Electrical and Computer Engr (ECE)

Introduces the student to the scope of the engineering profession and its role in society and develops a sense of professionalism in the student. Provides an overview of electrical engineering through a series of hands-on projects and computer exercises. Develops professional communication and teamwork skills.

Ohm's Law, Kirchhoff's Laws, and network element voltage-current relations. Application of mesh and nodal analysis to circuits. Dependent sources, operational amplifier circuits, superposition, Thevenin's and Norton's Theorems, maximum power transfer theorem. Transient circuit analysis for RC, RL, and RLC circuits. Introduction to Laplace Transforms. Laboratory experiments include analog and digital circuits; familiarization with test and measurement equipment; combinational digital circuits; familiarization with latches, flip-flops, and shift registers; operational amplifiers; transient effects in first-order and second-order analog circuits; PSpice software applications. Concurrent registration in MATH 252 and ECE 218.

Sinusoidal excitation and phasors. AC steady-state circuit analysis using phasors. Complex frequency, network functions, pole-zero analysis, frequency response, and resonance. Two-port networks, transformers, mutual inductance, AC steady-state power, RMS values, introduction to three-phase systems and Fourier series. Design-oriented experiments include counters, finite state machines, sequential logic design, impedances in AC steady-state, resonant circuits, two-port networks, and filters. A final project incorporating concepts from analog and digital circuit design will be required. Prerequisites: ECE 211 with a grade C or better.

Sinusoidal excitation and phasors. AC steady-state circuit analysis using phasors. Complex frequency, network functions, pole-zero analysis, frequency response, and resonance. Two-port networks, transformers, mutual inductance, AC steady-state power, RMS values, introduction to three-phase systems and Fourier series. Note: ECE 216 is for non-ECE majors.

Boolean algebra, switching devices, discrete and integrated digital circuits, analysis and design of combinational logic circuits. Karnaugh maps and minimization techniques. Counters and registers. Analysis and design of sequential circuits. Digital logic design using FPGA and VHDL programming.

Students will receive an introductory overview of major issues related to offensive and defensive cybersecurity. Key topics for this course include ethical hacking tools, penetration testing basics, exploit development, intrusion detection, cyber forensics, and cybersecurity law and regulations. Course projects will provide a hands-on experience using open-source tools and software to support concepts taught during the lecture. Students need to have basic programming skills.

Basic concepts in computer architecture, organization, and programming, including: integer and floating point number representations, memory organization, computer processor operation (the fetch/execute cycle), and computer instruction sets. Programming in machine language and assembly language with an emphasis on practical problems. Brief survey of different computer architectures.

Analysis of circuits using distributed network elements. Response of transmission lines to transient signals. AC steady-state analysis of lossless and lossy lines. The Smith Chart as an analysis and design tool. Impedance matching methods. Vector analysis applied to static and time-varying electric and magnetic fields. Coulomb's Law, electric field intensity, flux density and Gauss's Law. Energy and potential. Biot-Savart and Ampere's Law. Maxwell's equations with applications including uniform-plane wave propagation.

Time and frequency domain representation of continuous and discrete time signals. Introduction to sampling and sampling theorem. Time and frequency domain analysis of continuous and discrete linear systems. Fourier series convolution, transfer functions. Fourier transforms, Laplace transforms, and Z-transforms.

Physics of semiconductor devices. Diode operation and circuit applications. Regulated power supplies. Bipolar and field-effect transistor operating principles. Biasing techniques and stabilization. Linear equivalent circuit analysis of bipolar and field-effect transistor amplifiers. Laboratory experiments reinforce concepts.

Principles of electromechanical energy conversion. Fundamentals of the operations of transformers, synchronous machines, induction machines, and fractional horsepower machines. Introduction to power network models and per-unit calculations. Gauss-Seidel load flow. Lossless economic dispatch. Symmetrical three-phase faults. Laboratory considers operation, analysis, and performance of motors and generators. The laboratory experiments also involve use of PC-based interactive graphical software for load flow, economic dispatch, and fault analysis.

Introduction to Amplitude, Phase, and Frequency modulation systems. Multiplexing and Multi-Access Schemes; Spectral design considerations. Sampling theorem. Channel capacity, entropy; Quantization, wave shaping, and Inter-Symbol Interference (ISI), Matched filters, Digital source encoding, Pulse Modulation systems. Design for spectral efficiency and interference control. Probability of error analysis, Analysis and design of digital modulators and detectors.

The course addresses the fundamentals of wireless communications and provides an overview of existing and emerging wireless communications networks. It covers radio propagation and fading models, fundamentals of cellular communications, multiple access technologies, and various wireless networks including past and future generation networks. Simulation of wireless systems under different channel environments will be an integral part of this course.

Emphasis on the physical, data link, and medium access layers of the OSI architecture. Different general techniques for networking tasks, such as error control, flow control, multiplexing, switching, routing, signaling, congestion control, traffic control, scheduling will be covered along with their experimentation and implementation in a laboratory. Credit given for ECE 407 or ECE 408, not both.

Emphasis on the physical, data link and medium access layers of the OSI architecture. Different general techniques for networking tasks, such as error control, flow control, multiplexing, switching, routing, signaling, congestion control, traffic control, scheduling will be covered. Credit given for ECE 407 or ECE 408, not both.

Power electronic circuits and switching devices such as power transistors, MOSFET's, SCR's, GTO's, IGBT's and UJT's are studied. Their applications in AC/DC DC/DC, DC/AC and AC/AC converters as well as switching power supplies are explained. Simulation mini-projects and lab experiments emphasize power electronic circuit analysis, design and control.

Fundamentals of electric motor drives are studied. Applications of semiconductor switching circuits to adjustable speed drives, robotic, and traction are explored. Selection of motor drives, calculating the ratings, speed control, position control, starting, and braking are also covered. Simulation mini-projects and lab experiments are based on the lectures given.

Transmission systems analysis and design. Large scale network analysis using Newton-Raphson load flow. Unsymmetrical short-circuit studies. Detailed consideration of the swing equation and the equal-area criterion for power system stability studies. Credit will be given for ECE 418 or ECE 419, but not for both.

Transmission systems analysis and design. Large scale network analysis using Newton-Raphson load flow. Unsymmetrical short-circuit studies. Detailed consideration of the swing equation and the equal-area criterion for power system stability studies. Use of commercial power system analysis tool to enhance understanding in the laboratory.

Analytical Methods for the Economic operation of power systems with consideration of transmission losses. Analytical methods for the optimal scheduling of power generation, including real power and reactive power. Analytical methods for the estimation of power system state. Analytical methods for the modeling of smart grid cybersecurity.

Contemporary analog circuit analysis and design techniques. Bipolar, CMOS and BICMOS IC fabrication technologies, IC Devices and Modeling, Analog ICs including multiple-transistor amplifiers, biasing circuits, active loads, reference circuits, output buffers; their frequency response, stability and feedback consideration.

Processing, fabrication, and design of Very Large Scale Integration (VLSI) circuits. MOS transistor theory, VLSI processing, circuit layout, layout design rules, layout analysis, and performance estimation. The use of computer aided design (CAD) tools for layout design, system design in VLSI, and application-specific integrated circuits (ASICs). In the laboratory, students create, analyze, and simulate a number of circuit layouts as design projects, culminating in a term design project.

The goals of this course are to give the student an understanding of the physical and operational principles behind important electronic devices such as transistors and solar cells. Semiconductor electron and hole concentrations, carrier transport, and carrier generation and recombination are discussed. P-N junction operation and its application to diodes, solar cells, and LEDs are developed. The field-effect transistor (FET) and bipolar junction transistor (BJT) are then discussed and their terminal operation developed. Application of transistors to bipolar and CMOS analog and digital circuits is introduced.

Discrete-time system analysis, discrete convolution and correlation, Z-transforms. Realization and frequency response of discrete-time systems, properties of analog filters, IIR filter design, FIR filter design. Discrete Fourier Transforms. Applications of digital signal processing. Credit will be given for either ECE 436 or ECE 437, but not for both.

Signal-flow graphs and block diagrams. Types of feedback control. Steady-state tracking error. Stability and Routh Hurwitz criterion. Transient response and time domain design via root locus methods. Frequency domain analysis and design using Bode and Nyquist methods. Introduction to state variable descriptions.

This is a culminating major design experience course that involves smart and connected system applications including Internet of Things, healthcare system, artificial intelligence and machine vision, wireless sensor network, smart security system, smart city, smart power grid, smart power electronic devices, smart transportation, factory automation, agriculture automation, and home automation. Smart and connected system entails human machine interface, embedded computing, interrupt/exception handling, fault detection and recovery, standard and special peripheral interfacing to sensors and actuators, hardware and software codesign for data acquisition, encryption/decryption for secure system, information processing, data storage, and network communication protocols. The design project incorporates engineering standards and multiple constraints, building on knowledge and skills acquired from 100 to 300 level ECE coursework.

This course gives students a clear understanding of computer and cyber security as threats and defense mechanisms backed by mathematical and algorithmic guarantees. Key topics covered include introductory number theory and complexity theory, cryptography and applications, system security, digital forensics, software and hardware security, and side-channel attacks. Course projects will provide hand-on experiences on languages, libraries, and tools supporting state-of-theart cryptography applications. Students registering for ECE 518 are required to complete additional projects in advanced areas.

This course studies computer network security by covering topics such as fundamental cryptographic algorithms; protocol design and analysis for secure communications over Internet; efficient key management infrastructure; strong password protection; attack and security models; practical security protocols in application layer, transport layer, network layer, and link layer. Students registering for ECE 543 are required to complete additional projects in advanced areas.

The course provides introduction to languages and environments for application software development utilizing Software as a Service (SaaS) for electrical and computer engineers. Languages addressed include Java, Python, SQL, and JavaScript. Key topics covered include systems development life cycle, client-server architectures, database integration, RESTful service, and data visualization. Programming projects will include the development of a data-rich web application with server back-end that connects mobile devices and Internet of Things using Agile software engineering practices.

This course gives students a clear understanding of the fundamental concepts of object-oriented design/programming (OOD/OOP). Languages addressed include C++ and Python. Key topics covered include introduction to machine and deep learning, software development life cycle, core language and standard library of C++ and Python, class design and design patterns, OpenMP and CUDA platforms. Students will design a complex learning application using these concepts and Agile software engineering practices.

This course provides an introduction to Signals and Systems and illustrates the concepts using representative examples and applications. Basic concepts, including continuous-time and discrete-time signals and their properties, are covered. Properties and applications of continuous-time and discrete-time convolution, Fourier series, Fourier transform, Discrete Fourier transform, Laplace transform, and Z-transform are also covered. A significant number of examples are used to illustrate the basic concepts. This course is intended to provide a strong foundation for students who are entering graduate programs in Electrical and Computer Engineering (ECE) without an undergraduate degree in ECE. This course is not intended for credits as part of the degree programs at Illinois Tech.

This course provides introduction to Probability and Random Variables and illustrates the concepts using representative examples and applications. Basic concepts including probability axioms, random and repeated experiments, conditional probability, discrete, continuous, and mixed random variables, moments and characteristic function, and a function of multiple random variables are covered. Significant number of examples are used to illustrate the basic concepts. The intent of this course is to provide strong foundation for students who are entering the graduate programs in Electrical and Computer Engineering (ECE) without an undergraduate degree in ECE. This course is not intended for credits as part of the degree programs at Illinois Tech.

This course introduces students to cloud native systems that build on top of the cloud computing architecture to provide scalable services in dynamic environments. Key topics covered include virtualization and containerization, distributed database systems, communication mechanisms, batch and stream processing, resource management, consensus, security, and system design techniques for scalability, resilience, manageability, and observability. Course projects will provide hand-on experiences on state-of-the-art languages, libraries, and tools.

This course offers an in-depth introduction to data science for real-world engineering applications. It covers foundational topics, including linear algebra and statistics, before transitioning into data preprocessing, visualization, and various machine learning methods. Practical application is emphasized, and students will gain hands-on experience using Python and data science libraries such as NumPy, Pandas, and Matplotlib. The course also includes advanced topics, such as supervised learning, dimension reduction, clustering methods, ensemble methods, and graph methods. The course places a strong emphasis on solutions for engineering problems. Differentiation and assessment between ECE 474 and ECE 574 are provided through the use of projects and case studies. Undergraduate students can only be admitted to ECE 474, while graduate students can only be admitted to ECE 574.

Mathematical foundations of image processing, including two-dimensional discrete Fourier transforms, circulant and block-circulant matrices. Digital representation of images and basic color theory. Fundamentals and applications of image enhancement, restoration, reconstruction, compression, and recognition.

This course provides the students with understanding of the fundamental concepts of computer architecture, organization, and design. It focuses on relationship between hardware and software and its influence on the instruction set and the underlying Central Processing Unit (CPU). The structural design of the CPU in terms of datapath and control unit is introduced. The technique of pipelining and hazard management are studied. Advanced topics include instruction level parallelism, memory hierarchy and cache operations, virtual memory, parallel processing, multiprocessors and hardware security. The end to end design of a typical computer system in terms of the major entities including CPU, cache, memory, disk, I/O, and bus with respect to cost/performance trade-offs is also covered. Differentiation between ECE 485 and ECE 585 is provided via use of projects / case studies at differing levels. (3-0-3) Undergraduate students can only be admitted to ECE 485 Graduate students can only be admitted to ECE 585.

Independent work on a research project supervised by a faculty member of the department. Prerequisite: Consents of academic advisor and instructor.

Students undertake a project under the guidance of an ECE department faculty member. (1-4 variable) Prerequisite: Approval of the ECE instructor and academic advisor.

Design, development, analysis of advanced systems, circuits, or problems as defined by a faculty member of the department. Prerequisite: Consents of academic advisor and instructor.

ECE 501: Artificial Intelligence and Edge Computing This course is a project-oriented, team-based design experience focused on developing open-ended, AI-based solutions for contemporary smart and connected systems. It emphasizes edge computing, signal processing, computer communications, artificial intelligence, imaging, sensors, and their practical applications. Student teams are required to propose and develop projects that explore AI architectures capable of real-time operation and optimized for low-power, embedded edge computing platforms. Applications may span a variety of domains, including machine vision, Internet of Things, cybersecurity, smart grid technologies, health monitoring, robotics, and autonomous systems. Students registering for this course should have prior knowledge of artificial intelligence, deep learning, machine vision, signal processing, embedded computing, and programming.

The primary distinguishing features of 5th Generation (5G) wireless network are its operations in the mm wave region for effectively handling Machine Type Communication (MTC) for supporting secure and tactile Internet of Things (IoT) and cloud based virtualization and operations. This course covers the details of 5G Cloud based Radio Access Network (C-RAN) and the 5G Core along with how the cloud infrastructure creates a very powerful flexible, secure, and reliable network through virtualization and Network Slicing. Unique features of 5G New Radio (NR) including accessing and duplexing schemes, mm wave operation, and enhanced coverage are discussed. The capabilities of the 5G Core which provides a very flexible usage of network resources are discussed. Projects will entail application to a selected set of use cases in the domains of smart city, smart transportation, and e-Health among others.

Fundamentals of first (1G), second (2G), third (3G), and future generation cellular communication systems. This course covers the transition from 1G to 3G systems. Topics included are speech and channel encoders, interleaving, encryption, equalization, modulation formats, multi-user detection, smart antennas, technologies that are used in these transitions, and future generations of cellular systems. Compatibility aspects of digital cellular systems are discussed along with a review of the standards for the industry. TDMA and CDMA systems are covered in detail.

Principles of optimization for practical engineering problems, linear programming, nonlinear unconstrained optimization, nonlinear constrained optimization, dynamic programming.

This course covers the fundamentals of video coding and communications. The principles of source coding for the efficient storage and transmission of digital video will be covered. State-of-the-art video coding standards and error-resilient video coding techniques will be introduced. Recent technologies for robust transmission of video data over wired/wireless networks will be discussed. A detailed overview of architectural requirements for supporting video communications will be presented. Error control and cross-layer optimization techniques for wireless video communications will be covered.

This course is a project-oriented, team-based design experience focused on developing open-ended Internet of Things (IoT) and Cyber-Physical Systems (CPS). Students are introduced to the fundamentals of IoT and embedded computing through a series of design experiences involving embedded computing, sensors, wireless communications, data acquisition and analysis, signal processing, machine vision, and artificial intelligence. Student teams must design and develop projects that explore IoT architectures optimized for real-time performance and low-power edge computing platforms. Emphasis should be placed on developing applications that incorporate human-machine interfaces, robust data archiving and analysis, and seamless integration with cloud computing services. Applications may span a wide range of domains, including machine vision, smart grid technologies, smart cities, health monitoring, security systems, home monitoring, factory automation, smart transportation, environmental monitoring, smart agriculture, robotics, and autonomous systems. Students enrolling in this course should have a foundational understanding of electrical and computer engineering, including embedded computing, computer networks, signal processing, sensors, electronics, and programming.

Probability theory, including discrete and continuous random variables, functions and transformations of random variables. Random processes, including correlation and spectral analysis, the Gaussian process and the response of linear systems to random processes.

Fundamentals of electric motor drives are studied. Applications of semiconductor switching circuits to adjustable speed drives, robotic, and traction are explored. Selection of motor drives, calculating the ratings, speed control, position control, starting, and braking are also covered. Simulation mini-projects and lab experiments are based on the lectures given.

Review of probability and random processes. AM with noise, FM with noise. Introduction to digital communication. Source coding, signal space analysis, channel modulations, optimum receiver design, channel encoding.

This course introduces cutting-edge wireless networking technologies with focus on the network protocols and standards of the current and next generation wireless networks including cellular networks, wireless local area networks, and wireless ad hoc networks. Specifically, it will cover topics relevant to wireless communications, radio resource management, mobility management, wireless medium access control, wireless routing protocols, and wireless TCP protocols.

This course gives students a clear understanding of computer and cyber security as threats and defense mechanisms backed by mathematical and algorithmic guarantees. Key topics covered include introductory number theory and complexity theory, cryptography and applications, system security, digital forensics, software and hardware security, and side-channel attacks. Course projects will provide hand-on experiences on languages, libraries, and tools supporting state-of-theart cryptography applications. Students registering for ECE 518 are required to complete additional projects in advanced areas.

The goals of this course are to give students an understanding of the physical and operational principles behind important electronic devices. Semiconductor electron and hole concentrations, carrier transport, and carrier generation and recombination are discussed. P-N junction operation and its application to diodes, solar cells, and LEDs, are developed. The metal-oxide-semiconductor-field-effect transistor (MOSFET) and bipolar junction transistor (BJT) are then discussed. Applications of transistors in analog and digital circuits are introduced. A term project on a particular device topic is required.

Essentials of contemporary RF CMOS integrated circuit analysis and design. Typical RF building blocks in CMOS and BiCMOS technologies, including passive IC components, MOS transistors, RLC tanks, distributed networks, RF amplifiers, voltage reference and biasing circuits, LNA, mixers, power amplifiers, and feedback networks. RF device modeling, Smith chart applications, bandwidth estimation, and stability analysis techniques. RF IC team design projects.

The course provides introduction to languages and environments for application software development utilizing Software as a Service (SaaS) for electrical and computer engineers. Languages addressed include Java, Python, SQL, and JavaScript. Key topics covered include systems development life cycle, client-server architectures, database integration, RESTful service, and data visualization. Programming projects will include the development of a data-rich web application with server back-end that connects mobile devices and Internet of Things using Agile software engineering practices. Differential requirement from ECE 448 is a major final project.

Advanced design and applications in VLSI systems. The topics of this course include design tools and techniques, clocking issues, complexity management, layout and floor planning, array structures, testing and testability, advanced arithmetic circuitry, transcendental function approximations, architectural issues, signal processing architecture and sub-micron design. Design projects are completed and fabricated by student teams.

Background and insight into some of the most active performance-related research areas of the field is provided. Issues covered include CMOS delay and modeling, timing and signal delay analysis, low power CMOS design and analysis, optimal transistor sizing and buffer tapering, pipelining and register allocation, synchronization and clock distribution, retiming, interconnect delay, dynamic CMOS design techniques, asynchronous vs. synchronous tradeoffs, BiCMOS, low power design, and CMOS power dissipation. Historical, primary, and recent papers in the field of high-performance VLSI digital and analog design and analysis are reviewed and discussed. Each student is expected to participate in the class discussions and also lead the discussion surveying a particular topic.

Uncertain systems; multi-variable control design; linear fractional transformation; uncertainties and small-gain theorem; H-infinity norm; algebraic Riccati equations; H-infinity control; optimality and robustness; design considerations; loop shaping; uncertainty and disturbance estimator; applications and examples.

Paradigm change of power systems; Challenges faced during the paradigm change; Concept of synchronized and democratized (SYNDEM) smart grids; SYNDEM architecture for next-generation smart grids; Technical routes to implement SYNDEM smart grids; Enabling technologies: Three generations of virtual synchronous machines (VSM); Integration of renewables/EV/storage systems through VSM; Integration of flexible loads through VSM; Illinois Tech SYNDEM prototype smart grid.

Fundamentals of energy conversion will be discussed, which are the foundation of efficient design and operation of motors & generators in modern day automotive, domestic and renewable energy systems. It will further investigate the principles of structural assessment, electromagnetic analysis, dimensional and thermal constraints. Finite Element Analysis (FEA) software-based design projects will be used to model the performance and operation of electric machines.

This course will cover the probability and queueing theory fundamentals for modern communication networks performance analysis. Applications of the theoretical analysis to modern Internet protocols and machine learning are to be studied. The main topics include: Probability and distributions, random processes, discrete and continuous Markov Chains, queueing systems, probability in machine learning, and applications of theories in modern mobile access control protocols.

This course studies computer network security by covering topics such as fundamental cryptographic algorithms; protocol design and analysis for secure communications over Internet; efficient key management infrastructure; strong password protection; attack and security models; practical security protocols in application layer, transport layer, network layer, and link layer. Students registering for ECE 543 are required to complete additional projects in advanced areas.

This course covers the key technologies that enable the modern Internet with a top-down approach. The main topics include multimedia application and protocols, content distribution networks, edge computing, methodologies for reliable communications, next generation network architecture based on software defined networking, resource virtualization, and key techniques for mobile Internet. This course also deals with the concept of layer based design, strategy for quality of service provisioning, performance analysis based on mathematical modeling, and performance evaluation via practical simulations.

Fundamentals and applications of motion control systems, control techniques for high precision motion control, state variable feedback of linear and nonlinear systems, multivariable systems, physical system modeling, graphical analysis, and numerical analysis, and system performance analysis.

Modeling an analysis of solid-state switching circuits, parallel module dynamics, multi-converter interactions, resonant converters, feedback control, stability assessment, reduced parts converters, integrated structures, programmable switching regulators, digital switch-mode controllers, and power electronic converter-on-a-chip development.

Advanced power electronic convertors, techniques to model and control switching circuits, resonant converts, Pulse-Width-Modulation (PWM) techniques, soft-switching methods, and low-voltage high-current design issues are studied. Single-phase and multi-phase, controlled and uncontrolled rectifiers and inverters with different operating techniques and their design and control issues are explained.

Fundamentals of electric machines, basic principles of variable speed controls, field orientation theory, direct torque control, vector of AC drives, induction machines, switched reluctance and synchronous reluctance motors, permanent magnet brushless DC drives, converter topologies of DC and AC drives, and sensorless operation.

Model development. Interchange capability, interconnections, pooling. Economic generator size and site selection. Concept of reserves, transformers, relays and circuit breakers. Reactive planning AC and DC systems are explored thoroughly from a planning standpoint.

Market Design in Restructured Power Systems, Short-term Load Forecasting, Electricity Price Forecasting, Price Based Unit Commitment, Arbitrage in Electricity Market, Market Power Analysis, Asset Valuation and Risk Analysis, Security Constrained Unit Commitment, Ancillary Services Auction Market Design, Power Transmission Pricing, Regional Transmission Organizations.

This course covers simulation and scheduling tools used in restructured power system for studying the economics and security of power systems. Topics include modeling of generating units (thermal units, combined-cycle units, fuel-switching/blending units, hydro units, pumped-storage units, photovoltaic, wind), Lagrangian Relaxation-based scheduling, mixed integer programming-based scheduling, and Benders decomposition-based transmission security analyses. The simulation and scheduling tools consider different time scales including on-line security, day-ahead, operational planning, and long-term. The simulation and scheduling tools consider interdependency of supply (such as gas, water, renewable sources of energy) and electricity systems.

Critical fault events in a large power system, sparsity techniques. Contingency screening process. Modeling of local controls in load flow. Adaptive localization method. Injection outage analysis. Security constrained dispatch. LP-based OPF. Real-time security analysis. Dynamic security analysis.

The concept of reliability, reliability indices, component reliability, generation capacity reserve evaluation, transmission system reliability, bulk power system reliability, distributed system reliability, reliability modeling in context.

Overview of key issues in electric utilities restructuring, Poolco model, bilateral contracts, market power, stranded costs, transmission pricing, electric utility markets in the United States and abroad, OASIS, tagging electricity transactions, electric energy trading, risk in electricity markets, hedging tools for managing risks, electricity pricing, volatility in power markets, and RTO.

Power interchange transaction management in the deregulated electric power industry. Course topics include: power system security assessment, total and available transfer capability (TTC/ATC), transaction management system (TMS), transaction information system (TIS), tagging calculator (IDC), congestion management, transmission loading relief (TLR).

Introduction to artificial intelligence, artificial neural networks, machine learning, and advanced engineering applications in smart grid, including but not limited to energy forecasting, smart meter data analytics, nonintrusive load monitoring.

Multidimensional sampling and discrete Fourier transform; Image segmentation; Object boundary (edge) detection and description; shape representation and extraction; Matching and recognition; Image registration; Camera geometry and stereo imaging; Morphological processing; Motion detection and compensation; Image modeling and transforms; Inverse problems in image processing (restoration and reconstruction).

Overview of machine learning and deep learning; principle of learning; Bayesian methods; non-parametric classifiers, Fisher’s linear discriminant analysis, principal component analysis; training, validation, and testing; support vector machines; neural networks; history of deep learning; and applications of deep learning.

Detection theory and hypothesis testing. Introduction to estimation theory. Properties of estimators, Gauss-Markov theorem. Estimation of random variables: conditional mean estimates, linear minimum mean-square estimation, orthogonality principle, Wiener and Kalman filters. Adaptive filtering. LMS algorithm: properties and applications.

Review of basic DSP theory. Design of digital filters: FIR, IIR, frequency-transformation methods, optimal methods. Discrete Fourier Transform (DFT) and Fast Fourier Transform algorithms. Spectral estimation techniques, classical and parametric techniques. AR, MA, ARMA models. Estimation algorithms. Levinson, Durbin-Levinson and Burg's algorithms. eigenanalysis algorithms for spectral estimation.

Adversarial robustness, which is centered on attack and defense, has become an emerging topic to promote trust in machine learning (ML)/deep learning (DL) and enable a better understanding of the pros and cons of DL systems. More generally, the idea of learning with adversaries is crucial for expanding the learning capability, ensuring trustworthy decision-making, and enhancing the generalizability of ML in many applications. This course teaches students how to adapt fundamental techniques of robustness evaluation and enhancement into different use cases of adversarial ML in computer vision, signal processing, and power system.

This course introduces students to cloud native systems that build on top of the cloud computing architecture to provide scalable services in dynamic environments. Key topics covered include virtualization and containerization, distributed database systems, communication mechanisms, batch and stream processing, resource management, consensus, security, and system design techniques for scalability, resilience, manageability, and observability. Course projects will provide hand-on experiences on state-of-the-art languages, libraries, and tools. Students registering for graduate course section are required to complete additional project sections in advanced areas and review research papers in this field.

This course offers an in-depth introduction to data science for real-world engineering applications. It covers foundational topics, including linear algebra and statistics, before transitioning into data preprocessing, visualization, and various machine learning methods. Practical application is emphasized, and students will gain hands-on experience using Python and data science libraries such as NumPy, Pandas, and Matplotlib. The course also includes advanced topics, such as supervised learning, dimension reduction, clustering methods, ensemble methods, and graph methods. The course places a strong emphasis on solutions for engineering problems. Differentiation and assessment between ECE 474 and ECE 574 are provided through the use of projects and case studies. Undergraduate students can only be admitted to ECE 474, while graduate students can only be admitted to ECE 574.

The course is divided into four sub-components: current state of the distributed power grid, outlook for the distributed power grid, operation of the distributed power grid, and planning of the distributed power grid. This course will begin by providing an overview of exiting distribution systems and smart grid technologies, such as distribution automation and advanced metering infrastructure (AMI). With the emerging trends in power industry, the course will next focus on trends driving the change and the future components of distributed power grid, including but not limited to distributed generation (DG) and energy storage systems (ESSs). The next part of the course will be focused on the operation and control strategies for distributed power grid systems, including operational constraints, voltage and var control (VVC), and control of DERs and Smart Inverters. The final topic area for the course will be planning of distributed power grid with DERs, including lectures on DER impacts and their assessments, hosting capacity, and microgrid operations.

This course covers cross-disciplinary subjects on sustainable energy that relate to energy generation, transmission, distribution, and delivery as well as theories, technologies, design, policies, and integration of sustainable energy. Topics include wind energy, solar energy, biomass, hydro, nuclear energy, and ocean energy. Focus will be on the integration of sustainable energy into the electric power grid, the impact of sustainable energy on electricity market operation, and the environmental impact of sustainable energy.

This course covers cross-disciplinary subjects on smart grid that relates to energy generation, transmission, distribution, and delivery as well as theories, technologies, design, policies, and implementation of smart grid. Topics include: smart sensing, communication, and control in energy systems; advanced metering infrastructure; energy management in buildings and home automation; smart grid applications to plug-in vehicles and low-carbon transportation alternatives; cyber and physical security systems; microgrids and distributed energy resources; demand response and real-time pricing; and intelligent and outage management systems.

Microgrids are the entities that are composed of at least one distributed energy resource and associated loads which not only operates safely and efficiently within the local power distribution network but also can form intentional islands in electrical distribution systems. This course covers the fundamentals of designing and operating microgrids including generation resources for microgrids, demand response for microgrids, protection of microgrids, reliability of microgrids, optimal operation and control of microgrids, regulation and policies pertaining to microgrids, interconnection for microgrids, power quality of microgrids, and microgrid test beds.

This course aims to convey knowledge of advanced concepts in VLSI signal processing. Emphasis is on the architectural research, design and optimization of signal processing systems used in telecommunications, compression, encryption and coding applications. Topics covered include the principles of datapath design; FIR and IIR filtering architectures; communication systems including OFDM, multirate signal processing; fast transforms and algorithms including fast Fourier transform; discrete cosine transform; Walsh-Hadamard transform; and wavelet transform. Furthermore, advanced computer arithmetic methods including Galois fields, CORDIC, residue number systems, distributed arithmetic, canonic signed digit systems and reduced adder graph algorithms are examined.

This course provides the students with understanding of the fundamental concepts of computer architecture, organization, and design. It focuses on relationship between hardware and software and its influence on the instruction set and the underlying Central Processing Unit (CPU). The structural design of the CPU in terms of datapath and control unit is introduced. The technique of pipelining and hazard management are studied. Advanced topics include instruction level parallelism, memory hierarchy and cache operations, virtual memory, parallel processing, multiprocessors and hardware security. The end to end design of a typical computer system in terms of the major entities including CPU, cache, memory, disk, I/O, and bus with respect to cost/performance trade-offs is also covered. Differentiation between ECE 485 and ECE 585 is provided via use of projects / case studies at differing levels. (3-0-3)

This course focuses on designing computers and embedded computing devices from security and threat-mitigation perspectives. Advanced architecture topics such as instruction level parallelism, multi-threading and multi-instruction, multi-data stream processing are presented. Design for testability, hardware attacks, threat modeling and countermeasures against attacks are covered for the major entities for a computer system; including CPU, memory, and I/O. Case studies on recent examples of hardware security issues are discussed. * Students registering for this course should have a prior knowledge of Computer Organization and Design or equivalent course and be familiar with hardware description languages such as Verilog or VHDL.

Computer-aided techniques for the joint design of hardware and software: specification, analysis, simulation and synthesis. Hardware/software partitioning, distributed system cosynthesis, application-specific instruction set design, interface cosynthesis, timing analysis for real-time systems.

Students will learn the design of complex and high-performance AI systems from system level to circuit level. Introduction to Deep Learning, deep neural network architecture, deep learning system: hardware and software in CPU, GPU, and FPGA. FPGA fundamentals, arithmetic hardware, CIM and PIM memory structure for AI, RTL programming and optimization, power consumption, design techniques for low power at system-level and RT-level.

This course gives students a clear understanding of the fundamental concepts of object-oriented design/programming (OOD/OOP). Languages addressed include C++ and Python. Key topics covered include introduction to machine and deep learning, software development life cycle, core language and standard library of C++ and Python, class design and design patterns, OpenMP and CUDA platforms. Students will design a complex learning application using these concepts and Agile software engineering practices. Students are required to complete an open- ended project in one of the advanced areas, for example numerical optimization, tool integration, heterogeneous acceleration.

Seminar course for Master students.

Special projects.

Seminar course for graduate students.

This short course covers digital design techniques and hardware/software realization concepts in embedded computing systems using VHDL. Topics include: basics principles of VHDL programming; designing with FPGA; design of arithmetic logic unit; VHDL models for memories and busses; CPU design; system-on-chip design; efficient hardware realizations of FFT, DCT, and DWT.

This intensive two-day short course offers a comprehensive foundation and advanced understanding of cybersecurity and resilient control in microgrid systems. The curriculum builds from cybersecurity and microgrid basics to cutting-edge methods involving SDN, digital twins, risk assessment, and human factors. Designed for graduate students, the course is structured around six expert-led sections, with lectures, practical discussions, and interactive case studies.