Introduction to Discrete-Time Signal Processing

Introduction to Discrete-Time Signal Processing
Digital signal processing, fast Fourier transforms, digital filter design, spectrum analysis. Applications in speech processing, SONAR, communications, etc.
 Hours4.0 Credit, 3.0 Lecture, 3.0 Lab
 PrerequisitesEC EN 370 & EC EN 380
 ProgramsContaining EC EN 487
Course Outcomes

Probability Theory

Ability to apply principles of probablity theory for analysis and numerical estimation of power spectral densities.

DSP functions & Algorithms

Ability to use basic DSP functions and algorithms (i.e., FIR filters, IIR filters, polyphase systems, windows, FFTs, etc.) to formulate and solve engineering problems.


Ability to perform experiments with real-world signals and DSP systems, including discrete-time filters, spectrum analyzer, acoustic direction finding, etc.


Ability to use software tools such as Matlab (R) to design, implement, and debug DSP functions.

Application of Mathematics

Application of integral calculus, discrete math, and complex variables, and transform theory to discrete-time signal processing.