ECE 43800 - Introduction to Signal and Image Processing
Note:
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
Campus/Online:
On-campus only
Requisites:
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):
- 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):
None.
Learning Outcomes:
- an understanding of linear time invariant systems. [1]
- the ability to manipulate discrete parameter signals. [1]
- knowledge of how to use linear transforms. [1]
- 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:
none