Signal Processing Refresher

  • Overview
  • Course Content
  • Requirements & Materials
Overview

Signal Processing Refresher

Course Description

 Review basic techniques for representing and processing digital signals, with an emphasis on methods commonly used in sensor-processing systems, such as communications, radar, and infrared systems. Understand continuous and discrete signals and transforms, as well as the representation and properties of noise. Design and apply digital filters, discover basic data compression methods, and explore the important matched-filter concept from multiple viewpoints. You'll have the chance to use MATLAB™ to demonstrate concepts and properties.

Course Content

WAVEFORMS AS SINUSOIDS

  • Real and complex sinusoids
  • Complex waveforms and Fourier series
  • Nonperiodic waveforms: Fourier transforms

INTRODUCTION TO MATLAB

WINDOWING

  • Resolution and reciprocal spreading
  • Window effects on resolution, gain, and sidelobes

DIGITAL REPRESENTATION OF SIGNALS

  • Sampling
  • Quantization
  • Encoding

FOURIER ANALYSIS OF SAMPLED DATA

  • The discrete-time Fourier transform (DTFT) and its inverse
  • Aliasing
  • The DFT as a sampled DTFT
  • Filterbank interpretation
  • Interpretation of normalized and aliased frequencies

CONTINUOUS AND DISCRETE LSI SYSTEMS

  • Convolution
  • Frequency response
  • Fourier convolution theorem

DIGITAL FILTERING

  • FIR filters and their properties
  • FIR filter design
  • IIR filters and their properties
  • IIR filter design

NOISE

  • Random signal representations
  • Auto and cross-correlation
  • Signal-to-noise ratio
  • Filtering of random signals

INTRODUCTION TO DETECTION

  • Hypothesis testing and the likelihood ratio test
  • Threshold testing
  • Performance evaluation

INTEGRATION

  • Coherent integration
  • Integration gain
  • Non-coherent integration

THE MATCHED FILTER

  • Derivation
  • Interpretations
  • DFT as a matched filter
  • Colored noise: whitening filters

IMAGE PROCESSING BASICS

  • Representing images
  • Two-dimensional filters
  • Order statistic filters
  • Other image processing operations

INTRODUCTION TO DATA COMPRESSION

  • Lossless coding
  • Lossy coding
  • Transform coding
  • Image compression: jpeg

COMPUTATIONAL COMPLEXITY

  • Choosing efficient algorithms
  • The fast Fourier transform and its use
  • Relation to the DFT, DTFT, and Fourier transform
  • Fast convolution
Requirements & Materials
Prerequisites

Recommended

  • Familiarity with basic calculus, linear algebra and complex numbers and arithmetic
Materials

Provided

  • Hard copy and CD-ROM of course viewgraphs with accompanying online MATLAB demonstrations, solved exercises, and other resources
  • Textbook, Signal Processing First, second edition by J.H. McClellan, R.W. Schafer and M.A. Yoder (Pearson/Prentice-Hall, 2015)

Session Details

  • Georgia Tech Research Institute (GTRI) employees are eligible to receive a discount.  If you are a GTRI employee, please go to the Organizational Development website and look for the coupon code under GT Professional Development. Review coupon instructions for more information.

Who Should Attend

This course is designed for engineers, scientists, analysts, and technicians who are new to the field of signal processing and analysis or who would like to refresh or strengthen existing skills.

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What You Will Learn

  • Fourier representations for continuous and discrete signals
  • The effects of data windows
  • The best method for finding the response of systems to various input signals
  • Finite impulse response (FIR) and infinite impulse response (IIR) digital filters designs
  • The properties and effects of noise in signal processing systems
  • Basic concepts of threshold detection
  • The effects of coherent and non-coherent signal integration
Radar antenna

How You Will Benefit

  • Choose correct sampling rates to avoid aliasing artifacts.
  • Convert between normalized and analog frequencies.
  • Specify and design spectrum analysis systems using the discrete Fourier transofrm (DFT).
  • Use matched filters to maximize signal-to-noise ratio
  • Survey multiple application areas.
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    Taught by Experts in the Field
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The course schedule was well-structured with a mix of lectures, class discussions, and hands-on exercises led by knowledgeable and engaging instructors.

- Abe Kani
President

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