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MATLAB Access and Support for Everyone at

What's New

MATLAB Parallel Server

Scale MATLAB programs and Simulink simulations to clusters and clouds.

Deep Learning Onramp

This hands-on, two-hour course is an introduction to practical deep learning methods for image recognition.

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Universiti Teknologi Malaysia - MATLAB Access for Everyone

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MATLAB Access and Support for Everyone at What's New Scale MATLAB programs and Simulink simulations to clusters and clouds. This hands-on, two-hour course is an introduction to practical deep learning methods for image recognition.

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participants

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Using MATLAB with Other Programming Languages

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Learn how to use MATLAB with other programming languages including C and Java.

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Run in cloud environments from MathWorks Cloud to public clouds including AWS and Azure.

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MATLAB Onramp | Self-Paced Online Courses - MATLAB & Simulink

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Learn the basics of MATLAB® through this introductory tutorial on commonly used features and workflows. Get started with the MATLAB language and environment so that you can analyze science and engineering data.

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Educators in disciplines across the Sciences use MATLAB. Their main objective is to teach scientific concepts while instilling quantitative thinking in their students. To achieve that goal, educators must teach (and students must learn) computational skills and how to use them to express, analyze, and model scientific phenomena.

From code examples and how-to videos, to teaching activities, to assessment and auto-grading tools, there are multiple resources and tools to support learning in and out of the classroom. These resources lessen the educators' workloads.

Whether you're an individual educator, a member of a department-wide team, or a curriculum developer, these teaching resources can help you effectively teach the triad of science, analytical thinking, and computational skills.

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Educators in disciplines across the Sciences use MATLAB. Their main objective is to teach scientific concepts while instilling quantitative thinking in their students. To achieve that goal, educators must teach (and students must learn) computational skills and how to use them to express, analyze, and model scientific phenomena.

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Autonomous systems continue to be developed and deployed at an accelerated pace across a wide range of applications including automated driving, robotics, and UAVs. The four main components of an autonomous system are sensing, perception, decision making, and action execution and control.

In this webinar, we will focus on the perception component of an autonomous system. We will demonstrate a sensor fusion development framework that includes an integrated test bed, algorithms, and metrics for performance analysis. We will look at the strengths and weaknesses of different sensor modalities for localization and multi-object tracking. We will also explore how measurements from different sensor modalities such as radar and lidar can be combined to improve the quality of the perception system.

Multiple examples will be used to highlight concepts that are critical to architecting and integrating systems that perform sensor fusion.

Highlights

You will be able to learn:

- Why sensor fusion and tracking are a key part of perception systems

- How to combine measurements that are taken from different sensor to improve pose estimates and situational awareness

- How to explore system level trade-offs including sensor accuracy, location, and update rates

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Robotics & Autonomous Systems Technical Talks 2022

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Join the Robotics & Autonomous Systems Technical Talks 2022 and discover the new trends in AI for Robotics, Smart Factories , Mobile Robots, UAV and more!

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FEM Heat Structural and Thermal Analysis using MATLAB_TS

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The seminar will cover big data simulation with MATLAB, with the following schedule:

• Introduction – by TechSource Speaker, 10 minutes

• Getting started with MATLAB – by TechSource Speaker, 20 minutes

• Applications on PDE – by UTM’s speaker

• Demo & hands-on - by UTM’s speaker

• New features and beyond - by UTM’s speaker

• Closing remarks by TS and Dr. Norma

• TS will support any inquiries during the session on the chat box

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The Partial Differential Equation Toolbox™

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The Partial Differential Equation Toolbox™

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The Partial Differential Equation Toolbox™ offers various functions that allow for the analysis of structural mechanics, heat transfer, and other partial differential equations using finite element analysis. Here are five key features of the toolbox:

Linear Static Analysis: The toolbox provides the capability to perform linear static analysis to compute the deformation, stress, and strain of a given component or structure.

Modal Analysis: Users can analyze a component's structural characteristics by performing modal analysis to determine natural frequencies and mode shapes.

Heat Transfer Analysis: The toolbox enables the modeling of conduction-dominant heat transfer problems to calculate temperature distributions, heat fluxes, and heat flow rates through surfaces.

Mesh Generation: The toolbox allows for the automatic generation of meshes with triangular and tetrahedral elements, and the import of 2D and 3D geometries from STL or mesh data.

Postprocessing: Users can postprocess results to explore and analyze them, allowing for a deeper understanding of the modeled problem and its behavior.

Will include the new features introduced in R2023a.

The main reference Partial Differential Equation Toolbox - MATLAB (mathworks.com), Release Notes for Partial Differential Equation Toolbox - MATLAB & Simulink - MathWorks Benelux.