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Fall 2025
Quantum electronics, the study of light and matter interaction, has become the cornerstone in many areas of optical science and technology. This course reviews the principles of lasers then introduces the generalized nonlinear wave equations. This course will cover typical nonlinear effects and their applications in telecommunication, ultrafast laser, quantum computing/information and chemical/bio spectroscopy. Prerequisite: ECE 3209.
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Fall 2025
This course explores the intricacies of AI hardware, including the current landscape and anticipating the necessary developments in response to AI's rapid growth and widespread integration across all computing tiers. Through this exploration, you will gain an understanding of both the existing technologies and the future challenges in AI hardware design and implementation. Prerequisites: ECE 2330 or CS 2130.
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Spring 2026
This course explores advanced embedded systems topics such as design and validation of embedded computing systems, embedded C programming, real-time operating systems for microcontrollers, safety and security, cyber-physical systems, Internet of Things, and robotics. The course includes hands-on experience, paper presentations, and discussions. Prerequisite: ECE 3430
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Fall 2024
Focuses on the techniques for designing and analyzing dependable computer-based systems. Topics include fault models and effects, fault avoidance techniques, hardware redundancy, error detecting and correcting codes, time redundancy, software redundancy, combinatorial reliability modeling, Markov reliability modeling, availability modeling, maintainability, safety modeling, trade-off analysis, design for testability, and the testing of redundant digital systems. Cross-listed as CS 4434. Prerequisite: ECE 3430 or CS 3330 and APMA 3100 or APMA 3110.
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Spring 2026
This is a survey course in the theory and technology of modern wireless communication systems, exemplified in cellular telephony, paging, microwave distribution systems, wireless networks, and even garage door openers. Wireless technology is inherently interdisciplinary, and the course seeks to serve the interests of a variety of students.
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Spring 2026
Under faculty supervision, students plan a project of at least one semester's duration, conduct the analysis or design and test, and report on the results. If this work is to be the basis for an undergraduate thesis, the course should be taken no later than the seventh semester. Prerequisite: Instructor permission.
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Fall 2025
This course will teach students the required skills, concepts, and algorithms to develop mobile robots that act autonomously in complex environments. The main emphasis is on mobile robot locomotion and kinematics, control, sensing, localization, mapping, path planning, and motion planning. Besides theory, students are exposed to simulation environments and lab exercises with real robotic systems.
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Spring 2026
Explores measurement and behavior of high-frequency circuits and components. Equivalent circuit models for lumped elements. Measurement of standing waves, power, and frequency. Use of vector network analyzers and spectrum analyzers. Computer-aided design, fabrication, and characterization of microstrip circuits. Corequisite: ECE 5260 or instructor permission.
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Fall 2025
This course explores the intricacies of AI hardware, including the current landscape and anticipating the necessary developments in response to AI's rapid growth and widespread integration across all computing tiers. Through this exploration, you will gain an understanding of both the existing technologies and the future challenges in AI hardware design and implementation.
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Fall 2024
Focuses on techniques for designing and analyzing dependable computer-based systems. Topics include basic dependability concepts and attributes, fault models and effects, combinatorial and state-space modeling, hardware redundancy, error detecting and correcting codes, time redundancy, software fault tolerance, checkpointing and recovery, reliable networked systems, error detection techniques, and experimental dependability evaluation techniques.Prerequisites: A basic knowledge of probability and computer architecture is required. A working knowledge of programming is required for homework and mini projects.
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