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  • 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:    BME 3130 and ECE 3110 and ECE 3130 and ECE 3210 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

    This course delves into the advanced properties, processing, and applications of advanced materials in the context of electrical engineering. Students will explore the latest developments in materials science, including nanomaterials, semiconductors, biomaterials, and smart materials. The course combines theoretical knowledge with practical applications and research methodologies.
  • 4.00 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 3210 and ECE 3310 and ECE 3430 and EE 3310 and MATH 3410
  • 3.00 Credits

    Behavior of radiated electromagnetic waves in atmosphere, space, urban and indoor environments; path, frequency and antenna selection for practical communication systems; propagation prediction. Prerequisite:    ECE 3310
  • 3.00 Credits

    A study of communication circuits, modulation and decoding theory, spectrum usage, networks, and protocols. Prerequisite:    ECE 3210 and ECE 3430 and MATH 3410
  • 3.00 Credits

    This course provides in-depth coverage of the theory, analysis, and design of digital communications systems with an emphasis on advanced topics related to wired and wireless data communication. Students will develop computer models to emulate the concepts. The course is particularly beneficial to students interested in doing work/research in fields related to communications and networking. Prerequisite:    ECE 3210 and ECE 3430 and MATH 3410