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Course Criteria
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3.00 Credits
Deals with the design of both synchronous and asynchronous digital systems. The accent is on design methodologies for fi nal implementation on programmable logic devices. Design techniques are based on top-down design using ASM charts and bubble diagrams along with microprogramming applications. Students also learn how to rapidly develop digital systems with VHDL. Design strategies for testability are discussed along with their impact on performance. The practical aspects of component interconnection (crosstalk, noise, transmission line effects) with effects on performance are also surveyed. The laboratory portion consists of four distinct projects proposed, designed, simulated (two projects require actual hardware implementation), and tested by the student. The design laboratory is supported by the ALTERA MAX+PLUS II VHDL design tools and EPLD/FPGA programmers. (0301-240, 365) Class 4, Lab 3, Credit 4
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4.00 Credits
A technical elective that introduces students to the fundamental principles of Application Specifi c I.C. (ASIC) design. Both circuit design and system design are covered. The student also is introduced to CAD tools for schematic capture, placement and routing of standard cells. The projects are designed and simulated using commercial CAD tools. Top-down design using a hardware description language (VHDL) is included. (0301-650) Class 4, Credit 4
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3.00 Credits
Discussion of the use of the C Programming language in generating software specifi cally for microprocessor based systems. The tools and procedures necessary for the organized and effi cient development of high-level code for a target microprocessor including compilers, linkers, object code libraries, and symbolic debugging as well as monitor programs and real-time multi-tasking kernel principles will be presented. Programming projects with emphasis on the applications in electrical engineering will be assigned (0301-365, 346) Class 4, Lab 3, Credit 4
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4.00 Credits
Artificial Neural Networks (ANN) is the name given to a broad class of processing algorithms that are loosely based on how the brain processes information. The term "artificial" distinguishes the silicon-based systemsfrom the biological systems (such as ourselves). ANNs are used in numerous applications from manufacturing controls to handwriting recognition to optical visual processing, or in any application that can handle some "fuzziness"in the output. ANNs also form the foundation for artifi cial intelligence (AI) systems. This course begins with a discussion of what ANNs are and what features defi ne them, then examines a number of the most common neural algorithms and techniques such as backward error propagation ("Back-prop").Software implementations of the algorithms (requiring C programming skills) as well as hardware implementations (requiring PSPICE simulations) will be discussed. Class 4, Credit 4
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3.00 Credits
Gives the student detailed knowledge of the hardware and software organization of 8-bit microcontroller systems with an emphasis on design. Peripheral interfacing, serial and parallel I/O, including interrupts, are considered. Special attention is given to interfacing microcontroller with the analog world, including the use of A/D and D/A converters. Software organization as well as design tools are discussed. Design case studies of typical microcomputerembedded systems are examined. (0301-365) Class 3, Lab 3, Credit 4 (F)
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4.00 Credits
A continuation of the topics studied in 0301-554. Topics include study of the design methods for digital IIR fi lters via s-plane transformations, study of design methods for digital FIR fi lters, including emphasis on the question of linear phase response, a review of the discrete Fourier transform (DFT) and an in-depth study of fast algorithms (FFTs) for implementing the DFT, including radix 2, radix 4 and mixed radix algorithms, quantization effects in discrete systems; an introduction to digital signal processing computer chips and their use in the implementation of digital processing systems, and applications of digital signal processing, including speech processing and two-dimensional image processing. Includes several design projects in the digital signal processing laboratory. (0301-554) Class 4, Credit 4
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4.00 Credits
A study of the various techniques for the design of fi lters to meet given specifi - cations. Approximations to the ideal fi lter characteristic through Butterworth, Chebyshev and other polynomials are discussed in detail. The emphasis is on active network realizations using op amp stages. Topics include review of analysis of op amp circuits and transfer function of networks, magnitude and frequency scaling, ideal fi lter characteristics, Butterworth, Chebyshev and Bessel-Thompson approximations to the ideal fi lters, determination of transfer functions to meet given specifi cations, high-pass to low-pass and band-pass to low-pass transformations, standard op amp circuits for fi lter realizations, negative impedance converters, generalized impedance converters, and switched capacitor fi lters. (0301-453) Class 4, Credit 4
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2.00 Credits
An introduction to a wide range of robotics-related topics including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Prerequisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project, in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures. (0301-453, 346) Class 3, Lab 2, Credit 4
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4.00 Credits
Microelectromechanical systems (MEMS) are widely used in aerospace, automotive, biotechnology, instrumentation, robotics, manufacturing, and other applications. There is a critical need to synthesize and design high performance MEMS which satisfy the requirements and specifi cations imposed. Integrated approaches must be applied to design and optimized MEMS, which integrate microelectromechanical motion devices, ICs, and microsensors. This course covers synthesis, design, modeling, simulation, analysis, control and fabrication of MEMS. Synthesis, design and analysis of MEMS will be covered including CAD. (Fourth- or fi fth-year standing for undergraduates, or graduate standing) Class 4, Credit 4
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4.00 Credits
This course focuses on evaluation of MEMS, microsystems and microelectromechanical motion devices utilizing MEMS testing and characterization. Evaluations are performed using performance evaluation matrices, comprehensive performance analysis and functionality. Applications of advanced software and hardware in MEMS evaluation will be covered. (Senior standing required) Class 4, Credit 4
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