Understanding SLAM Using Pose Graph Optimization | Autonomous Navigation, Part 3 MATLAB 2024-10-19 | Understanding SLAM Using Pose Graph Optimization | Autonomous Navigation, Part 3
Back to School - Ask me Anything! MATLAB 2024-10-18 | Have you started a new school year? Know you’ll be using MATLAB this year? Whether you're excited, anxious, or somewhere in between, we've got you covered. Join us for our Back to School Ask Me Anything (AMA)! Be sure to drop your questions in the chat and we'll answer them during the livestream!In this interactive session, you'll have the opportunity to:• Ask Questions: Whether it's about study tips and tricks with MATLAB & Simulink, project ideas or even if you're a newbie wondering where to start, no question is too big or small!• Get Advice: Hear from our student programs team and experts who will share their insights and experiences to help you make the most out of MATLAB & Simulink. 🔔 How to Participate:• Subscribe to our channel and hit the notification bell to stay updated.• Submit your questions in advance in the comments section or during the live chat.• Join the live stream and interact with us in real-time!Build on your basic MATLAB and Simulink knowledge with code examples, free introductory tutorials, cheat sheets, and more in our student resource center: mathworks.com/academia/students/resources.htmlReady to learn more? Check out our free, self-paced online training courses: matlabacademy.mathworks.comAbout the presenters:Connell D’Souza has worked at MathWorks since 2016 and is a Manager of Education Programs focusing on Students. In this role he manages a team of engineers that help students adopt MATLAB, Simulink and Model-Based Design practices for Aerospace, Data Science, Automotive and Autonomous Systems applications. Connell holds a Master’s Degree in Mechanical Engineering from Northeastern University, Boston, MA and a Bachelor’s Degree in Mechanical Engineering from K.J. Somaiya College of Engineering, Mumbai, India where he competed at Formula Student Germany as team lead of Orion Racing India.Eric Hillsberg has been at MathWorks since 2022 and is a Product Marketing Engineer. He develops technical content such as videos, demos, and presentations, focusing on Aerospace, Autonomy, Controls, and Systems Engineering topics. He also conducts research to understand the marketplace of engineering tools. Eric holds a bachelor’s degree in aerospace engineering from the University of Michigan and graduated with a minor in Computer Science. While completing his degree, he completed a year-long internship with NASA Ames Research Center working on the Distributed Spacecraft Autonomy project.--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Object Recognition: Deep Learning and Machine Learning for Computer Vision MATLAB 2024-10-17 | Object recognition is enabling innovative systems like self-driving cars, image based retrieval, and autonomous robotics. The machine learning and deep learning these systems rely on can be difficult to train, evaluate, and compare.In this webinar we explore how MATLAB addresses the most common challenges encountered while developing object recognition systems. This webinar will cover new capabilities for deep learning, machine learning and computer vision.We will use real-world examples to demonstrate:- Training models using large image datasets- Training deep neural networks from scratch- Using transfer learning to re-use trained deep networks for new tasks- Exploring the tradeoffs between machine learning and deep learningRelated Resources:- What is Computer Vision? bit.ly/3NDmjbc- Get the example code used in this video: bit.ly/3VnTTZPChapters:0:00 What is object recognition and when do I want to use it?0:31 Demo #1: Scene Classification0:43 The Machine Learning Workflow for Object Recognition6:46 Classification Learner App for experimentation with different machine learning algorithms9:00 Export an object recognition model from the Classification Learner App10:27 Demo #1 Takeaways11:22 The Deep Learning Workflow for Object Recognition12:45 Demo #2: Fine-tune a pre-trained deep learning model (transfer learning)18:00 Visualizing and removing mis-identified images from training data19:06 Transfer Learning19:53 Real-world object recognition with the transfer learned model and a deployable video player20:30 Demo #2 Takeaways21:36 Demo #3: Deep Learning and Machine Learning combined approach for object recognition25:03 Demo #3 Conclusion25:13 Machine Learning vs. Deep Learning object recognition overall comparison--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Battery Modeling featuring Efficient Pack Design and Cell Characterization MATLAB 2024-10-16 | Learn about the latest tools for battery system modeling and simulation. Start with creating a single battery cell model using the new Battery Equivalent Circuit block, build a battery pack that includes thermal management, and see a new and efficient method for battery parameter estimation that finds equivalent circuit lookup tables using multiple characterization experiments simultaneously.Learn more:- Get Started with Battery Builder App: bit.ly/3V6Afj3- Characterize Battery (Table-Based) Block Parameters in CAGE: bit.ly/3KyglZi- Developing Battery Systems with Simulink and Simscape: bit.ly/3VaFb6t- Simscape Onramp: bit.ly/Simscape-OnrampChapters:0:00 Introduction to Battery Modeling2:06 Agenda3:04 Equivalent Circuit4:48 Battery Modeling - Single Cell6:38 Scale-Up to Module and Pack11:43 Cell Characterization21:14 Conclusion--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Why Convolution Matters MATLAB 2024-10-15 | Explore what convolution is and why it matters. Convolution is a mathematical operation between two functions. It is a foundational concept in fields such as signal processing and image processing. The convolution integral looks complex but can be broken into pieces to understand the operations behind it. Convolution describes ordinary phenomena seen every day, such as echoes in a cave. It is behind the techniques that allow smartphones to take pictures and receive phone calls. Learn the concepts behind convolution and why scientists and engineers use its properties to design systems.Learn more:- What Is Convolution?: bit.ly/3Y91BHY- Signal Processing Onramp: bit.ly/3jjGjWr- Image Processing Onramp: bit.ly/3PHmiVC- conv - Convolution and Polynomial Multiplication: bit.ly/3zMr8xyChapters:0:00 What is Convolution?2:45 Convolution in Sound4:36 Signal Convolution7:07 Image Convolution8:40 Convolutional Neural Networks9:25 Conclusion and Next Steps--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Managing Requirements and Advanced Model Checks MATLAB 2024-10-14 | Get started with Simulink® verification and validation products. Learn how to manage requirements in Simulink, perform advanced model checks, and check your model for run-time errors. The example in this video uses Requirements Toolbox™, Simulink Check™, and Simulink Design Verifier™.Try the example: bit.ly/3U8tsXiRelated Products:- Simulink Check: bit.ly/3KW2uKe- Requirements Toolbox: bit.ly/3yfRipb- Simulink Design Verifier: bit.ly/3sh0ZQi--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Model Description | MathWorks Minidrone Competition MATLAB 2024-10-10 | Get details about the Simulink® model that needs to be used for the MathWorks Minidrone Competition and learn how to use the Simulink Support Package for Parrot® Minidrones.Check out the MathWorks Minidrone Competition playlist for tips and tricks that will guide you through the different stages of the competition: youtube.com/playlist?list=PLn8PRpmsu08p6mFOfMUEO4-2bo3lynoMvRelated Resources:- Get started with Simulink Onramp: bit.ly/3BNzUJ9- Programming Drones with Simulink: bit.ly/2C99ynb- Explore the Simulink Support Package for Parrot Minidrones: bit.ly/2WE6NRz- Fly a Parrot Minidrone and Detect Objects: youtu.be/qPVnweXMguE- Ask questions on MATLAB Answers: bit.ly/2WD5YZq--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
MATLAB EXPO 2024 | November 13–14, 2024 MATLAB 2024-10-09 | Discover the latest MATLAB and Simulink capabilities at MATLAB EXPO 2024. Save the date: November 13–14, 2024.Learn more about MATLAB EXPO 2024: bit.ly/MATLAB-EXPO-2024At MATLAB EXPO, engineers, educators, researchers, and scientists from all over the world come together to learn about the latest applications and capabilities in MATLAB and Simulink. Join thousands of industry colleagues and leaders to hear customer success stories, participate in hands-on workshops, and see product demonstrations from MathWorks experts and partner companies.The MATLAB EXPO Experience:*Engage with Experts*Hear from MathWorks engineers and industry leaders in the field. Learn from their experience and be inspired by their success stories.*Deepen Your Knowledge*Discover the latest trends and advancements in engineering and science, and explore new techniques you can start applying right away to your own objectives.*Choose Your Agenda*Choose from a broad range of industry tracks and topics that align with your interests.--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Design Optimization with MATLAB MATLAB 2024-10-08 | Engineers use design optimization tools to automate finding the best design parameters while satisfying project requirements and to evaluate trade-offs among competing designs. Using these tools results in faster design iterations and allows evaluating a larger number of parameters and alternative designs compared with manual approaches.We will show how to use apps and functions in Optimization Toolbox and Global Optimization Toolbox to define and solve design optimization problems. Optimization can be applied to design models that are either analytic or black-box including those built with machine learning and simulations. We will use examples from different engineering domains to demonstrate these capabilities.Highlights- Defining objectives, constraints and design variables- Interactively creating and solving optimization problems with an app- Choosing the best solver for your problem- Setting options to improve results- Using parallel computing to accelerate design studiesChapters:00:00 Introduction to design optimization10:16 Multistage rocket design optimization example15:50 Current-carrying cables design optimization example28:26 Electrified powertrain gear ratios design optimization example41:42 Tips for selecting optimization tools45:21 Key takeaways --------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
What Is Computer Vision Toolbox? MATLAB 2024-10-07 | Computer Vision Toolbox™ provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. For 3D vision, the toolbox supports single, stereo, and fisheye camera calibration; stereo vision; 3D reconstruction; and lidar and 3D point cloud processing. Computer vision apps automate ground truth labeling and camera calibration workflows.Learn more:- Computer Vision Toolbox: bit.ly/3950wYd- Get started with Computer Vision Onramp self-paced online course: bit.ly/3HpoDkp- Learn more about What is Computer Vision here: bit.ly/3NDmjbc--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Learn to use FPGAs for Motor Control with Simulink MATLAB 2024-10-03 | FPGAs for Motor Control is becoming a pivotal technology in the development of high frequency switching power electronics and motor control systems, driven by highly mathematical computing algorithms. To implement such algorithms, engineers are increasingly looking towards System on Chip (SoC) devices, which blend the familiarity of embedded processors with the capabilities of FPGAs for parallel processing and deterministic behavior.In this session you will learn how to use Simulink to deploy a field-oriented control (FOC) algorithm, onto an AMD-Xilinx Zynq UltraScale+ SoC device, with minimal need for deep FPGA programming knowledge. Using model-based design we will control a permanent magnet synchronous motor (PMSM), illustrate the process of automatically generating C and HDL code for the ARM Cortex processor and FPGA fabric within the SoC device. The techniques are demonstrated using the Trenz Electronics Motor Control Development kit.Highlights:- Model, Simulate, Test and Deploy the FOC algorithm onto Zynq UltraScale+ SoC Device.- Explore the partition the design for ideal division of tasks between the ARM and FPGA.- Automate deployment of the algorithm into reference frameworks for the processor and programmable logic.Learn more about FPGA development: bit.ly/3IDqRgqChapters:00:32 Learn how to use FPGAs for Motor control with Simulink01:08 Overview of Model-Based Design for FPGAs01:47 Motivation to use of FPGAs for motor control03:20 Advantages of SoC Devices for control engineers04:08 Model-Based Design Workflow05:20 Simulink for Control Algorithms05:50 Introduction to Motor Control Blockset07:00 Introduction to the Trenz Electronic Motor Control Dev kit08:05 Introduction to Field Oriented Control algorithm model09:20 Introduction to HDL Coder10:20 System Testbench in Simulink11:05 Introduction to Hardware Support Package11:50 Hardware-Software Partition12:30 Generating C Code13:02 Generating the HDL IP core13:50 Reference design interface14:50 Generating the HDL code15:35 Introduction to Fixed-Point Conversion16:15 Embedded system integration17:00 Run the model on hardware17:45 Key Takeaways--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
SerDes Design and Verification for PAM3 and PAM4 High-Speed Digital Links MATLAB 2024-10-01 | Learn to optimize SerDes systems for PAM3 and PAM4 modulation using SerDes Toolbox. The requirements of emerging wireline communication systems for higher data rates is driving the need for SerDes systems to operate with higher order modulation schemes such as PAM3 and PAM4.SerDes systems architectures and adaption algorithms need to change for achieving this mandatory performance. Accurate system-level models of the physical layer including impairments such as jitter, crosstalk, and non-linearity are required in the study and development of innovative architectures.In this presentation, you will learn how to model PAM3 and PAM4 SerDes systems using measurement data and data sheet specifications, and integrate smarter adaptation and optimization algorithms.Learn more about SerDes Toolbox: bit.ly/4ejfMQLChapters:00:00 Introduction to SerDes Systems02:54 PAM3 SerDes Design and Analysis10:42 Adding Jitter and Crosstalk Impairments12:07 PAM4 Custom SerDes Equalization Algorithms16:34 PAM3 or PAM4 IBIS-AMI Model Generation18:41 Conclusion--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
What Is System Composer? MATLAB 2024-09-30 | System Composer™ is a Model-Based Systems Engineering tool for system and software architecture design that allows you to decompose complex systems or a system-of-systems into smaller, more manageable pieces. This easy-to-use tool bridges the gap between high-level requirements and low-level algorithm and simulation workflows, allowing for scalable specification and analysis of system and software architectures.The tool lets you author and edit architectures, link requirements to architecture and test cases, and define custom properties of architectural elements. With System Composer, you can generate new views of the model to gain insight into functional flows, component dependencies, and more. You can also edit those views directly. You can define software architectures from built-in templates that are generic, or specific like AUTOSAR Classic and Adaptive. You can even model the behavior using various formalisms such as block diagrams, state charts, and sequence diagrams. Another feature of System Composer is the ability to allocate or establish relationships between elements of different architecture models, which may represent different abstractions of a system, such as functional decomposition and logical or physical architectures. And of course, you can seamlessly integrate with Simulink®, FMUs, Legacy Code, and more for traceability and continuity from MBSE concepts through Model-Based Design and verification.- Software Architectures with Simulink and System Composer: bit.ly/3sSEUuI- System Composer Onramp: bit.ly/3vEC6mI--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
What Is Sliding Mode Control? MATLAB 2024-09-26 | Sliding mode control is a nonlinear control law that has a few nice properties, such as robustness to uncertainties and disturbances, and is relatively straightforward to implement and tune. However, one drawback is chattering where the system state, and therefore, the actuators trying to maintain the state, jitter back and forth quickly, which might not be desirable.This MATLAB Tech Talk covers mitigations for this chattering. Overall, sliding mode control is worth understanding because it is a really interesting controller, and you might find that it’s right for your application.This video is divided into three parts. In the first part, be walked through a graphical explanation of the controller to try to get some intuition into how it works. For the second part, see a derivation of it so you understand the mathematics. And in the third part, you’ll see an example of a controller in MATLAB® and Simulink®.To learn more, see this MATLAB example for sliding mode control design for a mass-spring-damper system: Sliding Mode Control Design for Mass-Spring-Damper System - bit.ly/476cNIxRelated Resources:- Capabilities for Modeling Dynamic Systems: bit.ly/4bGBNqr- Sliding Mode Control Design for a Robotic Manipulator: bit.ly/4dFWPamChapters:00:00 Introduction to sliding mode control01:04 Graphical explanation of sliding mode control11:13 Derivation of the sliding mode controller16:20 Example of sliding mode control in Simulink--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
MATLAB EXPO - Highlights from 2023 MATLAB 2024-09-25 | Take a look at the event highlights from MATLAB EXPO 2023, which was presented live and online with over 50 sessions.Learn more about MATLAB EXPO 2024: bit.ly/MATLAB-EXPO-2024--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
USB4 v2 SerDes Design & Signal Integrity Analysis with MATLAB MATLAB 2024-09-24 | Learn to design and analyze USB4 v2 systems using SerDes ToolboxTM and Signal Integrity ToolboxTM. In the rapidly evolving world of USB technology, the transition from NRZ to PAM3 modulation marks a significant leap forward for the next-generation USB standard, USB4 v2. This shift is driven by the demand for higher data rates and increased bandwidth, but it also introduces new challenges in SerDes design and signal integrity analysis.Through this detailed walkthrough, viewers will gain insights into the complexities of creating a USB SerDes system that leverages PAM3 modulation to effectively manage transmitter and receiver equalization, addressing various forms of impairments such as jitter.Starting with an introduction to the general SerDes and signal integrity workflow, the video demonstrates a hands-on approach to setting up a USB4 v2 SerDes model, beginning in the SerDes Designer app, progressing through detailed adjustments in Simulink®, and culminating in the analysis within the Signal Integrity Toolbox.Through statistical and time-domain channel analysis, including the generation of IBIS-AMI models for SerDes and channel verification, the video provides a thorough understanding of the tools and techniques necessary for optimizing USB4 v2 systems.Check out this example showing how to implement USB4 V2 (80Gbps PAM3) Transmitter and Receiver architectures with SerDes Designer and generate IBIS-AMI models using the library blocks in SerDes Toolbox: bit.ly/3z8J4S4 Chapters:00:00 Introduction 01:01 SerDes and Signal Integrity Workflow 02:04 USB4 v2 Demo: SerDes Designer App 12:44 USB4 v2 Demo: Simulink 26:22 USB4 v2 Signal Integrity Toolbox 30:01 Conclusion --------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Physical Modeling of Offroad Vehicles with Simscape MATLAB 2024-09-23 | Using the example of an excavator, this overview shows how to use Simscape Multibody™ to create dynamic models of offroad vehicles from CAD models and how to use these dynamics models to simulate control systems, verify machine performance, and test the integration of hydraulic, mechanical, and electrical subsystems.The presenter is Dr. Christoph Kammer, a senior application engineer at MathWorks in Switzerland. He supports customers in the robotics and autonomous systems domain in the areas of control and optimization, virtual scenario simulation, and digital twinning. Dr. Kammer has a master’s degree in mechanical engineering from ETH Zürich and a Ph.D. in electrical engineering from EPFL, where he specialized in control design and the control and modeling of electromechanical systems and power systems.--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Techno Economic Analysis of the Impact of EV Charging on the Power Grid MATLAB 2024-09-19 | With techno-economic analysis, you can assess the technical feasibility and economic viability of electric vehicle charging infrastructure.With more and more electric vehicles connecting to the power grid every day, there are concerns that existing grid infrastructure will be strained beyond acceptable operational limits. We can address these concerns by bringing operations, pricing, and forecasting into techno-economic models of power systems. Using these models, we can assess feasibility, risk, optimal operations, and profitability of charging infrastructure. These models provide key insights such as expected system performance over time, identification of factors that lead to bad outcomes, and right-sizing of components through optimization studies.In this talk, we consider a scenario where a system operator can command individual electric vehicle battery units to both store and supply electricity while connected to the grid. The operator applies techno-economic optimization to the charging profiles to minimize electricity cost while accounting for system requirements and constraints, such as limits on state of charge, grid supply, and charge/discharge rate. The optimization provides a fast and automated approach for leveraging all of the units connected to the grid for overall system benefit. Charging profiles are then assessed for the impact on voltage and power flow levels using a grid-level simulation.Chapters:0:00 Introduction to techno-economic analysis 4:38 Overview of EV charging case study 6:48 Modeling and solving the optimization problem 13:27 Grid simulation, analysis, and visualization 19:08 Deployment workflows 20:01 Key takeaways --------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Medical Image Analysis and AI Workflows in MATLAB MATLAB 2024-09-17 | Medical images come from multiple sources such as MRI, CT, X-ray, ultrasound, and PET/SPECT. The challenge is to visualize and analyze this multi-domain image data to extract clinically meaningful information and conduct other tasks such as training AI models.MATLAB provides tools and algorithms for end-to-end medical image analysis and AI workflows – I/O, 3D visualization, segmentation, labeling and analysis of medical image data. This webinar shows the complete medical image analysis workflow for AI applications. You will learn how to import visualize, segment and label medical image data and utilize these data in AI model training.Highlights- Importing and visualizing multi-domain DICOM medical images- Segmenting and labeling 2D and 3D radiology images- Designing and training AI and deep learning modelsLearn more:- Discover the new Medical Imaging Toolbox: bit.ly/3Yyc6Ch- AI for Medical Devices and Digital Health: bit.ly/3pSNfgtChapters:0:00 Introduction to medical image analysis in MATLAB 3:21 Image Preparation and LabelingMED 5:21 Image Preparation and Labeling Demo 20:52 Model Design and Training 25:31 Model Design and Training Demo 40:21 Beyond Training: Tuning, Verifying & Deployment 50:04 Summary --------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
What Is Satellite Communications Toolbox? MATLAB 2024-09-16 | Satellite Communications Toolbox provides standards-based tools for designing, simulating, and verifying satellite communications systems and links. The toolbox enables you to model and visualize satellite orbits, perform link analysis, and access calculations. You can also design physical layer algorithms together with RF components and ground station receivers, generate test waveforms, and perform golden reference design verification. With the toolbox, you can configure, simulate, measure, and analyze end-to-end satellite communications links. You can also create and reuse tests to verify that your designs, prototypes, and implementations comply with satellite communications and navigations standards, DVB-S2X, DVB-S2, CCSDS, and GPS.Learn more about Satellite Communications Toolbox: bit.ly/SatCom-Toolbox--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Discover Whats New: R2024b Release Highlights for MATLAB and Simulink MATLAB 2024-09-13 | Release 2024b offers hundreds of new and updated features and functions in MATLAB® and Simulink®. Learn more about what’s new in R2024b: bit.ly/2kMi0CrMajor Updates:• 5G Toolbox – Explore candidate 6G waveform generation; use the Wireless Waveform Analyzer app to perform signal quality assessments of the acquired 5G waveforms.• DSP HDL Toolbox – Use the interactive DSP HDL IP Designer app to customize, configure DSP algorithms, and generate HDL code and verification components.• Simulink Control Design – Design and implement nonlinear and data-driven control techniques such as sliding mode and iterative learning control.• System Composer – Edit subsetted views; describe system behavior with activity and sequence diagrams.Transitions:• Embedded Coder – As of R2024b, the SoC Blockset Support Package for Infineon® AURIX™ Microcontrollers has been merged into the Embedded Coder Support Package for Infineon® AURIX™ TCx4 Microcontrollers. Release Highlights:MATLAB:• Live Editor Fonts – Customize font, size, color, and formatting of text and code styles.• Help Center – View documentation in your system web browser.• Solve ODE Live Editor Task – Interactively solve systems of ordinary differential equations.• Reading Online Data – Read remote data over HTTP and HTTPS with 31 additional functions across multiple categories, including low-level I/O, datastores, and HDF5.• Graphics – Visualize grouped numeric data (violinplot); create one or more compass plots in polar axes (compassplot).• dbstop Function – Pause execution when unsuppressed output is returned to find missing semicolons.• Build Automation – Create and run a group of tasks.Simulink:• Component Interface View – Create, edit, and view interfaces in a perspective that highlights component boundaries and signal tracing.Runtime Variants – Change the active choice of a Variant Subsystem block during simulation or code generation by setting the activation time to runtime.Simulation Data Inspector – Save and load sessions with faster time and smaller file size with the new MLDATX 2.0 file format.Simulink Editor – Learn more about Simulink blocks and actions when using quick insert.5G Toolbox:• 6G Exploration Library – Explore 6G enabling technologies with MATLAB.Get more out of MATLAB and Simulink by downloading the latest release: bit.ly/3UcGlx2--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Modeling 5G Non-Terrestrial Network (NTN) Links in MATLAB MATLAB 2024-09-12 | Ubiquitous internet connectivity using satellites is no longer a dream as demonstrated by the 3GPP investment in 5G New Radio (NR) non-terrestrial networks (NTNs). The challenges of NTN links are very different in comparison to terrestrial links, as they must deal with larger propagation delays and high Doppler.In this webinar, you will learn about modeling 5G non-terrestrial links in MATLAB®. Start with an overview of the orbit propagation capabilities in MATLAB that allow you to model large satellite constellations in their orbits and generate coverage maps for different antenna types. Then focus on modeling a 5G NTN link by evaluating the throughput of an NR Physical Downlink Shared Channel (PDSCH) in an NTN channel—specifically, the tapped delay line (TDL) NTN channel. You will also learn about the different Doppler compensation strategies employed in an NTN link. Before concluding, narrowband Internet of Things (NB-IoT) NTN links will also be briefly discussed.Learn more:- Satellite Communications Toolbox — Examples: bit.ly/3AxQgIjChapters:00:00 Introduction, What Are 5G NTNs, and Why Are 5G NTNs Important?04:41 5G NTN Use Cases05:40 5G NTN Technology Challenges08:42 5G NTN Satellite and Link Architectures23:52 5G NTN Link Simulation39:25 Summary--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Econometric Modeler App: Detrending and Seasonal Adjustment MATLAB 2024-09-10 | Certain econometric models require that the time series is stationary. Many time series contain trends and seasonal effects. Thus, they need to be properly processed before being used further. In this session, learn how to quickly detrend and make seasonal adjustments with the Econometric Modeler app.Learn more:- Learn more about Econometrics Toolbox: bit.ly/3x40Fdi- Econometric Modeler App: Getting Started with the App: youtu.be/arXsTOjZyrI--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Meeting the Challenges of Design Optimization MATLAB 2024-09-09 | Engineers use design optimization tools to automate finding the best design parameters while satisfying project requirements and evaluating trade-offs among competing designs. Using these tools results in faster design iterations and allows the evaluation of a larger number of parameters and alternative designs compared with manual approaches.In this talk, you’ll learn how optimization can be used to meet design challenges for different types of models, from detailed finite element analysis to controls and system engineering. You’ll learn how to address challenges from conflicting design criteria and for leveraging available computing resources. You will also learn about new features that make design studies easier to perform and quicker to complete, including simpler ways to set up and run the optimization steps and new solvers designed for computationally expensive black-box models.Chapters:00:00 Introduction04:40 Disc brake design10:33 5G RF filter design13:03 EV gear design17:57 Quadcopter design22:33 Recap and resources--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Design Motor Cooling Systems with Motor-CAD and Simscape MATLAB 2024-09-05 | Explore this demonstration to see how cooling systems can boost a motor's performance, efficiency, and safety. Find out how Ansys Motor-CAD enables the evaluation of diverse cooling strategies and facilitates the integration of thermal models into Simulink and Simscape, allowing for a holistic approach to system design and performance optimization.Key takeaways include:- Thermal design is critical and non-trivial.- Cooling strategies determine machine continuous performance.- Integrating system models enables holistic design optimization.- You can import Motor-CAD thermal models to Simulink.Learn more:- Import a Motor-CAD Thermal Model in Simulink and Simscape: bit.ly/4cPTHYs- Import PMSM Flux Linkage Data from JMAG-RT: bit.ly/46rtfmk- Motor Thermal Circuit: bit.ly/3Z0t6Ve- Import Efficiency Map Data from Motor-CAD: bit.ly/3X9BUFQ- Field-Weakening Control (with MTPA) of Nonlinear Synchronous Reluctance Motors Using Lookup Table: bit.ly/474IT7x- Import a Motor-CAD Thermal Model into Simulink and Simscape: bit.ly/4cKfbFMChapters:00:00 Why cool your motor? 03.55 Most popular cooling solutions 08:50 Exporting Motor-CAD thermal model 11:43 Demo: Create Simulink Thermal Model 20:33 Demo: Validate the Thermal Model 25.51 Demo: System-Level Use of the Model 34:33 Conclusions--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Finding a Model for Categorical Data | Machine Learning Made Easy MATLAB 2024-09-03 | In this example, use supervised learning to find a classification model for fitness tracker data. Using the Classification Learner app and functions in Statistics and Machine Learning Toolbox™, perform common machine learning tasks such as:- Selecting and transforming features- Specifying cross-validation schemes- Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbors, and discriminant analysis- Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your dataCheck out our Machine Learning Made Easy Playlist: youtube.com/playlist?list=PLn8PRpmsu08pBihb7VFpRCDXd-g5JB-BRLearn more:- Human Activity Recognition Simulink Model for Smartphone Deployment Demo: bit.ly/46mZWkR- Machine Learning with MATLAB: bit.ly/2O9Sujp--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Machine Learning Basics MATLAB 2024-08-29 | From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. But what is Machine Learning and how does it work?Machine Learning may seem difficult to understand and even harder to use but in practice, incorporating machine learning in your workflow can be as easy as a couple of clicks.Check out our Machine Learning Made Easy Playlist: youtube.com/playlist?list=PLn8PRpmsu08pBihb7VFpRCDXd-g5JB-BRLearn more:-Machine Learning with MATLAB: bit.ly/2O9Sujp--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Simulating Torque Ripple for Motor Control System Design MATLAB 2024-08-26 | In this webinar, uncover the critical importance of torque ripple in motor drive systems. Discover its effects and how employing high-fidelity motor models, powered by JMAG’s finite element analysis (FEA) data, can elevate your understanding. Learn about the method for integrating FEA data into Simscape Electrical™, aimed at accurately modeling spatial harmonics and addressing high-frequency loss with precision.Related Resources:- Beyond PID: Exploring Alternative Control Strategies for Field-Oriented Controllers: bit.ly/4cWssvU- FEM-Parameterized PMSM: bit.ly/4bX2B5Q- MATLAB and Simulink for Motor Drives and Traction Motors: bit.ly/3uRvBfN- Understanding BLDC Motor Control Algorithms: bit.ly/4flfbPo- Import PMSM Flux Linkage Data from JMAG-RT: bit.ly/46rtfmk- Field-Weakening Control (with MTPA) of Nonlinear PMSM Using Lookup Table: bit.ly/FWC-MTPA-PMSM-LUT- Plot Constraint Curves and Drive Characteristics for PMSM and SynRM Directly from Block Parameters Dialog Box: bit.ly/4fjLhekChapters:0:00 - Introduction02:25 - Torque Ripple: Impacts & Sources07:29 - JMAG & Simscape: Torque Ripple Model10:36 - FEM Data for Motor Parameterization16:51 - FOC for High-Fidelity Motor Model22:24 - High-Fidelity Model with Iron Loss32:37 - Summary: Simulating Torque Ripple--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Assessing Climate Related Transition and Physical Risks to Mortgage Portfolios MATLAB 2024-08-22 | Physical risk is a critical aspect of the 2015 “Paris Agreement,” which places a binding obligation on the world’s governments to “make finance flows consistent with a pathway towards low greenhouse gas emissions and climate-resilient development.” Understanding physical risk is essential for effective decision-making. Regulators, customers, investors, and other stakeholders are driving financial institutions to do their part to transition to a low-carbon economy and manage exposure to climate-related risks, including physical risk. They’re using new data sources and developing new types of models, often leveraging methods from other scientific and engineering fields. Practitioners need software that provides a broad range of modeling functionality, flexible interfaces, rich visualization capabilities, collaboration, and review features to keep up with the pace of change in this area.Learn how MATLAB® can get you started modeling both physical and transition climate risks. In a live demonstration, you will learn how to:- Visualize flooding risk within a city (physical risk)- Understand the impact of policies aimed at increasing the energy efficiency of buildings (transition risk)- Model the impact of these risks on a portfolio of mortgagesMATLAB® offers powerful tools to help you assess physical risk and transition risk in various scenarios. By integrating physical risk data with other climate-related metrics, you can gain a comprehensive understanding of how physical risk influences financial outcomes. This session will also show you how to create models that account for physical risk, helping you make more informed decisions.Chapters:00:00 Introduction 00:16 Understanding Energy Ratings 01:40 Calculating Transition Costs 02:56 Visualizing Properties on a Map 05:09 Climate Risks and Financial Institutions 06:28 Data Acquisition for Modeling 06:59 Challenges of Climate Risk Modeling 07:40 Visualizing Flood Risk and EPC Ratings 09:21 Modeling Climate Impact on Mortgages 13:04 Summary and Conclusion--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
How to Visualize Text Data Using Word Clouds MATLAB 2024-08-20 | Learn how to create word clouds with MATLAB®. This guide shows you how to visualize text data using word clouds and demonstrates how you can modify word cloud properties for your projects.Additional Resources- Learn how to create word clouds from text data: bit.ly/4dbfYR8- Visualize text data using word clouds: bit.ly/4fBizW8- Learn how you can control word cloud appearance and behavior in MATLAB: bit.ly/4fBXf2P--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Spectral and Synchronous Averaging MATLAB 2024-08-13 | Watch this sequel on signal averaging in Simulink® to learn about extensions of the techniques covered in “The Many Ways to Average a Signal.” youtu.be/EIlH6-lA-BoSpecifically, learn how to combine running, exponential, sliding, and weighted moving averages in unique ways that lead to novel averaging approaches such as the triple exponent moving average and the Hull moving average. You’ll also see how these same averaging techniques are applied in the frequency domain to power spectrums using the Simulink Spectrum Estimator and Spectrum Analyzer blocks. Get started now by downloading the examples in this demonstration on GitHub®: github.com/kschutz68/AVERAGING_1.gitLearn more:- Moving Average: bit.ly/3W1Qi3y- dsp.MovingAverage: bit.ly/3L3oANp- Spectrum Analyzer: bit.ly/3XG32xSChapters: 00:00 Introduce Different Types of Signal Averaging 02:10 Walkthrough of a Running Average Implementation 03:46 Test Running Average Model 05:38 Brute-Force Moving Average Implementation 08:55 Efficient Vectorized Delay Line–Based Moving Average Implementation 10:10 FIR-Based Moving Average Implementation 10:30 CIC-Based Moving Average Implementation 11:30 Moving Average from Sliding Window in DSP System Toolbox 12:10 Using the Scope Legend to Turn Traces On and Off 12:34 Walkthrough of the Fixed-Coefficient Exponential Averaging Approach 14:22 Walkthrough of the Dynamic-Coefficient Exponential Averaging Approach 16:15 Exponential Averaging Block Implementation from DSP System Toolbox--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
What Is Simulink Test? MATLAB 2024-08-13 | Develop, manage, and execute simulation-based tests using Simulink Test™. For more on Simulink Test, visit: bit.ly/3FJOpyASimulink Test provides tools for authoring, managing, and executing systematic, simulation-based tests of models, generated code, and simulated or physical hardware. It includes simulation, baseline, and equivalence test templates that let you perform functional, unit, regression, and back-to-back testing using software-in-the-loop (SIL), processor-in-the-loop (PIL), and real-time hardware-in-the-loop (HIL) modes.With Simulink Test you can create nonintrusive test harnesses to isolate the component under test. You can define requirements-based assessments using a text-based language, and specify test input, expected outputs, and tolerances in a variety of formats, including Microsoft® Excel®. Simulink Test includes a Test Sequence block that lets you construct complex test sequences and assessments, and a test manager for managing and executing tests. Observer blocks let you access any signal in the design without changing the model or the model interface. Large sets of tests can be organized and executed in parallel or on continuous integration systems.You can trace tests to requirements (with Requirements Toolbox: bit.ly/3KAwWtr) and generate reports that include test coverage information from Simulink Coverage: bit.ly/3LuRapH.Support for industry standards is available through IEC Certification Kit: bit.ly/3KQjFgj (for ISO 26262 and IEC 61508) and DO Qualification Kit: bit.ly/2Y2Pugx (for DO-178 and DO-254)--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Why Work at MathWorks? MATLAB 2024-08-06 | At MathWorks, we believe in the importance of engineers and scientists. We created MATLAB® and Simulink® to help them do their best work. Every MathWorker brings a unique background and perspective, but we share a set of core values. We enjoy a stable workplace with a strong work-life balance.See what MathWorks can offer you: bit.ly/3wlo4CXLearn more:Join Our Talent Network: bit.ly/3ScNmhX-Explore MathWorks Offices Around the World: bit.ly/4cIYlYJ--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Flux Marine Redefines Marine Propulsion Through Electrification MATLAB 2024-08-01 | Flux Marine is a startup company designing electric outboards for marine propulsion in an effort to make the marine industry more sustainable. The Flux Marine team uses MATLAB® and Simulink® to design its control systems. Using Simscape™, they generate a digital twin model to inform software and hardware decisions when designing their propulsion system. They also use MATLAB for data analysis, curve fitting, and key statistics to ensure all tests are run accurately and safely.As part of the MathWorks Startup Program, Flux Marine has access to MATLAB at a startup-friendly price and engineering support from MathWorks experts. The partnership and MATLAB tools enable them to save resources, working efficiently to build out and validate their technology to go to market quickly.Learn more about MATLAB and Simulink for Startups: http://bit.ly/2X5zK9q--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
How to Generate a Fault Specification Report in MATLAB MATLAB 2024-07-30 | After you create faults in your model, you might want a summary of your faults in a clean, easy-to-read format such as a PDF that you can share outside of MATLAB®. Learn how to do that with Simulink Fault Analyzer™ by generating a fault specification report.Learn more:- Evaluate Fault Combinations on a Fault-Tolerant Fuel System: bit.ly/3RCFbfF- Generate Fault Specification Reports: bit.ly/4cveMHP--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Path Planning for Vehicles at Cluttered Construction Sites MATLAB 2024-07-25 | Learn how to design a global path planner and local path planner for a vehicle to drive autonomously at a cluttered construction site using the example of a wheel loader. Learn how to choose among different path-planning algorithms, incorporate human input to modify the path-planner output, and use a decision tree to supervise vehicle behavior.The presenter is Julia Brault, senior application engineer for the aerospace and defense industry at MathWorks. She specializes in the modeling and simulation of physical systems, with a focus on robotic and autonomous systems. Prior to joining MathWorks in 2017, Julia worked at companies such as iRobot and Johnson & Johnson in their mechanical engineering, systems engineering, and manufacturing engineering departments. Julia holds B.S. and M.S. degrees in mechanical engineering from Northeastern University.Chapters: 0:00 Introduction: Two types of path planning0:37 Formulate the global path planning problem2:18 Evaluate four choices of path planner algorithms5:07 Implement Hybrid A Star path planner in MATLAB7:41 Customize the path planner for different types of vehicles8:23 Add human inputs: no-go zones11:28 Add human inputs: stops and waypoints13:09 Visualize impact of human inputs on the path planner results13:33 Formulate the local path planning problem13:58 Translating desired obstacle avoidance behaviors into a supervisory logic, using Stateflow--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Manually Trigger Faults in an Aircraft Elevator Control System MATLAB 2024-07-23 | Learn how to use Simulink Fault Analyzer™ to manually trigger faults in a simulated model of an aircraft elevator control system.The tutorial begins with a discussion of why engineers would want to manually trigger faults in a simulation. The introduction sets the stage for this feature’s importance in the engineering design lifecycle.Then proceed to the practical application of these concepts through an example model of an aircraft elevator control system. This section includes a step-by-step setup of the model in Simulink®, detailing the function of each component and its relevance to the overall system. The choice of an aircraft elevator control system as an example serves to illustrate the application of fault analysis in a real-world context, given its critical role in aircraft operation.The main section of the tutorial is dedicated to demonstrating how to configure faults to be manually triggered in the simulation of the control system. Two fault configuration scenarios are covered: one in which a new fault is created from scratch, and another in which a previously configured fault is repurposed to be manually triggered. After the faults are configured, you’ll learn how to analyze the results of a simulation in which those faults are used.This tutorial is intended for an audience that includes engineering professionals seeking to deepen their knowledge of fault analysis, as well as students in system design and engineering disciplines. It aims to educate the viewers on how to properly use Simulink Fault Analyzer in their design process, taking advantage of the features available for rapid exploration of design choices and their implications.Learn more:- Verify Fault Detection Logic in Aircraft Elevator Control System: bit.ly/46wObap- Fault Modeling: bit.ly/FaultModeling--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Variational Quantum Eigensolver (VQE) with MATLAB MATLAB 2024-07-17 | The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed to find the lowest eigenvalue of a matrix. VQE is particularly useful in solving optimization problems and is widely applied in quantum chemistry and quantum simulations. Learn how to implement this algorithm using MATLAB® and MATLAB Support Package for Quantum Computing to determine the minimum ground state energy of a chemistry Hamiltonian. The VQE leverages the variational principle of physics to solve eigenvalues of matrices using classical computing methods, which can be challenging—especially for large matrices with complex numbers. By utilizing quantum computing, VQE offers a more efficient approach.Related Resources:- Download the Quantum Computing Support Package for MATLAB: bit.ly/3zyLNEK- Getting Started Guide for Quantum Computing with MATLAB: bit.ly/3We0VjN- Introduction to Quantum Computing: bit.ly/3zyJ9ir- Ground-State Protein Folding Using Variational Quantum Eigensolver (VQE): bit.ly/3Whb92MIn quantum mechanics, any measurable variable is called an observable, represented mathematically as a matrix. When measured, these observables yield discrete or quantized values known as eigenvalues, with corresponding eigenvectors. A Hamiltonian, representing the total energy of a system, is a key observable in quantum mechanics. The VQE algorithm estimates the lowest eigenvalue by applying the variational method. The VQE process involves two main parts: the ansatz and the classical optimizer. The ansatz is a quantum circuit with tunable parameters, mimicking a system’s ground state wavefunction or eigenvectors. The classical optimizer adjusts the parameters of the ansatz to minimize energy, iteratively finding the actual ground state. For this demo, explore a chemistry problem Hamiltonian in the second quantized form, using Pauli matrices X, Y, and Z and identity gates. Begin by defining the Hamiltonian and solving it classically to establish a benchmark for the VQE algorithm. Next, construct the ansatz and plot the quantum circuit. Then, define an optimizer using Global Optimization Toolbox to minimize the eigenvalues of the Hamiltonian. Download the files used in the demo: bit.ly/3W39IUrThe optimizer simulates the circuit iteratively, converging to a minimum eigenvalue close to the classically calculated value. Finally, extract the optimal rotational gate values and run the circuit on both the MATLAB simulator and a hardware device hosted on AWS®. By comparing the results, verify the successful implementation of the VQE.--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Modeling Dynamic Systems MATLAB 2024-07-16 | In this Tech Talk, you’ll gain practical knowledge on using MATLAB® and Simulink® to create and manipulate models of dynamic systems. You’ll learn different model structures and ways to determine model parameters such as white-box (first principles), black-box (data-driven), and gray-box (a combination of both) methods. You’ll also discover how to utilize tools such as Simulink for graphical modeling and Simscape™ for physical modeling. Additionally, you’ll learn techniques for transforming, linearizing, and reducing models to meet different development needs, all with the help of specialized apps in MATLAB.Check out these other references: Modeling Dynamic Systems Map and Links to More Resources: bit.ly/4bGBNqr--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
How to Create a State Transition Table MATLAB 2024-07-15 | State transition tables provide an easy on-ramp for learning Stateflow® and are an ideal solution for simple, process-oriented, sequential state machines. In this “how to” demonstration, follow an example of a quadcopter takeoff that explains the basic components of a state transition table.Watch to learn:- What finite state machines are used for- Why to use state transition tables- How to build a state transition table- How to visualize state transition tables in other waysTo learn more, check out:- Stateflow Onramp: bit.ly/stateflow-onramp- Stateflow Documentation: bit.ly/3Ju5tvi- What Is a State Machine?: bit.ly/State-Machine- “How To” with MATLAB and Simulink: youtube.com/playlist?list=PLn8PRpmsu08oBSjfGe8WIMN-2_rwWFSgr--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
How to Average a Signal Using Simulink MATLAB 2024-07-11 | Watch this detailed introduction to signal averaging using Simulink. Averaging is the one of the most commonly applied signal processing algorithms. There are many forms of averaging and each method has its own advantages. You’ll learn about running averages, sliding window or FIR-based averages, and exponential or IIR-based averages and when you would use one method versus another. Signal averaging serves one of two purposes, either to reduce noise or to better estimate it. You’ll learn how these averaging algorithms work at the detailed block diagram level as applied to streaming time series data. The focus is on signal processing applications but these same averaging techniques apply to other domains as well, e.g. financial or stock market data. You can download the R2024a examples used in this video here: github.com/kschutz68/AVERAGING_1.gitLearn more:- Moving Average: bit.ly/3W1Qi3y- dsp.MovingAverage: bit.ly/3L3oANp- Spectrum Analyzer: bit.ly/3XG32xSChapters: 0:00 Introduce Different Types of Signal Averaging 2:10 Walk Through of a Running Average Implementation 3:46 Test Running Average Model 5:38 Brute-Force Moving Average Implementation 8:55 Efficient Vectorized Delay-Line based Moving Average Implementation 10:10 FIR-based Moving Average Implementation 10:30 CIC-based Moving Average Implementation 11:30 Moving Average from Sliding Window in DSP System Toolbox 12:10 Using the Scope Legend to turn traces on and off 12:34 Walk-through of the Fixed-Coefficient Exponential Averaging Approach 14:22 Walk-through of the Dynamic-Coefficient Exponential Averaging Approach 16:15 Exponential Averaging block Implementation from DSP System Toolbox --------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Spectrum Monitoring for Cognitive Radio MATLAB 2024-07-10 | Cognitive radio is an advanced form of wireless communication technology. It allows devices to automatically detect available channels in the wireless spectrum and then dynamically adjust their transmission or reception parameters to utilize the unused spectrum. This ability to sense and adapt to the environment makes cognitive radio a key technology for optimizing the use of limited wireless spectrum resources.Spectrum monitoring is the key aspect of cognitive radio. It has multiple requirements such as spectrum sensing, management, mobility, and sharing.Learn more:- Wireless Testbench Examples: mathworks.com/help/wireless-testbench/examples.html- Building Datasets for AI-Enabled Radar, Communications, and EW Systems: mathworks.com/videos/building-datasets-for-ai-enabled-radar-communications-and-ew-systems-1701703375028.html- Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals: mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-lte-signals.html--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Three Ways to Analyze Requirements Traceability in MATLAB MATLAB 2024-07-09 | Requirements traceability analysis is the process of tracking the connections between requirements and other aspects of the development lifecycle, including design and testing. It ensures that every requirement is accounted for, has a reason to exist, has been implemented correctly, and has been verified through testing.Learn how to analyze requirements traceability in MATLAB® using the Document View, Traceability Matrix, and Traceability Diagram from Requirements Toolbox™.Learn more about Requirements Traceability: bit.ly/4cnZvIB--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
How to Import and Visualize NetCDF Data in MATLAB MATLAB 2024-07-08 | NetCDF and HDF are self-describing and portable formats for storing and distributing scientific data, enabling efficient analysis and sharing of complex multidimensional data sets. This demonstration will walk you through the steps for importing a netCDF file and exploring its contents using the Import Data Live Editor task in MATLAB®. You will also see how to visualize the imported netCDF data using the Create Plot Live Editor task. These tasks also automatically generate MATLAB code for your live script, allowing it to be shared or the steps to be replicated when needed- Explore ways to interact with a netCDF file: bit.ly/4ejDX1C- Learn more about Import Data Live Editor Task: bit.ly/3yWNiw5--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Use a Combination of Path-Planning Algorithms on an Autonomous Haulage Truck MATLAB 2024-07-03 | Learn how to divide a complex offroad navigation problem into smaller pieces and implement different path planners for each piece to obtain optimal overall performance and run-time speed using the example of an autonomous haulage truck driving at a pit mine.The presenter is Cameron Stabile, a senior developer for Navigation Toolbox™ working out of the MathWorks US Lakeside office. He has supported and authored features such as the 2D and 3D map objects, collision-checking configurations, highway planning tools, and search-, sample-, and control-based planners. More recently, he has been focused on offroad navigation applications, resulting in the open–pit mine reference application. Cameron has a master’s degree in mechanical engineering from Carnegie Mellon, where he focused on navigation and motion planning for tracked AGVs and robotic manipulators for autonomous shipbuilding.--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Creating a View or Controller That References a Model Class | Modular Apps in MATLAB, Part 4 MATLAB 2024-07-02 | Learn how to utilize a model class with custom components or charts. This enables you to have modular, independent building blocks that will be utilized by a larger app.Watch the Modular Apps in MATLAB series: youtube.com/playlist?list=PLn8PRpmsu08qMPrcrgplTHHH3nVc-TJVtAdditional Resources:- Advanced MATLAB Application Development training: bit.ly/3CV5OUs- Software Development with MATLAB consulting: bit.ly/46uYcFx- Widgets Toolbox - MATLAB App Designer Components: bit.ly/3yHGmP2- Volume Labeling Widgets - MATLAB App Building Components: bit.ly/4e1M1ny--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
What’s New in Quantitative Finance MATLAB 2024-07-01 | Explore the latest trends and advancements in quantitative finance with a focus on four key trends shaping the industry in 2024.The four topics covered in this webinar are:- The Rise of Generative AI: Discover how AI is transforming tasks, personalizing experiences, and generally improving efficiency and effectiveness.- Climate Risk: Explore how quantitative analysis is being applied to physical and transition risk assessments.- Low Code Workflows: Greatly reduce manual coding by using low-/no-code applications for workflows in econometrics, risk, and asset management.- Navigating the Evolving Regulatory Landscape: Learn how the Modelscape platform can aid in managing risks and ensuring compliance with ever-changing regulations.Chapters:0:00 Latest Trends in Quantitative Finance2:00 Generative AI4:07 Demo – Transformer networks for time series prediction6:17 Demo – AI Chat Playground8:15 Low Code Workflows11:45 Demo – Portfolio Backtesting App14:15 Climate Risk17:48 Demo – Physical and Transition Risk for Mortgages19:45 Regulatory Landscape20:44 Demo – Modelscape™ Governance--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Econometric Modeler App: Getting Started with the App MATLAB 2024-06-28 | The Econometric Modeler app is a user-friendly interface in Econometrics Toolbox™. This session shows you how to get started with the app, including how to:- Launch the app and import data- Visualize time series- Process time seriesLearn more:- Econometrics Toolbox: bit.ly/3x40Fdi- Econometric Modeler App: Detrending and Seasonal Adjustment: youtu.be/zAXs6OOJ7Yc--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Introduction to Variant Subsystems in Simulink MATLAB 2024-06-27 | Watch a thorough introduction to variant subsystems in Simulink®. Variant subsystems are subsystems in Simulink that can take on multiple behaviors. The user sets the active behavior before the model runs. You’ll learn why and where variant subsystems are used as well as how to create them yourself. Start by comparing variant subsystems against competing methods for modeling multiple behaviors in Simulink, such as enabled subsystems and multiport switch control. Then follow an example use case for variant subsystems where you’ll switch between two candidate algorithms for a given block in Simulink. In the last half of the demonstration, go step-by-step into the process of creating variant subsystems and switching between the implementation variants in different ways. The first method shown is a right-click menu-based switching method. The second method shown leverages MATLAB® code to switch between implementation variants. The latter method allows for full automation with variant subsystems requiring no user interaction. You can download the R2023b examples used in this video here: github.com/kschutz68/variant_ss.gitLearn more:- Implement Variations in Separate Hierarchy Using Variant Subsystems: bit.ly/42YM8uQChapters:00:00 The Many Ways of Modeling Multiple Behaviors under a common subysystem00:50 A Practical Use-Case for Variant Subsystems: Digital Pre-distortion (DPD)01:15 Digital Pre-distortion model walkthrough02:00 Two DPD Algorithms of interest02:30 Under the hood of the DPD coefficient subsystem03:30 Run the model using the LMS update DPD variant03:55 Visualize the DPD coefficients adapt using LMS04:50 Switch to the RPEM update variant05:50 Visualize the DPD coefficients update using RPEM07:00 Alternatives to Variant Subsystems07:15 Manually Rewire a Subsystem08:10 Use Manual Switches to modify subsystem behavior08:52 Use Enabled Subsystems and the Merge block09:50 Use Multiport Switches11:20 Creating Variant Subsystems: step-by-step14:10 Switching between Variants – the hard way15:40 Switching between Variants – an easier way using Label as control mode17:10 Automate the switching between variant choices using Expression as the control mode18:00 Using annotation callbacks and variant subsystems together19:00 Using set_param to set the active variant21:00 Relevant links in the documentation--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
How to Develop and Deploy a Neural Network for MCUs in 4 Steps MATLAB 2024-06-26 | Learn of a framework for designing neural networks for deployment onto MCUs, presented by MathWorks. Tailored for engineers and developers, this concise tutorial delivers a rapid overview of integrating neural network capabilities into microcontrollers, making it the perfect starting point for your embedded AI projects.Discover more about tinyML and MathWorks: bit.ly/3x0tO9nKickstart your journey with a brief walkthrough on designing and training a neural network. From importing your initial data to evaluating your model’s accuracy, you’ll learn the essentials to get your neural network up and running in no time.Discover the power of Bayesian optimization for hyperparameter tuning with MATLAB®, a method to fine-tune your network’s performance.Facing the memory constraints of microcontrollers? This tutorial highlights network compression techniques such as quantization, pruning, and data type conversion. Learn how to make your neural network compact and efficient, ready for embedded use.Concluding with a brief introduction to Embedded Coder® for generating C/C++ code, this tutorial ensures you’re well-equipped to deploy your neural network model onto microcontrollers.This fast-paced tutorial is your first step toward mastering AI deployment on MCUs. Embark on your own AI adventure and bring neural networks to your embedded devices today. For further exploration and resources, feel free to reach out to embedded-ai@mathworks.com.--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Medical Imaging Workflows in MATLAB MATLAB 2024-06-24 | Medical imaging involves multiple sources such as MRI, CT, X-ray, ultrasound, and PET/SPECT. Engineers and scientists must visualize and analyze multidomain image data to extract clinically meaningful information.In this webinar, explore tools and algorithms that MATLAB® provides to support end-to-end medical imaging analysis and AI workflows, such as I/O, 3D visualization, segmentation, labeling, and analysis of medical image data. Learn how to import, visualize, preprocess, register, segment, and label medical image data, and train and use AI models on the data.HighlightsIn this webinar, you will learn through demonstrations how to:Access and visualize medical images in the Medical Image Labeler- Interactively segment lung tissue- Create a machine learning model to characterize tissue- Explore segmenting with the MONAI Label platformExtract and characterize regions of interest- Create DICOM volumes- Use radiomics features to classify tumors as benign or cancerousProcess (huge) whole-slide images- Block-process arbitrarily large data- Use a pretrained deep learning model (Cellpose) to segment cellsLearn more: - Get Started with Medical Imaging Toolbox: bit.ly/4aH2XwP- Get Started with MONAI Label in Medical Image Labeler: bit.ly/4dYT5Bj- Get Started with Radiomics: bit.ly/3V2tsqz- Cellpose for Microscopy Segmentation: bit.ly/3VlcghvChapters:00:00 Introduction02:34 Medical Imaging Workflow and Capabilities: Importing, Visualization, Preprocessing, Registration, Segmentation and Labeling10:29 Demo 1: Lung Visualization, Segmentation, Labeling and Quantification using Medical Image Labeler app and MONAI20:26 What is Radiomics?22: 30 Demo 2: Classifying Tumors Using Radiomics29:20 Processing Large Images and What is Cellpose31:30 Demo 3: Processing Microscopy Images Using Blocked Images and Cellpose30:35 Medical Imaging Workflow and Capabilities: Apps, Analysis, Deployment, V&V42:24 - Learn More--------------------------------------------------------------------------------------------------------Get a free product trial: goo.gl/ZHFb5uLearn more about MATLAB: goo.gl/8QV7ZZLearn more about Simulink: goo.gl/nqnbLeSee what's new in MATLAB and Simulink: goo.gl/pgGtod© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
