Training - Courses

SLBE-G: Signal Processing with Simulink

Simulink for Signal Processing is a 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. Topics include:

Modeling single-channel and multichannel discrete-time systems
Implementing sample-based and frame-based processing
Modeling single-rate and multirate systems
Integrating filter designs into Simulink
Applying fixed-point math in Simulink models
Executing condition-based systems
Automating model simulations
Developing custom blocks and libraries

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	Detailed course outline

Day 1 of 2
Introduction	Objective: This section helps users understand MathWorks products with reference to Simulink and Signal Processing Blockset™. Course expectations Overview of Simulink and signal processing products Signal processing uses Implementing signal processing systems
Model-Based Design	Objective: This section introduces Signal Processing Blockset and discusses Model-Based Design. Overview of Simulink and signal processing products Overview of Model-Based Design
Simulink Interface	Objective: This section introduces the Simulink interface and teaches basic concepts that will help new users to get comfortable with the environment. Simulink Library Browser Setting up a model Add and Connect blocks Input from MATLAB workspace Model callbacks Processing vectors and matrices Exploring the time scope Exploring the spectrum scope Initializing parameters and defining data
Signal Analysis	Objective: This section uses a signal processing system to discuss important Simulink concepts such as multichannel frame-based systems, simulation from the command line, and defining system I/O. Following this section, students should be comfortable with how Simulink propagates signals and data during a simulation. Analyzing a signal Building an algorithm Frame-based processing Simulating models from the command line Multichannel signals Buffering Introducing noise Defining the system I/O using the Inport block
Filter I	Objective: This section begins the discussion on filtering. We build a filter out of basic components and analyze the behavior. The section ends with a discussion on fixed-point data types and filter quantization. Filtering basics Identifying the signal and noise Building a block diagram of the filter Port data types Working with fixed-point data types Controlling data types using Simulink numeric type objects Creating Simulink data type objects Automating the simulation using a script file
Filter II	Objective: This section introduces the various tools and components that help users design filters in Simulink. We introduce these filter components and apply them on various noisy signals. Filtering library Digital filter block Filter architectures Digital filter design block and FDATool Filter realization wizard Filter Design Toolbox™ library

Day 2 of 2
Multirate Systems	Objective: This section discusses the concept of multirate systems. A basic multirate model is used to illustrate multirate modeling features in Simulink. The section finishes with a case study of a digital audio rate converter. Multirate systems Discrete solvers Resampling Creating subsystems Aliasing and anti-aliasing filter Case study: digital audio rate converter
Signal-Driven Systems	Objective: This section highlights components in Simulink that model signal-driven systems. The two important categories of these types systemsare triggered and enabled subsystems. Virtual versus nonvirtual subsystems Block sorted order Zero crossings Modeling signal-driven systems Modeling condition-driven systems with enabled subsystems Modeling event-driven systems with triggered subsystems
Incorporating External Code	Objective: This section introduces tools and components in Simulink that allow users to import or incorporate custom or external MATLAB code and C code into the model. Custom and external code considerations Incorporating MATLAB code with Embedded MATLAB functionality Incorporating C code with S-Function Builder Incorporating C code with Legacy Code Tool
Combining Models	Objective: This section discusses the topic of model integration, an important topic for large-scale projects where several developers are developing different portions of a large system. Model referencing overview Subsystems and model referencing Setting up a model for referencing Defining model reference arguments Referencing models Simulating and analyzing response
Creating Custom Blocks	Objective: This section introduces the concept of custom blocks in Simulink. It begins by discussing the idea of masking and custom libraries, and concludes with creating configurable subsystems. Creating subsystems Creating custom blocks from subsystems Creating custom libraries Creating configurable subsystems

Prerequisites

Working experience with MATLAB and Signal Processing Toolbox is required. MATLAB Fundamentals and Signal Processing with MATLAB can be taken to satisfy the prerequisites.

Course Length - 2 days

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