An Introduction to Random Signals, Noise, and Digital Signal Processing
142155
Course Aims: To introduce the student to random signals and to the digital
processing of signals.
Prerequisites: A thorough understanding of the one-dimensional Fourier transform, a
linear algebra course, and a probability course. A course in complex variables would
be helpful.
Topics to be covered:
Random signals (3 1/3 weeks).
Stochastic Processes and the autocorrelation function.
The Weiner-Khinchin theorem—basic theory
Poisson Convergence
Noise—theory and some practice
Spectral Analysis (3 weeks).
Fourier Analysis and the Discrete Fourier Transform
Windowing
(If time permits, The Spectral Analysis of Random Signals.)
Digital Systems (3 weeks)
The Z-Transform
Sampled-data Systems
The Transfer Function of a Discrete-Time System
Stability of Discrete-Time Systems
The Behavior of the System in the Steady-State
Digital Filters (4 weeks)
Two Simple FIR Filters
Design of IIR Digital Filters—the Old-Fashioned Way
New Filters from Old
Implementing a Digital Filter
(IIR Filter Design Using MATLAB)
Design of FIR Filters
Texts:
S. Engelberg, Random Signals and Noise: A Mathematical Introduction, CRC
Press, 2006.
S. Engelberg, Digital Signal Processing: An Experimental Approach, Springer,
2008.
,‫ בית הספר הגבוה לטכנולוגיה בירושלים‬,‫ מהדורה שנייה‬,‫ עיבוד ספרתי של אותות‬,‫שמואל וינמן‬
.5991
Grades:
Homeworks—15%
Final exam—85%
There may be a midterm exam. If there is a midterm, the midterm will be 20% of the
grade and the final will only be 65% of the grade.