MATLAB
How to Fit a Linear Regression Model in MATLAB
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About 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.
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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 learning
Related Resources:
- What is Computer Vision? bit.ly/3NDmjbc
- Get the example code used in this video: bit.ly/3VnTTZP
Chapters:
0:00 What is object recognition and when do I want to use it?
0:31 Demo #1: Scene Classification
0:43 The Machine Learning Workflow for Object Recognition
6:46 Classification Learner App for experimentation with different machine learning algorithms
9:00 Export an object recognition model from the Classification Learner App
10:27 Demo #1 Takeaways
11:22 The Deep Learning Workflow for Object Recognition
12:45 Demo #2: Fine-tune a pre-trained deep learning model (transfer learning)
18:00 Visualizing and removing mis-identified images from training data
19:06 Transfer Learning
19:53 Real-world object recognition with the transfer learned model and a deployable video player
20:30 Demo #2 Takeaways
21:36 Demo #3: Deep Learning and Machine Learning combined approach for object recognition
25:03 Demo #3 Conclusion
25:13 Machine Learning vs. Deep Learning object recognition overall comparison
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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-Onramp
Chapters:
0:00 Introduction to Battery Modeling
2:06 Agenda
3:04 Equivalent Circuit
4:48 Battery Modeling - Single Cell
6:38 Scale-Up to Module and Pack
11:43 Cell Characterization
21:14 Conclusion
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© 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 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/3zMr8xy
Chapters:
0:00 What is Convolution?
2:45 Convolution in Sound
4:36 Signal Convolution
7:07 Image Convolution
8:40 Convolutional Neural Networks
9:25 Conclusion and Next Steps
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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.
Try the example: bit.ly/3U8tsXi
Related Products:
- Simulink Check: bit.ly/3KW2uKe
- Requirements Toolbox: bit.ly/3yfRipb
- Simulink Design Verifier: bit.ly/3sh0ZQi
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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.
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=PLn8PRpmsu08p6mFOfMUEO4-2bo3lynoMv
Related 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
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© 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 more about MATLAB EXPO 2024: bit.ly/MATLAB-EXPO-2024
At 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.
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© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
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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 studies
Chapters:
00:00 Introduction to design optimization
10:16 Multistage rocket design optimization example
15:50 Current-carrying cables design optimization example
28:26 Electrified powertrain gear ratios design optimization example
41:42 Tips for selecting optimization tools
45:21 Key takeaways
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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.
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
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© 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.
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/3IDqRgq
Chapters:
00:32 Learn how to use FPGAs for Motor control with Simulink
01:08 Overview of Model-Based Design for FPGAs
01:47 Motivation to use of FPGAs for motor control
03:20 Advantages of SoC Devices for control engineers
04:08 Model-Based Design Workflow
05:20 Simulink for Control Algorithms
05:50 Introduction to Motor Control Blockset
07:00 Introduction to the Trenz Electronic Motor Control Dev kit
08:05 Introduction to Field Oriented Control algorithm model
09:20 Introduction to HDL Coder
10:20 System Testbench in Simulink
11:05 Introduction to Hardware Support Package
11:50 Hardware-Software Partition
12:30 Generating C Code
13:02 Generating the HDL IP core
13:50 Reference design interface
14:50 Generating the HDL code
15:35 Introduction to Fixed-Point Conversion
16:15 Embedded system integration
17:00 Run the model on hardware
17:45 Key Takeaways
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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/4ejfMQL
Chapters:
00:00 Introduction to SerDes Systems
02:54 PAM3 SerDes Design and Analysis
10:42 Adding Jitter and Crosstalk Impairments
12:07 PAM4 Custom SerDes Equalization Algorithms
16:34 PAM3 or PAM4 IBIS-AMI Model Generation
18:41 Conclusion
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© 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.
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
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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.
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/476cNIx
Related Resources:
- Capabilities for Modeling Dynamic Systems: bit.ly/4bGBNqr
- Sliding Mode Control Design for a Robotic Manipulator: bit.ly/4dFWPam
Chapters:
00:00 Introduction to sliding mode control
01:04 Graphical explanation of sliding mode control
11:13 Derivation of the sliding mode controller
16:20 Example of sliding mode control in Simulink
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© 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 more about MATLAB EXPO 2024: bit.ly/MATLAB-EXPO-2024
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© 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.
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
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© 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.
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.
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© 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.
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
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© 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 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 models
Learn more:
- Discover the new Medical Imaging Toolbox: bit.ly/3Yyc6Ch
- AI for Medical Devices and Digital Health: bit.ly/3pSNfgt
Chapters:
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
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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
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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.
Major 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
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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.
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/3AxQgIj
Chapters:
00:00 Introduction, What Are 5G NTNs, and Why Are 5G NTNs Important?
04:41 5G NTN Use Cases
05:40 5G NTN Technology Challenges
08:42 5G NTN Satellite and Link Architectures
23:52 5G NTN Link Simulation
39:25 Summary
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Learn more:
- Learn more about Econometrics Toolbox: bit.ly/3x40Fdi
- Econometric Modeler App: Getting Started with the App: youtu.be/arXsTOjZyrI
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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 Introduction
04:40 Disc brake design
10:33 5G RF filter design
13:03 EV gear design
17:57 Quadcopter design
22:33 Recap and resources
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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/4cKfbFM
Chapters:
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
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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 data
Check out our Machine Learning Made Easy Playlist: youtube.com/playlist?list=PLn8PRpmsu08pBihb7VFpRCDXd-g5JB-BR
Learn more:
- Human Activity Recognition Simulink Model for Smartphone Deployment Demo: bit.ly/46mZWkR
- Machine Learning with MATLAB: bit.ly/2O9Sujp
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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-BR
Learn more:
-Machine Learning with MATLAB: bit.ly/2O9Sujp
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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.
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/4fjLhek
Chapters:
0:00 - Introduction
02:25 - Torque Ripple: Impacts & Sources
07:29 - JMAG & Simscape: Torque Ripple Model
10:36 - FEM Data for Motor Parameterization
16:51 - FOC for High-Fidelity Motor Model
22:24 - High-Fidelity Model with Iron Loss
32:37 - Summary: Simulating Torque Ripple
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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.
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 mortgages
MATLAB® 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
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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
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Specifically, 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.git
Learn more:
- Moving Average: bit.ly/3W1Qi3y
- dsp.MovingAverage: bit.ly/3L3oANp
- Spectrum Analyzer: bit.ly/3XG32xS
Chapters:
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
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Simulink 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)
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See what MathWorks can offer you: bit.ly/3wlo4CX
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Join Our Talent Network: bit.ly/3ScNmhX
-Explore MathWorks Offices Around the World: bit.ly/4cIYlYJ
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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.
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
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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 more:
- Evaluate Fault Combinations on a Fault-Tolerant Fuel System: bit.ly/3RCFbfF
- Generate Fault Specification Reports: bit.ly/4cveMHP
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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 planning
0:37 Formulate the global path planning problem
2:18 Evaluate four choices of path planner algorithms
5:07 Implement Hybrid A Star path planner in MATLAB
7:41 Customize the path planner for different types of vehicles
8:23 Add human inputs: no-go zones
11:28 Add human inputs: stops and waypoints
13:09 Visualize impact of human inputs on the path planner results
13:33 Formulate the local path planning problem
13:58 Translating desired obstacle avoidance behaviors into a supervisory logic, using Stateflow
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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
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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/3Whb92M
In 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/3W39IUr
The 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.
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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
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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 ways
To 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
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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.git
Learn more:
- Moving Average: bit.ly/3W1Qi3y
- dsp.MovingAverage: bit.ly/3L3oANp
- Spectrum Analyzer: bit.ly/3XG32xS
Chapters:
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
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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
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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
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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
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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.
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Watch the Modular Apps in MATLAB series: youtube.com/playlist?list=PLn8PRpmsu08qMPrcrgplTHHH3nVc-TJVt
Additional 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
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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 Finance
2:00 Generative AI
4:07 Demo – Transformer networks for time series prediction
6:17 Demo – AI Chat Playground
8:15 Low Code Workflows
11:45 Demo – Portfolio Backtesting App
14:15 Climate Risk
17:48 Demo – Physical and Transition Risk for Mortgages
19:45 Regulatory Landscape
20:44 Demo – Modelscape™ Governance
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- Launch the app and import data
- Visualize time series
- Process time series
Learn more:
- Econometrics Toolbox: bit.ly/3x40Fdi
- Econometric Modeler App: Detrending and Seasonal Adjustment: youtu.be/zAXs6OOJ7Yc
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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.
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.git
Learn more:
- Implement Variations in Separate Hierarchy Using Variant Subsystems: bit.ly/42YM8uQ
Chapters:
00:00 The Many Ways of Modeling Multiple Behaviors under a common subysystem
00:50 A Practical Use-Case for Variant Subsystems: Digital Pre-distortion (DPD)
01:15 Digital Pre-distortion model walkthrough
02:00 Two DPD Algorithms of interest
02:30 Under the hood of the DPD coefficient subsystem
03:30 Run the model using the LMS update DPD variant
03:55 Visualize the DPD coefficients adapt using LMS
04:50 Switch to the RPEM update variant
05:50 Visualize the DPD coefficients update using RPEM
07:00 Alternatives to Variant Subsystems
07:15 Manually Rewire a Subsystem
08:10 Use Manual Switches to modify subsystem behavior
08:52 Use Enabled Subsystems and the Merge block
09:50 Use Multiport Switches
11:20 Creating Variant Subsystems: step-by-step
14:10 Switching between Variants – the hard way
15:40 Switching between Variants – an easier way using Label as control mode
17:10 Automate the switching between variant choices using Expression as the control mode
18:00 Using annotation callbacks and variant subsystems together
19:00 Using set_param to set the active variant
21:00 Relevant links in the documentation
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© 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 more about tinyML and MathWorks: bit.ly/3x0tO9n
Kickstart 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.
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© 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.
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.
Highlights
In 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 platform
Extract and characterize regions of interest
- Create DICOM volumes
- Use radiomics features to classify tumors as benign or cancerous
Process (huge) whole-slide images
- Block-process arbitrarily large data
- Use a pretrained deep learning model (Cellpose) to segment cells
Learn 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/3Vlcghv
Chapters:
00:00 Introduction
02:34 Medical Imaging Workflow and Capabilities: Importing, Visualization, Preprocessing, Registration, Segmentation and Labeling
10:29 Demo 1: Lung Visualization, Segmentation, Labeling and Quantification using Medical Image Labeler app and MONAI
20:26 What is Radiomics?
22: 30 Demo 2: Classifying Tumors Using Radiomics
29:20 Processing Large Images and What is Cellpose
31:30 Demo 3: Processing Microscopy Images Using Blocked Images and Cellpose
30:35 Medical Imaging Workflow and Capabilities: Apps, Analysis, Deployment, V&V
42:24 - Learn More
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© 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.