ECE 43800 - Introduction to Signal and Image Processing


This course use to run under the title Digital Signal Processing with Applications

Course Details

Lecture Hours: 3 Lab Hours: 3 Credits: 4

Counts as:

  • EE Advanced Selective
  • EE Adv Level Lab
  • CMPE Selective - Special Content

Normally Offered:

Each Fall, Spring


On-campus only


ECE 30100 and ECE 30200

Catalog Description:

The course is presented in three units. Foundations: the review of continuous-time and discrete-time signals, and spectral analysis; design of finite impulse response and infinite impulse response digital filters; processing of random signals. Speech processing: vocal tract models and characteristics of the speech waveform; short-time spectral analysis and synthesis ; linear predictive coding. Image processing: two dimensional signals, systems, and spectral analysis; image enhancement; image coding; image reconstruction. The laboratory experiments are closely coordinated with each unit. Throughout the course, the integration of digital signal processing concepts in a design environment is emphasized.

Course Objectives:

This course will treat a broad range of Digital Signal Processing (DSP) topics. It will strengthen the student's understanding of the foundations of DSP, introduce the students to three major application areas: speech processing image processing and array signal processing, and provide extensive hands-on design experience.

Required Text(s):

  1. Digital Signal Processing, Principles, Algorithms, and Applications , 4th Edition , J. G. Proakis and D. G. Manolakis , Prentice Hall , 2006 , ISBN No. 978-0131873742

Recommended Text(s):


Learning Outcomes:

A student who successfully fulfills the course requirements will have demonstrated:
  1. an understanding of linear time invariant systems. [1]
  2. the ability to manipulate discrete parameter signals. [1]
  3. knowledge of how to use linear transforms. [1]
  4. The ability to apply linear system analysis to engineering problems. [1]

Lecture Outline:

Week(s) Topics
8 1.0 Foundations1.1 Continuous-time and discrete-time signals and spectral analysis (CTFT & DTFT;1.2 Continuous-time and discrete-time systems;1.3 Sampling;1.4 Decimation and interpolation;1.5 Z Transform ;1.6 Discrete Fourier Transform (DFT) and Fast Fourier Transform Algorithm (FFT) ;1.7 Digital filter design ;1.8 Filtering random signals ;1.9 Estimating distributions and correlation functions
3 2.0 Speech Processing;2.1 Speech models and characteristics ;2.2 Short-time Fourier analysis and synthesis;2.3 Linear predictive coding
3 3.0 Image Processing;3.1 2-D signals and systems;3.2 Image coding;3.3 Image enhancement;3.4 Computed tomography
1 Examinations

Lab Outline:

Lab Experiment Title or Activity
1 Discrete and Continuous Time Signals. Properties of discrete and continuous-time signals, sampling, processing of discrete signals using MatLab.
2 Discrete Time Systems. Properties of discrete time systems, difference equations, inverse systems.
3 Frequency Analysis. Synthesis of periodic signals using Simulink, modulation, discrete-time Fourier transform (DTFT).
4 Sampling and Reconstruction. Analysis of sampling, reconstruction using zero order hold, discrete-time interpolation and decimation.
5 Digital Filter Design I. Z transform analysis of difference equations, design of simple finite impulse and infinite impulse response filters (FIR and IIR), lowpass filter design parameters, FIR filter design via truncation.
6 Digital Filter Design II. FIR filter design using standard and Kaiser windows, FIR filter design via Parks-McClellan method, design of IIR filters via numerical optimization.
7 Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) Algorithm I. Computing the DFT, matrix representation for the DFT, computational complexity of the DFT.
8 Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) Algorithm II. Shifting frequency range, effect of zero padding, divide and conquer DFT, recursive divide and conquer.
9 Discrete-Time Random Processes and Spectrum Estimation I. Sample statistics for one and two random variables, approximating probability density functions, autocorrelation for filtered random processes, correlation of two random processes.
10 Discrete-Time Random Processes and Spectrum Estimation II. Power spectrum estimation, averaging periodograms, power spectrum of a linear-time-invariant system, power spectrum of a speech signal.
11 Speech Processing I. Characteristics of speech waveform, modeling of speech waveform.
12 Speech Processing II. Short-time discrete-time Fourier transform, spectogram, formant analysis.
13 Image Processing I. Histogram, pointwise transformation, gamma correction, linear and nonlinear smoothing, sharpening.
14 Image Processing II. Color images, color spaces, halftoning.

Assessment Method: