Photonic Communications Engineering (PCE) consists of two parts (I and II). Each part is further broken down into three courses: PCE IA, PCE IB, and PCE IC. PCE IA covers optical fiber light guiding, wave propagation characteristics, materials properties, and fabrication. PCE IB covers optical transmitters, receivers and amplifiers. PCE IC covers communications systems, fiber optics networks, and Internet infrastructure. Sections A, B, and C are each 1 credit and can be taken in any combination. When all three sections are taken together the course is designed as a survey, from the device to the systems level, of Photonic Communications Engineering. Reference material for the course is in a digital platform to allow dense hyper-linking between topics so that students from various disciplines can customize the reading material to their individual background knowledge.
 

Photonic Communications Engineering (PCE) consists of two parts (I and II). Each part is further broken down into three courses: PCE IA, PCE IB, and PCE IC. PCE IA covers optical fiber light guiding, wave propagation characteristics, materials properties, and fabrication. PCE IB covers optical transmitters, receivers and amplifiers. PCE IC covers communications systems, fiber optics networks, and Internet infrastructure. Sections A, B, and C are each 1 credit and can be taken in any combination. When all three sections are taken together the course is designed as a survey, from the device to the systems level, of Photonic Communications Engineering. Reference material for the course is in a digital platform to allow dense hyper-linking between topics so that students from various disciplines can customize the reading material to their individual background knowledge.
 

Photonic Communications Engineering (PCE) consists of two parts (I and II). Each part is further broken down into three courses: PCE IA, PCE IB, and PCE IC. PCE IA covers optical fiber light guiding, wave propagation characteristics, materials properties, and fabrication. PCE IB covers optical transmitters, receivers and amplifiers. PCE IC covers communications systems, fiber optics networks, and Internet infrastructure. Sections A, B, and C are each 1 credit and can be taken in any combination. When all three sections are taken together the course is designed as a survey, from the device to the systems level, of Photonic Communications Engineering. Reference material for the course is in a digital platform to allow dense hyper-linking between topics so that students from various disciplines can customize the reading material to their individual background knowledge.
 

This is a foundational course that offers an introduction to computer operating systems and their applications in engineering systems, the course delves into the principles and techniques involved in designing and implementing operating systems. It covers a wide range of topics, including operating system structures, process management, process synchronization, memory management, storage management, file systems, device management, and network and distributed operating systems. While the course covers important theoretical concepts, formal proofs are not emphasized. Instead, concepts are conveyed through real-world examples from modern engineering systems. A key focus of the course is providing students with practical experience in C programming for low-level operating system development, students will have the opportunity to work with and understand low-level tools for system diagnostics. By engaging in hands-on projects and assignments, students will gain significant exposure to the intricacies of operating systems and enhance their programming skills. May be convened with ECE402C.

Probability, random variables, stochastic processes, correlation functions and spectra with applications to communications, control and computers.

This course covers the basic mathematics needed for an in-depth understanding of the science and technology of fiber-optical communication systems. Every mathematical tool/technique developed in this course will first be motivated by the relevant application. The students are not expected to have a broad-based prior knowledge of the topics covered in this course, but they should generally be familiar with the basics of algebra, Euclidean geometry, trigonometry, integral and differential calculus, simple differential equations, and the rudiments of complex number analysis. The course will cover Complex Analysis, Fourier transform theory, and method of stationary phase (in the context of optical diffraction), vector algebra, linear algebra, ordinary and partial differential equations (e.g., Maxwell's electrodynamics, wave equation, diffusion equation), special functions (e.g., Bessel functions needed to study the guided modes of optical fibers), and probability theory (needed for understanding various sources of noise in communication systems, photodetection theory, digital communication via noisy channels, Information theory, etc.). Graduate-level requirements include completion of additional readings and additional problems on various homework assignments.

Enrollment Requirements

Familiarity with basic calculus, Euclidean geometry, algebra, trigonometry and the complex number system.

Provides a survey of optical spectroscopic methods and underlying phenomena for the study of materials. Graduate-level requirements include an individual research project with written report.

In this class, we investigate the state-of-the-art in reconfigurable computing both from a hardware and software perspective; understand both how to architect reconfigurable systems and how to apply them to solving challenging computational problems. The purpose of this course is to prepare students for engaging in research on reconfigurable computing. Initially, we review in detail the basic building blocks of most reconfigurable computers. Characteristics of FPGA architecture such as the organization of device logic and interconnection resources are examined to quantify hardware limitations. These physical limitations are then contrasted with computer-aided design issues such as the selection of circuit component locations in devices (the placement problem) and subsequent circuit interconnection between components (the routing problem). We then focus on the architecture for existing multi-FPGA systems and on compilation techniques for mapping applications described in a hardware description language to reconfigurable hardware. We will explore the question of “What makes an application suitable for reconfigurable computing?” with case studies in bioinformatics, image processing, video Processing, cryptography, molecular dynamics and computational fluid dynamics. We evaluate the FPGA based application acceleration with the emerging multicore architectures from the perspectives of price/performance and performance/watt. Specific contemporary reconfigurable computing systems are examined to identify existing system limitations and to highlight opportunities for research in dynamic and partial configuration areas.

Enrollment Requirements

Course Texts

Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation, Scott Hauck, André DeHon, Morgan Kaufman, 2007.

Other reading material will be either presented in the class or available as online papers.

Schedule

Assessment

• Homework: 3-5 assignments
• Project: 1 project
• Exams: 1 midterm exam
• Typical grading policy: 20% midterm, 50% project, 20% homework, 10% participation

This course covers the fundamental techniques for the design, analysis and layout of digital CMOS circuits and systems. Major topics include: MOSFET basics (structure and behavior of a MOSFET, CMOS fabrication, and design rules), detailed analysis of the CMOS inverter (static behavior, ratioed vs. ratioless design, noise margins, computing rise and fall times, delay models, resistance and capacitance estimation, design and layout of static CMOS logic gates, dynamic CMOS logic design, sequential circuit design (static and dynamic sequential circuit elements, clocking schemes and clock optimization), CMOS data path design. Graduate-level requirements include additional homework and term projects.

This course provides an introduction to technical aspects of cyber security. It describes threats and types of attacks against computers and networks to enable students to understand and analyze security requirements and define security policies. Security mechanisms and enforcement issues will be introduced. Students will be immersed in the cyber security discipline through a combination of intense coursework, open-ended and real-world problems and hands-on experiments.

Enrollment Requirements

This course combines themes from mechanics, electromagnetics, thermal physics, and neural networks with an introduction to numerical methods as well as the use of MATLAB. Students will become familiar with the underlying theory for a variety of systems in physics and biology (e.g., harmonic, anharmonic and coupled oscillators; electric fields of electric lenses; geo-thermal power station; and artificial neural networks), derive the necessary mathematical equations describing these systems, learn the necessary numerical methods to solve the underlying equations, and implement the system equations and numerical methods in MATLAB to simulate these systems. As a result, students will be prepared to formulate problems or model systems in physics, biology, and related disciplines, and to solve them numerically or in simulation.

The course focuses on the design, integration, and programming of web applications for the Internet of Things (IoT). Course topics include client-side dynamic web page development with HTML, CSS, JavaScript, and Ajax; server-side web application development with Node.js, MongoDB, and RESTful interfaces; and IoT device-side development using formal state-based programming and publish-subscribe interfacing.

Enrollment Requirements

This course will focus on presentation of the basic semiconductor processes which have direct environmental implications. Graduate-level requirements will include extended written analysis and oral presentation, which goes beyond the requirements for the students enrolled in CHEE 415.

Topics in biomedical instrumentation, sensors, physiological measurements, analog and digital signal processing, data acquisition, data reduction, statistical treatment of data, and safety issues. Course includes both lecture and structured laboratory components. Graduate-level requirements include building a biomedical instrument that implements a novel solution to a real-life problem. Examinations for graduate students will include additional essay questions that test ability to formulate creative solutions. Course includes both lecture and structured laboratory components.

Machine learning deals with the automated classification, identification, and/or characterizations of an unknown system and its parameters. There is an overwhelming number of application-driven fields that can benefit from machine learning techniques. This course will introduce you to machine learning and develop core principles that allow you to determine which algorithm to use, or design a novel approach to solving to engineering task at hand. This course will also use software technology to supplement the theory learned in the class with applications using real-world data.

Enrollment Requirements

Course Texts

Introduction to Machine Learning, 3rd Ed., Ethem Alpaydin, The MIT Press, 2014. (ISBN: 978-0262028189)

Schedule

Cloud computing is the model for ubiquitous, convenient, on-demand access to a shared pool of configurable computing resources. With the interest in cloud computing, the security challenges are raising concerns. This class discusses the cloud computing architecture and components along with the threat modelling and discusses physical, database, network, virtualization, services, and users level security concerns and their solutions.

Enrollment Requirements

Schedule

Deep Learning is revolutionizing artificial intelligence tasks such as language understanding, speech and image recognition, machine translation, autonomous driving, etc. This transformative impact of deep learning, which tries to model the neural networks in brains, was recognized with Nobel Prizes in 2024 and Turing Award in 2018. This course provides a comprehensive introduction to deep neural networks with a focus on underlying principles and engineering applications. Students will explore the fundamental concepts, optimization techniques, and software tools of deep learning starting from the basics of perceptron and progressing to advanced neural network models with convolutions and attentions. The course emphasizes an engineering perspective, hands-on learning, and integrating theory with practice, The course also introduces latest methods to enhance the efficiency of training and inference in deep learning models and systems. Designed for students from diverse engineering disciplines, this course aims to equip them with the skills and knowledge to effectively apply deep learning in their respective fields.

Enrollment Requirements

This course describes the nature of holographic and lithographically formed diffraction gratings and the tools necessary for their design and analysis. Course topics include a description of the interference and Fourier relations that determine the amplitude of diffracted fields, analysis of volume gratings, properties of holographic recording materials, computer generated holograms, binary gratings, analysis of applications of holography including data storage, imaging systems, photovoltaic energy systems, polarization control elements, and associative memories. We will also have a number of lab demonstrations fabricating holograms in a new type of photopolymer.

Enrollment Requirements

Discrete-time signals and systems, z-transforms, discrete Fourier transform, fast Fourier transform, digital filter design.

Enrollment Requirements

Course Texts

Oppenheim, Alan V., and Ronald W. Schafer. Discrete-Time Signal Processing. 3rd ed. Prentice Hall, 2010.

Schedule

Physics of optical communication components and applications to communication systems. Topics include fiber attenuation and dispersion, laser modulation, photo detection and noise, receiver design, bit error rate calculations, and coherent communications.

This course covers the fundamentals of designing fully functional software defined radio systems using a hardware radio peripheral and GNU Radio software. Using the provided hardware, students will implement and design core components of physical layer communication systems such as channel estimation, equalization, forward error correction, and modulation.

Enrollment Requirements

Course Texts

• T. F. Collins, et. al., Software-Defined Radio for Engineers, Artech House, 2018.

Digital image analysis, including feature extraction, boundary detection, segmentation, region analysis, mathematical morphology, stereoscopy and optical flow.

Enrollment Requirements

This course is designed to provide students with theoretical knowledge and practical experience to analyze and design digital image processing systems. The first part of this course covers two-dimensional signals and systems. Extension of key digital signal processing concepts (such as sampling, z-transforms, discrete Fourier transforms, and filtering) to two-dimensions are studied. During the second part of the course, properties of the human visual system are summarized and image enhancement, restoration, and compression methods are discussed. Course requirements include a project. Project ideas are developed in consultation with the instructor.

Enrollment Requirements

Course Texts

• R. C. Gonzales and R. E. Woods, Digital Image Processing, 4th Ed., Pearson, 2018. (optional)
• John Woods, Multidimensional Signal, Image, and Video Processing and Coding, 2nd Ed., Elsevier, 2012. (optional)

Schedule

Assessment

• Homework: 6-8 assignments
• Project: 1 project
• Exams: 1 midterm exam, 1 final exam
• Typical grading policy: 20% midterm, 20% final exam, 30% homework, 30% project

Topics to be selected from opto-electronics, wave guides, non-linear optics, nano-materials and semiconductor materials.

The purpose of the course is to give students a comprehensive introduction to digital communication principles. The major part of the course is devoted to studying how to translate information into a digital signal to be transmitted, and how to retrieve the information back from the received signal in the presence of noise and intersymbol interference (ISI).

Various digital modulation schemes are discussed through the concept of signal space. Analytical and simulation models for digital modulation systems are designed and implemented in the presence of noise and ISI. Optimal receiver models for digital base-band and band-pass modulation schemes are covered in detail.

Graduate work will include more challenging problem sets and exam problems, and a C/C++ simulation project.

Enrollment Requirements

Course Texts

S. Haykin, Digital Communication Systems. Wiley, 1st edition, 2014.

Schedule

Assessment

• Homework: ~10 problem sets
• Exams: 2 in-class exam, 1 mandatory final exam
• Computer usage: C/C++ exercises
• Contribution to professional component:
  • Math and basic science: 0.5 units
  • Engineering topics: 2 units
  • Significant design experience: 0.5 units

The purpose of the course is to give students a comprehensive introduction to free-space optical communication principles. This course offers in-depth exposition on propagation effects in free-space, both outdoor and indoor as well as deep-space; channel impairments in these media including atmospheric turbulence effects and scattering effects; noise sources, channel capacity studies, advanced modulation and multiplexing techniques for free-space applications, advanced detection, and channel compensation techniques; diversity techniques, MIMO techniques, adaptive optics techniques to deal with atmospheric turbulence effects; advanced coding and coded modulation techniques; software-defined free-space optical communications, physical-layer security, and quantum free-space optical communications.

Course Texts

Reference Textbooks (not required) are:

1. C. Andrews, R. L. Philips, Laser Beam Propagation through Random Media, 2nd Ed. SPIE Press, Bellingham, Washington, USA, 2005.
2. I. B. Djordjevic, Advanced Optical and Wireless Communications Systems, Springer, Dec. 2017/Jan. 2018.

Schedule

This is an advanced graduate course covering the principles of digital transmission of information. We rigorously define the amount of information and introduce information measures.

The largest portion of the course is devoted to studying how to translate information into a digital signal to be transmitted, and how to retrieve the information from the received signal. We study in-depth various digital modulation schemes through a concept of signal space. We build analytical and simulation models for digital modulation systems in presence of noise, and define the performances of digital communication systems through a probability of reliable transmission of information. We also build optimal receiver models for digital base-band and band-pass modulation schemes, and introduce iterative decoding on graphs, iterative decoding on intersymbol interference channels and constrained coding.

Enrollment Requirements

Course Texts

J. G. Proakis, Digital Communications, 5th Edition, McGraw-Hill, 2012.

Schedule

Assessment

• Homework: assigned but not graded
• Project: 1-2 projects
• Exams: 2 midterm exams, 1 final exam
• Typical grading policy: 30% midterms, 20% final exam, 0% homework, 0% laboratory, 50% project

Radar fundamentals: radar range equation, waveforms, ambiguity functions. Signal Processing: Pulse compression, synthetic aperture radar (SAR), inverse SAR, moving target indication (MTI), pulse-Doppler radar, space time adaptive processing (STAP).

Enrollment Requirements

This course covers the process of technology development in the photonics industry, both from the perspective of formal processes and case studies. Key aspects of the commercialization process including intellectual property, new product development processes, technical marketing and team building are treated in an interactive program informed by the instructor's 15 years of industry experience in both large corporate R&D organizations and entrepreneurial startups. Graduate-level requirements include completing an executive summary of their business plan/invention disclosure project that is a portion of the Group Gate 2 presentation grade.

Linear control system representation in time and frequency domains, feedback control system characteristics, performance analysis and stability, design of control.

Enrollment Requirements

Course Texts

Modern Control Systems, Twelfth Edition, R.C. Dorf and R.H. Bishop, Prentice Hall, Upper Saddle River, NJ, 2011.

Schedule

Silicon and compound semiconductor materials preparation, bulk crystal growth, wafering, epitaxial growth, photolithography, doping, ion implantation, etching, oxidation, metallization, silicon and compound semiconductor device processing.

Graduate-level requirements include an additional research paper requiring independent research.

Nonswitching aspects of analog integrated circuits using bipolar or CMOS technologies. Biasing, DC signal behavior, small behavior. Emphasis on use of physical reasoning, identification of circuit functions, and use of suitable approximations to facilitate understanding and analysis. Graduate-level requirement includes greater project scope.

Enrollment Requirements

Introduction to problems encountered at all levels of packaging: thermal, mechanical, electrical, reliability, materials and system integration. Future trends in packaging. 

Also offered as AME 554 and MSE 554.

Properties and applications of optoelectronic devices and systems. Topics include radiation sources, detectors and detector circuits, fiber optics, and electro-optical components. Graduate-level requirements include additional homework and a term project.

May be convened with ECE 456.

Enrollment Requirements

Course Texts

A. Yariv, Optical Electronics in Modern Communications, 5th Ed., Oxford University.

Schedule

Assessment

• Homework: ~8 problem sets
• Exams: 2 in-class exams, 1 final exam

Introduction to diffraction and 2-D Fourier optics, geometrical optics, paraxial systems, third-order aberrations, Gaussian beam propagation, optical resonators, polarization, temporal and spatial coherence, optical materials and nonlinear effects, and electro-optic modulators. Applications to holography, optical data storage, optical processing, neural nets, associative memory and optical interconnects.

May be convened with ECE 459. Graduate-level requirements include different exam questions and grading.

Enrollment Requirements

Course Texts

• Robert Guenther, Modern Optics, 2nd Ed., John Wiley, 2015.
• Joseph W. Goodman, Introduction to Fourier Optics, 4th ed., McGraw Hill, 2018.
• Donald C. O’Shea, Elements of Modern Optical Design, John Wiley, 1985.
• John E. Greivenkamp, "Field Guide to: Geometrical Optics", SPIE Press 2004
• Class notes will also be available on the d2L website.

Schedule

Assessment

• Homework: 8-10 assignments
• Project: 1 project
• Quizzes: 5 quizzes
• Exams: 2 midterm exams, 1 final exam
• Typical grading policy: 40% midterms, 20% final exam, 20% quizzes, 20% project

This course aims to provide a strong foundation for students to understand modern computer system architecture and to apply these insights and principles to future computer designs. It provides basic knowledge, fundamental concepts, design techniques and trade-offs, machine structures, technology factors, software implications, and evaluation methods and tools required for understanding and designing modern computer architectures, including multicores, embedded systems and parallel systems.

The course is structured around the three primary building blocks of general-purpose computing systems: processors, memories, and networks.

The first part of the course focuses on the fundamentals of each building block. Topics include processor microcoding and pipelining; cache microarchitecture and optimization; and network topology, routing, and flow control.

The second part goes into more advanced techniques and will enable students to understand how these three building blocks can be integrated to build a modern computing system. Topics include superscalar execution; branch prediction; out-of-order execution; register renaming and memory disambiguation; VLIW, vector and multithreaded processors; memory protection, translation and virtualization; and memory synchronization, consistency and coherence.

The third part addresses parallel computing, including multicore architectures, datacenters and cloud computing, and others.

Graduate-level students will be required to complete a term paper and extra homework.

Enrollment Requirements

Course Texts

Computer Architecture: A Quantitative Approach, J.L. Hennessy and D.A. Patterson, 5th Edition. Morgan Kaufmann Publishers, 2011.

Other reading material will be either presented in class or made available online.

Schedule

Summary

Intended to provide students with an in-depth study of computer architecture and design. Provides a basic knowledge and ability required for understanding and designing standard and novel computer architectures. Topics include design methodologies at various levels, instruction set design, ALU design, memory organization and design, cache design, virtual memories, interleaved memories, associative memories, control organization and design, hardwired control, micro-programmed control, pipelining, superscalar and super-pipelining, RISC design, vector processing, and others.

Assessment

• Homework: 4-6 homework problem sets
• Exams: 2 in-class exams
• Project: 1 semester-long project completed in 3 phases
• Computer usage: Assembly and C programming exercises

Design of microelectronic packaging systems based on the electrical, thermal and mechanical properties of materials. Chip, chip package, circuit board and system designs are considered. Graduate-level requirements include an additional term paper.

Knowledge systems are intelligent systems that totally or partially involve computational representation and processing of knowledge.

Objectives of this class are to:  

• Introduce students to the principles and techniques for engineering and developing knowledge systems
• Teach the alternative computational structures and methods for representation of knowledge
• Teach procedures and algorithms for computational processing (of knowledge structures), including automated reasoning and inference from knowledge, learning new knowledge, handling uncertain information, and complex knowledge-based-decision-making
• Discuss alternative system architectures (engines) for knowledge-based systems
• Graduate-level requirements include a more extensive and in-depth project,  and an additional assignment or question on the exam

Enrollment Requirements

Course Texts

Artificial Intelligence, A Modern Approach, latest edition by Stuart Russell and Peter Norvig, Prentice-Hall.

Additional reading provided/assigned by instructor.

Schedule

Assessment

• Homework: 3 assignments
• Project: 1 class project
• Activities: A few class activities
• Exams: 1 midterm exam, 1 final exam
• Typical grading policy: 25% midterms, 25% final exam, 20% homework, 10% activities, 20% class project

This course is intended to introduce graduate students to the field of modern computer architecture design stressing speedup and parallel processing techniques. The course is a comprehensive study of parallel processing techniques and their applications from basic concepts to state-of-the-art parallel computer systems. Topics to be covered in this course include the following: First, the need for parallel processing and the limitations of uniprocessors are introduced. Next, a substantial overview and basic concepts of parallel processing and their impact on computer architecture are introduced. This will include major parallel processing paradigms such as pipelining, superscalar, superpipeline, vector processing, multithreading, multi-core, multiprocessing, multicomputing, and massively parallel processing. We then address the architectural support for parallel processing such as 1) parallel memory organization and design; 2) cache design; 3) cache coherence strategies; 4) shared-memory versus distributed memory systems; 5) symmetric multiprocessors (SMPs), distributed-shared memory (DSM) multiprocessors, multicomputers, and distributed systems; 6) processor design (RISC, superscalar, superpipeline, multithreading, multi-core processors, and speculative computing designs); 7) communication subsystem; 8) computer networks, routing algorithms and protocols, flow control, reliable communication; 9) emerging technologies (such as optical computing, optical interconnection networks, optical memories); 10) parallel algorithm design and parallel programming and software requirements,; and 11) case studies of several commercial parallel computers from the TOP500 list of supercomputers.

Enrollment Requirements

Schedule

Assessment

• Homework: 3-5 assignments
• Project: 1 term paper
• Exams: 2 midterm exams
• Typical grading policy: 50% midterms, 20% project, 25% homework, 5% participation

Parallel models of computation, data flow, reduction, redi-flow, VLIW, superscalar, super-pipelining, multithreaded processors, multiprocessing, distributed computing, massively parallel systems, novel technologies, fundamentals of optical computing, optical architectures and neural networks.

Enrollment Requirements

Shannon's approach to cryptography. Symmetric key cryptography, cryptographic hash functions, and public key cryptosystems. Authentication, key management and key distribution. Wireless and network security. Graduate students will be required to submit a research paper.

Course Texts

• W. Stallings, Cryptography and Network Security: Principles and Practice, 7th Edition, Pearson, 2016.

Healthcare is changing at a very rapid pace. So does its attendant complexity and ever increasing reliance on high technology support. Technical medicine, where sophisticated, technology-based methods are used in education of healthcare professionals and in treatment of patients, is becoming a recognized discipline. Such methods require a new generation engineers, scientists, and systems designers to integrate medical and technical domains. With this in mind, this concept proposes a new honors engineering technical elective, to train innovators is this emerging domain. The course will be open to honors engineering students from all departments.

In this course students will investigate current medical and health care practices using high technology. The class will focus on systems design, modeling, and simulation technologies as applied to medicine.

This course is an introduction to computer-aided logic design. This is a highly active research area, enabling the design of increasingly complex digital systems. In this course we will mainly focus on three areas: specification, synthesis and optimization. We will look at how to specify functionality at a variety of abstractions, use industry-standard tools to simulate these designs, investigate some of the underlying optimization techniques utilized, as well as develop your own tools. Topics include, but are not limited to: 1) Register-Transfer Level, or RTL, Design, 2) Behavioral Synthesis, 3) Optimization and Tradeoffs of Combinational and Sequential Circuits, 4) Exact and Heuristic Minimization of Two-Level Circuits.

Students will be expected to implement a variety of Verilog and C/C++ projects throughout the semester. While specific programming assignments may change with the course offering, projects typically focus on the implementation of optimization and synthesis methods discussed in class, as well as the RTL design. 

Enrollment Requirements

Course Texts

No textbook is required. The class notes and slides are sourced from the following materials:

• Digital Design, Frank Vahid, John Wiley & Sons, ISBN 0470044373
• Verilog for Digital Design, Frank Vahid and Roman Lysecky, John Wiley & Sons, ISBN 9780470052624
• Logic Synthesis and Verification Algorithms, Gary D. Hachtel and Fabio Somenzi, Springer, ISBN 0387310045
• Logic Minimization Algorithms for VLSI Synthesis, Robert K. Brayton, Gary D. Hathtel, C. McMullen, and Alberto L. Sangiovanni-Vincentelli, Kluwer Academic Publishers, ISBN 0898381649
• Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest, McGraw-Hill, 0070131430
• Synthesis and Optimization of Digital Circuits, Giovanni De Micheli, McGraw-Hill, ISBN 0070163332

Schedule

Assessment

• Exam: 4 (lowest score dropped)
• Project: 4 programming projects
• Participation: 12-15 participation activities (1 dropped)
• Typical grading policy: 55% exams, 40% programming assignments, 5% participation/in-class exercises

The objective of this course is to provide students with methods and techniques for supporting engineering design of complex, computer-based systems. A design framework combining artificial intelligence, simulation modeling, and knowledge-based systems will be discussed and applied in term projects. This course focuses on the engineering of systems which comprise heterogeneous, distributed, software, hardware, communication, and other components. The class is an integral part of our design, computer simulation and modeling, codesign, and high-level systems analysis and synthesis track. A semester project will focus on an application selected to integrate students' background, expertise, current graduate work, and interests. This class provides a methodological and theoretical foundation for ECE 576B.

Enrollment Requirements

The focus of this course is on embedded system design, synthesis, and optimizations, also known as Electronic System Level (ESL) design. The coverage of ESL design will highlight the methods and challenges in developing embedded systems that require the tight integration of hardware and software components. In other words, the course will provide an under the hood look into how embedded systems (e.g. your smartphone, vehicle electronics) work. The course covers many aspects of embedded systems design, from system-level modeling to dynamic runtime optimizations to a brief overview of real-time software systems. The course also includes an in-depth discussion of the simulation and modeling aspects behind SystemC and transaction-level modeling (TLM), providing a detailed look into delta cycle simulation methods (similar to simulations methods used for Verilog and VHDL simulators).

Enrollment Requirements

ECE 274A, ECE 275, and ECE 576A

This course provides an introduction to the fundamental principles of computer networks and data communications. Emphasis is given on current technologies and architectures for establishing direct link and packet-switched networks, sharing access to a common communication medium, internetworking and routing, end-to-end flow control, congestion control and recourse allocation, and network security.

Enrollment Requirements

Course Texts

Computer Networks, A Systems Approach, 5th edition, Larry L. Peterson and Bruce S. Davie, Morgan Kaufmann, 2011.

References include:

• Data Networks, 2nd ed., D. Bertsekas, and R. Gallager, Prentice Hall, 1992.
• Computer Networks, 5th ed., A.S. Tanenbaum, and D. Wetherall, Prentice Hall, 2011.
• Computer Networking, A Top-down Approach, 5th ed., J. Kurose and K. Ross, Addison Wesley, 2009.

Assessment

There will be approximately eight weekly homework assignments on the topics covered in class. There will also be one midterm exam, three projects and a final exam.

Typical grading policy: 20% homework, 20% midterm, 30% projects, 30% final exam.

Provides an introduction to problems and techniques of artificial intelligence (AI). Automated problem solving, methods and techniques; search and game strategies, knowledge representation using predicate logic; structured representations of knowledge; automatic theorem proving, system entity structures, frames and scripts; robotic planning; expert systems; implementing AI systems; advanced topics: artificial neural networks, and learning, genetic algorithms; survey of modern applications

May be convened with ECE 479.

Enrollment Requirements

Course Texts

Artificial Intelligence: A Modern Approach. 3rd Edition. S. Russell and P. Norvig, Prentice Hall, 2009

Schedule

Course is structured to provide students with the fundamental concepts and analytical techniques associated with engineering electromagnetics (EM). The material is a complete exposure to Maxwell’s equations and their solutions for a variety of problems at an advanced graduate level. This theoretical study provides the student with the basis to deal with a wide range of practical topics including microwave engineering, electronic packaging, millimeter wave engineering, optical engineering, antennas, sensors, remote sensing, electromagnetic interference and electromagnetic compatibility. Understanding the fundamentals of electromagnetics is intrinsic to understanding how to analyze and design various types of components, devices, and systems for all of these applications and more.

Enrollment Requirements

Course Texts

Advanced Engineering Electromagnetics, C.A. Balanis, John Wiley and Sons Inc., New York, 1989.

Assessment

• Homework: 10-13 assignments
• Exams: 2 midterm exams, 1 final exam
• Typical grading policy: 50% midterms, 30% final exam, 20% homework

This course is structured as a sequential, second course that follows ECE 581A. In ECE 581A, the fundamental concepts and analytical techniques associated with engineering electromagnetics were introduced. These concepts and the associated analytical tools were then used to investigate a variety of canonical problems in the rectangular coordinate system. In ECE 581B, these concepts will be extended to the analysis of propagation, scattering, and diffraction problems in the cylindrical and spherical coordinate systems. These problems include metallic and dielectric waveguides, closed and open guiding structures, plane wave scattering from cylinders, wedges, and spheres; line source scattering from cylinders and wedges; and dipole scattering from spheres. Integral equation techniques and the method of moments will also be discussed.

As with ECE 581A, ECE 581B class material will emphasize understanding and analysis tools. The material is a complete exposure at an advanced graduate level. This theoretical study provides the student with the basis to deal with a wide range of practical topics including microwave engineering, millimeter wave engineering, optical engineering, antennas, sensors remote sensing, electromagnetic interference and electromagnetic compatibility. Understanding the fundamentals of electromagnetics is intrinsic to understanding how to analyze and design various types of components, devices, and systems for all of these applications and more.

Introduction to the fundamentals of radiation, antenna theory and antenna array design. Design considerations for wire, aperture, reflector and printed circuit antennas.

Enrollment Requirements

Course Texts

Antenna Theory and Design, 3rd edition with multimedia CD, Constantine Balanis, Wiley-Interscience.

Schedule

Assessment

• Approximately 2 homework problems per week
• Two 75-minute exams
• One take-home project
• Comprehensive final exam
• Computer assignments for visualization and understanding purposes may be given throughout the course
• Typical grading policy: 10% homework, 40% midterm exams, 25% project, 25% final exam

This course is structured to provide all students with the fundamental concepts and techniques associated with RF/microwave/wireless passive circuit design. Successful completion of this course will allow students to design and evaluate passive microwave circuits, as well as comprehend and analyze more advanced material in the field of microwave engineering. Microwave engineering is growing in importance with each passing year. It has application to the wireless industry and to high density electronic packaging for computer systems with fast clock speeds. Some common applications of passive microwave circuits include communication systems (e.g., satellite-to-ground link), mobile phones and wireless local-area-network, radar, navigation and guidance systems (e.g., GPS), antennas, radio astronomy, electronic warfare, remote sensing, and biomedical devices.

Enrollment Requirements

Course Texts

Microwave Engineering, 3rd Edition, David M. Pozar, John Wiley and Sons, 2005.

Schedule

Assessment

• Homework: 8-10 assignments
• Project
• Exams: 3 midterm exams, 1 final exam
• Typical grading policy: 45% midterms, 25% final exam, 15% homework 15% laboratory/project

Course Description

Planar active microwave circuits, diode and transistor characteristics, mixers, amps, oscillators, and frequency multipliers. Students will design circuits with CAD tools, fabricate in clean room, and measure performance in the lab. Graduate-level requirements include extra problems involving more challenging concepts and in-depth knowledge of the material.

Enrollment Requirements

Course Texts

Microwave and RF Design: A System Approach, Michael Steer, SciTech Publishing.

Suggested text: Microwave Transistor Amplifiers 2nd Edition, Guillermo Gonzales, Prentice-Hall, 1997.

Schedule

This course is a self-contained introduction to quantum information, quantum computation, and quantum error-correction. The course starts with basic principles of quantum mechanics including state vectors, operators, density operators, measurements, and dynamics of a quantum system. The course continues with fundamental principles of quantum computation, quantum gates, quantum algorithms, and quantum teleportation. A significant amount of time has been spent on quantum error correction codes (QECCs), in particular on stabilizer codes, Calderbank-Shor-Steane (CSS) codes, quantum low-density parity-check (LDPC) codes, subsystem codes (also known as operator-QECCs), topological codes and entanglement-assisted QECCs. The next topic in the course is devoted to the fault-tolerant QECC and fault-tolerant quantum computing. The course continues with quantum information theory. The next part of the course is spent investigating physical realizations of quantum computers, encoders and decoders; including photonic quantum realization, cavity quantum electrodynamics, and ion traps. The course concludes with quantum key distribution (QKD).

The course should alternate with ECE 638: Wireless Communications.

Enrollment Requirements

Course Texts

I.B. Djordjevic, Quantum Information Processing and Quantum Error Correction. Elsevier/Academic Press, 2012.

Summary

This course offers in-depth exposition on the design and realization of a quantum information processing and quantum error correction. The successful student will be ready for further study in this area, and will be prepared to perform independent research. The student completed the course will be able design the information processing circuits, stabilizer codes, CSS codes, subsystem codes, topological codes and entanglement-assisted quantum error correction codes; and propose corresponding physical implementation. The student completed the course will be proficient in fault-tolerant design as well.

Assessment

Homework will be assigned approximately every two weeks.  

Typical grading policy: 20% homework, 30% project, 15% midterm exam, 35% final exam.

Definition of a measure of information and study of its properties; introduction to entropy, mutual information, channel capacity, and rate-distortion theory.

Enrollment Requirements

Course Texts

Elements of Information Theory, T.M. Cover and J.A. Thomas, Wiley.

Schedule

Assessment

• Homework: 8-10 assignments.
• Exams: 2 midterm exams, 1 comprehensive final exam
• Typical grading policy: 10% homework, 30% exam 1, 30% exam 2, 30% final exam

This course will cover advanced topics in wireless communications for voice, data, and multimedia. It will also cover optical wireless communications, both indoor and free-space optical communications, and medical wireless communications. The course begins with a brief overview of current wireless systems and standards. It then characterizes the wireless channel, including path loss for different environments, random log-normal shadowing due to signal attenuation, and the flat and frequency-selective properties of multipath fading. Next it examines the fundamental capacity limits of wireless channels and the characteristics of the capacity-achieving transmission strategies. The next focus will be on practical digital modulation techniques and their performance under wireless channel impairments. A significant amount of time will be spent on multiple antenna techniques: MIMO channel model, MIMO channel capacity, and space-time coding. The section on multicarrier modulation provides comprehensive treatment of orthogonal frequency division multiplexing (OFDM). We will further study ultra wideband (UWB) communications, software defined radio and cognitive radio. Next section is related to optical wireless communications (OWC), in particular infrared OWC, visible light communications and free-space optical (FSO) communications. The section on wireless medical communications will cover implanted antennas inside biological tissue, antennas inside a human head, and antennas inside a human body. The course concludes with coding for wireless channels, adaptive modulation, adaptive coding and multiuser detection.

Course Texts

• A. Goldsmith, Wireless Communications. Cambridge: Cambridge University Press, 2005.
• R.A. Carrasco and M. Johnston, Non-Binary Error Control Coding for Wireless Communication and Data Storage. John Wiley & Sons, Ltd., 2005.
• M. Ghavami, L.B. Michael and R. Kohno, Ultra Wideband Signals and Systems in Communication Engineering. John Wiley & Sons, Ltd., 2007.
• D. Tse, and P. Viswanath, Fundamentals of  Wireless Communication. Cambridge University Press, 2005.
• T.M. Duman and A. Ghrayeb, Coding for MIMO Communication Systems. John Wiley & Sons, Ltd., 2007.
• E. Biglieri, R. Calderbank, A. Constantinides, A. Goldsmith, A. Paulraj and H.V. Poor, MIMO Wireless Communications. Cambridge University Press, 2007.

Schedule

Communication, detection and estimation as statistical inference problems. Optimal detection in the presence of Gaussian noise. Extraction of signals in noise via MAP and MMSE techniques. Performance evaluation including Chernoff and Cramer-Rao bounds.

Enrollment Requirements

Course Texts

H. Vincent Poor, An Introduction to Detection and Estimation, 2nd edition, Springer-Verlag, 1994

This course is designed to orient Electrical and Computer Engineering students to the PhD program. Students will learn about the requirements for the PhD program and develop strategies for successfully achieving those requirements. Students will be required to attend sessions to learn about different research opportunities associated with Electrical and Computer Engineering. Students will develop skills in effectively communicating technical concepts and ideas that are presented at these sessions.