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  • 4.00 Credits

    Topics related to control theory, analysis, and testing of systems in the time domain, frequency domain and state space. Lecture and lab combination. Prerequisite:    ECE 3110 and ECE 3210 and EE 3110 and EE 3210
  • 3.00 Credits

    Introduction to Digital VLSI design. Includes the development of standard cell library of common CMOS circuits. Use of hardware description language and CAD tools for the design and simulation of custom large-scale digital systems. Students will understand the impacts and tradeoffs from speed, power consumption, and thermal properties of large-scale custom ICs. Prerequisite:    ECE 3110 and ECE 3610 and EE 3610
  • 3.00 Credits

    Design of analog VLSI systems. Course includes design, modeling, and verification of analog circuits in large-scale systems. Students will develop custom analog system designs utilizing CAD programs. Prerequisite:    ECE 3120
  • 3.00 Credits

    Introduction to advanced semiconductor physics and devices. Topics include carrier transport theory, energy band diagrams, PN junctions, metal-semiconductor junctions, BJTs and MOSFETs. Study of current semiconductor process technologies and discussion of off-roadmap technologies. Prerequisite:    ECE 3110 and PHYS 2220
  • 3.00 Credits

    This course introduces a host of sensor technologies from both theoretical and practical perspectives. A study of the electronics for sensor signal conditioning will be complemented by lectures on the principles and operation of various sensor modalities including pressure, thermal, strain, displacement, inertial, magnetic field, optical, coustic, and/or bio-medical. Students will be introduced to precision analog circuit architectures, noise analysis, and signal processing algorithms commonly used in data acquisition systems. Prerequisite:    ECE 3110 and PHYS 2220
  • 3.00 Credits

    Thin films are shaping the future of electronic devices. Understanding how materials are grown and characterized is vital to understanding and mitigating limitations in device design. This course focuses on the materials used to create state of the art ultra-thin device quality layers and coatings as well as how they are grown, characterized, and then used in fabrication processes for electronic devices such as transistors. Prerequisite:    ECE 3430 and MATH 3410 and PHYS 2220
  • 3.00 Credits

    Theory, application, and implementation of digital signal processing (DSP) concepts, from the design and implementation perspective. Topics include: Fast Fourier transforms, adaptive filters, state-space algorithms, random signals, and spectral estimation. Prerequisite:    ECE 3210 and EE 3210
  • 3.00 Credits

    Advanced image processing theory and methods. Topics include digital image formation, transformation, filtering, enhancements, segmentation and morphological processing. Lectures, computer assignments and project (including term paper). Prerequisite:    ECE 3210
  • 3.00 Credits

    This course covers deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image and signal processing. Students will learn to implement, train and debug their own deep neural networks and gain a detailed understanding of cutting-edge research in this field. Strong emphasis will be placed on real-world applications for both solving engineering problems using these methods as well as practical techniques for training and fine-tuning the networks. Case studies will be drawn from medical imaging, semiconductors, and audio signal processing. Prerequisite:    ECE 3210 and ECE 3430 and ENGR 2240 and MATH 2250 and MATH 2270 and MATH 3410
  • 3.00 Credits

    A study of intermediate electromagnetic issues common to circuits, systems, and communication networks. Prerequisite:    ECE 3310 and EE 3310