Applying Signal Processing with SIMULINK

Course Highlights

This is a 2-day fundamental course for signal processing engineers who are new to system and algorithm modeling and design in Simulink. Through basic modeling techniques and tools, it shows how to develop Simulink block diagrams.

Course Benefits

Upon the completion of the course, the participants will gain knowledge on:

  • What is Simulink 
  • Using Simulink interface 
  • Modeling single-channel and multi-channel discrete dynamic systems
  • Implementing sample-based and frame processing
  • Modeling mixed-signal (hybrid) systems
  • Performing spectral analysis with SIMULINK
  • Integrating filter designs into SIMULINK
  • Modeling multirate systems
  • Incorporating external code
  • Developing custom blocks and libraries

Prerequisites

  • Working experience with MATLABĀ® and the Signal Processing Toolbox is required. 
  • "Comprehensive MATLAB " and "Applying Signal Processing with MATLAB" can be taken to satisfy the prerequisites.

Course Outline

Day 1

What is Simulink? 
Objective: Get an introduction to Simulink 

  • What is Simulink?
  • Benefits of using Simulink
  • Simulink add-ons 
  • A look at a Smulink model 

Creating and Simulating a Model 
Objective: Explorer the Simulink interface and block libraries. Build a simple model and analyze the simulation results. 

  • Creating and editing a Simulink model 
  • Defining system inputs and outputs
  • Simulating the model and analyzing results 

Modeling Discrete Dynamic System 
Objective: Model discrete dynamic systems, and visualize frame-based signals and multichannel signals using a scope. 

  • Modeling a discrete system with basic blocks
  • Finding sample times of block outputs
  • Using frames in your model
  • Using buffers 
  • Frames vs multichannel signals
  • Viewing frame-based signals
  • Behavior of delay bloacks with frame-based signals
  • Multichannel frame-based signal

Spectral Analysis
Objective: Perform spectral analysis in the Simulink environment, and use spectrum computation in an algorithm. 

  • Performing spectral analysis with the Spectrum Scope block
  • Choosing spectral analysis parameters
  • Analyzing power spectrum of a motor noise
  • Building a spectral classifier of speech 
  • Determining the frequency response of discrete system

Day 2 

Designing and Applying Filters 
Objective: Incorporate filters in a model, and explore different ways filters can be designed and implemented in a Simulink model 

  • Designing filters in Simulink
  • Converting filters to fixed point

Multirate Systems 
Objective: Model multirate systems. Resample data and explore multirate filter blocks 

  • Multirate systems 
  • Exploring blocks for multirate signal processing
  • Resampling  oversampled data 
  • Designing and implementing anti-imaging and anti-aliasing filters
  • Using mulitrate filter blocks 
  • Case study: Converting professional audio to CD format
  • Converting the design to fixed point 

Incorporating External Code
Objective: Import or incorporate custom or external MATLAB and C code into a Simulink model 

  • Working with custom and external code considerations 
  • Incorporating MATLAB code and C code with the MATLAB Function block

Subsystems and libraries 
Objective: Create custom blocks in Simulink, apply masks, and develop custom libraries 

  • Creating subsystems 
  • Understanding virtual and atomic subsystems
  • Using a subsystem as a model component
  • Masking subsystems 
  • Creating custom block libraries
  • Working with and modifying library blocks
  • Adding custom libraries to the Simulink Library Browser
  • Creating configurable subsystems 

Combining Models into Diagrams 
Objective: Explore model integration, an important topic for large-scale projects in which serveral developers are developing different portions of large system. 

  • Exploring model referencing and subsystems
  • Setting up a model reference
  • Setting up model reference arguments 
  • Exploring model reference simulation modes 
  • Viewing signals in referenced models
  • Browsing the model reference dependency graph

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