An introduction to computer architecture and hardware design. Computer abstractions and technology, performance evaluation, instruction set architectures, computer arithmetic, pipelining, memory systems, interfacing. Hardware description languages, machine languages and assembly languages.

An introduction to computer architecture and hardware design. Computer abstractions and technology, performance evaluation, instruction set architectures, computer arithmetic, pipelining, memory systems, interfacing. Hardware description languages, machine languages and assembly languages.

With lab. Continuation of ECE 211. Analysis techniques for ac circuits, frequency response, resonance, Bode plots, phasor representation of sinusoidal steady-state systems, complex frequency domain, transfer functions. MOSFETs as amplifiers; operational amplifiers. Transformers, two-port networks, Fourier series. Lab includes circuit hardware and PSPICE simulation experiments.

In-depth examination of semiconductor devices. The physics of semiconductors, p-n junction diodes, bipolar transistors, Schottky barriers, JFETs, MFSFETs, MIS diodes, CCDs, and MOSFETs. Prerequisite: E&C-Eng 344, or introductory semiconductor theory course.

Unified treatment of techniques for representation of signals and signal processing operations. Emphasis on physical interpre-tation of vector spaces, linear operators, transform theory, and digital signal processing with wavelet filter banks. Prerequisite: graduate standing.

Waveform coding, source coding and data compression. Pulse modulation systems: signal spaces, optimal receivers, probability of error. Baseband and bandpass data transmission. Introduction to channel coding. Prerequisite: E&C-ENG 563.

Introduction to the design and analysis of algorithms. Topics include basic algorithmic paradigms (e.g. divide-and-conquer, dynamic programming, the greedy approach and randomization), their application to core problems in graph theory and optimization, as well as analysis of time and space complexity.

Probability space, conditional probability, Bayes theorem. Combinatorial analysis. Random variables (r.v.'s), distribution and density functions. Expected value, moments, characteristic function. Function of r.v.'s, Multiple r.v.'s, conditional distributions, independent r.v.'s. Multivariate Gaussian r.v.'s. Parameter estimation, confidence intervals, hypothesis testing. Introduction to random processes: mean, autocorrelation, power spectral density. Prerequisite: E&C-ENG 313.

Probability space, conditional probability, Bayes theorem. Combinatorial analysis. Random variables (r.v.'s), distribution and density functions. Expected value, moments, characteristic function. Function of r.v.'s, Multiple r.v.'s, conditional distributions, independent r.v.'s. Multivariate Gaussian r.v.'s. Parameter estimation, confidence intervals, hypothesis testing. Introduction to random processes: mean, autocorrelation, power spectral density. Prerequisite: E&C-ENG 313.

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