Training Services

Learn to import data from mixed files, manipulate and group data, and create custom visualizations.

Learn to create regression, classification, and clustering models and improve their performance.

Learn to use and create deep neural networks for classification, regression, and object detection using image and sequence data.

Course topics include significance tests, distribution fitting, regression, and generating random simulations.

Prepare time-series data for machine learning analysis. Topics include importing signals, removing outliers, and extracting features in time and frequency domains.

Learn analytics, signal processing, and machine learning techniques needed for predictive maintenance and condition monitoring workflows.

Speed up your MATLAB code. Compile your MATLAB code into MEX files and solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters.

Learn to perform local and global optimization in MATLAB by translating the objective and constraints into a MATLAB code and choosing appropriate optimization solvers.

Gain insight into representing big data in MATLAB, adjust the existing code to work efficiently with it, and scale up the analysis to take advantage of your own computing resources or a cloud.

Learn to create flexible and robust applications, efficiently structure code and data, and leverage the unit testing framework.

Speed up your MATLAB code. Compile your MATLAB code into MEX files and solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters.

Learn to perform local and global optimization in MATLAB by translating the objective and constraints into MATLAB code and choosing appropriate optimization solvers.

Discover how to lay out apps in the App Designer, create callback functions for interactive components, and make responsive graphical objects.

Learn about namespaces, packages, and classes in MATLAB. Create extensible applications with inheritance. Enable object synchronization with events and listeners.

Learn to detect and segment objects in images based on shape, color, and texture. The course also covers preprocessing images using noise removal techniques.

Explore how to perform object detection, tracking, and motion estimation on images and videos. The course also covers camera calibration, point clouds, and 3D reconstruction.

Discover how to label ground truth data, detect lanes and objects, generate driving scenarios and modeling sensors, and visualize sensor data.

This two-day course provides hands-on experience with designing 3D scenes and scenarios for simulating and testing automated driving systems. You will acquire skills in creating detailed road networks and apply these techniques to reconstruct a real-world scene based on map data. Furthermore, you will learn how to create dynamic scenarios including vehicles and pedestrians. You will also export scenes and scenarios into common formats used by automated driving simulators.

This course demonstrates how to perform spectral analysis, and design and analyze digital filters including multirate and adaptive filters.

Model discrete dynamic systems and perform spectral analysis and filter design with Simulink. Learn to build custom blocks and libraries and to incorporate external code.

Prepare time-series data for machine learning analysis. Topics include importing signals, removing outliers, and extracting features in time and frequency domains.

Design single- and multi-carrier digital communications systems, create multi-antenna and turbo-coded communications systems, and work with radio-in-the-loop systems.

An in-depth introduction to LTE physical layer standards. Learn about generating reference LTE waveforms and simulating end-to-end LTE PHY models in MATLAB.

Learn to design receiver algorithms, add channel impairments, and analyze the bit error rate (BER) of a communication system.

This one-day course provides a comprehensive introduction to practical deep learning for signals. Attendees will learn how to create, train, and evaluate
different kinds of deep neural networks for signal processing using MATLAB.

This one-day course, targeted toward knowledgeable users of Simulink, uses modeling techniques and tools to demonstrate how to do Mixed-Signal Verification using Simulink.

This two-day course provides a comprehensive introduction to radar system design and modeling with a focus on Radar Toolbox and Phased Array System Toolbox .

Discover how to architect and manage Simulink models efficiently. Themes include requirement integration, source control, enforcement of modeling standards, and report generation.

Learn to analyze simulation results to verify model behavior, create test harnesses and test cases, test activity automation, and work with formal verification techniques.

Use Simulink Design Verifier™ to ensure that a design is devoid of possible design errors, is fully tested, and satisfies the necessary requirements.

Learn to configure Simulink models for Rapid Control Prototyping and hardware-in-the-loop simulation, interface with external motor and microcontroller hardware, and simulate and test against requirements in real-time.

This one-day course presents multiple methods for integrating C code into Simulink models. Topics discussed include the C Caller and C Function blocks, Legacy Code Tool for wrapping external C functions into Simulink, and manually written C MEX S-functions. This course is intended for intermediate to advanced Simulink users.

This five-day course describes guiding principles for applying Model-Based Design to meet ISO 26262 certification. It enables users to take advantage of the
Simulink® environment to synthesize, implement, and validate their software components in a manner consistent with the principles of ISO 26262.

This one-day course provides hands-on experience with designing 3D scenes for simulating and testing automated driving systems. You'll learn ways to create road networks and insert customized markings, signs and signals, as well as vegetation, buildings, and other 3D models. You will apply these techniques to reconstruct a real-world scene based on map data and export the scene into common formats used by automated driving simulators.

This three-day course is intended for software engineers that are using MathWorks tools to generate production code intended for DO-178C certification.

Learn to design and model control systems with Simulink. Topics include system identification, parameter estimation, control system analysis, and response optimization.

Acquire skills in analyzing simulation results to verify model behavior, create test harnesses and test cases, test activity automation, and work with formal verification techniques.

Study how to configure Simulink models for Rapid Control Prototyping and hardware-in-the-loop simulation, interface with external motor and microcontroller hardware, and simulate and test against requirements in real-time.

This one-day course presents multiple methods for integrating the C code into Simulink models. Topics discussed include the C Caller and C Function blocks, Legacy Code Tool for wrapping external C functions into Simulink, and manually written C MEX S-functions. This course is intended for intermediate to advanced Simulink users.

This five-day course describes guiding principles for applying Model-Based Design to meet ISO 26262 certification. It enables users to take advantage of the Simulink® environment to synthesize, implement, and validate their software components in a manner consistent with the principles of ISO 26262.

This two-day course focuses on modeling battery packs using Simscape™ and designing key control functionalities of battery management system using Stateflow®.

This one-day, hands-on course discusses the interpretation and review of Polyspace Bug Finder and Polyspace Code Prover results in Polyspace Access to remove algorithmic defects, improve software quality metrics, and improve product integrity. The course is intended for engineers who review results
with Polyspace Access and need to change their software in response to those results.

Get acquainted with using Simscape to model physical systems with components from various domains, such as the electrical, mechanical, or hydraulic; integrate Simscape models with Simulink models; and create custom user-defined Simscape components.

Understand how to model multibody mechanical systems; create custom geometries and compound bodies, assemble, guide, and verify mechanisms; and import CAD files.

Learn to model fluid power and fluid delivery systems; actuate and control fluid system models; connect fluid, mechanical, and thermal domains; and customize model components.

Become proficient in modeling three-phase systems, analyzing and controlling electrical power systems, modeling power electronic components, and speeding up simulation of electrical models.

Pick up on modeling power electronic systems in the Simulink environment using Simscape Electrical™ and grasp design control with Simulink Control Design.

Discover how to model vehicle bodies, tires, and mechanical power transmissions; design and optimize braking systems, and create multi domain automotive systems with closed-loop controllers.

This two-day course focuses on modeling battery packs using Simscape™ and designing key control functionalities of battery management system using Stateflow®.

Model discrete dynamic systems and perform spectral analysis and filter design with Simulink. Learn to build custom blocks and libraries and incorporate external code.

Learn to prepare Simulink models for HDL code generation, generate HDL code and testbench for a compatible Simulink model, and perform speed and area optimizations.

Get insight into optimizing DSP algorithms for efficient implementations using HDL code generation for FPGAs.

Learn about IP core generation and deployment using the AXI4 interface, processor-in-the-loop verification, and device driver integration.

Develop skills to deploy communication systems prototypes with real-time data on Zynq®-based radios via HW/SW co-design.

Learn to create a reference design in Vivado® and SDK, integrate user-space device drivers in Simulink, and build a custom Linux® image for Zynq.

This two-day course focuses on partitioning Simulink models intended for real-time execution on Speedgoat target machines to execute on the CPU and FPGA.

This hands-on, two-day course focuses on developing and configuring models in Simulink® and deploying on Xilinx® Zynq® UltraScale+ RFSoCs.

Develop Simulink models for deployment in embedded systems. Topics include code structure and execution, code generation options and optimizations, and deploying codes to target hardware.

Learn to configure Simulink models for Rapid Control Prototyping and hardware-in-the-loop simulation, interface with external motor and microcontroller hardware, and simulate and test against requirements in real-time.

Generate Simulink models from existing ARXML system descriptions, configure Simulink models for AUTOSAR compliant code generation, and model AUTOSAR events in Simulink.

Train in preparing MATLAB code for code generation, work with fixed-size and variable-size data, and integrate generated code into parent projects and external modules.

Learn to prove code correctness, review and understand verification results, handle missing functions and data, measure software quality metrics, and apply MISRA C rules.

This five-day course describes guiding principles for applying Model-Based Design to meet ISO 26262 certification. It enables users to take advantage of the Simulink® environment to synthesize, implement, and validate their software components in a manner consistent with the principles of ISO 26262.