Digital Commons Network^™

Assistive Technologies For Independent Navigation For People With Blindness, Howard Kaplan

USF Tampa Graduate Theses and Dissertations

The necessity for individuals to navigate from location to location is essential to daily life. The majority of humans use sight as the main source for determining how to move through their environment. However, there is a large population of people who are either blind or living with low vision. Based on a 2017 National Health Survey there are around 27 million adult Americans that have experienced vision loss (National Center for Health Statistics, 2017). According to the Eye Diseases Prevalence Research Group there are 3.4 million Americans 40 years and older that are legally blind or visually impaired (EDPRG, …

Development Of A Testing Station For Grid-Interactive Smart Inverter Systems, Yudong Lan

Dissertations and Theses

As renewable energy penetration increases within the electrical power grid, flexible power is required to ensure reliability. Reliability can be jeopardized by the fluctuation and non-dispatch ability of renewable generators. This instability is caused by an imbalance between the amount of power produced and the amount of power consumed. Grid-enabled battery inverter systems are considered a solution to offset voltage and frequency fluctuations, which arise due to this power imbalance.

The objective of this work is to develop a system for testing grid-enabled inverters. These inverters use one or more communication standards to control their interactions with the grid. This …

Highly-Individualized Physical Therapy Instruction Beyond The Clinic Using Wearable Inertial Sensors, Samir A. Rawashdeh, Ella Reimann, Timothy L. Uhl

Physical Therapy Faculty Publications

Musculoskeletal conditions, often requiring rehabilitation, affect one-third of the U.S. population annually. This paper presents rehabilitation assistive technology that includes body-worn motion sensors and a mobile application that extends the reach of a physical rehabilitation specialist beyond the clinic to ensure that home exercises are performed with the same precision as under clinical supervision. Assisted by a specialist in the clinic, the wearable sensors and user interface developed allow the capture of individualized exercises unique to the patient’s physical abilities. Beyond the clinical setting, the system can assist patients by providing real-time corrective feedback to repeat these exercises through a …

Eye-Gaze-Controlled Hmds And Mfd For Military Aircraft, Lrd Murthy, Abhishek Mukhopadhyay, Somnath Arjun, Varshith Yelleti, Peter Thomas, Dilli Babu Mohan, Pradipta Biswas

Journal of Aviation Technology and Engineering

Eye-gaze-controlled interfaces allow the direct manipulation of a graphical user interface by looking at it. This technology has great potential in military aviation, in particular, operating different displays in situations where pilots’ hands are occupied with flying the aircraft. This paper reports studies on analyzing the accuracy of eye-gaze-controlled interfaces inside aircraft undertaking representative flying missions. We report that using eye-gaze-controlled interfaces, pilots can undertake representative pointing and selection tasks at less than two seconds on average in a transport aircraft. Further, we analyzed the accuracy of eye-gaze-tracking glasses under various G load factors and analyzed the failure modes. We …

Single Event Transient Sensitivity Measurement And Worst-Case Test Vector Exploration For Asic Devices Exposed To Space Single Event Environment, Mohamed Wael

Theses and Dissertations

Space radiation and nuclear reactors produce single event effects (SEE) in electronic circuits and impact their performance. The SEE phenomena cause circuits and electronic devices to fail by producing faulty results. Therefore, today’s circuit’s reliability is a significant concern for all circuit designers.

This thesis suggests a new automated flow to measure the single-event-transient (SET) effects in combinational circuits in application-specific integrated circuits (ASIC) while reaching full fault coverage. The developed flow characterizes the whole circuit nodes by identifying the most sensitive paths to the propagated SET pulses from the node under test to an observable primary output, causing single …

Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao

Electrical and Computer Engineering Faculty Publications

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …

Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao

Electrical and Computer Engineering Faculty Publications

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …

A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran

Electrical and Computer Engineering Faculty Publications

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep …

Module 01: Introduction To Architectural Cad, Keith Johnson, Mohammad Moin Uddin

ENTC 2160: Architectural CAD

No abstract provided.

Performance Of Xor Rule For Decentralized Detection Of Deterministic Signals In Bivariate Gaussian Noise, Xingjian Sun, Shailee Yagnik, Ramanarayanan Viswanathan, Lei Cao

Faculty and Student Publications

In this paper, we consider the performance of exclusive-OR (XOR) rule in detecting the presence or absence of a deterministic signal in bivariate Gaussian noise. Signals, when present at the two sensors, are assumed unequal, whereas the noise components have identical marginal distribution but are correlated. The sensors send their one-bit quantized data to a fusion center, which then employs the XOR rule to arrive at the final decision. Here we prove that, in the limit as the correlation coefficient r approaches 1, the optimum fusion rule for both parallel and tandem topologies is XOR with identical, alternating partitions (XORAP) …

Design, Analysis, And Application Of Flipped Product Chaotic System, Md Sakib Hasan, Partha Sarathi Paul, Anurag Dhungel, Maisha Sadia, Md Razuan Hossain

Faculty and Student Publications

In this paper, a novel method is proposed to build an improved 1-D discrete chaotic map called flipped product chaotic system (FPCS) by multiplying the output of one map with the output of a vertically flipped second map. Two variants, each with nine combinations, are shown with trade-off between computational cost and performance. The chaotic properties are explored using the bifurcation diagram, Lyapunov exponent, Kolmogorov entropy, and correlation coefficient. The proposed schemes offer a wider chaotic range and improved chaotic performance compared to the constituent maps and several prior works of similar nature. Wide chaotic window and improved chaotic complexity …

Data-Enabled Distribution Grid Management, Zohreh Sadat Hosseini

Electronic Theses and Dissertations

In 2020, U.S. electric utilities installed more than 94 million advanced meters, which brought the percentage of residential customers equipped with smart meters to 75%. This significant investment allows collecting extensive customer data at the distribution level, however, the data are not currently leveraged effectively to help with system operations. This dissertation aims to use the smart meters’ data to improve the grid’s reliability, stability, and controllability by solving two of the most challenging problems at the distribution level, namely distribution network phase identification and outage identification.

Distribution networks have typically been the least observable and most dynamic and locally …

Classification Of Electropherograms Using Machine Learning For Parkinson’S Disease, Soroush Dehghan

Electronic Theses and Dissertations

Parkinson’s disease (PD) is a neurodegenerative movement disorder that progresses gradually over time. The onset of symptoms in people who are suffering from PD can vary from case to case, and it depends on the progression of the disease in each patient. The PD symptoms gradually develop and exacerbate the patient’s movements throughout time. An early diagnosis of PD could improve the outcomes of treatments and could potentially delay the progression of this disorder and that makes discovering a new diagnostic method valuable. In this study, I investigate the feasibility of using a machine learning (ML) approach to classify PD …

Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman

All Undergraduate Theses and Capstone Projects

Low-cost PM sensors have garnered interest for their ability to reduce the cost of investigating PM concentrations in both indoor and outdoor spaces. They perform well in high concentration lab testing with correlation coefficients greater than 0.9. In real-world applications, the correlation coefficients drop significantly because of sensing floors and adverse ambient conditions. There are plenty of supervised machine learning techniques that aim to correct the measurements ranging from linear regression to more advanced neural networks and random forests. This work aims to use those more complicated techniques to adjust the measurements using other data sets gathered by a sensor …

Clarity, Organization, Precision, Economy: A Technical Writing Guide For Engineers, David J. Adams, University Of New Haven

Civil Engineering Faculty Book Series

This fourth edition of COPE was sparked by my involvement with PITCH (Project to Integrate Technical Communication Habits) at the Tagliatela College of Engineering at the University of New Haven. This new edition contains additional material on data displays, as well as some additional material on writing about data—including how to avoid rhetorical shifts that undermine the precision of a technical report.

Dielectrophoretic Trapping Of Carbon Nanotubes For Temperature Sensing, Kaylee Burdette

Theses, Dissertations and Capstones

Conventional sensors are rapidly approaching efficiency limitations at their current size. In designing more efficient sensors, low dimensional materials such as carbon nanotubes (CNTs), quantum dots, and DNA origami can be used to enable higher degrees of sensitivity. Because of the high atomic surface to core ratio, these materials can be used to detect slight changes in chemical composition, strain, and temperature. CNTs offer unique advantages in different types of sensors due to their electromechanical properties. In temperature sensing, the high responsiveness to temperature and durability can be used to produce an accurate, reliable sensor in even extreme temperatures. This …

Handwritten English Word Recognition Using A Deep Learning Based Object Detection Architecture, Riktim Mondal, Samir Malakar, Elisa H. Barney Smith, Ram Sarkar

Electrical and Computer Engineering Faculty Publications and Presentations

Handwriting is used to distribute information among people. To access this information for further analysis the page needs to be optically scanned and converted to machine recognizable form. Due to unconstrained writing styles along with connected and overlapping characters, handwriting recognition remains a challenging task. Most of the methods in the literature use lexicon-based approaches and train their models on large datasets having near 50 K word samples to achieve good results. This results in high computational requirements. While these models use around 50 K words in their dictionary when recognizing handwritten English text, the actual number of words in …

The Development Of An Inexpensive Alternative To Industry Solar Simulators, Raines A. Vanhuss

OUR Journal: ODU Undergraduate Research Journal

Solar cells are a prominent renewable energy source used in the twenty-first century. In order for the technology to grow, research lab equipment is needed to measure and characterizes these cells. This report covers solar simulator devices and proposes an alternative design solution to lessen the cost of solar cell lab equipment. Common household light sources were tested by generating I-V characteristics using an amorphous silicon solar cell. The light bulb that generated the best I-V curve was selected and then tested for the optimal distance between the light source and the solar panel. The alternative solar simulator was then …

Optimal Speed Control Of Electric Vehicles In Traffic With Wireless Charging, Amina Jemi

All Graduate Theses, Dissertations, and Other Capstone Projects

With the increase in electric vehicle use in recent years and expected increase in the future, a wireless network provides a possible option to allow vehicles with a route while maintaining an approximate net-zero energy balance under certain scenarios. In this type of network, a collection of wireless charging pads is installed under the road, and energy transfer occurs, while a vehicle with wireless charging capability is driven over the charging pads. This thesis describes a methodology and modeling to solve an optimization problem involving a speed-controlled vehicle in traffic. The methodology incorporates a receding horizon dynamic model with constraints. …

A Measurement System For Detection Of Intestinal Motility In Neonates By Monitoring Slow Wave Activity, Garett Goodale

Electronic Theses and Dissertations, 2020-2023

Similar to how electrocardiographic waves are the pace making signals of the heart, slow waves are the pace making signals of the intestines. Slow waves are electrical signals in the intestines that determine the speed at which food can move through the intestine ensuring proper digestion and uptake of nutrients. It has been shown that slow waves can be measured in adults using non-invasive, surface electrodes. However, no study has investigated the measurements of slow waves in neonates, specifically pre-term neonates. Around 7% of pre-term neonates suffer from necrotizing enterocolitis (NEC) which is a condition that causes damage to the …

The Proof Of Concept Of The Hurricane Imaging Radiometer: Hurricane Wind Speed And Rain Rate Retrievals, Jonathan Coto

Electronic Theses and Dissertations, 2020-2023

This dissertation presents the proof of concept for the Hurricane Imaging Radiometer (HIRAD), where remote sensing retrievals of the 2-dimensional wind and rain fields for several hurricanes are validated with independent measurements. A significant contribution of this dissertation is the development of a novel statistical calibration technique, whereby the HIRAD instrument is radiometrically calibrated, using modeled brightness temperatures (Tb) generated using a priori hurricane wind and rain fields that are statistically representative of the actual hurricane conditions at the time of the HIRAD brightness temperature measurements. For this calibration technique, the probability distribution function of the measured HIRAD Tb's is …

Speaker Encoding For Zero-Shot Speech Synthesis, Tristin W. Cory

Graduate Theses/Dissertations

Spoken communication, for many, is an essential part of everyday life. Some individuals can lose or not be born with the ability to speak. To function on a day-to-day basis, these individuals find other ways of communication. Adaptive speech synthesis is one of those ways. It recreates a user’s previous voice or creates a voice that blends with their regional dialect. Current adaptive speech synthesis techniques that achieve human-like speech require thirty minutes, to a few hours of high-quality audio recordings of a target speaker. This amount of recorded audio is not commonly possessed by people in need of a …

Reduced Footprint Probabilistic Inference Networks Using Novel Hybrid She-Mtj/Cmos Based Majority Gate, Harshavardhan Reddy Thummala

Electronic Theses and Dissertations, 2020-2023

In recent years, innovations in machine learning using artificial neural networks (ANN) have significantly increased and led to various applications like image recognition, text classification, machine translation, sequence recognition, etc. Earlier, research was focused on software-based DBNs, which are implemented on conventional von-Neumann architectures that provided flexibility but had few limitations. Recent studies have implemented hardware-based designs like FPGA-based, CMOS based, RRAM-based, and MRAM-based designs to overcome these limitations. Hybrid CMOS-MTJ-based RBMs provided significant area and energy improvements compared to other techniques. We herein implemented Spatial and Temporal redundant probabilistic interpolation network to improve the accuracy and provide fault tolerance …

Module 06: Hvac, Keith Johnson, Mohammad Moin Uddin

ENTC 2160: Architectural CAD

No abstract provided.

Future Needs Of The Cybersecurity Workforce, Connie Justice, Char Sample, Sin Ming Loo, Alex Ball, Clay Hampton

Electrical and Computer Engineering Faculty Publications and Presentations

Expected growth of the job market for cyber security professionals in both the US and the UK remains strong for the foreseeable future. While there are many roles to be found in cyber security, that vary from penetration tester to chief information security officer (CISO). One job of particular interest is security architect. The rise in Zero Trust Architecture (ZTA) implementations, especially in the cloud environment, promises an increase in the demand for these security professionals. A security architect requires a set of knowledge, skills, and abilities covering the responsibility for integrating the various security components to successfully support an …

A Progressive Learning Strategy For Large-Scale Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, the worldwide temperature increase has resulted in rapid deglaciation and a higher risk of glacier-related natural hazards such as flooding and debris flow. Due to the severity of these hazards, continuous observation and detailed analysis of glacier fluctuations are crucial. Many such analyses require an accurately delineated glacier boundary. However, the complexity and heterogeneity of glaciers, particularly debris-covered glaciers (DCGs), poses a challenge for glacier mapping when using conventional remote sensing or machine-learning techniques. Some examples exist about small-scale automated glacier mapping, but large or regional-scale mapping is challenging. Previously, a deep-learning-based approach named GlacierNet2 had been …

Volumetric Microfabrication With Structured Light, He Cheng

Electronic Theses and Dissertations, 2020-2023

Multiphoton polymerization (MPP) as one of the direct laser writing techniques is capable of manufacturing three-dimensional (3D) micro-structures with complex shapes and novel functionalities. However, current MPP methods rely on point-by-point or layer-by-layer scanning and therefore are time-consuming. The low fabrication throughput of conventional MPP is the key factor that limits its wider adoption for industrial manufacturing over large surface area. One way to increase the fabrication speed is to turn layer-by-layer process into a volumetric process. An ideal volumetric printing method can fabricate structures with complex 3D geometry by single exposure and should be easy to implement. As a …

Patterned Liquid Crystal Devices For Near-Eye Displays, Kun Yin

Electronic Theses and Dissertations, 2020-2023

As a promising next-generation display, augmented reality (AR) and virtual reality (VR) have shown attractive features and attracted broad interests from both academia and industry. Currently, these near-eye displays (NEDs) have enabled numerous applications, ranging from education, medical, entertainment, to engineering, with the help of compact and functional patterned liquid crystal (LC) devices. The interplay between LC patterns and NEDs stimulates the development of novel LC devices with unique surface alignments and volume structures, which in turn feedback to achieve more compact and versatile NEDs. This dissertation will focus on the patterned LC with applications in NEDs. Firstly, we propose …

Light Guiding And Concentrating Using Self-Collimating Spatially-Variant Photonic Crystals, Chun Xia

Electronic Theses and Dissertations, 2020-2023

Advances in integrated photonic devices require low loss, easy-to-integrate solutions for chip-to-chip and chip-to-fiber interfacing. Among the most common solutions are traditional lenses. However, circular lenses require additional mounting mechanisms to ensure proper alignment. Additionally, the beam routing functionality cannot be added to the traditional lenses unless they are combined with mirrors and operate in the reflection mode. In this dissertation, we investigate lens-embedded photonic crystals (LEPCs) as a solution to flat and multifunctional lenses. The concept is demonstrated by creating self-collimating lattices containing a gradient refractive index lens (GRIN-LEPC), a binary-shaped lens (B-LEPC), and a Fresnel-type binary-shaped lens (F-B-LEPC). …

Diffractive Liquid Crystal Optical Elements For Near-Eye Displays, Jianghao Xiong

Electronic Theses and Dissertations, 2020-2023

Liquid crystal planar optics (LCPO) with versatile functionalities is emerging as a promising candidate for overcoming various challenges in near-eye displays, like augmented reality (AR) and virtual reality (VR), while maintaining a small form factor. This type of novel optical element exhibits unique properties, such as high efficiency, large angular/spectral bandwidths, polarization selectivity, and dynamic modulation. The basic molecular configuration of these novel reflective LCPO is analyzed, based on the simulation of molecular dynamics. In contrast to previously assumed planar-twist structure, our analysis predicts a slanted helix structure, which agrees with the measured results. The optical simulation model is established …