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Electrical Engineering - ELEN 504

Digital Signal Processing

• Define and illustrate the following systems characteristics:
• Causal systems
• Lumped constant systems
• Distributed parameter systems
• Deterministic systems
• Stochastic systems
• Linear systems
• Nonlinear systems
• Time varying systems
• Time invariant systems
• Continuous time systems
• Discrete time systems
• Interpret the meaning of magnitude vs. frequency filter specifications.
• Design filters in the s-plane
• Design Butterworth Filters in s-plane based on particular specifications
• Design Chebychev filter in s-plane based on particular specifications
• Design Bessel filter in s-plane based on particular specifications
• Define the z-transform for use with sampled data
• Convert time series into z-plane function
• Take the inverse of a z-plane function
• Convert a filter specification from the s-plane to the z-plane
• Plot the frequency response of a function specified in the z-domain
• Use Matlab to design both analog and digital filters
• Convert a digital filter to a computer algorithm
• Perform a convolution in the discrete domain
• Apply a digital filter in the time domain using convolution.
• Convert from the time domain to the frequency domain using DFT
• Apply a digital filter by converting the data into the frequency domain, multiplying by the filter and transforming the results back into the time domain
• Use FFT (Fast Fourier Transform) to enhance the application of a digital filter in the frequency domain
• Compare the fundamental performance of applying a filter in the time domain to that of applying the filter in the frequency domain
• Describe Huffman coding and its use in data compression in DSP
• Describe the .jpg data compression algorithm for digital pictures
• Describe the performance achievable with .jpg and the cost
• Describe .mpg for data compression of video
• Calculate the spectrum of a finite data sample using straight DFT
• Calculate the spectrum of a finite data sample using FFT and zero filling
• Calculate the spectrum of a finite data sample using the Burg algorithm