Electrical and Computer Engineering Commons^™

Utilizing Machine Learning To Predict The Charge Storage Capability Of Lithium-Ion Battery Materials, Manoj Chhetri, Karen S. Martirosyan

Physics & Astronomy Faculty Publications

With the increasing demand for high-performance batteries in applications such as electric vehicles and portable electronics, accurately predicting the charge storage capacity of battery materials is crucial for developing more efficient and reliable energy storage systems. Machine Learning (ML) and data-driven approaches, plays a vital role in enhancing our understanding of Li-ion battery performance, guiding materials design, optimizing system efficiency, and accelerating innovation in energy storage technologies. In this study, an ML-based approach was applied to a dataset of 2345 rechargeable Li-ion battery materials, obtained from the Materials Project online portal, to predict gravimetric charge storage capacity ─ a key …

A Novel Preprocessing Model For Multi Modal Brain Mri Image Classification For Stroke Prognosis, Alwin Joseph, Chandra J

Northeast Journal of Complex Systems (NEJCS)

Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data – Fluid Attenuated Inversion Recovery (FLAIR), Apparent …

Fault Diagnosis And Fault Tolerant Structure For Multilevel Inverters Using Machine Learning Techniques, Sudha V

Theses and Dissertations

A paradigm shift towards electric drives in domestic and industrial sectors has significantly increased the use of multilevel inverters (MLI). MLIs are constructed using more semiconductor devices, which hinders safety and reliability. Literature states 31.2% of failures in MLIs are due to semiconductor devices. Hence, there is a need for fault detection and tolerant mechanisms to ensure the safety and reliability of MLIs.

MLIs like Cascaded H-bridge(CHB) and Packed U cell(PUC) are mostly preferred due to low harmonic distortion, which is considered in this work. The complexity associated with fault diagnosis with more components in MLIs is addressed by machine …

Novel Approach For The Micro Cracks Detection Of Solar Wafers And Cells, Mohd Israil, Arvind Kumar Sharma, Ekta Gupta

Al-Bahir

This paper deals with the review of various existing technique for the microcracks detection in silicon solar cell and wafer. In addition to this, we proposed a novel approach for the machine learning technique for the inspection of the cracks those are existed in the solar cell and wafer and not able to detect by the naked eyes. There are many techniques have been developed by the various researchers around the world to inspect solar cells for defect. All the techniques discussed in this article having some features and some weakness too. This paper present here gives the two-fold solution …

Advancing Electrical Stimulation: Full-Head Mri Segmentation For Abnormal Brain Anatomy With Tdcs, Andrew Birnbaum

Dissertations and Theses

Evaluating the effectiveness of transcranial direct current stimulation (tDCS) is essential for guiding its integration into therapeutic and performance-enhancement applications. In our laboratory, we investigate the efficacy of tDCS across multiple experimental models, including both animal and human studies. I have contributed significantly to the execution and analysis of these experiments, which include studies in rats and healthy human participants aimed at evaluating whether electrical stimulation of the motor cortex can enhance motor learning. These studies assess improvements in fine motor performance resulting from tDCS. In stroke patients, I contribute to our investigation of tDCS as a rehabilitative intervention, particularly …

A Multiscale Ai Framework For Forest And Agriculture Health Monitoring: Drone-Based Object Recognition And Segmentation For Automated Ecological Assessment, Sruthi Keerthi Valicharla

Graduate Theses, Dissertations, and Problem Reports (ETD)

Forest and agricultural ecosystems are increasingly at risk due to invasive species, pests, and diseases, necessitating scalable, automated, and intelligent monitoring solutions. Traditional field based forest and agriculture health assessments are limited by cost, time, and spatial coverage. This dissertation presents a multiscale deep learning framework that automates forest and agriculture health monitoring using drone imagery and computer vision techniques. The system operates across three spatial levels: forest level, tree level, and leaf level, combining object detection, segmentation, and classification models to support large scale ecological assessment.

At the forest level, high-altitude drone imagery is processed using object detection and …

Deep Target Recognition: Semi-Supervised Annotation, Sensor Fusion And Super-Resolution, Shoaib Meraj Sami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Despite the recent expansion of machine learning algorithms to cover a wide range of disciplines, several areas of automatic target recognition (ATR) remain underexplored. This dissertation presents tools developed to improve performance in three significant aspects of ATR: semi-supervised annotation, sensor fusion, and image super-resolution. The aim of the semi-supervised methods is to automatically annotate targets in scenarios where labeled data are scarce in the target domain but available in the source domain. Secondly, to address the limitations of individual image sensors and enhance robustness under different environmental conditions and man-made constraints, a sensor fusion algorithm was developed to improve …

Electromagnetic Integral Equation Methods For High-Order Field Predictions, Jordon N. Blackburn

Theses and Dissertations--Electrical and Computer Engineering

Methods like the Method of Moments (MoM) or the locally-corrected Nyström (LCN) method are employed to discretize and solve electromagnetic integral equations. This process results in large, dense systems of linear equations that must be solved. In many cases, the elements of the system matrix can be computed analytically or approximated with high-order numerical methods. In this thesis, various approaches are presented to improve the accuracy and efficiency of integral equation solutions.

The second chapter derives a modified form of the low-rank matrix approximation algorithm known as the adaptive cross approximation (ACA). The original ACA has been observed to lose …

Performance Evaluation Of Routing Protocols And Ml-Based Enhancements For Uav-Assisted Post-Disaster Communication Networks, Prachi Choudhary

Doctoral Dissertations and Master's Theses

During natural disasters, the existing communication systems collapse, and the disaster-affected areas become disconnected without any means of exchanging information. The collapse of existing communication networks poses challenges for First-Responders (FRs) in locating survivors during Search and Rescue (SAR) operations and for survivors to communicate for emergency aid. To alleviate post-disaster consequences and save lives, Uncrewed Air Vehicles (UAVs), commonly known as drones, can be employed to establish adaptable and reliable emergency communication networks. UAVs offer portability and rapid deployment, making them effective in crises. This thesis presents two main contributions: 1) Evaluation and Comparison of Routing Protocol Performance: The …

Applying Positive Unlabeled Learning Techniques And Using The Kullback-Leibler Divergence To Improve Geothermal Surveying Assessments, Martín Thomas Rodriguez

Dissertations and Theses

As we face the current climate crisis, the discovery of geothermal energy resources has the potential to greatly reduce our dependence on fossil fuels worldwide. However, the development of any new energy infrastructure is expensive and depends on the willingness of energy agencies and developers to make initial investments based on calculated risk measures. One such measure, called geothermal favorability, is the likelihood that a site has conditions favorable for geothermal systems containing recoverable energy potential. Its prediction from existing geophysical datasets proves to be a nontrivial task. The prediction of geothermal favorability can be framed as a binary classification …

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 …

Enhancing Fundraising Strategies In Higher Education Through Machine Learning, Laith Alatwah

Electrical Engineering Theses

This thesis presents a comprehensive application of machine learning techniques, namely Fine Gaussian SVM and RUS Boosted Trees, to enhance fundraising strategies in higher education institutions. Analyzing a rich dataset from Blackbaud Raiser's Edge NXT, spanning 2012 to 2022, the study focuses on donor profiles, including demographics, donation history, and engagement patterns. Key demographic insights include the increasing engagement of younger donors (20-29 age group) and significant contributions from older donors (70-99 age group). Geographical trends are also examined, revealing distinct patterns based on donors' city, state, and ZIP code. The Fine Gaussian SVM model demonstrates moderate discriminatory power, with …

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 …

Automated Measurement Of The Water Drop Penetration Time For The Analysis Of Soil Water Repellency, Danxu Wang

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this work, we develop an innovative system for the automated measurement of Water Drop Penetration Time (WDPT) - a parameter that is conventionally used for evaluating soil water repellency (SWR). Increased SWR can be a reason for plant stress and poor crop yields, create a risk of potential water runoff and floods and thus can pose risks to life and property loss. Timely evaluation of soil conditions can save resources and win time for responding to environmental disasters. Manual measurements of WDPT are labor-intensive, subjective, tend to produce variability of outcomes, and also not always available in remote or …

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 …

Studying The Performance Of Object Recognition With Fusion Of Visible Light And Infrared Images With Neural Networks, Plamen Petkov

Doctoral Dissertations and Master's Theses

Neural networks have been used for object detection and recognition in both color and intensity camera images. As the use of infrared cameras, colloquially termed thermal cameras, has increased and costs have decreased, object detection and recognition in infrared camera images have been increasingly studied. An infrared image is treated as an intensity image, just like a grayscale camera image, except the intensity corresponds to infrared radiation instead of visible light. The information provided by these two types of images are different, especially in different lighting and environmental situations, and some types of objects are more easily recognized in visible …

Advancing Adversarial Audio: Human-In-The-Loop Black-Box Attacks, Rui Duan

USF Tampa Graduate Theses and Dissertations

Adversarial audio attacks pose significant security challenges to real-world audio applications. Attackers may manipulate speech to impersonate a speaker, gaining access to smart devices like Amazon Echo. In audio applications, there are two key areas: music and speech. In music, most attackers create a small noise-like perturbation on the original signal to evade copyright detection. However, this method degrades music's perceived quality for human listeners. In the speech, creating an adversarial example often requires many queries to the target model, a process too cumbersome for practical use in real-world scenarios, like interacting with smart devices numerous times.

In this dissertation, …

Generative Data Augmentation: Using Dcgan To Expand Training Datasets For Chest X-Ray Pneumonia Detection, Ryan D. Maier

Master's Theses

Recent advancements in computer vision have demonstrated remarkable success in image classification tasks, particularly when provided with an ample supply of accurately labeled images for training. These techniques have also exhibited significant potential in revolutionizing computer-aided medical diagnosis by enabling the segmentation and classification of medical images, leveraging Convolutional Neural Networks (CNNs) and similar models. However, the integration of such technologies into clinical practice faces notable challenges. Chief among these is the obstacle of acquiring high-quality medical imaging data for training purposes. Patient privacy concerns often hinder researchers from accessing large datasets, while less common medical conditions pose additional hurdles …

Machine Learning For Graph Algorithms And Representations, Allison Gunby-Mann

Dartmouth College Ph.D Dissertations

This thesis explores a variety of common graph theoretic problems from a machine learning perspective. The topics covered include fundamental network problems such as distance approximation, distance sensitivity, community detection, cross-network alignment, and graph embedding dimension reduction. These projects are unified by the theme of machine learning on graphs, graph embeddings, and representations of graphs.

Machine Learning For Intrusion Detection Into Unmanned Aerial System 6g Networks, Faisal Alrefaei

Doctoral Dissertations and Master's Theses

Progress in the development of wireless network technology has played a crucial role in the evolution of societies and provided remarkable services over the past decades. It remotely offers the ability to execute critical missions and effective services that meet the user's needs. This advanced technology integrates cyber and physical layers to form cyber-physical systems (CPS), such as the Unmanned Aerial System (UAS), which consists of an Unmanned Aerial Vehicle (UAV), ground network infrastructure, communication link, etc. Furthermore, it plays a crucial role in connecting objects to create and develop the Internet of Things (IoT) technology. Therefore, the emergence of …

Head Impact Measurement Using Piezoelectric Sensors, Huda Abdulla Alnuaimi

Theses

The importance of safety measures cannot be overstated, especially when it comes to protecting the human head. Head injuries can have severe, life-altering consequences, as the head is crucial for controlling the entire body. Unlike machines that store data, the human brain's capacity to retain thoughts and memories can be significantly affected by even a single injury. This thesis introduces a method for predicting the specific area of the head that might be injured during an impact. The prediction is based on the intensity and duration of the impact. The innovation of this thesis lies in the use of piezoelectric …

Multi-Task Learning For Hybrid Communication Waveforms: Exploring Model Enhancement Techniques And Establishing Task Relationships, Saksham Dewan

Legacy Theses & Dissertations (2009 - 2024)

Wireless communications have become ubiquitous, enabling seamless connectivity and driv- ing innovations across various domains. As we look to the future, visible light communication (VLC) is a promising technology that offers the potential to revolutionize how we transmit and receive data. It seamlessly integrates multiple functionalities, including localization, control/sensing, and high-speed data transmission.This thesis proposes a multi-task learning deep convolutional neural network approach to optimize a hybrid waveform for VLC-enabled networks. By integrating Beacon Posi- tion Modulation (BPM), Beacon Phase Shift Keying (BPSK), and OFDM symbols within a virtual Pulse Width Modulation (PWM) envelope, this waveform supports localization, control/sensing, and …

Database And Machine Learning Model For Classifying Autism Spectrum Disorder From Smartphone Based Electroretinography, Rory Harris

Honors Scholar Theses

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that negatively affects a patient’s cognitive and communication aptitude and, therefore, can severely impact that patient’s quality of life. Because of this, early diagnosis is paramount. In recent studies, electroretinography (ERG), which is a measure of the retina’s electrical response to a brief flash of light into the eye, has shown promise in detecting ASD. Access to these scans can provide early diagnosis, improving well-being. Current ERG devices are very expensive due to their on board processing capabilities. This paper aims to create an ERG device using a smartphone as the main …

Effects Of Unobservable Bus States On Detection And Localization Of False Data Injection Attacks In Smart Grids, Moheb Abdelmalak

USF Tampa Graduate Theses and Dissertations

In an era increasingly marked by sophisticated cyber-attacks, this thesis investigates the critical issue of bus unobservability in smart grids and its impact on the effectiveness of cyber-attack detection and localization models. Given that unobservability is a prevalent challenge in smart grids due to various factors, researchers have developed numerous algorithms for optimal Phasor Measurement Unit (PMU) placement under scenarios of limited observability. However, these models primarily focus on enhancing network observability, often without considering whether this placement optimally facilitates attack detection. This research is driven by the hypothesis that a deeper understanding of the effects of unobservable buses can …

Exploring Machine Learning Techniques For Embedded Hardware, Neel R. Vora

Computer Science and Engineering Theses - Archive

This thesis delves into the intricate symbiosis between machine learning (ML) methodologies and embedded hardware systems, with a primary focus on augmenting efficiency and real-time processing capabilities across diverse application domains. It confronts the formidable challenge of deploying sophisticated ML algorithms on resource-constrained embedded hardware, aiming not only to optimize performance but also to minimize energy consumption. Innovative strategies are explored to tailor ML models for streamlined execution on embedded platforms, with validation conducted across various real-world application domains. Notable contributions include the development of a deep-learning framework leveraging a variational autoencoder (VAE) for compressing physiological signals from wearables while …

Neural Net Estimation Of Discriminants Posterior Probability Vector, Harshvardhan Harshvardhan

Electrical Engineering Theses - Archive

Interpreting multi-layer perceptron (MLP) classifier outputs as posterior probabilities is a well-established practice in machine learning and is supported in the literature. However, several authors point out that MLP outputs are very poor estimates of the posterior probabilities. This is demonstrated for classifiers with and without nonlinear output activation. Achieving this reliability depends on key factors such as model complexity, sufficient training data availability, and optimization techniques' effectiveness. In practice, these requirements are not met, resulting in suboptimal probability estimates. Our approach introduces an innovative method based on the softmax output. The method aim to refine MLP discriminants into more …

Machine Learning Based Intrusion Detection Framework For Can Bus Vulnerabilities In Modern Vehicles, Obinna C. Agbo

Graduate Theses, Dissertations, and Problem Reports (ETD)

The Controller Area Network (CAN) bus is a crucial communication backbone in modern vehicles, connecting various Electronic Control Units (ECUs). However, inherent design weaknesses such as the lack of encryption and authentication make CAN networks vulnerable to cyber-attacks, including spoofing, Denial of Service (DoS), and fuzzing attacks. This thesis thoroughly evaluates these vulnerabilities and the limitations of existing security frameworks like Message Authentication Codes (MACs) and encryption, advocating for the adoption of Intrusion Detection Systems (IDS) as a more practical solution for CAN bus security. The proposed IDS leverages advanced machine learning techniques to accurately detect intrusions, even under complex …

Multi-Magnetic Material Transcranial Magnetic Stimulation Coils Development And Electric Field Measurement & Modeling Using Machine Learning, Mohannad Tashli

Theses and Dissertations

Transcranial Magnetic Stimulation (TMS) is a safe, effective, and non-invasive therapy for treating several psychiatric and neurological disorders. TMS is Food and Drug Administration (FDA) approved treatment and is commonly applied to patients who do not respond to medications for the treatment of clinical depression, smoking cessation, obsessive-compulsive disorder and migraine. Recently, there has been an increase in the development of electromagnetic neuromodulation techniques targeted at enhancing the effectiveness of TMS devices for the treatment of mental diseases. In TMS stimulation, focality is an important factor which determines the specificity of the pulses induced in different brain tissues. The electromagnetic …

Applications Of Computational Intelligence And Data Fusion Techniques For Biomedical Images, Anand Krishnadas Nambisan

Doctoral Dissertations

"The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall …