Signal Processing Commons^™

Calibration And Demonstration Of A Dropped Channel Polarimetric Compressive Sensing Radar System, Cameron G. Goodbar

Theses and Dissertations

The Air Force Institute of Technology (AFIT) Dropped Channel Polarimetric Compressive Sensing (DCPCS) Radar System is a polarimetric radar utilizing four horn antennae with a unique cross-coupling architecture that enables direct control of system parameters to embed signals into adjacent channels. This thesis characterizes the nature of the system, develops system calibration, and illustrates the performance of the DCPCS technique under multiple system configurations. As shown in the results, DCPCS can successfully reconstruct full-polarization data from a subset of polarization measurements. In many cases, the target estimation and signal reconstruction is precise despite less-than-ideal conditioning of the canonical target dictionary …

Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros

Theses and Dissertations

Pacific Islands under U.S. jurisdiction are highly vulnerable to natural disasters, yet many lack the infrastructure to effectively respond and recover. Clear communication during and after such events is critical for evacuation, hazard awareness, and first responders’ coordination. This research explores a simulation-based approach using Bluetooth communication to relay messages across Guam, assessing its efficiency through statistical analysis. By examining regional differences and geographic impacts on Bluetooth messaging, the study aims to identify key factors that enhance peer-to-peer communication for timely and effective disaster response.

Statistical Analysis Of Spreading Code Authentication (Sca) Performance Under Varying Signal Conditions And Marker Quantization Schemes, Joseph Quinones-Ocasio

Theses and Dissertations

This thesis analyzes Spreading Code Authentication (SCA) in the GPS L1C signal using the PyChips software-defined receiver (SDR)framework. Monte Carlo simulations evaluate authentication performance under varying signal conditions, assessing the impact of double-precision and quantized data on signal integrity. Results demonstrate that authentication remains achievable at low signal-to-noise ratio(SNR) conditions but introduces trade-offs in memory usage and authentication time. These findings provide insights into optimizing SCAfor resource-constrained environments, contributing to secure GPS operations in critical applications such as aviation, autonomous systems,and national defense.

Development Of An Advanced 16-Channel High-Fidelity Multi-Frequency Software-Defined Rf Front-End For Advanced Satnav Signal Monitoring Applications, Melbourne T. Ketteridge

Theses and Dissertations

Multi-element antenna array technology provides significant performance advantages in satellite timing and navigation (satnav) receiver applications. It is the most effective anti-jamming method with the ability to place steep nulls in the direction of jammers. Until recently, satnav systems with 4 or more antenna elements were designated as weapons technology and restricted under ITAR regulations. This opens the door to commercial multi-element satnav receivers. Due to advancements in wireless broadband technology a receiver built entirely using commercial off-the-shelf (COTS) components is possible. This thesis presents an architecture for a high fidelity 16-channel RF front-end (RFFE) for research and development of …

Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles: Process, Application, And Comparison, William R. Smith

Theses and Dissertations

Lightning waveforms in the low frequency (LF; 30-300 kHz) and the very low frequency (VLF; 3-30 kHz) can be exploited to produce data-driven ionospheric Dregion electron density profile (EDP) estimates with significantly higher spatial and temporal coverage than previously available. The lightning waveforms used in this paper are signals detected in the LF/VLF of negative cloud-to-ground lightning by the Earth Networks Total Lightning Detection Network. Each waveform contains a ground wave and a time-delayed ionospheric reflection. The time delay between the ground wave and ionospheric reflection has previously been used to estimate a single specular reflection altitude, where LF/VLF emissions …

Deep Learning-Based Gain Estimation For Multi-User Software-Defined Radios In Aircraft Communications, Viraj K. Gajjar, Kurt L. Kosbar

Electrical and Computer Engineering Faculty Research & Creative Works

It may be helpful to integrate multiple aircraft communication and navigation functions into a single software-defined radio (SDR) platform. To transmit these multiple signals, the SDR would first sum the baseband version of the signals. This outgoing composite signal would be passed through a digital-to-analog converter (DAC) before being up-converted and passed through a radio frequency (RF) amplifier. To prevent non-linear distortion in the RF amplifier, it is important to know the peak voltage of the composite. While this is reasonably straightforward when a single modulation is used, it is more challenging when working with composite signals. This paper describes …

Deep Reinforcement Learning For Leo Satellite Grouping, James E. Minteer

Theses and Dissertations

This research investigates jamming evasion using DRL to provide an autonomous solution that repositions a geostationary satellite experiencing directed, terrestrial based jamming. Second, this thesis applies DRL to an area of research for LEO satellite constellations, user grouping, using a portion of an Air Force Research Lab reinforcement learning framework. As LEO satellites orbit the earth, they must constantly re-evaluate not only which users they are able to connect to, but on which of its multiple beams. This model succeeds in finding a balance between maximizing signal strength and minimizing overhead from switching user assignments, all while requiring fewer costly …

Harmonicthreads – An Interface That Supports Accessibility In Musical Interaction, Ellie Nguyen, Miyuki Weldon, Franceli L. Cibrian

Engineering Faculty Articles and Research

Traditional musical instruments often can create boundaries due to their cost, training, mobility, and cognitive requirements, making musical expression inaccessible. To address this challenge, we developed HarmonicThreads, a novel pervasive computing interface consisting of a responsive, flexible fabric. HarmonicThreads provides a tactile and auditory experience, allowing users to easily create and control sounds. Using embedded sensors and real-time processing, HarmonicThreads interprets the user's natural movements and interactions to create adaptable musical outputs. This enables context-aware musical interaction, demonstrating the potential of pervasive interfaces in reducing barriers and making musical expression more accessible.

Increasing Guard Band Size To Decrease Interference In V2x Communication, Nakira Oglesby, Mackenzie Prescott, Billy Kihei, Ph.D.

Symposium of Student Scholars

As technologies evolve and new devices are introduced, the demand for fast and reliable vehicle-to-everything (V2X) communication increases. As this demand increases, the interference level in the 5.9GHz Dedicated Short Range Communications (DSRC) band will inevitably increase. And thus, the task of somehow minimizing this interference becomes increasingly important as time passes. This report investigates the effects of increasing the guard band size of the lower 5.9 GHz DSRC band on the adjacent channel interference from Unlicensed National Information Infrastructure 4 band (U-NII-4) devices and to try and see if there is a significant decrease in the interference level. The …

Novel Electrophysiological Biomarkers In Pediatric Drug Resistant Epilepsy And Genetic Epilepsy Syndromes, Sakar Rijal

Bioengineering Dissertations - Archive

Pediatric epilepsies, particularly those that are drug-resistant or genetically driven, represent some of the most complex neurological disorders encountered in childhood. Central to their pathophysiology is a disruption in the delicate balance between cortical excitation and inhibition (E/I), often resulting from impaired GABAergic interneuron function. This imbalance manifests as aberrant network dynamics and altered neural oscillations, giving rise to seizures and long-term cognitive impairments. In this thesis, we developed a translational framework to identify electrophysiological biomarkers that (i) assess cortical E/I imbalance and (ii) map epileptogenic zones, with the aim of enhancing diagnosis, guiding surgical planning, and informing therapeutic monitoring …

Smart Irrigation System Using Iot And Lstm For Optimal Water Management, Farley Y. Ruiz

Electrical Engineering Theses - Archive

This thesis presents the design and implementation of a smart irrigation system that combines Internet of Things hardware with a Long Short-Term Memory (LSTM) neural network for predictive soil moisture management. The goal is an affordable and reliable solution that uses real-time sensor data and environmental data to schedule irrigation before the substrate moisture drops below its target range. The system integrates soil moisture, temperature, humidity, and sensors on an Arduino Nano that communicates wirelessly with a Raspberry Pi. The Raspberry Pi runs a Python/Flask backend that collects and processes data, executes the LSTM model, and serves a secure web …

Power Quality Event Diagnosis Using Multi-Rate Neural Networks, Jordan D. Lloyd

Electrical Engineering Theses - Archive

Power quality disturbances (PQDs) are among the primary challenges facing modern electrical systems, as they degrade the performance and lifespan of connected equipment. This thesis investigates the relationship between the rate at which voltage waveform data are sampled, the reliability of these measurements, and the ability of deep neural networks to classify PQDs accurately. A one-dimensional convolutional neural network (CNN) was trained and evaluated across multiple sampling rates and signal-to-noise ratios to quantify how information loss in the temporal and spectral domains affects classification reliability. The results demonstrate that model accuracy degrades nonlinearly as sampling rate and signal-to-noise ratio (SNR) …

Revealing Spatiotemporal Neural Activation Patterns In Electrocorticography Recordings Of Human Speech Production By Mutual Information, Julio Kovacs, Dean Krusienski, Minu Maninder, Willy Wriggers

Mechanical & Aerospace Engineering Faculty Publications

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, …

Study Of Deep Neural Network Trained With Salient, Compressed Medical Video Data For Enhanced Predication, Aileen Sengupta

Electrical Engineering Dissertations - Archive

The rapid growth of surgical video analysis presents a need for efficient deep learning models for surgical training, while reducing the need for excessive image and video image storage. Traditional training approaches typically rely on uniformly compressed video data, instead of selectively preserving the most surgically relevant regions. This dissertation investigates the impact of training deep neural networks (DNNs), both convolutional and transformer-based architectures, on saliency-guided, differentially compressed surgical video sequences. The study systematically evaluates how such compression influences prediction accuracy, computational efficiency and storage requirements. Experimental results demonstrate that models trained on ROI-focused compressed data combined with motion vectors …

Neurovascular Coupling Impairments In Acute Traumatic Brain Injury: An Eeg-Nirs Analysis, Zachary Armstrong

Bioengineering Theses - Archive

Traumatic brain injury (TBI) is a major cause of neurological impairment, often leading to variable recovery and uncertain prognosis in the neurocritical care setting. There is a pressing clinical need for robust, physiologically grounded biomarkers to inform prognosis and therapeutic decision-making in acute TBI. This thesis investigates neurovascular coupling (NVC), the physiological coordination between neuronal activity and cerebral blood flow, as a candidate biomarker for brain function and recovery after injury.

A prospective cohort study was performed using simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings in patients with moderate-to-severe TBI and healthy controls. Wavelet transform coherence (WTC) analysis was …

Green Bank Chime/Frb Outriggers Commissioning And Analog System Development, Kholoud Sharif Tag Alkhatem Khairy

Graduate Theses, Dissertations, and Problem Reports (ETD)

The main objective of this thesis is to document the development and commissioning of the Canadian Hydrogen Intensity Mapping Experiment. (CHIME) outrigger at the Green Bank Observatory. (GBO) in Green Bank, WV. This novel cylindrical wide-field radio transient telescope is currently operating in conjunction with CHIME. The CHIME outrigger at GBO aims to contribute significantly to the field of radio astronomy, with implications for both Fast Radio Burst. (FRB) science and broader astronomical research. The construction and commissioning of the CHIME outrigger at the GBO mark a pivotal step forward in pursuing high-precision wide-field detection and localization of radio transients, …

Transformer-Based Symbolic Music Generation, Ben Buentello

Master’s Theses

This thesis investigates the capacity of transformer-based architectures to learn generalized musical patterns through symbolic generation. To support this exploration, a complete music generation pipeline was developed, beginning with the construction and classification of a large-scale dataset of over 170,000 MIDI files. The dataset was processed using rule-based heuristics and custom neural classifiers to separate tracks by musical function and contour. A novel tokenization scheme, MINTii, was introduced to encode musical information compactly through interval-based representations, reducing redundancy and promoting generalization. Using this infrastructure, a transformer model was trained to generate single-track melodic sequences. Its performance was evaluated through both …

Trace Flight Computer, Zachary Stratton, Vandad Mossavand Varkaneh, Nicholas Ely

Williams Honors College, Honors Research Projects

In the world of collegiate rocketry, there are currently no commercial-off-the-shelf flight computers capable of being fitted to liquid engine rockets with thrust vectoring control. Currently, amateur rocketeers utilize unreliable Arduino-based systems or expensive drone computers. The objective of this project is to design a flight computer capable of data collection and filtering, telemetry transmission, and real-time controls of critical safety systems using readily available commercial-off-the-shelf components to allow rocketeers to complete their designs within a reasonable budget. The system will collect data from two IMUs, a barometric altimeter, and a magnetometer, then filter the collected data using custom Kalman …

Cochlear Electrode Insertion Training Model, Sarah Powell, Kaelyn E. Kraley, Nathan J. Smith

Williams Honors College, Honors Research Projects

Cochlear implant surgery is a delicate procedure performed by Otolaryngologists (ENTs) to implant an electronic device into the inner ear to provide a sense of sound for people who are profoundly deaf or hard of hearing. The current practices of training involve cadavers and 3D-printed models. Cadavers are commonly used but are expensive, single-use, and do not provide visual and haptic feedback, which are essential for medical students. 3D printed models are less commonly used and are hard to fabricate and not as realistic. If medical students are not properly trained for this delicate procedure, then risks are significantly increased …

Vector Estimation For Continuous Tracking Of Observed Radio Signals (V.E.C.T.O.R.) Lunar Navigation System, Dimitry Melnikov, Evan Bartel, Andrew Burrier, Goran Gjorgievski

Williams Honors College, Honors Research Projects

NASA's Artemis program requires precise navigation capabilities to establish the first sustained presence on the lunar surface. However, as launches bring necessary orbital infrastructure, the Artemis program will face a critical period during which reliable lunar navigation is not possible. To address this challenge, the V.E.C.T.O.R. system tracks assets, such as rovers and astronauts, as User Terminals relative to a pre-existing cell tower, or Base Station. To do so, the system leverages existing Base Station hardware to calculate the location of User Terminals in conjunction with existing communications infrastructure.