UNIVERSITY of WASHINGTON | BOTHELL

Electrical Engineering | Division of Engineering and Mathematics

BEE 341

BEE 442

BEE 417

BEE 511

BEE 512

BEE 341 Discrete-time Linear Systems

The course studies mathematical representation of discrete-time signals and systems, analysis of the input-output relationship of linear and time-invariant (LTI) systems (convolution), representation of discrete-time systems and signals using DTFT and DFT, and application of z-transform for the analysis and design of LTI systems.  Major topics covered in the course include:

• Mathematical description and representation of discrete-time signals and systems.
• Time-domain analysis of input-output relationships of linear time-invariant (LTI) systems.
• Solutions of difference equations and their application for time-domain representation of LTI systems.
• Discrete-time Fourier Transform and its application for determining frequency response of LTI systems.
• Discrete Fourier Time (and FFT) and its applications for frequncy-domain representation of signals.
• z-transform and its application for analysis discrete-time systems.

BEE 442 Digital Signal Processing

The course provides students with fundamental knowledge of the characteristics, specification, and methods of designing digital filters. The course covers review of sampling theorems, time-domain analysis of linear time-invariant systems, Z-transform, system functions, DTFT and FFT (DFT) techniques, digital filter structures, design of digital IIR and FIR filters, and brief introduction of adaptive filters. MATLAB is extensively used for analysis and design of filters. Major topics covered are:

• Review of Discrete-Time Fourier Transform (DTFT) and Z Transform
• Review of Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT)
• Digital Filter Structures
• Digital Finite Impulse Response (FIR) Filter Design
• Digital Infinite Impulse Response (IIR) Filter Design
• Adaptive Filter Design

BEE 417 Digital Communications

The course studies digital signal formatting, baseband and bandpass modulation and detection, design of optimum receivers, analysis of probability error of PSK and FSK systems and introduction to error correcting codes. Major topics covered in the course include

• Review of Signal analysis and random processes
• Digital formatting: Sampling, Quantization, encoding and PCM waveforms
• Baseband Modulation and demodulation
• Bandpass Modulation and Demodulation
• Link budget analysis
• Introduction to error control coding

BEE 511 Signal Processing I

BEE 511 provides an introduction to analysis of digital signals and systems as well as design of digital filters. The course covers time and frequency-domain methods of describing and characterizing discrete-time signals and systems, analysis of LTI systems using convolution, the z-transform and its applications to analysis of LTI systems, techniques for designing FIR and IIR digital filters, and analysis of multi-rate systems. MATLAB is extensively used for analysis and design of digital filters. Major topics covered in the course are the following:

• Introduction to Discrete Time-Signals and Systems
• Analysis of LTI Systems
• The Z-Transform and its Application to the Analysis of LTI Systems
• Frequency Analysis of LTI Systems
• Discrete Fourier Transform and FFT
• Sampling and Reconstruction of Signals
• Implementation of Discrete-Time Systems
• Design of Digital Filters
• Multi-rate Digital Signal Processing.

BEE 512 Signal Processing II (Statistical Signal Processing)

B EE 512 is an introduction to statistical signal processing. The course deals with random signals and their modeling, characterization and transformation to extract information about the underlying processes or mechanisms that generate them. The course covers representation of signals using parametric modeling, parametric and non-parametric methods of spectrum estimation, optimum filtering in time and frequency domains, least-square estimation and prediction, and adaptive filtering and their applications. MATLAB is used in the course to simulate signals and implement algorithms. Major topics of the course are the following:

• Introduction to statistical signal processing and its applications.
• Review of random signals and stochastic processes.
• Signal modelling.
• Least-squares filtering and prediction.
• Power spectrum estimation.
• Optimum linear filtering.
• Kalman Filtering
• Adaptive filtering.