Course Highlights

This 2-day course provides an overview of signal processing capabilities in MATLAB & SIMULINK and equips participants with the necessary signal processing techniques.  Participants will learn how MATLAB, SIMULINK and featured Toolboxes and Blocksets enable users to more effectively analyze signal processing systems and to better model, design, implement and algorithms and systems.  The course presents signal processing in the MATLAB environment, including the capabilities of the Signal Processing and Filter Design toolboxes, as well as the basics of using the Signal Processing Blockset in SIMULINK to analyze and design a signal processing system.

The first part includes an introduction to signal processing with a concentration on representations of signals in MATLAB, special analysis, and working with linear, time- independent system models. Also, it covers filter design, with comprehensive instruction on FIR, IIR, adaptive, and multirate filters. Filter quantization and implementation are also discussed. The second part will emphasizes discrete-time simulations and includes topics on buffering and vector operations, digital filter design and implementation, transforms, power spectrum estimation and Frame-based processing is also discussed.

Course Objectives

The aim of the training is to provide participants with the fundamentals and hands-on experience in using Signal Processing Toolbox and Signal Processing Blockset to design and apply Signal Processing techniques

Who Should Attend

This hands-on course is designed for engineers who wish to design and simulate Signal Processing algorithm in the MATLAB and SIMULINK environment. Engineers, researchers, scientists, and managers working with systems level design will be shown an easy-to-use approach in using Signal Processing Toolbox and Signal Processing Blockset.

Course Benefits

Upon the completion of the course, the participants will gain a comprehensive understanding of system and algorithm modeling and design validation for Signal Processing application in MATLAB and SIMULINK environment.

Prerequisites

Attended "Comprehensive MATLAB " and "Comprehensive SIMULINK", or equivalent experience using MATLAB & SIMULINK. Participants are expected to have some exposure to signal processing techniques prior to taking this course.

Course Outline

Day One

Signals in MATLAB
Objective: Learn how to create and manipulate signals using the command line and the SPTool, a graphical user interface (GUI) in the Signal Processing Toolbox. Throughout the course, we will use the SPTool to analyze digital signals, filters, and spectra.

• Creating and importing signals
• Sampling and resampling
• Visualizing signals
• Modeling noise

Spectral Analysis
Objective: Gain an understanding of statistical signal processing. Explore visualization and analysis of signals in the time and frequency domains using spectral analysis.

• Signal statistics
• Discrete Fourier transform
• Power spectral density estimation
• Time-varying spectra

LTI Systems
Objective: Gain an understanding of linear time-independent systems, the basis for filtering applications and the subject of the majority of functions in the Signal Processing Toolbox. We discuss various ways to represent such systems, both mathematically and in MATLAB. Investigation of the basic input/output behavior of these systems introduces filtering.

• LTI system representations
• The z-Transform
• Frequency and impulse response
• Introduction to filtering

IIR Filter Design
Objective: Apply LTI system analysis to filter design and discuss the use of IIR filters from initial performance specifications to analog prototyping and digital design. The Filter Design and Analysis Tool (FDATool) GUI is introduced, and will be used for the remainder of the course to assist in filter design.

• Filter specifications
• Filter design functions
• Introduction to the Filter Design and Analysis Tool (FDATool)
  

FIR Filter Design
Objective: Continue the application of LTI system analysis to filter design, and discuss the use of FIR filters from specification to digital design. Explore a variety of specialized filters.

• FIR design methods
• Windowing
• Standard band filters
• Arbitrary response filters
• Multiband filters
• Raised cosine filters
• Frequency domain filtering

Day Two

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

Filtering
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

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