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Course Description
ECE 500 - Mathematical Methods for Electrical & Computer Engineering
Catalog Data | Prerequisite: graduate standing. (3) Topics include: Transform Techniques using Fourier series, Fourier transforms, Laplace transforms and Sampling Theorem. Linear Algebra using eigen expansions, polynomial functions and matrices and determinants. Random Variables using probability density and distribution functions, functions of a random variable, and conditional & joint probabilities. Three lecture hours per week. |
Textbook | Probability, Random Variables and Stochastic Processes, A. Papoulis, & S. U. Pillai Signals, Systems, and Transforms, Phillips & Parr Linear System Theory and Design, C-T. Chen |
Coordinator | Prof. Sridhar Lakshmanan |
Prerequisites by Topic | None |
Topics | Review of signals, systems and transforms (3 weeks) Review of probability and random variables (3 weeks) Review of determinants, and matrices (3 weeks) Exams (9 hours) |
Course Objectives | Review of undergraduate ECE mathematics material Prepare students for subsequent courses such as ECE550, ECE555, ECE560, ECE565, ECE580, etc. |
Course Outcomes | Ability to analyze signals and systems using transform techniques Ability to set-up and analyze random experiments Familiarity with matrices and determinants |
Assessment Tools | Exams Separate assessments of the student's ability in each of the above outcomes Consultation with instructors of down-the-line courses to ensure that the objective of the course is fulfilled |
ECE/AE 510 Vehicle Electronics I
Catalog Data
(Revised 2006-03) | Prerequisite: graduate standing, this course is for non-EE students only (3) This course discusses the principles of electrical engineering and applications of electrical and electronic systems in automobiles, including resistive, inductive, and capacitive circuit analysis, semiconductor diodes, junction transistors, FETS, rectifiers, and power supplies, small signal amplifiers, biasing considerations, gain-bandwidth limitations, circuit models. Some automotive E/E applications are used for case study. Three lecture hours per week. (Not open to students with EE degree.) |
Textbook | Principles and Applications of Electrical Engineering, A. Hambley, Third edition, Prentice Hall, 2002 |
Coordinators | Prof. Chris Mi, Electrical and Computer Engineering |
Prerequisites by Topic | physics and math |
Topics | Basic theory Introduction: basic concepts about current, voltages, power, energy, KCL, KVL, ohm's law, and basic circuit topologies Resistor Circuits, Inductor, and Capacitor Circuits: series and parallel connections, voltage and current dividers, circuit analysis Transient Circuit Analysis: RC and RL circuits. Matlab/Simulink simulation of circuit transients Steady-State Sinusoidal Analysis and Frequency response: sinusoidal currents and voltages, complex impedance, AC analysis, filters, resonance and PSpice simulation of circuits Diodes: basic device physics, terminal characteristics, and analysis Basic Amplifier Concepts and Operational Amplifiers MOSFETs and Bipolar Junction Transistors (BJT) Electromechanics: Magnetic circuits and transformers DC machines and AC machines Automotive application: Overview of automotive electrical and electronic applications, automotive wiring and power supply, 14V vs. 42V systems Automotive lighting circuits, inductive loads (motors and solenoids), automotive EMC transients Automotive inductive and capacitive discharge ignition circuits Telematics, radio/car phone, GPS, sensors, and digital filtering Rectifier and Zener diodes in automotive E/E systems Analog and digital circuits in automotive E/E systems Semiconductor devices and silicon integrated circuits in automobiles Solenoids, actuators, step motors, transformers and other magnetic components motors in EVs and HEVs |
| Analysis, simulation and design of ignition circuit Alternators, starters, rectifier circuits, and dc-dc converters EV, HEV and fuel cell vehicles, electric drive train design Midterm and final exams | |
Laboratory projects: | Various projects related to vehicle electronics |
Computer Usage | Simulation using Simulink, Simplorer, etc. |
Course Objectives | Knowledge of the basic concepts of vehicle electronic systems A good understanding of micro electronics and power electronics circuits An understanding of the basic concept of control and practical design issues of vehicle electronics systems. |
Course Outcomes | Ability to analyze basic electronics circuits and use them in automotive systems Ability to analyze electronics circuits contains diodes, BJT and MOSFETs and use them in automotive electronics systems Ability to simulate electric and electronics circuits using Simplorer and/or PSpice Ability to analyze and design magnetic circuits including transformers, motors understand the emerging issues related to vehicle electronics circutis |
Assessment Tools | Course assignments related to the course material covered in lectures. Mid-term and final exams to assess the student competence of the material covered. Computer simulation and design project to assess the students capability to use the knowledge |
Other references | Automobile Electric/Electronic Systems, Robert Bosch GmbH, 1995. Understanding Automotive Electronics, 5th Edition, W. B. Ribbens, SAE, 1998. Auto Electricity and Electronics, Technology, J. Duffy, the Goodheart-Willcox Company, 1998. |
ECE 512 Filter Design
Catalog Data
(Revised 2006-03) | Prerequisite: graduate standing. (3) This course deals with the analysis and design of continuous-time (analog) and switched-capacitor filters. Students will learn how to analyze and design analog filter, whether they are passive, active or switched-capacitor filters. Effect of tolerances of circuit elements on the performance of the circuit behavior will be discussed Also, students will learn how to use simulation tools to design filters and verify circuit performance. Please refer to my website for additional information. Three lecture hours per week. |
Textbook | Rolf Schaumann and Mac E. Van Valkenburg, "Design of Analog filters," Oxford University Press, 2001. |
Coordinators | Prof Selim S. Awad, Electrical and Computer Engineering |
Prerequisites by Topic | Laplace transform and Fourier transform methods. Circuit analysis. Fundamentals of electronic components and systems. |
Topics | Review of continuous-time signals and systems fundamentals (3 hours) Passive and active circuits of simple filters (first and second order) (3 hours) Impedance and frequency scaling (3 hours) Filter transformations (3 hours) Filter approximations (Butterworth, Chebyshev, Elliptic, etc.) (9 hours) High order realizations of passive and active filters (3 hours) Sensitivity analysis of filters (6 hours) Switched-capacitor filters (6 hours) Selected topics (3 hours) Exams (3 hours) |
Laboratory projects: | Projects dealing with the simulation and design of analog and switched-capacitor filters |
Computer Usage | Pspice, Matlab, Simulink, and relevant software packages. |
Course Objectives | 1. Proficiency in the analysis of continuous-time (analog) signals and systems. 2. Proficiency in the design of analog and switched-capacitor filters. 3. Proficiency in the use of software tools to simulate and design of analog and switched-capacitor filter applications. |
Course Outcomes
(Revised Mar. 24, 06) | Ability to analyze continuous-time signals and systems Ability to design analog filters to meet magnitude and phase specifications. Ability to use software tools to simulate and verify the design of analog and switched-capacitor filter applications. |
Assessment Tools | 1. Exams and tests quizzes 2. Reports from project assignments 3. Discussions with students throughout the course duration. |
ECE 5121 Modeling and Design of Electronic Cir. & Sys.
Catalog Data
| Prerequisite: graduate standing. (3) The purpose of this course is three fold. First, we review the basic semiconductor circuit elements in detail in order to model each device from a circuit point of view. Such devices include: all types of diodes, bipolar junction transistors, MOS field-effect transistors and operational amplifiers. For each device, we will discuss large signal and small signal (ac) models, frequency effects and non-ideal models. Hence, the second purpose of the course, is to use the above mentioned semiconductor devices in designing electronic circuits such as: switching circuits, power suppliers, amplifiers, oscillators, non-linear circuits (logarithmic amplifiers, multivibrators, etc.). A final purpose of the course would be to give the students the opportunity to acquire hands-on experience in terms of designing, simulating and implementing electronic circuits. In order to achieve this goal, students will learn in depth how to use software tools to verify their designs. Also, students will have a number of hardware oriented projects to gain practical experience. Three lecture hours per week. |
Textbook | A. Sedra and K. Smith, "Microelectronic Circuits," Oxford University Press, Fifth edition, 2004. |
Coordinators | Prof Selim S. Awad, Electrical and Computer Engineering |
Prerequisites by Topic | Basic Circuit analysis. Fundamentals of semiconductor materials and devices. |
Topics | Review of circuit theory and semiconductor material fundamentals (3 hours) Analysis and modeling of semiconductor diodes and zener diodes (6 hours) Switching times and applications of diodes (3 hours) Analysis of the MOSFET transistor and its dc and ac models (6 hours) Basic MOSFET applications (3 hours) Analysis of the operational amplifier as a circuit element (3 hours) Linear and Nonlinear applications of OPAMPS (3 hours) Studying the imperfections and frequency limitations of OPAMPS (3 hours) Analysis of the bipolar junction transistor and its applications (6 hours) Selected topics (3 hours) Exams (3 hours) |
Laboratory projects: | Projects dealing with the simulation and design of electronic devices and systems |
Computer Usage | Pspice, Matlab, Simulink, and relevant software packages. |
Course Objectives | Proficiency in the analysis of electronic circuits (DC analysis, AC analysis, large signal analysis, etc.) Proficiency in the design electronic circuits (amplifiers, rectifiers, limiters, power supplies, log-amplifiers, differential amplifiers, oscillators, etc.). Proficiency in the use of software tools to simulate and design electronic circuits and systems. |
Course Outcomes | Ability to analyze electronic circuits and systems. Ability to design electronic circuits and systems according to given specifications. Ability to use software tools to simulate and verify the design of electronic circuits. |
Assessment Tools | 1. Exams and tests quizzes 2. Reports from project assignments 3. Discussions with students throughout the course duration. |
ECE 514 - VLSI Design
Catalog Data | Prerequisite: graduate standing. (3) Topics relevant to the design and analysis of VLSI circuits are investigated. These include an introduction to CMOS circuits, their characterization and performance estimation. Logic design and testing of VLSI circuits. Analysis of layout and the design of subsystems. VHDL and commercial CAD packages for VLSI design. Three lecture hours per week. |
Textbook | Physical design of CMOS integrated circuits using L-EDIT, John Uyemura, PSW publishing Company, latest edition |
Coordinator | A. El Kateeb, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Introductory Electronics 2. Computer Architecture 3. Digital system design |
Topics | Introduction to digital systems and VLSI VLSI CAD tools CMOS fabrication Layout and CMOS integrated circuits Basic principles of MOSFET theory CMOS logic circuits Subsystem design Architecture design and VHDL VLSI chip design |
Laboratory projects | Various projects involving digital subsystems design, chip design used for different applications |
Computer Usage | Tanner tools for chip layout, chip routing, circuit simulation, and chip design |
Course Objectives | A good understanding of the CMOS circuits operations A good understanding of the CMOS fabrication process Knowledge of the chip layout, design, and testing. Use of the MOSIS sub-micron standard cells and the Tanner auto-routing tools to implement CMOS chip. |
Course Outcomes | Ability to analyze the CMOS circuits using PSPICE tool. Ability to design CMOS systems using networks of p and n transistors. Ability to perform the layout for p and n MOS transistors and to integrate them to construct simple digital gates. Ability to use the MOSIS standard cells to construct digital subsystems Ability to use the auto routing tools to implement CMOS chips. |
Assessment Tools | Course assignments related to the course material Mid-term exam to evaluate students understanding to the material covered in the course Final project to assess students competence of the use of VLSI tools and the standard cells package Project paper to assess students in conducting research in the state-of-the-art topics in the VLSI field |
ECE 517 Advanced Industrial Drives and Motor Control
Catalog Data
(Revised 2006-03) | Prerequisite: EE graduate stand; AE510 for non-EE students (3) This is an advanced course on power electronics and electric drives. Motor drives including DC, induction, synchronous and reluctance; industrial and residential application of power electronics; practical aspects of the design of power electronics devices including heat sink design and magnetic components design. Three lecture hours per week. |
Textbook | Toliyat, and Campbell, DSP based electromechanical motion control, CRC Press, 2004 |
Coordinators | Profs. Chris Mi, Electrical and Computer Engineering |
Prerequisites by Topic | Introductory electronics Introductory electric circuit theory |
Topics | General review (3 hours) Review of power electronics and electrical machines (6 hours) DC motor drives (6 hours) Induction motor drives (6 hours) Permanent magnet synchronous motor drives (6 hours) industrial and residential applications ((6 hours) Heat sink and other practical design issues (6 hours) Exam (3 hours) |
Laboratory projects: | Design projects involving the design, simulation of induction motor vector control and PM motor control using Matlab/Simulink, implorer, and implementation in the laboratory using dSPACE. |
Computer Usage | Simulation using Simulink, Simplorer, dSPACE, etc. |
Course Objectives | A good knowledge of induction motor control A good understanding PM motor control Industrial and residential applications of industrial electronics An understanding of the emerging technologies related to EV |
Course Outcomes | Ability to perform analysis of power electronics circuits using the basic principles. Ability to design, model, and simulate the performance of power electronics circuits in application areas of m induction motor control, PM motor control, and other residential and industrial applications using engineering tools such as Simulink and Simplorer Ability to implement control of induction and PM motors using rapid prototyping tools such as dSPACE and Simulink Ability to program a DSP device for the purpose of motor control and other application using industrial electronics circuits. |
| Course assignments related to the course material covered in lectures. Mid-term and final exams to assess the student competence of the material covered. Computer simulation and design project to assess the students capability to use the knowledge | |
Other references | Chan, "Modern Electric Vehicle Technology", Oxford 2002 Technical publications and papers Gao, Gay, and Emadi, "Modern Electric, Hybrid Electric and Fuel Cell Vehicles: Fundamentals, Theory and Design", CRC Press 2003 |
ECE 519 Advanced Topics in EMC
Catalog Data
| Prerequisite: Graduate Standing. (3) This course covers the EMC requirements and EMC test methods for large systems. Examples involving various types of applications (automotive, communications, computers) will be discussed. Discussion of design practices used in large installation, including component segregation, cable routing, connectors, grounding, shielding, common impedance coupling, ground planes, screening and suppression. Classification of electromagnetic environments will also be discussed. Three lecture hours per week. |
Textbook | Clayton R. Paul, "Introduction to Electromagnetic Compatibility", 2nd edition, 2006, Wiley-Interscience |
Coordinators | Prof. Chris Mi, Electrical and Computer Engineering |
Prerequisites by Topic | Introduction to electromagnetic - Physics 151 (or Introduction to EMC - ECE 319) Introduction to electrical circuits. |
Topics | EMC requirements for systems (3 hours) Analysis techniques for EMC (6 hours) Allocation of EMC requirements to components (2 hours) System level issues/characteristics (6 hours) "Design for EMC' methodology (6 hours) EMC Test methods (3 hours) EMC data review methodology (2 hours) Circuit/system corrective actions for EMC (8 hours) Demonstration of EMC corrective actions (3 hours) Exams (3 hours) |
Laboratory projects: | One and two-week experiments covering such topics as: Radiated and conducted emissions characteristics and causes. Transient voltage generation and impact upon circuit design. Radiated and conduct immunity (susceptibility) measurements and corrective actions. |
Computer Usage | SPICE analysis of electric circuits to determine EMC characteristics, project reports |
Course Objectives | 1. Proficiency in the analysis of AC, DC, and RF circuits 2. Proficiency in the construction, testing and verification of circuits. 3. Proficiency in the use of electronic equipment including power supplies, signal generators, oscilloscopes, spectrum analyzers, and test antennas. |
Course Outcomes
| Ability to analyze AC and RF circuits using basic circuit theory and mesh/node analysis techniques. Ability to evaluate system impedance coupling characteristics. Ability to determine equivalent circuits and antenna systems. Ability to evaluate radiated emissions characteristics analytically and experimentally. Ability to analyze source of immunity (susceptibility) issues. Ability to use SPICE to model component parasitic elements. Ability to use electronic instruments to measure and test RF circuit characteristics. Ability to design a simple circuit to meet EMC requirements and write project report. |
Assessment Tools | 1. Exams and frequent quizzes 2. Reports from laboratory and project assignments 3. Instructor will conduct several informal course evaluations during the term of the course and use student feedback to enhance the course. |
ECE 525 Multimedia Data Storage and Retrieval
Catalog Data
| Prerequisite: graduate standing (3) This course will cover the fundamental concepts and techniques used in multimedia data, storage and retrieval including storage and retrieval images, videos, audio and text documents. Selected multimedia applications will be discussed and students will be required to work on a project related to multimedia applications such as advertising and marketing, education and training, entertainment, medicine, surveillance, wearable computing, biometrics, and remote sensing. Three lecture hours per week |
Textbook | 1. Fundamentals of Multimedia, By Ze-Nian Li and Mark S. Drew, Prentice Hall, 2003 2. Storage and Retrieval: An Algorithmic Approach, Jan Korst, Verus Pronk, Wiley and Sons, 2005 |
Coordinators | Prof. Y. L. Murphey, Electrical and Computer Engineering |
Prerequisites by Topic | linear algebra, discrete mathematics, probability theory |
Topics | Introduction to Introduction to multimedia data ( 3 hours) Multimedia data storage ( 9 hours) Images data retrieval ( 6 hours) Video data retrieval ( 6 hours) Audio data retrieval (6 hours) Text document retrieval (6 hours) Project presentations (4.5 hours) Exams (1.5 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | high level computer programming language, web search and web programning |
Course Objectives | 1. Have a good understanding of the principles and fundamentals of multimedia data storage, indexing, searching and retrieval 2.Learn the major techniques used in multimedia information storage and retrieval 3. capable of applying the learnt technology to various real world multimedia application problems. |
Course Outcomes
| Have a good knowledge on the multimedia information data: images, videos, audios, web documents, etc. Ability to understand important algorithms for multimedia storage Ability to understand the major techniques in multimedia information retrieval Ability to apply learnt technology to a real world application problem |
Assessment Tools | 1. Exams (1, 2, 3) 2. research project and presentations (3, 4) |
ECE 5251: Multimedia Design Tools I
Catalog Data | Prerequisite: graduate standing. (3) This course will introduce students to multimedia design tools. Basic concepts of digital images will be reviewed, such as resolution and color theory. Various methods of image editing and enhancement will be covered including masks, gradients, filters, and image compositing. The basic concepts of vector graphics will be introduced, including Bezier curves, groups, and symbols. Also, basic concepts in fonts and print media will be discussed. Basic design principles will be utilized through the course, such as grid schemes, layout, color mixing. Part of the coursework involves a project of communicating technical information. Three lecture hours per week |
Textbook | Course materials will be available from the instructor |
Coordinator | P. Watta |
Prerequisites by Topic | Computer programming (Matlab or C). |
Topics | 1. Introduction to digital images (1 week) 2. Color Theory (1 week) 3. Channels, Masks, and Image Compositing (3 weeks) 4. Gradients, Filters, and Effects (1 week) 5. Image Enhancement (1 week) 6. Bezier Curves and Vector Graphics (2 weeks) 7. Grids Schemes and Design Basics (2 weeks) 8. Fonts, Text Layout, and Printer Parameters (2 weeks) 9. Information Graphics and Technical Communication (1 week) 10. Design Project (1 week) |
Computer Usage | Engineering software (Matlab). Design software (Adobe Creative Suite) |
Course
Objectives | 1. Become familiar with multimedia design tools. 2. Learn principles of layout and design. 3. Explore effective strategies for information graphics and technical communication. |
Course
Outcomes | 1. Students will be familiar with the basic image editing tools. 2. Students will be able to edit and composit images effectively. 3. Students will be able to create and edit vector graphics using Bezier curves (pen tool). 4. Students will be able to layout and organize technical information in a variety of ways for both computer and print media. 5. Students will learn design principles and best practices. |
Assessment Tools | 1. In-class tests 2. Computer assignments (in-class and take-home). 3. Design project |
ECE 5252: Multimedia Design Tools II
Catalog Data | Prerequisite: ECE 5251. (3) This course will introduce students to multimedia design tools for dynamic media (video and audio). Basic concepts of digital video will be reviewed, such as resolution and compression standards. Various methods of video editing and enhancement will be covered. Basic timeline operations and animation techniques will be covered. Both MPEG and web-based techniques will be covered. Part of the coursework involves a project of communicating technical information. Three lecture hours per week |
Textbook | Course materials will be available from the instructor |
Coordinator | P. Watta |
Prerequisites by Topic | Image editing, vector graphics, computer programming (Matlab or C). |
Topics | 1. Introduction to digital video (1 week) 2. Light and color theory (1 week) 3. Basic video editing (1 week) 4. Effects (1 week) 5. Basic animation: key frames, position, rotation, opacity (2 weeks) 6. Advanced animation: masks (3 weeks) 7. Animation for web and user interaction (3 weeks) 8. Basics of sound editing for video (1 week) 9. Rendering and compression (1 week) 10. Design Project (1 week) |
Computer Usage | Engineering software (Matlab). Design software (Adobe Digital Video Suite), Flash and Actionscript |
Course
Objectives | 1. Become familiar with multimedia design tools. 2. Learn principles of animation and motion graphics. 3. Explore effective strategies for information graphics and technical communication. |
Course
Outcomes | 1. Students will be familiar with the basic video editing tools. 2. Students will be able to edit and composit video effectively. 3. Students will be able to create and edit animations. 4. Students will continue to learn strategies for effective design and layout. 5. Students will complete a design project. |
Assessment Tools | 1. In-class tests 2. Computer assignments (in-class and take-home). 3. Design project |
ECE 521 Advanced Topics in EMC
Catalog Data
| Prerequisite: Graduate Standing. (3) This course covers the EMC requirements and EMC test methods for large systems. Examples involving various types of applications (automotive, communications, computers) will be discussed. Discussion of design practices used in large installation, including component segregation, cable routing, connectors, grounding, shielding, common impedance coupling, ground planes, screening and suppression. Classification of electromagnetic environments will also be discussed. Three lecture hours per week. |
Textbook | Clayton R. Paul, "Introduction to Electromagnetic Compatibility", 2nd edition, 2006, Wiley-Interscience |
Coordinators | Prof. Chris Mi, Electrical and Computer Engineering |
Prerequisites by Topic | Introduction to electromagnetic - Physics 151 (or Introduction to EMC - ECE 319) Introduction to electrical circuits. |
Topics | EMC requirements for systems (3 hours) Analysis techniques for EMC (6 hours) Allocation of EMC requirements to components (2 hours) System level issues/characteristics (6 hours) "Design for EMC' methodology (6 hours) EMC Test methods (3 hours) EMC data review methodology (2 hours) Circuit/system corrective actions for EMC (8 hours) Demonstration of EMC corrective actions (3 hours) Exams (3 hours) |
Laboratory projects: | One and two-week experiments covering such topics as: Radiated and conducted emissions characteristics and causes. Transient voltage generation and impact upon circuit design. Radiated and conduct immunity (susceptibility) measurements and corrective actions. |
Computer Usage | SPICE analysis of electric circuits to determine EMC characteristics, project reports |
Course Objectives | 1. Proficiency in the analysis of AC, DC, and RF circuits 2. Proficiency in the construction, testing and verification of circuits. 3. Proficiency in the use of electronic equipment including power supplies, signal generators, oscilloscopes, spectrum analyzers, and test antennas. |
Course Outcomes
| Ability to analyze AC and RF circuits using basic circuit theory and mesh/node analysis techniques. Ability to evaluate system impedance coupling characteristics. Ability to determine equivalent circuits and antenna systems. Ability to evaluate radiated emissions characteristics analytically and experimentally. Ability to analyze source of immunity (susceptibility) issues. Ability to use SPICE to model component parasitic elements. Ability to use electronic instruments to measure and test RF circuit characteristics. Ability to design a simple circuit to meet EMC requirements and write project report. |
Assessment Tools | 1. Exams and frequent quizzes 2. Reports from laboratory and project assignments 3. Instructor will conduct several informal course evaluations during the term of the course and use student feedback to enhance the course. |
ECE 526 Multimedia Communication Systems
Catalog Data
| Prerequisite: Graduate Standing. (3) Object of this course is to introduce current techniques in multimedia communications. This course will cover in-depth study of existing multimedia compression standards such as, MPEG, JPEG, JPEG2000, etc. The course will introduce the basic issues in multimedia communications and networking and is designed to give the student hands-on experience in various aspects of multimedia communications through the various assignments and projects. Three lecture hours per week. |
Textbook | There is no single textbook that covers all topics of interest. Some chapters from the reference texts will be used as the core material. Some journal and magazine articles will also be used as part of the regular course material. Reference Texts: C-H Wu and J.D. Irwin, "Emerging Multimedia Computer Communication Technologies," Prentice-Hall: Upper Saddle River, New Jersey, 1998 Fred Halsall, "Multimedia Communications Applications, Networks, Protocols and Standards," Addison Wesley, 2001, ISBN 0-21-39818-4 K.R. Rao and Z.S. Bojkovic, "Packet Video Communications over ATM Networks," Prentice-Hall, 2000, ISBN: 0-13-011518-5 |
Coordinators | Prof. H. Malik, Electrical and Computer Engineering |
Prerequisites by Topic | Introductory linear algebra, calculus, basic probability Expertise in C, C++ or MATLAB will be assumed |
Topics | Introduction to multimedia communications (2 hours) Multimedia information representation - text, images, audio, video; (3 hours) Introduction to Source coding, channel coding (3 hours) Multimedia compression standards (7 hours) Standards for multimedia communications (4 hours) Transmission protocols (5 hours) Wireless networks (4 hours) Television (3 hours) Videoconferencing (3 hours) Applications: Business Entertainment Medicine Education (3 hours) Project presentations (3 hours) Exams (2 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | Computer programming , web search can be used for the class project |
Course Objectives | 1. Have a good understanding of the multimedia communication systems 2. knowledge of multimedia compression standards 3. hand-on experience of multimedia compression and communications techniques 4. capable of conducting research in multimedia systems and apply multimedia compression and communication techniques to various real-world applications |
Course Outcomes
| Ability to understand the multimedia systems Ability to understand basics of source coding, data compressions and its applications to multimedia compression Ability to design multimedia system for a given application problem Ability to design multimedia processing algorithm for a given multimedia system Ability to apply advanced multimedia processing tools to develop efficient multimedia communication system Ability to do conduct research in a specific area of multimedia systems and communication |
Assessment Tools | 1. Biweekly homework assignments (programming and theory): 20% 2. Two open book/notes test: 40% 3. Design project and presentation: 40% |
ECE 527 Multimedia Security and Forensics
Catalog Data
| Prerequisite: Graduate Standing. (3) Object of this course is to introduce current techniques information security in general and multimedia security in particular. This course will cover existing information hiding techniques such as digital watermarking, steganography, and fingerprinting. The course will also cover basics of cryptography and coding theory. This course will cover the basic issues in multimedia security and forensics and is designed to give the student hands-on experience in various aspects of information security and forensic analysis through the various assignments and projects. Three lecture hours per week. |
Textbook | There is no single textbook that covers all topics of interest. Some chapters from the reference texts will be used as the core material. Some journal and magazine articles will also be used as part of the regular course material. Reference Texts: I. Cox, M. Miller, J. Bloom, J. Fridrich, and T. Kalker, "Digital Watermarking and Steganography," 2nd Ed. Morgan Kauffman, 2007, ISBN 0-12-372585-2 |
Coordinators | Prof. H. Malik, Electrical and Computer Engineering |
Prerequisites by Topic | Introductory linear algebra, calculus, basic probability, communication theory Expertise in C, C++ or MATLAB will be assumed |
Topics | Introduction to information security (2 hours) Introduction to human perceptual systems - human audio/visual system (3 hours) Secure communication - capacity, security, and robustness analysis (6 hours) Introduction to traditional data protection tools - cryptography, hashing (7 hours) Information hiding - digital watermarking, fingerprinting, steganography (8 hours) Steganalysis (3 hours) Digital forensics (5 hours) Case study: Digital Rights Management (DRM), Digimark (3 hours) Project presentations (3 hours) Exams (2 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | Computer programming, web search can be used for the class project |
Course Objectives | 1. Have a good understanding of the information security 2. knowledge of existing state-of-the-art in information security 3. hand-on experience to develop tools for secure communication and multimedia forensics 4. capable of conducting research in the areas of information security and digital forensics |
Course Outcomes
| Ability to understand the information security Ability to understand basics of cryptography, DRM Ability to using source and channel coding tools for secure communication Ability to design and develop tools for digital forensic analysis Ability to apply emerging information security tools to develop a secure communication system Ability to do conduct research in a specific area of information security and digital forensic analysis |
Assessment Tools | 1. Biweekly homework assignments (programming and theory): 30% 2. One open book/notes test: 30% 3. Design project and presentation: 40% |
ECE 529: Introduction to Computer Music
Catalog Data | Prerequisite: Graduate standing or consent of instructor. This course will introduce students to methods and technologies of computer music. The basics of digital audio will be covered, including sampling, quantization, and compression standards. Various analysis tools will be covered, including the Fourier transform and windowing techniques. Mathematical models of physical instruments will be introduced. Various sound synthesis strategies will be introduced: wave tables, additive synthesis, subtractive synthesis, frequency modulation, and granular synthesis. |
Textbook | Course materials will be available from the instructor |
Coordinator | P. Watta |
Prerequisites by Topic | Computer programming (Matlab or C). |
Topics | 1. Introduction to digital audio (1 week) 2. Frequency and pitch (1 week) 3. Elementary music theory: scales, modes, time signature (1 week) 4. MIDI (1 week) 5. Audio effects and filters (2 weeks) 6. Physical modeling (2 weeks) 7. Synthesis (3 weeks) 8. Algorithmic composition (2 week) 9. Design Project (1 week) |
Computer Usage | Engineering software (Matlab). Csound |
Course
Objectives | 1. Become familiar with digital audio concepts. 2. Become familiar with the science and mathematics of basic musical concepts 3. Become familiar with audio editing tools and techniques. |
Course
Outcomes | 1. Students will know the features and limitations of various formats for encoding music. 2. Students will be able to mathematically model various aspects of music production. 3. Students will be familiar with various sound synthesis strategies. 4. Students will be able to create original musical instruments and musical scores. 5. Students will complete a design project. |
Assessment Tools | 1. In-class tests 2. Computer assignments (in-class and take-home). 3. Design project |
ECE 531 Intelligent Vehicle Systems
Catalog Data
| Prerequisite: Graduate Standing. (3) The course covers important technologies relevant to intelligent vehicle systems including: systems architecture, in-vehicle electronic sensors, traffic modeling and simulation. Students will design and implement algorithms and simulate driver-highway interactions. Three lecture hours per week |
Textbook | No textbook; Powerpoint presentations will be provided electronically |
Coordinators | Prof. Joe Tolkacz Electrical and Computer Engineering |
Prerequisites by Topic | Basic electrical/electronic circuit knowledge Matlab/Simulink |
Topics | Introduction/Background (3 hours) Intelligent Vehicle Architecture (3 hours) Intelligent Vehicle Subsystems (15 hours) Intelligent Highways (9 hours) Exams (3 hours) |
Laboratory projects: | Not applicable |
Computer Usage | Matlab/Simulink for homework and student projects |
Course Objectives | Proficiency in understanding intelligent vehicle subsystems Proficiency in understanding intelligent highways Proficiency in understanding intelligent vehicle and intelligent highways |
Course Outcomes
| Knowledge of intelligent vehicle systems Knowledge of intelligent highway systems Knowledge of interaction between intelligent vehicles and intelligent highways Knowledge of how to model intelligent vehicle systems and intelligent highway systems |
Assessment Tools | 1. Exams (1-2) 2. Homework (1-3) 3 Project (1) |
ECE 532 Automotive Sensors and Actuators
Catalog Data
| Prerequisite: Graduate Standing. (3) Study of automotive sensory requirements; types of sensors; available sensors and future needs. Study of functions and types of actuators in automotive systems. Dynamic models of sensors and actuators. Integrated smart sensors and actuators. Three lecture hours per week |
Textbook | Automotive Electronics Handbook, 2ndEdition, R. Jurgen, McGraw Hill, 2000 |
Coordinators | Prof. TaeHung Kim of Electrical and Computer Engineering |
Prerequisites by Topic | Basic electrical/electronic circuit knowledge Matlab/Simulink |
Topics | Signal delivery systems (6 hours) Interface circuits (6 hours) Switches, thermistors, and pressure sensors (6 hours) Chassis sensors/actuators (9 hours) Powertrain sensors/actuators (6 hour) Electric motors (6 hours) Exams (3 hours) |
Laboratory projects: | Not applicable |
Computer Usage | Matlab/Simulink for homework and student projects |
Course Objectives | Proficiency in understanding automotive sensors and actuators Proficiency in modeling automotive sensors and actuators |
Course Outcomes
| Knowledge of automotive sensors and actuators Knowledge of interaction between sensors and subsystems on a vehicle Knowledge of variation of automotive sensors and actuators |
Assessment Tools | 1. Exams (1-2) 2. Homework (1-3) 3 Project (1) |
ECE 533 Active Automotive Safety Systems
Catalog Data
| Prerequisite: Graduate Standing. (3) The course covers enabling technologies relevant to active automotive safety systems. The study of such intelligent vehicle systems includes their architecture, sensors, and underlying algorithms. Modeling and simulation will also be covered. Students will design and simulate systems that embody the essential elements. Three lecture hours per week |
Textbook | Automotive Electronics Handbook, 2nd Edition, R. Jurgen, McGraw Hill, 2000 |
Coordinators | Prof. Sridhar Lakshmanan of Electrical and Computer Engineering |
Prerequisites by Topic | Basic electrical/electronic circuit knowledge Matlab/Simulink |
Topics | Techniques for designing a safety critical system (6 hours) Electronic throttle control (6 hours) Brake by wire (3 hours) Steer by wire (2 hours) Shift by wire (1 hour) Air bag and occupant sensing (3 hours) Roll over detection (3 hours) Lane departure (3 hours) Adaptive cruise control (3 hours) Collision avoidance (3 hours) Parking assist (3 hours) Tire pressure monitoring (3 hours) Exams (3 hours) |
Laboratory projects: | Not applicable |
Computer Usage | Matlab/Simulink for homework and student projects |
Course Objectives | Proficiency in understanding active safety systems Proficiency in modeling active safety systems |
Course Outcomes
| Knowledge of active safety systems Knowledge of interaction between active safety systems on a vehicle Knowledge of design process for active safety systems |
Assessment Tools | 1. Exams (1-2) 2. Homework (1-3) 3 Project (1) |
ECE 536 - All Weather Automotive Vision
Catalog Data | Prerequisite: Graduate Standing. (3) Studies of the next generation of active automotive safety systems--intelligent cruise control, lane departure warning, virtual camber, and back-up and blind spot warning systems. Topics include active safety system architecture, enabling technologies for such systems, and future directions. Three lecture hours per week |
Textbook | Course Pack |
Coordinators | Prof. Sridhar Lakshmanan |
Topics | Lane detection (3 weeks) Obstacle detection (3 weeks) Active Vehicle Control (1 weeks) Human Factors (1 weeks) Project (half-term) |
Course projects | In-depth familiarity, analysis, and simulation of a system belonging one of the first four topics |
Computer Usage | Computer simulation of vehicle systems |
Course Objectives | Broad background in all-weather automotive systems In-depth knowledge in at least one of four critical areas Partial specialty in at least one more critical area 3 hour lectures by area experts |
Course Outcomes | Ability to exhibit breath of background in all four critical areas Ability to exhibit in-depth knowledge in at least one critical area Ability to exhibit partial specialty in one other critical area |
Assessment Tools | Bi-weekly term reports Cross grading across topical areas Course project |
ECE 537 Data Mining
Catalog Data
| Prerequisite: Graduate Standing. (3) Introduction to the fundamental concepts of data mining. In depth study of the principles, algorithms, techniques, implementations and applications of data mining, including mining sequential and structured data, stream data, text data, spatiotemporal data, Web data and other forms of complex data. Three lecture hours per week |
Textbook | Jiawei Han and Micheline Kamber, "Data Mining - Concepts and Techniques," 2nd edition, Morgan Kaufmann, 2006 |
Coordinators | Prof. Y. L. Murphey, Electrical and Computer Engineering |
Prerequisites by Topic | Introductory linear algebra, calculus artificial intelligence |
Topics | Introduction to data mining ( 1.5 hours) Data Warehouse management ( 3 hours) Data pre-processing ( 4.5 hours) Data mining primitives, concept description ( 6 hours) Mining frequent patterns and associations (7 hours) Classification and prediction (6 hours) Cluster analysis ( 3 hours) Wed data mining (5 hours) Project presentations (3 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | high level computer programming language, web search can be used for the class project |
Course Objectives | 1. Have a good understanding of the principles and fundamentals of data mining 2. knowledgeable about the major techniques used in various aspects of data mining 3. capable of conducting research in data mining and applying data mining techniques to various applications |
Course Outcomes
| Ability to understand the techniques used in data warehouse management and data pre-processing (i) Ability to design data concept description for a given application problem (iii) Ability to design algorithms for mining association rules (iii) Ability to apply advanced techniques in data mining to various data types (iii) Ability to do conduct research in a specific area of data mining (i) |
Assessment Tools | 1. Exams (1-3) 2. research project and presentations (1-5) |
ECE 539 - Production of Electronic Products
Catalog Data
(Revised 2006) | Prerequisite: Graduate Standing. (3) This course discusses the manufacturing of discrete components, integrated circuits, hybrid circuits and modules, advanced packages, printed circuit boards, optical components, and MEMS products. Special topics on product testing, reliability assurance, accelerated reliability testing, product lifetime models, and automotive environments will also be addressed. The course will be organized as a combination of conventional lectures, workshop-style discussion, and design review sessions. Three lecture hours per week |
Textbook | Microchip Fabrication, 4th edition, Peter Van Zant, McGraw Hill, 2000. |
Coordinators | Profs. Chris Mi and Taehyung Kim |
Prerequisites by Topic | Introductory electric circuit theory Introductory electronics |
Topics | Review of circuit theory and electronics Introduction to microelectronic fabrication Wafer processing technologies: oxidation, photolithography, implant, and deposition Semiconductor devices and integrated circuit formation Yield and factory economics of wafer fabrication Electronic packaging technologies Printed circuit boards, hybrid modules, and assemblies Failure mechanisms and reliability analysis of electronic products Testing and operating environments of electronic products |
Laboratory projects | Various projects involving design of power electronics modules, automotive electronic products, and manufacturing optical electronic products. |
Computer Usage | Simulation using Matlab/Simulink, etc. |
Course Objectives
| A good understanding of microelectronic fabrication. Knowledge of semiconductor devices and integrated circuit formation An understanding of electronic packaging and reliability issues of electronic products. Basic knowledge of maintaining plant and equipment and the plant environment. |
Course Outcomes | Ability to analyze nonlinear electronic circuits using circuit theory and analysis techniques. Ability to analyze manufacturing process of discrete components, ICs, advanced packages, PCBs, and MEMS products. Ability to perform failure mechanism and reliability analysis of electronic products. Ability to specify and use automated test equipments. Ability to consider thermal design issues for power electronic modules. Ability to perform reliability testing for automotive electronic products. |
Assessment Tools | Final exam to assess the student competence of the material covered. Project report and presentation based on selected topics to assess the students capability to use the knowledge |
Other References | 1. Manufacturing Challenges in Electronic Packaging, Y. C. Lee and W. T. Chen, Chapman & Hall, 1998. 2. Failure Modes and Mechanisms in Electronic Packages, P. Viswanadham and P. Singh, Chapman & Hall, 1998. |
ECE 546 Electric Vehicles
Catalog Data
(Revised 2006-03) | Prerequisite: EE graduate stand; AE510 for non-EE students (3) To introduce fundamental concepts and specifications of electric and hybrid vehicles; vehicle design fundamental; motors for electric vehicles; controllers and power electronics; energy sources; engineering impact of electric vehicles and practical design considerations. Three lecture hours per week. |
Textbook | Iqbal Husain, "Electric and Hybrid Vehicles Design Fundamentals" Published by: CRC Press, Boca Raton, Florida, USA, 2003. |
Coordinators | Prof. Chris Mi, Electrical and Computer Engineering |
Prerequisites by Topic | Introductory electronics Introductory electric circuit theory |
Topics | Introduction (3 hours) EV fundamentals (3 hours) Basic simulation of vehicle systems (3 hours) Energy sources (battery) (3 hours) Alternative Energy Sources (3 hours) Hybrid vehicles (3 hours) Electrical motors fundamentals (3 hours) Advanced modeling and simulation of vehicle EV/HEV systems (3 hours) Power electronics converters: (3 hours) The speed control of motors (3 hours) ABS control of EV (3 hours) EV testing and special applications of EV Emerging issues in EV (3 hours) Midterm and Final exam (3 hours) |
Laboratory projects: | Various projects involving the design, modeling and control of electric vehicles. |
Computer Usage | Simulation using Simulink, Simplorer, etc. |
Course Objectives | Knowledge of the basic electric vehicle systems, design and modeling fundamentals A good understanding of EV components such as energy source and energy storage, electric propulsions, etc. An understanding of the emerging technologies related to EV |
Course Outcomes | Ability to perform electric vehicle fundamental designs, such as size of the powertrain, acceleration performance, etc. Ability to model an electric vehicle system using engineering tools such as Simulink and Simplorer Ability to select and design EV components, such as energy storage, electric propulsions systems, etc. Ability to perform systematic analysis of the EV and related environmental issues, society issues, etc. |
| Course assignments related to the course material covered in lectures. Mid-term and final exams to assess the student competence of the material covered. Computer simulation and design project to assess the students capability to use the knowledge | |
Other references | Chan, "Modern Electric Vehicle Technology", Oxford 2002 Technical publications and papers Gao, Gay, and Emadi, "Modern Electric, Hybrid Electric and Fuel Cell Vehicles: Fundamentals, Theory and Design", CRC Press 2003 |
ECE 5462 Electric Aspects of Hybrid Electric Vehicles
Catalog Data
(Revised 2006-03) | Prerequisite: EE graduate stand; AE510 for non-EE students (3) To introduce fundamental concepts and the electrical aspects of HEV, including the fundamentals, design, control, modeling, battery and other energy storage, electric propulsion systems. It covers vehicle dynamics, energy sources, electric propulsion systems, regenerative braking, parallel and series HEV design, and practical design considerations, specifications of hybrid vehicles. Three lecture hours per week. |
Textbook | None. |
Coordinators | Profs. Chris Mi, Electrical and Computer Engineering |
Prerequisites by Topic | Introductory electronics Introductory electric circuit theory |
Topics | Introduction to Hybrid Electric Vehicles HEV Fundamentals HEV Modeling and Simulation Energy Storage for HEV Applications Electric Propulsion for HEV Systems Regenerative Braking of HEV Series HEV Design, Modeling and Control Parallel HEV Design, Modeling and Control A Look into the Current Hybrids Fuel Cell Vehicles Antilock Braking of HEV HEV Testing Emerging Technologies in HEV Topologies, Heavy Hybrids, Diesel Hybrids, Hybrid Locomotive, and Plug-in Hybrid, Military Applications, Reliability, Diagnostics, and Prognostics of HEV Systems Midterm and Final exam |
Laboratory projects: | Projects involving the design, modeling and control of hybrid electric vehicles. |
Computer Usage | Simulation using Simulink, Simplorer, Advisor, etc. |
Course Objectives | Knowledge of the basic hybrid electric vehicle systems, design and modeling fundamentals A good understanding of HEV components such as energy source and energy storage, electric propulsions, etc. An understanding of the emerging technologies related to HEV development |
Course Outcomes | Ability to perform hybrid electric vehicle design, such as size of the powertrain, vehicle performance, etc. Ability to model an electric vehicle system using engineering tools such as Simulink, Simplorer and Advisor, etc. Ability to select and design EV components, such as energy storage, electric propulsions systems, power split, etc. Ability to perform systematic analysis of the EV and related environmental issues, society issues, cost, and other issues, etc. |
| Course assignments related to the course material covered in lectures. Mid-term and final exams to assess the student competence of the material covered. Computer simulation and design project to assess the students capability to use the knowledge | |
Other references | Chan, "Modern Electric Vehicle Technology", Oxford 2002 Technical publications and papers Gao, Gay, and Emadi, "Modern Electric, Hybrid Electric and Fuel Cell Vehicles: Fundamentals, Theory and Design", CRC Press 2003 |
ECE 550 - Communication Theory
Catalog Data | Prerequisite: Graduate Standing. (3) The basic limitations and alternatives for communications signaling are studied, using appropriate mathematical tools. The topics include: review of information measure; random process and vector description of signals and noise; optimum receiver principles; signaling alternatives; channel capacity; block and convolutional coding; waveform estimation concepts. Practical system examples are stresses. Three lecture hours |
Textbook | Digital Communications, S. Haykin |
Coordinators | Prof. Sridhar Lakshmanan |
Topics | Source coding (2 weeks) Channel capacity (2 weeks) Optimal detection (2 weeks) Channel coding (2 weeks) Digital modulation (2 weeks) Inter-symbol interference (1 week) Exams (6 hours) |
Course Objectives | In-depth understanding of the mathematics behind modern communication systems Familiarity with the essential elements of these systems Use computers to model and simulate the systems |
Course Outcomes | Ability to understand in-depth the mathematical elements of modern communications systems Ability to apply these elements to analyzing/designing such systems Ability to use the computer to model and simulate these systems |
Assessment Tools | Homeworks Exams Computer-based "focus" projects |
ECE 552 Fuzzy Systems
Catalog Data
| Prerequisite: Graduate Standing. (3) A study of the concept of fuzzy set theory including operations on fuzzy sets, fuzzy relations, fuzzy measures, fuzzy logic, with an emphasis on engineering applications. Topics include fuzzy set theory, applications to image processing, pattern recognition, artificial intelligence, computer hardware design, and control systems. Three lecture hours per week |
Textbook | John Yen and Reza Langari, Fuzzy Logic - Intelligence, Control, and Information, 1999 by Prentice-Hall, Inc. |
Coordinators | Prof. A. Shaout, Electrical and Computer Engineering |
Prerequisites by Topic | Introductory complex algebra, calculus Introduction to control theory |
Topics | The Theory of Fuzzy subsets ( 6 hours) Operations on Fuzzy Subsets ( 3 hours) Fuzzy Relations and graphs ( 6 hours) Fuzzy measures ( 4 hours) Fuzzy Logic (3 hours) Fuzzy Applications - AI and Expert Systems ( 3 hours) Pattern Recognition ( 3 hours) Fuzzy Controllers ( 8 hours) Research paper presentations (3 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | MatLab or any other high level computer programming language can be used for the class project |
Course Objectives | 1. Knowledgeable with the fuzzy subset theory and applications 2. Design fuzzy controllers 3. Be able to conduct research in contemporary and current applications using the fuzzy logic |
Course Outcomes
| Ability to understand and analysis fuzzy systems Ability to design and analysis fuzzy rules Ability to design and analysis fuzzy metric measures Ability to design a fuzzy controller through a project related automotive applications Ability to do research in the area of fuzzy logic |
Assessment Tools | 1. Exams (1-3) 2. Project report (4-5) 3. Research paper presentations (1-5) |
ECE 554 Embedded Systems
Catalog Data
| Prerequisite: Graduate standing. (3) Survey of real time, sampled data systems and embedded applications, e.g. digital controllers, diagnostic systems. Principles and characteristics of embedded micro- processors: processor/device interfaces: time critical I/O handling: data communications in embedded environments. Overview of embedded operating systems, cross-development techniques & tools. Design of real time systems. The software life Cycle. Embedded specification and design techniques. Real Time Kernels. Multi-Tasking. Real Time Memory management. Performance Analysis. Reliability & Fault Tolerance. Project oriented course. Three lecture hours per week |
Textbook | P. A. Laplante, Real-time Systems Design and Analysis, 3rd edition, ISBN 0-471-22855-9, IEEE PRESS/WILEY-INTERSCIENCE, 2004. |
Coordinators | Prof. A. Shaout, Electrical and Computer Engineering |
Prerequisites by Topic | Fundamentals of logic design and computer organization The knowledge of any programming language |
Topics | Introduction - Real Time Concepts ( 3 hours) Computer Hardware basic issues ( 3 hours) Computer language issues ( 3 hours) The software life Cycle ( 3 hours) Embedded specification and design techniques (6 hours) Real Time Kernels ( 3 hours) Multi-Tasking ( 3 hours) Real Time Memory management ( 3 hours) Performance Analysis (3 hours) Reliability & Fault Tolerance (3 hours) Embedded Systems Applications (3 hours) Research paper presentations (3 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | Students can use an assembly or high level computer language for their class project |
Course Objectives | 1. Knowledge of real time and embedded systems 2. Design of embedded controllers 3. To analysis real time systems 4. To design reliable and fault tolerant embedded systems 5. Be able to conduct research in contemporary and current applications of real time embedded systems |
Course Outcomes
| Ability to understand and analysis real time systems through software process methods Ability to design and analysis reliable and fault tolerant embedded systems Ability to design and analysis multithreaded embedded systems Ability to design an embedded real time system through a class project Ability to do research in the area of real time systems |
Assessment Tools | 1. Exams (1-3) 2. Project report (4-5) 3. Research paper presentations (1-5) |
ECE 560 - Modern Control Theory
Catalog Data
(Revised 2001-03) | Prerequisite: Graduate Standing. (3) Introduction to linear spaces and operators; mathematical description of multiple input-output systems; state variables and state transition matrix; controllability and observability and its application to irreducible realization of transfer function matrices; state variable estimation; controller synthesis by state feedback; stability of linear systems; analysis of composite systems. Three lecture hours per week. |
Textbook | C. T. Chen "Linear System Theory and Design," HRW Inc. |
Coordinators | Profs. N. Natarajan and M. Shridhar, Electrical and Computer Engineering |
Prerequisites by Topic | Classical Control Theory Analog Signals and Systems Concepts |
Topics | Basic system concepts, transfer functions, convolution (3 hours) Linear Spaces and Linear Operators (2 hours) Vectors, Matrices, Eigen Values, Eigen Vectors (2 hours) Functions of matrices, Caley-Hamilton's theorem (3 hours) Introduction to state variable methods (4 hours) Analysis of stability, observability and controllability (6 hours) Design of Observer (4 hours) Design with eigen value placement (6 hours) Controller design using observer and state variable feedback (6 hours) Case Studies (3 hours) Exams (3 hours) |
Laboratory projects: | Projects implemented in Matlab environment |
Computer Usage | Extensive use of Matlab and associated tool boxes |
Course Objectives | 1. Proficiency in the state space analysis of linear systems 2. Proficiency in the construction, testing and design of observers and controllers 3. Proficiency in the use of Matlab for analysis and design using state variable methods |
Course Outcomes
| Ability to describe linear systems in state space Ability to analyze linear systems in state space Ability to design an observer for generating estimates of system state Ability to design a controller with eigen value placement Ability to utilize an observer in the implementation of controller with state variable feedback Ability to use Matlab to analyze linear systems |
Assessment Tools | 1. Exams and frequent quizzes (1-5) 2. Reports from laboratory and project assignments (1-8) 3. Instructor will conduct several informal course evaluations during the term of the course and use student feedback to enhance the course. |
ECE 565 - Digital Control Systems
Catalog Data
(Revised 2001-03) | Prerequisite: Graduate Standing. (3) Mathematical representation of digital control systems; z-transform and difference equations; classical and state space methods of analysis and design; direct digital control of industrial processes. Three lecture hours per week. |
Textbook | M. S. Santina, A. R. Stubberud, G. H. Hostetter"Digital Control System Design," Saunders College Publishing Inc. |
Coordinators | Profs. N. Natarajan and M. Shridhar, Electrical and Computer Engineering |
Prerequisites by Topic | Classical Control Theory Signals and Systems Concepts |
Topics | Basic system concepts, transfer functions, convolution (2 hours) Overview of z-transform and propeties (2 hours) Analysis of discrete-time systems and difference equations (3 hours) Linear Spaces and Linear Operators (2 hours) Vectors, Matrices, Eigen Values, Eigen Vectors (2 hours) Functions of matrices, Caley-Hamilton's theorem (3 hours) Introduction to state variable methods in discrete-time (4 hours) Analysis of stability, observability and controllability (4 hours) Design of Observer (4 hours) Design with eigen value (pole) placement (4 hours) Controller design using observer and state variable feedback (6 hours) Case Studies (3 hours) Exams (3 hours) |
Laboratory projects: | Projects implemented in Matlab environment |
Computer Usage | Extensive use of Matlab and associated tool boxes |
Course Objectives | 1. Proficiency in the state space analysis of linear systems 2. Proficiency in the construction, testing and design of observers and controllers 3. Proficiency in the use of Matlab for analysis and design using state variable methods |
Course Outcomes
| Ability to describe discrete-time linear systems in state space Ability to analyze linear discrete-time systems in state space Ability to design an observer for generating estimates of system state Ability to design a controller with eigen value placement Ability to utilize an observer in the implementation of controller with state variable feedback Ability to use Matlab to analyze linear systems |
Assessment Tools | 1. Exams and frequent quizzes (1-5) 2. Reports from project assignments (1-8) 3. Instructor will conduct several informal course evaluations during the term of the course and use student feedback to enhance the course. |
ECE 567: Nonlinear Control Systems
Catalog Data | Prerequisite: Graduate Standing. (3) Nonlinearities in control systems; phase plane analysis; isoclines, equilibrium points, limit cycles, optimum systems; heuristic methods; harmonic balance, describing function, frequency response and jump phenomena, oscillations in relay systems; state space; optimum relay controls; stability; Liapunov's method. Three lecture hours per week. |
Textbook | Hassan K. Khalil, Nonlinear System, |
Coordinator | N. Natarajan, Electrical and Computer Engineering |
Prerequisites by Topic | Undergraduate course in linear algebra Calculus through differential equations |
Topics | Nonlinear phenomena Second order systems and phase plane analysis Linearization and stability characterization in terms of eigenvalues Higher order systems and Lyapunov's indirect method Lyapunov direct method Region of attraction Absolute stability and circle criteria Describing functions Feedback linearization Lyapunov redesign Sliding mode control |
Laboratory projects | None |
Computer Usage | As a computational aid |
Course Objectives | An in-depth treatment of nonlinear systems |
Course Outcomes | Ability to model non-linear systems Ability to analyze non-linear systems for both stability and performance Control design taking into account nonlinear effects. |
Assessment Tools | Home-work Mid-term and final exams An open-ended final project |
ECE 570 Computer Networks and Data Communications
2004 - 2005 Catalog Data | Prerequisite: Graduate Standing. (3) An examination of existing and emerging networks, LANs, WANs, emerging standardization efforts; computer and telecom architecture will be contrasted to existing products such as Ethernet, MAP, TOP. Three lecture hours per week. |
Textbook | Halsall, "Data Communications, Computer Networks, and Open Systems," Addison Wesley, 1997 |
Coordinators | Prof. Paul Richardson |
Prerequisites by Topic | Knowledge of a high level programming language (preferably C). Logic design. |
Topics | Introduction to ISO/OSI standard models (1 hours) Data transmission media and signal format (4 hours) Modulation and demodulation (4 hours) Communication Protocols (4 hours) Data Link Layer Protocols (4 hours) Error control and Flow Control (5 hours) Local area network, topology, hardware and MAC (8 hours) Internetwork Protocols (3 hours) Transport Protocols (3 hours) Overview of wireless communications (3 hours) Overview of Embedded Networks and Network Design (2 hours) Exams (5 hours) |
Laboratory projects | Term Project or Report Required |
Course Objectives | A good understanding of data communications and computer networks. Understanding fundamental concepts for data communications including signaling issues and data transmission issues. Understanding of local area networks, inter-networking issues, and their impact on wireless communications systems |
Course Outcomes
| An ability to use the techniques, skills, and modern engineering tools necessary for understanding engineering practice (c) An ability to recognize a problem, formulate different strategies to understand the problem, and use engineering principals to solve them. (b) |
Assessment Tools | Final Exam and Mid Term Exam Quizzes in between exams |
ECE 5701 Special Topics: Introduction to Wireless Communications
2004 - 2005 Catalog Data | Prerequisite: Graduate Standing. (3) Prerequisite by course: ECE 550 or ECE 570, and Matlab Programming A basic introduction to modern wireless communication principles and architectures. Channel models, signal generation and reception are explored. Examples of current protocols and architectures of wireless data and voice networks are studied. Self guided lab assignments. A project is required. Three lecture hours per week. |
Text Book | Required: Theodore S. Rappaport, "Wireless Communications: Principles and Practice", Second Edition, Prentice Hall Reference: Modern Wireless Communications, Simon Haykin, Prentice Hall |
Coordinators | Profs. Paul Richardson and Weidong Xiang |
Prerequisites by Topic | Probability and Statistics Signals representations and classifications, Frequency response, Fundamentals of Data Communication at the physical and link layer |
Topics | Overview of Wireless Communications (1 hours) Large Scale Channel Propagation Models (5 hours) Small Scale Channel Propagation and Fading (5 hours) Modulation and Demodulation Techniques (6 hours) Overcoming Channel Impairments- Adaptive Equalizers, Diversity Techniques, Fundamentals of Channel Coding (4 hours) Overview of Wideband Systems: Direct Sequence Spread Spectrum, Frequency Hopping (4 hours) Applications, such as, Bluetooth, IEEE 802.11, GSM (5 hours) Miscellaneous wireless technologies (2 hours) Wireless systems and software architectures (3 hours) Project/Term Paper presentations (3 hours) Exams (5 hours) Total 43 hours |
Lab and Projects | Requires student projects or term paper |
Computer Usage | Modeling tools, such as, MATLAB and a high level programming language to model and simulate wireless communications techniques and architectures. |
Course Objectives
| 1. A basic introduction to wireless communications systems 2. An overview of current practices in wireless voice and data network design |
Course Outcomes
| A strong background in mathematics and physical sciences and a good understanding of their importance to electrical and computer engineering disciplines (a) An ability to use the techniques, skills, and modern engineering tools necessary for understanding engineering practice (c) |
Assessment Tools
| Midterm, Final, exams Matlab laboratory assignments Course project, demonstrations, and oral presentations |
ECE 5702- High-speed and Advanced Networks
Catalog Data | Prerequisite: Graduate Standing. (3) This course deals with the high-speed and advanced networks concepts, protocols, and architectures. The course emphasis on ATM networks, IPv6, Internetworking, and wireless sensor networks. This course has assignments, midterm exam, and final Exam and project. Three lecture hours per week. |
Textbook | Research papers selected by the instructor. |
Coordinator | A. El Kateeb, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Data communications and Networks 2. Computer Architecture |
Topics | Communications concepts Traditional telecommunications networks The converged voice/data networks Broadband ISDN SONET Frame relay ATM concepts ATM physical layer ATM adaptation layer ATM applications IPv6 protocol Internetworking and SS7 Wireless sensor networks |
Laboratory projects | Various projects involving networks protocols programming and sub-network architecture and design. |
Computer Usage | Networks simulators such as NS-2 and TOSSIM. |
Course Objectives | A good understanding to different networks protocols A good understanding to high-speed networks architecture Hand-on experience on wireless sensor networks. |
Course Outcomes | Ability to understand the high-speed networks protocols. Ability to analyze the sensor networks nodes architecture for different applications. Ability to design an ATM networks and sensor networks. |
Assessment Tools | Course assignments related to the course material Mid-term exam to evaluate students understanding to the material covered in the course Final exam to evaluate students overall performance in the course. Final project to assess students' competence in designing sub-system of the high-speed or wireless sensor networks. |
ECE 571 Switching Theory
Catalog Data
| Prerequisite: Graduate Standing. (3) Combinational and sequential logic design, minimization of combinational and sequential circuits, functional decomposition, reliable design and fault diagnosis; incompletely specified sequential machine design, asynchronous sequential circuits and iterative methods. Three lecture hours per week |
Textbook | Switching theory for logic synthesis by Sasao and the ISB# is 07923-8456-3. Recommended Text: Kohavi, "Switching and Finite Automata Theory", Second Edition, McGraw-Hill, 1978 |
Coordinators | Prof. A. Shaout, Electrical and Computer Engineering |
Prerequisites by Topic | Fundamentals of logic design |
Topics | Combinational Logic ( 3 hours) Minimization of Switching Functions ( 6 hours) Logic Design - Applications ( 3 hours) Reliable Design and Fault Diagnosis ( 6 hours) Functional Decomposition (3 hours) Synchronous Machines - Completely Specified ( 3 hours) Incompletely Specified Machines ( 6 hours) Asynchronous Machine Design (6 hours) Research paper presentations (3 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | Students can use a high level computer language for their class project |
Course Objectives | 1. To design combinational and sequential logic circuits 2. Design of reliable combinational logic circuits 3. To analysis sequential logic circuits 4. Optimization techniques for combinational and sequential logic circuits 5. Be able to conduct research in contemporary and current applications of switching theory |
Course Outcomes
| Ability to design and analysis combinational logic circuits Ability to design and analysis sequential logic circuits Ability to optimize a combinational and sequential logic design Ability to do research in the area of switching theory |
Assessment Tools | 1. Exams (1-3) 2. Project report (4) 3. Research paper presentations (1-4) |
ECE 572 Sequential Machines
Catalog Data
| Prerequisite: Graduate Standing. (3) Theoretical aspects and algebraic structure of sequential machines. Characterization of complete and incomplete machines, decomposition and state assignment problems. Deterministic and nondeterministic finite state machine identification. State-identification and fault-detection experiments. Three lecture hours per week |
Textbook | "Switching and Finite Automata Theory", Second Edition, McGraw-Hill, 1978 |
Coordinators | Prof. A. Shaout, Electrical and Computer Engineering |
Prerequisites by Topic | Combinational and sequential machine design State assignment State reduction Completely and incompletely specified machines |
Topics | Mathematical Background ( 6 hours) Review of Synchronous Sequential Machines ( 3 hours) Structure of Sequential Machines ( 9 hours) State Identification and Fault Detection ( 6 hours) Finite Memory Machines and Information Losslessness (6 hours) Linear Sequential Machines ( 3 hours) Regular Expressions and Finite State Recognizers ( 3 hours) Research paper presentations (3 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | A high level computer programming language can be used for the class project |
Course Objectives | 1. Design structured sequential machines 2. Design state identification experiments for Finite State Machines (FSM) 3. Design fault detection experiments for FSM 4. Be able to conduct research in contemporary and current applications structured sequential machines |
Course Outcomes
| Ability to design and analysis structured FSM Ability to design state identification experiments for FSM Ability to design fault detection experiments for FSM Ability to design and analysis linear sequential machines Ability to do research in the area of structured FSM |
Assessment Tools | 1. Exams (1-4) 2. Homework (1-4) 2. Project report (5) 3. Research paper presentations (1-5) |
ECE 574 - Adv. Software Techniques in Engr Appl.
Catalog Data | Prerequisite: Graduate Standing. (3) Graduate-level introduction to data structures, high-level engineering analysis languages, hardware description languages, algorithm complexity analysis; engineering applications. Three lecture hours per week |
Textbook | M. A. Weiss, Data structures and Algorithm Analysis,Benjamin/Cummings |
Coordinator | N. Natarajan, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Undergraduate programming experience preferably in C/C++ |
Topics | Concept of algorithms Complexity Analysis, Big Oh notation Comparing Complexity, Polynomial and exponential complexities Time-space tradeoffs Experimental determination of complexity Projects: Linked Lists, Sorting and Searching, Sparse Matrices Graphs, shortest path, minimum cost, network flows Search trees and backtracking algorithms Game programming: Games of strategy, pruning search trees |
Laboratory projects | Projects are done outside the class room |
Computer Usage | Extensive programming and quantitative analysis of time-space complexity |
Course Objectives | A good understanding of advanced programming techniques Analysis of algorithms |
Course Outcomes | Design and analyze algorithms Implement algorithms and measure complexity Knowledge of advanced programming concepts and data structures. |
Assessment Tools | Programming assignments In-class presentation Final report |
ECE 575- Computer Architecture I
Catalog Data | Prerequisite: Graduate Standing. (3) This course deals with the basics of the computer architecture. The course cover the central processing unit architecture, the instructions set design, input/output and RAID, memory and virtual memory. This course has assignments, midterm and final exam, and final project. Three lecture hours per week |
Textbook | Computer Organization and Design by D. Patterson and J. Hennessy, Morgan Kaufman Publishers Inc., latest edition. |
Coordinator | A. El Kateeb, Electrical and Computer Engineering |
Prerequisites by Topic | Digital system design |
Topics | Introduction Instruction set principles Performance evaluation Arithmetic for computers Processor datapath and control Instruction level parallelism Pipelining Memory and cache Input/output and RAID Multiprocessor Advanced topics |
Laboratory projects | Various projects involving with the simulation of different parts of the general-purpose computer architecture. |
Computer Usage | Hardware Description Language (HDL) such as VHDL and Verilog HDL . |
Course Objectives | A good understanding of the non-pipeline architecture A good understanding of the pipeline architecture A good understanding to computers I/O devices, main memory, caches, and multiprocessors architectures. Hand-on experience on HDL languages and their use in computer simulation and design. |
Course Outcomes | Ability to analyze the simple processor instructions set. Ability to understand the RISC and CISC architectures. Ability to analyzer processors data paths and control unit. Ability to understand the basic principles of the Superscalar, VLIW, and multiprocessors architectures. |
Assessment Tools | Course assignments related to the course material Mid-term and final exam to evaluate students understanding to the material covered in the course Project paper to assess students in conducting research in the state-of-the-art computer architecture topics. |
ECE 5751- Advanced Computer Design
Catalog Data | Prerequisite: Graduate Standing. (3) This course deals with the advanced computer architecture and design techniques and tools used in computer design. The pipelined and superscalar processor architecture, memory, and multi-core architecture is discussed. The hardware description language such as VHDL and Verilog HDL will be used in this course. The System-On-chip design approach will be addressed and used in students' final project. This course has assignments, midterm exam, and final project. Three lecture hours per week |
Textbook | Research papers selected by the instructor |
Coordinator | A. El Kateeb, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Computer Architecture 2. Digital system design |
Topics | Processor design pipelined processors Memory and I/O systems Superscalar organization Superscalar techniques Advanced instruction flow techniques Advanced register data flow techniques Hardware Description Languages System-On-Chip |
Laboratory projects | Various projects involving general-purpose computer architecture design and specialized architecture for different applications. |
Computer Usage | VHDL and Verilog HDL languages, FPGA SOC design tools such as Xilinx or Altera |
Course Objectives | 1. A good understanding of the pipeline architecture 2. A good understanding of the Superscalar architecture 3. Hand-on experience in HDL languages and processors design Processor implementation on FPGA SOC chips |
Course Outcomes | 1. Ability to design the processor instructions set. 2. Ability to design processors subsystems such as ALU, Register file, pipeline buffers. 3. Ability to integrate processor subsystems and implement the design on SOC chips. |
Assessment Tools | 1. Course assignments related to the course material 2. Mid-term exam to evaluate students understanding to the material covered in the course 3. Final project to assess students' competence of the use of computer design tools and the processor implementations. 4. Project paper to assess students in conducting research in the state-of-the-art topics in the processors design field |
ECE 5752- Reconfigurable Computing
Catalog Data | Prerequisite: Graduate Standing. (3) This course deals with the advances in reconfigurable computing techniques, design, and research. The techniques and design concepts for reconfigurable computing is discussed. The hardware description language such as VHDL and Verilog HDL will be used in this course. The System-On-chip and network-op-chip design approach will be addressed and used in students' final projects. This course has assignments, and final project. Three lecture hours per week |
Textbook | Research papers selected by the instructor |
Coordinator | A. El Kateeb, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Computer Architecture 2. Digital system design 3. HDL languages |
Topics | Introduction to Reconfigurable Computing Fundamentals of FPGA System-On-Chip (SOC) and Network-On-Chip (NOC) Programming Languages for RC CAD Tools RC design for general-purpose computing RC design for special purpose applications Case studies and design projects |
Laboratory projects | Various projects involving specialized reconfigurable computing engine to enhance the performance of specialized applications and computer subsystems. |
Computer Usage | Xilinx Virtex 5 boards and ISE design tools |
Course Objectives | A good understanding of the reconfigurable computing concept and design. A good understanding of using HDL languages for reconfigurable computing design. Using reconfigurable computing for improve computer performance and applications. |
Course Outcomes | Ability to design a reconfigurable computing system. Ability to apply the reconfigurable computing design approach to improve computer performance and to support the processing required by different applications. |
Assessment Tools | Course assignments related to the course material Final project to assess students' competence in the use of the reconfigurable computing design approach to improve processors and applications performance. Project paper to assess students in conducting research in the state-of-the-art topics for the reconfigurable computing field. |
ECE 576 Information Engineering
Catalog Data
| Prerequisite: Graduate Standing. (3) This course will cover the fundamental concepts of information engineering including computation, storage, communication, and application. Examples of topics are multimedia data such as video, audio, image and text, multimedia transmission through local & wide area networks, multimedia data representations, storage & compression. Information engineering applications will be discussed and students are expected to complete a project in a selected application. Three lecture hours per week |
Textbook | 1. Fundamentals of Multimedia, By Ze-Nian Li and Mark S. Drew, Prentice Hall, 2003 2. Information Theory, Inference, and Learning Algorithms, By David J. C. Mackay, Cambridge University Press, 2005 |
Coordinators | Prof. Y. L. Murphey, Electrical and Computer Engineering |
Prerequisites by Topic | linear algebra, discrete mathematics, probability theory, artificial intelligence |
Topics | Introduction to Introduction to information engineering ( 2 hours) Information theory ( 6 hours) Images, processing and analysis, and applications ( 9 hours) Video encoding, processing and applications ( 6 hours) Audio signal processing and applications (6 hours) Text document processing (7 hours) Project presentations (3 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | high level computer programming language, web search can be used for the class project |
Course Objectives | 1. Have a good understanding of the principles and fundamentals of information theory and information technologies 2. knowledgeable about the major techniques used in processing multimedia information 3. capable of conducting research in information technology and applying various information processing and analysis techniques to solve real application problems. |
Course Outcomes
| Ability to understand important theories in information technology (i) Ability to understand the major techniques in multimedia information processing (i) Ability to design an information processing and analysis system for a given application problem (iii) Ability to do conduct research in a specific area of information technology (i) |
Assessment Tools | 1. Exams (1, 2) 2. research project and presentations (3, 4) |
ECE 577 Engineering in Virtual World
Catalog Data
| Prerequisite: Graduate Standing. (3) An in-depth study of selected topics in design and development of virtual systems in industrial environment. Topics include cyberspaces, techniques for generating virtual world in engineering application, building blocks of virtual environments including hardware and software. Case studies. Three lecture hours per week |
Textbook | 1. Virtual Reality: Interface, Application, and Design, William R. Sherman and Alan Craig, Morgan Kaufmann, 2003. 2. Virtual Reality Technology, Grigore Burdea, Philippe Coiffet , John Wiley 2003 |
Coordinators | Prof. Y. L. Murphey, Electrical and Computer Engineering |
Prerequisites by Topic | linear algebra, computer graphics |
Topics | 1. Introduction to virtual reality ( 2 hours) 2. input and output devices ( 6 hours) 3. computing architecture for virtual engineering (6 hours) 4. Modeling: geometric, kinematics, physical, behavior ( 9 hours) 5. VR programming ( 9 hours) 6. Engineering Applications of VR (4 hours) 7. Project presentations (3 hours) 8. Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | high level computer programming language, web search can be used for the class project |
Course Objectives | 1. Have a good understanding of the fundamentals of virtual reality systems 2. knowledgeable about the major techniques used in virtual reality modeling 3. capable of applying various VR techniques to solve engineering problems. |
Course Outcomes
| Ability to understand the major input and output devices used in VR Ability to understand the modeling techniques used in VR Ability to do VR programming Ability to apply VR technologies to engineering problems. |
Assessment Tools | 1. Exams (1, 3) 2. research project and presentations (3,4) |
ECE 578 Advanced Operating Systems
Catalog Data
| Prerequisite: Graduate Standing. (3) Advanced techniques in operating system design. Distributed operating systems. Message-based operating systems. Operating systems for parallel architectures. Layered techniques in operating systems. Formal models of operating systems. Current trends in operating system design. Three lecture hours per week |
Textbook | Operating Systems Concepts, seventh edition, 2005 by Silberschatz, Galvin, and Gagne |
Coordinators | Prof. Yi L. Murphey, Electrical and Computer Engineering |
Prerequisites by Topic | Introduction to operating systems The knowledge of a high level computer language |
Topics | Processes and threads ( 6 hours) CPU Scheduling (3 hours) Process Synchronization in a distributed system ( 6 hours) Deadlocks a distributed system ( 4 hours) Memory and files in a distributed system (3 hours) Distributed Systems ( 3 hours) Protection and security ( 3 hours) Current trends in operating system design ( 3 hours) I/O systems ( 3 hours) Research paper presentations (5 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | A high level computer programming language will be used for the class homework and class project |
Course Objectives | 1. Design of operating systems for parallel architecture 2. Design of process synchronization for distributed systems 3. Knowledge of current trends in operating systems 4. Be able to conduct research in contemporary and current issues in distributed systems |
Course Outcomes
| Ability to understand and analyze distributed systems (i) Ability to design and analyze process synchronization in a distributed systems (i) Ability to use distributed systems in projects related to real world applications (iii) Ability to conduct research in the area of advanced operating systems (i) |
Assessment Tools | 1. Exams (1-2) 2. Project report (3-4) 3. Research paper presentations (1-4) |
ECE 579 Intelligent Systems
Catalog Data
| Prerequisite: Graduate Standing. (3) This course provides a broad technical introduction and a survey of core concepts of intelligent systems. Topics include: Intelligent system design, training and evaluation, decision trees, rule based systems, Bayesian learning, Support Vector Machines and neural network systems. A project will be required. Three lecture hours per week |
Textbook | 1. Computer Systems That Learn, Sholom M. Weiss and Casimir A. Kulikowski, Morgan Kaufmann, 1991. 2. Machine Learning, Tom M. Mitchell, The McGraw-Hill Companies, Inc., 1997. |
Coordinators | Prof. Y. L. Murphey, Electrical and Computer Engineering |
Prerequisites by Topic | linear algebra, discrete mathematics, probability theory, artificial intelligence |
Topics | Introduction to intelligent systems ( 2 hours) Intelligent system design, training, and evaluation ( 6 hours) Decision trees and rule based systems ( 9 hours) Support Vector Machines ( 6 hours) Neural network systems (6 hours) Bayesian learning (7 hours) Project presentations (3 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | high level computer programming language, web search can be used for the class project |
Course Objectives | 1. Have a good understanding of the principles and fundamentals of intelligent systems 2. knowledgeable about the major techniques used in intelligent system design, train, and evaluation. 3. capable of conducting research in intelligent systems and applying various intelligent systems techniques to solve real application problems. |
Course Outcomes
| Ability to understand the important techniques used in intelligent system design, training, test and evaluation (i) Ability to design an intelligent system for a given application problem (iii) Ability to understand the strengths and weakness of major techniques in intelligent system design. (i) Ability to do conduct research in a specific area of intelligent systems (iii) |
Assessment Tools | 1. Exams (1, 3) 2. research project and presentations (2, 4) |
ECE 580 Digital Signal Processing
Catalog Data
(Revised 2006-03) | Prerequisite: Graduate Standing. (3) The objective of this course is to teach students the analysis and design of discrete-time signals and systems. Students will be made familiar with the mathematical tools needed in the area of digital signal processing such as: the Fourier transform of discrete time signals, the sampling theorem and z-transform method. Topics covered will include the design of digital filters (IIR and FIR filters), characteristics of analog-to-digital and digital-to-analog converters and spectral analysis of signals. Discrete Fourier transform of sequences will also be covered. Three lecture hours per week. |
Textbook | A. Oppenheim, R. Schafer, and J. Buck, "Discrete-time signal processing," Prentice-Hall, 1999. |
Coordinators | Prof Selim S. Awad, Electrical and Computer Engineering |
Prerequisites by Topic | Complex algebra, linear algebra, calculus, Laplace transform and Fourier transform. Fundamentals of signals and systems. |
Topics | Fundamentals of discrete-time signals and systems (6 hours) Fourier transform of discrete-time signals (3 hours) Frequency response of discrete-time systems (3 hours) The z-transform and its applications (6 hours) Processing of continuous-time signals (3 hours) Design of IIR digital filters (6 hours) Design of FIR digital filters (6 hours) Spectral analysis of signals (3 hours) Selected topics (3 hours) Exams (3 hours) |
Laboratory projects: | Projects dealing with the simulation and design of discrete-time and hybrid (discrete-time and continuous-time) systems. |
Computer Usage | Matlab, Simulink, and other digital signal processing software packages. |
Course Objectives | 1. Proficiency in the analysis of discrete-time signals and systems. 2. Proficiency in the design of digital filters. 3. Proficiency in the use of software tools to simulate and design digital signal processing applications. |
Course Outcomes
(Revised Mar. 24, 06) | Ability to analyze discrete-time signals and systems Ability to design digital filters to meet magnitude and phase specifications. Ability to apply signal processing methods to perform spectral analysis on continuous-time signals. Ability to use software tools to simulate and verify the design of digital signal processing application. |
Assessment Tools | 1. Exams and tests quizzes 2. Reports from project assignments 3. Discussions with students throughout the course duration. |
ECE 5802 Multirate signal processing with applications
Catalog Data
| Prerequisite: ECE 580 or equivalent (3). The purpose of this course is to provide an introduction to multirate digital signal processing and their typical applications in different fields of engineering (speech processing, communications, instrumentation, measurements, signal compression, etc). Thus the first part of this course will focus on laying the theoretical foundation for all aspects of multirate digital signal processing. Once the fundamentals are well established, we move on to the second part of the course, which deals with the modern applications of multirate digital signal processing. These, applications include: design of efficient multirate filter banks, using the wavelets transforms in order to efficiently encode signals for signal compression purposes, spectral analysis and synthesis of signals, etc. A final part of the course is devoted to teaching students the necessary software tools to analyze, design and simulate multirate digital signal processing systems. Three lecture hours per week. |
Textbook | P. P. Vaidyanathan, "Multirate Systems And Filter Banks,"Prentice-Hall, 1993. |
Coordinators | Prof Selim S. Awad, Electrical and Computer Engineering |
Prerequisites by Topic | Digital signal processing fundamentals. Continuous-time signals and systems fundamentals. |
Topics | Review of digital signal processing fundamentals (3 hours) Digital filter design methods (3 hours) Fundamentals of multirate systems (6 hours) Multirate filter bank theory (9 hours) Linear phase perfect reconstruction QMF banks (6 hours) Quantization effects (3 hours) The wavelet transform and its relation to multirate filter banks (6 hours ) Selected topics (3 hours) Exams (3 hours) |
Laboratory projects: | Projects dealing with the simulation and design of multirate systems and filter banks. |
Computer Usage | Matlab, Simulink, Digital signal processing toolbox, and relevant software packages. |
Course Objectives | 1. Proficiency in the analysis of continuous-time (analog) signals and systems. 2. Proficiency in the design of analog and switched-capacitor filters. 3. Proficiency in the use of software tools to simulate and design of analog and switched-capacitor filter applications. |
Course Outcomes
| Ability to analyze multirate signals and systems Ability to design multirate systems to meet given specifications Ability to use software tools to simulate and verify the design of multirate systems and their applications. |
Assessment Tools | 1. Exams and tests quizzes 2. Reports from project assignments 3. Discussions with students throughout the course duration. |
ECE 581 - Architecture for Digital Signal Processing
Catalog Data | Prerequisite: Graduate Standing. (3) This course deals with technical fundamentals of the architecture and features of programmable digital signal processors. Numeric representations and arithmetic concepts are discussed, which include fixed-point, floating-point representation of numbers, native data word width, IEEE-754 floating-point representation. Memory architecture and memory structures of digital signal processors are examined. Programming concepts for DSP processors such as addressing, instruction set, execution control, pipelining, parallel processing and peripherals are discussed. Finally, students will work on related projects, where digital signal processors are used in speech processing, instrumentation, image processing and other relevant applications. Three lecture hours per week. |
Textbook | P. Lapsley, J. Bier, A. Shoham, and E. Lee, "DSP Processor Fundamentals, "Berkeley Design Technology, Inc., California, 1994-1996. |
Coordinator | S. Awad, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Fundamental of digital signal processing 2. Basic concepts of computer architecture 3. Fundamental programming skills |
Topics | Review of DSP concepts Numeric representations and arithmetic concepts Quantization effects DSP processor architectures Comparison of current DSP processors Programming of DSP processors and debugging tools Systolic arrays Selected topics |
Laboratory projects | Various projects involving the application of digital signal processing in real-time and non-real-time applications. Such projects include: speech processing, image processing, instrumentation and measurement. |
Computer Usage | Simulation tools such as Matlab, Simulink and DSP simulators/emulators. |
Course Objectives | A good understanding of the architecture of fixed-point and floating-point DSP processors. Knowledge of software tools for testing and debugging DSP algorithms. Knowledge of methods of interfacing DSP processors Hands-on experience in relevant applications. |
Course Outcomes | Ability to analyze the effect of fixed-point arithmetic on digital filters and related algorithms. Ability to understand the different DSP architecture. Ability use simulators and software tools for debug DSP systems. |
Assessment Tools | Course assignments related to the course material Project paper to assess the understanding of the basic concepts of DSP architecture and how to apply software tools to ensure a successful design. |
ECE 583 - Artificial Neural Networks
Catalog Data | Prerequisite: Graduate Standing. (3) Students will gain an understanding of the language, formalism, and methods of artificial neural networks. The student will learn how to mathematically pose the machine learning problems of function approximation/supervised learning, associative memory, and self-organization, and analytically derive some well-known learning rules, including backprop. In addition, the student will learn how to perform computer simulations of various neural net models, and learn how to select appropriate model parameters, such as network architecture, hidden layer size, and learning rate. Three lecture hours per week. |
Textbook | Fundamentals of Artificial Neural Networks, by M. H. Hassoun MIT Press |
Coordinator | P. Watta, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Calculus, Differential and Difference Equations 2. Linear Systems Analysis, Linear Algebra, Matrix Computations 3. Computer Programming (Matlab) |
Topics | Threshold Gates Computational Capabilities of Artificial Neural Networks Learning Rules Adaptive Multilayer Neural Networks I Adaptive Multilayer Neural Networks II Associative Neural Memories |
Laboratory projects | Various projects involving simulation of neural network models. |
Computer Usage | Matlab |
Course Objectives | A good understanding of the terminology and mathematics of neural systems. A good understanding of how network parameters affect performance. Familiarity of the typical applications of neural networks. |
Course Outcomes | Ability to formally state various machine learning problems. Ability to derive gradient descent-based learning rules. Ability to choose suitable network parameters to solve practical problems. Ability to use Matlab to simulate given neural models. An understanding of how neural networks can be used to solve practical problems. |
Assessment Tools | In-class and take-home quizzes. In-class exams to evaluate students understanding to the material covered in the course. Final project to assess student ability to set up their own simulations and experiments. |
ECE 584 Speech Processing
Catalog Data
(Revised 2006-03) | Prerequisite : Graduate Standing. (3) The purpose of this course is to introduce students to the fundaments of speech processing using digital signal processing methods and techniques. First, students learn how speech is produced from the human vocal system and the different types of the basic speech sound components. Second, students learn the method to analyze speech signal in both the time domain and frequency domain. Third, applications of speech processing are discussed. Such applications include: speech synthesis, speech coding and speech recognition application. Finally, students in groups of 2 or 3 students work on a term project to gain hands-on experience. Three lecture hours per week. |
Textbook | L. R. Rabiner/ R. W. Schafer, "Digital Processing of Speech Signals," Prentice-Hall, 1978. |
Coordinators | Prof Selim S. Awad, Electrical and Computer Engineering |
Prerequisites by Topic | Complex algebra, linear algebra, calculus, Laplace transform and Fourier transform. Fundamentals of discrete-time signals and systems. |
Topics | Fundamentals of the human vocal systems and speech production (3 hours) Classification of basic human speech sounds and properties (3 hours) Models of speech production (3 hours) Analysis of speech in the time-domain (6 hours) Analysis of speech in the frequency domain (3 hours) Linear predictive speech coding and its application (6 hours) Speech processing applications (speech synthesis and coding) (6 hours) Software tools to analyze speech signals (3 hours) Selected topics (6 hours) Test and exams (3 hours) |
Laboratory projects: | Projects dealing with the simulation and design of speech processing systems (speech synthesis, speech therapy, speech recognition, etc.). |
Computer Usage | Matlab, Simulink, and other digital speech signal processing software packages. |
Course Objectives | 1. Proficiency in the understanding of the human speech system and speech production. 2. Proficiency in the analysis of speech signals in time-domain and frequency-domain. 3. Proficiency in the use of software tools to simulate and design digital speech processing applications. |
Course Outcomes
(Revised Mar. 24, 06) | Ability to analyze speech signals (time-domain and frequency domain). Ability to design digital speech processing systems for speech synthesis, recognition, coding and speech therapy. Ability to use software tools to simulate and verify the design of digital speech signal processing applications. |
Assessment Tools | 1. Exams and tests quizzes 2. Reports from project assignments 3. Discussions with students throughout the course duration. |
ECE 585 - Pattern Recognition
2006 - 2007 Catalog Data | Prerequisite: Graduate Standing. (3) Introduction to pattern recognition (PR as a process of data analysis. Representation of features in multidimensional space as random vectors. Similarity and dissimilarity measures in feature space. Bayesian decision theory, discriminant functions and supervised learning. Clustering analysis and unsupervised learning. Estimation and learning. Feature extraction and selection. Introduction to interactive techniques in PR. Applications of PR. Three lecture hours per week. |
Textbook | Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, (2nd Ed.) Wiley Interscience, 2001 |
Coordinators | Profs. M. Shridhar |
Prerequisites by Topic | Probability Theory and Probability Distributions Statistics and Hypothesis Testing. |
Topics | Introduction to Pattern Recognition, Problem Formulation and Machine Perception (3 hours) Random Vectors and their distributions (3 hours) Linear Transformation (3 hours) Hypothesis Testing (3 hours) Parametric Classifiers (3 hours) Nonparametric Classification (6 hours) Feature Extraction and Linear Mapping for Classification (6 hours) Clustering Techniques, Supervised and Unsupervised Learning (3 hours) Neural Networks in PR (6 hours) Case Studies of PR Applications (3 hours) Exams (3 hours) |
Laboratory projects | Students will design, construct and test different types of classifiers |
Computer Usage | Use of Matlab and Visual Studio for developing PR systems |
Course Objectives | In depth understanding of pattern recognition problems. In depth understanding of different techniques and strategies for pattern classification. A good understanding of formal methods to solve PR problems |
Course Outcomes | Ability to manipulate random signals and vectors to achieve specified covariance matrices Ability to implement statistical classifiers using features extracted from raw data Ability to implement supervised and unsupervised clustering Ability to implement neural networks trained using back propagation |
Assessment Tools | In-class exams Project assignments including project report |
ECE 586 - Digital Image Processing
Catalog Data | Prerequisite: Graduate Standing. (3) Monochrome and color vision in man and machines, image quantization edge detection, image enhancement, image restoration, color analysis and processing, multispectral image processing, texture analysis, image coding and compression, morphology, geometrical image modifications. Three lecture hours per week. |
Textbook | Digital Image Processing, Fourth Edition, William K. Pratt , Wiley |
Coordinator | J. W. V. Miller, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Introductory digital and analog signal processing 2. computer programming |
Topics | 1. Overview of digital image processing and insights from biological vision 2. Lighting, optics, sensor properties 3. Convolution-based digital image processing 4. Applications of the 2D fast Fourier transform 5. Classical nonlinear image processing techniques 6. Mathematical morphology 7. Binarization, feature extraction and shape description 8. Fast methods for image processing and analysis |
Laboratory projects | Various software projects involving two-dimensional signal processing and feature extraction. |
Computer Usage | C/C++ programming |
Course Objectives | A good understanding of digital image processing A good understanding of color and spectral properties of real-world scenes Ability to use FFT techniques in image processing and analysis Use of the MOSIS sub-micron standard cells and the Tanner auto-routing tools to implement CMOS chip. |
Course Outcomes | Ability to improve visual image quality. Ability to enhance features of interest. Ability to eliminate nonessential image information. Ability to design and algorithms for real-time image processing |
Assessment Tools | Course assignments related to the course material Midterm and final exams to evaluate students understanding to the material covered in the course Final project to assess students understanding and ability to apply principles of digital image processing. Paper review and class presentation to assess students in conducting research in digital image processing topics. |
ECE 588 Applied Machine Vision
200X-200Y Catalog Data | Prerequisite: Graduate Standing. (3) Image binarization, mathematical morphology, connectivity, boundary extraction, shape analysis, color analysis, applications in metrology and inspection, illumination and optics for machine vision, and hardware and software for vision systems. Three lecture hours per week. |
Textbook | Intelligent Vision Systems for Industry by Batchelor and Whelan and of Image Processing by Young, Gerbrands and van Vliet |
Coordinators | Prof. John Miller |
Prerequisites by Topic | Knowledge of programming Basic knowledge of signal processing |
Topics | Optics and Lighting (3 hours) Sensor characteristics and image acquisition (3 hours) Color and Spectral Analysis (3 hours) Gray-Scale Processing (6 hours) Binary Processing and Connectivity analysis (3 hours) Automatic Inspection (3 hours) Metrology (3 hours) Software and Hardware for vision applications (9 hours) Applications (6 hours) Tests (3 hours) |
Laboratory projects | Image processing and analysis software will be provided to enhance understanding. A final project will also be required. |
Computer Usage | Computer Usage: All laboratory reports, including diagrams and graphs, must be prepared on a computer. Office tools and Spice are used generally for problems and in the preparation of laboratory reports, as well as in the process of converging to a final design for design projects. |
Course Objectives
| A basic understanding of digital images A basic understanding of the elements of a machine vision system An understanding of software techniques required for image processing and analysis |
Course Outcomes | Knowledge of digital image representation and processing Knowledge of image processing including mathematical morphology Understanding of hardware and software requirements for a machine vision system Fundamental understanding of color perception, representation and spectral properties Knowledge of optics and lighting required to extract information from a scene |
Assessment Tools | Exams outcomes (1-5) Final project report (1-5) Oral presentation (3) |
ECE 589 - Multi-Dimensional Digital Signal Processing
Catalog Data | Prerequisite: Graduate Standing. (3) Topics include multidimensional signal analysis methodologies, signal representation, 2-D FIR filter, 2-D recursive systems and IIT filters, spectral estimation and methods, multidimensional signal restoration, applications in 2-D image processing and 3-D image processing, reconstruction, and feature estimation. Three lecture hours per week. |
Textbook | Multidimensional Signal, Image, and Video Processing and Coding, John W. Woods, Elsevier (2006) Multidimensional Digital Signal Processing, Dudgeon & Mersereau, Prentice Hall |
Coordinator | Dongming Zhao, Electrical and Computer Engineering |
Prerequisites by Topic | 1. Linear systems 2. Signals and systems 3. Digital signal processing |
Topics | Introduction to multidimensional signal processing Two-dimensional (2-D) discrete signal representations Multidimensional signals and systems Discrete Fourier analysis of multidimensional signals 2-D FIR filters 2-D recursive systems and IIR filters Spectral estimation Signal restoration Selected topics in multidimensional signal analysis 2-D and 3-D imaging applications |
Laboratory projects | Individual projects in the field. Do not require a lab presence. Do require computer simulations. |
Computer Usage | Software design tools such as MatLab |
Course Objectives | Understanding of multidimensional signals and signal generations Understanding of various sensors that produce multidimensional signals Being able to implement multidimensional algorithms using MatLab and/or C++ Being able to design algorithms according to applications |
Course Outcomes | Ability to perform 2-D and 3-D signal processing Ability to perform spectral analysis on multidimensional signals Ability to select and modify multidimensional signal processing algorithms according to the principles and solve practical problems in engineering field Ability to develop simple algorithms for 2-D image processing and analysis Ability to implement algorithms in 3-D signal/image processing |
Assessment Tools | Course reading and homework assignments Mid-term exam to evaluate students understanding to the material covered in the course Final project to assess student competence in developing algorithms for some applications in audio or video Individual paper reading and presentation of the topics of interest |
ECE 675 Advanced Computer Architecture
Catalog Data
| Prerequisite: ECE 575. (3) Parallel and non-Von Neumann architectures. Supercomputers. SIMD and MIMD structures. Pipelining, vector processing and array processing techniques. Associate processors. Data flow computers. RISC computers. VLSI computer structures. Advances in computer architecture. Three lecture hours per week |
Textbook | "Computer Architecture - Single and Parallel Systems", Mehdi R. Zargham |
Coordinators | Prof. A. Shaout, Electrical and Computer Engineering |
Prerequisites by Topic | Computer architecture concepts Introduction to parallel processing |
Topics | Introduction to Parallel Computer Architecture ( 3 hours) Pipeline Computers (3 hours) Array Processors (3 hours) Multiprocessor Systems (3 hours) Interconnection Network Structures ( 3 hours) VLSI computer structure (3 hours) Data-flow computers ( 3 hours) Parallel Processing Classification (3 hours) Parallel Processing Applications ( 3 hours) Queuing Theory ( 3 hours) Research paper presentations and seminars by students (9 hours) Exams (3 hours) |
Laboratory projects: | No lab is assigned for this class |
Computer Usage | A high level computer programming language can be used for the class project |
Course Objectives | 1. Knowledgeable with the parallel processing structures 2. Design and analysis of parallel processing systems 3. Be able to conduct research in area of parallel processing |
Course Outcomes
| Ability to understand and analysis pipeline systems Ability to design and analysis data flow computers Ability to design and analysis interconnected network structures for parallel processing systems Ability to classify parallel processing systems Ability to do research in the area of parallel processing |
Assessment Tools | 1. Exams (1-4) 2. Project report (5) 3. Research paper presentations (1-5) |