Signal Processing Commons^™

Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover

Theses and Dissertations

The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …

Exploring The Limits Of Multimodal Foundation Models For Visual Temporal Reasoning And Gesture Recognition Tasks, Ziyao Shangguan

Computer Science Theses

Multimodal foundation models (MFMs) have demonstrated impressive capabilities in static vision-language tasks such as image captioning, video summarization, and cross modal retrieval. However, their ability to reason over time—especially in gesture-rich video inputs—remains limited. This thesis investigates the temporal reasoning capabilities of MFMs in the context of gesture understanding, a critical component for enabling more expressive human-robot interaction. Through a preliminary study, we show that prompting-based strategies offer only marginal improvements in temporal reasoning, despite producing accurate frame-by-frame descriptions.

To more rigorously evaluate these limitations, we introduce TOMATO, a benchmark designed to assess visual …

Adaptive Noise Estimation And Denoising With Deep Learning For Nmr Spectroscopy, Naveen Asokan

McKelvey School of Engineering Graduate Student Theses & Dissertations

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique widely used for molecular structure elucidation in chemistry, biology, and medicine. However, spectral accuracy is often degraded by noise—particularly in low acquisition time settings—resulting in reduced resolution and obscured chemical features. While traditional noise reduction techniques such as signal averaging can improve spectral quality, they require longer acquisition times, limiting their utility in real-time and high-throughput applications.

This thesis presents a deep learning-based denoising framework designed to enhance the quality of complex-valued NMR spectra. The proposed model, built upon a U-Net architecture, incorporates both real and imaginary components of the …

Development Of A Cost-Effective Daq For Measuring Brake Performance In Race Cars, Adrin Alias

2025 Spring Honors Capstone Projects - Archive

This project explores the feasibility of creating a cost-effective data acquisition (DAQ) system for high-speed, real-time brake performance testing of Formula SAE racecars. The research addresses the limitations of the current MoTeC DAQ system currently employed by the team, which is costly and time-consuming to set up for on-car testing. The team will use a brake dynamometer for steady-state comparisons of different brake pad compounds (senior design project), but evaluating real-world performance requires on-car testing. By systematically comparing various hardware platforms, sensors, communication protocols, and storage solutions, this project aims to balance cost-efficiency with reliability and performance. The research evaluates …

A Heuristic Approach To Portrait Segmentation And Its Application To Synthetic Bokeh Generation, Charles J. Snead

Master's Theses

Segmentation of portrait images is an important technique used to separate the foreground and background of an image. This separation of layers is useful for selectively applying post-processing techniques to enhance the quality of the image, such as blurring the background. Automatic portrait segmentation is a complex process that can be completed with a high degree of accuracy using deep learning with neural networks, but training and inference are often very computationally expensive. This thesis aims to take a heuristic approach to portrait segmentation by combining classical image processing and computer vision techniques into a solution that can be run …

Optimizing And Training An Svm-Based Breast Cancer Tumor Classifier, Kevin Lopatka

Master's Theses

With advancements in technology, turning to machine learning has become a popular choice for aiding clinicians in the diagnoses of breast cancer malignancies. While the neural networking approach has been vetted thoroughly, this work aims to take advantage of traditional machine learning techniques; mainly support vector machine learning and the optimizing of feature extraction. The discrete-wavelet transform is used in the feature extraction stage of machine learning. Previous works that use this feature extraction technique are analyzed and expanded upon by utilizing a variety of different wavelets as well as other color-spaces with the goal of achieving higher result metrics …

Enhanced Post-Capture Automatic White Balance For Srgb Images, Eric Huang

Master's Theses

Automatic white balancing (AWB) aims to correct color casts caused by varying illumination conditions, typically assuming access to RAW sensor data. However, many real-world applications involve only sRGB images that have already been processed by in-camera pipelines. In these cases, traditional AWB algorithms often underperform due to the nonlinear transformations done by these pipelines.

This thesis builds upon a data-driven color correction framework introduced by Afifi et al. that relies on RGB-UV histograms and learned color transforms. A revised automatic white balancing (AWB) framework that improves both color accuracy and runtime efficiency is proposed. A fallback routine is implemented to …

Characterizing Human Mobility Patterns In Saudi Arabia Using Cellular Data, Meshal Alnefaie

Theses and Dissertations

The study analyzes human mobility in Saudi Arabia. Using crowd-source data, Riyadh mobility is analyzed to find trends and highlight mobility patterns of individuals in Riyadh. Then, the mobility of Riyadh is compared with that of Jeddah and Dammam in a similar data collection and analysis. Four mobility metrics are utilized: Number of Visited Locations (NLOC), Number of Unique Locations (NULOC), Radius of Gyration (RGYR), and Distance Traveled (DTRV). The results show interesting outcomes about individuals in the three cities. Although these cities are far from each other, they observe the same mobility patterns. These findings have the potential to …

Deep Learning In Lung Cancer Pre- And Post-Radiation Therapy: Diagnosis Of Malignancy And Radiation-Induced Lung Injury From 3d X-Ray Ct., Benjamin Peter Veasey

Electronic Theses and Dissertations

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection and accurate diagnosis being critical for improving patient outcomes. Additionally, the progression of Radiation-Induced Lung Injury (RILI) following Stereotactic Body Radiation Therapy (SBRT) for lung cancer presents a significant diagnostic challenge. This dissertation addresses these challenges by developing deep learning-based diagnostic tools for both pre-treatment lung nodule malignancy classification and post-treatment RILI identification using 3D X-ray CT imaging. The research is divided into two primary objectives. First, for lung nodule malignancy classification, we developed a biopsy-confirmed dataset, called NLSTx, to train and evaluate deep learning models while …

Finding Groundwater With Electricity: Research And Testing Of An Electro-Resistive Ground-Surveying Device For Use In Well-Drilling Applications, Samuel L. Heath

Senior Honors Theses

A custom-built electro-resistive water detection device is tested under various conditions to evaluate its overall performance and sensitivity to changing variables. The tests, designed to assess accuracy and precision, revealed that the prototype exhibits high reliability (~ 99%) for both Schlumberger and Wenner arrays across different electrode spacings and soil conditions. Moreover, the measured resistivity values from the soil tests aligned with established literature ranges for each soil type. The device also showed the ability to consistently detect changes in soil water content by producing measurable variations in resistivity. At a total cost of $181, this prototype can serve as …

Lightlink: Visible Light Communication For Batteryless Devices With Variable Computational Load, Charles Phillips

All Theses

Visible Light Communication (VLC) devices have been experimentally proven to work as a suitable communication medium for batteryless devices. However, the effects of practical load have yet to be fully explored. To that end, we have developed LightLink, a new MAC and PHY layer protocol for VLC within batteryless devices, and have studied various ways that computational load can affect transmission accuracy in realistic scenarios. Our key findings point us towards an adaptive VLC reception system based on inferred environmental variables.

Adaptive Delay Compensation Frameworks For Distributed Real-Time Co-Simulation In Power Systems, Elutunji Buraimoh

All Dissertations

This dissertation presents a model-free, adaptive delay prediction and compensation framework for geographically distributed real-time power system co-simulation environments. Communication delays—both constant and real-time-varying—significantly degrade the accuracy, fidelity, and stability of co-simulated systems, particularly in dynamic and transient analyses of partitioned power systems. To address this, a predictor-based framework is developed that compensates for delays without requiring system models, computationally intensive signal transformations, or manual intervention.

The proposed solution leverages a Damping Impedance Method as the interface algorithm, combined with a sliding-mode control-inspired predictor system. Both single-parameter and multi-parameter predictor configurations are implemented, with the multi-parameter design providing an additional …

Artificial Intelligence Applications For Grid-Connected Solar Inverters, Utkirjon Ubaydullaev, Sarvinoz Mirzaeva, Hasan Mustafoev

Chemical Technology, Control and Management

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications.

This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, …

Static Characteristics Of An Electromagnetic Three-Phase Reactive Power Sensor For Converting Asymmetric Currents, Timur Urunbayevich Kurbaniyazov

Chemical Technology, Control and Management

This article discusses the processes in the elements and structures of three-phase electromagnetic current sensors used in measuring and controlling three-phase asymmetric reactive power in power supply systems, research models of quantities and parameters, physical mechanisms, and mathematical formulas based on graphical models.

Simultaneous Observations Of Irregular Sporadic E Structures Using The Lwa And A Dps4d, Kenneth S. Obenberger, C. A. Taylor, Jonah J. Colman, Eugene Dao, J. Dowell, J. D. Eccles, Daniel J. Emmons, C. T. Fallen, J. M. Holmes, G. B. Taylor

Faculty Publications

Multi-instrument studies have recently shed new light on the morphology of sporadic E, especially intense sporadic E. Here we present simultaneous observations of dense sporadic E (E_s) structures using the Long Wavelength Array (LWA) radio telescopes and a Digisonde Portable Sounder 4D (DPS4D). Our coordinated observations show that the LWA radio telescopes in central New Mexico can reliably locate regions of dense Es structures as they pass over a Digisonde located over 500 km away in Texas. The LWA appears to be most sensitive to the densest E_s structures, which also appear to contain irregularities with vertical …

Navajo Speech Recognition Using Low-Resource Language Models, Emery M. Sutherland

Electrical and Computer Engineering ETDs

This thesis describes the development of a speech recognition system to classify Navajo (Dine) words using Low Resource Language (LRL) datasets. Presently there are no recognized high-quality open-sourced datasets for the Dine language needed to train models for speech recognition. A small balanced dataset was designed to train several models. To overcome the scarcity of the LRL dataset, the audio recordings were augmented to account for time-stretching, amplitude variations, time shifts, small amounts of white Gaussian noise, and SpecAugmentation. The models included a Recurrent Neural Network (RNN), a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM) model with …

Examining Physiological Responses To Misophonic Triggers, Christian O'Reilly, Xuan Yang, Sewon Oh, Doug Wedell, Svetlana Shinkareva

Faculty Publications

We collected and analyzed an array of biosignals (face electromyogram, skin electrodermal activity, peripheral temperature, and electrocardiogram) in 60 participants with and without misophonia, a condition characterized by decreased tolerance to innocuous sounds. Our goal was to objectively characterize the physiological response to misophonia triggering sounds. We found that misophonic responses can be objectively identified in some cases through atypical physiological reactions to triggering stimuli, though not all participants exhibited this response. Our analyses suggest a large interindividual variability in response to misophonic triggers and highlights the need for methodological adjustments in future experiments to increase the detectability of misophonic …

On The Provenance Of Software Systems: Automating Software Traceability With Knowledge Graph And Large Language Model Synergy, Tyler Procko

Doctoral Dissertations and Master's Theses

The present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life …

Painting Rich Six-Dimensional Pictures Using Polarized Fluorescence Microscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

No abstract provided.

Non-Orthogonal Multiple Access Empowered Physical Layer Security Systems: Review, Issues And Challenges, Aml Fawzy, Mahmoud Selim, Maha Elsabrouty, Sameh Napoleon, Mustafa M. Abd Elnaby

Journal of Engineering Research

As 5G networks advance, the demand for higher data rates, enhanced spectral efficiency, and increased connectivity intensify. Non-orthogonal multiple Access (NOMA) addresses these needs by allowing multiple users to share the same time and frequency resources, thus optimizing network resource utilization and significantly boosting system capacity and throughput. NOMA's influence extends beyond traditional communication scenarios, impacting various vertical industries that require extensive connectivity, such as the Internet of Things (IoT). This transformative approach is crucial for industrial and critical mission applications. Given the importance of safeguarding these communications from potential eavesdroppers, Physical Layer Security (PLS) emerges as a vital tool. …

The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz

Faculty Publications

Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth …

Misalignment Uncertainty In Near-Field Thz Scattering Experiments, Philip Patterson

Theses and Dissertations

This research investigates the effect of misalignment on the near-field scattering of cylinders in the 550-700 GHz frequency band. A Type-1 calibration is performed on previously collected data, using a near-field physical optics solution to simulate scattering at various positions and orientations. The alignment of the cylinders at the time of measurement is predicted by comparing the range profiles of the theoretical and calibrated responses. The data with the most similar range profiles had a mean calibration difference metric of -2.78 dB and a standard deviation of -0.57 dB, demonstrating the presence of sources of error that are dominant over …

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.

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 …

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.

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 …

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 …

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 …