Digital Commons Network^™

Reinforcement Learning Assisted Communication Resources Optimization In Advanced Air Mobility., Ruixuan Han

Electronic Theses and Dissertations

Advanced air mobility (AAM), which envisages a safe and efficient aviation transportation system, has drawn significant attention to support the increasing mobility demand in metropolitan areas. Communication services for AAM aerial vehicles (AVs) are crucial for ensuring flight safety. This dissertation explores three research topics on communication resource allocation problems in AAM applications. The first topic, addressed in Chapter II, investigates the joint velocity selection and spectrum allocation problem for AAM applications to enhance spectrum utilization efficiency (SUE). In the AAM scenario, multiple AVs travel along predefined paths for passenger and cargo deliveries. Given that AAM aims to provide fast …

Federated Learning In Wireless Networks, Xiang Ma

All Graduate Theses and Dissertations, Fall 2023 to Present

Artificial intelligence (AI) is transitioning from a long development period into reality. Notable instances like AlphaGo, Tesla’s self-driving cars, and the recent innovation of ChatGPT stand as widely recognized exemplars of AI applications. These examples collectively enhance the quality of human life. An increasing number of AI applications are expected to integrate seamlessly into our daily lives, further enriching our experiences.

Although AI has demonstrated remarkable performance, it is accompanied by numerous challenges. At the forefront of AI’s advancement lies machine learning (ML), a cutting-edge technique that acquires knowledge by emulating the human brain’s cognitive processes. Like humans, ML requires …

Fault Detection And Location In Renewable-Fed Distribution Systems Using Low Voltage Sensors, Bushra Farhat

All Theses

The penetration of Distributed Energy Resources (DERs) into the Electric Power System (EPS) is increasing. These (DERs) are mostly inverter-based resources (IBRs) that are integrated into (EPS) at the distribution level. Therefore, it has become necessary to leverage the integration of (IBRs) for both commercial and residential distribution systems through the deployment of Microgrid (MG). However, existing distribution systems that use fuses for sensing and isolating faults are not equipped to handle protection of microgrids where fault currents are fed from multiple sources and can flow downstream or upstream. Bulk of the protection schemes proposed in literature are heavily dependent …

Training Uav Teams With Multi-Agent Reinforcement Learning Towards Fully 3d Autonomous Wildfire Response, Bryce Hopkins

All Theses

As climate-exacerbated wildfires increasingly threaten landscapes and communities, there is an urgent and pressing need for sophisticated fire management technologies. Coordinated teams of Unmanned Aerial Vehicles (UAVs) present a promising solution for detection, assessment, and even incipient-stage suppression – especially when integrated into a multi-layered approach with other recent wildfire management technologies such as geostationary/polar-orbiting satellites and CCTV detection networks. However, there remains significant challenges in developing the necessary sensing, navigation, coordination, and communication subsystems that enable intelligent UAV teams. Further, federal regulations governing UAV deployment and autonomy pose constraints on real-world aerial testing, creating a disconnect between theoretical research …

Using Efficient Vision Transformers To Improve Perception Systems In Autonomous Off-Road Vehicles, Adam S. Pickeral

All Theses

The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also be quick to make decisions. In Autonomous Vehicle research, the environment perception system is one of the key com- ponents of development. The environment perception system allows the vehicle to understand its surroundings using cameras, light detection and ranging (LiDAR), and other sensor systems or modalities. Deep learning computer vision algorithms have shown to be the …

Mmwave Tx-Rx Self-Interference Suppression Through A High Impedance Surface Stacked Ebg, Adewale K. Oladeinde, Ehsan Aryafar, Branimir Pejcinovic

Electrical and Computer Engineering Faculty Publications and Presentations

This paper proposes a full-duplex (FD) antenna design with passive self-interference (SI) suppression for the 28 GHz mmWave band. The reduction in SI is achieved through the design of a novel configuration of stacked Electromagnetic Band Gap structures (EBGs), which create a high impedance path to travelling electromagnetic waves between the transmit and receive antenna elements. The EBG is composed of stacked patches on layers 1 and 2 of a four-layer stack-up configuration. We present the design, optimization, and prototyping of unit antenna elements, stacked EBGs, and integration of stacked EBGs with antenna elements. We also evaluate the design through …

Power Controller For Insulated Solar Electric Cooker, Ryan Koshy, Justin Tae-Hoon Kim

Electrical Engineering

Insulated Solar Electric Cookers, or ISECookers, are devices created to aid those in impoverished regions improve safety, sustainability, and quality of life regarding their cooking practices. ISECookers present an alternative to traditional biofuel/biomass energy sources and provide a closed-loop, self-sustaining system that can be used in a variety of environments. These devices present solutions to widespread issues such as pollution, deforestation, and hazardous emissions as a result of traditional cooking in developing regions around the world. A notable obstacle of these devices is power delivery. Given the varying conditions experienced by a solar panel (inclement weather, irradiance irregularities, etc.), it …

Ensemble Machine Learning At The Edge Using The Codec Classifier Structure And Weak Learners Guided By Mutual Information, Aj Beckwith

All Graduate Theses and Dissertations, Fall 2023 to Present

The Codec Classifier is a low-computation, low-memory tree ensemble method that dramatically improves feasibility of image classification on resource-constrained edge devices. It achieves advantages over other tree ensemble methods due the separation of encoder and decoder tasks in the classifier. The encoder partitions feature space, and the decoder labels the regions in the partition. This functional separation of tasks enables the encoder design (partitioning) to be guided by maximizing the mutual information (MI) between class labels and the features (i.e. the encoded representation of the data) without regard to the error performance of the classifier. Experiments show maximizing MI leads …

Thermal Management Of Roadway-Embedded Power Electronics For Electric Vehicle Dynamic Wireless Charging Systems, Conner R. Sabin

All Graduate Theses and Dissertations, Fall 2023 to Present

Electric vehicles (EVs) are gaining popularity worldwide. However, concerns raised by the public about purchasing EVs include the limited availability of charging infrastructure, high costs, and the inconvenience of current charging methods. Dynamic wireless charging systems enable electric vehicles to charge while driving, thereby extending the range of EVs and reducing the necessity for large, expensive batteries. Moreover, these systems can initiate charging automatically without user interaction, enhancing charging convenience.

The ground assembly of existing dynamic wireless charging systems consists of an electromagnetic assembly, power electronics for the transmitter pad, and power electronics for grid connection. Installation of these systems …

Time-Domain Line Protection In Presence Of Renewables, Prabin Adhikari

All Dissertations

Inverter based resources (IBRs) are crucial in integrating renewable energy sources into the power grid. However, their unique fault characteristics, significantly different from synchronous generators (SGs), present several challenges for existing line protection schemes at both transmission and distribution levels. These schemes, reliant on distance and directional relays designed in phasor domain, are not well-suited for IBRs. Most published literature addressing this problem concentrates on altering the control design of inverters. However, this approach faces practical limitations. Inverter controls, often proprietary, are not readily accessible to utilities, rendering the control-based solutions impractical for widespread implementation.

To address this challenge, this …

Modeling, Optimization, And Characterization For Choke Horn Antennas, Ibrahim Nasser I Alquaydheb

Graduate Theses and Dissertations

This dissertation presents a comprehensive study focusing on the modeling, optimization, and characterization of choke horn antennas (CHAs). An analytical model designed to capture the parameters of CHA and derive the total radiated fields from the choke and waveguide elements is primarily focused on in this work. Compared to the use of simulation software, such as ANSYS HFSS (High Frequency Structure Simulator) or CST Studio, which employs numerical methods to simulate and calculate antenna performance, numerous advantages are offered by the analytical model. Analytical models provide deeper insight into electromagnetic interactions and the principles governing antenna behavior, leading to a …

A Real-Time Iot-Based Data Acquisition And Monitoring System For Photovoltaic Applications, Adam Barbosa, Hamza Mubarak, Fazel Mohammadi, Mohammad J. Sanjari, Mehrdad Saif

Electrical & Computer Engineering and Computer Science Faculty Publications

The transition to low-carbon energy systems, driven by climate change and fossil fuel scarcity, highlights technologies, such as Photovoltaic (PV) technology, for sustainable energy generation. This paper focuses on enhancing the efficiency of PV monitoring systems by leveraging Internet of Things (IoT) technology for accurate and real-time monitoring of essential parameters, such as voltage, current, and output power. Significant gaps in cost-effective and reliable IoT integration for PV monitoring are addressed, with an emphasis on predictive modeling. In this regard, a low-cost real-time IoT-based data acquisition and monitoring system for PV systems, as a proof of concept for future endeavors …

Hardware-Oriented Protection And Acceleration For Machine Learning Application, Antian Wang

All Dissertations

The security of Machine Learning (ML) grows along with the development of high-performance models and expanding application scenarios. Numerous users are benefiting from the convenience brought by transformative ML applications. In the meantime, various attackers are trying to find vulnerabilities within ML deployment service models, thereby undermining the performance of ML and jeopardizing stakeholders’ interests. The dissertation focuses on the two aspects of secure ML applications: acceleration and protection. Homomorphic Encryption (HE) emerges as a widely recognized security primitive suitable for the cloud computing service model, where the computation can be performed over ciphertext without decryption. However, evaluations in the …

Computer Vision Algorithms For Assessment Of Surgical Suturing Skill Using Hand And Needle Motion, Jianxin Gao

All Dissertations

Surgical suturing skill assessment is a crucial part of surgical education. Vascular surgery educators have developed a simulation-based examination called Fundamentals of Vascular Surgery, which includes a clock-face model for assessing open surgical suturing skills. The clock-face model, however, requires the valuable time of expert surgeons to determine examinees' skills. Moreover, expert surgeons have different judgments for appropriate sutures, which leads to inconsistent grading. These limitations motivate us to use sensors to measure examinees' needle motions and hand motions during the clock-face suturing exercises, and then use the measurements for objective suturing skill assessment.

To assess suturing skills based on …

Data-Driven Enhanced Energy Management System Applications For Energy Control Centers, Dulip Madurasinghe

All Dissertations

This dissertation explores the pressing needs of modern bulk power system operation and control and delves into enhancements to EMS applications with minimal infrastructure development. The dissertation investigates three main EMS application enhancements. The transmission network topology processing (TNTP) is a foundational application of the EMS. A physics-based hierarchical transmission network topology processing (H-TNTP), including substation configuration identification, to improve efficiency and reliability is proposed. Secondly, a multi-level distributed linear state estimation (D-LSE) approach solely based on PMUs is proposed utilizing H-TNTP as the network modeling tool. The D-LSE can conduct linear state estimation (LSE) at either substation, area or …

Deep Reinforcement Learning-Based Dynamic Routing And Spectrum Access In Aeronautical Networks., Zhe Wang

Electronic Theses and Dissertations

As the airspace is experiencing an increasing number of aircraft, spectrum sharing among Air Vehicles (AVs) and Terrestrial Users (TUs) emerges as a compelling solution to improve spectrum utilization efficiency. I investigated three types of aeronautical communication including the single-hop Air-Air Communication Network (AACN), the multi-hop Air-Air Ad-hoc Network (AAAN), and the Aerial and Terrestrial Hybrid Network (ATHN). I assume a spectrum-limited scenario through all communication networks. Thus, the number of communication pairs is greater than that of the available channels, resulting in co-channel interference due to the reuse of the same channel among communication links. In a single-hop AACN, …

Image Processing Techniques For Water Droplet Penetration Time And Contact Angle Estimation, Sai Balaji Jai Kumar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Water droplet behavior on soil surfaces plays a critical role in numerous environmental processes, including soil erosion, hydrological dynamics, and ecosystem health. Accurate characterization of soil water repellency, quantified by parameters such as water droplet penetration time (WDPT) and contact angles (WDCA), is essential for informed decision-making in agricultural management, forestry practices, and land-use planning. Despite the significance of these parameters, challenges exist in reliably estimating them due to the complex and dynamic nature of soil-water interactions. This thesis address challenges in estimating WDPT and WDCA, by leveraging state-of-the-art image processing techniques and machine learning algorithms. The research focuses on …

A Heuristic Approach To Operations And Control Of Btm-Ders For Bill Management And Grid Services, Md Arshad Rashid Shaon

UNLV Theses, Dissertations, Professional Papers, and Capstones

Behind-The-Meter (BTM) distributed energy resources (DERs) have emerged as a critical and transformative force within the energy sector. These decentralized energy assets, which include solar photovoltaic (PV) systems, battery energy storage systems (BESS), and thermostatically controlled loads (TCLs), are increasingly essential for empowering customers by granting them greater control over their energy production and consumption, thereby reducing reliance on centralized power sources. Additionally, they have the potential to play a pivotal role in enhancing grid resilience by providing grid services. This study investigates the management of customer electricity bills and grid services through the integration of various BTM-DERs, particularly solar …

High-Power Power Conditioning Systems For Medium-Voltage Applications, Ahmed Rahouma Fares Rahouma

Graduate Theses and Dissertations

The purpose of this dissertation is to address the background, theoretical analyses, design methodologies, and evaluation techniques relevant to high-power power conditioning systems (PCSs) tailored for medium-voltage (MV) applications. Conventional PCSs, reliant on line-frequency transformers (LFTs), suffer from many problems including low power densities, inflexible designs, and scalability constraints. Employing multilevel converters (MLCs), particularly cascaded H-bridge topology, eliminates the need for these LFTs. A key focus lies in minimizing the number of cascaded building blocks by harnessing MV power switching modules. The dissertation makes two primary contributions: firstly, it offers a design methodology for MV-PCS, facilitating the selection of the …

Periodic Information Leakage Fault Detection On A Risc-V Microprocessor, Idris Somoye

Electrical and Computer Engineering ETDs

The execution behavior of a Microprocessor (μP) in the presence of a fault is difficult to predict because of the complex interactions across pipeline stages and between functional units within the architecture. Fault effects are known to not introduce any type of anomaly in the input-output behavior for 10s of thousands to millions of clock cycles. These characteristics increase the difficulty of evaluating μP architectures for resilience to information leakage events, i.e., scenarios where a fault causes sensitive data such as an encryption key to be inadvertently diverted to a primary output channel. This dissertation explores two promising strategies for …

Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, Sinam Ashinikumar Singh, Sinam Ajitkumar Singh, Aheibam Dinamani Singh

Turkish Journal of Electrical Engineering and Computer Sciences

The analysis of heart sound signals constitutes a pivotal domain in healthcare, with the prediction of imbalanced heart sounds offering critical diagnostic insights. However, the inherent diversity in cardiac sound patterns presents a substantial challenge in predicting imbalanced signals. Many scientific disciplines have focused a great deal of emphasis on the problem of class inequality. We introduce an ensemble learning approach employing a convolutional neural network model-based deep learning algorithm to effectively tackle the challenges associated with predicting imbalanced heart sound signals. We use a Gammatone filter bank to extract relevant features from the heard sound signal. Our approach leverages …

Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu

Turkish Journal of Electrical Engineering and Computer Sciences

The decarbonisation of electricity generation requires the real-time monitoring and control of grid components in order to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge …

Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Environmental sound classification (ESC) is one of the important research topics within the non-speech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a …

Distributed Energy Trading Models Utilizing Reinforcement Learning And Game-Theoretic Based Approaches In Smart Grids, Nicholas Kemp

Electrical and Computer Engineering ETDs

Community-driven energy initiatives have become crucial for effective energy management, particularly in trading and management. The rise of Distributed Energy Resources in smart grids demands a redesign of traditional Demand Response Management (DRM) models to account for prosumers' dynamic behavior in energy markets. Decentralization, including peer-to-peer (P2P) energy trading, is vital for resilience and sustainability. This thesis introduces two coalitional DRM models: one based on hedonic community formation games, and the other combining matching theory with coalition games. These models empower prosumers to autonomously select energy trading communities using partially available data. To optimize energy consumption, two additional models are …

Embedding Direction Of Arrival And Antenna Beamforming Algorithms On Automated Software Defined Radio Platforms, Adrian J. Lewis

Electrical and Computer Engineering ETDs

Antenna arrays are an essential technology in the modern era in both communications and radar applications. The algorithms used for antenna arrays are beginning to be embedded in smart device applications to enable greater communication abilities. This thesis explores the idea of embedding direction of arrival and beamforming algorithms using a software defined radio platform. Specifically, the Multiple Signal Classification (MuSiC) algorithm and Minimum Variance Distortionless Response (MVDR) algorithm are combined. The combination of similar algorithms has the potential to be used in many internet of things applications to enable more dynamic communication ability.

Adaptive Screen Capture Video Analysis, Ugesh Egala

Electrical and Computer Engineering ETDs

This thesis presents a system for analyzing student activities during class sessions to gain insights into the learning process. A dataset consisting of 14 screen recordings, with 2 videos labeled across two stages, was used for training, validation, and testing. The methodology employs adaptive sampling, initially at 10 frames per minute to identify active regions, followed by detailed analysis at 1 frame per second using OCR to detect typing activities through character changes. The results demonstrate over a 50% reduction in computational load while maintaining high accuracy in detecting student engagement. A total of 26 hours of screen capture videos …

Bio-Inspired Robotic Framework For Spatiotemporal Analyses In Environmental Monitoring Applications, Maliha Kabir

Electrical Engineering Theses

Frequent fluctuations in the environment's temperature and climate significantly impact our crop fields. Floods and droughts occur in different places in different seasons, which is very unusual and affects the crops' health and productivity. During these calamities, they are also not able to monitor their crop fields. However, if it is possible by providing farmers with information about their fields' conditions like temperature, pressure, and soil moisture, we can help them assess environmental conditions for specific areas. This data can also aid farmers in determining the appropriate amounts of fertilizers and pesticides to apply to their crops and which crops …

Experimental Validation Of An Analytical Transient Model For Saturated Boosting Gain In Dc–Dc Converters With Variable Duty Cycle, Goliana Samir, Simon Ezzat, Wagdy Anis, Sameh O. Abdellatif

Electrical Engineering

No abstract provided.