Electrical and Computer Engineering Commons^™

Chipless 3d Microfluidic Rf Sensing, Sheikh Dobir Hossain

Open Access Theses & Dissertations

Effective management of the cold chain is essential to uphold the quality and safety of products vulnerability to physical factors like temperature, humidity, pressure, etc., including perishable foods, pharmaceuticals, and vaccines. Chipless Radio Frequency Identification (RFID) sensors have become increasingly favored as a viable technology for tracking, inventory, and sensing industries due to their wireless and non-line of sight (NLOS) situations, straightforward fabrication, cost efficiency, and adaptability to challenging environmental conditions. However, there is still a need for advancements in RFID sensors to make them promise for cold chain applications. Integration of flexibility and non-volatile memory into the existing RFID …

Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …

Fast Neutron & Gamma-Ray Scintillation And Broadband Photoluminescence From Gallium Nitride & Gallium Oxide: Developing Radiation-Hard Diagnostic Platforms, Daniel Jesus Valdes

UNLV Theses, Dissertations, Professional Papers, and Capstones

Integrated scintillation and electronics processing materials are of critical importance in environments exposed to high levels of radiation, such as nuclear fusion diagnostics and space missions. This dissertation focuses on the radiation detection capabilities of gallium oxide (Ga2O3) and gallium nitride (GaN), exploring their responses to fast neutrons and gamma rays. Using high-energy neutron beam facilities at Los Alamos Neutron Science Center (LANSCE) Flight Path 4FP60R, we exposed both Ga2O3 and GaN crystals to neutron irradiation spanning an energy spectrum ranging from 1 to 400 MeV. A Pi-Max 4 fast-gated Intensified CCD (ICCD) camera captured the transient scintillation responses for …

Low-Cost Vehicle Controller Testing System, Kevin R. Jung

Electrical Engineering

This project aims to create a system to facilitate easier evaluation of PCBs designed for low-voltage vehicle applications with an emphasis on accessibility for student teams in collegiate design series' such as Formula SAE. Student teams or smaller vehicle electronics manufacturers often need to verify designs and validate functionality during both development and manufacturing. An inexpensive, small, and portable, yet capable, system for taking measurements and simulating inputs would allow designers to put their boards into vehicle-representative conditions and environments without needing to connect to actual vehicle hardware. Additionally, while systems exist off-the-shelf that could fulfill the requirements needed by …

Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo

Knowledge Engineering and Data Science

Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …

Optimal Strategy For Handling Unbalanced Medical Datasets: Performance Evaluation Of K-Nn Algorithm Using Sampling Techniques, Yulita Salim, Aulia Putri Utami, Abdul Rachman Manga, Huzain Azis, Fadhila Tangguh Admojo

Knowledge Engineering and Data Science

This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung …

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …

Extracting Permittivity And Permeability Using The Position-Insensitive And Calibration-Independent Method On A Rectangular Waveguide, James Conrad Denemark

Theses and Dissertations

Classic methods for extracting material characteristics require known measurements to accurately calibrate the network analyzer. Previous work demonstrated a position-insensitive and calibration-independent (PiCi) transmission/reflection method to extract a material’s permittivity. This thesis proposes a method with the same function, manipulated to use one empty measurement and then two samples of different thicknesses. The PiCi method is first adopted for rectangular waveguide which resulted in inaccurate permittivity data when compared to the calibrated solution. Once detector mismatch corrections were applied, the PiCi method produced accurate results. Using a 2-D numerical root search, permittivity and permeability material characteristics are now successfully extracted …

Building Energy Management: A Data-Driven Approach Using Clustering And Load Forecasting, Aviral Kandel

LSU New Orleans Theses and Dissertations

The increase of smart meters in the grid has led to the generation of a vast amount of high dimensional energy data with improving temporal resolution. During analysis, relying on short samples like a day or week of data, could lead to wrong conclusion due to seasonal dynamics and customer behavior variations. To effectively utilize the vast amount of information, it must be compressed into a low-dimensional representation. This thesis explores the state-of-the-art dimensionality reduction techniques for a high-dimensional, non-linear energy dataset and proposes a novel deep learning based method to address the limitation of existing approaches. The proposed method …

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred

Knowledge Engineering and Data Science

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …

Theoretical Advancements In Hawkes Processes And Their Practical Applications, Xi Zhang

Theses and Dissertations

Hawkes self-exciting point processes have been widely used in fields such as seismology, finance and social media analysis. Despite decades of research focusing on these processes, several key aspects remain under-explored. These include broadly applicable methods for model evaluation and selection, intuitive nonparametric inference approaches akin to kernel density estimation for probability density functions, and strategies to enhance the performance of generative point process models in predictive tasks. In this dissertation, first, we extend the time-rescaling theorem, which is traditionally limited to non-terminating processes with complete observations, to accommodate terminating processes and incomplete observations as well. This extension allows for …

Techno-Economic Factors Impacting The Intrinsic Value Of Behind-The-Meter Distributed Storage, Ingrid Hopley, Mehrdad Ghahramani, Asma Aziz

Research outputs 2022 to 2026

With the increasing adoption of renewable energy, there is a growing need for efficient storage solutions. Battery storage is becoming an essential tool for maintaining grid reliability and handling the variable nature of renewable energy sources. This research focuses on behind-the-meter, grid-connected household systems in Western Australia, adopting a consumer perspective to evaluate the financial viability of residential batteries. Using the HOMER Grid for techno-economic modeling, eight factors influencing financial viability were analyzed, with results validated through two external case studies. The findings suggest that photovoltaic (PV) systems paired with batteries can be cost-effective at current prices, depending on load …

Investigation Of Conductive Die-Top Thermal Capacitors During Short Circuit Operation Of Silicon Carbide Devices, Youssef Abotaleb

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

Silicon carbide (SiC) power devices have garnered significant attention in recent years due to their superior thermal and electrical properties compared to traditional silicon devices. However, SiC power devices suffer from severe reliability issues arising from their mechanical properties. This is because the Young's modulus of SiC is about three times larger than that of silicon, which correspondingly raises the mechanical stresses acting on the bonding structure, often leading to device failure under power shock events.

Traditional packaging materials and designs tend to constrain SiC performance, as hot-spot temperature resulting from a power shock is one of the key factors …

Model Reference Adaptive Control For Mobile Manipulators And Beyond, Srivatsan Srinivasan

All Dissertations

In recent years, robotics has expanded into various sectors, including manufacturing, transportation, and household services, making the integration of autonomy a critical area of research. This shift aims to ensure safety and enhance the utility of autonomous systems. Traditionally, robotic applications focused separately on mobility, like automated guided vehicles, and manipulation, such as serial-chain arms in manufacturing. Today, however, we see a merging of these capabilities in the growing field of mobile manipulator robots that combine movement with purposeful interactive functionalities.

A typical mobile manipulator is a robotic arm mounted on a wheeled base. This thesis focuses on advancing control …

A Techno-Economic Perspective On Efficient Hybrid Renewable Energy Solutions In Douala, Cameroon’S Grid-Connected Systems, Reagan Jean Jacques Molu, Serge Raoul Dzonde Naoussi, Mohit Bajaj, Patrice Wira, Wulfran Fendzi Mbasso, Barun K. Das, Milkias Berhanu Tuka, Arvind R. Singh

Research outputs 2022 to 2026

Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, …

Feasibility Study On Microwave Glucose Level Monitoring, Ricardo Cepeda

Theses and Dissertations

Diabetes management heavily relies on regular blood glucose monitoring, which traditionally involves invasive techniques. In recent years, significant research has been directed towards developing non-invasive glucose monitoring methods, particularly using electromagnetic waves. This study explores the feasibility of detecting blood glucose levels non-invasively by measuring the resonant frequency shifts of an antenna, influenced by the dielectric properties of blood. Microwave measurement techniques have garnered attention due to their ability to safely penetrate human tissue and detect biochemical markers like glucose. While previous studies have established a link between glucose concentration and dielectric properties, many techniques suffer from poor sensitivity or …

Virtual Control: A Comparison Of Methods For Hand-Tracking Implementation, Nathan Roberts

Honors Program: Senior Projects (Public)

This thesis examines the design philosophy of modern virtual reality applications that utilize hand-tracking as a primary form of user input. The analysis presented hopes to provide ideas for future implementations of this technology so that more immersive experiences are developed. This analysis starts with the discussion of a modern example of successful hand-tracking implementation, then comparing that implementation to a recent senior design project. This comparison is primarily based on each experience’s ability to create interactivity and immediacy. Interactivity is the degree to which the user can quickly and reliably make changes to their virtual environment, while immediacy is …

Dynamic Optimization Of Directed Energy Deposition Build Conditions Using Real-Time Monitoring Via Closed-Loop Control, Callan Herberger

Open Access Theses & Dissertations

Directed Energy Deposition (DED) is an additive manufacturing process that is being rapidly adopted by industry and is well suited for the fabrication of complex components in various metal alloys. DED provides unique benefits such as design flexibility, the potential for in-situ alloying, and an open environment that allows for unobstructed monitoring within the build chamber. Despite these benefits, fully exploiting additive manufacturing's (AM) potential remains a complex task for designers. This dissertation presents a framework for controlling Directed Energy Deposition process variables through in-situ monitoring. An exploration into modifying AM build conditions through the development and implementation of a …

Uncovering The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Michelle Lara

Open Access Theses & Dissertations

This thesis evaluates the effectiveness of the Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm under varying network load conditions. Repeated simulation experiments using Mininet were conducted for four different network-wide load levels: 100 Mbps, 500 Mbps, 1 Gbps, and 5 Gbps. Using statistical inference, our experimental results indicate that NLOF: MLL is ineffective under light load conditions (i.e., 100Mbps load) due to the limited network flow data available for its learning process. This limitation highlights a key challenge in applying the algorithm to lightly loaded networks. A preliminary algorithm was proposed to address this light-load performance …

Advancing Grid Modernization Through Data-Driven Resilience Modeling And Hosting Capacity Of Distributed Energy Resources, Oscar Samuel Acosta

Open Access Theses & Dissertations

This Ph.D. dissertation focuses on advancing the integration of distributed energy resources (DERs) through concepts surrounding their impacts on power system stability, resilience, and hosting capacity (HC). This dissertation addresses crucial topics in renewable energy deployment, transient fault response, and dynamic modeling. The work begins with the development of renewable energy source (RES) models tailored for offsetting residential heating, ventilation, and air conditioning~(HVAC) and commercial cooling loads. These models utilize solar photovoltaic (PV) and wind energy systems to produce scalable frameworks adapted across diverse climates and building types in application of a partial-load targeting methodology. The dissertation then transitions from …

Reference Dependence In Queue Design And Pricing Strategies, Jian Liu, Yongpin Zhou, Jian Chen, Peng Li

Electrical and Computer Engineering Faculty Research & Creative Works

This research investigates the effect of reference dependence on waiting times in service systems which formerly used a first-in-first-out (FIFO) service but have introduced a priority line with a fee. Our model combines reference-dependent gain-loss utility with standard customer utility, and we posit that customers are pleased with shorter-than-expected waiting times, whereas longer-than-expected times lead to dissatisfaction and an increased likelihood of balking. The study explores two scenarios: a captive customer system (CCS) and a noncaptive customer system (NCCS), with a focus on optimal pricing and segmentation strategies for revenue and social welfare maximization. The results reveal that, in a …

Impact Of Electrical Testing Strategies On The Performance Metrics Of Bio-Organic-Based Resistive Switching Memory, Muhammad Awais, Hao Zhe Leong, Nadras Othman, Mohamad Danial Shafiq, Feng Zhao, Kuan Yew Cheong

Electrical and Computer Engineering Faculty Research & Creative Works

Resistive Random-Access Memory (ReRAM) is considered as one of the most promising non-volatile memory technologies because of its high scalability, fast switching speed, and low power consumption. While many review papers are focused on investigating material types, material properties, device fabrication methods, and device structures, the influence of electrical testing strategies on ReRAM performance has yet been reviewed, particularly for bio-organic-based ReRAM. This review compiled, analyzed, and discussed how compliance current, voltage sweep rate, voltage sweep range, and voltage sweeping direction affect the ON/OFF ratio, read memory window, and both SET and RESET voltages of ReRAM.

An Overview Of Planning For Vehicle-To-Grid Systems With Large-Scale Adoption Of Electric Vehicles, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

The rapid adoption of Electric Vehicles (EVs) has the potential to transform energy infrastructure worldwide. Vehicle-to-Grid (V2G) systems, which enable bidirectional energy flow between EVs and power grids, offer a promising solution for balancing electricity demand, supporting renewable energy integration, and enhancing the resilience of power grids. This paper explores different strategic planning considerations necessary for the large-scale adoption of V2G-enabled EVs, aiming to support a more resilient and sustainable energy infrastructure.

Study Of Temperature Effects Of Glucose And Microwave Dielectric Dispersion Model, Ethan Grant Boone

Theses and Dissertations

In this study, the temperature effect of dielectric properties in various aqueous solutions containing glucose are examined. A design for a new testing chamber is introduced to provide improved conditions for testing to ensure accurate and precise measurements. A N-SMA Adaptor is used when obtaining dielectric characteristics of glucose solutions ranging 100-300mg/dl. Dielectric parameters are obtained adopting a modified Debye dispersion model. MATLAB is used to confirm coaxial dimensions are suitable fabrication designs using fixed dielectric parameters of air and water. Simulations and design models were implemented utilizing ANSYS High Frequency Structure Simulator and 3-D Modeler software to develop the …

Hardware Applications Of High-Speed Fault Detection Algorithms, Daniel Zintsmaster

All Theses

The growing need for a sustainable electric energy infrastructure has driven research into control, protection, and optimization of power systems. A key challenge is that the changing grid must operate at increasingly higher speeds, but many current hardware devices cannot meet these demands. This thesis focuses on power system protection, aiming to design a hardware solution that can detect and isolate faults in microseconds, ensuring faster, more reliable grid operations. The first hardware developed was for a low-voltage direct current (LVdc) microgrid, which faces challenges due to a lack of protection schemes and novel speed requirements. This thesis presents protection …

Higher Order Bessel Beams Integrated In Time (Hobbit) With Engineered Light Frequencies And Applications In Computational Imaging, Tyler Cramer

All Theses

This thesis presents an adaptive optical system which leverages the frequency shifts from an acousto-optic deflector (AOD) form an array of uniformly spaced frequency diverse beamlets. These beamlets are then spatially transformed (wrapped) into a circular array forming a non-diffracting ring with an unprecedented orbital angular momentum (OAM) mode switching rate of 50Mhz with an OAM range of ±64. Along with frequency diversity, the non-diffracting range of the beams are analyzed 6 Rayleigh ranges of an equivalently sized gaussian beam. Inherent in the frequencies superposed on the AOD are associated relative phases which can be controlled to form sets of …