Jameson D. Morgan

J. D. Morgan and B. D. Rigling, "Radar Surveillance using Learned Beam Placement Strategies in
Non-homogeneous Environments," in 2024 DAGSI Student Research Presentations, Virtual, 2024. Link: https://www.soche.org/college-students/dagsi-student-research/2024-dagsi-student-research-presentations/#JamesonMorgan.

J. D. Morgan and B. D. Rigling, "Surveillance Performance of Digital Radars in Non-uniform Target Behavior," in Proc. 2023 IEEE Radar Conf. (RadarConf23), San Antonio, TX, USA, 2023, pp. 1-6, doi: 10.1109/RadarConf2351548.2023.10149742. Link: https://ieeexplore.ieee.org/abstract/document/10149742.

J. D. Morgan, “GeoAware-A Simulation-based Framework for Synthetic Trajectory Generation from Mobility Patterns,” M.S. thesis, Dept. Comput. Sci. Eng., Wright State Univ., Dayton, OH, USA, 2020. Link: https://corescholar.libraries.wright.edu/etd_all/2374/.

J. D. Morgan and D. Doran, "[POSTER] GeoAware: An R-based Framework for Controlling SUMO and Generating Synthetic Vehicle Datasets," presented at the Eclipse SUMO User Conf. 2020, Virtual, 2020. Link: https://youtu.be/TxDkCr4qANU?si=RJ3OZGqjjqKZFvdj.

J. D. Morgan and D. Doran, "[POSTER] GeoNet: A Framework for Intrinsic Geospatial Anomaly Detection," presented at the 2019 SIAM Workshop on Network Science (NS19), Snowbird, UT, USA, 2019. Link: https://meetings.siam.org/sess/dsp_talk.cfm?p=100696.

For conference citations, the bolded and underlined author is the one who presented the research at the conference.

• Wright State University's College of Engineering and Computer Science 2024 Ph.D. in Electrical Engineering “Top Student” - April 26, 2024
  

• Random Processes
  • This course provided a thorough introduction to discrete and continuous probability. Topics addressed included basic probability theory and axioms; conditional, joint, and marginal probabilities; Bayes' rule; random variables (scalar and vector); moments; conditional probability; limit theorems; random processes (including wide sense stationary processes); autocorrelation; power spectral density; and ergodicity. Many homework exercises required writing MATLAB code to provide hands-on experience.
• Detection, Estimation, and Optimal Filter Design
  • This class provided an extensive overview of the basics of estimation and detection theory. The topics addressed in estimation theory included linear estimators, maximum likelihood estimation (including numerical methods), expectation maximization, least squares, Bayesian estimators, and Kalman filtering. In detection theory, the topics covered included Neyman-Pearson, minimum Bayes risk detectors, generalized likelihood ratio tests, Bayesian composite hypothesis testing, and logistic regression. The various principles presented in class were frequently derived and both scalar and vector domains were considered. A final class project provided a student-led opportunity to investigate and apply the various topics discussed.
• Systems Simulation
  • This course provided an introduction to simulation concepts and techniques, including random number generation; empirical and statistical modeling; and discrete and continuous simulations. Knowledge was tested through homework assignments, a midterm, and a final simulation project.
• Optimization Techniques
  • This class discussed unconstrained optimization, linear programming, and constrained optimization with a focus on the mathematics behind such techniques. Specific topics covered included one-dimensional search methods; gradient methods; Newton's method; conjugate direction methods; necessary and sufficient conditions; least squares; the simplex method; duality; problems with equality constraints (including the Lagrange condition and second order conditions); and problems with inequality constraints (with respect to the Karush-Kuhn-Tucker condition).
• Applied Time Series
  • This class investigated time series analysis and used the R programming language to reinforce the topics discussed in class. Beyond the basics of time series analysis (mean, variance, covariance, and stationarity), advanced topics included trend estimation, general linear processes, moving average processes, autoregressive processes, ARIMA modeling, model selection, parameter estimation, and forecasting. Homework and tests were used to provide experience with the covered topics.
• Digital Communication
  • This class provided provided an introduction to a variety of complex-valued digital communication principles relevant to the field of electrical engineering. Topics discussed included convolution, Fourier transform, linear time-invariant systems, sampling theory, Hilbert transform, spectrum plotting, amplitude modulation, frequency modulation, phase modulation, matched filters, bit-error probability, analog-to-digital conversion, pulse shaping (OOK, ASK, PSK, QAM, FSK, QPSK), error corrective coding, and the Viterbi algorithm. Hands-on experience was gained via MATLAB assignments in a dedicated lab section and frequent quizzes were used to reinforce concepts.
• Modern Radar Theory
  • This class provided an introduction to radar theory through once-a-week lectures and MATLAB class exercises. Topics discussed included the radar range equation, basic radar operation principles, pulse modulation, pulse compression, Doppler frequency (velocity) extraction, and beamforming. The final project for this class required one to build a radar pulse domodulator that was capable of extracting radar pulses from returned signals. Various statistics of the pulse were also reported.
• Advanced Wireless Communication Techniques
  • This class introduced the student to wireless communication technologies and signal processing. Application was emphasized in this class and hands-on exercises were performed in MATLAB. Topics discussed included analyzing power spectral density for random signals; autocorrelation sequences; code-division multiple access (CDMA); multipath; orthogonal frequency division multiplexing (OFDM); multiple input, multiple output (MIMO) antennas; statistical modeling of wireless communication; baseband modulation; the Viterbi algorithm; and the cellular long-term evolution (LTE) standard.
• Image Processing
  • This class provided an introduction to image processing fundamentals and included topics on image storage (format, sampling, and quantization); image shifting and scaling; image transformations; histogram equalization; image filtering; frequency interpretation of images and filtering; noise removal; image compression; wavelet transform; color images; watermarking; encoding; image morphology; and image segmentation. A class project required implementing an image processing algorithm from a journal publication. For my class project, I choose to implement an image encryption algorithm using a gray code decomposition.
• Modern Control
  • This class provided a graduate-level introduction to the field of control theory for continuous and discrete systems. Topics discussed included linear time-invariant (LTI) systems, state-space decomposition, stability, observability, controllability, Jordan form representation (Eigen-analysis), canonical decomposition of LTI systems, state estimators/observers, and pole placement. 
• Embedded Systems
  • A class discussing microprocessor-based embedded systems. Topics include system architecture; embedded processors; field-programmable gate arrays; hardware and software co-design; and real-time scheduling operating systems. Topics reinforced through weekly labs and a final project where teams were tasked to design their own oscilloscope/digital logic analyzer. Hardware used included the DE0-Nano and the Arduino Uno.
• Algorithm Design and Analysis
  • This course introduced concepts related to the design and analysis of algorithms. Topics included recurrence relations (and their role in asymptotic and probabilistic analysis of algorithms), greedy strategies, divide-and-conquer techniques, dynamic programming, and the max flow - min cut theory. Topics were emphasized through the illustration of well-known problems and applications. Homework and tests were used to demonstrate competency.
• Advanced Programming Languages
  • This class provided a foundation in programming language specification and design. Topics addressed included the history of programming languages, the purpose of language specification, functional languages, recursion, abstract data types, abstract syntax, interpreters, user-defined functions, scoping, closure, streams, variable assignment, object-oriented languages, and attribute grammars. The class used the Scheme meta-language for in-class discussion, homework, and testing. The class required building various components of an object-oriented interpreter in Scheme.

• IEEE Student Member; Columbus, Ohio Section

• U.S. Citizen