ECE 43800 - Digital Signal Processing with ApplicationsLecture Hours: 3 Lab Hours: 3 Credits: 4 Professional Attributes
Advanced EE Selective
Upper Level Lab
Normally Offered: Each Fall, Spring
ECE 30100 and ECE 20800 and ECE 30200
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.
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.
- 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:A student who successfully fulfills the course requirements will have demonstrated:
- an understanding of linear time invariant systems. 
- the ability to manipulate discrete parameter signals. 
- knowledge of how to use linear transforms. 
- The ability to apply linear system analysis to engineering problems. 
|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|
|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.|