Signal Processing II

S. Kay (1988). Modern Spectral Estimation, Prentice Hall, Englewood Cliffs, NJ.

B. Widrow and Stearns (1995). Adaptive Signal Processing, Prentice Hall, Englewood Cliffs:NJ

M. El-Sharkawy (1996). Digital Signal Processing Applications with Motorola’s DSP56002 processor, Prentice Hall, Englewood Cliffs, NJ.


  1. Linear prediction and optimum linear filters -Forward and backward linear prediction, solution of normal equations: Levinson-Durbin algorithm, Wiener filters
  2. Power spectrum estimation - Estimation of spectra using the DFT from finite-duration observations of signals, non-parametric methods for power spectrum estimation (Welch, Bartlett methods), parametric methods for power spectrum estimation (Yule-Walker method, Burg method for the AR model parameters, sequential estimation methods)
  3. Adaptive signal processing - The LMS algorithm, Newton algorithm, applications of adaptive signal processing: Noise cancellation, adaptive interference canceling (e.g., canceling 60 Hz in ECG)
  4. Signal processing using the Motorola DSP56002 - Introduction to Motorola’s assembler program, DSP56002 architecture and addressing modes, designing FIR filters and implementing them on the DSP56002 processor, implementing the FFT with the DSP56002
  5. Multirate signal processing - sampling rate conversion, decimation and interpolation, applications of multirate signal processing - oversampling A/D and D/A conversion, sigma-delta converters

Assignments and projects:
The projects and assignments will require the use of MATLAB’s Signal Processing Toolbox. Students will be asked to write MATLAB programs to process, filter and analyze real-life signals including speech signals and biological signals (e.g., EEG and ECG waveforms). In another project, students will be asked to develop code for the Motorola DSP56002.

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