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  • EE 5300: Microwave and RF Circuits

    3.00 Credits
    University of Wyoming

    Analysis and design of microwave and RF circuits with applications to communication and radar systems. Review of transmission line concepts and the Smith Chart, scattering parameters, microstrip lines, and matching networks. Analysis and design of microwave and RF amplifiers, oscillators, and mixers. Dual listed with EE 4300. Prerequisite: EE 3150 or concurrent enrollment in EE 3330.
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  • EE 5330: Adv Electr Sys Des

    3.00 Credits
    University of Wyoming

    Theory of errors. High accuracy: multiplexers; voltage references; sample and hold circuits. Amplifiers: programmable gain; high speed voltage feedback; current feedback. Noise in integrated circuits. Pulse code modulation ADC; sigma delta ADC; oversampling; undersampling. Analog and digital audio systems. CD players. Superheterodyne and digital receivers. Signal transmission and conditioning. Hardware design techniques. Prerequisite: EE 4330 and 4210.
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  • EE 5340: Adv Semi Mat & Dev

    3.00 Credits
    University of Wyoming

    Advanced semiconductor materials and device concepts including noise in semiconductors, heterostructure and quantum fundamentals, high power materials and devices, high performance transistors including the MESFET, HEMT, and HBT. Also discusses GUNN and IMPATT diodes, Resonant Tunneling devices, and future computing devices based on the quantum properties of semiconductors. Prerequisite: EE 4340.
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  • EE 5350: Optoelec Mat & Dev

    3.00 Credits
    University of Wyoming

    Optoelectronic properties of semiconductor materials and devices. Includes a review of the basic electronic properties of semiconductors materials, epitaxial growth, optical properties including absorption and emission of light, effects of quantum confinement and strain, and Heterostructures. Operation and optimization of basic optoelectronic devices including: photodetectors, LEDs Lasers, and modulators. Prerequisite: EE 4340.
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  • EE 5360: Digital VLSI Design

    3.00 Credits
    University of Wyoming

    Digital building blocks, stick diagrams, CMOS cells and arrays, CMOS digital subsystems and systems. Chip design software such as layout, simulators and digital synthesis using HDL. Digital design verification and timing issues. Prerequisite: EE 4360.
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  • EE 5390: Comp Architecture

    3.00 Credits
    University of Wyoming

    Examines the various methodologies used in the design of high-performance computer systems. Topics include CISC and RISC architecture and instruction sets, pipelining, instruction-level parallelism, memory hierarchy (including cache) design and computer networks. Prerequisite: EE 4390.
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  • EE 5400: Intro To Robotics

    3.00 Credits
    University of Wyoming

    Representation of pose using Euler angles, quaternions and homogeneous coordinate transformations. Forward and inverse kinematics of rigid body manipulators. Velocity and force transformation in a rigid robot using Jacobians. Trajectory generation using splines. Robotic vision for depth measurement. Analysis of actual robotic systems. Prerequisite: ES 2250.
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  • EE 5410: Neural & Fuzzy Sys

    3.00 Credits
    University of Wyoming

    Theory of feedforward and recurrent neural networks. Supervised and unsupervised learning theories. Fuzzy logic and systems. Associative memories. Matching and self-organizing networks. Application of neural and fuzzy systems. Prerequisite: ES/COSC 3070, EE 3220.
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  • EE 5430: 3-D Computer Vision

    3.00 Credits
    University of Wyoming

    Provides a mathematical framework for describing three dimensional imaging and computer vision. Topics include 3-D coordinate transforms, image formation, camera calibration, reconstruction from two views, SIFT detection, hidden Markov models, Markov random fields, and "bag-of-words" visual description. Prerequisites: EE 4220 and MATH 2250.
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  • EE 5440: Geometric/Deep Comp Vision

    3.00 Credits
    University of Wyoming

    Geometric methods including exponential coordinates for describing rigid motion, quaternions, pinhole models of cameras, and models of stereo cameras. Reconstruction of a 3D scene. Deep learning methods using convolutional and other neural networks will be used for computer vision. CNN architectures, classification, optimization, detection, identification, segmentation, GANs, and transformers are covered. Prerequisites: MATH 2250.
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