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Course Criteria
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1.00 - 3.00 Credits
Independent study under a faculty. The proposal must be approved by the graduate advisor if the course is to apply toward degree requirements. Prerequisite: consent of instructor.
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3.00 Credits
Lecture, 3 hrs. Review of probability theory, fundamentals of transform theory, Fourier and Z-transforms. Markovian and discrete time queuing systems, single and multi-server queues, queuing networks and their applications. The course may require significant lab and/or project activity. Prerequisites: MATH 345 and 261 or consent of instructor. Sonoma State University 2006-2008 Catalog Engineering Science Page 181
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3.00 Credits
Lecture, 3 hours. Introduction to adaptive systems: neural networks, genetic algorithms (GAs), fuzzy logic, simulated annealing, tabu search, etc. Specific topics include perceptions, backpropagation, Hopfield nets, neural network theory, simple GAs, parallel GAs, cellular GAs, schema theory, mathematical models of simple GAs, and using GAs to evolve neural networks. Prerequisites: CS 315 and CES 400, or consent of instructor.
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3.00 Credits
Lecture, 3 hrs. Review of data structures and basic algorithms for sorting and string processing. Basics of logic, formal systems, grammars, and automata. Applications to some of the following areas: design of language processing tools (editor, translator etc.), software specification, testing and verification, non-numerical problem solving. The course may require significant lab and/or project activity. Prerequisite: CS 315 or consent of instructor.
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3.00 Credits
Lecture, 3 hrs. Introduction to data models, data warehousing, associationrule mining, searching the Web, Web Mining: Clustering. AI techniques (neural networks, decision trees), applications, and case studies. The course may require significant lab and/or project activity. Prerequisite: CS 315 or consent of instructor.
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3.00 Credits
Lecture, 3 hrs. Algorithmic tools and techniques for problems hard to solve on a standard uniprocessor model, such as problems involving large data sets or real-time constraints; development of computational models to analyze the requirements and solutions and special hardware-based solutions; case studies to illustrate the developed models, tools, and techniques. The course may require significant lab and/or project activity. Prerequisite: CS315 or consent of instructor.
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3.00 Credits
Lecture, 3 hrs. Three major topics covered in this course are: controlling specialized I/O devices with particular attention to bit patterns and priority interrupts; waveshapes and measurement tools, both hardware and software; and real-time operating systems. Prerequisites: ES 230-231 and CS 351, or consent of instructor.
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3.00 Credits
Lecture, 3 hrs. IC technology review; hardware description languages and describing hardware using one of the languages, modern VLSI design flow; circuit partitioning; clustering. Floorplanning; placement; global routing; area-efficient design; area-time tradeoffs. The course may require significant lab and/or project activity. Prerequisite: CES 530 or consent of instructor.
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3.00 Credits
Lecture, 3 hrs. Concept of advanced computing architectures, pipelining; multiprocessing and multiprogramming, single and multi-stage interconnection networks, applications/algorithms for parallel computers; local and system bus architectures; CPU and computer system performance analysis. The course may require significant lab and/or project activity. Prerequisites: CS 351 and CS 450, or consent of instructor.
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3.00 Credits
Lecture, 3 hrs. Introduction to analog/digital integrated circuits; bipolar and MOS transistor models; analysis and design of monolithic operational amplifiers; frequency response; non-linear circuits and CMOS, and Bipolar Logic Circuits. The course requires lab and/or project activity. Prerequisites: ES 230-231 and CES 432, or consent of instructor.
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