MATLAB and Simulink Videos

Learn about MATLAB® and Simulink®, watch demonstrations about how to get started, and explore what’s new.

Applications: Internet of Things, Robotics, Wireless Communications, Image Processing and Computer Vision, Data Analytics, Motor and Power Control

MATLAB Videos

Introduction

Get an overview of MATLAB , the language of technical computing.
2:05
MATLAB Overview
Get an overview of MATLAB, the language of technical computing.
Explore, visualize, and model your data with MATLAB .
3:26
Analyzing and Visualizing Data with MATLAB
Write programs and develop algorithms using the high-level language and development tools in MATLAB .
4:32
Programming and Developing Algorithms with MATLAB
Develop and share MATLAB applications as code, executables, or software components.
3:51
Developing and Deploying Applications with MATLAB
Use MATLAB® to analyze data, develop algorithms, and create applications. Solve problems for a range of applications, including signal processing and communications, image and video processing, control systems, and test and measurement.
2:38
Technical Computing with MATLAB

Getting Started

Get started with MATLAB and learn how to get more information.
7:00
Getting Started with MATLAB
Working in the Development Environment
Access tools such as the command history workspace browser and variable editor, save and load your workspace data, and manage windows and desktop layout.
5:21
Working in The Development Environment
Create plots programmatically using basic plotting functions.
5:52
Using Basic Plotting Functions
Write a basic MATLAB Program using Live Scripts and learn the concepts of indexing, if-else statements, and loops.
13:57
How to Write a MATLAB Program
Learn how to create a graphical user interface using GUIDE, the graphical user interface development environment of MATLAB.Learn how to create a graphical user interface using GUIDE, the graphical user interface development environment of MATLAB.
8:13
How to Create a GUI with GUIDE

What's New

R2018a includes new releases of MATLAB and Simulink as well as updates and bug fixes to all other products.
3:34
Release 2018a Highlights
R2018a includes new releases of MATLAB and Simulink as well as updates and bug fixes to all other products.
Visually explore and analyze problems in a single, interactive environment, and turn your code into formatted, executable documents that tell your story.
1:54
MATLAB Live Editor Overview
Develop MATLAB applications with an enhanced design environment and expanded UI component set.
5:06
App Designer
Use MATLAB®` to manipulate and analyze data that is too big to fit in memory.
5:24
Using Tall Arrays for Big Data
Learn how to use transfer learning in MATLAB to re-train deep learning networks created by experts for your own data or task.
3:59
Deep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code

Data Analytics and Machine Learning

Get an overview of using MATLAB to develop your data analytics applications.
3:55
MATLAB for Data Analytics
Automate your analysis workflows using thousands of prebuilt mathematical and analysis functions and versatile visualization tools.
Use MATLAB®` to manipulate and analyze data that is too big to fit in memory.
5:24
Using Tall Arrays for Big Data
Learn how machine learning tools in MATLAB(R) can be used to solve regression, clustering, and classification problems.
3:02
Machine Learning with MATLAB Overview
The Classification Learner app lets you train models to classify data using supervised machine learning.
5:12
Classify Data Using the Classification Learner App
Using a mining case study, we will show how to get started using machine learning tools to detect patterns and build predictive models from your datasets.
39:11
Predictive Modeling Using Machine Learning - A Mining Case Study

Deep Learning

See how to use MATLAB, a simple webcam, and a deep neural network to identify objects in your surroundings. This demo uses AlexNet, a pretrained deep convolutional neural network that has been trained on over a million images.
2:38
Deep Learning with MATLAB: Deep Learning in 11 Lines of MATLAB Code
See how to use MATLAB, a simple webcam, and a deep neural network to identify objects in your surroundings. This demo uses AlexNet, a pretrained deep convolutional neural network that has been trained on over a million images.
Use MATLAB for fine-tuning a pretrained convolutional neural network for image classification.
4:05
Deep Learning with MATLAB: Transfer Learning with Neural Networks in MATLAB
Learn how MATLAB addresses common challenges encountered while developing object recognition systems and see new capabilities for deep learning, machine learning, and computer vision.
27:59
Deep Learning for Computer Vision
Learn how MATLAB can help with any part of the deep learning workflow: from preprocessing through deployment. Get a high-level overview of deep learning with MATLAB, and explore several applications.
3:03
MATLAB for Deep Learning
Learn the high-level workflow for semantic segmentation using a deep learning network. Additionally, learn how the Image Labeler app can expedite your workflow for ground truth labeling at the pixel level.
7:56
Semantic Segmentation Overview

Image Processing and Computer Vision

Explore the latest features in image processing and computer vision such as interactive apps, new image enhancement algorithms, data preprocessing techniques, and 3D algorithms.
Image Processing and Computer Vision with MATLAB
Explore the latest features in image processing and computer vision such as interactive apps, new image enhancement algorithms, data preprocessing techniques, and 3D algorithms.
Edge detection can be a versatile and powerful image processing tool. See the type of problems edge detection can help solve and view a detailed example in MATLAB .
3:24
Edge Detection with MATLAB
Discover how to use <code>configureKalmanFilter</code> and <code>vision.KalmanFilter</code> to track a moving object in video. Learn how to handle the challenges of inaccurate or missing object detection while keeping track of its location in video.
7:01
Introduction to Kalman Filters for Object Tracking
Use the OpenCV interface to bring OpenCV based code into MATLAB .
10:19
Using OpenCV with MATLAB
Explore camera calibration capabilities in MATLAB . Calibrate a camera using the camera calibrator app, perform image undistortion, and measure the actual size of an object using a calibrated camera.
4:56
Camera Calibration with MATLAB

Computational Finance

Through the example of risk management, find out how the MATLAB Computational Finance Suite can help you solve risk problems.
3:13
MATLAB Computational Finance Suite Overview
Through a risk management example, find out how the MATLAB Computational Finance Suite can help you solve risk problems.
Learn how machine learning is used for risk modeling from MathWorks, Quant University, and PRMIA.
47:40
Machine Learning and AI in Risk Management
Learn how to create seasonal ARIMA models for time-series analysis using Econometric Modeler App..
5:43
Creating ARIMA Models Using Econometric Modeler App
Using MATLAB for advanced portfolio construction including stock selection models.
30:28
MATLAB for Advanced Portfolio Construction and Stock Selection Models
Using the functionalities in MATLAB and Financial Toolbox, you can perform a strategy backtesting in just 8 lines of code.
4:13
Backtesting Trading Strategies in Just 8 Lines of Code

Signal Processing and Wireless Communications

This webinar teachs how to easily perform signal analysis and signal processing tasks with MATLAB. You will learn techniques for visualizing and measuring signals in time and frequency domains, spectral analysis, and designing FIR and IIR filters.
31:18
Signal Processing with MATLAB
Perform signal analysis and signal processing tasks using MATLAB. Explore techniques for visualizing and measuring signals in time and frequency domains, spectral analysis, and designing FIR and IIR filters.
Denoise signals with weighted moving average and Savitzky-Golay filters.
3:39
Signal Smoothing
Find a signal of interest within another signal, and align signals by determining the delay between them using Signal Processing Toolbox.
4:33
Determining Signal Similarities
Design wireless systems by performing antenna-to-bits simulation, smart RF design, over-the-air testing, LTE and LTE-advanced modeling, and airborne and automotive radar simulation using MATLAB and Simulink products.
2:57
Design Wireless Systems with MATLAB and Simulink
This session highlights new wireless design capabilities featuring multiple antennas, smart RF devices, receiver algorithms, and connecting MATLAB algorithms to SDR devices and RF instruments for verification and over-the-air testing.
30:54
What’s New in Wireless System Design

Simulink Videos

Introduction

Use Model-Based Design with MATLAB® and Simulink® to improve product quality and reduce development time by 50% or more.
3:17
Model-Based Design with MATLAB and Simulink
Use Model-Based Design with MATLAB and Simulink to improve product quality and reduce development time by 50% or more.
Get an overview of Simulink modeling and simulation capabilities.
2:04
Simulink Overview
Design and simulate state charts using Stateflow .
1:57
Stateflow Overview
Model and simulate multidomain physical systems.
2:15
Simscape Overview
Generate C and C++ code optimized for embedded systems using Embedded Coder.
2:11
Embedded Coder Overview

Getting Started

Learn how to get started with Simulink. Explore the Simulink start page and learn how to use some of the basic blocks and modeling components.
8:57
Getting Started with Simulink, Part 1: Building and Simulating a Simple Simulink Model
Learn how to get started with Simulink. Explore the Simulink start page and learn how to use some of the basic blocks and modeling components.
Explore how to create a plant control model using Simulink. The example walks you through how to create both open- and closed-loop systems.
5:31
Getting Started with Simulink, Part 2: Adding a Controller and Plant to the Simulink Model
Visualize simulation results using tools such as the Simulation Data Inspector to view and compare signal data from multiple simulations, or the Dashboard Scope to see your results directly in the Simulink editor.
6:04
Getting Started with Simulink, Part 3: Viewing Simulation Results
Automatically tune PID gain values using the PID controller block and instantly see the results of your changes in Simulink.
2:44
Getting Started with Simulink, Part 4: Tuning a PID Controller
Use the Simulation Data Inspector in Simulink to compare the results of multiple simulation runs. Open the results in MATLAB Figures to further annotate and add information to your figures.
4:30
Getting Started with Simulink, Part 5: Comparing and Saving Simulation Data

What's New

Simulink directly runs multiple parallel simulations from the parsim command​ using Parallel Computing Toolbox.
1:06
Parallel Simulations
Simulink directly runs multiple parallel simulations from the parsim command​ using Parallel Computing Toolbox.
Run simulations at wall clock speed or other specified pace for improved visualization
1:41
Simulation Pacing in Simulink
Add inports and outports to blocks when routing signals​ in Simulink.
1:16
Automatic Port Creation
Easily identify dependencies and impacts of block or requirement changes in your Simulink project.
1:36
Impact Analysis with Simulink Projects
Use Simulink Functions to model software services. A Function Caller block can model and generate code for the client’s call to services, especially I/O services, and a Simulink Function block provides those services.
3:07
Run-Time Software Modeling, Part 4: Modeling Software Services with Simulink Functions

Large-Scale Modeling & Project Management

Easily update all the models in your Simulink project to the latest release.
1:08
Simulink Project Upgrade
Easily update all the models in your Simulink project to the latest release.
Integrate the Simulink
4:26
When and How to Use a Data Dictionary
Easily turn a folder into a Simulink Project and manage your files, data, and environment in one place.
1:00
Automatic Project Creation
Create Model blocks from scratch or by converting existing subsystems.
3:25
Getting Started with Model Referencing
Create, configure, and manage design variants for large-scale models in Simulink .
2:00
Variant Configuration Management in Simulink

Modeling Multidomain Physical Systems (Simscape)

Model and simulate multidomain physical systems.
2:15
Simscape Overview
Model and simulate multidomain physical systems.
Model and simulate rotational and translational mechanical systems.
1:56
Simscape Driveline Overview
Model and simulate electronic and mechatronic systems.
2:02
Simscape Electronics Overview
Model and simulate fluid systems.
1:48
Simscape Fluids Overview
Model and simulate multibody mechanical systems.
1:49
Simscape Multibody Overview

Run-Time Software Modeling

Run-Time Software Modeling helps you implement large-scale run-time systems by defining the components in a composition; simulating and testing them; generating code; and integrating the code into a larger system.
1:01
Run-Time Software Modeling, Part 1: Introduction to Run-Time Software Modeling with Simulink
Run-time software modeling helps you implement large-scale run-time systems by defining the components in a composition, simulating and testing them, generating code, and integrating the code into a larger system.
Partition and schedule models with the Export Function modeling style to integrate functions into your software environment easily and have a clear mapping from model to code.
3:15
Run-Time Software Modeling, Part 2: Partitioning and Scheduling with Export Functions
Rate-based subsystems can be partitioned and scheduled as well, allowing you to combine modeling styles with schedulable components.
3:10
Run-Time Software Modeling, Part 3: Combining Modeling Styles with Schedulable Components
Use Simulink Functions to model software services. A Function Caller block can model and generate code for the client’s call to services, especially I/O services, and a Simulink Function block provides those services.
3:07
Run-Time Software Modeling, Part 4: Modeling Software Services with Simulink Functions
The Initialize and Terminate Functions in Simulink® allow you to compute the initial condition and save the final condition of block states.
3:12
Initialize and Terminate Functions

Verify and Validate Your Design

The Requirements Perspective allows you to view, edit, link and organize requirements from within Simulink or Stateflow.
3:57
Work with Requirements in Simulink Using Requirements Perspective
The Requirements Perspective allows you to view, edit, link, and organize requirements from within Simulink or Stateflow.
Use Simulink Verification and Validation to identify duplicate patterns in a model and refactor into reusable components.
3:16
Improving Reuse with Clone Detection and Refactoring
Detect and fix potential standards compliance issues in your model at design time
3:27
Edit-Time Checking to Find Compliance Issues Earlier
Detect untested elements of your design using model coverage in Simulink Coverage
2:06
Model Coverage
Identify design errors, generate test cases, and verify designs against requirements using Simulink Design Verifier.
1:49
Simulink Design Verifier Overview

Automatic Code Generation

Hear how Simulink and Embedded Coder let you model classes and generate C++ code. Object-oriented design and realization is critical for services-oriented architecture and middleware including Adaptive AUTOSAR, ROS 2.0, DDS, and more.
3:43
Coder Summit 2018: C++ Class Modeling and Code Generation with Simulink and Embedded Coder
Simulink and Embedded Coder let you model classes and generate C++ code. Object-oriented design and realization is critical for services-oriented architecture and middleware including Adaptive AUTOSAR, ROS 2.0, DDS, and more.
View the highlights of AUTOSAR support from Embedded Coder.
2:33
AUTOSAR Support from Simulink and Embedded Coder
Control two 3-phase, brushless motors using an F28069 LaunchPad and the TI C2000 support package for Simulink .
4:56
Using a TI F28069 LaunchPad with Simulink
Using Fixed-Point Designer, you can convert your ideal double-precision floating-point design to an embedded efficient fixed-point design for your target microprocessor or FPGA hardware.
2:07
Converting Double-Precision Design to Embedded Efficient Fixed-Point Design
Learn how to use of Model-Based Design and formal methods with Simulink, Stateflow, Embedded Coder, and the DO Qualification Kit in a process compliant with DO-178C, DO-331, DO-333, and DO-330.
4:59
Using Qualified Tools in a DO-178C Development Process, Part 1: Introduction to Model-Based Development for DO-178C

Connecting to Hardware

Simulink provides built-in support for prototyping, testing, and running models on low-cost target hardware, such as Arduino , LEGO MINDSTORMS NXT, and Raspberry Pi.
1:50
Introduction to Simulink Support for Target Hardware
Simulink provides built-in support for prototyping, testing, and running models on low-cost target hardware, such as Arduino, LEGO MINDSTORMS NXT, and Raspberry Pi.
Create mobile apps directly from Simulink and run models on your Android or iOS phone or tablet.
1:24
Introduction to Simulink Hardware Support for Android and iOS Mobile Devices
The Simulink Support Package for PARROT Minidrones lets you design, simulate, and deploy algorithms to fly PARROT minidrones.
2:08
Introduction to Simulink Hardware Support for PARROT Minidrones
Build, run, and test real-time applications with Simulink Real-Time.
1:50
Simulink Real-Time Overview
Run a typical hardware-in-the loop simulation for industrial automation applications. An industrial programmable logic controller runs the control algorithm that is connected to a plant simulation, which is a Simulink Real-Time application.
5:42
Hardware-in-the-Loop Simulation with Simulink Real-Time Using PLCs and Ethernet/IP Protocol for Validating Industrial Automation Control Algorithms

Featured Applications

Deep Learning

Learn how MATLAB can help with any part of the deep learning workflow: from preprocessing through deployment. Get a high-level overview of deep learning with MATLAB, and explore several applications.
3:03
MATLAB for Deep Learning
Learn how MATLAB can help with any part of the deep learning workflow: from preprocessing through deployment. Get a high-level overview of deep learning with MATLAB, and explore several applications.
See additional videos
Learn about using MATLAB for deep learning

Data Analytics

Get an overview of using MATLAB to develop your data analytics applications.
3:55
MATLAB for Data Analytics
Get an overview of using MATLAB to develop your data analytics applications.
See additional videos
Learn about using MATLAB for data analytics

Wireless Communications

Design wireless systems by performing antenna-to-bits simulation, smart RF design, over-the-air testing, LTE and LTE-advanced modeling, and airborne and automotive radar simulation using MATLAB and Simulink products.
2:57
Design Wireless Systems with MATLAB and Simulink
Design wireless systems by performing antenna-to-bits simulation, smart RF design, over-the-air testing, LTE and LTE-advanced modeling, and airborne and automotive radar simulation using MATLAB and Simulink products.
See additional videos
Learn about using MATLAB for wireless communications

Quantitative Finance and Risk Management

In this talk, Ray discusses how financial risk technology stacks are evolving in response to regulatory and geopolitical change, bigger datasets, new modelling techniques, and rapidly changing development cultures.
18:44
Two Worlds Coincide: Financial Risk Management and Model-Based Design
In this talk, Ray discusses how financial risk technology stacks are evolving in response to regulatory and geopolitical change, bigger datasets, new modelling techniques, and rapidly changing development cultures.
See additional videos
Learn about using MATLAB for quantitative finance and risk management

Motor and Power Control

Implement the three most common Maximum Power Point Tracking (MPPT) algorithms using MATLAB and Simulink : Perturb and Observe (P&O), Incremental Conductance, and Fractional Open Circuit Voltage algorithms.
3:46
Implement Maximum Power Point Tracking Algorithms Using MATLAB and Simulink
Implement the three most common Maximum Power Point Tracking (MPPT) algorithms using MATLAB and Simulink : Perturb and Observe (P&O), Incremental Conductance, and Fractional Open Circuit Voltage algorithms.
See additional videos
Learn about using MATLAB and Simulink for motor and power control