Digital Signal Processing Fundamentals with Hands-On
Experiments(3 days)
Includes Lecture and Hands On Experiments
Instructor: A. Spanias
Description of Course
This combined theory and practice course provides an introduction to the
principles of digital signal processing including the areas of discrete-time
spectral analysis and adaptive signal processing. The course begins with an
introduction to discrete-time signals and systems and continues with a lecture
on digital filters, the FFT, random signal processing, direct and parametric
methods for digital spectral analysis, linear prediction, and adaptive LMS
algorithms. Computer experiments using a DSP software package on the PC will
give participants the opportunity to generate, process, and analyze signals.
Each participant will get a set of notes for the class sessions, and a copy of
the DSP educational software package for the laboratory (experimental) portion.
Who Should Attend
The course is designed for engineers and managers who need to understand the
fundamental theory and applications of DSP. The course should be of particular
interest to engineers who need to prepare for projects that involve DSP hardware
and software. Participants should have an understanding of basic engineering
mathematics.
Course Content
Introduction to Signals and Linear Systems: continuous and discrete time
signals, time and frequency domain analysis, Fourier representations, uniform
sampling, linear systems, transient and steady-state response, frequency
response, convolution and impulse response, stability considerations -
z-transform: region of convergence, properties, inverse z-transform, transfer
function, poles-zeros and stability, z transform and linear systems - Digital
Filters: FIR and IIR digital filter realizations, transfer function and
frequency response, linear phase FIR filters, FIR and IIR filter design, the
bilinear transform - Discrete and Fast Fourier Transform: properties and
important transform pairs, time and frequency windows, circular and linear
convolution, implementation issues - Random Signal Processing Fundamentals:
stationary and ergodic signals, mean, variance, autocorrelation,
cross-correlation, power spectral density, white noise, response of linear
systems to random inputs - Direct and Parametric Methods for Spectral Analysis:
estimators, periodograms and correlograms, ARMA, AR, and MA models for
parametric spectral estimation, Pade approximations, linear prediction, Levinson
algorithm, Adaptive Signal Processing: least squares, performance surfaces,
adaptive gradient algorithms, the LMS algorithm, the RLS algorithm, sequential
and block algorithms, frequency-domain algorithm, adaptive noise cancellation.
Computer Labs: signal generation and convolution, z-transform and the
transfer function, poles and zeros and frequency response, FIR and IIR Filter
Design, FFT and its applications.
If you need information on any of the above (date/location/in house) send
email at spanias@asu.edu.
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