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GET3D cria objetos em 3D para realidade virtual, apps e games.

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GET3D cria objetos em 3D para realidade virtual, apps e games.

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https://blogs.nvidia.com/blog/2022/09/23/3d-generative-ai-research-virtual-worlds/

A Nvidia desenvolveu uma inteligência artificial que pode facilitar bastante a vida de quem trabalha com desenvolvimento de jogos e de aplicações para o metaverso. A ferramenta GET3D é, de acordo com a empresa, capaz de criar objetos em 3D prontos para serem usados em games ou apps de realidade virtual, por exemplo, podendo formar até 20 objetos por segundo usando uma única GPU.

A ferramenta consegue criar objetos “com texturas em alta fidelidade e detalhes geométricos complexos”, de acordo com a Nvidia. Com ela, objetos 3D como personagens, prédios e veículos, entre outros, são formados em questão de segundos. Esses modelos em 3D podem depois ser usados no desenvolvimento de jogos ou de apps para o metaverso.

Isso significa que desenvolvedores de games ou da web3 podem ter em poucos segundos diversos modelos para usar em suas criações. Prédios criados pela inteligência artificial da Nvidia podem ajudar a formar o cenário de uma aplicação do metaverso, enquanto os diversos personagens podem servir como NPCs (non-playable characters, ou personagens não controláveis) em games.

Os pesquisadores da Nvidia criaram o GET3D ao alimentar a inteligência artificial com cerca de 1 milhão de imagens 2D mostrando múltiplos ângulos diferentes de modelos em 3D.

Além de jogos e do metaverso, a tecnologia também poderia ser aplicada em robótica e arquitetura, segundo a Nvidia. Confira uma demonstração do GET3D abaixo:

https://www.youtube.com/watch?v=KfcsdhGKb1U

A ferramenta GET3D ainda pode trabalhar em conjunto com outra tecnologia de inteligência artificial da Nvidia, a StyleGAN-NADA, para aplicar diferentes estilos a um objeto a partir de texto. Isso possibilitaria ao desenvolvedor, por exemplo, aplicar um estilo de pintura para um carro com facilidade, assim como criar uma casa mal-assombrada rapidamente.

Os pesquisadores da Nvidia ainda planejam continuar melhorando as capacidades da tecnologia, incluindo a possibilidade de gerar diversos tipos de imagens 3D ao mesmo tempo em vez de focar em apenas uma.

Leandro Kersting de Freitas

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The massive virtual worlds created by growing numbers of companies and creators could be more easily populated with a diverse array of 3D buildings, vehicles, characters and more - thanks to a new AI model from NVIDIA Research.

Trained using only 2D images, NVIDIA GET3D generates 3D shapes with high-fidelity textures and complex geometric details. These 3D objects are created in the same format used by popular graphics software applications, allowing users to immediately import their shapes into 3D renderers and game engines for further editing.

The generated objects could be used in 3D representations of buildings, outdoor spaces or entire cities, designed for industries including gaming, robotics, architecture and social media.

GET3D can generate a virtually unlimited number of 3D shapes based on the data it's trained on. Like an artist who turns a lump of clay into a detailed sculpture, the model transforms numbers into complex 3D shapes.

With a training dataset of 2D car images, for example, it creates a collection of sedans, trucks, race cars and vans. When trained on animal images, it comes up with creatures such as foxes, rhinos, horses and bears. Given chairs, the model generates assorted swivel chairs, dining chairs and cozy recliners.

"GET3D brings us a step closer to democratizing AI-powered 3D content creation," said Sanja Fidler, vice president of AI research at NVIDIA, who leads the Toronto-based AI lab that created the tool. "Its ability to instantly generate textured 3D shapes could be a game-changer for developers, helping them rapidly populate virtual worlds with varied and interesting objects."

GET3D is one of more than 20 NVIDIA-authored papers and workshops accepted to the NeurIPS AI conference, taking place in New Orleans and virtually, Nov. 26-Dec. 4.

It Takes AI Kinds to Make a Virtual World

The real world is full of variety: streets are lined with unique buildings, with different vehicles whizzing by and diverse crowds passing through. Manually modeling a 3D virtual world that reflects this is incredibly time consuming, making it difficult to fill out a detailed digital environment.

Though quicker than manual methods, prior 3D generative AI models were limited in the level of detail they could produce. Even recent inverse rendering methods can only generate 3D objects based on 2D images taken from various angles, requiring developers to build one 3D shape at a time.

GET3D can instead churn out some 20 shapes a second when running inference on a single NVIDIA GPU - working like a generative adversarial network for 2D images, while generating 3D objects. The larger, more diverse the training dataset it's learned from, the more varied and detailed the output.

NVIDIA researchers trained GET3D on synthetic data consisting of 2D images of 3D shapes captured from different camera angles. It took the team just two days to train the model on around 1 million images using NVIDIA A100 Tensor Core GPUs.

Enabling Creators to Modify Shape, Texture, Material

GET3D gets its name from its ability to Generate Explicit Textured 3D meshes - meaning that the shapes it creates are in the form of a triangle mesh, like a papier-mâché model, covered with a textured material. This lets users easily import the objects into game engines, 3D modelers and film renderers - and edit them.

Once creators export GET3D-generated shapes to a graphics application, they can apply realistic lighting effects as the object moves or rotates in a scene. By incorporating another AI tool from NVIDIA Research, StyleGAN-NADA, developers can use text prompts to add a specific style to an image, such as modifying a rendered car to become a burned car or a taxi, or turning a regular house into a haunted one.

The researchers note that a future version of GET3D could use camera pose estimation techniques to allow developers to train the model on real-world data instead of synthetic datasets. It could also be improved to support universal generation - meaning developers could train GET3D on all kinds of 3D shapes at once, rather than needing to train it on one object category at a time.
For the latest news from NVIDIA AI research, watch the replay of NVIDIA founder and CEO Jensen Huang's keynote address at GTC:

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NVIDIA AI Research Helps Populate Virtual Worlds With 3D Objects | NVIDIA Blog

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The massive virtual worlds created by growing numbers of companies and creators could be more easily populated with a diverse array of 3D buildings, vehicles, characters and more - thanks to a new AI model from NVIDIA Research. Trained using only 2D images, NVIDIA GET3D generates 3D shapes with high-fidelity textures and complex geometric details.

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Sistema de Relatórios Meteorológicos - Joel Tassinari

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Sistema de Relatórios Meteorológicos - Joel Tassinari

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Esta é uma das excelentes ideias de projetos de IoT para iniciantes. Este sistema de relatório meteorológico baseado em IoT foi projetado especificamente para facilitar o relatório de parâmetros climáticos pela Internet. Este é um dos melhores projetos de IoT onde o sistema é incorporado com sensores de temperatura, umidade e chuva que podem monitorar as condições climáticas e fornecer relatórios ao vivo de estatísticas meteorológicas.

É um sistema automatizado e sempre ativo que envia dados por meio de um microcontrolador para o servidor da Web usando uma conexão WIFI. Esses dados são atualizados ao vivo no sistema do servidor online. Assim, você pode verificar diretamente as estatísticas meteorológicas on-line sem precisar depender dos relatórios das agências de previsão do tempo. O sistema também permite definir valores de limite e alertas para instâncias específicas e notifica os usuários sempre que os parâmetros climáticos ultrapassam o valor limite.

Alguns projetos de IoT para o último ano têm como objetivo evoluir o uso eficiente de dispositivos para reduzir a pegada de carbono, que é uma necessidade do momento. Desde o monitoramento consistente das emissões de carbono até a aplicação de equipamentos padrão e uso de energia para operar em níveis restritos, o papel da IoT está evoluindo. Os desenvolvedores estão aproveitando tecnologias inteligentes para manter um equilíbrio consistente entre natureza e tecnologia.

https://nevonprojects.com/iot-weather-reporting-system/

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Here we propose a smart weather reporting system over the internet. Our proposed system allows for weather parameter reporting over the internet. It allows the people to directly check the weather stats online without the need of a weather forecasting agency. System uses temperature, humidity as well as rain sensor to monitor weather and provide live reporting of the weather statistics. The system constantly monitors temperature using temperature sensor, humidity using humidity sensor and also for rain. The system constantly transmits this data to the microcontroller, which now processes this data and keeps on transmitting it to the online web server over a wifi connection. This data is live updated to be viewed on the online server system. Also system allows user to set alerts for particular instances, the system provides alerts to user if the weather parameters cross those values. Thus the IOT based weather reporting system provides an efficient internet based weather reporting system for users.

Hardware Specifications

Atmega Microcontroller

DHT11 Digital Temperature Humidity Sensor

Rain Drop Sensor

ESP8266 Wifi Module

LCD Display

Crystal Oscillator

Resistors

Capacitors

Transistors

Cables and Connectors

Diodes

PCB and Breadboards

LED

Transformer/Adapter

Push Buttons

Switch

IC

IC Sockets

Software Specifications

Arduino Compiler

MC Programming Language: C

IOTGecko

Block Diagram

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Here we propose a smart weather reporting system over the internet. Our proposed system allows for weather parameter reporting over the internet. It allows the people to directly check the weather stats online without the need of a weather forecasting agency.

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Transporte por caminhões autônomos - Marcelo Pancinha

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Transporte por caminhões autônomos - Marcelo Pancinha

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O Transporte por caminhões no Brasil é o principal meio logístico de transporte de mercadoria, sendo essencial para a economia do país, visto que os caminhoneiros são responsáveis por movimentar 60% de toda a carga brasileira.

Muitos países pelo mundo têm situação semelhante, tendo grande parte da logística baseada sobre transporte de cargas pelos caminhões e caminhoneiros. Tendo em vista a importância deste setor na economia além da necessidade destes para a sociedade e indústrias, algumas empresas começaram a trabalhar com a ideia de caminhões ‘.

A startup TuSimple, empresa que desenvolve tecnologias para caminhões autônomos, lançou a primeira rede autônoma de cargas nos Estados Unidos. Batizado de Autonomous Freight Network (AFN), o sistema já está em operação em sete rotas no país. Os caminhões autônomos podem rodar sem motorista.

O sistema é composto por caminhões autônomos, rotas mapeadas digitalmente e terminais em locais estratégicos. Tudo é controlado por meio do aplicativo de monitoramento TuSimple Connect. O conjunto compõe a tecnologia chamada L4. O objetivo é tornar a operação mais segura e eficiente.

Um estudo recente da Universidade de Michigan demonstrou que os caminhões autônomos podem abocanhar cerca de 90% do mercado de transportes de longa distância.

Um dos pontos em destaque é a capacidade dos caminhões conseguirem lidar com  muita chuva, tempestades, buracos na estrada e acidentes.

Além de a tecnologia tornar a operação de transporte mais segura, permitirá o aumento da eficiência. Isso se traduz em redução do consumo de combustível. Estudo elaborado pela startup TuSimple com base nos primeiros testes mostram que a tecnologia autônoma pode gerar 10% de economia em relação aos caminhões tradicionais.

https://estradao.estadao.com.br/caminhoes/caminhoes-autonomos-comecam-rodar/

https://clickpetroleoegas.com.br/o-futuro-da-logistica-caminhoes-autonomos-podem-assumir-ate-90-das-viagens-de-longa-distancia/

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O setor de logistica será completamente mudada com a entrada dos caminhões autonomos no mercado. Essa tendência veio para ficar?

Com a digitalização de processos e outras grandes mudanças da tecnologia, o setor de logística está passando por transformações profundas em todo o mundo. Por exemplo, em um curto prazo, os caminhões autônomos devem ganhar mais espaço nas rodovias, assumindo o lugar de caminhoneiros.

Um estudo recente da Universidade de Michigan demonstrou que os caminhões autônomos podem abocanhar cerca de 90% do mercado de transportes de longa distância. Acreditando ser um desafio menos complicado, os engenheiros de direção destacam que os caminhões autônomos devem ser focados para viagens de longas distâncias.

Viagens curtas de caminhões autônomos são muito mais complexas

Como nas viagens de longas distâncias não existem muitas curvas ou complexidades extras, os especialistas destacam que é muito mais fácil projetar um caminhão autônomo para esse fim. Por outro lado, as viagens curtas nas cidades a história é outra, já que existem milhares de curvas, pedestres e carros estacionados para o sistema autônomo dar conta.

Vídeo sobre inovações e testas destas tecnologia no Brasil

Sem Motorista! Conheça o caminhão autônomo projetado no Brasil, na USP - Via Olhar Digital

Para acelerar o uso dos caminhões autônomos, uma das soluções seria a montagem de estações entre as etapas. Os motoristas poderiam lidar com a primeira etapa, que é muito mais complexa. Já as viagens longas e demoradas podem ser feitas pela inteligência artificial.

" Quando conversamos com motoristas de caminhão, literalmente todos disseram: 'Sim, essa parte do trabalho pode ser automatizada'', disse Aniruddh Mohan, que fez um estudo sobre o tema na Universidade de Michigan, nos EUA.

O estudo destaca que se os caminhões autônomos superarem essas barreiras iniciais poderão substituir até 90% da condução humana no mercado de transportes de longas distâncias.

Os sistemas dos caminhões autônomos ainda precisam evoluir bastante. Um dos pontos em destaque é a capacidade dos caminhões darem conta de muita chuva, tempestades, buracos na estrada e acidentes. Outro ponto é conseguir que as autoridades autorizem caminhões sem motorista nas estradas. Para isso acontecer os mecanismos de segurança devem estar em um nível elevadíssimo.

Curtiu? Apoie nosso trabalho seguindo nossas redes sociais e tenha acesso a conteúdos exclusivos. Não esqueça de comentar e compartilhar.

Jornalista brasileiro, amo esportes, notícias e política. Já contribui em diversos outros portais relacionados a esportes, economia e informações regionais.

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O futuro da logistica: Caminhões autônomos podem assumir até 90% das viagens de longa distância

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Com a digitalização de processos e outras grandes mudanças da tecnologia, o setor de logística está passando por transformações profundas em todo o mundo. Por exemplo, em um curto prazo, os caminhões autônomos devem ganhar mais espaço nas rodovias, assumindo o lugar de caminhoneiros.

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GitHub copilot | Your AI pair programmer

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O Github copilot é uma ferramenta assistente desenvolvida em parceria do GitHub com a Open IA que utiliza inteligência artificial para  fazer sugestões de código a medida em que o programador desenvolve suas linhas de código.

O assistente foi apresentado em 2021 e em somente em sua fase de teste foi utilizado por 1,2 milhão de desenvolvedores. Dentre as principais sugestões estão o auto completar de linhas de códigos simples, testes unitários e funções na íntegra.  Para chegar na média de 40% de código sugerido no total de código programado pelos seus utilizadores, o algoritmo treinou com códigos abertos disponibilizados em repositórios públicos.

O GitHub Copilot é compatível com vários editores de código como Visual Studio Code e os ambientes JetBrains

e algumas linguagens de programação como JavaScript, Python, C++, Ruby e Go.

A ferramenta levantou algumas discussões sobre o direitos autorais dos códigos sugerido e se veio para substituir de vez os programadores. A resposta para esse último questionamento só o futuro dirá.

Para saber mais sobre o GitHub Copilot:
https://docs.github.com/en/copilot/overview-of-github-copilot/about-github-copilot

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Clinical Evidence for Use of a Noninvasive Biosensor for Tear Glucose as an Alternative to Painful Finger-Prick for Diabetes Management Utilizing a Biopolymer Coating - Émerson Alves Beier

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Clinical Evidence for Use of a Noninvasive Biosensor for Tear Glucose as an Alternative to Painful Finger-Prick for Diabetes Management Utilizing a Biopolymer Coating - Émerson Alves Beier

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O artigo aqui citado já é uma tencologia alcançável que foca em um biosensor para controlar o nível da glicose de pacientes com diabetes do tipo 1 através da lágrima produzida para lubrificar o olho e transmite os níveis de glicose para o celular do paciente por nfc, fazendo assim não ser mais necessário o uso invasivo e doloroso de agulhas para perfurar o dedo.

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MELOMICS - Sistema computacional para composição automática de músicas, baseado em algoritmos bioinspirados.

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MELOMICS - Sistema computacional para composição automática de músicas, baseado em algoritmos bioinspirados.

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Melomics (derivado de “genômica das melodias”) é um sistema computacional para composição automática de música (sem intervenção humana), baseado em algoritmos bioinspirados.O sistema Melomics codifica cada tema em um genoma, e toda a população de peças musicais passa por uma dinâmica evo-devo (ou seja, peças lidas imitando um complexo processo de desenvolvimento embriológico). O sistema é totalmente autônomo: uma vez programado, compõe música sem intervenção humana.
Essa tecnologia foi transferida para a indústria como um spin-off acadêmico, a Melomics Media, que forneceu e reprogramou um novo cluster de computadores que criou uma enorme coleção de música popular. Os resultados dessa computação evolutiva estão sendo armazenados no site da Melomics, que hoje constitui um vasto repositório de conteúdo musical.

https://www.artificia.pro/melomics/

Aluno: Diogo Gnutzmann Santos

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Detecção e Classificação de Bots Utilizando Redes Neurais Artificiais e Análise de Sentimentos

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Detecção e Classificação de Bots Utilizando Redes Neurais Artificiais e Análise de Sentimentos

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Com a forte utilização de bots para propagação de notícias falsas, o artigo apresenta um estudo que utiliza técnicas de aprendizado de máquina com processamento de linguagem natural e tem como objetivo detectar contas bot no twitter analisando a periculosidade da conta verificando o sentimento envolvido nos tweets.

Aluno: Alexsander de Oliveira da Silva

https://www.researchgate.net/profile/Eliaquim-Ramos/publication/351528995_Deteccao_e_Classificacao_de_Bots_Utilizando_Redes_Neurais_Artificiais_e_Analise_de_Sentimentos/links/609c69cd92851cca59850be3/Deteccao-e-Classificacao-de-Bots-Utilizando-Redes-Neurais-Artificiais-e-Analise-de-Sentimentos.pdf

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Software para detectar Autismo - Rafael Farias Batista

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Software para detectar Autismo - Rafael Farias Batista

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Link: https://portal.ifma.edu.br/2021/12/21/pesquisadores-do-ifma-desenvolvem-software-livre-para-detectar-autismo/

Aluno: Rafael Farias Batista

Comentários: Através do trabalho em conjunto (multidisciplinar) de uma psicóloga e pesquisadores do departamento de Eletroeletrônica do Instituto Federal do Maranhão - IFMA, foi criado/desenvolvido um software livre capaz de identificar a presença do Transtorno do Espectro Autista (TEA), com um nível de acerto superior a 90% em crianças na faixa etária de 1 a 10 anos, diminuindo assim a complexidade e tempo do diagnóstico.

Diferente da prática convencional que se utiliza do DSM-5 (Manual Diagnóstico e Estatístico de Transtornos Mentais) além de testes neurológicos para realização do diagnóstico, o software em questão utiliza o método DIR/Floortime (desenvolvimento emocional e funcional, diferenças individuais e relacionamentos), introduzido, em 1980, pelo pesquisador norte-americano Stanley Greenspan, que identifica as características apresentadas pelo indivíduo e faz uma correlação com seu estágio de desenvolvimento e sua faixa etária para identificar características e padrões do Transtorno do Espectro Autista.

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Pesquisadores do IFMA desenvolvem software livre para detectar Autismo : IFMA

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Instituto Federal de Educação, Ciência e Tecnologia do Maranhão

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Inteligência Artificial para diagnóstico de imagem - Gustavo Nor

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Inteligência Artificial para diagnóstico de imagem - Gustavo Nor

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A VizioMed é uma startup brasileira que utiliza IA para o diagnóstico de imagem.

Após o paciente realizar o exame, a plataforma que tem acesso a uma grande base de dados e diagnósticos, analisa as imagens e emite o diagnóstico.

Caso a solução identifique algo crítico no exame, o médico é avisado de forma automática, permitindo que o tratamento seja iniciado com agilidade.

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A ConnectON Ambientes Inteligentes é uma startup Acadêmica voltada para a área de Automação Predial e Residencial.
Criada em parceira com o Departamento de Engenharia Elétrica da Universidade Federal de Pernambuco (UFPE), estamos prontos para proporcionar o conforto, atender as suas necessidades e trazer praticidade para o seu dia a dia, seja em sua residência ou no ambiente de trabalho, inovando por meio de uma automação pouco invasiva e de alta qualidade.

link da startup:
https://www.connecton.com.br/

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A SYOS é uma startup carioca que desenvolveu uma plataforma de gestão da cadeia de frios para o varejo alimentar brasileiro. Por meio de IoT e Inteligência Artificial (IA), ela otimiza a operação dos refrigeradores, garantindo a qualidade dos produtos em uma era que o consumidor está cada vez mais preocupado com sua saúde.

A startup traz disrupção a um mercado (refrigeração comercial) que passou ao largo da transformação digital, até o momento, e quer ser reconhecida como referência na gestão do frio alimentar, item fundamental para varejistas e consumidores. Por meio de metodologia proprietária, usando sensores sem fio e alarmes inteligentes, sua plataforma garante aos varejistas que todos os produtos estejam sempre na temperatura certa, prevenindo perdas, garantindo a qualidade dos produtos e, como consequência, aumentando vendas.

A solução pode ser facilmente instalada em qualquer equipamento, sem a necessidade de investimento, obras ou contratação de novos funcionários. A tecnologia é comercializada no modelo SaaS (Software as a Service) + HaaS (Hardware as a Service), com cobrança por equipamento monitorado (refrigeradores e câmaras frias).

A empresa recentemente recebeu um aporte de 12 milhões de reais. Link do site deles: https://syos.com/

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Um serviço de assinatura que constantemente monitora e coleta dados da saúde de seus pacientes com o objetivo de detectar doenças em seus estágios iniciais e consequentemente mais fáceis de tratar. A ideia é fazê-los viver mais e melhor, ultrapassando os 100 anos de vida.

Diversos diagnósticos são realizados, algumas anualmente como ressonâncias magnéticas do corpo inteiro, cérebro e coração e outros mais frequentes como exames de sangue, controle do peso, eletro e ecocardiogramas.

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At Human Longevity, everything we do is in service of helping you live a healthier, longer life. We've designed a leading-edge precision health care program using today's best technology to detect and help preempt cancer, cardiac, metabolic and neurodegenerative disease, and more.

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A Horizon Robotics é uma empresa chinesa de inteligencia artificial, focada em sua maioria para a industria automotiva como veiculos autonomos  e smart mobility. Além da parte de IA, também tem produção independente de chips para redes neurais, e ainda atua com visão computacional para toda essa cadeia produtiva. Uma das empresas com mais rápido crescimento na China neste setor. Contando com parcerias como  Audi, Chery, Bosh e Continental Pneus.

Segue alguns links para melhor conhecimento da empresa:

https://www.linkedin.com/company/horizonrobotics?original_referer=

https://techcrunch.com/2020/12/21/horizon-robotics-700-million-funding/

https://www.chinadaily.com.cn/a/202206/30/WS62bd5b11a310fd2b29e6993f.html

https://pandaily.com/ai-firm-horizon-robotics-wins-strategic-investment-from-faw-group/

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Your browser does not support HTML5 video. Efficient AI Computing for Automotive Intelligence Discover Horizon Robotics AI Inference Platform.Open. Scalable. Low Energy. Your browser does not support HTML5 video. Efficient AI Computing for Automotive Intelligence Discover Horizon Robotics AI Inferen…

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Evolução sistemas de software - Rodrigo Freund de Moraes

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Evolução sistemas de software - Rodrigo Freund de Moraes

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Na minha visão o conteúdo apresentado gera possibilidades para que sistemas de software gerem valor de diferencial competitivo, e não apenas para solucionar problemas de necessidade básica do clientes  (como a maioria dos sistemas gerenciais presentes no mercado).
Contudo ainda vejo muitas barreiras a serem transpostas, principalmente à educação das pessoas em relação aos seus próprios dados. Atualmente a permissão de acesso é concedida através de uma caixa de seleção onde o usuário, que provavelmente não leu as regras de acesso aos dados, concorda ou não com a divulgação dos seus dados. Penso que com a evolução do uso dos sistemas inteligentes a educação básica terá que abordar esse tema tendo em vista uma racionalização das informações disponibilizadas pelos sistemas.

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Aluno: Jonhson Santos
Como mencionado na palestra aplicações inteligentes estão presentes em diferentes ambientes áreas de negócio. Na agricultura não é diferente, solução do link anexado é um exemplo disto!

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Smart Agri - Soluções para o Agronegócio

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O QUE MUDA NA ERA DIGITAL?

A tecnologia está causando uma verdadeira revolução na Agricultura.

É possível hoje gerenciar uma fazenda do mesmo modo que uma indústria, controlando efetivamente as entradas e saídas e conhecendo cada aspecto da produção. Isso significa uma grande quantidade de dados e informações que poderão não apenas auxiliar os produtores a tomarem melhores decisões gerenciais, mas também proporcionar uma evolução exponencial de todo conhecimento agrícola, à medida que esses dados são trabalhados e utilizados para promover melhorias em todas as áreas do cenário agrícola.

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Smart Agri - Soluções para o Agronegócio - Home

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É possível hoje gerenciar uma fazenda do mesmo modo que uma indústria, controlando efetivamente as entradas e saídas e conhecendo cada aspecto da produção. Isso significa uma grande quantidade de dados e informações que poderão não apenas auxiliar os produtores a tomarem melhores decisões gerenciais, mas também proporcionar uma evolução exponencial de todo conhecimento agrícola, à medida que esses dados são trabalhados e utilizados para promover melhorias em todas as áreas do cenário agrícola.

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A SVA Tech é uma startup de Belo Horizonte que utiliza câmeras de monitoramento para realizar análise de vídeos para empresas, através de inteligência artificial com automações. Acho interessante pois poderíamos utilizar até mesmo para não utilizar chaves por exemplo, ou para validarmos placas de carros roubados na cidade sem precisar de uma fiscalização ativa. Ao mesmo tempo voltamos para a questão da privacidade dados gerados por essa análise. Essa empresa recebeu um aporte de 6 milhões em 2021.

segue o link:
https://startupi.com.br/startup-que-transforma-cameras-de-seguranca-em-aparelhos-inteligentes-recebe-aporte-r-6-milhoes/

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Olhos digitais que enxergam e ensinam um sistema de Inteligência Artificial deep learning através de câmeras convencionais, que vai aprendendo mais e mais. Essa é a proposta da SVA Tech, empresa de tecnologia fundada em Belo Horizonte, em 2014, e que oferece uma sofisticada solução de análise por vídeo em tempo real, transformando imagens em dados.

Quatro anos depois do primeiro investimento da KPTL com recursos do Fundo Criatec 3, a companhia recebe agora uma nova rodada de R$ 6 milhões, que tem entre os coinvestidores a Cedro Capital e a FC Partners.

A especialidade da SVA Tech é criar soluções sob medida para empresas com base na chamada Análise Inteligente de Vídeo (SVA - Smart Video Analytics, na sigla em inglês). A companhia mineira criou um software de Vídeo Software as a Service (VSaaS) para resolver as mais variadas dores dos clientes à medida em que o sistema vai sendo treinado e aprimorado a cada uso. O software de Inteligência Artificial desenvolvido pela SVA transforma imagens em dados que permite analisar a conformidade e comportamentos de ativos da indústria. No portfólio de clientes, grandes nomes como Vale, Gerdau, Air Liquide, SegurPro, CPFL.

Segundo o CEO Roberto Fernandino, parte do investimento será utilizado na reconfiguração do quadro societário e parte para impulsionar a operação, sempre focado no crescimento. "Vamos aumentar a equipe, fazer as demandas de expansão do software que grandes players nos solicitam. E evoluir a plataforma, dando ao cliente a liberdade de treinar as suas redes neurais para usar dentro das mais variadas aplicações. Além disso teremos um incremento das equipes operacionais, desenvolvimento e comercial", afirma Fernandino.

Para Paulo Tomazela, sócio da KPTL e conselheiro da SVA Tech, a companhia já confirmou que pode ir mais além com estes recursos. "Como todo investimento de Venture Capital, os primeiros aportes são uma aposta. E as novas rodadas premiam as teses mais vencedoras, que demonstraram resultados mais consistentes e grande potencial de crescimento e retorno próximo", explica Tomazela.

Uma das estratégias da SVA Tech para os próximos anos é a internacionalização, hoje a empresa já tem clientes na Europa e pretende fornecer a plataforma como serviço em outros países, expandindo soluções hoje já aplicadas em clientes no Brasil, inclusive em multinacionais brasileiras. Assim, busca aumentar a receita de exportação de forma relevante através de roll-out, alcançando às plantas no exterior de clientes já ativos no Brasil.

Alessandro Machado, sócio-diretor da Cedro Capital, afirma que a investida vem demonstrando nos últimos anos uma ótima competência tecnológica e comercial. "A empresa explora as mais diferentes aplicações da sua tecnologia de visão computacional em soluções voltadas para processos industriais e de segurança. A adoção dessas tecnologias ainda está em um estágio relativamente inicial em muitos setores, e há muito por ser desenvolvido ainda, gerando oportunidades em diferentes mercados para a SVA Tech acelerar sua expansão nos próximos anos", detalha.

Ricardo Resende, sócio da FC Partners, conta que a SVA Tech entrou no radar em 2015, quando estava captando sua primeira rodada de investimento. "Apesar de não termos investido na época, continuamos acompanhando de perto a evolução da empresa, uma vez que esta solução de análise de vídeo sempre nos impressionou muito. Além de ser muito inovadora, possui um custo benefício superior à de seus concorrentes, em sua maioria multinacionais. O investimento permitirá à empresa expandir sua operação e alcançar novos mercados ainda inexplorados, o que nos deixa muito entusiasmados", relata Resende.
Grandes cases deixam clara as potenciais aplicações do software
Os usos do VSaaS são os mais diversos e podem aprimorar processos nos mais variados segmentos. Na chamada Indústria 4.0 o sistema pode monitorar peças e componentes em tempo real, evitando e alertando para possíveis falhas. Um exemplo como a tecnologia dessa startup está transformando o processo de mineração de uma grande multinacional como a Vale.

Com milhares de vagões que transportam minério pelas ferrovias, os trens da Vale são supervisionados por centenas de câmeras que extraem dados das imagens. Por meio das câmeras são analisados em tempo real 98 itens de um vagão, como sistemas de freio, rolamentos, entre outros. O sistema também detecta se os funcionários estão usando capacete, óculos de proteção e outros equipamentos de segurança.

Outro case de sucesso é um projeto elaborado em conjunto com a CPFL, uma das principais companhias distribuidoras de eletricidade do País. Por meio de câmeras, o sistema acompanha em tempo real o andamento das obras de uma subestação. É uma plataforma de gestão de obras automatizada, com câmeras que vão avaliando se os trabalhos estão evoluindo, comparando constantemente com o programado e o cronograma. E sugerindo potenciais melhorias aos gestores.

Aplicado tanto em condomínios quanto empresas e indústrias, o controle de acesso por reconhecimento facial é outra tecnologia da SVA Tech com muita demanda. Com a pandemia, câmeras que controlam temperatura e uso de máscaras foram instaladas em diversas companhias. A plataforma também pode ser configurada para a detecção de presença, de elementos mascarados, fadiga de funcionários e motoristas, de objetos retirados e até para flagrar o uso de celular em locais proibidos. Enfim, a capacidade de aprendizado do software de vídeo análise e interpretação em tempo real é imensa, bem como as possibilidades de aplicação.
Foto de destaque: Roberto Fernandino, CEO da SVA.

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Startup que transforma câmeras de segurança em aparelhos inteligentes recebe aporte R$ 6 milhões | Startupi

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Mais tradicional portal de mídia brasileiro sobre o mercado de Startups, Inovação, Investimentos, Empreendedorismo e Tecnologia. Desde 2008 vem colaborando para a construção do ecossistema brasileiro de Startups, informando e educando todo o mercado. Quer falar com a gente? Clique aqui e envie uma mensagem para redação, parceria ou comercial.

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Engenharia de software e ambientes inteligentes 

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Engenharia de software e ambientes inteligentes 

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Engenharia de Software e Ambientes Inteligentes.

A engenharia de software e ambientes inteligentes existe como linha de pesquisa acadêmica e como hipótese de tecnologia disruptiva.

Não se trata de nada essencialmente novo, mas do amadurecimento do conhecimento e das ferramentas que hoje tornam possível realizações intencionadas há muito tempo por assim dizer.

O que temos hoje e transforma as possibilidades nestes domínios e em outros tantos são os recursos para captura e tratamento de dados habilitados com a internet e os dispositivos móveis.

Em resumo, a engenharia de software e ambientes inteligentes trata de desenvolvimento de software orientado a orquestrar ambientes com dispositivos conectados - internet das coisas - e racionalizar a captura e o tratamento de dados de forma a produzir conhecimento e com ele realizar inferências em e sobre determinados domínios que possam auxiliar as pessoas a melhorar sua experiência de vida no âmbito dos domínios explorados.

Trata-se, portanto, da combinação de diferentes tecnologias aplicadas para produzir uma experiência de produção de conhecimento utilizando dados capturados das pessoas em seus contextos e circunstâncias.

Essa experiência propoe uma esteira de processamento de dados do passado e do presente para inferir o futuro baseada em modelos matematicos e aprendizado de máquina suportados em estrutura de “big data”.

Importante: não se tratada tão somente do armazenamento e tratamento de um grande volume de dados, mas de um grande volume de dados armazenados segundo determianados padrões e métricas.

Algumas iniciativas estão ligadas às seguintes  aplicações:

1. Agricultura

2. Sinais Vitais

3. Saúde mental

4. Gerenciamento de projetos.

O poder desta tecnologia pode ser exemplificado pelas experiência em saúde mental dedicadas a detecção e prevenção de comportamentos suicidas.

Existem perguntas importantes que já estão respondidas por trabalhos de mestrado e doutorado na Universidade do Vale do Sinos, Rio Grande do Sul. Da mesma forma, existem outras tantas, igualemente importantes ainda sem resposta mas sendo objeto de pesquisa e desenvolvimento. Certamente, posto o que está realizado e o que está disponível em conhecimento aplicado, apostar que a vida poderá ser mais inteligente e o mundo poderá ser mais inclusivo para mais pessoas é uma escolha com chances reais de se traduzir em um acerto de muitos ganhos.

Luciano Veronese Monteiro

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Cinco aplicações de IA na medicina - Felipe Amadeu Junges

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Cinco aplicações de IA na medicina - Felipe Amadeu Junges

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https://medicalbox.com.br/blog/descubra-5-aplicacoes-da-inteligencia-artificial-na-medicina/

A AI pode potencializar o atendimento aos pacientes, em cinco aspectos: Diagnóstico, Exames, Tratamento, Alertas e Atendimento Remoto.

O acompanhamento de pacientes pode agilizar em alertas de possíveis emergências médicas, monitorando diversos sensores de forma à prever problemas de saúde.

Em caso de exames, os dados poderão "munir" os médicos com informações para aumentar a eficácia dos exames.

Em caso de internações, os sensores podem gerar alertas de emergências, iniciando atendimento imediato quando necessário.

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Não há dúvida de que a tecnologia vem impactando o dia a dia da sociedade de maneira muito intensa. Com o desenvolvimento de softwares e equipamentos específicos para todos os segmentos da ciência, manter-se atualizado é essencial para sobreviver no mercado.

No setor da saúde, o que vem ganhando cada vez mais espaço é o uso da Inteligência Artificial (IA). Pensando nisso, veremos no artigo de hoje algumas das principais aplicações da Inteligência Artificial na medicina. Interessado? Então, continue lendo para conferir!

1. Associação de dados para diagnósticos

Uma das grandes aplicações da Inteligência Artificial na medicina é a possibilidade de associar todos os dados disponíveis para fazer diagnósticos. Isso é possível não apenas pelas plataformas e hardwares de armazenamento bem mais potentes que surgiram nos últimos anos, mas também pela criação da tecnologia de nuvem.

Essa associação garante aos médicos e demais profissionais de saúde mais praticidade e precisão para chegar às suas conclusões. Ao digitarem um prontuário, por exemplo, o banco de dados é atualizado em tempo real, ficando disponível para qualquer outro médico ao redor do mundo com facilidade de acesso e total proteção dos arquivos.

O desenvolvimento das tecnologias de big data, cloud computing e IA, portanto, possibilita que se una as atualizações com softwares chamados tecnicamente de " agentes autônomos ", cuja função é recuperar, organizar e localizar informações relevantes sobre algum assunto específico, de acordo com as necessidades do usuário.

Imagine um ortopedista que tenha dúvidas sobre uma radiografia complexa, ou um dermatologista que não está muito certo sobre o diagnóstico de uma lesão de pele. Esses são apenas alguns exemplos nos quais o uso do banco de dados pode ajudar a criar uma conduta mais adequada, minimizando as chances de erros.

Outra aplicação atual disso é o Registro IRIS (sigla para Intelligent Research in Sight), que promete ser de grande contribuição para a prática clínica. Esse sistema pertence à Academia Americana de Oftalmologia, e é o primeiro banco de dados com resultados oftalmológicos provenientes dos prontuários eletrônicos de milhões de pacientes.

2. Mais precisão no resultado de exames

Bom, se existem tecnologias aplicadas para melhorar a capacidade diagnóstica, não é difícil imaginar que também estão sendo produzidas ferramentas para trazer maior precisão aos exames. Em alguns países, como os EUA, a China e o Reino Unido, diversos pesquisadores já estão desenvolvendo algoritmos específicos para isso.

Eles são compostos por vastos bancos de dados com imagens de exames radiológicos - como tomografias ópticas que analisam as distintas camadas de nossa retina, por exemplo - capazes de encontrar possíveis alterações patológicas. O resultado é que, com essas informações, o programa comete menos erros e consegue até superar os especialistas em retina nos diagnósticos.

Outro benefício importante é que isso universalizará o acesso dos pacientes mais carentes, uma vez que a automatização diminui os custos dos processos. Assim, em vez de passar por vários médicos e fazer uma bateria de exames, o paciente poderá utilizar esse sistema de Inteligência Artificial para descobrir se existe algum problema em sua visão.

3. Tratamento mais eficientes de doenças

Outra aplicação importante da IA acontece no aperfeiçoamento do tratamento de doenças, que é algo fundamental na evolução da medicina.

Existem hoje algoritmos sendo desenvolvidos por empresas de informática que usarão o deep learning - uma técnica de aprendizado complexo de máquina - para aproveitar os conteúdos da literatura científica, bem como os dados genéticos dos pacientes, para sugerir tratamentos.

Esses programas não servirão como substitutos dos médicos, dizendo com exatidão quais caminhos devem ser tomados, mas poderão atuar complementarmente, exibindo as opções mais indicadas para cada caso. Vale lembrar que isso é feito considerando os efeitos colaterais e o grau de risco de cada alternativa.

Na realidade, o grande benefício desse tipo de mecanismo é que, muito antes de recorrer às inúmeras combinações de remédios e princípios ativos diferentes, um especialista poderá direcionar o seu paciente para práticas mais seguras e eficientes, melhorando o seu prognostico e a sua qualidade de vida.

4. Emissão de alertas sobre o quadro do paciente

Uma grande vantagem da Inteligência Artificial é que ela trabalha em tempo real - o que também representa um desafio para os desenvolvedores e cientistas que trabalham para aprimorar esse diferencial. Ainda assim, mesmo nos dias de hoje, já existem gadgets e aparelhos que podem salvar vidas.

A ideia é criar dispositivos capazes de alertar imediatamente os médicos (ou algum estabelecimento de saúde de referência) sobre qualquer piora no quadro de um paciente. Tais alertas em situações de emergência podem ser cruciais para portadores de males severos, como cardiopatas e diabéticos, por exemplo.

Em situações de menor urgência, a coisa não muda de figura. Equipamentos modernos podem analisar resultados de exames laboratoriais ou a eficácia das prescrições de medicações, enviar lembretes e até fazer alguns pequenos ajustes ou sugestões - de acordo, obviamente, com a conduta estabelecida.

5. Possibilidade de atendimento remoto por telemedicina

Mesmo com a globalização e a eficácia de diversos tipos de transporte, o mundo ainda possui muitos locais isolados. Mas a Inteligência Artificial também pode ser muito interessante quanto a isso, visto que uma das suas aplicações é a telemedicina e o atendimento remoto feito por meio de imagens em tempo real.

Cidades nas quais o acesso de médicos e demais profissionais de saúde é mais difícil já estão fazendo uso dessa tecnologia para o envio de exames laboratoriais, dados clínicos ou mesmo consultas. A novidade agora são os softwares capazes de ajudar nesse contexto, interpretando informações e até sugerindo diagnósticos.

Enfim, como podemos ver, a Inteligência Artificial na medicina já faz parte da rotina de muitos profissionais do campo. A grande questão é que o seu desenvolvimento promete revolucionar a área de saúde, trazendo mais possibilidades para os médicos e mais qualidade de vida para os pacientes, mesmo em locais isolados.

Então, gostou de aprender um pouco mais sobre a IA aplicada à medicina? Agora, aproveite para nos seguir nas redes sociais e não perca nossos próximos conteúdos! Estamos no Facebook.

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Descubra 5 aplicações da Inteligência Artificial na medicina - Software médico MedicalBox BLOG

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Não há dúvida de que a tecnologia vem impactando o dia a dia da sociedade de maneira muito intensa. Com o desenvolvimento de softwares e equipamentos específicos para todos os segmentos da ciência, manter-se atualizado é essencial para sobreviver no mercado.

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Machine Learning para previsão em tempo real de internação hospitalar para pacientes de emergência - Bruno Adam Alves

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Machine Learning para previsão em tempo real de internação hospitalar para pacientes de emergência - Bruno Adam Alves

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Comentário: Este estudo apresenta um pipeline de predição que utiliza registros de saúde de pacientes de um hospital para prever a curto-prazo, as probabilidades de admissões nas alas de emergência

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Introduction

To date, most applications of Artificial Intelligence (AI) to healthcare have been applied to address clinical questions at the level of individual patients1. Now that many hospitals have electronic health records (EHRs) and data warehouse capabilities, there is the potential to exploit the promise of AI for operational purposes2. Hospitals are highly connected systems in which capacity constraints in one area (for example, lack of ward beds) impede the flow of patients from other locations, such as the emergency department (ED)3 or those ready for discharge from intensive care4. Arrivals to the ED show diurnal and seasonal variations, with predicted peaks in the morning and early evening, but workflows elsewhere in a hospital mean that discharges from the hospital happen late in the day, creating flow problems ( https://www.nuffieldtrust.org.uk/resource/understanding-patient-flow-in-hospitals). This mismatch of cadence between different parts of the hospital results in patients boarding in ED, or being admitted to inappropriate wards, with adverse consequences including longer stays5, greater risk of medical errors6 and worse long-term outcomes in elderly patients7.

Hospital services can be managed more efficiently if accurate short-term forecasts for emergency demand are available8,9. Currently, most hospitals use simple heuristics to make short-term forecasts of numbers of emergency admissions, which are based on rolling averages for each day of the week10. Scholars have suggested improvements using Bayesian approaches or auto-regressive inductive moving averages with meteorological, public health and geographic data8,11,12. However, such methods do not take account of the stochastic nature of ED arrivals13 and cannot be adapted to reflect the case mix of people in the ED at a given point in time. In hospitals with EHRs, where staff are recording patient data at the point of care, there is an opportunity to use EHR data to generate short-horizon predictions of bed demand. These would help the teams responsible for allocating beds make best use of available capacity and reduce cancellations of elective admissions.

Maching Learning (ML) is attractive for such predictions because its aggregation of weak predictors may create a strong prediction model2. Emergency medicine scholars have compared predictions made by ML algorithms against conventional approaches like linear regression and naïve Bayes9,14. It is common for such studies to use arrival characteristics (e.g. arrival by ambulance or on foot), triage data and prior visit history15,16,17 to make predictions, although recent studies have included a wider variety of data captured by EHRs, including medical history, presenting condition and pathology data9,18,19,20. Hong et al.9 showed that ML algorithms like gradient-boosted trees and deep neural networks, applied to a large EHR dataset of 972 variables, improved predictive performance. By including data on lab test results and procedures, El-Bouri et al.20 were able to predict which medical specialty patients would be admitted to. Barak-Corren et al'.s study18 is one of few in emergency medicine to address the challenges of making predictions during a patient's visit to ED. They built progressive datasets from historical data, each intended to reflect the data usually available at 10, 60 and 120 minutes after presentation to the ED. Notwithstanding their use of chief complaint data that was entered by ED receptionists as free text and retrospectively coded by the researchers, they were able to show that the later datasets offered better predictions than at 10 minutes. Their study demonstrates the potential that EHRs offer for improving on approaches that use triage data only.

Although these studies demonstrate the predictive utility of ML, they do not unlock its potential to generate predictions in real-time to help managers address problems of patient flow. Building a model for implementation involves several additional challenges to those encountered when simply optimising the technical performance of a prediction model. These include preparing training examples of incomplete visits from historic data in which visits have been completed21, making decisions about the temporal framing of the model (for example, at what point in the visit to check if the outcome of interest has occurred)22, and planning for a drift in model performance over time23. Models for real-time prediction have been trained in clinical contexts such as circulatory failure in critical care24 and post-operative complications25,26. These are contexts where patient observations are taken with high frequency whereas the frequency of data collection and the type of data collected varies greatly from patient to patient in the ED. A patient in the resuscitation area of an ED may have frequent observations, while a patient in the waiting room has no data collected. These heterogeneous data profiles are themselves indicative of likelihood of admission.

From the bed planners' point of view, knowing the probability that a particular patient will be admitted is less valuable than knowing in aggregate how many patients to plan for. In this respect a prediction tool that can provide a probability distribution for the number of admissions in a given time frame is more useful than one that solely estimates probability of admission at the patient level. One study in emergency medicine derived an expected number of admissions among a roomful of patients in ED by summing their individual probabilities of admission27, but there was no presentation of the uncertainty of their point estimates. Also, when making predictions for admissions within a time-window after the prediction is made, projections must allow for the number of patients not in the ED at the prediction time who will arrive and be admitted within the window28.

If models are to be used operationally, their performance needs to be sustained over time as care provision, patient characteristics and the systems used to capture data evolve23. Real-time operational models also need to cover the 'last mile' of AI deployment; this means that the applications that generate predictions can run end-to-end without human intervention. This last mile is the most neglected29, leading to calls for a delivery science for AI, in which AI is viewed as an enabling component within an operational workflow, rather than an end in itself30.

This research aimed to harness the heterogenous stream of real-time data coming from patients in the ED of a UK hospital to make predictions of aggregate admissions in a short time horizon. Bed planners at the hospital were closely involved with the research team to specify their requirements. They requested predictions for bed requirements in the next four and eight hours to be sent at four times daily, to coincide with their own capacity reporting. As part of the project, we developed an application that formats and sends an email to the bed planners at the four report times. See Supplementary Note 9 for details of the bed planners' workflow and the application we created. In this paper, we explain how the predictions are generated, evaluate their performance and compare them with standard benchmarks.

The contributions of the research are: the development and deployment of a ML-based information product in use in hospital operations; the demonstration of a method to train ML models for real-time use when patient-level data is variable between patients and over the course of individual visits; the incorporation of a method to aggregate individual-level predictions for operational planning purposes; and an exposition of some of the challenges associated with developing models for real-time implementation.

Results

Overview of prediction pipeline

Figure 1 illustrates a real example of predictions generated at 16:00 on 11 May 2021 using the seven-step pipeline built through this work. As noted above, the bed planners wanted these predictions at four times daily (06:00, 12:00, 16:00 and 22:00).

a Illustrates the roomful of patients in the ED at the prediction time on the day of interest, grouped according to how long they had been in the ED since arrival. b Shows each patient's probability of admission, generated using a set of ML models. These are combined in c into a probability distribution for the number of admissions among this roomful of patients. d Shows how, for each patient, the probability of admission within 4 hours would be calculated from recent data on time to admission, taking into account the time the patient has been in the ED up to the prediction time. e Shows a probability distribution for the number of admissions among the roomful of patients within the prediction window of 4 hours. f Shows a probability distribution over the number of patients who have not yet arrived, who will be admitted in the prediction window, generated by a Poisson equation from recent data on number of admissions. g Shows the final probability distribution for the number of admissions, including patients in the ED now and those yet to arrive, within the prediction window.

At each prediction time, EHR data on the set of patients in the ED was retrieved (Step 1). A ML prediction was made for each about their probability of admission at Step 2. At Step 3, the individual probabilities were combined to give a probability distribution for the number of admissions from the patients currently in the ED. At Step 4, the individual probability of admission for each patient was combined with survival analysis to give for each patient the probability that they would be admitted within the prediction window, accounting for when they arrived and the number of patients in the ED when they arrived. At Step 5 the individual probabilities from Step 4 were combined to give a probability distribution for the number of admissions within the prediction window from patients currently in the ED. At Step 6 Poisson regression was used to give a probability distribution for the number of additional patients that would arrive and be admitted within the prediction window. Finally, at Step 7, the distributions obtained at Steps 5 and 6 were convoluted to give a probability distribution for the total number of admissions within the prediction window by patients currently in the ED and others yet to arrive. The following paragraphs present an evaluation of the predictions made at the four prediction times on a test set of 97 days from 13 December 2019 to 18 March 2020.

Results for steps 1-2: machine learning feature importances

The most important features for admission prediction selected by the XGBoost classifier are shown in Fig. 2a for 12 distinct models developed for use with patients that have been on the ED for increasing periods of time, where model T15 is based on data available within the first 15 min of a visit and T240 based on data available within the first 240 min etc. See Supplementary Table 2 for a glossary of features.

a Shows the feature importances, presented for ease of interpretation in four groups (visit data, location history, observations and consults, and pathology). The colour intensity reflects the relative importance of each feature within each model. For simplicity of presentation, a feature is excluded from the figure if it had a raw importance of less than 0.01 in all models. b Shows the number of visits, admission proportion and performance of each model. See Supplementary Table 2 for a glossary of features and Supplementary Note 6 for equivalent analysis of later visits during the Covid-19 pandemic.

Among the visit features, age, arrival method and previous admission are initially important but these diminish as elapsed time passes and signals from other features become stronger. Among the location features, being in a waiting area, or being in the resuscitation area (Resus), are important between 15 and 120 minutes. (See Supplementary Note 1 for more information on the locations within the ED). After 180 minutes, having visited or being in the Mental Health Stream (MHS) is important; this location is used for people with mental health disorders who are usually discharged to a specialist facility but who often stay in MHS for a long time. That explains why MHS is so important in the T720 model, whose training examples include a higher proportion of MHS visits. After 240 min, being in the Clinical Decision Unit (CDU) becomes important; this location is for people waiting for test results or being observed, prior to discharge.

Among the observation and consults features, the number of consults with inpatient specialists, signalling likely admission, is important in all models, especially between 180 and 360 minutes. Triage scores are important up to 60 minutes, and the National Early Warning Score (NEWS) remains important up to 240 minutes. The cumulative number of measurements taken, and the number of times certain indicators are recorded, like nurse checks of body pressure areas, are more important later in the ED stay, presumably reflecting sustained attention by staff to more unwell patients. Among the pathology features, test result values and the number of out-of-range results become important from 60 min onwards, as lab tests results start to be returned to the ED. Requests for certain sets of lab tests (bone profile and C-reactive protein) are important for longer-staying patients.

Results for steps 1-2: evaluation of machine learning model performance

The performance metrics for each model are shown in Fig. 2b. The models achieved lowest log loss when presented with patients with elapsed times of between 30 and 120 minutes, and the best Area Under the Receiving Operating Curve (AUROC) of 0.90 between 90 and 180 minutes. Up to this point, few patients have departed, so the models can differentiate well between likely admissions and discharges. As time goes on, the more straightforward discharges and admissions are made, the number of training examples diminishes (see Fig. 2b) and the case mix includes a higher proportion of more clinically complex cases which are harder to predict.

Calibration plots for each ML model are shown in Fig. 3, applied to all visits in the test set. All models are well calibrated, up to the final two models which related to a very small subset of visits where patients remain in the ED after 8 hours.

Results for steps 1-2: alternatives to XGBoost

In additional analyses we found XGBoost to outperform Random Forest (RF) in terms of log loss on all 12 models (Fig. 4). Logistic Regression with Lasso regularisation gave performance almost as good as XGBoost, with increased regularisation (and as a consequence sparser regression models) giving less good performance (Fig. 4). Both Random Forrest and Logistic Regression models drew on similar feature sets to XGBoost (Supplementary Note 8).

Results for steps 3-7: evaluation of aggregated predictions

In the remaining steps, individual probabilities at the patient level were aggregated into probability distributions over the number of admissions at each prediction time. Figure 5 uses QQ plots to evaluate the concordance between observations and the predicted distributions created at Steps 3, 5 and 7 of the prediction pipeline (see Methods for more about the intervening steps). From visual inspection of the QQ plots, there is very good concurrence between the predicted distributions and observations after Step 3. (Concurrence at Step 3 was worse when using only 3 rather than 12 models at Step 2, as explained in Supplementary Note 7 and shown in Supplementary Fig. 17). After Step 5, concurrence remains good, although (especially for an eight-hour prediction window) the predicted distributions underestimate slightly the number of admissions within the prediction window, suggesting that patients were taking less time to be admitted than predicted. Similar concurrence is observed after Step 7.

Figure 6 compares the model predictions with the conventional six-week rolling average benchmark for daily admissions, adjusted for use at 16:00 h (see Methods for how this was derived). Mean Absolute Error (MAE) was used to compare the approaches, as this avoids positive and negative deviations cancelling each other out, and the error was also expressed as a percentage of observed admissions to derive a mean percentage error (MPE). The prediction pipeline underestimated the number of admissions within the 8-hour window but performed better than the benchmark (MAE of 4.0 admissions with MPE of 17%, compared with 6.5 and 32% for the benchmark).

a Shows the difference between the observed number of admissions and the expected value from the probability distribution for the model predictions (the red dots) and between the observed number of admissions and expected value from the benchmark (the blue dots). Where the expected value equals the observed value, the dots fall on the x axis ( y = 0). The grey shaded band represents the range of probability between the 10 th and the 90 th centile of the cumulative probability distribution of the model. b Shows the distribution of errors (difference between observed and expected). See Methods for a rationale for conducting the evaluation at 16:00 with an 8-hour prediction window.

The results were achieved using data from May 2019 to March 2020, before the Covid-19 pandemic took hold in the UK. In Supplementary Note 6 we present the results of modelling two alternative datasets; one which starts at the point of the Covid outbreak, and one trained only on the period after Same Day Emergency Care (SDEC) was introduced. Patterns of feature importance (Supplementary Fig. 11) and model performance (Supplementary Fig. 12) were largely consistent across the three datasets. The seven-step pipeline needed one adjustment to accommodate the greater variation in both patients and operational conditions after the outbreak of Covid; this was to refine the survival curve used in Step 4 to draw only on rolling data from the past six weeks.

Discussion

Our work provides a practical example of a ML-based modelling approach that is designed and fit for the purpose of informing real-time operational management of emergency admissions. The predictions outperformed the conventional six-week rolling average benchmark. Moreover, the prediction pipeline improved on the benchmark (which only projects up to midnight) by enabling predictions for short time-horizons of 4 and 8 hours at various times throughout the day. When adapted to take account of operational variations in how long it took a patient to be admitted during the state of flux introduced by the pandemic, the predictions made using models trained on data following the Covid outbreak were also able to improve on the benchmark. The results were achieved using only data that are available for inference in real-time.

The predictions were based on ML models that have equivalent or better performance to other studies. Using logistic regression (LR), Barak-Corren et al.18 achieved an AUROC of 0.82 in their 10 minute model (augmented by free-text chief complaint data that was retrospectively encoded), compared with 0.85 at 15 minutes here without chief complaint; they achieved an AUROC of 0.83 after 120 minutes, compared with 0.90 here. Using XGBoost, Barak-Corren et al.31 achieved AUROC of 0.87-0.93 with a 60 minute model, compared with 0.89 here, and Hong et al9 achieved 0.87 with triage data. The challenge for predictions using ED data is the sparse and heterogenous nature of the recorded data. This study created complex models with many features, without dropping visits with missing data as other scholars have done20, and included real-time location features that proved to be important for the long-staying patients who are more difficult to predict for. To exploit the evolving predictive power of features, including data-completeness during patients' visits to ED, 12 temporally framed models were used; Supplementary Note 7 shows the value added by the use of 12 models compared to 3 (the number chosen by Barak-Corren et al.31), confirming that additional models contribute to superior performance. We found that XGBoost outperformed Random Forest and that LR optimised with Lasso regularisation was a close second. More heavily regularised LR models also performed well, suggesting that sparser models could potentially achieve acceptable performance in data-poor settings. That said, we note that that the full set of variables was available for inclusion in the regression models. In addition to good predictive performance, the XGBoost and other classifiers used here have the benefit of being more interpretable than other algorithms such as neural networks, giving some insight into how the models derive their individual-level predictions. Patterns of feature importance and model performance were largely consistent across three different datasets, confirming that the ML models are relatively robust to changes brought about by the pandemic.

This study is also, to our knowledge, one of few to aggregate patient-level probabilities into predictions of bed demand. Peck et al.'s27 proposed summation of probabilities to calculate running bed need does not quantify uncertainty, and England et al.'s32 simulations do not benefit from real-time data. The prediction pipeline generated output that could be evaluated at each step, which means that, when applied to data from later time periods, it is possible to identify (and potentially address), any weak step(s) in the pipeline. For example, a post-hoc change to the step that uses survival curves was made for the period following the Covid outbreak to address fluctuations in the flow of patients through the ED after the pandemic began. Other changes also required adjustments. Same Day Emergency Care (SDEC) was introduced late in 2020 and significantly changed how the ED is run. So, for the real-time information product currently in use, a training set starting in November 2020 was used to train the models, and the survival curves are updated nightly to use only time-to-admission data from the last six weeks.

The need for a post-hoc adjustment to models is reflective of wider difficulties with drift for all predictive applications that learn from past data, including but not limited to those using ML. Methods have been proposed for dealing with non-stationary learning problems33,34. Here a sliding window was applied to one step of the model, but changing patterns of ED presentations and/or changing operational practice may introduce drift or sudden changes in model performance, necessitating continuous monitoring. Here, the modular nature of the aggregation made it easy to swap out the bit that changed and retrain. No model of an evolving system can be expected to predict accurately in perpetuity, so some human action to monitor and revise models is acceptable and indeed expected by patients35. It is also important to alert users of the application if prediction quality deteriorates and we intend to do this in a future release. Nonetheless there is evidence for the robustness of the models. The ML models draw on similar features before and during Covid (see Supplementary Note 6) suggesting that the signals of likelihood of admission remain somewhat consistent.

The hospital where this work was conducted is urban with a student and commuter case mix and no major trauma centre. As ED organisation varies between hospitals (especially location and pathways) the cross-site generalisability of the findings may be restricted to similar hospitals. But aside from those based on location, the risk factors for admission found here are familiar in ED practice and therefore likely to be common to other sites31. There are limitations to this study. Some features used in other studies18,20 are not available here, particularly chief complaint and imaging. Because presentations at ED are seasonal, ideally models have more than one complete annual cycle to learn patterns from11; in fact the two years in this study were very different, affected by organisation changes and the impact of Covid-19 on health services. Future research in this setting could benefit from longer training periods, and focus on predictions of onward destinations, following El-Bouri20, to distinguish between demand for medical versus surgical admissions, and perhaps to differentiate demand according to infection status with respect to Covid-19 or other infectious diseases.

This study was original in two important respects. First, from a research point of view, the use of real-time EHR data for prediction is rare in the literature on emergency admissions, especially in combination with the aggregation of patient-level predictions into output for operational use. In future studies, researchers may find it useful to deploy the prediction pipeline proposed here in healthcare settings with sparse and heterogeneous flows of patient data, such as outpatient clinics. Second, it presented an information product in use, co-designed with bed managers with continued, and ongoing, iterations to meet their needs. Few published studies complete the 'last mile' of AI deployment by reporting on models in production36. Here we demonstrate an application of ML in that last mile, providing an example of how ML for healthcare will need to be delivered if it is to become a dependable and reliable tool. This work draws attention to some challenges that make technically high-performing systems perform poorly for their intended use, including model drift, and considers how to address them.

In a resource-constrained environment, the benefits of ML implementations must be weighed against the costs. Our study was designed under the assumption that better information would improve bed planners' ability to manage the complexities of patient flow, but its benefits are hard to isolate from the contribution of other workflows and initiatives in a complex and changing environment. The costs of implementation were small as the hospital had already invested in the infrastructure to utilise data from the EHR, and no data entry was required by clinical staff in real-time. Other settings without the upfront investment in infrastructure might be able to achieve similar results with fewer data points and simpler models, as suggested by our LR results, and it may be possible to implement some predictive models within an EHR. Ultimately of course, crowding in EDs is an outcome of system-wide issues downstream such as bottlenecks and capacity constraints6,37. AI does not do anything on its own; to succeed, it must be connected to real-world processes38.

Methods

Data source

The source of the data is HL7 messages generated by Epic, the hospital's EHR system. These are captured as they are issued, and stored in EMAP, a PostgresSQL relational database that is kept up to date with latency of less than 5 min. The database records a subset of the full patient record, including observations, pathology orders and results, location of patients, consult requests, and a summary of prior visit history. Data were analysed with R version 4.0.0 using MLR3 packages39 to manage the ML pipeline. The real-time application is run on a security-enhanced Linux machine within the hospital network.

The study was deemed exempt from NHS Research Ethics Committee review as there is no change to treatment or services or any study randomisation of patients into different treatment groups. It was considered a Service Evaluation according to the NHS Health Research Authority decision tool ( http://www.hra-decisiontools.org.uk/research/).

Study population and datasets

The data include all inpatient and emergency visits involving an ED location from 1 May 2019 to 19 July 2021. Pre-processing steps are shown in Supplementary Fig. 1. Patients under 18 on the day of admission were excluded, as were the very few who died in ED and those who self-discharged. After filtering, there were 213,985 visits. In total 36,225 (20.7%) visits ended in admission. Supplementary Note 2 explains how the outcome of admission was derived. The distribution of the outcome variable at different report times, and in periods before and during Covid, is shown in Supplementary Fig. 2.

From March 2020 onward, three notable changes happened that could be material to the modelling process. First, as Covid-19 case numbers have fluctuated through various surges, EDs have had to deal with the uncertainties of a new disease and changing clinical case mix. Second, operational conditions, in terms of levels of busy-ness, have been highly variable. Numbers of presentations fell dramatically in late March 2020 (see Supplementary Fig. 3) and patients were processed quickly. After a quiet period in April to June 2020, presentations gradually climbed, reaching pre-pandemic levels by mid-2021 and EDs have become progressively busier and slower to admit patients. Third, structural changes have led to various reconfigurations of care pathways, including: the introduction of Same Day Emergency Care (SDEC) in December 2020 intended to avoid unnecessary admissions ( https://www.england.nhs.uk/urgent-emergency-care/same-day-emergency-care/access-to-same-day-emergency-care/); the resumption of primary care and urgent paediatric services; the accelerated vaccination campaign; and the establishment of ambulatory services for the treatment of mild to moderate Covid cases. We therefore modelled three datasets; a pre-Covid dataset up to the point of the Covid outbreak in the UK on 19 March 2020, a Covid dataset running from then to 19 July 2021, and one trained only on the period after SDEC was introduced, which runs from 17 November 2020 to 19 July 2021.

Visits were added to training, validation and test sets chronologically with the test set containing the most recent 20% of days, and the validation set the 10% before that, as shown in Supplementary Fig. 3. This temporal split avoids any leak of future information into the past, and allows for a fairer test for problems with temporal drift that are commonly in real-time implementation23. See Supplementary Table 1 for a detailed explanation of how the training, validation and test sets were used to prepare models and regression equations, and to evaluate the predictions, at each step of the pipeline.

Design of prediction pipeline

Our pipeline was designed to generate bed-level predictions from real-time patient-level data streams. We have four prediction times in the day and use data from an observation window to make predictions about the number of admissions in prediction windows of 4 and 8 hours after each prediction time ( italics refer to the terminology of Lauritsen et al.22). We constructed the aggregate predictions in a series of seven steps (see Fig. 1). Figure 7 shows the temporal detail for each step at a hypothetical moment when four patients were in the ED at the prediction time, and an unknown number of patients could be expected to arrive after the prediction time and be admitted within the prediction window.

Methods for Steps 1-2: machine learning for predictions of individual probability of admission

Using Lauritsen et al.'s22 terminology, samples were left-aligned for training and right-aligned for prediction, as shown in Fig. 8. A series of 12 models, each trained on successively longer elapsed times in ED, was created. For example, model T60 was trained on all visits lasting more than 60 minutes and any data timestamped as recorded within 60 min of the patient's arrival. For ease of interpretation, features are described here in four groups. The visit features included demographics, prior visit history and the nature and timing of the patients' arrival. Location features included the patient's location within the ED at the time of the prediction, and locations visited previously. Observation and consults features included triage scores, vital signs, the use of respiratory support and other data recorded by staff. Counts of each type of observation and latest values were used as features. Observations were also summarised by counting: how many observations were recorded; how many different types of observations were recorded; how many recording events took place; and how many specialist consultations were requested. Pathology features included requests for sets of lab tests, and latest results on a selection of tests clinically associated with acute illness. Pathology features were summarised by counting the number of out-of-range results that were higher, and the number that were lower, than the relevant target range for the patient. A glossary and descriptive statistics for features used T0, T90 and T240 models for the pre-Covid period are in Supplementary Table 2. Supplementary Table 3 has descriptive statistics for the T90 model in the various periods analysed (pre-Covid, during Covid and post-SDEC). Supplementary Fig. 4 shows how a Covid surge feature was derived for the periods after the Covid outbreak.

A series of left-aligned datasets was constructed for training as shown in a with fictional examples. In each, the observation window began at the patient's arrival time in the ED. Successive models were trained on longer observation windows. Model T0 had a zero minute cutoff, ie was given only the data known at the arrival time. Model T15 was trained on any data known up to 15 mins. Thus visit A, which lasted less than 15 min, only appears in T0. Model T30 was trained on any data known up to 30 min, so would only include data from visits D, E and F. To make predictions, right-aligned datasets of all patients in the ED at the time of prediction, as shown in b, were created. For each patient, the elapsed time of their visit determined which model was used to predict their probability of admission. Visit I began just less than 30 minutes before the prediction time, so this would be predicted by model T15. Visit K began just more than 30 minutes before the prediction time, so model T30 would be used for this visit.

The size and class balance of the dataset used for each model are shown in Supplementary Fig. 5. As elapsed time increases, fewer patients remain and the proportion of admissions increases. An extreme gradient boosting (XGBoost) classifier40 was trained on each of the 12 datasets using a binary logistic loss function to generate a probability of admission. XGBoost was chosen due to its efficiency and capability of processing large datasets, its ability to handle missing data and imbalanced datasets, and (relative to other ML algorithms) its interpretability. Ten-fold cross-validation was used to find hyperparameters for each model, optimising for log loss, and the validation set used to assess held-out performance of individual-level models and the whole pipeline during development41. See Supplementary Note 3 for details on class balance, tuning and feature generation. To evaluate the individual-level probabilities, predictions were generated for all visits in the test set period and scored using log loss and AUROC as shown in the Results. Log loss was considered the most important metric, because, for input into the aggregation steps, accurate probabilities are more important than classification.

Predictions from XGBoost were compared with Random Forest (RF) and from Logistic Regression (LR). For RF and LR, missing values were imputed at the median. Lasso regularisation was applied to LR using the Lasso and Elastic-Net Regularized Generalized Linear Models (GLMNET) algorithm developed by Friedman et al.42. A series of Logistic Regression equations were created, using the lambda parameter-ranging from lambda.min, the optimal value, to lambda.4se, which is the most regularised model with a mean loss that was 4 standard errors away from that of the lambda.min model-to force greater model sparsity. More details are provided in Supplementary Note 8.

A post-hoc analysis evaluated the use of 3 models at Step 2, whereby visits were stratified as less than 90 min, less than 240 min and the remainder, with the T0, T90 and T240 models applied respectively (see Supplementary Note 7 for details).

Methods for Step 3: aggregation into a probability distribution

At each prediction time, the probabilities of admission for every individual in the ED estimated at Step 2 were combined to give a predicted cumulative distribution function (cdf) for the aggregate number of admissions among this group (see for instance Utley et al.28). The observed number of admissions associated with each prediction time was mapped to the midpoint of the relevant portion of its respective predicted cdf43. A plot of the cumulative distribution of these mapped observations against the predicted cdf was constructed to give a visual guide to the concurrence between the predicted distributions and the observations, analogous to a QQ plot41.

Methods for Step 4-5: survival analysis for time to admission and aggregation

Survival analysis applied to ED visit durations among admitted patients was used to estimate the probability of a patient that had been on the ED for a given time being admitted within the prediction window conditional on them being admitted eventually, with Cox regression used to adjust such probabilities to account for the time of the patient's arrival (time of day, weekday or weekend, quarter of year) and the occupancy of the ED at that time. (See Supplementary Note 4 for more details and Supplementary Table 4 for regression coefficients.) This analysis was combined with the probabilities of admission estimated at Step 2 to give a probability for each patient in the ED at the prediction time of being admitted within the prediction window. These probabilities were then combined to give a predicted cumulative distribution function for the aggregate number of admissions within the prediction window among this group (as per Step 3).

Methods for Step 6-7: Poisson analysis for patients who have not yet arrived and aggregation

At each prediction time during the training set periods, a count was made of the number of patients not on the ED at the prediction time who were admitted via ED within the prediction window. A Poisson regression was fitted to the count data, with coefficients for the prediction time of day (06:00, 12:00, 16:00 and 22:00), quarter of year, and weekday or weekend. (See Supplementary Note 5 for more details and Supplementary Table 5 for regression coefficients.) The resulting coefficients were used to generate a probability distribution for the relevant prediction time of patients who have not yet arrived, and this was convoluted with the output from Step 5 to generate the final aggregated predictions of number of admissions within the prediction window, which was evaluated using QQ plots as in Step 3.

Methods for final evaluation: comparison with benchmarks

Comparison with the commonly used six-week rolling average was not straightforward, as this metric is for a 24 hour prediction window from midnight. A direct comparison with it is only possible for a prediction window that ends at midnight. Following practice in the hospital, the observed number of admissions up to 16:00 h was subtracted from the daily rolling average to derive a prediction for the period from 16:00 h to midnight for the benchmark. This was compared with the models' 8 hour predictions at 16:00 h for all report days in the test set, illustrated in Fig. 6.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Acknowledgements

We acknowledge Lorraine Walton and Craig Wood for help with user requirements, Mohamed Abdalla, Jennifer Hunter, Dan Stein and Rebecca Wong for clinical advice, Nel Swanepoel for help building the real-time application, Sarah Keating, Tom Keen, Roma Klapaukh and Stefan Piatek for assistance with EMAP and Tim Roberts for modelling advice. The work was funded by a grant from the Wellcome Institutional Strategic Support Fund (ISSF) UCL and Partner Hospitals: AI in Healthcare Funding Call 2019 (award number BRC717/HI/RW/101440), with an extension funded by NIHR UCLH Biomedical Research Centre HIGODS Theme (award number BRC824/HG/ZK/110420). The contribution of MU and JG was funded under the National Institute for Health Research (Artificial Intelligence, Digitally adapted, hyper-local realtime bed forecasting to manage flow for NHS wards, AI_AWARD01786) and NHSX. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research, NHSX or the Department of Health and Social Care. This work was supported by the Wellcome Trust (reference: 204841/Z/16/Z)

Author information

Authors and Affiliations

1. Clinical Operational Research Unit, University College London, 4 Taviton Street, London, WC1H 0BT, UK

Zella King, Martin Utley, Enoch Kung & Sonya Crowe

2. Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK

Zella King, Joseph Farrington & Kezhi Li

3. University College London Hospitals NHS Foundation Trust, 250 Euston Road, London, NW1 2PG, UK

Samer Elkhodair, Steve Harris, Richard Sekula & Jonathan Gillham

Contributions

Z.K. performed the literature search, conducted the modelling and evaluation, contributed to the modelling design, generated the figures, and co-wrote the paper. S.C. and M.U. contributed to the modelling design and model evaluation, and co-wrote the paper. K.L. and J.F. contributed to the data interpretation and model evaluation and drafting. E.K. contributed to model evaluation. S.C. conceived the study with R.S. and S.E., and secured funding. S.H. secured further project funding and contributed to model evaluation and drafting. J.G. contributed to the software engineering of the application. R.S. and S.E. contributed to drafting.

Corresponding author

Correspondence to Zella King.

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Supplementary information

Supplementary Material: Machine Learning for Real-Time Aggregated Prediction of Hospital Admission for Emergency Patients

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King, Z., Farrington, J., Utley, M. et al. Machine learning for real-time aggregated prediction of hospital admission for emergency patients. npj Digit. Med. 5, 104 (2022). https://doi.org/10.1038/s41746-022-00649-y
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Machine learning for real-time aggregated prediction of hospital admission for emergency patients - npj Digital Medicine

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Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital's emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction.

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Construindo as cidades do futuro - Lenoir Schneider

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Construindo as cidades do futuro - Lenoir Schneider

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A tecnologia permite que os municípios entendam as demandas dos cidadãos, invistam em segurança pública, melhorem o trânsito, entre outros quesitos das cidades inteligentes

https://proximonivel.embratel.com.br/tecnologia-habilita-cidades-inteligentes/

Cidades inteligentes proporcionam um engajamento melhor das pessoas, dentro do municipio, maior qualidade de vida, mobilidade e segurança pública.
Mobilidade urbana, videomonitoramento conectando camerâs particulares no monitoramento da cidade, controle melhor do tráfego, dentro outros tantos beneficios.

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Por Redação em 12/07/2022

Ferramentas como Internet das Coisas (IoT), videomonitoramento, inteligência artificial (IA) e analytics são essenciais para viabilizar as chamadas cidades inteligentes - ou seja, municípios que usam a inteligência para melhorar os serviços públicos e utilizar os recursos de forma mais eficiente.

Entre os benefícios estão o aumento da segurança pública, uso racional de energia para iluminação, melhorias no trânsito e maior compreensão das demandas dos cidadãos. "O céu - ou a criatividade - é o limite", disse Maria Teresa de Azevedo Lima, diretora-executiva da vertical Governo na Embratel, em entrevista à Folha de São Paulo, mencionando o projeto City Câmeras, da capital paulista, que tem por objetivo promover o aumento da segurança pública.

Cidadão omnichannel

Maria Teresa destacou, na entrevista, que uma cidade inteligente pressupõe cidadãos conectados e engajados no centro das decisões. "E como promover esse engajamento? Um exemplo é o omnichannel, que amplia e facilita a comunicação do governo com a sociedade, favorecendo a participação mais ativa das pessoas nas decisões e na destinação de recursos públicos", afirmou.

A executiva também citou a importância de ferramentas como Internet das Coisas (IoT), videomonitoramento, inteligência artificial (IA) e analytics, que ajudam as cidades a garantirem, por exemplo, mais segurança, redução do tempo perdido no trânsito, destinação correta de resíduos e melhor gestão de recursos como água e energia.

Cidades inteligentes promovem maior qualidade de vida

Segundo Maria Teresa, as ferramentas possibilitam melhor qualidade de vida para os cidadãos, com benefícios que se refletem em áreas como educação e saúde. "Um exemplo disso foi o uso de dados durante a fase mais aguda da Covid-19. "Estados e municípios utilizaram plataforma de mapa de calor disponibilizada, sem custos, pelas operadoras móveis. Com isso, foi possível atuar para evitar aglomerações e aumento de contágio", ressaltou.

Ela citou também as melhorias no atendimento à saúde com o avanço do 5G. "A coleta e a análise de dados oferecem muitas possibilidades, como desenvolvimento de programas educativos específicos por região e por faixas etárias, controle de endemias, acompanhamento de cobertura vacinal e de gestantes, crianças e idosos, entre outros", explicou, destacando que tudo isso é feito com o uso de dados anonimizados, em respeito à Lei Geral de Proteção de Dados Pessoais (LGPD).

Mobilidade e segurança pública também são beneficiados

Na entrevista, a executiva da Embratel citou ainda alguns benefícios da tecnologia para a mobilidade urbana e também para a adoção de estratégias de segurança. "IoT, videomonitoramento e analytics permitem coletar e analisar, em tempo real, dados sobre a condição de trânsito nas cidades e alterar tempos de abertura e fechamento de semáforos, tornando o tráfego mais fluido", exemplificou, citando a parceria com a Escola Politécnica da USP e a Ericsson para uso de IoT nessa área.

Segundo ela, o videomonitoramento também permite melhorias na segurança pública, ao conectar câmeras particulares aos equipamentos da prefeitura. "Isso possibilita não somente maior segurança, mas também a maior participação dos cidadãos", disse.

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Por Redação em 12/07/2022 Ferramentas como Internet das Coisas (IoT), videomonitoramento, inteligência artificial (IA) e analytics são essenciais para viabilizar as chamadas cidades inteligentes - ou seja, municípios que usam a inteligência para melhorar os serviços públicos e utilizar os recursos de forma mais eficiente.

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A Neavon Project trabalha para o desenvolvimento de software baseado em pesquisa, sistemas integrados/eletrônicos e mecânicos para fins de pesquisa e desenvolvimento. É possível adquirir kits para desenvolvimento de projetos com IoT.

Lucas Baumgart

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Internet of things is the coming together of internet and physical devices in a network of unlimited possibilities using microcontrollers, arduino and raspberry pi. IOT allows for physical devices to wirelessly communicate over networks which has led to a growing number of applications for iot devices. Nevon Projects brings you the list of most innovative IOT projects for students and engineers using micocontroller arduino raspberry pi etc. Also all iot projects require internet connection using either esp8266 nodemcu or gsm connection. These IOT ideas are updated every week to bring you new unique IOT project ideas every week.

#TrendingIOTProjects

Nevon Projects brings you a list of most innovative IOT Projects an internet of things ideas. IOT stands or internet of things viz hardware devices interacting over the internet. This is done using either ESP8266 and NodeMcu which are frequently used to develop a wide variety of iot devices and diy projects or using Gsm connectivity. Here are some of the top IOT projects made using nodemcu and esp8266 wifi module and GSM. This are ideas for the growth of internet of things in many applications including consumer electronics, automotive, industrial, smart cities etc.

Need Custom Made IOT Based Project / System ?

These iot projects using arduino and the iot projects using raspberry pi board are an inspiration to students and researchers for further iot research. Our researchers focus on the use of IOT for home/industry automation and monitoring various physical parameters over the internet. Here you may find a wide list of projects related to internet of things along with free synopsis abstract, PPT and source codes for building up practical IOT knowledge.
Nevonprojects constantly researches on internet of things ideas and concepts that have the potential to have numerous iot applications.

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Top IOT Projects 2022 IOT Ideas and Topics | NevonProjects

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Internet of things is the coming together of internet and physical devices in a network of unlimited possibilities using microcontrollers, arduino and raspberry pi. IOT allows for physical devices to wirelessly communicate over networks which has led to a growing number of applications for iot devices.

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City Brain - Keith Eberhardt Sarate

Aluno: Keith Eberhardt Sarate

City Brain  é um sistema de software que utiliza inteligência artificial e coleta de dados para gestão urbana.

O City Brain é usado principalmente para gerenciamento de tráfego, mas têm sido usados e expandido para cobrir outras necessidades também tornando as cidades mais inteligentes.

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The City Brain Lab is committed to building new infrastructure for future cities by using data, also opening up a "pipeline" of city data. The solutions have been deployed in Hangzhou, Suzhou, Shanghai, Macau, Malaysia and etc., which assists to solve problems of transportation, security, municipal construction, urban planning and etc.

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Monitoramento de condição de mercadorias em trânsito baseado em IoT

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Monitoramento de condição de mercadorias em trânsito baseado em IoT

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Aluno: Nícolas Savaris Ludwig

O monitoramento das condições das mercadorias é essencial em indústrias farmacêuticas ou de alimentos e bebidas. Muitas vezes, produtos dessas indústrias são transportados para locais distantes e/ou remotos e dependem de condições ideais de temperatura para sua preservação. Nesse contexto, dados de temperatura são um dos valores mais importantes, podendo garantir a segurança do produto final através de um acompanhamento rigoroso ao longo de toda a cadeia de abastecimento, tanto em trânsito como em armazenamento.

Uma empresa global de ciências da vida trabalhou com a DHL para habilitar paletes de mercadorias com sensores inteligentes. Os sensores são equipados com conectividade de área ampla de baixa potência (LPWA) e rastreiam localização geográfica, movimento, atraso, choque e temperatura. Essa capacidade permitiu que a empresa de ciências da vida reduzisse seus custos de ciclo de vida em 6 a 7%, com confiabilidade relatada de quase 100%.

Mais informações em: https://iot-analytics.com/top-10-iot-use-cases/

Este caso relatado refere-se ao número 8 do artigo.

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In short

Adoption of various Internet of Things (IoT) use cases is on the rise, according to the latest 2021 IoT Use Case Adoption report.

Of the 200+ companies from various industries interviewed, the average company has adopted eight different IoT use cases to date (out of a total of 48).

IoT use cases related to the smart operations of a company are the most adopted, followed by smart supply chain use cases and use cases involving connected IoT products.

Read-only remote asset monitoring is the most widely adopted use case (34% of companies interviewed have adopted it).

Why it matters?

With various IoT topics prioritized by organizations post-COVID, getting a feel for what is hot and what is not is important for both vendors and IoT adopters.

Vendors should prioritize those use cases that yield a high return on investment (ROI) and those that are on the investment priority list in the coming years.

IoT adopters can learn from the best practices and examples of existing users to prevent costly mistakes.

The IoT Use Case Adoption Report 2021

In 2021, the average large manufacturing, healthcare, automotive, retail, or energy company has rolled out eight different IoT use cases, according to IoT Analytics' latest IoT Use Case Adoption Report.

The 430-page report, which is part of IoT Analytics' ongoing market coverage of IoT applications, is the first such in-depth report and is based on 200+ interviews with IoT end users who have rolled out more than 1,600 IoT projects over the last few years across 48 different IoT use cases. The report shows where companies have been investing and are planning to invest, which industries and regions are ahead, and which use cases promise the highest ROI.

Oil and gas companies and energy companies are ahead of others. They have rolled out an average of 15 use cases. Make no mistake: For many companies, IoT is still a small fraction of their business. The average company we interviewed had $9.6 billion in revenue and only currently spends $33 million on IoT use cases (0.34% of the revenue). The fact that the top use case today has only been adopted by 34% of respondents (but with a predominantly positive ROI) shows how much bigger the IoT opportunity is.

Note: The report looks at IoT use cases that are applicable to most organizations and does not consider industry-specific use cases such as smart heating, ventilation, and air conditioning (HVAC) (applicable only to buildings) or smart transportation systems (applicable to cities). The analysis also does not consider consumer IoT use cases such as Smart Home devices or wearables. For a broader analysis of enterprise IoT applications, see this analysis from 2020. For a deep dive on Smart City IoT use cases, see . A deep dive on Smart Building use cases will be published later this year.

Six of the top 10 IoT use cases today (ranked by adoption) aim at making operations smart, thus improving companies' production processes for manufacturing, enhancing maintenance operations, or advancing any other operations (e.g., energy generation in the case of an energy company, running healthcare operations in the case of a hospital, or running store operations in the case of a retail company). Three of the top 10 use cases are related to smart supply chains, and only one is related to smart products in the field.

Here are the 10 most adopted use cases, ranked by their adoption rate:

1. Read-only Remote Asset Monitoring

Not surprisingly, the simplest IoT use case is also the most adopted. Read-only remote asset monitoring refers to assets that are connected from afar in a read-only manner (i.e., one can visualize the asset data, but one cannot send back any commands to the asset itself). This use case is one of the easiest and cheapest to set up due to its simplicity. In many cases, remote asset monitoring supersedes the error-prone and costly manual task of checking and documenting asset states in person. Adoption in 2020 was clearly accelerated by the pandemic and is expected to grow even further, as 36% of interviewees say they plan to invest significantly in this use case in the next two years.

Adopter's quote

"In the future, our engineers and plant operations staff will not need to sit in cars to go from their office location to another sub-station or satellite equipment room to perform daily routine activities. Routine read-only activities could be done more frequently, which could indirectly contribute to the reliability of control systems." Senior operations manager of a mining company in Qatar

Implementation example

Hindustan Coca-Cola Beverages Pvt. Ltd., located near Pune, India, adopted remote asset monitoring for its PET and can-bottling lines. The real-time and continuous monitoring of critical parameters (e.g., syrup flow rate, can-rinse pressure, and water temperature) replaced its MS Excel-based manual reporting. In this specific case, the company combined the remote connectivity of its assets with a condition-monitoring use case that generates alarms based on predefined conditions.

| Use case definition (by IoT Analytics)

2. IoT-based Process Automation

IoT-based process automation has been rolled out by 33% of companies. This type of process automation describes operational processes that were either entirely manual in the past or relied on antiquated, industrial-automation setups but have now been upgraded with state-of-the-art hardware and software. Companies that introduce this use case to upgrade their existing setups often do so to add flexibility and agility in the operations process so that specific process steps can be changed in the future. This becomes important because companies are increasingly interested in aligning their manufacturing and operations processes to ever-changing customer demands.

Adopter's quote

Implementation example

An Australian farmer, reported he was able to use 20% less water thanks to a new IoT-based irrigation system supplied by Lindsay, a US-based manufacturer of pivot-irrigation systems. Farmers increasingly adopt smart IoT-based irrigation systems to automate the process of irrigation and thereby enhance their crop yields.

| Use case definition (by IoT Analytics)

3. Remote Asset Monitoring and Control (read/write)

"Read/Write remote asset monitoring and control" is the extension of "Read-only remote asset monitoring." On top of "reading" the assets data, with this setup one can also communicate back and thereby influence asset control from afar (i.e., "writing" data back).

Just like the read-only asset monitoring use case, this use case was accelerated by the COVID-19 pandemic as service teams, engineers, and other staff needed to find ways to reach an asset that needed their attention while being in a different location.

Because of the added complexity and security risk of controlling an asset, on top of just simply monitoring, this use case comes with significantly higher costs of installation and maintenance. However, it has been proven that such solutions pay off in a relatively short time: Of those decision makers whom IoT Analytics interviewed, 51% reported amortization in less than 24 months.

Adopter's quote

Implementation example

Schlumberger, an oilfield service company, recently adopted a monitoring and control solution from Advantech and ReStream. These companies had partnered up to supply and develop a new oilfield fluid-monitoring system based on LTE connectivity. The project helped to achieve flow assurance, ensure asset integrity, and optimize production. It also helped to increase labor safety. Oilfield companies suffer from a dynamic chemical environment that, if unchecked, can lead to premature wear, lost productivity, and the release of lethal poisonous gases.

| Use case definition (by IoT Analytics)

4. Vehicle Fleet Management (track/trace)

The management of vehicles in a fleet is the number one IoT supply chain use case right now. The larger the fleet of trucks (or other means of transport) to manage, the higher the complexity. Cross-border fleet management can be especially overwhelming, which is why 31% of companies have rolled out a professional vehicle fleet management solution to gather real-time information.

Today, most fleet management solutions are reliant on wide-area connectivity, such as cellular (2G, 3G, 4G). Several new, low-cost IoT satellite technologies have recently been launched and promise to add another option to this use case so that vehicle fleets are always truly connected at a reasonable price. New companies include Hiber (IoT-focused satellite network) and Starlink (part of SpaceX). Both plan to send thousands of new satellites into space in the coming years to provide low-latency, high-bandwidth connectivity globally.

Adopter's quote

Implementation example

Lineas, the largest private rail freight operator in Europe, managed to increase capacity utilization of its fleet by more than 40 percent by implementing the Bosch fleet management solution.

| Use case definition (by IoT Analytics)

5. Location Tracking (e.g., GPS)

Not every company sells smart-connected IoT products, but of those that do, tracking the location of the asset is the number one use case (31% of companies in our interview set have adopted it). The results from this interview series are in line with results from the IoT Commercialization & Business Model Report 2020, which found that geolocalization was also the number one use case in terms of customer adoption when original equipment manufacturers (OEMs) were asked which smart product features their end customers were adopting as part of their IoT-enabled assets. Location tracking is therefore important for developing a successful IoT business model.

Tracking the location of an asset can be beneficial to both the vendor of the products (e.g., by understanding usage patterns) and to the user (e.g., by finding a lost item or by mitigating theft).

Adopter's quote

Implementation example

VanMoof, an Amsterdam-based e-bike manufacturer, offers an anti-theft service to its customers that is based on the location tracking of a bike. For a monthly fee, the company sends its "bike hunters" if a customer's bike is stolen. The bikes are connected and tracked by a combination of Bluetooth and cellular networks. This allows the company to keep its promise of recovering the bike in up to 70% of cases.

| Use case definition (by IoT Analytics)

6. IoT for Asset/Plant Performance Optimization

Asset performance management (APM) is a term in manufacturing to describe methods of capturing and integrating data, visualizing it, and analyzing it to improve the reliability and availability of physical plant-floor assets. IoT for asset/plant performance optimization is the modern version of APM. It integrates state-of-the-art data-capture and integration tools (like IoT gateways) and software tools (like IoT platforms) to analyze how assets can be run and maintained at optimum levels (e.g., optimized asset speed settings, optimized material input settings, or optimized maintenance intervals).

More than 30% of companies in our interview set have adopted IoT for asset/plant performance optimization. With 42% of companies planning to invest significantly in IoT for this purpose, it is among the use cases with the highest expected growth rate.

Adopter's quote

Implementation example

A beverage manufacturer that was running mostly older equipment used an IoT solution provided by Relayr in one of its bottling plants, which produces 1.2 million bottles per day per production line. It achieved improvements of 11% in performance and 8% in quality. Implementing an IoT retrofit kit and an IoT gateway, the company was able to identify the processes responsible for 90% of the plant's downtime. Based on the IoT data collected from the legacy machinery of different manufacturers, the company was able to address several issues (e.g., changing the control logic of the machines).

| Use case definition (by IoT Analytics)

7. IoT- based Quality Control & Management

IoT-based quality control and management involves the use of machine vision or other IoT sensor data to detect quality issues in real time during operations. The use case pays off particularly quickly: Of the 30% of companies that have implemented this IoT use case, two-thirds report amortization in fewer than 24 months.

Adopter's quote

Implementation example

A leading global automobile manufacturer automated the testing of the electronic functionality of its cars during the manufacturing process. As the company was adding greater electronic functionality to the cars to stay current with technological developments, it was looking to find ways to optimize its time-consuming, manual method of checking the operation of electronic parts. With the help of Cognizant, the company was able to completely automate vehicle testing and also implement a mixed-mode validation to simulate missing parts that would arrive late due to unforeseen supply chain issues. The solution realized a positive return on investment in four months.

| Use case definition (by IoT Analytics)

8. IoT-based goods condition monitoring in transit

Monitoring the conditions of goods is essential in industries such as pharmaceuticals or food and beverage and can even be seen as a way to help tackle the global food shortage. Temperature sensor data is one of the most important sensor values for this use case; it guarantees the safety of the final product by close monitoring throughout the entire supply chain, both in transit and in storage.

Condition-monitoring solutions were used by 29% of the interviewed decision makers as part of their digital supply chain initiatives.

Adopter's quote

Implementation example

A global life sciences company worked with DHL to enable pallets with smart sensors. The sensors are equipped with low-power wide-area (LPWA) connectivity and track geographic location, movement, delay, shock, and temperature. This ability has enabled the life science company to reduce its life-cycle costs by 6 to 7%, with reported reliability of nearly 100%.

| Use case definition (by IoT Analytics)

9. Predictive Maintenance

To forecast the remaining useful life of assets and ensure they are repaired before they fail, 29% of all companies have invested in solutions at the intersection of artificial intelligence and maintenance. As IoT Analytics highlighted in a recent blog post, predictive maintenance (PdM) is an en vogue topic, with 40% of interviewed decision makers planning to invest significant amounts of money in the coming two years.

Adopter's quote

Implementation example

Colgate-Palmolive Company, a US-based consumer goods manufacturer, leveraged a predictive maintenance solution to optimize its manufacturing capacity and prevent costly downtime. It installed wireless sensors on 2,000 machines (including tube makers and liquid machines) to collect machine data. The predictive maintenance solution detected increased temperatures and alerted staff, thereby preventing nearly 200 hours of downtime and a missing output of 2.8 million tubes of toothpaste.

| Use case definition (by IoT Analytics)

10. On-Site Track and Trace

The availability of low-cost sensors and trackers has made it easy and relatively cheap to track goods and tools at construction sites, ports, and even inside buildings (e.g., factories). Of the experts interviewed, 29% have implemented such a solution. The median spending for a fully functional, on-site track-and-trace solution (in our interview set) is only $25,000-one of the lowest in the data set. Costs can scale up, however, as more and more assets get connected.

Adopter's quote

"Having On-Site Track & Trace has improved our efficiency [and] reduced labor time for tracking items (we have almost 1,000 stock-keeping units). We are now also able to check stock balances in real time." Head of IT at a retailer in Singapore

Implementation example

Continental, a German automotive supplier, implemented KINEXON'S real-time locating solution at its logistics center and production site in Regensburg. More than 50 transportation vehicles and more than 3,000 load carriers are tracked live inside the 30,000 m² building sites, allowing Continental to locate each of these items within a precision of several centimeters (inches), thereby creating a transparent material flow in the supply chain and increasing overall throughput.

| Use case definition (by IoT Analytics)

More information and further reading

Are you interested in learning more about IoT Use Cases

The IoT Use Case Adoption Report 2021 is a comprehensive 430-page report examining the adoption of 48 IoT use cases across 4 types: smart operations, smart supply chain, connected products, and connected transport. It includes the adoption (by region/country, industry), a detailed breakdown of spending, ROI per use case, key vendors & vendor satisfaction, as well as best practices and lessons learnt. It is part of IoT Analytics' ongoing coverage of IoT in general.

This report provides answers to the following questions (among others):

What are the most adopted IoT use cases (out of a list of 48 use cases)?

Which IoT use cases have the highest ROI?

How much do companies spend to implement IoT use cases?

How much do they spend on hardware, software and services respectively?

Which IoT use cases do companies plan to invest in in the next 2 years?

Who are the leading vendors for IoT use cases and how satisfied are end-users?

What supporting technologies do IoT end-users implement as part of their IoT use case projects?

What are the best practices and lessons learned of companies that have adopted the IoT?

And more...

Sample

The sample of the report gives you a holistic overview of the available analysis (outline, key slides). The sample also provides additional context on the topic and describes the methodology of the analysis. You can download the sample here:

Related articles

You may also be interested in the following recent articles:

Are you interested in continued IoT coverage and updates?

Subscribe to our  newsletter and follow us on  LinkedIn and  Twitter to stay up-to-date on the latest trends shaping the IoT markets. For complete enterprise IoT coverage with access to all of IoT Analytics' paid content & reports including dedicated analyst time check out   Enterprise subscription.

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Adoption of various Internet of Things (IoT) use cases is on the rise, according to the latest 2021 IoT Use Case Adoption report. Of the 200+ companies from various industries interviewed, the average company has adopted eight different IoT use cases to date (out of a total of 48).

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Software usa inteligência artificial para diagnosticar risco de doenças do coração - Everton Goulart

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Software usa inteligência artificial para diagnosticar risco de doenças do coração - Everton Goulart

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Algoritmo aponta, a partir da análise de imagens comuns de ultrassonografia, alterações nas artérias que tornam o diagnóstico mais eficaz, rápido e barato. Criação do sistema foi em parceria com a UFSC.

Pesquisadores da Unicamp desenvolveram um sistema de computador capaz de analisar e apontar com precisão, a partir de imagens comuns de ultrassonografia, alterações nas artérias que antes só seriam possíveis em exames específicos e até invasivos. O avanço, graças ao uso de inteligência artificial, pode tornar o diagnóstico de doenças cardiovasculares mais eficaz, rápido e barato, além de acessível a mais pacientes.

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Software desenvolvido na Unicamp usa inteligência artificial para diagnosticar risco de doenças do coração

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Algoritmo aponta, a partir da análise de imagens comuns de ultrassonografia, alterações nas artérias que tornam o diagnóstico mais eficaz, rápido e barato. Criação do sistema foi em parceria com a UFSC.

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Automação de processos jurídicos – Thomas Vieira

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Automação de processos jurídicos – Thomas Vieira

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Diversos procedimentos jurídicos podem ser automatizados através de algoritmos computacionais, de forma a desonerar profissionais de trabalhos repetitivos e burocráticos, permitindo que atuem em processos complexos os quais serão julgados com mais agilidade.

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Robôs inteligentes garantem mais otimização em processos jurídicos -

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No mercado há 20 anos, a Catho é um marketplace de tecnologia que conecta empresas e candidatos . Pioneira no seu segmento, a companhia conta com cerca de 800 colaboradores e já ajudou centenas de candidatos a encontrarem oportunidades de trabalho.

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Inteligência de decisão: o novo Business Intelligence - Marcelo da Silva

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Inteligência de decisão: o novo Business Intelligence - Marcelo da Silva

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https://itforum.com.br/noticias/inteligencia-de-decisao-o-novo-business-intelligence/

Comentários:
Este artigo discorre sobre um tema muito relevante para o mundo dos negócios, em que trata do uso de IA e Machine Learning sendo adicionados às plataformas de BI, potencializando o seu uso e transformando-o numa poderosa ferramenta na tomada de decisão tornando-as mais abrangentes. É feito um detalhamento maior sobre esse movimento para a "Inteligência de Decisão" e a importância do uso dessas informações para conhecer melhor o seu público e oferecer produtos ou serviços relevantes para a sociedade.
Penso que muito do que estamos vivendo atualmente está forçando as empresas e o mundo de um modo geral se reinventar para se manterem num mercado cada vez mais competitivo.

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A Digifarmz é uma startup de Porto Alegre - RS
que possui uma plataforma que analisa diversas variáveis do processo do plantio de soja.

através de algoritmos matemáticos, a solução reúne dados de pesquisa sobre clima, solo, genética, datas de semeadura, local entre outros parâmetros com o objetivo de apresentar recomendações eficazes à tomada de decisão do produtor.

Assim auxiliando os produtores e agrônomos, indicando quais fungicidas devem ser utilizados melhor data para pulverização e colheita e alerta sobre doenças.
Todos esses dados impactam diretamente no aumento da produtividade e qualidade das lavouras.

A solução entrega parâmetros e recomendações em tempo real para os clientes, baseados em pesquisas e crowdsourcing, que servem como base para a tomada de decisões técnicas.

Site da empresa:
https://www.digifarmz.com/

Notícias sobre a empresa:

https://ventiur.net/digifarmz-atua-na-reducao-de-custos-e-perdas-na-producao-de-soja/

https://www.startse.com/noticia/startups/agrotech-digifarmz-algoritmo-doencas-soja/

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Elon Musk fundou a Neuralink ainda em 2016, na Califórnia, como uma empresa de pesquisa médica, criando chips que podem ajudar pacientes em tratamentos. Em setembro do mesmo ano, os interesses iam além e as coisas ficaram um pouco mais claras, com o executivo explicando o seu desejo de criar uma forma de integrar a inteligência humana com a artificial, literalmente conectando o cérebro ao computador.
https://canaltech.com.br/inteligencia-artificial/neuralink-o-que-e-como-funciona-170585/

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Neuralink | O que é e como funciona o projeto que conecta um chip ao cérebro

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Elon Musk vai fazer mais uma apresentação contando os avanços obtidos pela sua empresa Neuralink, que consiste na implementação de um chip no cérebro humano

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https://institutolaura.org/solucoes/

Comentários: utilizando grande captura de dados (big data) o software acompanha o quadro de saúde de bebês e assim com base em dados prévios pode auxiliar a equipe médica antecipando situações mais críticas que exigem maior atenção, permitindo que a equipe concentre esforços onde é mais prioritário.

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Instituto Laura Fressatto | O Instituto Laura Fressatto ajuda a salvar centenas de vidas ao levar tecnologia de ponta, acessível e eficiente aos hospitais filantrópicos e públicos no Brasil.

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Soluções - Instituto Laura Fressatto | O Instituto Laura Fressatto ajuda a salvar centenas de vidas ao levar tecnologia de ponta, acessível e eficiente aos hospitais filantrópicos e públicos no Brasil.

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A saúde precisa chegar onde estão as pessoas. O Laura Care nasceu para chegar na ponta e conectar diretamente pacientes e profissionais de saúde ao mesmo tempo que coleta informações, analisa riscos e classifica o potencial de gravidade dos pacientes. Tudo de forma muito natural, por meio de conversas pelo Whatsapp (entre outras interfaces).

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Naurotecnologia aborda tecnologias para o entendimento do cérebro e aqui eu coloco o neuralink que o objetivo principal é o implante de chips cerebrais para ajudar pacientes com problemas neurológicos.

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Our Mission

Brain-computer interfaces have the potential to change lives for the better. We want to bring this technology from the lab into peoples' homes.

Create a generalized brain interface to restore autonomy to those with unmet medical needs today and unlock human potential tomorrow.

Implant

Our brain-computer interface is fully implantable, cosmetically invisible, and designed to let you control a computer or mobile device anywhere you go.

Surgical Robot

The threads of our implant are so fine that they can't be inserted by the human hand. Our surgical robot has been designed to reliably and efficiently insert these threads exactly where they need to be.

A Seamless BCI Experience

To restore independence and improve lives, we've built a brain-computer interface (BCI) experience that enables fast and reliable computer control and prioritizes ease of use.

Patient Registry

If you're interested in learning whether you may qualify for current and/or future Neuralink clinical trials, consider joining our Patient Registry.
Join Our Patient Registry

Careers

Developing brain-computer interfaces is an interdisciplinary challenge. We are looking to hire a wide range of people with diverse engineering, scientific, and operations expertise.

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Creating a generalized brain interface to restore autonomy to those with unmet medical needs today and unlock human potential tomorrow.

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Aluno: Rodrigo Lemos Obach

Fiware busca melhorar a eficiência da indústria de saúde utilizando inteligência artificial, internet of things e Blockchain. A solução é uma plataforma open-source para coleta de dados contextuais em tempo real e automatização de tarefas a partir da análise destes dados.

https://www.fiware.org/2021/10/15/open-source-hospital-intelligent-automation-server/

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Empatica - Monitoramento da Saúde Humana - Fernanda Rapellini Padilha

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Empatica - Monitoramento da Saúde Humana - Fernanda Rapellini Padilha

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Empatica é uma platafarma de sistema de inteligência artificial voltada para o monitorameno da saúde humana. Por meio de sensores de coleta de dados fisiológicos.  é feita a interpretação, para que seja possível antecipar a previsão, monitoramento, pesquisa e o tratamento. Os principais dados coletados são frequência cardíaca, atividade eletrodérmica, aceleração e movimento, temperatura, dentre outros. Com base nestes dados, Empatica esta conseguindo diminuir os casos de morte em pacientes com epilepsia e convulsões, tendo já sido aprovado pelo FDA. Ademais, está sendo utilizado pela NASA para a verificação da saúde dos astronautas e também para a previsão de casos simtomáticos da COVID-19.
Links relacionados:
https://www.mobihealthnews.com/39465/empatica-crowdfunding-embrace-a-wearable-for-epilepsy
https://techport.nasa.gov/view/96036
https://www.newscientist.com/article/dn21795-sweat-sensing-bracelet-could-detect-fatal-seizures/
Link da empresa:
https://www.empatica.com/en-int/index.html?utm_source=Google&utm_medium=Paid&utm_campaign=ADW_AO_BRND_CNV_1021&gclid=EAIaIQobChMIz9qEpLOt-gIVYehcCh0VNAM9EAAYASAAEgLxuPD_BwE

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Análise individual de videiras em um contexto multitemporal usando imagens multissensor baseadas em UAV

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Análise individual de videiras em um contexto multitemporal usando imagens multissensor baseadas em UAV

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O uso de veículos aéreos não tripulados (VANTs) para aplicações de sensoriamento remoto na viticultura de precisão aumentou significativamente nos últimos anos. A capacidade dos VANTs de adquirir alta resolução espaço-temporal e imagens georreferenciadas de diferentes sensores os torna uma ferramenta poderosa para um melhor entendimento da heterogeneidade espacial e multitemporal dos vinhedos, permitindo a estimativa de parâmetros que impactam diretamente o estado de saúde das plantas. Desta forma, o processo de apoio à decisão na viticultura de precisão pode ser muito melhorado.

https://www.mdpi.com/2072-4292/12/1/139/htm

Arthur Boeck

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Engineering Department, School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal

Centre for Robotics in Industry and Intelligent Systems (CRIIS), INESC Technology and Science (INESC-TEC), 4200-465 Porto, Portugal

Author to whom correspondence should be addressed.

The need to assess vineyard spatiotemporal variability is crucial in viticulture, as it is directly related to grapevine health status and yield [ 1], which can be achieved through precision viticulture (PV). Derived from precision agriculture (PA) concepts [ 2], in PV, different technologies for vineyard management and grape production are employed for data acquisition and processing, aiming, among others, to maximize the oenological potential of vineyards, according to their spatiotemporal variability, by adopting site-specific management practices to increase both quality and yield [ 3, 4]. Thus, individual grapevine identification is important to precisely assess the vineyard status by estimating several metrics for each plant. In this way, a better knowledge of grapevine (Vitis vinifera L.) development and spatial heterogeneity within the vineyard [ 1], along with the factors influencing it [ 5], can be reached enabling individual plant treatments.

Traditional airborne remote sensing platforms such as satellites and manned aircraft, both suitable for applications requiring a regional coverage, were used in PV to detect grapevine varieties [ 6], vigour assessment [ 7, 8], vineyard disease mapping [ 9], leaf area index (LAI) and canopy density estimation [ 10, 11]. However, given their coarser spatial resolution, crop and non-crop data are often mixed or represent multiple plants, lacking true individual grapevine information [ 12]. Nevertheless, data from these platforms can still deliver a general overview of vineyards, at least at a plot level [ 13]. To overcome this scale-related issue, some approaches rely on proximal remote sensing [ 14, 15, 16]. However, these approaches are time-consuming, requiring passage through the whole vineyard and some issues can occur due to the terrains' topography and possible obstacles in between the vine rows [ 17]. Vibrations induced by the vehicles can interfere in data quality and the high costs of LiDAR sensors constitute a drawback to their widespread adoption.

On the other hand, unmanned aerial vehicles (UAVs) provide aerial remote sensed data, with high temporal and spatial resolutions, and at lower costs for small to medium area coverages when compared to traditional airborne platforms [ 18]. UAVs are capable of acquiring high-resolution georeferenced data from different sensors exploring different parts of the electromagnetic spectrum [ 19]. The georeferenced images driven from these sensors can be used to compute orthorectified outcomes through photogrammetric processing [ 20]: orthophoto mosaics, digital elevation models (DEM) and spectral indices [ 19] are among the most used. The normalized difference vegetation index (NDVI) [ 21] is a vegetation index widely used in different remote sensing platforms for different purposes [ 19]. In the scope of PV, it is known to be correlated with LAI [ 22], vegetative vigour [ 23] and yield [ 24]. In turn, the crop water stress index (CWSI) [ 25] is used in different studies to assess vineyard water status [ 26, 27, 28, 29]. In PA, crop surface models (CSM) were used in different annual crops [ 30, 31, 32, 33], where good agreements with crop height and biomass were observed. CSMs were also used in olive groves [ 34], chestnut trees [ 35] and lychee trees [ 36]. As for PV, CSMs demonstrated a high agreement with grapevines' height [ 22, 37, 38] and with vineyard vigour [ 39, 40]. Its usage also enabled the estimation of grapevine volume [ 22, 24, 37, 38]. Regarding vineyard vegetation detection, several methods were already proposed based on different approaches using the photogrammetric outcomes from UAV-based imagery by applying image processing techniques, machine learning methods and by filtering dense point clouds and DEMs [ 41, 42, 43, 44, 45, 46, 47, 48, 49]. Those methods are capable of distinguishing grapevine from non-grapevine vegetation and to extract different vineyard macro properties such as the number of vine rows, row spacing, width and height, potential missing plants and vineyard vigour maps.

In what concerns UAV-based approaches for individual identification of plants, the published studies mostly focus on tree detection within both forest and agriculture contexts [ 50, 51, 52]. The outcomes resulting from photogrammetric processing can be used to estimate individual geometrical and biophysical grapevine parameters, providing a plant-specific application for PV [ 53]. In this scope, De Castro et al. [ 38] proposed an object-based image analysis (OBIA) method using very high-resolution vineyard DSMs (1 cm ground sample distance-GSD) to estimate grapevine vegetation within vineyard plots. The method is based on a chessboard algorithm to consider pixels as grapevines. Grapevines are then divided by considering the spacing between plants, wherein missing plants are also estimated. Different geometrical properties were extracted per grapevine for: area, height, width, length, and volume. Matese and Di Genaro [ 54] assessed missing plant detection in a semi-automatic procedure by filtering the DSM (1 cm GSD) in a 40 × 60 m experimental vineyard plot and by manually placing polygons of 1.00 × 0.60 m, representing each grapevine plant and, then, analysing the number of pixels intercepted by each polygon by using a five-classes approach based on quantiles to verify the probability of a missing plant presence. Primicerio et al. [ 53] used a binary multivariate-logistic regression model for the individual detection of grapevines, including missing grapevines, in orthophoto mosaics. Grapevine vegetation segmentation was addressed by the method proposed by Comba et al. [ 41]. While De Castro et al. [ 38] only focused on the extraction of grapevine geometric parameters, Primicerio et al. [ 53] and Matese and Di Genaro [ 54] mainly relied on the detection of the presence/absence of grapevines. In the previously mentioned studies, it is pointed out that the integration of data from other sensors can enable the extraction of single plant vigour, health and water status, allowing some of the problems of correct representation of vigour zones within the vineyard to be solved. Usually, vineyard vigour maps rely on the averaging and/or interpolation of vegetation indices values [ 53]. Moreover, De Castro et al. [ 38] state that grapevine multi-temporal analysis would provide a rapid way to monitor its status when compared to time-consuming and inconsistent in-field observations.

In this study, it is intended to address the gaps that were not covered in those implementations, by performing an individual grapevine estimation for site-specific management in a multi-temporal context, helping winegrowers fully explore the potential of the high-resolution data provided by UAVs and to combine data resultant from the different imagery sensors for a more precise decision support and a quick vineyard inspection. This way, grapevine biophysical and geometric parameters extraction is performed using UAV-based data from different imagery sensors, namely: RGB, for grapevine vegetation detection and geometrical features extraction; multispectral, for feature extraction from vegetation indices; and thermal infrared (TIR) imagery for grapevine temperature and water status estimations.

Next section characterizes the study areas, describes the data acquisition and processing, presents the method used for vineyard segmentation and individual grapevine estimation, as well as the validation procedures. The obtained results are shown in Section 3, considering grapevine estimation accuracy and the multi-temporal analysis. Section 4 discusses this study's findings. Section 5 addresses the main conclusions and presents potential future developments using the proposed method.

Aerial surveys were conducted in different vineyards, in the context of this study, and the most significant of their characteristics are shown in Table 1. Except for vineyard B, the analysed plots are located in the campus of the University of Trás-os-Montes e Alto Douro (UTAD, Vila Real, Portugal), in the Douro demarcated region. They are rainfed irrigated and trained in double guyot system. Vineyard B is located at Quinta do Suco (Amares, Braga, Portugal), in the Vinhos Verdes region, it is trained in a single cordon and is irrigated through an irrigation system. Both training systems are the most common in these wine regions [ 55]. The selection of these vineyard plots was based on the fact they present different row orientation, diverse levels of missing grapevines (0% to 33%) and plant height. The surveyed vineyard plots are presented in Figure 1.

Aerial imagery acquisition was performed using two UAVs, a multi-rotor UAV, the DJI Phantom 4 (DJI, Shenzhen, China) and a fixed-wing UAV, the senseFly's eBee (senseFly SA, Lausanne, Switzerland). The Phantom 4 was used to acquire RGB and multispectral imagery at lower flight heights, whereas eBee was used to survey a larger area for TIR imagery acquisition, which included the studied areas. RGB imagery was acquired using Phantom 4 native camera, FCC 3 model, a CMOS sensor with 12.4 MP resolution mounted in a 3-axis electronic gimbal. Multispectral imagery acquisition was conducted using the Parrot SEQUOIA (Parrot SA, Paris, France), using green (550 nm), red (660 nm), red-edge (735 nm), and near-infrared (790 nm) bands, with 1.2 MP resolution. The radiometric calibration is performed based on the irradiance measured by the sensor located at the top of the UAV and using the reflectance from the calibration target (Airinov, Paris, France) prior to each flight. TIR imagery was acquired using thermoMAP (senseFly SA, Lausanne, Switzerland) which can acquire TIR data between 7500 nm to 13500 nm with 640 × 512 pixels and with a temperature resolution of 0.1 °C. The sensor's calibration is automatically performed in-flight.

UAV flight campaigns performed with the multi-rotor UAV were conducted with 80% front overlap and 70% side overlap at ≈40 m height from the take-off point in a double-grid configuration, resulting in GSDs of approximately 2 cm for RGB imagery and approximately 5 cm for multispectral imagery. While the flights conducted with the fixed-wing UAV had 90% front overlap and 70% side overlap and were performed at 75 m height in a single grid, these specifications were selected according to manufacturer recommendations, obtaining an approximate GSD of 18 cm. The preparation and execution of flight campaigns of the multi-rotor UAV took 20 min, while the fixed-wing UAV took 30 min.

2.1. Data Acquisition

2.1.1. Validation Dataset

A validation dataset, composed of all the surveyed vineyard plots (A to F), was created for accuracy assessment of the individual grapevine detection procedure. Specifically, for the estimation of the number of grapevines and for canopy gaps detection, i.e., parts of vine rows where no grapevine canopy is present (missing plants). For this purpose, only RGB data was considered. The data was collected between May to August 2018 at the following at days of the year (DOY):136 (vineyard plot B); 186 (vineyard plot A); 197 (vineyard plot D); 215 (vineyard plot C); and 219 (vineyard plots E and F).

2.1.2. Multi-Temporal Dataset

Vineyard plots A and B were used for multi-temporal analysis and were surveyed using different UAV-based sensors, namely, RGB, MSP and TIR. The context of these vineyard plots is different: vineyard plot A, located at UTAD campus, is mainly used for experimental purposes, while plot B is a commercial vineyard. Vineyard plot B was selected to be surveyed throughout the vegetative growth cycle. It is composed of seven rows of two white grapevine varieties, four rows of cv. Alvarinho and three rows of cv. Loureiro. Vineyard plot A had a higher incidence of missing grapevines and suffered from powdery and downy mildew due to the conjugation of high air temperature and high humidity levels. These fungi affect the grapevines yield and leaves causing potential losses [ 56]. It is composed of a collection of recommended Portuguese grapevine varieties.

In the case of vineyard plot A, flight campaigns were conducted in the first and fourth weeks of July and in the third week of August and September (2018), with an average time distance of approximately 26 days in between flight campaigns, covering most parts of the fruit set and veraison phenological stages. As for vineyard B, the flight period is broader encompassing most parts of the grapevine's phenological development, from the third week of May 2018 until the second week of October 2018, after grape harvesting. The remaining campaigns were performed on the third week of June and July, and on the second week of August 2018. The temporal mean distance between campaigns is 37 days.

For an accurate multi-temporal analysis, six ground control points (GCP) were acquired in different points from the surroundings of each analysed vineyard plot using a GNSS receiver in RTK mode in the TM06/ETRS89 coordinate system to perform imagery alignment during the photogrammetric processing. In order to ensure GCPs' recognition in TIR imagery, aluminium foil was used, as in Hartmann et al. [ 57]. Figure 2 presents an example of the aluminium target appearance thermal and RGB images.

2.2. Data Processing

The data acquired in each flight campaign passed through a pre-processing stage by means of photogrammetric processing for computation of different orthorectified outcomes. After this stage, the data was used as input for individual grapevine estimation and computation of different vineyard-related parameters.

2.2.1. Data Pre-Processing

Pix4Dmapper Pro (Pix4D SA, Lausanne, Switzerland) was used for photogrammetric processing. It supports the imagery from all the sensors used in this study, allowing the generation of 3D point clouds using Structure from Motion (SfM) algorithms and, therefore, different orthorectified outcomes. However, depending on the source data, different outcomes were computed. This way, when processing RGB imagery, orthophoto mosaics, DSMs and DTMs were generated. Moreover, crop surface models (CSM) were computed subtracting the DTM altitude values from the DSM. The G% index [ 58] was computed according to [ 48], from the RGB orthophoto mosaic, as in (1), by normalizing the green band with the sum of all bands. The data from other sensors used in this study can also generate CSM, but they suffer from smoothing effects since they have less spatial resolution and, therefore, lack of detail [ 39].

G%= Green / ( Red+Green+Blue)

The NDVI was computed using multispectral imagery. Using TIR imagery, the land surface temperature (LST) was computed. However, since crop temperature varies along the day and epoch of the year, it can penalize the multi-temporal data analysis representativeness [ 28]. Thus, to ensure the most representative results from TIR data, LST from each flight campaign was used to compute the CWSI [ 25]. This index relies on the usage of upper and lower temperature bounds (T wet and T dry) which, respectively, correspond to the temperatures of a well-watered leaf and a non-transpiring leaf. Since GSDs from different sensors were also different, to enable data integration, it was necessary to standardize the GSD. This operation was directly performed in the photogrammetric software, using the gaussian average, being the data resampled to 5 cm GSD. Obviously, this procedure did not improve the resolution of the thermal data, as in practice the original pixel was split divided allowing a direct comparison. This ensures the resolution of the remaining information is maintained and, at the same time, making the image processing stage quicker, and preserving all relevant information. CSM and G% were computed using QGIS software, an open source geographical information system (GIS).

2.2.2. Vine Rows Estimation and Individual Grapevine Estimation

The detection of vineyard vegetation and vine rows was accomplished using the method from Pádua et al. [ 48]. Figure 3 presents the main stages of the method for individual grapevine estimation and parameters extraction.

Vegetation detection relies on the combination of CSM and G% with image processing techniques for a given vineyard plot P. Both CSM and G% are binarized by thresholding, automatically using the Otsu's method [ 59] for G%, and using a height range for the CSM. The resulting images are combined resorting in a new binary image V representing the estimated vineyard vegetation, forming a different group of pixels (clusters). Then, the detected clusters are dilated according to their orientation resulting in the vine rows estimations. By eroding this image, a new image S with the rows central lines is obtained. Considering the equidistance space d between grapevines' trunks along with S, grapevine plants can be estimated. This is the common scenario in modern vineyards where grapevines are mechanically implanted [ 53]. This way, points are positioned along the vine rows and then dilated with a morphological line structuring element with the same orientation as the vine rows. Depending on the vineyard training system, the trunk's position can vary, being in the middle or in edges of the grapevine area. Taking this into account, the grapevines are correctly positioned. The resulting binary image is subjected to a thickening morphological operation, by adding pixels to the border of the clusters but maintaining the clusters as unconnected. The binary image G forms the area where vegetation from each grapevine is confined since they are trained with wires to grow vertically and horizontally in between rows.

2.2.3. Parameters Extraction

By estimating the area of where each grapevine is present within a given plot, it is possible to individually estimate different parameters from biological and physical characteristics. This way, the outcomes from different sensors can be useful to provide grapevine parameters.

This procedure uses the estimated grapevine vegetation V along with G, both are combined as in (2). The result of this combination in a new image E, representing the vegetation for each estimated grapevine, enabling the extraction of biophysical and geometric grapevine parameters from the UAV-based photogrammetric outcomes.

e i,j= { 1,if v i,j=1∧ g i,j=1 NaN,otherwise

Depending on the available data, different parameters can be extracted for each grapevine, namely:

the area of each grapevine, which is computed by the number of pixels present in each cluster from E multiplied by its squared GSD value;

the grapevine height, these values are extracted from the CSM, only retrieved in the pixels of E (the remaining CSM pixels are not considered), mean, maximum and minimum values are estimated;

the volume of the grapevine, it is estimated using both height and area, the different height estimates (mean, maximum and minimum estimated height values) are used to calculate different volume values;

features are driven from multispectral and TIR imagery, in the case of this study, NDVI, LST and CWSI are masked according to E and its mean, maximum and minimum values are estimated;

Mean value refers to mean value of a given cluster, maximum and minimum values are estimated from the mean value of the higher/lower 10% values, this way, potential outlier pixels are discarded independently from the area of the clusters. Since CWSI needs upper and lower bound temperature values, this was achieved by using the mean temperature values retrieved from LST, considering the mean temperature value of the 10% lowest and highest temperature values to compute T dry and T wet, respectively. The estimation of other vegetation indices from different sensors is also supported. In the scope of this study, only NDVI and CWSI are estimated. Still, other parts of the electromagnetic spectrum can also be used. The grapevine mask clusters are associated with the extracted grapevine parameters, which, in turn, are converted to a point shapefile or in a table format. Statistical parameters as the standard deviation of the estimated values are also calculated, but such values are not in the scope of this study.

2.2.4. Multi-Temporal Analysis

The estimated grapevine positions enable its multi-temporal analysis which is crucial to track the grapevine vegetative development over time. This way, the analysis is performed by considering the position of each detected grapevine in a given flight campaign, aiming to individually monitor them regarding the various estimated parameters extracted from the multi-sensor data. This analysis is also performed at the vineyard plot scale, as proposed in Pádua et al. [ 37].

The multi-temporal analysis is performed by using the detected grapevine estimation in each flight campaign. Using the mask created in G in the first flight campaign upon the estimated grapevine vegetation V from a flight campaign k, it is possible to estimate the different parameters from the available UAV-based data for n flight campaigns. Thus, data from flight campaign k can be used to evaluate the current vineyard status, by statistically assessing the distribution of the different extracted parameters, and to compare it with other flight campaigns. This enables the observation of the grapevines vegetative evolution through the extracted biophysical and geometrical parameters.

2.3. Grapevine Counting Accuracy

For accuracy purposes, the number of estimated grapevines was evaluated. This process was conducted by counting the plants in the vineyard plots, in each vine row, and then different cases were evaluated:

the total number of estimated grapevines, this value helps to understand the robustness of the method, by considering the total number of grapevines for an analysed vineyard plot;

the number of existing plants, by cross-referring the actual number of plants observed in-field with the ones estimated from the method; and

the number of missing grapevines, i.e., grapevines that were missing causing a canopy gap in the vine row.

These values are compared at the vineyard plot level and at the row level.

Grapevine detection was evaluated based on true and false positives (TP/FP) which refer to the number of correct/incorrect estimated grapevines as real grapevines and, similarly, true and false negatives (TN/FN) for non-grapevines. For this purpose, different parameters were evaluated (equation presented in Table 2), namely:

precision, the fraction of estimated grapevines that are correctly estimated (TP) rather than wrongly estimated (FP);

recall, the fraction of grapevines that are correctly detected rather than wrongly estimated;

false negative rate (FNR), percentage of grapevines falsely classified as being missing;

F ₁ score, the harmonic mean of precision and recall measures; and

overall accuracy, considering all correctly estimated grapevines and missing grapevines in all data.

2.4. Data Alignment

Data alignment is crucial in multi-temporal analysis to accurately extract individual grapevine parameters with minor alignment errors. Thus, the photogrammetric processing of the UAV-based imagery from the different sensors was evaluated for each vineyard plot in the flight campaigns encompassed in the multi-temporal dataset. The mean error and root mean square error (RMSE) were used. The RMSE equation is shown in (3), where e i is the error of each point in a given direction (X, Y, Z) and n the total number of GCPs. This evaluation can further provide the deviation of the acquired UAV-based imagery taken from each sensor along the flight campaigns.

3.1. Grapevine Counting Accuracy

Individual grapevine estimation for different plots of the study area is presented in Table 3. A perfect agreement was observed in vineyards A, B, D, and F, where the number of estimated grapevines per row matched with the ground-truth values. However, for vineyards C and E, the number of estimated vine rows differed by one in each vineyard plot, which was expected since in these vine rows there were no grapevines, thus, these missing grapevines were discarded from further validation. The number of estimated grapevine plants differed by two for vineyard C and seven for vineyard E. Discarding the plants belonging to the undetected vine rows, the overall agreement was 99.88%, missing solely by nine plants (total: 7786, estimated: 7777).

Figure 4 presents the estimated grapevines in each vineyard plot, along with the corresponding numerical assessment shown in Table 4. The precision rate was above 99% (mean value of 99.59%), reaching 100% in vineyard B. Regarding recall, also quantifying FN; a mean value of 98.06% was obtained, being the lowest recall value obtained in vineyard C (93.98%). These cases reflect the number of grapevines that were incorrectly classified as missing grapevines (FN). Consequently, FNR was approximately 3% (vineyards C, D and F), while the mean FNR reached 1.94%. In what concerns the harmonic mean of precision and recall metrics (F1score), its mean value is above 98%. The lower value for this metric was observed for vineyard plot F (96.75%), in opposition to vineyards A and B (99%), which had better ranks. For the remaining plots, F1score was within the range of 97-98%. Focusing on accuracy metrics, an overall value of 98% was obtained, with minimum occurrences above 96%. These results seem to represent a good overall agreement concerning individual grapevine classification.

3.2. Multi-Temporal Vineyard Monitoring

Since the used methods anticipated a multi-temporal context application supported through different photogrammetric outcomes from which individual grapevine parameters can be extracted, an analysis using UAV-based RGB, multispectral, and TIR imagery was conducted in vineyards A and B. Moreover, for this type of application, it is important to ensure a reliable imagery alignment, which was also evaluated.

3.2.1. Data Alignment

The spatial accuracy of each orthorectified outcomes, generated from the photogrammetric processing, were evaluated in their mean spatial deviations (X, Y, Z) and mean RMSE. In this case, the UAV-based imagery acquired from RGB, multispectral and TIR sensors, with different spatial resolutions, were used along with georeferenced GCPs. The projection errors obtained in vineyards A and B for each sensor are presented in Table 5, for each flight campaign. Overall, the errors are lower for RGB, below 5 cm; followed by multispectral imagery, with errors ranging from 5 cm to 10 cm; and when processing TIR imagery, higher error rates were obtained ranging from 10 cm to 20 cm. Pixel projection error was similar in all sensors, i.e., from approximately 0.5 to 1 pixel, being generally higher for RGB imagery. The overall mean RMSE values in vineyard A were 3.61 cm for the RGB imagery, 7.35 cm for the multispectral imagery, and 15.50 cm for the TIR imagery, while in vineyard B they were, respectively, 2.24 cm, 4.84 cm, and 14.34 cm. The mean pixel projection error of all campaigns was lower than one pixel in all sensors; it was higher in the RGB, followed by the multispectral and TIR imagery.

3.2.2. Vineyard Plot A

The applied method enables the estimation of vegetation (grapevine and. inter-row vegetation) present in the vineyard plot. In between the flight campaigns, an increment of grapevine vegetation area was observed from the first to the second flight campaign (from 675 m ² to 782 m ², corresponding to a 16% increase). From the second to the third flight campaign, there was a decline of 35% (−272 m ²). For the remaining flight campaigns, the grapevine vegetation area remained unchanged (around 500 m ²). The same behaviour was observed in grapevine volume, where 877 m ³ was estimated in the first flight campaign, 939 m ³ (growth of 7%) for the second flight campaign, followed by 619 m ³ (decline of 34%) and 610 m ³ (decline of 1%), for the third and fourth flight campaigns, respectively. Focusing on the inter-row vegetation, its area declined from the first to second flight campaigns (from 1221 m ² to 6 m ²), and then remained unchanged on subsequent flights (lower than 90 m ²).

Figure 5 provides the distribution of the mean values of the different parameters extracted from the estimated grapevine plants. The flight campaigns were carried out with a significant vegetation development from fruit set to harvest. This way, the height distribution in the different flight campaigns ( Figure 5a) did not vary significantly, starting with a mean height of 1.23 m in the first flight campaign, and stabilizing around 1.13 m in the remaining campaigns. In what concerns the grapevine area and volume ( Figure 5b,c), both parameters presented a similar behaviour, declining from the first to the last flight campaign. The grapevines mean area starts at 0.44 m ² in the first campaign and a progressive decrease can be observed until the last campaign, in which the grapevine mean area ends up at 0.32 m ². In terms of the mean grapevine canopy volume, it is 0.69 m ³ in the first flight campaign, 0.58 m ³ in the second one and 0.50 m ³ in the remaining flight campaigns. Considering the distribution of the NDVI values ( Figure 5d), a decline was verified when comparing the values of the previous campaigns sequence in which 0.80, 0.73, 0.66, and 0.55 of grapevine mean NDVI were respectively reached. Regarding the parameters extracted from the thermal infrared imagery, both grapevine temperature and CWSI ( Figure 5 e,f) presented distinct distributions. The temperature values were lower in the first flight campaign and higher in the third flight campaign. Despite the mean temperature variability through the flight campaigns, CWSI distribution is similar.

Figure 6 presents the main parameters extracted for the estimated grapevines. The grapevine's height distribution ( Figure 6a) suffered a decline along the flight campaigns. In fact, 87% of estimated heights were higher than 1.0 m in the first flight campaign, falling to 77% in the second flight campaign, 64% in the third and 72% in the fourth flight campaign. Regarding plants with heights lower 0.5 m, no estimates were presented in the first flight campaign, whereas in the following campaigns this number represented 0.3%, 8% and 2%, of the total estimates. Individual grapevine canopy volume ( Figure 6b) presented a similar trend. In the first flight campaign, 53% of the grapevines showed a volume greater than 0.75 m ³, while in the following flight campaigns only 39%, 24% and 19% were greater than 0.75 m ³. Regarding plants with a volume greater than 1 m ³, they represented 19%, 10%, 6% and 4% of the estimated grapevines. Considering individual grapevine NDVI values ( Figure 6c) bigger than 0.7, a total of 85% higher were identified in the first flight campaign, 60% in the second, 38% in the third, and 16% in fourth flight campaign. Focusing on the CWSI ( Figure 6 d), 75% of the grapevine plants were lower than 0.6 in the first three flight campaigns, while in the last flight campaign this value is 70%.

3.2.3. Vineyard Plot B

In what concerns vineyard B, a broader period was analysed (from May to October 2018), encompassing most of the vineyard vegetative development from flowering to harvesting. A growth trend of the grapevine vegetation area and volume is observed across the first four flight campaigns (growth of 218%, from 262 m ² to 833 m ² and 463%, from 339 m ³ to 1301 m ³). In the last campaign, a decline of 15% (−128 m ²) in area and 21% (−401 m ³) in volume was verified. From the second to third flight campaigns, there was a canopy management operation (leaf removal), due to the smaller growth area in between these flights (20% and 101 m ²). As for inter-row vegetation, this value increases until July (from 34 m ² to 546 m ²) and decreases in August (34 m ²), with a small growth in October (to 83 m ²).

The estimation of the individual grapevines position in the vineyard enabled us to estimate several parameters. Their distribution is presented in Figure 7. Height distribution ( Figure 7a) was lower in the first flight campaign with a mean value of 1.24 m, then increased until the fourth flight campaign and declined in the last one. Similar trends were observed in other geometrical parameters such as area and volume ( Figure 7b,c), as well as in the NDVI ( Figure 7d). Indeed, from the first to the second flight campaigns, a significant growth was registered. The mean grapevine area increased almost to double (from 1.10 m ² to 2.12 m ²), and the mean grapevine volume increased 193% (from 1.38 m ³ to 4.04 m ³). In what concerns the grapevines NDVI values, the mean value ranged from 0.56 in the first flight campaign, to 0.84 in the second, 0.86 in the third, 0.89 in the fourth and declined to 0.76 in the last flight. As for LST ( Figure 7e) and CWSI ( Figure 7 f) values, a different trend was observed, the first flight campaign presented a higher temperature than the remaining ones (approximately 10 °C higher), while CWSI values spanned towards higher values in the first flight campaign and lower values in remaining flight campaigns, where most of the data is located below 0.5.

The individual grapevine values estimated for each parameter in the most representative flight campaigns are presented in Figure 8. From the first to the remaining flight campaigns ( Figure 8a-c), an overall numerical increase of assessed characteristics can be noted, while a decline from the fourth to fifth flight campaign is better observed in the NDVI, rather than with height or volume. Most of the height values ( Figure 8a) in the first flight campaign are below 1.5 m, whereas in the remaining flight campaigns they are higher than 1.5 m. As for grapevine canopy volume ( Figure 8b), in the first flight campaign all plants show a value below 4 m ³, estimations made upon data from the second flight campaign point out a rate of 57% of grapevine plants greater than 4m ³, being above 94% in the remaining flight campaigns. NDVI values ( Figure 8 c) are lower than 0.7 in the first flight campaign (0.56 mean value) and higher than it in the remaining flight campaigns, excluding the last one where 82% of the grapevines were higher than 0.7. For CWSI, 53% of the grapevines presented a value higher than 0.6 in the first flight campaign, but in the remaining campaigns it is lower, as an example, it represents 21% and 28%, respectively, in the fourth and fifth campaigns.

4.1. Grapevine Counting Accuracy

The results presented in Section 3.1 document the method's effectiveness in the individual grapevine estimation using the six vineyard plots analysed. As for the number of grapevines presented in each vineyard plot ( Table 3), the method showed 100% accuracy for vineyard plots A, B, D and F. However, for vineyard plots C and E, there was an under-estimation in the number of grapevines in both cases, due to the lack of one plant per vine row. Still, a mean accuracy value of 99.88% was achieved. This is related to the way that the vine rows were estimated, the distance of the rows was smaller than its ground-truth, possibly related to the automatic vine row orientation used during their estimation. Furthermore, as stated in the results section, there were two vine rows that were not detected, one in vineyard plot C (three grapevines) and another in vineyard D (five grapevines). However, since there were no grapevines present in those plots, they were discarded from this evaluation procedure.

As for grapevine identification, the results from the performed evaluation were slightly lower ( Table 4), which is related to FP and FN estimations for grapevines. Nevertheless, a mean overall accuracy of 97.5% was achieved. It can be stated that the method tends to overestimate grapevines rather than underestimate since more FN cases were identified (a mean of 2.0% and a mean of 0.4% FP). In vineyard A, the percentage of FP and FN was 1.6% representing, respectively, 8 and 10 cases. The higher number of FP was verified in vineyard F with 10 cases (1%), whereas a higher number of FN was reached in vineyard E with 49 cases (2.4%). However, vineyard F had the highest percentage of FN cases with 3% (30 occurrences). Cases of grapevines classified as missing (FP) can be justified by erroneous three-dimensional reconstruction of the surveyed vineyard plots. Increasing the imagery overlap in the mission plan stage can help to mitigate this issue since more common tie points will be found in correspondent images. The grapevine plants from the commercial vineyard (vineyard plot B) were correctly estimated, as this plot only composed seven vine rows and had only two missing grapevine plants. In Primicerio et al. [ 53], this issue was addressed by evaluating a total of 211 missing plants (incidence of 9.4%), in which the parameter found with most correlation was the grapevine area. Different area thresholds were considered, based on the cardinality of each cluster attributed to grapevine plants, wherein lower values showed to be more suitable for missing plant discrimination but inducing the FP number growth, while higher values result in the inverse behaviour. In Matese and Di Genaro [ 54], the results demonstrated 80% accuracy in missing plants detection with the application of their method. Authors stated that FP results (plants estimated as a missing plant) were related to their low vegetative development, while FN results (missing plant classified as a plant) were related to the high presence of weeds and overlap of adjacent plants. However, neither of these studies considered height properties. In De Castro et al. [ 38] an overall accuracy of 95.3% was obtained in the analysis of three vineyard plots in Northeastern Spain at two different epochs (July and September). Similarly, to this study, according to the authors, most of the misclassification cases were related to grapevines lower vegetative development (thin branches and fewer leaves) which led to issues in the 3D canopy reconstruction through photogrammetric processing. Regarding FN cases, it can be justified by the growth of adjacent grapevines which tends to cover missing spots in the wires, making it difficult to estimate missing plants. This way, as a future direction, machine learning classification should be considered to detect the number of missing grapevines. Moreover, as stated by Matese and Di Genaro [ 54], by selecting an anticipated period to conduct this type of survey would increase the results accuracy, by having plants with less vegetative development, which would avoid the presence of adjacent grapevine vegetation in missing plant areas.

4.2. Data Alignment

Since one of the main goals of this study is to extract grapevine properties from different outcomes through photogrammetric processing of UAV-based imagery from different sensors, a correct data alignment must be ensured to mitigate data alignment errors. Aiming to reduce geolocation errors from a few metres to some centimetres [ 60]. In Turner et al. [ 61] the same tendency was inferred, when compared to the results obtained in this study. More specifically, the overall RMSE values were approximately 1.9 cm for RGB imagery (1 cm GSD), 6.4 cm for multispectral imagery (3 cm GSD), and 17.8 cm for TIR imagery (10 cm GSD). In their study, the authors used a higher number of GCPs along with a lower flight height and almost half of the spatial resolution used in this study. The need of GCPs for multi-sensor data alignment will be mitigated by the technological development of sensors by combining visible, multispectral and TIR data into a single sensor, this is the case of the MicaSense Altum (MicaSense Inc., Washington, United States of America) which provides radiometrically corrected blue, green, red, red edge, NIR spectral bands along with TIR imagery. This way, in a single UAV flight, all data is acquired and photogrammetric processing can be achieved in a single project.

4.3. Multi-Temporal Vineyard Monitoring

By analysing the results obtained through the method application in both vineyards A and B ( Section 3.2.2 and Section 3.2.3), the spatial grapevine variability is noticeable. Furthermore, the multi-temporal analysis enabled us to track the changes throughout the analysed periods.

In what concerns the experimental vineyard (vineyard A), it presented a higher incidence of missing grapevines, which is associated with the occurrence of phytosanitary problems and to the presence of different grapevine varieties, wherein a higher data variability is clearly visible ( Figure 6). Areas with a better grapevine overall status, are located in the upper right and bottom left parts of the vineyard. Higher NDVI values and grapevine volume were detected in those areas throughout the flight campaigns, as this is more notorious in all flight campaigns for the estimated grapevine volume and in the first three flight campaigns for NDVI. With respect to CWSI, only the first and last two flight campaigns highlighted this behaviour. On the other hand, the southern central part showed the lowest results. The grapevines in this region presented a lower volume, particularly in the last two flight campaigns, along with higher CWSI values (potential water stress) and lower NDVI values.

The commercial vineyard plot (vineyard B) presented a high vegetative dynamic over the analysed period, its parameters grew throughout the flight campaigns and declined after the harvest season ( Figure 7). Its geometrical features (height, area, and volume) along with NDVI showed a higher development from the first to second flight campaigns. These results are related to both phenological stages and training systems implemented in the Vinho Verde wine region. This vineyard presented overall good results as confirmed by the estimated grapevine values in the flight campaigns ( Figure 8). The northern part presented a higher grapevine volume when analysing each flight campaign, while for NDVI this was only verified in the last two campaigns. The southern part presented some water stress signs ( Figure 8 d) in the last two flight campaigns, along with lower grapevine volume verified on all flight campaigns, and lower NDVI values were only observed in the last flight campaign. Regarding CWSI, the inferior grapevine vegetative development verified in the first flight campaign led to the estimation of potential water stress in the upper part of the vineyard, while in the last flight campaign the higher CWSI agreed with the lower NDVI values.

When comparing the commercial (vineyard B) and the experimental (vineyard A) vineyard, the former obtained better results in almost all parameters. A higher spatial heterogeneity was verified in vineyard A, while vineyard B presented a more homogeneous development, as can be seen when analysing the grapevine canopy volume and NDVI. However, some similar trends were detected in both vineyards, an overall decline of the NDVI values in the last flight campaigns and an agreement between NDVI with the estimated grapevine volume and with the CWSI values was observed. The decline of NDVI in the last flight campaigns can be related to the leaf senescence and, consequently, its discolouration [ 62, 63]. The relationships among the NDVI and volume relationships were verified in other studies [ 24] that showed that both of these parameters are related with LAI, which in fact, is also verified in this study.

Grapevines located in the southern parts (first plant of the rows) of the analysed vineyards presented potential water stress in the CWSI, an observation that gains meaning in the last two flight campaigns of vineyard B and in the first, third and fourth flight campaigns in vineyard A. This can be considered as outliers, since higher temperatures are observed due to heat advection from the soil in proximity to the edges of the rows of the grapevine canopy, which can affect, in a significant manner, the air temperature, with an increase in the loss of water by evapotranspiration [ 64]. These results corroborated with Tucci et al. [ 65] where external rows in a terraced vineyard presented higher temperatures due to solar exposition and shadowing effects when compared with internal vine rows. Although the individual grapevine temperature is estimated, this parameter showed to be ineffective to be evaluated in a multitemporal analysis. It is useful to understand the global temperature context on the flight campaign day. The CWSI trends shown a more stabilized distribution since this index represents a normalization throughout the flight campaigns. Moreover, other TIR-based indices can be computed, as it is the case of stomatal conductance indices Ig and I3 [ 66], which, respectively, implicated increases with stomatal conductance and correlates it with stomatal resistance. The same applies to other multispectral-based vegetation indices and to hyperspectral data, as long as it comes in an orthorectified format. Usually, published studies resort to NDVI to produce vigour maps and use that information to improve the decision support at the vineyard plot scale [ 13, 23, 67]. The approach proposed in this study provides a more incisive analysis. Multi-sensor parameters are extracted at the plant level and are not exclusively relying on qualitative analysis.

This study explores the usage of UAV-based photogrammetric outcomes to extract individual grapevine geometrical and biophysical parameters within vineyard plots. Missing plants and other vegetation are detected but not considered to perform multi-temporal analysis over a series of flight campaigns. Three different types of sensors (RGB, multispectral and TIR) were used. By estimating vine rows and the individual position of each plant, several parameters can be extracted at the plot level, namely the: number of vine rows; number of plants; and number of missing plants and grapevine vegetation area; and at the plant level, the: position; length; width; area; volume; vigour (driven from NDVI); temperature from (TIR) and water status (driven from CWSI). The two multi-temporal analyses conducted in the two different vineyards presented in this study confirmed the method's suitability for plant-specific analysis, allowing us to assess the different estimated parameters and to establish relationships among them and between flight campaigns. This way, the methods employed in this study enable an overview of the current status of the grapevine plants and the monitoring of their evolution over time. The estimated geometrical and biophysical parameters can significantly help farmers and/or winemakers understand the current vineyard overall status and can be used as a decision support tool to apply treatments in certain plants and to observe their response with multi-temporal analysis, helping to improve grapevines health. Moreover, it can be used to compare growth seasons from different years by extending the flight campaigns.

The potential of the method can be extended for different applications, it can help in the decision support by means of grapevine growth and status evaluation, and the individual grapevine water status estimation. Moreover, the different extracted parameters can be used to create datasets for supervised and unsupervised classification methods for disease detection and to improve the results in the detection of missing grapevines. The extracted individual grapevine parameters can be used for computation of prescription maps for individual grapevine treatment in PV plant-specific applications and to estimate individual grapevine production. The topographical data produced from the photogrammetric processing along with the position of each grapevine and its estimated parameters can be used to reproduce a 3D virtual vineyard environment. As such, this information can be used in augmented reality applications for easier vineyard in-field inspections.

Conceptualization, L.P. and J.J.S.; data curation, L.P.; formal analysis, L.P.; funding acquisition, A.S., E.P., and J.J.S.; investigation, L.P., and T.A.; methodology, L.P. and J.J.S.; project administration, L.P., E.P., and J.J.S.; resources, L.P., T.A., and J.J.S.; software, L.P., and T.A.; supervision, A.S., E.P., and J.J.S.; validation, L.P. and J.J.S.; visualization, L.P.; writing-original draft, L.P.; writing-review and editing, T.A., A.S., E.P., and J.J.S. All authors have read and agreed to the published version of the manuscript.

Financial support provided by the FCT-Portuguese Foundation for Science and Technology (SFRH/BD/139702/2018) to Luís Pádua.

The authors declare no conflict of interest.

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Figure 2. Thermal target, indicated by the arrow, used for data alignment: ( left) its representation in thermal infrared imagery and ( right) in RGB.

Vineyard plot

Coordinates (Lat., Lon.)

Area (ha)

Number of Grapevines

Rows

Original

Missing

Number

Spacing (m)

Height (m)

A
41°17′12.1″ N 7°44′15.2″ W

0.36

1440

381

34

1.20

1.4-1.7

Parameter

Equation

Precision

P r e c i s i o n= T P

Recall

S e n s i t i v i t y= T P

False Negative Rate

F N R=1− T P

F1score

F 1 s c o r e=2 ( P r e c i s i o n×R e c a l l P r e c i s i o n+R e c a l l)

Overall accuracy

A c c u r a c y= T P+T N+F P+F N

Vineyard Plot

Number of Grapevines

Percentage (%)

Observed

Estimated

A
1436

1436

100.00

Vineyard plot

Precision (%)

Recall (%)

F1score (%)

FNR (%)

OA (%)

Vineyard Plot

Sensor

F#

Mean (cm)

RMSE (cm)

Projection Error (Pixel)

# data[].attributes.attachment_link.title
Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

# data[].attributes.attachment_link.description
The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status.

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Aluno: Pedro Lumertz

Nos últimos anos surgiram diversas ferramentas de geração de imagens baseado em inteligência artificial. Os mais famosos acredito que sejam o DALL-E, da OpenAI, e o Mid Journey. O interessante é que esses algoritmos funcionam com base na obtenção de uma quantidade massiva de dados em uma base de dados geral (sem customização por usuário ou contexto, apenas com a entreda individual de cada chamada). Imagino que essas tecnologias, unindo a ferramentas de armazenamento de contexto inteligente mencionadas na palestra, como a UbiTrail, o Odin, e outros, poderiam ser utilizados conjuntamente para criação de artes conceituais mais precisas de acordo com um certo indivíduo ou até um público alvo. Por um lado, isso poderia resultar em imagens mais específicas e objetivas para cada caso, mas por outro, poderia limitar a ou substituir em parte a liberdade criativa de produtos e serviços que utilizassem dele.
DALL-E: https://openai.com/dall-e-2/
MidJourney: https://www.midjourney.com/home/

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Baseado no nessa temática de Ambientes Inteligentes e processamento de contextos históricos e presentes para predições, compartilho aqui uma empresa de software Open Source que oferece uma solução alternativa e popular para assistência de trânsito e carros “serlf-driving”. O dispositivo oferece predições de transito e monitora o comportamento do motorista, evitando possíveis acidentes.

O projeto é chamado Open Pilot, mantido pela empresa Comma AI.

https://comma.ai/

https://github.com/commaai/openpilot

Postado por Andre Ilges

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A empresa tem como seu principal objetivo fornecer:

Detecte Falhas em Estágio Inicial
Inteligência Artifical monitorando sua máquina a todo momento.

Insights Automáticos
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A empresa participou do Y Combinator 2021 onde buscou investimentos necessários para a expansão dos seus hardwares e software de monitoramento. Mais informações em: https://www.ycombinator.com/companies/tractian

Link da Empresa: https://tractian.com/

Notícias sobre a empresa:

https://finance.yahoo.com/news/tractian-raises-15m-series-machine-152800376.html

https://techcrunch.com/2022/05/17/2314910/

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https://quantasmarthome.com.br/?gclid=Cj0KCQjw1bqZBhDXARIsANTjCPJfCap2Hp--WDCZc_c31WrwKsZAmZo7gD7LW3S9esU4yTEaDvPNziUaAi_uEALw_wcB#

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Por imagem é possível realizar:
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- Tomografia computadorizada de Tórax;
- Tomografia computadorizada de Crânio;
- Eletrocardiograma (ECG).

E por texto tem o sistema harpIA que analisa laudos de radiologia via Processamento de Linguagem Natural (conhecido em inglês como NLP), extraindo informações extremamente importantes de dentro do texto não estruturado.

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VIA INTELIGÊNCIA ARTIFICIAL, A RÁPIDA ANÁLISE DE EXAMES É CAPAZ DE ORGANIZAR AUTOMATICAMENTE O FLUXO DOS PACIENTES

Por meio de sistemas de Inteligência Artificial, nossa ferramenta é capaz de analisar:
‍
- Radiografia de Tórax,
- Tomografia computadorizada de Tórax
- Tomografia computadorizada de Crânio
- Eletrocardiograma (ECG)
‍
A ferramenta é integrada diretamente no PACS, RIS ou HIS e, em instantes, uma análise automatizada dos exames possibilita a ordenação da lista de pacientes por prioridade clínica ao invés de organizar por ordem de chegada. Assim, nossa tecnologia faz com que pacientes com exames alterados e potencialmente mais urgentes possam ser priorizados.

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VIA INTELIGÊNCIA ARTIFICIAL, A RÁPIDA ANÁLISE DE EXAMES É CAPAZ DE ORGANIZAR AUTOMATICAMENTE O FLUXO DOS PACIENTES Por meio de sistemas de Inteligência Artificial, nossa ferramenta é capaz de analisar:‍- Radiografia de Tórax,- Tomografia computadorizada de Tórax- Tomografia computadorizada de Crânio- Eletrocardiograma (ECG)‍A ferramenta é integrada diretamente no PACS, RIS ou HIS e, em instantes, uma análise automatizada dos exames possibilita a ordenação da lista de pacientes por prioridade clínica ao invés de organizar por ordem de chegada.

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Soluções digitais para melhorar a experiência dos pacientes -- Aluno: Matheus Gomes Reis Pinto

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Soluções digitais para melhorar a experiência dos pacientes -- Aluno: Matheus Gomes Reis Pinto

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A Healthcare Innovation Show (HIS) apresenta soluções digitais que contribuem para aprimorar a jornada do cliente de ponta a ponta e melhorar a experiência dos pacientes. Entre as soluções e serviços apresentados, destacam-se:

1 - Experiências envolventes e centradas no usuário, que combinam tecnologia e ciência comportamental, ajudando pessoas e pacientes a atingirem suas metas de saúde, como a adesão ao tratamento crônico, aumento do exercício físico ou manutenção de consultas com o médico.

2 - Soluções tecnológicas que aumentam e aceleram a taxa de diagnóstico de doenças raras e doenças crônicas não transmissíveis, como hipertensão, câncer e enxaqueca.

3 - Estratégias e soluções de ponta a ponta para gerenciar todo o ciclo de vida dos dados de saúde, seja para uso em saúde, projetos de pesquisa baseados em evidências do mundo real ou em geração de insights para a tomada de decisões.

4 - Modelagem de doenças com base em dados da vida real para auxiliar nas decisões de reembolso, preços e cuidados baseados em valor.

5 - Transformação digital é a solução para alcançar a meta de um prestador de cuidados de saúde inteligente, com foco na experiência do usuário, no resultado clínico e na otimização de recursos.

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Pela primeira vez no HIS - Healthcare Innovation Show, a Globant, empresa nativa digital focada em reinventar negócios por meio de soluções tecnológicas inovadoras, participa do evento com a intenção de apresentar a marca como uma parceira na transformação digital do setor de saúde, com soluções e projetos de inovações personalizados para cada cliente.

"A tecnologia e as ciências da vida estão agora interligadas. E existe uma necessidade de acelerar a descoberta de medicamentos e seu tempo de lançamento no mercado, integrar dados para melhorar os processos de tomada de decisão clínica e de acesso, gerar eficiências no ecossistema e melhorar a jornada do cliente de ponta a ponta, particularmente no acesso a medicamentos, adesão e experiência do consumidor. A Globant visa preencher essa lacuna para ajudar as ciências da vida e as organizações de saúde a cumprir a missão de fornecer inovação e serviços com mais rapidez e eficiência, com o objetivo de para aumentar o valor do paciente e melhorar os resultados", afirma o diretor Global de Life Science da Globant, Lucas Najun Dubos.

Para o executivo da Globant, o cenário atual do mercado de saúde neste momento de pós-pandemia acentua algumas tendências que estão em crescimento como, por exemplo, uma maior aceitação da descentralização do atendimento aos pacientes, por telemedicina ou por meios não presenciais, a necessidade de automatizar certos processos, principalmente devido à baixa oferta de profissionais de saúde e a integração na prestação de cuidados por meio de um canal unificado e híbrido.

Já no sentido de tecnologias digitais que estão em expansão e devem ter um impacto nos próximos anos no setor de saúde, Dubos aponta o 5G como facilitador para muitas das plataformas e mídias futuras, terapias digitais, o metaverso (em seu conceito geral de criar uma experiência diferente para o paciente ou mesmo para treinar profissionais de saúde), serviços em nuvem, inteligência artificial como auxiliar do profissional e do paciente, e wearables ou Internet das Coisas (IoT), permitindo a descentralização do atendimento.

Experiência do usuário

Para a participação Globant no HIS - Healthcare Innovation Show, a empresa pretende destacar com suas soluções e serviços os seguintes tópicos:

1 - Experiências envolventes e centradas no usuário, que combinam tecnologia e ciência comportamental, ajudando pessoas e pacientes a atingirem suas metas de saúde, como a adesão ao tratamento crônico, aumento do exercício físico ou manutenção de consultas com o médico.

2 - Soluções tecnológicas que aumentam e aceleram a taxa de diagnóstico de doenças raras e doenças crônicas não transmissíveis, como hipertensão, câncer e enxaqueca.

3 - Estratégias e soluções de ponta a ponta para gerenciar todo o ciclo de vida dos dados de saúde, seja para uso em saúde, projetos de pesquisa baseados em evidências do mundo real ou em geração de insights para a tomada de decisões.

4 - Modelagem de doenças com base em dados da vida real para auxiliar nas decisões de reembolso, preços e cuidados baseados em valor.

5 - Transformação digital é a solução para alcançar a meta de um prestador de cuidados de saúde inteligente, com foco na experiência do usuário, no resultado clínico e na otimização de recursos.

A 8ª edição do HIS - Healthcare Innovation Show apresentará mais de 50 horas de conteúdo com a participação de mais de 200 especialistas do Brasil e do exterior, entre profissionais de saúde, gestores, executivos e acadêmicos que vão debater sobre os mais variados aspectos da inovação, tecnologia, empreendedorismo, negócios e finanças aplicadas à saúde.

Para saber como participar do evento e ver a programação completa, acesse o site do HIS - Healthcare Innovation Show

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Globant visa aumentar o valor do paciente e a eficácia de resultados

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Pela primeira vez no HIS - Healthcare Innovation Show, a Globant, empresa nativa digital focada em reinventar negócios por meio de soluções tecnológicas inovadoras, participa do evento com a intenção de apresentar a marca como uma parceira na transformação digital do setor de saúde, com soluções e projetos de inovações personalizados para cada cliente.

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Pulverizadores inteligentes para o agronegócio - Aluna: Ana Carolina Müller

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Pulverizadores inteligentes para o agronegócio - Aluna: Ana Carolina Müller

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O agronegócio vem crescendo a cada ano, gerando assim, novas soluções tecnológicas que visam uma maior economia aos produtores e mais benefícios ao meio ambiente. Os pulverizadores inteligentes com tecnologia de aplicação estão ganhando espaço nas agrícolas, pois com a combinação do hardware e o software, conseguem detectar em tempo real as ervas daninhas nas plantações, em qualquer período do dia e temperatura. As ervas daninhas são detectadas em milissegundos e o pulverizador recebe o comando para aplicar o herbicida somente onde é necessário, reduzindo assim os custos do agricultor e sendo ecologicamente mais sustentável.

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Para alcançar posição relevante no mercado, a joint-venture Bosch BASF Smart Farming (BBSF) investe hoje em uma tecnologia com alto potencial de crescimento no futuro. Segundo Florian Gwosdz, Diretor Executivo da BBSF, os pulverizadores inteligentes com tecnologia de aplicação localizada podem chegar a representar, na próxima década, metade da frota de grande porte usada no trato de culturas agrícolas em todo o mundo. Esse tipo de tecnologia visa reduzir a utilização de insumos, gerando economia ao produtor rural e benefícios ao meio ambiente.

"A tecnologia de pulverização localizada de defensivos ganhará espaço e acreditamos que metade de todos os pulverizadores de maior porte estarão equipados com essa inteligência em 8 a 10 anos. Nossa meta é alcançar um market share significativo nesse mercado", diz Gwosdz. A solução já atende produtores de soja e milho e está em testes em lavouras de algodão. A sede da empresa fica em Colônia, na Alemanha, de onde é gerida por Gwosdz (que veio da Bosch) e Silvia Cifre Wibrow (vinda da Basf).

O avanço da BBSF já começou e desde maio, quando a joint-venture entre Bosch e Basf passou a operar comercialmente, foram fechadas parcerias com fabricantes de máquinas de diferentes partes do globo para comercialização de pulverizadores com a tecnologia vinda de fábrica. Na América Latina, foi firmada parceria com a Stara. Na Europa, com as alemãs Daman e Amazone, além da multinacional AGCO, que também é parceira da empresa nos EUA. A tecnologia de aplicação localizada da BBSF foi batizada Smart Spraying.

Rodrigo Lima, Diretor Executivo da Bosch BASF Smart Farming na América Latina, destaca que o desenvolvimento da solução passa pela cooperação de múltiplos atores. " Se a gente olhar a história da agricultura, você tem de um lado empresas que fabricam sementes, fertilizantes e herbicidas e que investem um montante gigantesco de dinheiro para entregar o melhor produto ao agricultor. Ao mesmo tempo em que empresas como a Stara estão focadas na excelência das máquinas e um e fornecedor de automação, como a Bosch, tem todo o mindset do hardware, integração de sistema, mas sem o know-how da agricultura e da máquina. Nossa proposta foi integrar esse tripé", diz.

Combinação de expertises

Hoje, a Bosch fornece o hardware e software da solução. Isto inclui câmeras, unidades de controle e luzes que possibilitam a detecção de ervas daninhas nas plantações em tempo real, faça chuva ou faça sol, de dia e de noite. A Basf, por sua vez, entrega a inteligência agronômica xarvio Field Manager, que se traduz em um módulo de pulverização acoplado à máquina dos parceiros de maquinários. E a Stara provê toda essa tecnologia embutida, por hora, no pulverizador Stara Imperador 4000, que já teve pelo menos uma dezena de unidades vendidas na Agrishow, em abril. Mais adiante, a ideia é os parceiros da BBSF também operarem com retrofit (introduzindo a tecnologia em máquinas que já estão no campo).

Segundo a BBSF, em questão de milissegundos, as ervas daninhas são detectadas e o pulverizador recebe o comando para aplicar herbicida somente onde é necessário. A tecnologia pode ser usada em situação de pré-emergência das culturas (verde sobre marrom) e pós-emergência (verde sobre verde). A redução no uso de herbicidas chegou a até 70% em testes nas lavouras.

"Baseando-se no que o produtor comprou ou pretende comprar de herbicidas, auxiliamos de ponta a ponta no processo de tomada de decisão. Fazemos uma recomendação entendendo quais são as daninhas que ele tem, qual a realidade do campo, estádio fenológico da cultura e a janela de pulverização", explica o executivo.

A semente do negócio

Além do pulverizador localizado Smart Spraying, a BBSF já tem outra solução no mercado, que inclusive contribuiu para a aproximação entre a Bosch e a Basf e a formação da joint-venture. O Intelligent Planting Solution (IPS) é uma solução que visa aumentar a produtividade e eficiência da lavoura, ao otimizar a população e colocação de sementes de acordo com o solo, cultura e fertilidade.

O hardware possui motores elétricos que, ao serem empregados na semeadura, evitam sobreposição de linhas de plantio e controlam melhor a aplicação de fertilizantes no sulco. As marcas Jumil, Baldan, Case e New Holland comercializam a solução para retrofit.

Lima conta que a demanda pelo produto de plantio inteligente veio de um cliente da Bosch na Argentina, a Siembra Neumática, que comprava eletrônicos da empresa alemã e foi sondar a possibilidade de obter ajuda com motores elétricos para atender um cliente da área agrícola. A Siembra Neumática atua como distribuidora e integradora de tecnologias, fornecendo equipamentos para fabricantes de plantadeiras.

"Já se vão três anos comercializando essa solução no Brasil, uma solução que nasceu na América Latina. A Siembra Neumática é nossa parceira, que solicitou ajuda para eletrificar dosadores quando eu trabalhava na Bosch, o que levou à criação de uma casa de projetos em parceria com o time do xarvio, da Basf", explica Lima, que depois de 10 anos no mercado automotivo aceitou primeiro o desafio de integrar uma divisão de negócios orientada para o agronegócio na Bosch e agora migrou para a JV.

Segundo ele, outras novidades vêm por aí. "Começamos com as soluções de plantio e pulverização inteligente e a gente continua desenvolvendo soluções sempre em cooperação entre as duas empresas. A entidade de vendas dessas soluções é a BBSF, mas a casa de projetos, onde são desenvolvidos os produtos, continua dentro da Basf e da Bosch. Nosso objetivo é caminhar para entregar soluções cada vez mais holísticas para o agricultor, que permeiam todas as etapas de produção, desde o plantio até a colheita", afirma Lima.
LEIA MAIS

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BBSF aposta em pulverizadores inteligentes, que podem responder por até metade do mercado global

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Para alcançar posição relevante no mercado, a joint-venture Bosch BASF Smart Farming (BBSF) investe hoje em uma tecnologia com alto potencial de crescimento no futuro. Segundo Florian Gwosdz, Diretor Executivo da BBSF, os pulverizadores inteligentes com tecnologia de aplicação localizada podem chegar a representar, na próxima década, metade da frota de grande porte usada no trato de culturas agrícolas em todo o mundo.

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UFS inicia implantação de salas inteligentes em Itabaiana - Everton Borba

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UFS inicia implantação de salas inteligentes em Itabaiana - Everton Borba

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A Universidade Federal de Sergipe inicia a implantação de um projeto de hardware e software combinados para obter o controle mais eficiente do consumo de energia das salas de aula do Campus de Itabaiana com o envolvimento direto dos alunos do curso de Sistema de Informação, uma solução de "salas inteligentes" onde os equipamentos se comunicam via bluetooth  e podem ser acionados de forma automática via aplicativo ou manual a partir de sensores de presença.

Aluno: Everton Correa de Borba

Fonte: https://www.ufs.br/conteudo/70225-ufs-inicia-implantacao-de-salas-inteligentes-em-itabaiana

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UFS amplia oferta de refeições para os campi de Lagarto, Laranjeiras e Aracaju
Ampliação começa na próxima segunda-feira, 26
Serviço de Psicologia Aplicada da UFS abre 40 vagas para atendimento psicológico
As inscrições deverão ser feitas no dia 27 de setembro
Proex divulga Chamada Pública para 9º EIX
Discentes da graduação devem submeter projetos até 21/10 na VIII SEMAC
UFS e empresa petrolífera reúnem-se para avaliar parceria
Representantes das instituições projetam novas ações conjuntas
Seminário aborda os 25 anos da Política de Recursos Hídricos em Sergipe
Evento contou com palestra, lançamento de livro e mesa redonda

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Portal UFS - UFS inicia implantação de salas inteligentes em Itabaiana

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UFS amplia oferta de refeições para os campi de Lagarto, Laranjeiras e Aracaju Ampliação começa na próxima segunda-feira, 26 Serviço de Psicologia Aplicada da UFS abre 40 vagas para atendimento psicológico As inscrições deverão ser feitas no dia 27 de setembro Proex divulga Chamada Pública para 9º EIX Discentes da graduação devem submeter projetos até 21/10 na VIII SEMAC UFS e empresa petrolífera reúnem-se para avaliar parceria Representantes das instituições projetam novas ações conjuntas Seminário aborda os 25 anos da Política de Recursos Hídricos em Sergipe Evento contou com palestra, lançamento de livro e mesa redonda

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Edição de imagens por texto com rede neural - Camila Canali Schmitz

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Edição de imagens por texto com rede neural - Camila Canali Schmitz

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Após a divulgação de modelos de aprendizado de máquina que geram imagens baseadas em texto, a NVIDIA e pesquisadores da universidade de Tel Aviv desenvolveram um modelo em que é possível editar uma imagem baseado em texto.

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Artificial Intelligence

Personalizing Text-to-Image Generation using Textual Inversion

Watch the video

Text-to-Image models like DALLE or stable diffusion are really cool and allow us to generate fantastic pictures with a simple text input. But would it be even cooler to give them a picture of you and ask it to turn it into a painting? Imagine being able to send any picture of an object, person, or even your cat, and ask the model to transform it into another style like turning yourself into a cyborg of into your preferred artistic style or adding it to a new scene.

Basically, how cool would it be to have a version of DALLE we can use to photoshop our pictures instead of having random generations? Having a personalized DALLE, while making it much more simple to control the generation as "an image is worth a thousand words". It would be like having a DALLE model that is just as personalized and addictive as the TikTok algorithm.

Well, this is what researchers from Tel Aviv University and NVIDIA worked on. They developed an approach for conditioning text-to-image models, like stable diffusion I covered last week, with a few images to represent any object or concept through the words you will send along your images. Transforming the object of your input images into whatever you want.

Of course, the results still need work, but this is just the first paper tackling such an amazing task that could revolutionize the design industry. As a fantastic YouTuber colleague would say: just imagine two more papers down the line.

So how can we take a handful of pictures of an object and generate a new image following a text-conditioned input to add the style or transformation details?

To answer this complex question, let's have a look at what Rinon Gal and his team came up with. The input images are encoded into what they call a pseudo-word that you can then use within your text generation. Thus the paper name, "an image is worth one word". But how do they get this pseudo-word and what is it?

They start with three to five images of a specific object. They also use a pre-trained text-to-image model (image below, right). In this case, they use latent diffusion, the model I covered not even a week ago which takes any kind of inputs like images or text and generates new images out of them. You can see it as a cooler and Open-Source DALLE. If you haven't watched my video yet, you should pause this one, learn about this model and come back here. You'll love the video and learn about the hottest architecture of the moment!

So you have your input images and the base model for generating images conditioned on inputs such as text or other images. But what do you do with your 3-5 images of your object? And how do you control the model's results so precisely that your object appears in the generations?

This is all done during the training process of your second model: the text encoder (image above, left). Using your pre-trained and fixed image generator model, latent diffusion in this case, already able to take a picture and reconstruct it, you want to teach your text encoder model to match your pseudo-word to your encoded images, or, in other words, your representations taken from your five images. So you will feed your images to your image generator network and train your text encoder in reverse to find out what fake words, or pseudo-word, would best represent all your encoded images.

Basically, find out how to correctly represent your concept in the same space as where the image generation process I described in my previous video happens. Then, extract a fake word out of it to guide future generations. This way, you can inject your concept into any future generations and add a few words to condition the generation even further using the same pre-trained text-to-image model. So you will simply be training a small model to understand where your images lie in the latent space to convert them into a fake word to use in the regular image generation model. You don't have to touch the image generator model, and that's quite a big deal considering how expensive they are to train!

And voilà! This is how you can teach a DALLE-like model to generate image variations of your preferred object or perform powerful style transfers.

Of course, this is just an overview of this new method tackling a very very interesting task and I invite you to read their paper linked below for a deeper understanding of the approach and challenges.

It's a very complicated task and there are still a lot of limitations like the time it takes to understand such a concept in a fake word, which is roughly 2 hours. It is also not yet capable of completely understanding the concept but is pretty damn close. There are also a lot of risks in having such a product accessible that we need to consider. Imagine being able to embed the concept of a specific person and generate anything involving the person. This is quite scary and this kind of technology is just around the corner. I'd love to hear your thoughts in the comments section or discuss this on our Discord server.

Thank you for reading and I will see you next week with another amazing paper!

References

►Paper: Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A.H., Chechik, G. and Cohen-Or, D., 2022. An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion. https://arxiv.org/pdf/2208.01618v1.pdf
►Code: https://textual-inversion.github.io/
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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Guiding Stable Diffusion with your Images

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Artificial Intelligence Personalizing Text-to-Image Generation using Textual Inversion Watch the video Text-to-Image models like DALLE or stable diffusion are really cool and allow us to generate fantastic pictures with a simple text input. But would it be even cooler to give them a picture of you and ask it to turn it into a painting?