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         Pipelining Computer Science:     more books (15)
  1. A Code Mapping Scheme for Dataflow Software Pipelining (The Springer International Series in Engineering and Computer Science) by Guang R. Gao, 1990-12-31
  2. Wave Pipelining: Theory and CMOS Implementation (The Springer International Series in Engineering and Computer Science) by C. Thomas Gray, Wentai Liu, et all 1993-11-30
  3. Compiling for dataflow software pipelining (Technical report / McGill University. School of Computer Science) by Guang R Gao, 1989
  4. University of California, Irvine. Dept. of Information and Computer Science. Technical report by Frederic M Tonge, 1978
  5. Specification and verification of pipelining in the ARM2 RISC microprocessor (Technical report / University of Michigan, Computer Science and Engineering ... Electrical Engineering and Computer Science) by James K Huggins, 1998
  6. Perfect pipelining: A new loop parallelization technique (Technical report. Cornell University. Dept. of Computer Science) by Alexander Aiken, 1987
  7. Pipelining techniques for vector reduction arithmetic (Technical report) by Lionel M Ni, 1983
  8. Computer Organization by Carl Hamacher, Zvonko Vranesic, et all 2001-08-02
  9. Fault-tolerance and two-level pipelining in VLSI systolic arrays by H. T Kung, 1983
  10. A study of instruction prefetching and pipelining of 8088/286/386 microprocessors (DISCS publication) by K. T Lua, 1988
  11. The force on the flex global parallelism and portability (SuDoc NAS 1.26:178161) by Harry F. Jordan, 1986
  12. Complexicty of Kronecker operations on sparse matrices with applications to the solution of Markov models (SuDoc NAS 1.26:206274) by NASA, 1997
  13. Parallelization of the pipelined Thomas algorithm (SuDoc NAS 1.26:208736) by A. Povitsky, 1998
  14. A parallel pipelined renderer for the time-varying volume data (SuDoc NAS 1.26:206275) by Tzi-cker Chiueh, 1997

1. Pipelining - Free Computer Science Tutorials - Provided By
Overview of pipelining computer processors. The purpose of this document is to provide an overview of pipelining computer processors.
CS 01 CS 02 CS 03 CS 04 ... CS 17
Pipelining: Pipelining is sometimes compared to a manufacturing assembly line in which different parts of a product are being assembled at the same time although ultimately there may be some parts that have to be assembled before others are. Even if there is some sequential dependency, the overall process can take advantage of those operations that can proceed concurrently. Computer processor pipelining is sometimes divided into an instruction pipeline and an arithmetic pipeline. The instruction pipeline represents the stages in which an instruction is moved through the processor, including its being fetched, perhaps buffered, and then executed. The arithmetic pipeline represents the parts of an arithmetic operation that can be broken down and overlapped as they are performed.

2. Computer Science
computer science. Starting Points. Search Engines Covering the computer architecture concepts of caches and pipelining, this tutorial is aimed at undergraduate students
Computer Science

Last Updated: May 28, 2004 This page is maintained by Michael Knee Starting Points
The Ada Project (TAP)
Named in honor of Ada Lovelace, TAP is a clearinghouse for information and resources related to women in computing. It includes publications, conferences, employment resources, fellowships and grants, news, organizations, plus projects and programs.
AI Topics
AI Topics is a starting point for finding information on artificial intelligence. This AAAI site links to basic information along with news, education resources, reference materials (dictionaries, encyclopedias, handbooks, and journals), courses, forums, and career information.
Artificial Intelligence
A WWW Virtual Library site containing links to research sites and projects, newsgroups, programming languages, journals, bibliographies, interactive demonstrations, and commercial sites and products.
Artificial Intelligence Resources on the Web
An AI Internet resources listing containing links to information on agents, artificial life, case-based reasoning, conferences, constraint programming, data mining, expert systems, fuzzy logic, genetic algorithms, journals, knowledge engineering, neural networks, SIGs, and university research groups.
Cetus Links
Cetus Links is portal to several thousand Internet sites about about object-orientation and component-orientation. It is divided into seven main areas: general information; distributed objects and components; Internet and intranets; architecture and design; languages and development environments; databases and repositories; and related topics. There is an overview page for each of the main area pages and an introductory statement on the individual subject pages. The individual subject pages contain a variety of links to articles, bibliographies, books and publications, conferences and meetings, FAQs, forums, glossaries, libraries, newsgroups, organizations, people, software, standards, starting points, tools; tutorials, and utilities. Cetus Links is also fully searchable.

3. RISC - Free Computer Science Tutorials - Provided By
pipelining is a design technique where the computer s hardware processes more than one instruction at a time, and doesn t wait for one instruction to complete
CS 01 CS 02 CS 03 CS 04 ... CS 17
RISC (Reduced Instruction Set Computer)
The RISC concept has led to a more thoughtful design of the microprocessor. Among design considerations are how well an instruction can be mapped to the clock speed of the microprocessor (ideally, an instruction can be performed in one clock cycle); how "simple" an architecture is required; and how much work can be done by the microchip itself without resorting to software help. Besides performance improvement, some advantages of RISC and related design improvements are:

4. USC Computer Science Department Technical Reports
94563 compressed A pipelining Mechanism to Minimize the 92-515 compressed Pricing in computer Networks Motivation Towards an Art and science of Knowledge
Technical Reports
This following is a listing of all the technical reports published by the USC Computer Science Department. You can access them as either compressed or uncompressed postscript documents. If a report's link is not highlighted, then it is not available online. Related Information: Submission instructions for USC faculty and students.
Send to the USC Computer Science Tech Report Librarian Tech reports of affiliated centers and institutes: CSE Tech Reports
IRIS Tech Reports

ISI Tech Reports

00-736 compressed
Feature Matrices: A Model for Efficient and Anonymous Mining of Web Navigations
by Cyrus Shahabi, Farnoush Banaei-Kashani, Jabed Faruque, Adil Faisal
00-735 compressed
Pushing the Limits of Multicast in Ad Hoc Networks
by Katia Obraczka, Gene Tsudik, Kumar Viswanath
00-734 compressed
Multicast-based Architecture for IP Mobility: Simulation Analysis and Comparison with Basic Mobile IP
by Ahmed Helmy
00-733 compressed
Resiliency and Robustness of Alternative Shape-Based Image Retrieval Techniques
by Maytham Safar, Cyrus Shahabi and Chung-hao Tan

5. References
scientists; in addition to detailed explanations of pipelining and memory organization (suitable for graduate level courses in computer science) there are
Next: About this document Up: CA Chapter Previous: 4 Exercises
Amdahl, G., The Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities , AFIPS Conf. Proc. 30, pp. 483-485, 1967.
Andrews, G., and Schneider,F., Concepts and Notations for Concurrent Programming , Computing Surveys, Vol. 15, pp. 3-43, 1983.
Bell, G. The Future of High Performance Computers in Science and Engineering , Comm. ACM, Vol. 32, pp. 1091-1101, 1989.
Bhuyan, L., Yang, Q., and Agrawal, D., Performance of Multiprocessor Interconnection Networks, Computer , Vol. 22, No. 2, pp. 25-37, 1989.
Bouknight, W.J., et al., The ILLIAC-IV System . Proc. IEEE, April 1972, pp. 369-388. (reprinted in CSPE)
Buzbee, B., Remarks for the IFIP Congress '83 Panel on How to Obtain High Performance for High Speed Processors, Los Alamos National Laboratory Report LA-UR-84-1392, Los Alamos, NM, 1983.
Denning, P. and Tichy, W., Highly Parallel Computation , RIACS Report TR-90.35, NASA Ames Research Center, Moffet Field, CA, August, 1990.

6. Subject Test Descriptions
Highperformance architectures. 1. pipelining superscalar and out-of-order execution processors background in the areas of calculus and linear algebra as applied to computer science.

General Test

Subject Tests

General Test

Subject Tests
Complete Directory
Subject Test Descriptions
Directory Biochemistry, Cell and Molecular Biology Literature in English Biology Mathematics ... Psychology For detailed information about test registration, test day activities, test preparation, and the Subject Test scoring process, please refer to the Subject Test portion of this Web site.
The test contains about 180 multiple-choice questions, a number of which are grouped in sets toward the end of the test and based on descriptions of laboratory situations, diagrams, or experimental results. The content of the test is organized into three major areas: biochemistry, cell biology, and molecular biology and genetics. In addition to the total score, a subscore in each of these subfield areas is reported. Because these three disciplines are basic to the study of all organisms, test questions encompass both eukaryotes and prokaryotes. Throughout the test, there is an emphasis on questions requiring problem-solving skills (including mathematical calculations that do not require the use of a calculator) as well as a content knowledge. While only two content areas in the following outline specifically mention methodology, questions on methodology and data interpretation are included in all sections. In developing questions for the test, the committee that develops the test keeps in mind both the content of typical courses taken by undergraduates and the knowledge and abilities required for graduate work in the fields related to the test. Because of the diversity of undergraduate curricula, few examinees will have encountered all of the topics in the content outline. Consequently, no examinee should expect to be able to answer all questions on the edition of the test he or she takes. The committee is aware that the three content areas are interrelated. Because of these interrelationships, individual questions or sets of questions may test more than one content area. Therefore, the relative emphases of the three areas in the following outline should not be considered definitive. Likewise, the topics listed are not intended to be all-inclusive but, rather, representative of the typical undergraduate experience.

7. Computer Science
computer science. Starting Points. Search Engines Covering the computer architecture concepts of caches and pipelining, this tutorial is aimed at undergraduate students
has moved to

Please adjust your bookmark or link. You will be automatically forwarded to the new location.
updated: Novermber 7, 2001 This page is maintained by Michael Knee
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8. BGSU Computer Science: CS 607
Flynns classification of multiprocessors; Vector computers(*) Numerical algorithms on a vector computer; pipelining in vector computers;
BGSU Computer Science Graduate Program Graduate Courses
CS 607: Architecture of Computers
  • Introduction to Technology and Architecture(*)
  • Impact of technology on computer architecture Evolution of computer architecture Associative Memory (CAM)
  • Hardware concepts of CAM Lewin's O(n) sorting algorithm Cache Memory(*)
  • Basic cache structure Set associative caches Evaluating Cache performances Determining Cache parameters Replacement Policies Implementing LRU replacement policies Detail example of a cache memory system Virtual Memory(*)
  • Basic virtual memory structure Translation lookaside buffer Segment tables Replacement algorithms Detail example of a virtual memory system Pipeline Techniques(*)
  • Principles of Pipelined computers Evaluating performance of pipelined computers Reservation tables and collision vectors Maximizing pipeline performance Conditional branches in pipelined computers Internal forwarding and deferred instructions Reconfigurable computer architectures
  • Reconfigurable busses Addition in time O(1) Multiprocessors
  • Flynns classification of multiprocessors Vector computers(*)
  • Numerical algorithms on a vector computer Pipelining in vector computers Examples of vector computers, e.g. Cray
  • 9. Computer Science
    bachelor of science degree with a major in computer science is designed to prepare design, basic processor implementation techniques, pipelining, memory design, caches, I/O systems
    Graduate Courses
    Schedule of Classes
    Computer Science (Com S)
    Leslie L. Miller, Chair of Department
    Professors: Fernandez-Baca, Kothari, Leavens, J. Lutz, Miller, Slutzki, Wong
    Professors (Emeritus): Brearley, Oldehoeft, Thomas
    Associate Professors: Baker, Chaudhuri, Gadia, Honavar, R. Lutz, Prabhu, Strawn, Tyagi
    Associate Professors (Adjunct): Kendall
    Assistant Professors: Aluru, Chou, Cruz-Neira, Eulenstein, Jia, Lavalle, Lumpe, Miner, Tavanapong
    Assistant Professors (Adjunct): Boysen, Mitra
    Instructors (Adjunct): Rose Undergraduate Study The curriculum in Liberal Arts and Sciences leading to a bachelor of science degree with a major in computer science is designed to prepare students for positions as computer scientists with business, industry, or government, or for graduate study in computer science. This program has been accredited by the Computing Sciences Accreditation Board, Inc. To complete an undergraduate degree in Computer Science, a student must satisfy the requirements of the College of Liberal Arts and Sciences (see Liberal Arts and Sciences, Curriculum) and include the following courses within the group requirements: Phil 442; Sp Cm 212; 14 credits of math and statistics including Math 165, 166 and at least one math course from Math 265, 266, 304, 307, 314, or 317, and at least one stat course from Stat 105, 231, 305, 333, or 341; a minimum of 12 credits of natural science including Phys 221, 222, and at least one additional natural science course from the following list: A Ecl 312, Anthr 202, 307, BBMB 221, Biol 201, 201L, 202, 202L, 312, Bot 102, 202, 304, Chem 163-231, Ent 370, Env S 324, FS HN 167, Gen 260, Geol 101, 102, 201, 306, 311, 412, Mat E 207, 211, Mteor 206, 301, Psych 310, Zool 155, 156, 258, 310; English proficiency requirement: Engl 104, 105 and one of Engl 302, 305, 309 or 314. The minimum grade accepted in each of the three required courses is a C-.

    10. SETI @ UNC Computer Science
    Who. Our group is composed of several people from the computer science department, and some that we ve never heard of (who are you guys?). pipelining.
    SETI @ UNC Computer Science
    Welcome to our page about how we contribute to the SETI@Home project. We're not running it at home- we're running it on a machine in the Computer Science Department at UNC that has spare cycles (mostly at night). On this page, we explain what the hardware is, how we run it all automatically to favor anyone else who has anything to do (even run a runaway emacs processes), who we are, why our average time to finish a work unit is lower than average, and some musings on the accumulated stats at the SETI@Home site.
    Our primary hardware is an SGI Onyx2 (aka an Origin2000 with graphics accelerators). It has 32 processors, each of which is a MIPS R12000 processor running at 300 mHz (read below for reasons why a 300, or even 180, mHz processor can outperform a 500 mHz PentiumIII). We run the Irix6.2 binary compiled with the mips3 instruction set. For a little while, we ran the Irix6.4-mips4 binary, which ran more than twice as fast, but produced unusable results. We also contribute to SETI@Home with several other machines- a few Sun sparcs, a macintosh, a few PCs, and some HP workstations. Not one of these is nearly as fast as a single processor on the Oynx2, so the contributions do not even add up to 5% of our total contribution.

    11. Computer Science
    computer science. Department Office. Darwin Hall 125 and secondary storage; CISC, RISC, stack architectures; pipelining; I/O interfacing; comparative examples of existing
    Computer Science
    Department Office
    Darwin Hall 125 Department Chair
    George Ledin Jr. Department Secretary
    Gayle Walker Faculty
    Jagan Agrawal, Richard H. Gordon, V. Scott Gordon,
    Ali Kooshesh, George Ledin Jr., Robert G. Plantz,
    B. Ravikumar, Lynn M. Stauffer, Tia Watts Course Plan Sample Four-Year Program for Bachelor of Science in Computer Science Minor in Computer Science Individual Class Descriptions Programs offered
    Bachelor of Science in Computer Science
    Minor in Computer Science Computer science is the scientific study of computing devices, the software that drives them, and the computational tasks they are capable of performing. As such, computer science includes both hardware science and software science; and as with all sciences, each of these possesses both theoretical and applied components. Computing theory shares knowledge and techniques with the fields of mathematics, physics, engineering, philosophy, psychology, and linguistics. Its applications span the range of human endeavors: the physical, life, and social sciences; the literary, visual, and performing arts; law, government, recreation, and virtually every sector of the commercial world. Thus, computer science is by its very nature an interdisciplinary subject that offers both a solid, unifying foundation for a liberal arts education and valuable career skills. The curriculum consists of a rigorous course of study in computer science and mathematics, and provides the student with a thorough grounding in programming, fundamentals of computer organization, data structures, and algorithm design. It is designed to prepare students for careers in the computer industry and graduate work in computer science.

    12. COMP 206: Official Syllabus (UNC-CH Computer Science)
    Software solutions pipeline scheduling, loop unrolling, software pipelining. Out of Department of computer science Campus Box 3175, Sitterson Hall College of
    Search our Site ON THIS PAGE: Course Objectives Prerequisites Approach Typical Text ... Course Outline COMP 206: Computer Architecture and Implementation
    (3 hours)
    Syllabus approved April 1989; renamed 206 in Spring 1994;
    syllabus revised April 1996
    Course Objectives
    Develop an understanding of the architecture and implementation of von Neumann computer systems. Understand the interdependence of architectural and implementation decisions through the detailed examination of one simple, complete computer. Prerequisites
    COMP 120 and digital logic (PHYS 102) Approach
    Study architecture by topics, using relevant portions of various real computers to illustrate each topic. Study implementation chiefly through the detailed examination of one simple, complete computer. Supplement the textbook with selected readings from the literature. Do not emphasize programming or hardware laboratory. Typical Text
    Hennessy and Patterson, Computer Architecture: A Quantitative Approach (2nd edition), Chapters 1-6. Course Outline
    Numbers in parentheses indicate approximate number of weeks
    • Basics of machine organization (review) (0.5)

    13. Computer Science Course Descriptions
    Exception the designation computer science 49S is required by Trinity College and Topics include processor design, pipelining, caches (memory hierarchies), virtual memory, and
    Next: Computer Science Faculty Up: Guide to Degree Programs Previous: Ph.D. Program
    Computer Science Course Descriptions
    By University convention, the numbering of courses is grouped by hundreds according to level of difficulty:
    Introductory undergraduate courses
    Intermediate and advanced undergraduate courses. These courses may also be suitable for graduate students under certain circumstances; the Director of Graduate Studies (DGS) should be consulted.
    Courses for graduate students and advanced undergraduate students. Most courses are open to juniors and seniors with adequate preparation.
    Courses for graduate students
    Within each hundred, the courses are numbered in order to reflect their particular subareas of computer science:
    x x
    General, programming, and programming theory.
    x x
    x x
    Architecture and modeling.
    x x
    x x
    Foundations and complexity. (Exception: the designation Computer Science 49S is required by Trinity College and is used to specify the first year seminar course.)
    x x
    Numerical analysis.
    x x
    Computational science.

    14. Computer Science Course Descriptions
    Topics may include pipelining, multiprocessors, data flow, and reduction machines. Same course as ECEN 6253. cs6300*. computer science Department.
    1000 Level Classes 2000 Level Classes 3000 Level Classes 4000 Level Classes ... 6000 Level Classes Computer Literacy. For students with little or no personal computer skills. Use Internet and productivity software such as word processing, spreadsheets, databases, and presentation software. (A) Computer Programming. Lab 2. Prerequisite: MATH 1513 or equivalent. Introduction to computer programming using a high-level computer language, including subprograms and arrays. Principles of problem solving, debugging, documentation, and good programming practice. Elementary methods of searching and sorting. Course is not intended for Computer Science majors. Honors section (A) Computer Science I. Prerequisite: MATH 1513 or equivalent. Introduction to computer science using a block-structured high-level computer language, including subprograms, arrays, recursion, records and abstract data types. Principles of problem solving, good programming practice. Elementary methods of searching and sorting. Use of operating system commands and utilities. Computer Science II. Prerequisites: CS 1113. Recursive algorithms, intermediate methods of searching and sorting. Mathematical analysis of space and time complexity, worst case, and average case performance.

    15. Redirected Page: Columbia University Continuing Education, New York, NY
    Take a course for credit at Columbia University. to computer science. Because computer science concepts pipelining, caches, memories, storage systems, multiprocessors. computer science W4995x. Special Topics In computer science
    Continuing Education Home Ask a Question Request a Bulletin Contact the Webmaster ... Columbia Home
    This Page Has Moved
    The content of this page has been moved to a new location. You will be automatically redirected. If the new page does not load within 5 seconds, click here to continue.

    16. Computer Engineering Courses
    Cpr E) 585/computer science (Com S videotape) Quantitative principles of computer architecture design design, processor architecture pipelining and superscalar
    CALENDAR ISU EXTENSION ISU HOME PAGE CONTACT US COURSE INDEX A B C-D E ... Home To ensure you have the latest information, we update course listings regularly on this site. We no longer publish a print catalog. College credit courses by delivery
    by discipline

    ... Extension news Fall 2004 Courses Reconfigurable Computing Systems
    Course: Computer Engineering (Cpr E) 583/Computer Science (Com S) 583, section XC (CD-ROM), section XL (streaming media), section XM (videotape)

    Introduction to adaptive/reconfigurable computing, FPGA technology and architectures, spatial computing architectures, systolic and bit serial architectures, adaptive network architectures, bus-based and static dynamic rearrangeable interconnection structure architectures, reconfigurable computing architectures for processors, pipeline, and caches.
    Prerequisite: background in computer architecture, design, and organization

    17. Computer Science
    MAJOR. The computer science Department offers a B.S Prerequisite(s) computer science 161 or equivalent. Parallel programming languages. pipelining and supercomputing
    Thomas H. Payne , Ph.D., Chair Department Office, A242 Bourns Hall Professors: Mart L. Molle, Ph.D. Teodor C. Przymusinski, Ph.D. Professor Emeritus: Lawrence L. Larmore, Ph.D. Associate Professors: Marek Chrobak, Ph.D. Yang-Chang Hong, Ph.D. Yu-Chin Hsu, Ph.D. Thomas H. Payne, Ph.D. Assistant Professors: Brett D. Fleisch, Ph.D. Frank N. Vahid, Ph.D. Adjunct Associate Professor: Halina Przymusinska, Ph.D. Cooperating Faculty: Gerardo Beni, Ph.D.
    (Engineering) Bir Bhanu, Ph.D.
    (Engineering) John de Pillis, Ph.D.
    (Mathematics) Gerhard Gierz, Ph.D.
    (Mathematics) Susan Hackwood, Ph.D.
    (Engineering) Lawrence Harper, Ph.D.
    (Mathematics) Ping Liang, Ph.D.
    (Engineering) Jing Wang, Ph.D.
    The Computer Science Department offers a B.S. degree in Computer Science and an M.S. and a Ph.D. degree in Computer Science. These programs provide the basis for careers in research and development in the computer science field as well as technical and nontechnical related fields that are dependent on a working knowledge of computers. The Computer Science major has been designed to provide the student with a broad background in science and humanities and to provide an understanding of fundamental principles of computing and modern computing technology. It prepares the student for professional work with computer systems as well as for graduate work in computer science.

    University of Virginia, computer science Department; Supervisor Developed a new computer microarchitecture called space exploration and software pipelining.
    P.O. Box 3033 Department of Computer Science Oakton, Virginia 22124 Thornton Hall, University of Virginia Voice: (301) 589-7042, Fax: (804) 982-2214 Charlottesville, Virginia 22903 E-mail: Web: OBJECTIVE Tenure-track assistant professor with a speciality in compilers/computer architecture. PROFESSIONAL INTERESTS Compilers and software development tools, computer architecture, application-specific and reconfigurable processors, electronic design automation, processor and system simulation, and embedded and portable computer systems. EDUCATION Ph.D. (Sept. 1991-present), Computer Science, University of Virginia , Charlottesville, Virginia. Thesis title: "Custom pipelines for embedded applications." Advisor: Prof. Jack W. Davidson. Expected Spring 1999. B.S. (May 1991), Computer Science, College of William and Mary , Williamsburg, Virginia. Graduated cum laude. Honors advisor: Prof. Phil Kearns. RESEARCH EXPERIENCE Research assistant (May 1992-present), University of Virginia, Computer Science Department; Supervisor: Prof. Jack W. Davidson.

    19. Computer Science (C S) Course Descriptions
    include processor, control, and memory design and organization, pipelining and vector degree requirements for the MS or Ph.D. programs in computer science. (F).
    Computer Science (C S) 1313 Programming for Nonmajors. Prerequisite: Mathematics 1523 or equivalent. Introduction to the design and implementation of computer programs. Emphasis on problem solving. (F, Sp) 1323 Introduction to Computer Programming. Prerequisite: Mathematics 1523 or equivalent. Introduction to the design and implementation of computer software with an emphasis on abstraction and program organization. (F, Sp) 1813 Discrete Mathematics. Prerequisite: 1323 and Mathematics 1823. Introduction to the mathematical foundation of computer science. Topics include logic, sets, relations, functions, proof techniques including mathematical induction, counting, graphs and trees, and recursion. (F, Sp) 2281 Engineering Co-Op Program (Crosslisted with AME, CH E, C E, ECE, ENGR, EPHY, E S, G E, I E, P E 2281). Prerequisite: student participation in the program. The Co-Op program provides student placement in jobs outside the University, but in a position related to the student's major. On completion of a semester work period, the student submits a brief written report. One hour of credit (elective) granted for each work period, with a maximum credit of six hours. (F, Sp, Su) 2334 Programming Structures and Abstractions.

    20. UCF Computer Science: Course Descriptions
    set architectures, processor implementation, memory hierarchy, pipelining, computer arithmetic, vector CGS 1060C Introduction to computer science AS 3(2,2
    Computer Science Course Descriptions
    • CAP 4020 Digital Media AS 3(3,0)
      PR: Senior standing or C.I. Information structures, algorithms and interactive tools for creation, compression, storage, indexing and transmission of multimedia (visual images, sound, tactile displays, etc.) Project-oriented.
    • CAP 4021 Building Virtual Worlds AS 3(3,0)
      PR: Senior standing or C.I. Design and construction of software for networked interactive learning environments, entertainment and communication systems. Tools for enabling dramatic, artistic and technical creativity. Project oriented.
    • CAP 4453 Introduction to Robot Vision AS 3(3,0)
      PR: COP 2501, MAC 2312, or C.I. Pin hole camera and eye, perspective and orthographic projections, the processing of edges, regions, motion, shading, texture, object; robot arm usage.
    • CAP 4630 Introduction to Artificial Intelligence AS 3(3,0)
      PR: COP 3530 and COT 3100. Current methods in Al: knowledge-based systems, representation, inference, planning, natural language. Programming in Lisp or Prolog required.
    • CAP 4702 Seminar in Digital Arts AS 3(3,0)

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