This course introduces the student to the basic components and methodologies used in digital systems design. It is usually the student's first exposure to engineering design. The laboratory component consists of small design, implement, and debug projects. The complexity of these projects increases steadily throughout the term, starting with circuits of a few gates, until small systems containing several tens of gates and memory elements. Topics include: Boolean algebra, synthesis and analysis of combinational logic circuits, arithmetic circuits, memory elements, synthesis and analysis of sequential logic circuits, finite state machines, and data transfers.

In the first part, the course covers the design of digital systems using a hardware description language. In the second part, it covers the design of large digital systems using the computer design methodology, and culminates with the design of a reduced instruction set central processing unit, associated memory and input/output peripherals. The course focuses on the design, capture, simulation, and verification of major hardware components such as: the datapath, the control unit, the central processing unit, the system memory, and the I/O modules. The lab sessions enforce and complement the concepts and design principles exposed in the lecture through the use of CAD tools and emulation in a commercial FPGA. This course assumes a background in C programming.

This is an introductory course in digital MOS circuit analysis and design. The course covers the following topics: (1) MOSFET I-V behavior in aggressively scaled devices; (2) Static and dynamic characteristics of NMOS and CMOS inverters; (3) Combinational and sequential logic networks using CMOS technology; (4) Dynamic CMOS logic networks, including precharge-evaluate, domino and transmission gate circuits; (5) Special topics, including static and dynamic MOS memory, and interconnect RLC behavior.

The purpose of this course is to expose students to both the hardware and the software components of a digital embedded system. It focuses on the boundary between hardware and software operations. The elements of microcomputer architecture are presented, including a detailed discussion of the memory, input-output, the central processing unit (CPU) and the busses over which they communicate. C and assembly language level programming concepts are introduced, with an emphasis on the manipulation of microcomputer system elements through software means. Efficient methods for designing and developing C and assembly language programs are presented. Concepts of program controlled input and output are studied in detail and reinforced with extensive hands-on lab exercises involving both software and hardware, hands-on experience.

This is an introductory course in analog electronic circuit analysis and design. The course covers the following topics: (1) Diode circuit DC and small-signal behavior, including rectifying as well as Zener-diode-based voltage regulation; (2) MOSFET current-voltage characteristics; (3) DC biasing of MOSFET circuits, including integrated-circuit current sources; (4) Small-signal analysis of single-transistor MOSFET amplifiers and differential amplifiers; (5) Multi-stage MOSFET amplifiers, such as cascade amplifiers, and operational amplifiers; (6) Frequency response of MOSFET-based single- and multi-stage amplifiers; (7) DC and small-signal analysis and design of bipolar junction transistor (BJT) devices and circuits; (8) Feedback and stability in MOSFET and BJT amplifiers.

One semester of paid work experience in electrical engineering.

The course will start with the history of artificial intelligence and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many artificial intelligence techniques have been developed and applied to solve real life problems. This course will explore variety of artificial intelligence techniques, and their applications and limitations. Some of the AI techniques to be covered in this course are intelligent agents, problem-solving, knowledge and reasoning, uncertainty, decision making, learning (Neural networks and Bayesian networks), reinforcement learning, swarm intelligence, Genetic algorithms, particle swarm optimization, applications in robotics, controls, and communications. Students are expected to have any of the following programming skills listed above. Students will write an IEEE conference paper.

An introduction to a wide range of robotics-related topics, including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Pre-requisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project, in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures.

The course will start with the history of artificial intelligence (AI) and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many AI techniques have been developed and applied to solve real life problems. This course will explore a variety of AI techniques and their applications and limitations. Some of the AI topics to be covered in this course are intelligent agents, problem-solving, knowledge and reasoning, uncertainty, decision making, machine learning, reinforcement learning, and real-world applications of AI. Students are expected to have solid programming skills, understanding of probability and linear algebra, and statistics. Students will write a conference-style paper based on a research project.

An introduction to a wide range of robotics-related topics, including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Pre- requisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project, in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures. Students are required to write an IEEE conference paper on their projects.

An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits.

This course is used to fulfill the graduate paper requirement under the non-thesis option for the MS degree in electrical engineering. The student must obtain the approval of an appropriate faculty member to supervise the paper before registering for this course.