Electrical and Computer Engineering Commons^™

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 …

Learning To Represent Temporal Dynamics And Generative Factors For Intelligent Visual Navigation, Sahand Khoshdel

All Theses

Visual navigation systems are crucial in various applications, including autonomous driving, unmanned aerial systems (UAS), and industrial automation. For these systems to operate efficiently in dynamic environments, they must not only interpret complex surroundings but also anticipate changes over time. Temporal prediction—forecasting environmental changes like moving obstacles or shifting lighting conditions—enables navigation systems to act proactively, enhancing both safety and performance. This dissertation investigates representation learning methods both as a backbone feature extractor for RL agents as well as a proxy for systems oriented for Explainable AI (XAI). Two main projects are presented as case studies to achieve the aforementioned …

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 …

Myofibroblast Targeted Liposomes For The Treatment Of Skin Fibrosis, Elfa Beaven

Open Access Theses & Dissertations

Continuous overactivation of myofibroblasts leads to excessive deposition of profibrotic proteins, which in turn causes tissue stiffness and, ultimately, organ failure. One of those proteins, Cadherin-11 (CDH11), is reported to be overexpressed in the tissue of fibrotic skin, specifically on myofibroblasts. Similarly, chronic inflammation drives fibrosis progression in autoimmune-related illnesses like systemic sclerosis (SSc). Therefore, it is hypothesized that CDH11 could be a target for cell-specific delivery of antifibrotic therapies to fibrotic skin tissue. Here, a CDH11 peptide conjugated liposome loaded with Tofacitinib, a JAK inhibitor, was developed to understand the potential of targeted therapies to treat skin fibrosis. The …

Developing A Bioengineered Nanoparticle For Improving Oral Absorption Of Iron Supplements., Mila Biswas

Open Access Theses & Dissertations

Iron deficiency (ID) and iron deficiency anemia (IDA) are widespread nutritional issues, affecting millions globally. Conventional iron supplements often suffer from low absorption rates and gastrointestinal side effects. We investigated β-glucan derivatives as potential carriers to enhance iron absorption and mitigate these drawbacks. This study explored a novel β-glucan-based carrier system loaded with ferrous sulfate heptahydrate. In-vitro studies demonstrated sustained iron stability for over six hours in simulated gastric fluids due to the carrier's affinity for stomach mucin. Particle size analysis and scanning electron microscopy (SEM) images confirmed this specific binding. Additionally, drug release studies revealed a pH-dependent release profile, …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill

Theses and Dissertations

This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …

Three-Dimensional Environmentally Sustainable Neuromorphic Computing System Based On Natural Organic Memristor, Mohammed Rafeeq Khan

Graduate Theses and Dissertations (2019 - present)

A three-dimensional neuromorphic (3D) computing architecture based on environmentally sustainable natural organic honey memristors is proposed in this thesis. A set of comprehensive and experimental results indicate that the proposed systems exhibit remarkable inference accuracy, consistently surpassing the 90% threshold, even with different challenges such as device variations and nonlinearity. This study also considers four different conductance drift situations, the effects of analog-to-digital converter (ADC) quantization, and multiple algorithms, such as VGG8 and DenseNet-40. The deliverable of this thesis will test the stability of the proposed systems and explore their potential applications and scalability in real-world situations.

The 9th National Conference Of Solid State Battery Successfully Held In Xiamen, Fujian, Zheng-Liang Gong

Journal of Electrochemistry

No abstract provided.

Aqueous Supercapacitors With Wide Operative Voltage Window And Long Cycling Life Enabled By Electrolyte Hybridization Effect, Shi-Hua Ma, Qi Yin, Jin-Ping Zhao

Journal of Electrochemistry

Developing supercapacitors (SCs) with long cycling life and wide operative voltage window is a significant topic in the field of aqueous electrolytes. Although the design of water in salt (WIS) electrolytes has pushed the development of aqueous electrolytes to a new height, the WIS electrolytes with an operative voltage window of up to 2.5 V is still very scarce. Herein, in order to enrich the type of aqueous electrolyte with high operative voltage, tetramethylammonium trifluoromethanesulfonate (TMAOTf) based WIS electrolyte was used as a model to construct WIS based hybrid electrolyte with acetonitrile (ACN) co-solvent and LiTFSI co-solute. In view of …

The Award Ceremony Of The 2nd Xiamen University Short Video Abstract Contest For Science And Technology Journal Papers Came To A Successful Conclusion, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Journal Of Electrochemistry, The Flagship Journal Of Chinese Chemical Society Electrochemical Committee, Made A Public Appearance At The 22nd National Electrochemical Congress, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Characterization Of Silicon Carbide Low-Voltage N/P-Channel Mosfets At High Temperatures, Hui Wang, Pengyu Lai, Affan Abbasi, Maksudul Hossain, Asif Faruque, Alan H. Mantooth, Zhong Chen

Electrical Engineering and Computer Science Faculty Publications and Presentations

SiC-based n-channel and p-channel MOSFETs fabricated by Fraunhofer IISB SiC CMOS technology are characterized from room temperature up to 300◦C. The behaviors of these low voltage devices including the short-channel effect (SCE), p-type ohmic contact with high resistivity, and the low channel mobility due to the SiC/SiO2 interface are presented. A thorough analysis is performed to understand the cause of low channel mobility, with TCAD simulations specifically on p-channel MOSFET, providing an insight into the impact of channel length, interface traps, and contact resistivity on device performance. The analysis in this paper is important in the comprehension of the low-voltage …

Modeling, Control And Validation Of A Three-Phase Single-Stage Photovoltaic System, Eubis Pereira Machado, Adeon Cecílio Pinto, Rodrigo Pereira Ramos, Ricardo Menezes Prates, Jadsonlee Da Silva Sá, Joaquim Isídio De Lima, Flavio Bezerra Costa, Damásio Fernandes, Alex Coutinho Pereira

Michigan Tech Publications

The central inverter topology presents some advantages such as simplicity, low cost and high conversion efficiency, being the first option for interfacing photovoltaic mini-generation, whose shading and panel orientation studies are evaluated in the project planning phase. When it uses only one power converter, its control structures must ensure synchronization with the grid, tracking the maximum power generation point, appropriate power quality indices, and control of the active and reactive power injected into the grid. This work develops and contributes to mathematical models, the principles of formation of control structures, the decoupling process of the control loops, the treatment of …

Data Security In The Apple Ecosystem: An Evaluation, Kayrene Woods

Cybersecurity Undergraduate Research Showcase

This study provides a comprehensive evaluation of data security within the Apple ecosystem, focusing on the company’s privacy policies, user perceptions, and the effectiveness of its App Store review processes. Employing an interdisciplinary methodology, the research examines Apple’s commitment to data protection, emphasizing transparency and user trust. A survey of user experiences revealed varying levels of engagement and understanding of Apple’s privacy practices, with only 32.8% of respondents having read the Privacy Policy and mixed opinions on its clarity. Additionally, concerns persist about third-party app security, with 39.7% of users expressing apprehension and skepticism about Apple’s App Store review process. …

Intelligent Transportation System With 5g Vehicle-To-Everything (V2x): Architectures, Vehicular Use Cases, Emergency Vehicles, Current Challenges, And Future Directions, Vaishali Pawar, Nilima Zade, Deepali Vora, Vaishali Khairnar, Aurenice M. Oliveira, Ketan Kotecha, Ambarish Kulkarni

Michigan Tech Publications

In recent years, connected vehicle technologies emerged to provide a safer and more connected environment for transportation systems, which has further evolved into cellular vehicle-to-everything (C-V2X) technology. The current study contributes in the direction of future research of smart India @2047 in connected vehicles. The study presents the diverse aspects of 5G V2X as a comprehensive overview of Vehicle-to-Everything (V2X) technology that covers the significant scope of V2X technology, including its development, components, functionality, use cases, and future potential. It is observed that few surveys are available that cover limited aspects of 5G V2X communication technologies, with one or two …

An Improved Conditional Integrator Sliding Mode Controller Based On Swarm Intelligence For A Magnetic Levitation System, Abdelkader Kerraci, Mohamed Fayçal Khelfi, Zoubir Ahmed-Foitih

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes an enhanced Conditional Integrator Sliding Mode Controller using Particle Swarm Optimization (CISMCPSO) for a magnetic levitation system (MLS). The main advantage of this controller is its robustness to uncertainties and disturbances, which also avoids chattering and ensures zero static steady-state error. The main idea of CISMCPSO is to activate its integral action only when the sliding surface reaches the boundary layer while it is reduced to zero or close to zero elsewhere, which avoids destroying the transient response caused by the conventional integral sliding-mode controller. A particle swarm optimization algorithm schedules the conditional integral term parameter of …

Uniform 3d Scattering Point Model For Simulating The Dynamic Radar Echo From Wind Farm, Bo Tang, Zhendong Zhu, Zhiyu Shang, Huanghai Xie, Feng Wang, Jiaxu Chen

Turkish Journal of Electrical Engineering and Computer Sciences

The calculation scale of simulating wind farm dynamic radar echo is gradually growing with the increasing scale of wind farms, which can hardly meet the requirements of real-time radar echo simulation. Considering that the method of surface element division can greatly influence the result of simulation, uniform surface element division is applied to accelerate the traditional simulation algorithm based on the refined 3D scattering point model and enhance the main characteristics of the radar echo. The solution time of dynamic radar echoes from 1-8 wind turbines is calculated to test the average speed that the uniform 3D scattering point model …

Developing Linguistic Patterns To Mitigate Inherent Human Bias In Offensive Language Detection, Toygar Tanyel, Besher Alkurdi, Serkan Ayvaz

Turkish Journal of Electrical Engineering and Computer Sciences

With the proliferation of social media, there has been a sharp increase in offensive content, particularly targeting vulnerable groups, exacerbating social problems such as hatred, racism, and sexism. Detecting offensive language use is crucial to prevent offensive language from being widely shared on social media. However, the accurate detection of irony, implication, and various forms of hate speech on social media remains a challenge. Natural language-based deep learning models require extensive training with large, comprehensive, and labeled datasets. Unfortunately, manually creating such datasets is both costly and error-prone. Additionally, the presence of human-bias in offensive language datasets is a major …