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| Course
No. |
Course
Description |
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EE 101
3 Units |
Introduction to Digital Logic
Boolean algebra; number systems; binary arithmetic;
codes; gates; Boolean expressions; Boolean switching function synthesis; iterative arrays; sequential machines;
state minimization; flip/flops; sequential circuits; simple processors. |
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EE 102L
2 Units |
Introduction to Digital Circuits
Practical digital design using MSI/SSI TTL devices; practical aspects and specifications,
open-collector/three-state outputs, timing and triggering; logical analyzers; finite state
controllers; lab experiments; digital logic simulation.
Prerequisite: EE 101
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EE 357
3 Units |
Basic Organization of Computer Systems
Organization and operation of the processor, memory and I/O of a minicomputer at
the machine language level; assembly language programming; data representation and
computer arithmetic.
Prerequisite: 101, EE 102, and a high level programming language.
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EE 450
3 Units |
Introduction to Computer Networks
Network architectures; layered protocols, network service interface; local networks;
long-haul networks; internal protocols; link protocols; addressing; routing; flow control;
higher level protocols.
Prerequisite: junior standing.
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EE 454L
4 Units |
Introduction to Systems Design Using Microprocessors
Operation and timing of 8-bit microprocessors; design of microprocessor-based systems; 16-bit microprocessors;
bit sliced microprocessors.
Prerequisite: EE 102L and EE 357
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EE 457x
3 Units |
Computer Systems Organization
Register transfer level machine organization; CPU data paths and control; micro-programming;
timing, simple arithmetic units; basic I/O organization; design using register transfer languages.
Not available for graduate credit to computer science majors.
Recommended preparation: EE 357, EE 102L
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EE 465
3 Units |
Probabilistic Methods in Computer Systems Modeling
Review of probability; random variables; stochastic processes; Markov chains; and simple queueing
theory. Applications to program and algorithm analysis; computer systems performance and reliability
modeling.
Prerequisite: MATH 407
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EE 552
3 Units |
Logic Design and Switching Theory
State minimization of incompletely specified sequential circuits; asynchronous sequential circuits; races;
state assignments; combinatorial and sequential hazards in logic circuits.
Prerequisite: graduate standing.
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EE 553
3 Units |
Computational Solution of Optimization Problems
Computer algorithms for system optimization. Search techniques, gradient methods, parameter optimization
in control systems. Optimization with constraints; linear and nonlinear programming. Random search techniques.
Prerequisite: EE 441
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EE 554
3 Units |
Real Time Computer Systems
Structure of real-time computer systems; analog signals and devices; scheduling,
synchronization of multiprocessors; reliability, availability; serial/parallel computations;
real-time operating systems and languages; design examples.
Prerequisite: EE 457x and CS 455x
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EE 557
3 Units |
Computer Systems Architecture
Comparative studies of computer system components: the CPU, memory, and I/O;
analytical modeling techniques to allow comparative evaluation of architectures;
parallelism and supercomputers.
Prerequisite: EE 457x and CS 455x
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EE 559
3-3 Units |
Mathematical Pattern Recognition
Distribution free classification, discriminant functions, training algorithms;
statistical classification, parametric and nonparametric techniques, potential
functions; non-supervised learning.
Prerequisite: EE 464
Corequisite: EE 441
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EE 658
3 Units |
Diagnosis and Design of Reliable Digital Systems
Fault models; test generation; fault simulation; self-checking and self-testing circuits; design for
testability; fault tolerant design techniques; case studies.
Prerequisite: graduate standing
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MATH 458
4 Units |
Numerical Methods
Rounding errors in digital computation; solution of linear algebraic systems; Newton's method for
nonlinear systems; matrix eigenvalues; polynomial approximation; numerical integration; numerical
solution of ordinary differential equations.
Prerequisite: MATH 225 or MATH 245.
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MATH 501
3 Units |
Numerical Analysis and Computation
Linear equations and matrices, Gauss elimination, error estimates, iteration techniques; contractive mappings,
Newton's method; matrix eigenvalue problems; least-squares approximation, Newton-Cotes and Gaussian
quadratures; finite difference methods.
Prerequisite: linear algebra and calculus.
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MATH
502ab
3-3 Units |
Numerical Analysis
Computational linear algebra; solution of general nonlinear systems of equations; approximation
theory using functional analysis; numerical solution of ordinary and partial differential equations
Prerequisite: MATH 425a and MATH 471.
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MATH
504ab
3 Units |
Numerical Solutions of Ordinary and Partial Differential Equations
a: Initial value problems; multistep methods, stability, convergence and error estimation, automatic stepsize control,
higher order methods, systems of equations, stiff problems; boundary value problems; eigenproblems. b: Computationally
efficient schemes for solving PDE numerically; stability and convergence of difference schemes, method of lines;
fast direct and iterative methods for elliptic equations.
Prerequisite: MATH 501 or MATH 502a or departmental approval
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MATH
505ab
3-3 Units |
Applied Probability
a: Populations, permutations, combinations, random variables, distribution and density functions
conditional probability and expectation, binomial, Poisson, and normal distributions; laws of large numbers,
central limit theorem. b: Markov processes in discrete or continuous time; renewal processes; martingales;
Brownian motion and diffusion theory; random walks, inventory models, population growth, queueing models,
shot noise
Prerequisite: departmental approval
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MATH 533
3 Units |
Combinatorial Analysis and Algebra
Advanced group theory; algebraic automata theory; graph theory; topics in combinatorial analysis
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MATH 578
3 Units |
Dna and Protein Sequence Analysis |
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MATH
587ab
3-3 Units |
Mathematical Models of Neurons and Neural Networks
a: Dynamics of discrete and analog neural networks; qualitative and numerical analysis; computer
simulation; learning algorithms and convergence; Kolmagorov theory of feed-forward networks.
b: Nernst-Planck and Goldman-Hodgkin-Katz equations; Hodgkin-Huxley theory; cable theory; compartment
models of dendritic structures; McCulloch-Pitts networks; perceptron theory.
Prerequisite: a: MATH 465 and either MATH 501 or MATH 502a, b: MATH 587a.
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PHYS 495
2 Units |
Senior Project
An original project will be constructed applying computer technology (in either hardware or software) to produce
a result useful in the physics classroom or laboratory.
Prerequisite: departmental approval.
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