Electrical and Computer Engineering Commons^™

Gait Characteristics In People With Friedreich Ataxia: Daily Life Versus Clinic Measures, Hannah L. Casey, Vrutangkumar V. Shah, Daniel Muzyka, James Mcnames, Mahmoud El-Gohary, Kristen Sowalsky, Delaram Safarpour, Patricia Carlson-Kuhta, Christian Rummey, Fay B. Horak, Christopher M. Gomez

Electrical and Computer Engineering Faculty Publications and Presentations

Gait assessments in a clinical setting may not accurately reflect mobility in everyday life. To better understand gait during daily life, we compared measures that discriminated Friedreich ataxia (FRDA) from healthy control (HC) subjects in prescribed clinic tests and free, daily-life monitoring.MethodsWe recruited 9 people with FRDA (median age: 20, IQR [12, 48] years). A comparative healthy control (HC) subject cohort of 9 was sampled using propensity matching on age (median age: 18 [13, 22] years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory during a 2-min walk at a natural pace, followed by …

Distributed Repeaters For Extending The Range Of Harmonic Rf Tags, Trevor Saunders

USF Tampa Graduate Theses and Dissertations

Passive harmonic Radio Frequency (RF) tags are highly advantageous compared to theiractive counterparts due to their ability to operate effectively in environments that are cluttered or where access to the tag for battery maintenance is challenging, dangerous and/or time consuming. They are also resistant to the local oscillator (LO) phase noise in radar applications where the doppler frequency from slow moving targets is buried beneath the noise. One of the major constraints of passive harmonic RF tags, however, lies in their operating range and the significant amount of power required to overcome the conversion loss and activate the nonlinear device …

Introducing A Novel Figure Of Merit For Evaluating Stability Of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks, Zahraa Ismail, Ahmed Alali, Mahmoud Ashraf, Ahmad Muhammad, Sameh O. Abdellatif

Electrical Engineering

This study introduces a novel figure of merit for evaluating the stability of perovskite solar cells (PSCs) by employing advanced Long Short-Term Memory (LSTM) neural networks to investigate degradation mechanisms. By harnessing the power of artificial intelligence and data analytics, we analyzed extensive datasets encompassing PSC parameters, experimental results, and environmental conditions, revealing critical insights into the degradation patterns affecting cell performance over time. Our findings indicate that the LSTM model effectively captures and predicts the complex relationships between key design parameters—efficiency, fill factor, and open-circuit voltage—and degradation-induced changes in PSCs. Specifically, we identified three degradation coefficients associated with the …

Modeling And Simulations Of An Input Coupler For A High-Power Traveling Wave Tube Amplifier As A Preliminary Investigation Into A Multi-Stream Traveling Wave Tube Amplifier, Ethan Wade

Electrical and Computer Engineering ETDs

The University of New Mexico (UNM) is working on developing a multi-stream Traveling Wave Tube (TWT) amplifier. One of the first steps to be taken during the design of a TWT amplifier is to design an input coupler to couple power from a lower power High Power Microwave (HPM) seed source to the slow wave structure (SWS) of the amplifier. In order to aid in the development of a TWT which operates in X-band, multiple options for input couplers have been designed for use in a greater TWT project at UNM. The primary tasks to be addressed with these input …

Emg-Based Intraoperative Neuromonitoring Using Advanced Machine Learning Approaches, Abdalla Nabil Elsharkawy, Nourhan Zayed

Mechanical Engineering

Intraoperative neuromonitoring (IONM) plays a critical role in minimizing nerve damage during surgeries by providing real-time feedback on neural integrity. This study evaluated models associated with deep learning and machine learning models for electromyography classification of signal during intraoperative neuromonitoring (IONM). The CNNLSTM model achieved the highest accuracy (85.2%), outperforming traditional models like KNN (53%), RF (62%), and CNN (76%). This demonstrates the degree to which the CNN-LSTM model can gain insight into temporal and spatial dependencies throughout the EMG signals, which makes it optimal for real-time classification in IONM applications. This implies that deep learning techniques can improve surgical …

Neurophisology Biosignals Of Cognitive Training Classification In Virtual Reality Environment Using Deep Learning Model, Nourhan Zayed, Mohamed Reda

Mechanical Engineering

This research investigates the potential of neurophysiological biosignals fusion, such as electroencephalogram (EEG) and Eye Tracking signals (ET), to classify cognitive states during virtual reality (VR) training, specifically for the rehabilitation of neurodegenerative diseases. By analyzing EEG data collected from participants engaged in VR-based cognitive exercises, we aim to identify patterns associated with different cognitive states and develop a robust classification system. A Convolutional Neural Network (CNN) model was developed to predict task performance utilizing neurophysiological inputs in an immersive world. This system could be used to monitor cognitive function, assess treatment efficacy, and provide real-time feedback to adapt the …

Electrochemical Detection Of Dopamine Using Screen-Printed Graphene Electrode For Cancer Diagnosis And Therapy, Pritu P. Sarkar, Nazmul Islam

Research Symposium

Background: Dopamine plays a critical role in various essential functions, including motor control, hormone regulation, cognition, learning, and the reward system. In healthy individuals, dopamine levels are extremely low, with concentrations ranging from 0 to 0.25 nM in blood and 0.3 to 3.13 µM in urine. Abnormal levels are linked to disorders like Parkinson’s, schizophrenia, Alzheimer’s, epilepsy, hypertension, and arrhythmia. Abnormal dopamine levels can indicate the presence of certain cancers. Dopamine receptors may be therapeutic targets for treating cancer, especially breast and colon cancer. Thus dopamine can increase the efficacy of anticancer drugs in breast and colon cancer. Detecting dopamine …

Conversational Voice User Interfaces Supporting Individuals With Down Syndrome: A Literature Review, Franceli L. Cibrian, Concepción Valdez, Lauren Min, Vivian Genaro Motti

Engineering Faculty Articles and Research

Conversational Voice User Interfaces (CVUIs) are widely used in commercial applications such as personal assistants. CVUIs are beneficial for most users as they enable interaction through speech and natural language. However, recent studies indicate that underrepresented user groups, such as individuals with speech impairments and specifically those with Down syndrome, face challenges in using voice commands to control CVUIs. The anatomical and physiological differences affecting the voice, speech, fluency, and prosody of users with Down syndrome hinder their experience with CVUIs. This article presents the results of 43 papers related to the use of voice user interfaces supporting individuals with …

Free Space Optical Communications: Challenges, Mitigation Techniques, Classification Framework, And Standardization, Moataz Mohamed Roshdy, Ahmed Hosny Elghandour, Kamel Mohamed Hassan

Future Engineering Journal

Over the past several years, there has been a renewed interest in Free Space Optical (FSO) communication technology, commonly referred to as Optical Wireless Communications (OWC). FSO is sometimes viewed as a substitute for current technology, such radio frequency. In other situations, FSO is viewed as a potent contender to work in tandem and complement next-generation technology, such 5G wireless networks. FSO technology is therefore widely used in a variety of indoor (such as data centers), terrestrial (such as mobile networks), space (such as intersatellite and deep space communication), and underwater systems (such as underwater sensing). Nevertheless, despite the enormous …

Mg And Fe Co-Doped Perovskites For The Electrochemical Oxidative Coupling Of Methane In Solid Oxide Electrolyzers, Luke H. Denoyer

Chemical and Biological Engineering ETDs

More efficient usage of shale gas reserves and natural gas resources will allow for higher olefin yields and lower greenhouse gas emissions. The oxidative coupling of methane (OCM) is a direct pathway for converting methane to ethylene at temperatures >700 °C. The OCM process is typically regulated via the choice of catalyst, reactor conditions, temperature range and gas mixing. The electrochemical oxidative coupling of methane (E-OCM) is a modification to the OCM reaction by controlling the oxygen content via externally applied potentials to an SOEC. Perovskite oxide catalysts are useful for E-OCM due to their flexible bond networks and desired …

Gratings For Enhanced Sensitivity: Advancing D-Shaped Optical Fiber Surface Plasmon Resonance Sensors With Ag-Fe2o3 Structures, Yomna Seliem, Sameh O. Abdellatif

Electrical Engineering

This research delves into enhancing biosensing sensitivity by optimizing D-shaped optical fiber surface plasmon resonance (SPR) sensors employing - structures. By investigating the influence of different grating structures—rectangular, triangular, and elliptical—on sensor performance, a comprehensive analysis was conducted to ascertain the impact of these structural variations on sensitivity and detection precision. The study revealed that while the rectangular structure exhibited a sensitivity of 6.4 µm/RIU, the triangular structure outperformed with an impressive sensitivity of 7.2 µm/RIU. Moreover, the detection accuracy, quantified by the detection angle (DA), reached µ for the triangular grating, surpassing the rectangular grating’s detection angle of µ. …

Multiscale Materials Characterization And In-Situ Process Monitoring In Additive Manufacturing Via Non-Contact Optical Thermometry Techniques, Rifat-E-Nur Hossain

Doctoral Dissertations

While additive manufacturing (AM) is experiencing rapid growth, its development is uneven across different branches. Some areas are still emerging, while even the more established branches are still facing ongoing challenges that require further development. Regardless of their development stage, both emerging and mature AM require process monitoring and part characterization. Process monitoring helps to achieve more control over the process and build a self-adaptive system, while characterization of printed parts speeds up process optimization and ensures required quality. Together, process monitoring and build characterization will transform AM into a more dependable and commercially viable technique. Build surface temperature is …

Discrete-Time Signal Processing, Carl Greco

ATU Faculty OER Books and Materials

No abstract provided.

Novel Approach To Traveling-Wave-Based Fault Location In Nonhomogeneous Transmission Lines, John Parker Burrell

Master's Theses

Accurate fault location is a critical aspect of power system protection, ensuring grid reliability and minimizing downtime. Traditional traveling-wave-based fault location methods face limitations when applied to nonhomogeneous transmission lines due to the reliance on precise segment velocities and propagation time data. This thesis addresses these challenges by proposing a novel algorithm that leverages historical fault data to estimate segment velocities and refine these estimates as more faults occur. The algorithm was rigorously tested using the digital model of an 11-segment, 65.694 km real overhead transmission line and validated using both simulations and hardware tests using commercially available time-domain protective …

Real-Time Prediction Of Dynamical Systems Using A Hybrid Analog Computer: Network Traffic Modeling, Majd Zuhair Tahat

Doctoral Dissertations

As the number of online users grows exponentially, the number and severity of cyber threats escalate, urgently requiring advancements in real-time network modeling and response. Swiftly predicting and analyzing network traffic is crucial for effective network monitoring and control, preventing cyber breaches, and maintaining healthy network functionality. This research presents a novel approach to real-time modeling based on analyzing evolving properties and patterns in a dynamical network system using a hybrid analog-digital computer. An analog computer was utilized as a co-processor to compute differential equations that model the Transmission Control Protocol (TCP) window size. A comparative analysis was conducted between …

Performance Of Neural Networks On Fpga For Embedded Devices Using Hls4ml Framework, Alam N. Romo Lopez

Master's Theses

As machine learning models such as neural networks are investigated for their applications across many fields, the demand for models that can be implemented on an embedded device grows. Field Programmable Gate Arrays (FPGAs) have become an attractive option for implementing these models in hardware. This thesis considers the viability of FPGA implementations for machine learning on low-resourced embedded systems. The hls4ml project is a promising prospect for machine learning on FPGA devices. To test hls4ml, we used a model trained on the MNIST digits dataset, which was then synthesized for the PYNQ-Z2 device. We fine tuned the performance by …

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.

Numerical Studies Of Semiclassical Light Storage Using The Coherent Atomic Transfer Function, Zachary T. Johnson

Theses and Dissertations

Quantum communication through photons relies on photonic storage to preserve quantum states. However, when photons interact with matter, quantum information becomes distorted. A recently developed semi-classical analytical model predicts output light pulses from an electromagnetically induced transparency (EIT) system. Using the predictions, the model known as the Coherent Atomic Transfer (CAT) Function, is capable of predicting the stored pulse or reconstructing the original pulse. Using numerical convolution and deconvolution with the CAT function as an analog of the point spread function of Fourier optics can provide insights on the effects of EIT storage on the retrieved pulse. Blind deconvolution is …

Applying Image Processing And Computer Vision For Damage Detection In Photovoltaic Panels, Montaser Abdelsattar, Ahmed Abdelmoety, Ahmed Emad-Eldeen

Mansoura Engineering Journal

As the use of Photovoltaic (PV) panels as a sustainable energy source grows, there is a need for effective and precise techniques to monitor and manage these systems. Conventional techniques for identifying faults in PV panels, such as manual inspections, require a significant amount of effort and are susceptible to mistakes made by humans. This study introduces an innovative automated method that utilizes image processing techniques implemented using the OpenCV library to identify panel faults, namely hotspots, which are important indications of possible failures. The study approach combines grayscale conversion, histogram analysis, and adaptive thresholding to accurately detect and evaluate …

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.

Quantum Ghost Imaging Through An Atmospheric Turbulence Simulator, Mikaela R. Morris

Theses and Dissertations

The use of single photons and photon pairs has become a cornerstone for new developments in the sensing and imaging community for their ability to improve image resolution and signal to noise readings in low light environments and large standoff distances. Quantum imaging techniques have an ability to overcome the fundamental limits of classical imaging, like diffraction. Quantum ghost imaging, a specific type of quantum imaging, uses a bi-photon pair produced during the process of spontaneous parametric down conversion to produce an image. The pair is split using a beam splitter, the signal photon is transmitted through an aperture and …

Development And Validation Of An Artificial Intelligence System For Surgical Case Length Prediction, Adhitya Ramamurthi, Bhabishya Neupane, Priya Deshpande, Ryan Hanson, Kellie R. Brown, Kathleen K. Christians, Douglas B. Evans, Anai N. Kothari

Electrical and Computer Engineering Faculty Research and Publications

Background

Accurate case length estimation is a vital part of optimizing operating room use; however, significant inaccuracies exist with current solutions. The purpose of this study was to develop and validate an artificial intelligence system for improved surgical case length prediction by applying natural language processing and machine-learning methods.

Methods

All inpatient elective surgical cases longer than 30 minutes completed between 2017 and 2023 at a single, quaternary care hospital were considered. Data were split into training, test, and hold-out validation for model training and testing. Linear regression, CategoricalBoost, and feed-forward neural network each were trained and used embeddings created …

Industry Applications Society And Conferences [President’S Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Industry Applications Society And Conferences [Presidents Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

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 …

A Survey On Energy Efficient Medium Access Control For Acoustic Wireless Communication Networks In Underwater Environments, Walid K. Hasan, Iftekhar Ahmad, Daryoush Habibi, Quoc Viet Phung, Mohammad Al-Fawa'reh, Kazi Yasin Islam, Ruba Zaheer, Haitham Khaled

Research outputs 2022 to 2026

Underwater communication plays a crucial role in monitoring the aquatic environment on Earth. Due to their unique characteristics, underwater acoustic channels present unique challenges including lengthy signal transmission delays, limited data transfer bandwidth, variable signal quality, and fluctuating channel conditions. Furthermore, the reliance on battery power for most Underwater Wireless Acoustic Networks (UWAN) devices, coupled with the challenges associated with battery replacement or recharging, intensifies the challenges. Underwater acoustic communications are heavily constrained by available resources (e.g., very limited bandwidth, and limited energy storage). Consequently, the role of medium access control (MAC) protocol which distributes available resources among nodes is …

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 …