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Computer Engineering Courses
The number and title of each course is followed by the number of semester hours it carries, the semester(s) during which it is taught (F=fall, S=spring, U=summer), its prerequisites, its corequisites, and any courses with which it is cross-listed.Where a course has both a 5000- and 6000-level number, the 5000-level version is intended for undergraduates, and the 6000-level version is for honors and graduate students. The two versions of the class will meet together, but extra work will be expected of honors and graduate students.
Courses that have only 6000-level numbers may be taken by graduate and advanced undergraduate students.
Current class schedules and registration information are available on line.
Some elective classes are not offered every year. Check the on-line schedule or the Computer Engineering advisor to see which classes will be offered in upcoming semesters.
- CS 1010 Introduction to Unix (0.5,FSU)
- An introduction to the Unix workstations used in the College of Engineering CADE Lab. Topics include the X Windows system, Unix shell commands, file system issues, text editing with Emacs, accessing the World Wide Web, and electronic mail. Self-paced course using online teaching aids.
- Introduction to the field of Electrical Engineering through programming in the Matlab language. Students design various components of a prototype communication system while learning about the following aspects of Matlab: script and function files, math functions, commands for array construction and manipulation, string expressions, logical operators, control flow, and graphics. No prior knowledge of Electrical Engineering is assumed.
- Survey of the social issues that surround the increasingly pervasive roles that computers play in society. Topics include privacy of personal information, encryption and interception of communications, risks posed by unreliable computer systems, freedom of speech in cyberspace, intellectual property as it relates to downloadable media, computer-based crime, and computers in the workplace. Case studies will focus on computer-related issues of current public interest. No background in computer technology is required or assumed beyond the ability to use a computer to send e-mail, browse the web, and write papers.
- The basics of analog and digital circuits as an introduction to electrical and computer engineering. Concepts of voltage, current, power, resistance, capacitance, and inductance. Circuit analysis techniques such as Kirchhoff's Laws, node voltages, and mesh currents. Thevenin's and Norton's equivalent circuits. Simple op-amp and timing circuits. Alternating current and impedance.
- The first course required for students intending to major in computer science and computer engineering. Introduction to the engineering and mathematical skills required to effectively program computers, and to the range of issues confronted by computer scientists. Roles of procedural and data abstraction in decomposing programs into manageable pieces. Introduction to object-oriented programming. Extensive programming exercises that involve the application of elementary software engineering techniques.
- Introduction to propositional logic, predicate logic, formal logical arguments, finite sets, functions, relations, inductive proofs, recurrence relations, graphs, and their applications to Computer Science.
- Fundamental electric-circuit techniques, including Kirchhoff's laws, impedance, superposition, phasor transforms, RLC solutions in the time domain, sinusoidal steady-state systems, frequency response, filters, Fourier-series methods, Laplace-transform techniques, transformers.
- Fundamentals of electronic circuits and components, network models of amplifiers, basic semiconductor device physics, diodes, bipolar and MOS transistors, basic analog and digital circuit elements, frequency response, feedback and stability. Introduction to computer circuit simulation.
- The second course required for students intending to major in computer science and computer engineering. Introduction to the problem of engineering computational efficiency into programs. Classical algorithms (including sorting, searching, and graph traversal) and data structures (including stacks, queues, linked lists, trees, hash tables, and graphs). Analysis of program space and time requirements. Extensive programming exercises that require the application of elementary techniques from software engineering.
- Models of sequential computation, including finite-state automata, push-down automata, and Turing machines.
- Analog and digital integrated circuit techniques, filters and tuned amplifiers, signal generator, waveform shaping circuits, power amplifier and power semiconductor devices, computer models and computer simulations of complex devices and circuits.
- Scientific computation relevant to computer science and engineering; floating-point arithmetic, systems of linear equations (direct and iterative techniques), nonlinear equations (univariate and multivariate), interpolation and differentiation (divided differences), integration (mechanical and Gaussian quadratures, optimal quadratures), approximation by spline functions (natural splines and B-splines, optimality of splines).
- Brief introduction to vector calculus, definition of electric and magnetic fields. Maxwell's equations in integral and differential forms, electromagnetic-wave propagation in free space and in material regions, Poynting theorem, and electromagnetic power. Transmission lines (transient and steady-state analysis), Smith chart, and impedance matching techniques. Basic principles of radiation and propagation in waveguides.
- Practical exposure to the process of creating large software systems, including requirements specifications, design, implementation, testing, and maintenance. Emphasis on software process, software tools (debuggers, profilers, source code repositories, test harnesses), software engineering techniques (time management, code and documentation standards, source code management, object-oriented analysis and design), and team development practice. Much of the work will be in groups and will involve modifying preexisting software systems.
- Transform domain analysis of passive circuits. Linear and time invariant systems in continuous-time and discrete-time domains. System representations using impulse response functions, frequency responses and transfer functions. Realizations of linear time-invariant systems. Fourier analysis of continuous and discrete-time signals. Sampling theorem. Filter design from specifications.
- An in-depth study of traditional software development (using UML) from inception through implementation. The entire class is team-based, and will include a project that uses an agile process.
- Laplace transforms, boundedness, and convergence of signals. Transfer functions, stability, steady-state responses and transient responses, effect of initial conditions, state-space representations. Feedforward and feedback control, steady-state error and integral control, Routh-Hurwitz criterion, root-locus method, application to phase-locked loops. Bode plots, Nyquist criterion, gain and phase margins. The z-transform and the analysis of discrete-time signals and systems. Sampled-date systems, conversion between continuous-time and discrete-time systems.
- An introduction to probability theory and statistics, with an emphasis on solving problems in electrical and computer engineering. Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions, correlation, regression, and hypothesis testing. Engineering applications include failure analysis, process control, communication systems, and speech recognition.
- Techniques for reasoning about, designing, minimizing and implementing digital circuits and systems. Combinatorial (logic and arithmetic) and sequential circuits are covered in detail leading up to the design of complete small digital systems using finite state machine controllers. Use of computer-aided tools for design, minimization, and simulation of circuits. Laboratory is included involving circuit implementation with MSI, LSI, and field programmable gate arrays.
- Working in teams, students employ the concepts of digital logic design and computer organization to design, implement, and test a computing system. Interface I/O devices and develop associated software/firmware. Extensive use of CAD and software tools.
- An in-depth study of computer architecture and design, including topics such as RISC and CISC instruction set architectures, CPU organizations, pipelining, memory systems, input/output, and parallel machines. Emphasis is placed on performance measures and compilation issues.
- Special topics courses are taught every year. Check the on-line schedule for a current listing of offerings.
CS/EE 3991 Computer Engineering Junior Seminar (0.5,F) Prereq: CE major status.
- Presentations from faculty and industry representatives to discuss trends in computer engineering, professionalism, ethics, the impact of engineering in global and societal contexts, lifelong learning, and contemporary issues.
- Fundamentals of project planning (scoping, group selection, risk assessment, scheduling, backup planning, strategy, etc.) are covered in the first half of the course. The second half involves student presentations and critique of proposals in progress. The final result of the course will be an approved project or thesis proposal.
- Issues confronted by undergraduate teaching assistants in introductory computer science courses, including leading lab sections, conducting office hours, grading assignments, communicating with students. Each student must currently be an undergraduate teaching assistant in the School of Computing. May be taken for credit up to three times.
- Study of algorithms, data structures, and complexity analysis beyond the introductory treatment from CS 2420. Balanced trees, heaps, hash tables, string matching, graph algorithms, external sorting and searching. Dynamic programming, exhaustive search. Space and time complexity, derivation and solution of recurrence relations, complexity hierarchies, reducibility, NP completeness. Laboratory practice.
- Introduction to computer systems from a programmer's point of view. Machine level representations of programs, optimizing program performance, memory hierarchy, linking, exceptional control flow, measuring program performance, virtual memory, concurrent programming with threads, network programming.
- Development of significant software systems by small student groups, with emphasis on applying sound, disciplined software engineering practice.
- Software architectures, programming models, and programming environments pertinent to developing web applications. Topics include client-server model, multi-tier software architecture, client-side scripting (JavaScript), server-side programming (Servlets and JavaServer Pages), component reuse (JavaBeans), database connectivity (JDBC), and web servers.
- Basic simulation modeling, modeling complex systems, basic probability and statistics for simulation, building valid simulations, random numbers, and output data analysis. Both discrete event and continuous simulation may be covered.
- This is the capstone project course for Computer Engineering majors who do not choose to do a thesis. Projects are done in groups and are of the student's choosing. Classroom sessions are devoted to improving presentation skills and serve as peer reviews of the ideas and work done to date. Multiple in-progress oral presentations are required as is a final written project report and a final oral presentation.
CS 4960-4964 Special Topics (1-4)
- Special topics courses are taught every year. Check the on-line schedule for a listing of offerings.
CS/EE 4991 Computer Engineering Senior Thesis I (2,F) Prereq: CS/EE 3992 and approved senior thesis proposal.
- Students work on original senior thesis project under the direction of their approved thesis advisor. This course along with CS/EE 4992 substitute for CS/EE 4710 (Computer Engineering Senior Project) for students who have chosen to do a thesis.
- Students work on an original senior thesis project under the direction of their approved thesis advisor, make an oral presentation at the annual student technical conference, and prepare and submit their senior thesis for approval. This course along with CS/EE 4991 substitute for CS/EE 4710 (Computer Engineering Senior Project) for students who have chosen to do a thesis.
- A survey of topics in theoretical computer science, focusing on computability and complexity. Turing machines, decidability, relative computability, recursion theorem, non-deterministic TMs, complexity measures, time and space hierarchies, P and NP, NP-completeness, program specification and verification. Undergraduates only.
- Physical principles that underlie operation of semiconductor electronic devices with emphasis on silicon integrated circuits. Physics of semiconductor materials, equilibrium in electronic systems, metal semiconductor contacts, p-n junction theory, junction field effect transistors, introduction to operation of bipolar transistors.
- Continuation of EE 5201. Bipolar transistors, silicon-silicon dioxide system, insulated gate field effect transistors (IGFETs). Mathematical models for computer simulation of bipolar and MOS devices. Second order effects associated with very small geometry devices, and other devices of current interest.
- Hands on experience in the fabrication of silicon devices. Use of oxidation, donor and acceptor diffusion, and high resolution photolithography in a clean room facility. Characterization of silicon, measurement of basic parameters, oxide thickness, dopant diffusion. Introduction to metalization and contacts.
- Integrated knowledge of individual processing steps with more complex processing equipment. Fabricate and characterize simple transistors and integrated circuits.
- Introduction to the principles of micromachining technologies. Topics include photolithography, silicon etching, thin film deposition and etching, electroplating, polymer micromachining, and bonding techniques. A weekly lab and a review of micromachining applications is included. Undergraduates only.
- Use of the technologies from the first course in the series (ECE 5221) to investigate biomedical applications of micromachining. Course focuses on the design and development of micro sensor/actuator systems. Laboratory focus is on the fabrication and testing of microscale sensor/actuator systems. Undergraduates only.
- Third in a 3-course series on Microsystems Engineering. This course generalizes microsystems design considerations with practical emphasis on MEMS and IC characterization/physical analysis. Two lectures, one lab per week, plus 1/2 hour lab lecture. Must also register for ME EN 6056 (0-credit lab with fees).
- Introduction to field of artificial intelligence, including heuristic programming, problem-solving, search, theorem proving, question answering, machine learning, pattern recognition, game playing, robotics, computer vision. Undergraduates only.
- The mechanics of robots, comprising kinematics, dynamics, and trajectories. Planar, spherical, and spatial transformations and displacements. Representing orientation: Euler angles, angle-axis, and quaternions. Velocity and acceleration: the Jacobian and screw theory. Inverse kinematics: solvability and singularities. Trajectory planning: joint interpolation and Cartesian trajectories. Statics of serial chain mechanisms. Inertial parameters, Newton-Euler equations, D'Alembert's principle. Recursive forward and inverse dynamics. Undergraduates only.
- Basic pattern-recognition and image-analysis techniques, low-level representation, intrinsic images, ``shape from'' methods, segmentation, texture and motion analysis, and representation of 2-D and 3-D shape. Undergraduates only.
- Brief review of transmission line theory and Smith Chart, general theory of waveguides, TE, TM, TEM modes, some commonly used waveguides and transmission lines including microstripline and its variations for microwave integrated circuits, matching techniques including conjugate matching, passive components, scattering matrices and signal-flow graphs, ABCD parameters, directional coouplers and hybrids, power dividers and combiners, signal-flow graphs for microwave amplifiers, microwave resonators and filters including design considerations, filter design by image parameter method, constant-k and m-derived filters, maximally flat and equal-ripple filters, coupled-line filters, ferrite components. Biweekly laboratory assignments to design, fabricate, and test microstrip circuits: e.g., low and band-pass filters, coupled-line filters, directional couplers, etc., using professional-level computer sofware and network analyzers.
- Nonlinear and active microwave devices including diodes, mixers, transistors, and negative resistance devices; compressed Smith Chart; balanced and double-balanced mixer design; transistor amplifier theory and design for best gain, stability, and noise performance. Oscillator theory and design using transistors, tunnel diodes, IMPATTs, and Gunn diodes. PIN diode switching circuits and phase shifters. Survey of design and performance of microwave systems and auxiliary components; antennas, signal modulation and multiplexing, transceiver and radar systems, signal-to-noise ratios, atmospheric effects, microwave heating, biological effects and safety. Course includes bi-weekly laboratory assignments using microstrip integrated circuits with professional level design and test equipment. Demonstrations of other active components such as traveling wave tubes, klystrons, and backward oscillators are also provided.
- General theory of conduction current antennas; linear antennas including dipoles and monopoles; antenna equivalent impedance; design of AM, FM, TV and shortwave broadcast antennas of one or more elements including ground and mutual impedance effects; matching techniques including lumped, shunt, and series elements, transmission lines and conjugate matching; receiving antennas; antennas used for mobile communication systems and their radiation characteristics; antenna arrays and their design; wave propagation including propagation via ionosphere or troposphere; loop antennas and Yagi-Uda arrays; antenna synthesis for specified radiation patterns. UHF and microwave antennas including corner reflector antennas, helical antennas, theory of aperture antennas including rectangular and circular apertures; broadband log-periodic antennas; microstrip antennas and phased arrays including applications for wireless communication systems; slot antennas, turnstile, horn and parabolic radiators; considerations for radar antennas and communication links. Antenna ranges and measurement techniques. Laboratory demonstrations of radiation patterns of portable wireless antennas with and without the model of the head. Visits to various antenna installations in the Salt Lake valley by groups of three students.
- Introduction to wireless transmission systems. This course will emphasize how individual parameters affect overall system design and performance. Topics include: basic cellular systems and parameters, multi-path channels and modulation techniques.
- Introduction to design, operation, and application of microwave and millimeter-wave vacuum tubes; klystrons, traveling-wave tubes, backward-wave oscillators, magnetrons, gyrotrons, free-electron lasers.
- Computational models and methods for understanding written text. Introduction to syntactic analysis, semantic analysis, discourse analysis, knowledge structures, and memory organization. A variety of approaches are covered, including conceptual dependency theory, connectionist methods, and statistical techniques. Applications include story understanding, fact extraction, and information retrieval. Undergraduates only.
- Review of basic numerical techniques including matrix methods and numerical methods for error minimization and convergence. Comparison of differential and integral formulations including finite difference, finite element, and moment methods. Emphasis on frequency domain method of moments and time domain finite difference (FDTD). Computer exercises require Fortran, C, or equivalent programming and computerized data display techniques. Undergraduates only.
- Techniques for developing computer systems that can acquire new knowledge automatically or adapt their behavior over time. Topics include concept learning, decision trees, evaluation functions, clustering methods, explanation-based learning, language learning, cognitive learning architectures, connectionist methods, reinforcement learning, genetic algorithms, hybrid methods, and discovery. Undergraduates only.
- Physics and applications of lasers. All major laser types are studied, including semiconductor, gas, dye and solid-state lasers. Emphasis is placed on the properties of laser light and how they are used in a myriad of applications. Hands-on laboratory experience is included.
- Systematic study of modern optical-fiber communication systems; Loss-limited systems vs. dispersion-limited systems; Point to point links, broadcast and distribution systems, and optical networks; Wavelength-division multiplexing (WDM); and sub-carrier multiplexing (SCM); optical amplifiers and dispersion compensation; Emphasis is on system design. Includes hands-on laboratory experience.
- Characteristics, objectives, and issues concerning computer operating systems. Hardware/software interactions, process management, memory management, protection, synchronization, resource allocation, file systems, security, and distributed systems. Extensive systems programming.
- Lexical analysis, top-down and bottom-up parsing, symbol tables, internal forms and intermediate languages, runtime environments, code generation, code optimization, semantic specifications, error detection and recovery. Use of software tools for lexical analysis and parsing.
- A comprehensive study of the principles and practices of data communication and networks. Topics include: transmission media, data encoding, local and wide area networking architectures, internetwork and transport protocols (e.g., IPv4, IPv6, TCP, UDP, RPC, SMTP), networking infrastructure (e.g., routers, name servers, gateways), network management, distributed applications, network security, and electronic commerce. Principles are put into practice via a number of programming projects. Undergraduates only.
- Acoustic-wave propagation in biological materials with examples of practical medical instrumentation resulting from ultrasound interactions with biological structures. Includes one lab experience.
- Ideas behind the design and implementation of programming languages. Syntactic description; scope and lifetime of variables; runtime stack organization; parsing and abstract syntax; semantic issues; type systems; programming paradigms; interpreters and compilers.
- Review of probability theory; multivariate distributions; Gaussian distributions; weak and strong law of large numbers; random processes; stationarity and ergodicity; mean-value function; auto- and cross-correlation functions; power spectral densities; Wiener-Khinchine theorem; Karhunen-Loeve expansion; Gaussian random processes; random processes in linear filters; white Gaussian noise.
- Requirements, challenges, and techniques for designing a modern programming language, currently focusing on Java as a case study. Syntactic and lexical issues, semantic specification, modularity concepts, support for object-oriented programming, types and subtypes, type safety and security, portability, compilability, dynamic linking and loading, program evolvability, use of meta data (reflection), multi-threading, native code generation and linkage, generic types, persistence.
- Modern communications; probabilistic viewpoint; vector representation of signal; signal spaces; vector channels; additive white Gaussian noise; optimum receivers; maximum-likelihood detection; error probabilities; memoryless modulation methods: PAM, BPSK, M-PSK, FSK, QAM; message sequences; intersymbol interference (ISI); Nyquist signaling; complex baseband models; noncoherent detection.
- Representing information about real world enterprises using important data models including the entity-relationship, relational and object-oriented approaches. Database design criteria, including normalization and integrity constraints. Implementation techniques using commercial database management system software. Selected advanced Topics such as distributed, temporal, active, and multi-media databases. Undergraduates only.
- Discrete-time signals and systems; the z-transform. Input-output relationships; discrete-time networks. The discrete-time Fourier transform and sampling; practical sampling issues; signal quantization. The discrete Fourier transform, the fast Fourier transform, and high-speed convolution. Filter design from analog models; impulse-invariant, bilinear, and spectral transformations. FIR filter design, windowing, and frequency-sampling methods. Equiripple filter design. Coefficient quantization. Examples of DSP applications and implementations. Undergraduates only.
- Fundamentals of input/output devices, user interfaces, and human factors in the context of designing interactive applications. Undergraduates only.
- Industrial problems requiring function approximations, Fourier series, universal series approximations, fuzzy logic, radial basis functions, neural networks, linear interpolation, triangulation, window reticulation, response surfaces, polynomials, cubic splines, sinc functions, Bezier curves. Undergraduates only.
- Principles of operation, mathematical models, and control techniques for electric motors. Types of motors include brush DC motors, stepper motors, brushless DC motors, synchronous motors and induction motors. Topics covered: steady-state and dynamic characteristics, torque limits and field weakening operation, characteristics under voltage and current sources, open-loop and closed-loop control of position and velocity, and field-oriented operation for AC motors.
- Review of common DSP systems and functional elements; number representations. Implementation of bit-parallel, bit-serial, and digit-serial multiplier and adder structures; carry-save arithmetic; register minimization. Architectural transformation techniques: folding and unfolding, pipelining, and retiming of computations. Performance and hardware tradeoffs in VLSI DSP system design. Pipelined and parallel direct-form FIR and IIR filter structures. Pipelined adaptive filter structures. Architectures for the fast Fourier transform. Undergraduates only.
- Basic display techniques, display devices, and graphics systems. Homogeneous coordinates, transformations, and clipping. Introduction to lighting models. Introduction to raster graphics and hidden-surface removal.
- Interactive 3D computer graphics, polygonal representations of 3-D objects. Interactive lighting models. Introduction to interactive texture mapping, shadow generation, image-based techniques such as stencils, hidden-line removal, and silhouette edges. Introduction to image-based rendering, global illumination, and volume rendering. Undergraduates only.
- Introduction to the techniques and tools needed for the visual display of data. Students will explore many aspects of visualization, using a "from concepts to results" format. The course begins with an overview of the important issues involved in visualization, continues through an overview of graphics tools relating to visualization, and ends with instruction in the utilization and customization of a variety of scientific visualization software packages. Undergraduates only.
- Design of analog and mixed-signal CMOS integrated circuits. Fundamental building blocks for analog circuits, including the basic principles of op amp, current mirror, and comparator design. The basics of sample-and-hold circuits. Students complete integrated circuit design, simulation, layout, and verification using computer-aided design tools. Undergraduates only.
- Introduction to theory and algorithms used for computer-aided synthesis of digital integrated circuits. Topics include algorithms and representations for Boolean optimization, hardware modeling, combination logic optimization, sequential logic optimization and technology mapping. Undergraduates only.
- Introduction to systematic methods for the design of asynchronous VLSI systems from high-level specifications to efficient, reliable circuit implantations. Topics include specification, protocols, graphical representations, synthesis, optimization using timing information, and verification. Undergraduates only.
- Introduction to issues in embedded system design using microcontrollers. Topics include: microcontroller architecture, memory interfacing, serial and parallel I/O interfacing, analog interfacing, interrupt synchronization, and embedded software. Undergraduates only.
- This class is about building reliable and efficient embedded systems, with a bias toward software issues and a bias toward whole-system issues. Students complete several projects in C running on ARM-based embedded development boards. The course covers a number of special topics such as embedded software architectures, digital signal processing, feedback control, real-time scheduling, verification and validation, wired and wireless embedded networks, and safety-critical embedded systems. Undergraduates only.
- Project-based study of a variety of Topics related to VLSI systems. Use of field programmable gate arrays to design, implement, and test a VLSI project. Undergraduates only.
EE 5950 Undergrad Special Study (1-6) Prereq: Instructor's consent.
CS 5960-5969 Special Topics (1-4)
- Special topics courses are taught every year. Check the on-line schedule for a listing of offerings.
CS 6100 Foundations of Computer Science (3,S) Prereq: CS 3100, CS 4100.
- Graduate and honors students only. Extra work required.
- Study of methods for formally specifying and verifying computing systems. Specific techniques include explicit state enumeration, implicit state enumeration, automated decision procedures for first-order logic, and automated theorem proving. Examples selected from the areas of superscalar CPU design, parallel processor memory models, and synchronization and coordination protocols.
- An introduction to existing classical and modern numerical methods and their algorithmic development and efficient implementation. Topics include: numerical linear algebra, interpolation, approximation methods and parallel computation methods for nonlinear equations, ordinary differential equations, and partial differential equations.
- A study of the numerical solution of two and three dimensional partial differential equations that arise in science and engineering problems. Topics include: finite difference methods, finite element methods, boundary element methods, multigrid methods, mesh generation, storage optimization methods, and adaptive methods.
- Graduate students only. Extra work required.
- Graduate students only. Extra work required.
- Graduate students only. Extra work required.
- Overview of parallel computing; processors, communications topologies and languages. Use of workstation networks as parallel computers. Design of parallel programs: data composition, load balancing, communications and synchronisation. Distributed memory and shared memory programming modules; MPI, PVM, threads. Performance models and practical performance analysis. Case studies of parallel applications.
- The course builds on EE 5221/6221, Fundamentals of Micromachining. Topics include definitions, categorization and application fields of microsensors and actuators, an introduction to solid state physics, piezoresistive sensors, semiconductor-based temperature sensors, magnetoresistive sensors, thermoelectric sensors, photoelectric sensors, micro gas and fluid concentration sensors, molecular diagnostics arrays, and various actuators (relays, micromotors, inkjet printheads, micropumps), sensor packaging and assembly. Registration for a weekly lab (1) is required.
- The lab is compulsory when taking EE 6231 or 7231. The lab will include design and simulation of microsensors and actuators, process design, packaging and assembly, characterization and testing of microsensors and actuators as well as reliability issues. The first part of the lab will focus on the acquirement of additional technological skills and understanding of sensor characteristics. The second part of the lab will lead to the fabrication, characterization and presentation of a variety of fully functional microsensors or actuators. Examples of these are pressure, force, acceleration, gas sensors and inkjet printheads.
- Development of a thorough, working knowledge of the physics of semiconductor materials and devices, including quantum effects. Examination of advanced devices, including light emitting diodes, solar cells, detectors, and injection lasers.
- Introduce the technology of ultrafast diode lasers from the basic physical principles through to the applications in communications and ultrafast optoelectronic and applications of semiconductor diode laser arrays. All of the major types of arrays will be discussed including coherent, incoherent, edge- and surface-emitting, horizontal- and vertical-cavity, individually addressed, lattice-matched and strained-layer systems.
- A lecture/laboratory course focusing on advanced principles of operation, physical design considerations, and testing of advanced Si, SiGe, SiC, and III-V compound semiconductor devices. Ohmic and Schottky contact technologies will be discussed in detail. Advanced applications of MESFETs and JFETs will also be presented. The primary thrust of this course will be on HEMTs, HBTs, MBTs, graded junction/alloy transistors, resonant tunneling transistors and other quantum and superlattice devices. Trade-offs, theoretical considerations, modeling and simulation, testing, and the correlation between theory and experiment for various device parameters will be covered.
- Current topics in silicon device physics. Review of MOS device theory, rules for scaling devices to submicron dimensions, theoretical limits to scaling. Short channel, device models including two-dimensional numerical models. Hot carrier effects and other reliability issues. Yield statistics, lifetime prediction.
- Development of a through, working knowledge of the thermodynamic and kinetic aspects of epitaxy. This material is used to illustrate the advanced epitaxial techniques of organometallic vapor phase epitaxy, chemical beam epitaxy, and molecular beam epitaxy.
- A lecture/laboratory course focusing on advanced characterization, measurement, and testing of semiconductor devices. Topics include: MIS/MOS interface and bulk trap measurement and analysis using HF/Ideal, LF/HF, LF/Ideal, Multifrequency (Conductance) capacitance versus voltage (C-V) curves, BTS and TVS testing of oxides, Fowler Nordheim and Poole Frenkel currents in oxides and insulators, Charge Pumping, two-, three-, and four-terminal MOS current vs. Voltage (I-V) measurements, measuring hot Electron/Short Channel Effects, C-t/Zerbst Plots, Silicide technology, Electronmigration effects, DLTS, I-V versus temperature of MOS and BJTs.
- Graduate and honors students only. Extra work required.
- Graduate and honors students only. Extra work required.
- Review of Maxwell's macroscopic equations in integral and differential forms including boundary conditions, power and energy computations, and time-harmonic formulations. Macroscopic electrical properties of matter. Oblique incidence planewave propagation and polarization in multi-layered media. Separation of variable solutions of the wave equation in rectangular, cylindrical and spherical coordinates. Vector potential theory and the construction of solutions using Green's theorem. Electromagnetic theorems of duality, uniqueness, reciprocity, reaction, and source equivalence. Waveguide, cavity, antenna, and scattering applications in rectangular, cylindrical, and spherical geometries.
- Graduate and honors students only. Extra work required.
- This class deals with design and technology of microwave integrated circuits (MICs) and Monolithic Microwave Integrated Circuits (MMICs). Microwave integrated circuits such as small-signal amplifiers, power amplifiers, and oscillators are studied. Nonlinear circuits such as frequency multipliers and mixers are also covered in detail. Active devices are studied for microwave circuit and system applications. Transistors, both bipolars and FETs, and various two terminal devices are also discussed. This class deals with fabrication techniques and measurements related to microwave integrated circuits. Testing, packaging and reliability issues are studied. This class also covers monolithic microwave integrated circuit techniques. This class involves extensive computer-aided designs, circuit layout and fabrication, and circuit characterization and testing of MICs and MMICs.
- State-of-the-art course in microwave thermionic devices: Formation and control of electron beams. Llewellyn Peterson equations, space-charge waves, klystrons, traveling-wave tubes.
- State-of-the-art course in microwave thermionic devices: Continuation of traveling-wave tubes, backward-wave oscillators, crossed-field devices, parametric amplifiers, gyrotron devices, and free-electron lasers.
- Graduate and honors students only. Extra work required.
- Graduate students only. Extra work required.
- Graduate and honors students only. Extra work required.
- Human interfaces: visual, auditory, haptic, and locomotory displays; position tracking and mapping. Computer hardware and software for the generation of virtual environments. Networking and communications. Telerobotics: remote manipulators and vehicles, low-level control, supervisory control, and real-time architectures. Applications: manufacturing, medicine, hazardous environments, and training.
- Geometric computation is the study of practical algorithms for solving queries about geometric properties of computer models and relationships between computer models. Robot motion planning uses these algorithms to formulate safe motion through a modeled environment. In addition, algorithms for geometric computation are used in computer animation, simulation, computer-aided design, haptics, and virtual reality. Topics to be covered in this course are spatial subdivision and model hierarchies, model intersection, distance queries and distance fields, medial axis computations, configuration space, and motion planning. The course will rely on lectures, readings, and projects to provide understanding of current practices in the field.
- Covers fundamental notions of (1) software agents, including: autonomy, communication, persistence, and intelligence; and (2) multiagent systems, including: communication standards, cooperation, competition and coordination. Methods will be applied to a practical application (usually in Matlab or C).
- Analysis of optical systems by use of spatial Fourier transforms. A systems approach to optics using spatial frequencies and transfer functions to analyze diffraction, filtering, and imaging. Holography and holographic optical elements used in optical signal processing techniques. Includes two laboratory experiences.
- Coherence properties of light, including partial temporal and spatial coherence, as measured by statistical functions. Review of basic statistical concepts. Intensity fluctuations of thermal and laser light. Michelson interferometry, Wiener-Khinchin theorem, Young's experiment and the Van Cittert-Zernike theorem. Origins and statistics of optical noise. Comparison of various detection techniques. Includes two laboratory experiences.
- Planar and rectangular waveguides and their mode properties. Fabrication techniques, input and output couplers, and coupling between guides. Integrated optic modulators. Applications of integrated optical devices. Optical sensors for biomedical and environmental monitoring. Includes two laboratory experiences.
- Advanced quantum mechanical analysis of the interaction of light with matter, including quantization of lattice vibrations and the electromagnetic field. Analysis of laser principles based on quantum mechanical principles.
- Theoretical development and applications of nonlinear optical processes including harmonic generation, sum and difference frequency generation, parametric oscillation. Nonlinear refractive indices and multiphoton absorption.
- Compilation of modern languages. Optimization techniques, register allocation and instruction scheduling, garbage collection, exception handling. Linkers and late-stage compilation and optimization.
- Graduate and honors students only. Extra work required.
- Comprehensive introduction to the principles and practices of network security, especially Internet Security. Topics to be covered include: cryptography, authentication, access control, web security, denial-of-service, digital pests, anonymity, and intrusion detection. Existing network security standards will be used for case studies. Includes laboratory practice.
- Practical programming with functional language (e.g., Scheme, ML, Haskell) and functional techniques (e.g., fold operators, continuation-passing style, monads, parametric polymorphism). No previous experience with functional language is required. Course work includes writing programs, presenting programs in class, and critiquing peer programs.
- Efficient modulation; the capacity theorem; Shannon bound; signal constellations, lattices; maximum-likelihood sequence detection; maximum-aposteriori symbol detection; communication channels; statistical description of channels; multipath fading channels; Optimal detection; diversity detection; spread-spectrum communications; spreading sequences; Gold codes; multiple-access communications; code-division multiple access (CDMA); Aloha and random access communications.
- Modern communications systems; additive white Gaussian noise; bandwidth and power constraints; soft-decision decoding; tree codes; tree decoders; the M-algorithm; convolutional codes; trellis codes; decoding methods; maximum aposteriori symbol detection (MAP), soft information processing; iterative decoding, Turbo coding principles.
- Graduate and honors students only. Extra work required.
- Graduate students only. Extra work required.
- Graduate and honors students only. Extra work required.
- Bayesian parameter estimation; unbiased estimators; minimum variance estimators. Sufficient statistics; maximum-likelihood estimation; the Cramer-Rao bound. Linear estimation; minimum-mean-square-error estimation and its geometrical interpretation. Wiener filtering; spectral factorization. Kalman filtering and state-space estimation. Applications of estimation to practical problems including system identification and spectrum estimation.
- Basics of minimum mean-square and least squares estimation. Lattice orthogonalization. Stochastic gradient adaptive filters: derivations, performance analyses and variations. Recursive least-squares adaptive filters: fast algorithms, least-squares lattice filters, numerical issues, and performance comparisons with stochastic gradient adaptive filters. Adaptive IIR filters. Fundamentals of adaptive nonlinear filtering. Selected applications.
- Neural networks, gradient and Hessian descent, conjugate gradient, random search, simulated annealing, prejudicial search, least-squares, regression, downhill simplex, genetic algorithms, linear programming, simplex algorithm, Karmarkar algorithm, quadratic and dynamic programming, Riccati equation, Beard-Galerkin optimal control.
- Graduate students only. Extra work required.
- State-space models, controllability, observability, model reduction, and stability. Matrix fraction descriptions, coprimeness, properness, state-space realizations, multivariable poles and zeros, and canonical forms. Linear quadratic control, pole placement, and model reference control. Frequency-domain analysis and optimization.
- Analysis and control of uncertain systems. Representation of uncertain systems and their performance requirements using linear fractional transformation (generalized plant framework). Design of robust controllers, including frequency-weighted linear quadratic regulators, minimax, H-infinity and H-2 synthesis methods.
- Identification using gradient and least-squares algorithms. Indirect adaptive control: pole placement control, model reference control, predictive control, and problems with singularity regions. Direct adaptive control: strictly positive real transfer functions, Kalman-Yacubovitch-Popov lemma, passivity theory, and stability of pseudo-gradient adaptive algorithms. Persistency of excitation and sufficient richness conditions for parameter convergence. Averaging methods and robustness issues. Disturbance rejection.
- Graduate students only. Extra work required.
- Graduate and honors students only. Extra work required.
- Introduction to ray-tracing. Intersection methods for 3-D objects, reflection and refraction. Introduction to surface and solid texturing. Introduction to continuous-tone pictures and the aliasing problem. Special effects such as soft shadows, depth-of-field, motion-blur, and indirect lighting.
- Graduate and honors students only. Extra work required.
- Project-oriented class on advanced topics of current interest in signal processing. Examples of topics include image compression, nonlinear signal processing, active noise control, blind deconvolution and equalization.
- Project-oriented class on advanced topics of current interest in signal processing. Examples of topics include image compression, nonlinear signal processing, active noise control, blind deconvolution and equalization.
- Introduction to current concepts and issues in CAGD systems with emphasis on free- form surface design; mathematics of free-form curve and surface representations, including Coons patches, Bezier method, B-splines, triangular interpolants, and their geometric consequences; classical surface geometry; local and global design tradeoffs and explicit and parametric tradeoffs; subdivision and refinement as techniques in modeling; current production capabilities compared to advanced research. Laboratory experiments with current CAD systems.
- Project based on material covered in CS 6670.
- Introduction to basic concepts of the design of CMOS integrated circuits. Static and dynamic properties of CMOS circuits, composite layout of CMOS circuits, and modeling of transistors. Commonly encountered CMOS circuits. Students complete design, composite layout, and simulation of an integrated circuit project using computer-aided design tools.
- This course is designed for students who fabricated an integrated circuit in CS/EE 6710 or 6770. Students will test their chiips independently and report on the experimental results.
- Graduate and honors students only. Extra work required.
- Optional lab that accompanies CP SC/ECE 5720/6720. Students will test and characterize transistors, circuits, and systems on modern CMOS chips.
- This course is designed for students who fabricated an integrated circuit in CP SC/ECE 5720/6720. Students will test their chips independently and report on the experimental results.
- Graduate and honors students only. Extra work required.
- Graduate and honors students only. Extra work required.
- Introduction to methods for modeling and analyzing biological networks such as genetic regulatory networks, metabolic networks, and signal transduction networks. A particular emphasis will be given to methods inspired by models used by engineers for circuit analysis. Other topics include: stochastic analysis using Monte Carlo methods, differential equation models, Bayesian network models, flux balance analysis, learning methods, pathway databases, and synthesized gene circuits.
- This course addresses advanced issues in VLSI design, covering the following topics: design methodologies and IP design, CMOS circuit scaling, advanced logic circuit styles, noise sources and signal integrity in digital design, design techniques for dynamic and static power reduction, power supply issues, interconnect analysis, clocking and synchronization, process variation, and performance verification. Students are expected to complete a substantial design project as part of the course, which involves extensive use of CAD tools.
- Graduate students only. Extra work required.
- Graduate students only. Extra work required.
- Principles of modern high performance computer and micro architecture: static vs. dynamic issues, pipelining, control and data hazards, branch prediction and correlation, cache structure and policies, cost-performance and physical complexity analyses.
- Graduate and honors students only. Extra work required.
- Special topics courses are taught every year. Check the on-line schedule for a listing of offerings.
CS 7120 Information-Based Complexity (3) Prereq: CS 3200, MATH 2270, MATH 3210.
- Analysis of optimal computational methods for continuous problems. Introduction to the general worst case theory of optimal algorithms, linear problems, and spline algorithms as well as selected nonlinear problems. Examples include optimal integration, approximation, nonlinear zero finding, and fixed points.
- The course builds on EE 5221/6221, Fundamentals of Micromachining. Topics include definitions, categorization and application fields of microsensors and actuators, an introduction to solid state physics, piezoresistive sensors, semiconductor-based temperature sensors, magnetoresistive sensors, thermoelectric sensors, photoelectric sensors, micro gas and fluid concentration sensors, molecular diagnostics arrays, and various actuators (relays, micromotors, inkjet printheads, micropumps), sensor packaging and assembly. Registration for a weekly lab (1) is required.
- Sinc methods for solving difficult computational problems, such as partial differential and integral equation problems, that arise in science and engineering research. Emphasis on parallel computation. Applications vary, depending on participants in the class. Students are given projects--whenever possible in their areas of research--that lead to publishable research articles.
- In-depth study of research topics of current interest in scientific computing. Topics will typically have been surveyed in CS 6210 and 6220. This course can be repeated for credit since the focus will be changed from semester to semester.
- Covers the kinematics, dynamics, and control of robotic manipulators. Projects controlling robots will be an integral part of the course.
- In-depth study of physics and mathematics of MR imaging and MR spectroscopy as they relate to imaging of biologic systems: NMR physics, Block's equations, pulse sequences, flow and diffusion phenomena, spectroscopy principles, methodology. Laboratory.
- Modeling and identification of the mechanical properties of robots and their environments. Review of probability and statistics. Parametric versus nonparametric estimation. Linear least squares parameter estimation, total least squares, and Kalman filters. Nonlinear estimation and extended Kalman filters. State estimation. Specific identification methods for kinematic calibration, inertial parameter estimation, and joint friction modeling.
- The course focuses on the problem of three-dimensional (3D) image reconstruction from line integrals, which constitute a mathematical model of measurements in computed tomography (CT), and particularly x-ray computed tomography. Analytical and iterative reconstruction methods are investigated for various geometries of data acquisition. A critical goal is to provide the student with the essential tools required to understand papers on tomographic image reconstruction, from x-ray CT to emission CT, and also with a clear understanding of how efficient and accurate reconstruction algorithms are designed, using the Fourier slice theorem and backprojection techniques. MATLAB laboratories and a computer project are given in support of the theory.
- Practical distributed operating systems concepts from basics through the state of the art. Topics include interprocess communication, client-server systems, distributed shared memory, distributed file systems, distributed databases, portable computing, software fault tolerance, and wide-area (e.g. web) applications. Work includes individual oral presentations, a group project, and a written research report.
- Examination of the formal and pragmatic ideas behind programming language design. Imperative, functional, logic, object-oriented, and multi-paradigm languages. Lambda calculus, fixpoints, type systems, and predicate logic. Denotational semantics and models of concurrency.
- Concept of information and uncertainty; source and channel models; entropy and its properties; relative entropy; mutual information; Shannon's source coding theorem; the Asymptotic Equipartitioning Property (AEP); concepts of source codes; Huffman code; arithmetic coding; variable to fixed source codes; typical sequences; rate distortion theory; channel coding; Shannon's channel coding theorem.
- An introductory course in processing grey-scale and color images that covers both mathematical funadmentals and implementation. It introduces students to the basic principles of processing digital signals and how those principles apply to images. These fundamentals include sampling theory, transforms, and filtering. The course also covers a series of basic image-processing problems including enhancement, reconstruction, segmentation, feature detection, and compression. Assignments include several projects with software implementations and analysis of real data.
- Project-oriented class on advanced topics of current interest in signal processing. Examples of topics include image compression, nonlinear signal processing, active noise control, blind deconvolution, and equalization.
- Project-oriented class on advanced topics of current interest in signal processing. Examples of topics include image compression, nonlinear signal processing, active noise control, blind deconvolution, and equalization.
- Using camera and sensor simulation along with physical simulation to generate realistic synthetic images.
- Issues in the design of modern microprocessors, with emphasis on current research topics in the field. Offered in alternate years.
- Architecture, design, and analysis of parallel computer systems: vector processing, data vs. control concurrency, shared memory, message passing, communication fabrics, case studies of current high performance parallel systems. Offered in alternate years.
- Special topics courses are taught every year. Check the on-line schedule for a listing of offerings.
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