Dive into the workflow of the DynamiΣ PRC as they develop a driver-in-the-loop simulator for the Formula Student competition. Leveraging the power of Simulink® and Unreal Engine®, the team created a custom simulator tailored for specific Formula Student dynamic events, providing a comprehensive environment for a full car model. Learn how Simulink 3D Animation™ was utilized to seamlessly integrate the Simulink model with Unreal Engine. You’ll gain insights into the simulator’s capabilities and learn essential considerations for designing high-performance simulation environments. Related resourc ..read more

Learn how to use MATLAB AI Chat Playground to extract vehicle information from LIDAR data. You'll learn how to use AI Chat playground to: - Load and visualize LIDAR data - Pre-process LIDAR data - Denoise and downsample LIDAR data Related resources: - AI Chat Playground launch: https://bit.ly/49Tt84E - Access AI chat playground here: https://bit.ly/3RCOg7D -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl ..read more

Sliding mode control is a robust control technique that ensures precise tracking of desired trajectories, even in the presence of model uncertainties and disturbances. This demonstration includes a quick overview of sliding mode control. It also shows how you can design and tune a sliding mode controller in Simulink® to make a robotic manipulator follow reference trajectories in the presence of model uncertainties and external disturbances. Check out the example demonstrated in the video: https://bit.ly/4dFWPam Additional Resources: - Learn what sliding mode control is and how it works: https ..read more

In this webinar, go through the process for an end-to-end digital twin project, focusing on how you can use real-world CAN bus data to drive your understanding and model development. Here’s what you’ll learn: - Data Collection: Discover how to log and decode raw CAN bus data from vehicles using the CANedge family of data loggers from CSS Electronics. - Data Preparation: Once you have logged data, understand the process used to prepare the data for analysis and modeling. - Digital Twin Development: Develop and deploy a digital twin model of an EV battery, designed to estimate the battery’s stat ..read more

Image labeling tools are a crucial component of many machine learning workflows. The Image Labeler app in MATLAB enables you to conduct individual and team-based image labeling projects. Learn how to label ground truth data in a collection of images using the Image Labeler app, as well as how to increase the scale of image labeling projects by distributing tasks across a range of team members through a collaborative, multi-user labeling workflow. You will also learn how to review labeled images, provide feedback, and track progress for all labeling and review tasks. Highlights: - Discover how ..read more

Discover how to automate the tuning of gain-scheduled PID controllers for vertical takeoff and landing (VTOL) aircraft using Simulink®. We focus on a VTOL system that combines the unique flight modes of hover, fixed-wing flight, and the critical transition phase between them. Gain-scheduled PID controllers, commonly used in such applications, can be complex to design and tune manually across different operating points, especially for seamless transition control. The demonstration highlights how the Gain-Scheduled PID Autotuner block in Simulink accelerates this tuning process. Instead of manua ..read more

Fault data is critical when designing predictive maintenance algorithms but is often difficult to obtain and organize. Many organizations are faced with a growing sea of time series sensor data, most of which represents normal operation. How can engineers analyze this data and design anomaly detection algorithms to identify potential problems in industrial equipment? Using real-world examples, this webinar will introduce you to a variety of statistical and AI-based anomaly detection techniques for time series data. Learn about: - Organizing, analyzing, and preprocessing time series sensor data ..read more

Learn how to apply the least mean squares (LMS) algorithm to the problem of system identification. The LMS algorithm is the most popular adaptive algorithm in the world because of its simplicity and flexibility. You’ll learn how to set up a Simulink® model to perform system identification and verify its performance using visualization tools such as scopes and array plots. The focus is on rapid prototyping, i.e., constructing a system identification model with the least amount of effort. The LMS Filter block from DSP System Toolbox™ is leveraged for this purpose. In later videos, you’ll see how ..read more

Qualcomm® Hexagon® processors are integral to DSP and NPU applications. Learn how Embedder Coder Support Package for Qualcomm Hexagon Processors assists in designing and verifying algorithms using the QHL scalar processor library and Hexagon Vector eXtension. Execute your code on the Qualcomm Hexagon Simulator, Hexagon QEMU emulator, or directly on hardware boards for improved performance. Explore how code replacement library optimizations work specifically for Qualcomm Hexagon. Get started by installing the support package from the Add-On Manager in MATLAB®, available from R2024b, and streaml ..read more

Learn how to use MATLAB and Simulink to create machine learning models that can be trained on an embedded device to adapt to new data. We'll cover: - The basics of on-device learning techniques and typical applications and devices - Motivation for training machine learning models on embedded devices - Challenges involved in on-device learning - Two main approaches to on-device learning: using passive or active model updates - The steps in a typical workflow using an audio classification example About the Presenters: Jack Ferrari is a product manager at MathWorks, focused on code generation for ..read more