• EE 5450: Topics in Robotics

  3.00 Credits

  University of Wyoming

  
  Topics vary between offerings, but include exponential coordinates for describing rigid motion, parallel machines, robotic vision, actuators and sensors, calibration, quaternions, motion planning, multifinger grasp dynamics, singularities, and singularity-free design, and limited-DOF machines. Prerequisite: MATH 2250, senior or higher level standing and permission of the instructor.
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• EE 5460: Probabilistic Robotics

  3.00 Credits

  University of Wyoming

  
  Fundamental theory underlying the robust sensing and planning used in self-driving machines is developed. Topics covered are: Bayesian, Kalman, and Particle Filters; simple ground robot motion models; mobile robot localization; simultaneous localization and mapping; partially observable Markov decision processes. Prerequisite: EE 4220.
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• EE 5490: Convex Optimization

  3.00 Credits

  University of Wyoming

  
  Covers fundamentals of numerical convex optimization. These methods have potential applications in many fields, so the goal of the course is to develop the skills and background needed to recognize, formulate, and solve convex optimization problems. Covers convex sets, convex functions, convex optimization problems and applications. Prerequisites: MATH 2250 and senior or higher level standing.
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• EE 5590: Real Time Emb Sys

  3.00 Credits

  University of Wyoming

  
  Emphasizes a systems approach to real time embedded systems. Students are expected to apply methodical system design practices to designing and implementing a microprocessor-based real time embedded system. Students employ a robot-based educational platform to learn the intracacies of real time embedded systems, distributed processing, and fuzzy logic. Students learn processor input/output interfacing techniques. Students use state-of-the-art design and troubleshooting tools. Dual listed with EE 4590. Prerequisites: EE 4390.
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• EE 5600: Stat Signal Proc in:

  2.00 - 4.00 Credits

  University of Wyoming

  
  Topics vary between offerings but include signal detection, feature extraction and pattern recognition, information theory and coding, spectral analysis, identification, speech processing, image processing, and seismic processing. Prerequisite: EE 4220.
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• EE 5610: Randon Proc Thy I

  3.00 Credits

  University of Wyoming

  
  Introduction to statistical models. Applications of sampling theorems. Correlation functions and spectra. Shot noise and thermal noise. Introduction to measurements and computational techniques. Nonlinear random processes. Term papers on special problems. Prerequisite: EE 4220.
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• EE 5620: Digital Image Processing

  3.00 Credits

  University of Wyoming

  
  Methodologies and algorithms for processing digital images by computer. Includes gray level transformations, histogram analysis, spatial domain filtering, 2D Fourier transforms, frequency domain filtering, image restoration, and reconstruction of computed tomography (CT) medical images. Prerequisite: EE 3220 or equivalent background. (Offered fall of even-numbered years)
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• EE 5625: Spectral Analysis

  3.00 Credits

  University of Wyoming

  
  Spectral estimation including nonparametric methods such as Welch and Blackman-Tukey; modern parametric methods for AR, MA and ARMA spectra including Yule-Walker and Levinson-Durbin. Parametric line spectral subspace methods including MUSIC and ESPRIT. Filterbank and spatial methods such as beamforming. Prerequisites: EE 3220, 4220 or equivalent.
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• EE 5630: Advanced Image Processing

  3.00 Credits

  University of Wyoming

  
  Introduces students to advanced aspects of image processing (IP), using specific applications to demonstrate these principles. Concepts such as medical imaging; color IP; wavelets and multiresolution IP; image compression; morphological IP; image segmentation, representation, description and understanding are covered. Prerequisites: EE 4530.
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• EE 5640: Adaptive Filters

  3.00 Credits

  University of Wyoming

  
  Adaptive filtering including eigenanalysis, low-rank modeling, Wiener filters, linear prediction, steepest descent methods, least mean-squares and recursive least squares methods, adaptive beamforming. Performance, convergence, and stability issues. Realization techniques. Prerequisites: EE 4220.
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