Applying Signal Processing with MATLAB

Overview

Signals are time series data that contain information of some physical attributes, such as sound and sensor’s measurement. Signal processing involves the analysis, synthesis and modification of the signals to improve the quality of the signal or to detect the specific information of interest from the signals. Signal processing techniques include signal acquisition, signal preprocessing through filtering and signal analysis.

This training is hands-on training using MATLAB which includes basic theory of spectral analysis, filter design, multirate system and adaptive filter design. The comprehensive exercise and case studies throughout the training will enable participants to import the signals from file, generating simulated signal, preprocess the signal using filtering method and analyze the spectrum of the signal.

Course Highlights

This two-day course is designed for users who want to learn basic theory and practical implementation of signal analysis and on how to design signal processing systems using MATLAB®, Signal Processing Toolbox and DSP System Toolbox. Topics include:

  • Creating and analyzing signals
  • Performing spectral analysis
  • Designing and analyzing filters
  • Designing multirate filters
  • Designing adaptive filters


Course Benefits

After completing this training, you will gain knowledge and skills on:

  • Signal generation through file import, sampled and synthesized signals
  • Visualization of the signals in time-domain and frequency domain
  • Identifying the characteristics of different spectrum analysis methods
  • Understanding of Linear-time invariant systems
  • Designing different kind of filters and understand the benefits and the drawback of different kind of filters

 

Who Must Attend

Engineers who wish to design and simulate Signal Processing algorithm in the MATLAB environment. Engineers, researchers, scientists, and managers working with systems level design will be shown an easy-to-use approach in using Signal Processing Toolbox.

 

Course Outline

Day 1 of 2

Signals in MATLAB

Objective: Generate sampled and synthesized signals from the command line and visualize them. Create noise signals for a given specification. Perform signal processing operations like resampling, modulation and correlation.

  • Creating discrete signals
  • Sampling and resampling
  • Visualizing signals
  • Modeling noise
  • Performing resampling, modulation and correlation
  • Generating streaming signals

 

Spectral Analysis

Objective: Understand different spectral analysis techniques and the use of windowing and zero padding. Become familiar with the spectral analysis tools in MATLAB and explore nonparametric (direct) and parametric (model-based) techniques of spectral analysis.

  • Discrete Fourier transform
  • Windowing and zero padding
  • Power spectral density estimation
  • Time-varying spectra
  • Using a spectrum analyzer in MATLAB

Linear Time Invariant Systems

Objective: Represent linear time-invariant (LTI) systems in MATLAB and compute and visualize different characterizations of LTI systems.

  • LTI system representations
  • z-transform
  • Frequency and impulse response
  • Visualizing filter properties
  • Applying filters to finite and streaming signals

 

Day 2 of 2

Filter Design

Objective: Design filters interactively using the Filter Design and Analysis app. Design filters from the command line using filter specification objects.

  • Filter specifications
  • Interactive filter design
  • Common filter design function
  • Filter design with filter specification objects
  • Reducing filter delay
  • Frequency-domain filtering

 

The Signal Analysis App

Objective: Learn to use a powerful all-in-one app for importing and visualizing multiple signals, performing spectral analysis on them, and designing and applying filters to the signals. Make simple statistical and cursor measurements on signals.

  • Browse signals and make simple measurements
  • Perform interactive spectral analysis
  • Design and apply filters to signals interactively

Multirate Filters

Objective: Understand principles of polyphase multirate filter design. Design multirate interpolating and decimating filters. Design multistage and narrow-band filters.

  • Downsampling and upsampling
  • Noble identities and polyphase FIR structures
  • Polyphase decimators and interpolators
  • Design multistage and interpolated FIR filters


Adaptive Filter Design

Objective: Design adaptive filters for system identification and noise cancellation.

  • Basics of adaptive filtering
  • Perform system identification
  • Perform noise cancellation
  • Improve adaptive filter efficiency
 

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