Signal Processing Commons^™

Ground Truth Images, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Laboratory and scenario ground truth images for the Model Desert Terrain Monochromatic DoT dataset.

Asl File Reader (Matlab), Bradley M. Ratliff

Source Code

This zip file contains the necessary files for reading binary ASL data and reading and parsing ASL header file metadata in MATLAB.

Polarimetric Data: Scenario 01, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric data Scenario 01 collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.

Data Annotations, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Pixel-wise object masks for each polarimetric scene in ASL file format for the Model Desert Terrain Monochromatic DoT data.

Dataset Description, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric dataset containing data collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. A model desert terrain model was constructed and imaged for eight different scenarios consisting of different model panel and vehicle targets. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.

A Yolo-Based Deep Learning Approach For Vibration-Based Rotating Shaft Imbalance Detection, Abdelhameed Zayed Dr., Ali Siam Dr.

Journal of Engineering Research

One of the prevailing causes of vibrations in machines is rotor imbalance. Rotor balancing can be used to fix the majority of rotating machinery issues. When it comes to high-speed running equipment, even a slight imbalance can lead to serious issues and decrease the operational efficiency of rotating machinery. This work proposed a deep learning approach for the detection of binary and multiclass imbalance in rotating shafts. A YOLOv11 model-based approach is developed to detect imbalance and identify unbalanced rotor positions. To precisely identify unbalanced positions, this method trains the YOLOv11 model using numerous sets of measured response data and …

Multi-Modal Covid-19 Detection Using Cough Sounds And Medical Information With Attention-Enhanced Deep Learning, Mohamed Talaat Saidahmed, Reda Elbasiony, Marwa Reda Bastwesy, Asmaa Aly Hagar

Journal of Engineering Research

The COVID-19 pandemic has highlighted the need for fast, non-invasive, and cost-effective diagnostic tools. Cough sounds, as a prominent symptom of respiratory diseases, present a promising modality for automated COVID-19 detection. In this study, we propose a novel multi-modal deep learning framework for COVID-19 detection that leverages cough sounds and patient-specific medical information. Our approach extracts two types of acoustic features—Mel-Frequency Cepstral Coefficients (MFCCs) and Mel spectrograms—and integrates them with clinical metadata to improve diagnostic ac-curacy. The MFCC branch employs 1D convolutional layers followed by Efficient Channel Attention mechanism. The Mel spectrogram branch utilizes ResNet-50 combined with ECA to capture …

Leveraging Machine-Learning Algorithms In Two Car Crash Detection Systems On A Custom Dataset, Addison Jacob Sandvik

Master's Theses

Traffic accidents pose a significant threat to public safety, causing millions of deaths and injuries worldwide each year. While efforts to reduce accidents have seen limited progress in recent years, improving emergency response times through automated detection systems is a promising avenue for saving lives. This thesis describes the development of machine learning-based traffic accident detection systems, exploring both video classification and image detection models. The models are trained on a new dataset deemed the Cal Poly Traffic Accident Dataset, an extension of the existing Car Accident Detection and Prediction (CADP) dataset with a precise collision annotations. Two systems were …

Coherent Synchronization For Distributed Digital Phased Arrays, Zachary C. Numa

Master's Theses

Distributed digital phased arrays are rising technologies that help enable applications such as search and rescue operations, wireless communication, radar navigation, and military operations, among many others. Due to their improved angular resolution, digital phased arrays offer superior direction-finding capabilities compared to traditional analog phased array systems. However, this improvement comes at the cost of increased complexity—specifically, the need for precise synchronization of phase, time, and frequency across physically separated nodes. Without synchronization, the distributed phased array's gain and direction-of-arrival (DoA) estimations deteriorate significantly.

There are multiple aspects to implementing and synchronizing a non-stationary distributed digital phased array. This research …

Single-Sideband Pulse Width Modulation For Parametric Acoustic Arrays, Douglas Liu

Master's Theses

Parametric acoustic arrays are directional loudspeakers that operate using ultrasonic carriers to project sound within a narrow beam. Input audio is first modulated onto an ultrasonic carrier and transmitted through air, where it self-demodulates into audible frequencies in the far field.

This thesis introduces a method for preprocessing audio into scaled quadrature signals using a passive analog polyphase filter. These signals are modulated using microcontroller-generated waveforms to create quadrature ultrasonic pulse-width modulation (PWM) signals. The modulated outputs are combined through a wired-OR summer, producing single-sideband ultrasonic content at the desired frequency of 40 kHz. This signal is amplified through high-efficiency …

Single-Sided Hearing Assistance Application, Harryson Nguyen, Jp Haratani

Electrical Engineering

The product is a hearing assistance smartphone application which aims to improve the quality of life of people with single-sided hearing loss/deafness by providing them with a cheaper and more reliable alternative to medically-prescribed hearing aids. Due to development setbacks, the product instead uses open-ear earbuds. Since the earbuds are not directly inserted into the user’s ear canals, they will not obstruct the hearing of the user’s non-deaf ear, and the user will still be able to hear the sounds that come from their non-deaf side. Some users may find that Bluetooth earbuds can be quite expensive, but are ultimately …

Piezoelectric Actuator Driver System, Daniel Hoefer

Electrical Engineering

This work presents the design, fabrication, and testing of a high-voltage piezoelectric actuator driver system purposed for integration into defense and aerospace precision motion control applications, particularly those in laser optics. The core challenge addressed was the mismatch between the standardized 28V DC power bus used in military systems and the significantly higher voltage requirements of piezoelectric actuators. Existing commercial drivers were evaluated and found unsuitable for field deployment due to inadequate ruggedness, excessive complexity, or power limitations. Consequently, a modular system architecture was designed featuring four independent driver channels, each using a high-voltage, high-current linear amplifier topology to scale …

Synthetic Aperture Radar Processing For Increased Resolution Of Englacial Strata, Ava Stockman

Electrical Engineering

Glaciological research relies on a variety of remote-sensing methods to study the internal structure of glaciers and other bodies of ice. Synthetic Aperture Radar (SAR) serves as one method for imaging large cross sections of englacial strata, reducing the need for labor-intensive, costly, and hazardous ice coring expeditions and operations. Characterizing englacial structures using SAR images allows scientists to learn about the ice dynamics of past and present which provides the basis for discoveries about climate history and glacial dynamics. This project explores the benefits of employing various SAR processing methods to raw phase history data acquired through the British …

Field Deployable Mobile Manipulator For Autonomous Apple Harvesting, Gabriel K. Basus, Antonio Bowen, Andrew Daouda

Electrical Engineering

Increasing labor costs and agricultural demands have created a need for automated apple harvesting. However, automated apple harvesting via robotic manipulation must overcome certain challenges to be an effective and efficient method. First, the system must compete with or perform better than manual human labor. Second, it must safely and accurately navigate an apple orchard autonomously. Third, the robotic manipulator must be able to securely grasp and pick apples without causing damage. To address these challenges, this project utilizes a Husky UGV equipped with a 2D LiDAR sensor, an RGB-D camera, an IMU, an OpenManipulator-X robot arm, and a soft …

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 …