Other Electrical and Computer Engineering Commons^™

Synthetic Aperture Optical Imaging Interferometric Microscopy With Improved Image Quality, Preyom K. Dey

Electrical and Computer Engineering ETDs

The resolution limit of optical microscopy can be extended by using Imaging Interferometric Microscopy (IIM), which uses a low numerical aperture (NA) objective lens to achieve resolution equivalent to that of a high-NA objective lens with multiple sub-images. Along with the resolution enhancement challenge, IIM often suffers from poor image quality. In this dissertation, several image quality improvement methods are proposed and verified with simulation and experimental results. Next, techniques to extend the resolution limit of IIM to ≤ 100nm using a low-NA objective lens are demonstrated. An experimental technique of using a grating coupler on …

Unmanned Ground Vehicle System To Collect Soil Moisture Data, Austin Edward Flynt

Theses and Dissertations

With an increased interest in precision agriculture, it is important to identify efficient ways to monitor soil moisture. Soil moisture can be monitored using handheld sensors, but this method is laborious and time consuming. Remote methods, such as radar systems can be used as well, but these methods require ground truth data to verify their accuracy. It becomes clear that to collect this data regularly and reliably, a mobile robotic device is necessary. This thesis proposes to implement mobile robot take soil moisture measurements with less human effort than existing methods while maintaining the same accuracy. This soil moisture data …

Low-Noise, Low-Power Analog Front End For Dual Detector, Event-Driven Radioactive Isotope Identification, Joseph Medinger

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

An analog front end (AFE) design for a low-noise, low-power, event-driven radioactive spectroscopy system is implemented in a 65 nm CMOS technology. The AFE is optimized for use with two scintillation based detectors, CsI(Na) and LaBr3(Ce), that utilize photo-multiplier tubes for charge amplification. The amplification within the AFE is accomplished through charge sensitive amplifier designs that are tailored to each detector type. The AFE includes adjustable bias generation circuits to allow amplifier tuning for process, voltage, and temperature variations. The presented AFE is implemented along with analog to digital acquisition circuits and a microcontroller to provide a single-chip radioactive spectroscopy …

Deep Learning-Guided Prediction Of Material’S Microstructures And Applications To Advanced Manufacturing, Jianan Tang

All Dissertations

Material microstructure prediction based on processing conditions is very useful in advanced manufacturing. Trial-and-error experiments are very time-consuming to exhaust numerous combinations of processing parameters and characterize the resulting microstructures. To accelerate process development and optimization, researchers have explored microstructure prediction methods, including physical-based modeling and feature-based machine learning. Nevertheless, they both have limitations. Physical-based modeling consumes too much computational power. And in feature-based machine learning, low-dimensional microstructural features are manually extracted to represent high-dimensional microstructures, which leads to information loss.

In this dissertation, a deep learning-guided microstructure prediction framework is established. It uses a conditional generative adversarial network (CGAN) …

An Analysis Of Camera Configurations And Depth Estimation Algorithms For Triple-Camera Computer Vision Systems, Jared Peter-Contesse

Master's Theses

The ability to accurately map and localize relevant objects surrounding a vehicle is an important task for autonomous vehicle systems. Currently, many of the environmental mapping approaches rely on the expensive LiDAR sensor. Researchers have been attempting to transition to cheaper sensors like the camera, but so far, the mapping accuracy of single-camera and dual-camera systems has not matched the accuracy of LiDAR systems. This thesis examines depth estimation algorithms and camera configurations of a triple-camera system to determine if sensor data from an additional perspective will improve the accuracy of camera-based systems. Using a synthetic dataset, the performance of …

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya

Electrical and Computer Engineering Faculty Publications

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …

Genome Annotation Using Average Mutual Information, Garin P. Newcomb

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Advancements in high-throughput DNA sequencing technologies and ambitious goals for their use are resulting in the generation of a deluge of unannotated sequenced genomes. This makes computational tools that can aid in annotation increasingly valuable.

Here, we provide a detailed exploration of the utility as well as the limitations of average mutual information (AMI) in several steps of genome annotation. For a genomic sequence, AMI is a measure of the information a base contains about the base separated by a fixed lag. A profile is constructed by calculating AMI at multiple lags. In addition to traditional AMI, we employ two …

Risk-Based Machine Learning Approaches For Probabilistic Transient Stability, Umair Shahzad

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive …

Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay

All Theses

The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at …

Machine Learning For Unmanned Aerial System (Uas) Networking, Jian Wang

Doctoral Dissertations and Master's Theses

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex …

Let's Read: Designing A Smart Display Application To Support Codas When Learning Spoken Language, Katie Rodeghiero, Yingying Yuki Chen, Annika M. Hettmann, Franceli L. Cibrian

Engineering Faculty Articles and Research

Hearing children of Deaf adults (CODAs) face many challenges including having difficulty learning spoken languages, experiencing social judgment, and encountering greater responsibilities at home. In this paper, we present a proposal for a smart display application called Let's Read that aims to support CODAs when learning spoken language. We conducted a qualitative analysis using online community content in English to develop the first version of the prototype. Then, we conducted a heuristic evaluation to improve the proposed prototype. As future work, we plan to use this prototype to conduct participatory design sessions with Deaf adults and CODAs to evaluate the …

Digital Markers Of Autism, Ivonne Monarca, Franceli L. Cibrian, Monica Tentori

Engineering Faculty Articles and Research

Autism Spectrum Disorder (ASD) is a neurological condition that affects how a people communicate and interact with others. The use of screening tools during childhood is very important to detect those children who need to be referred for a diagnosis of ASD. However, most screening tools are based on parents' responses so the result can be subjective. In addition, most screening tools focus on social and communicative skills leaving aside sensory features, which have shown to have the potential to be ASD markers. Tactile processing has been little explored due to lack of tools to asses it, however with the …

Feel And Touch: A Haptic Mobile Game To Assess Tactile Processing, Ivonne Monarca, Monica Tentori, Franceli L. Cibrian

Engineering Faculty Articles and Research

Haptic interfaces have great potential for assessing the tactile processing of children with Autism Spectrum Disorder (ASD), an area that has been under-explored due to the lack of tools to assess it. Until now, haptic interfaces for children have mostly been used as a teaching or therapeutic tool, so there are still open questions about how they could be used to assess tactile processing of children with ASD. This article presents the design process that led to the development of Feel and Touch, a mobile game augmented with vibrotactile stimuli to assess tactile processing. Our feasibility evaluation, with 5 children …

Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli

Electrical and Computer Engineering Faculty Publications

Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR …

Parents’ Perspectives On A Smartwatch Intervention For Children With Adhd: Rapid Deployment And Feasibility Evaluation Of A Pilot Intervention To Support Distance Learning During Covid-19, Franceli L. Cibrian, Elissa Monteiro, Elizabeth Ankrah, Jesus A. Beltran, Arya Tavakoulnia, Sabrina E. B. Schuck, Gillian R. Hayes, Kimberley D. Lakes

Engineering Faculty Articles and Research

Distance learning in response to the COVID-19 pandemic presented tremendous challenges for many families. Parents were expected to support children’s learning, often while also working from home. Students with Attention Deficit Hyperactivity Disorder (ADHD) are at particularly high risk for setbacks due to difficulties with organization and increased risk of not participating in scheduled online learning. This paper explores how smartwatch technology, including timing notifications, can support children with ADHD during distance learning due to COVID-19. We implemented a 6-week pilot study of a Digital Health Intervention (DHI) with ten families. The DHI included a smartwatch and a smartphone. Google …

Recent Advances And Trends Of Predictive Maintenance From Data-Driven Machine Prognostics Perspective, Yuxin Wen, Md. Fashiar Rahman, Honglun Xu, Tzu-Liang Bill Tseng

Engineering Faculty Articles and Research

In the Engineering discipline, prognostics play an essential role in improving system safety, reliability and enabling predictive maintenance decision-making. Due to the adoption of emerging sensing techniques and big data analytics tools, data-driven prognostic approaches are gaining popularity. This paper aims to deliver an extensive review of recent advances and trends of data-driven machine prognostics, with a focus on their applications in practice. The primary purpose of this review is to categorize existing literature and report the latest research progress and directions to support researchers and practitioners in acquiring a clear comprehension of the subject area. This paper first summarizes …

A Unified Framework Of Deep Learning-Based Facial Expression Recognition System For Diversified Applications, Sanoar Hossain, Saiyed Umer, Vijayan K. Asari, Ranjeet Kumar Rout

Electrical and Computer Engineering Faculty Publications

This work proposes a facial expression recognition system for a diversified field of appli- cations. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution …

Biometric Features Modeling To Measure Students Engagement., Islam Mohamed Ahmed Mohamed Mahmoud Alkabbany

Electronic Theses and Dissertations

The ability to measure students’ engagement in an educational setting may improve student retention and academic success, revealing which students are disinterested, or which segments of a lesson are causing difficulties. This ability will facilitate timely intervention in both the learning and the teaching process in a variety of classroom settings. In this dissertation, an automatic students engagement measure is proposed through investigating three main engagement components of the engagement: the behavioural engagement, the emotional engagement and the cognitive engagement. The main goal of the proposed technology is to provide the instructors with a tool that could help them estimating …

Variable Autonomy Assignment Algorithms For Human-Robot Interactions., Christopher Kevin Robinson

Electronic Theses and Dissertations

As robotic agents become increasingly present in human environments, task completion rates during human-robot interaction has grown into an increasingly important topic of research. Safe collaborative robots executing tasks under human supervision often augment their perception and planning capabilities through traded or shared control schemes. However, such systems are often proscribed only at the most abstract level, with the meticulous details of implementation left to the designer's prerogative. Without a rigorous structure for implementing controls, the work of design is frequently left to ad hoc mechanism with only bespoke guarantees of systematic efficacy, if any such proof is forthcoming at …

Distributed Neural Network Based Architecture For Dddos Detection In Vehicular Communication Systems, Nicholas Jaton

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

With the continued development of modern vehicular communication systems, there is an ever growing need for cutting edge security in these systems. A misbehavior detection systems (MDS) is a tool developed to determine if a vehicle is being attacked so that the vehicle can take steps to mitigate harm from the attacker. Some attacks such as distributed denial of service (DDoS) attacks are a concern for vehicular communication systems. During a DDoS attack, multiple nodes are used to flood the target with an overwhelming amount of communication packets. In this thesis, we investigated the current MDS literature and how it …

An Improved Earned Value Management Method Integrating Quality And Safety, Brian Briggs

LSU Doctoral Dissertations

The construction industry invests significant time and money to improve quality and safety while reducing cost and schedule impacts. The industry has a sincere desire to improve construction project management methods to improve efficiency. Historically, quality and safety underperformances result from undermanaged quality control and safety activities. The cost and schedule impacts associated with poor quality work have always had an impact on construction operations. The unprecedented challenges and uncertainties of COVID-19 highlighted the need to improve the Earned Value Management (EVM) method within construction to reflect these quality and safety activities. The central goal of this dissertation is to …

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

Pier Ocean Pier, Brandon J. Nowak

Computer Engineering

Pier Ocean Peer is a weatherproof box containing a Jetson Nano, connected to a cell modem and camera, and powered by a Lithium Iron Phosphate battery charged by a 50W solar panel. This system can currently provide photos to monitor the harbor seal population that likes to haul out at the base of the Cal Poly Pier, but more importantly it provides a platform for future expansion by other students either though adding new sensors directly to the Jetson Nano or by connecting to the jetson nano remotely through a wireless protocol of their choice.

Ai Alzheimer's Early Detection Via Mri Processing, Luke Frey, Siddharth Sharma, Arti Jain

Electrical Engineering

Alzheimer’s Disease (AD) is an irreversible, progressive brain disorder that impairs memory, thinking, and language. Known as the most common form of dementia, AD is the 6th leading cause of death in the United States. It is estimated that currently, nearly 6 million Americans suffer from AD and moreover, the prevalence of AD is projected to grow to 13.8 million being diagnosed by 2050. Considering these projections, hospitals are expected to be diagnosing nearly half a million patients a year. This high volume will lead to technological growth within the diagnosis process along with more effective treatments.

As of now, …

Pilltank, Lucas Chang, Hayden Tam, Aaron Teh, Krista Round

Electrical Engineering

Imagine an elderly family member, going through their daily routine of taking their pills. They find their pill box; however, they are having trouble identifying all the pills in there. Is there a name on the tablet? Can they read what it says? Do they just trust that the medication in their box is correct? How can they properly take care of themselves if they can not even confirm that what they are taking is the right medication? To combat this issue that many face, we present PillTank.

To decrease the risk of consuming the wrong medication, PillTank identifies the …

Wildfire Early Detection System (Weds), Mason Mciver, Vincent Liang, Jeanreno Racines

Electrical Engineering

With climate change causing an increase in temperature over the past several decades, wildfires have been burning hotter and moving quicker leaving a trail of destruction in their path. Detecting a wildfire early allows firefighters to respond efficiently and effectively to ensure containment. With the rise of advanced computer vision and algorithms, autonomous systems can be used to monitor and report any fire activity. Having multiple devices spread out across a large area will allow first responders to map out the fire location and track the fire. By utilizing smart technologies, property damage can be minimized and residents living in …

Robot Path Planning System, Filippo Cheein, Tim Berry, Bruce Rogstad, Bob Entezar, Jamari Ducre

Electrical Engineering

The future is autonomous. It is estimated that by 2025 there will be 8 million autonomous cars in the market. Millions more will be autonomous devices employed in homes, malls, offices, and warehouses. There are many aspects of these future devices that are necessary for proper autonomous functionality, with none potentially more critical than the devices’ path planning ability. This project aims to create an effective, reliable, and safe path planning algorithm for the autonomous devices of the future. Indoor autonomous devices, such as warehouse or office robots, benefit from being able to always use the same floor plan rather …

Online Laboratory Course Using Low Tech Supplies To Introduce Digital Logic Design Concepts, Dhanya Nair

Engineering Faculty Articles and Research

This paper describes a Digital Logic Design Laboratory Course developed to engage students with hardware systems within an online setting. This is a junior level core course for students from Computer Science (CS), Computer Engineering (CE) and Electrical Engineering (EE). Hence, the laboratories are designed to provide the hands-on experience of breadboarding, testing and debugging essential to CE and EE while accommodating CS students with no prior hardware experience. Commercially available low-cost electronic trainers (portable workstations) are loaned to the students in addition to basic electronic components. To ensure a strong foundation in debugging, prior to utilizing these workstations, students …

First Order Self-Oscillating Class-D Circuit With Triangular Wave Injection, Matthew J. Carroll

Master's Theses

An investigation into performance improvements to the modulator stage of a class-D amplifier is conducted in this thesis. Two of the standard topologies, namely class-D open-loop pulse-width modulation (PWM), and the improved self-oscillating feedback system are benchmarked against a topology which includes both a hysteretic comparator in a feedback loop and triangle wave injection. Circuit performance is analyzed by comparing how the triangle injection circuit handles known issues with open-loop and self-oscillating circuits. Using this analysis, it is shown that the triangle injection topology offers an improved power supply rejection ratio relative to open-loop PWM and reduces distortion generated by …

An Artificial Neural Network For Bankruptcy Prediction, Walter D. Magdefrau

Master's Theses

Assessing the financial health of organizations remains a topic of great interest to economists, financial institutions, and invested stakeholders. For more than a century, research into financial distress has focused primarily on traditional applications of statistical analysis; however, modern advances in computational efficiency have created a significant opportunity for more sophisticated approaches. This thesis investigates the application of artificial intelligence on company bankruptcy prediction. The proposed neural network model is evaluated using the Polish Companies Bankruptcy dataset and yields a 5-year prediction accuracy of 96.5% and an AUC (area under receiver operating characteristic curve) measure of 92.4%.