General Information Edit

Introduces statistical signal processing. Signal representation and manipulation are covered via correlation and using the DFT/FFT to estimate other transforms; applications of these topics are then covered, including quantization, quantization effects in digital filters, multirate DSP, filter banks, delta-sigma modulation, power spectrum estimation, and introductions to Wiener and Kalman filtering and image processing.

Prerequisites Edit

Prerequisite:  ECE 3250

Outcomes Edit

  • Be able to draw Fourier spectra in both discrete-time frequency and continuous-time frequency, while undergoing common operations such as filtering, upsampling, downsampling, A/D and D/A.
  • Given a finite-duration, discrete-time signal, be able to estimate the discrete-time Fourier Transform and the original frequency content in the signal (e.g., the continuous-time Fourier Transform) and give bounds on the accuracy of the estimate.
  • Given a continuous-time signal with certain frequency- and time-based characteristics, be able to design a (real-world, non-ideal) system to appropriately sample the signal such that desired characteristics are maintained to within given tolerances.
  • Given a digital audio (1-d) or image (2-d) signal, be able to select an appropriate frequency- based transform suitable for the desired or specified processing.
  • Quickly prototype and debug signal processing algorithms using Matlab.

Workload Edit

SP17 - Light. 2 problem sets. 2 exams. 1 project (+report+presentation).

Advice Edit

SP17- Be careful not to fall behind on material as there is virtually no formal work for this class.

Past Offerings Edit

Semester Time Professor Median Grade
Spring 2017

TR 11:40-12:55

David Delchamps A

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