ENES 222 Elements of Discrete Signal Analysis

All consumer and specialized electronics today involve measuring and recreating the world around us. This course introduces the concepts necessary to understand how to record and play back including: discrete-time and continuous-time signals, sampling, linear transformations, orthogonal projections, Discrete Fourier Transform, Fourier Series, discrete-time linear time and frequency filters. One or more application oriented projects will be required that use a modeling tool such as MatLab.

Credits

4

Prerequisite

MATH 182

Hours Weekly

3 hours lecture, 3 hours lab weekly

Course Objectives

1. 1. Use concepts of discrete time signal frequency analysis to analyze signal characteristics.
   
2. 2. Apply basic linear algebra concepts in modeling real-world signals and systems.
3. 3. Recall the basics of Fourier Analysis including orthogonal projection, circular convolution, etc.
4. 4. Use the fast Fourier Transforms (FFT) feature of a variety of software packages.
   
5. 5. Apply basic digital filters to projects involving spectral analysis, voice recognition, and compression.
   

Course Objectives

1. 1. Use concepts of discrete time signal frequency analysis to analyze signal characteristics.
   
2. 2. Apply basic linear algebra concepts in modeling real-world signals and systems.
3. 3. Recall the basics of Fourier Analysis including orthogonal projection, circular convolution, etc.
4. 4. Use the fast Fourier Transforms (FFT) feature of a variety of software packages.
   
5. 5. Apply basic digital filters to projects involving spectral analysis, voice recognition, and compression.