Computer Engineering Commons^™

Future-Ready Digitalized Education: Unraveling The Dynamics Of Sustainable And Ethical Digital Transformation, Vaishnavi Rode

Electronic Theses, Projects, and Dissertations

Amid the brisk advancement of digital technologies, higher educational institutions and universities are finding themselves at a crucial turning point, with significant obstacles and new prospects in the realm of digital transformation. This culminating experience project delves deeply into the compounded terrain of digital transformation in higher education, emphasizing the need for sustainable practices in the face of rapidly evolving technical advancements. The research questions are: (Q1) What strategies can universities adopt to foster digital literacy among students and faculty while promoting sustainability values within their digital education programs and Why? (Q2) What ethical considerations, concerning data privacy and digital …

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 …

Vision-Based Autonomy Stacks For Farm Tractors And Intelligent Spraying Systems In Orchards, Shengli Xu

All Dissertations

Autonomous tractors equipped with intelligent sprayers have become a pivotal aspect of smart farming (SF), marking a transformative shift in traditional agricultural practices and holding the potential to revolutionize the farming industry. With 2,453,620 fruit-bearing acres in the United States as of 2022, there is a pressing need for the implementation of autonomous systems for farm tractors and intelligent spraying systems in orchards. These advancements can significantly reduce labor costs, address labor shortages, and minimize spray loss. Furthermore, to enhance profitability and productivity, it is essential to develop low-cost yet effective vision-based autonomy systems that can operate efficiently across various …

Convex Approach To Data-Driven Optimal Control With Safety Constraints Using Linear Transfer Operator, Joseph Raphel Moyalan

All Dissertations

This thesis is concerned with the data-driven solution to the optimal control problem with safety constraints for a class of control-affine nonlinear systems. Designing optimal control satisfying safety constraints is a problem of interest in various applications, including robotics, power systems, transportation networks, and manufacturing. This problem is known to be non-convex. One of this thesis's main contributions is providing a convex formulation to this non-convex problem. The second main contribution is providing a data-driven framework for solving the control problem with safety constraints. The linear operator theoretic framework involving Perron-Frobenius and Koopman operators provides the convex formulation and associated …

Efficient And Secure Data Transmissions In Emerging Heterogeneous Wireless Networks, Sihan Yu

All Dissertations

The widespread deployment of wireless devices facilitates the Internet of everything, greatly enhancing communication efficiency and improving people’s daily experiences. The interconnectivity of wireless devices relies on wireless communication technologies. With the development of various emerging communication technologies, wireless networks have become increasingly vast and complex, giving rise to numerous new challenges such as efficiency and security concerns.

In wireless networks, different devices may utilize different communication protocols, resulting in heterogeneous wireless networks. Communication among heterogeneous devices is challenging, often leading to conflicts and low communication efficiency when utilizing limited communication resources (e.g., spectrum resources). Moreover, the coexistence of heterogeneous …

A Hierarchical Framework For Interpretable, Safe, And Specialised Deep Reinforcement Learning, Ammar Abbas

Doctoral

Safety-critical systems, which are crucial for human safety and the environment, are difficult to control and operate. Traditional controllers need precise models of these complex systems, which is hard to develop. \acrfull{drl} offers a potential solution by learning from interactions rather than detailed models, but it faces limitations such as non-transparent decision-making and an expensive, unsafe learning process. Additionally, a key challenge in DRL is ensuring effective decision-making in rare situations.

This thesis proposes a novel approach called the \acrfull{prop_frame} that enables safe and reliable control of critical systems. SRLA combines probabilistic modelling with reinforcement learning to create an interpretable …

Crowdstrike Cyber Incident Vs. Past Major Cyber Incidents: Analysis And Solutions, Priyant Banerjee

Himalayan Research Papers Archive

On July 19, 2024, a technical malfunction in CrowdStrike’s Falcon sensor software led to a global ITdisruption, affecting millions of devices across multiple sectors. This incident, although not a direct cyber-attack, caused significant operational upheavals reminiscent of major past cyber incidents. This paperexplores the CrowdStrike incident in detail, compares it with previous major cyber events, and proposescomprehensive solutions to mitigate such risks in the future.The faulty update from CrowdStrike resulted in widespread system crashes, notably the "Blue Screen ofDeath," paralyzing operations in critical sectors such as healthcare, finance, and transportation. The paperexamines the immediate and cascading effects of the incident, …

Adaptive Robot Collaboration Using Robotic Skin And Motion Similarity., Jordan Dowdy

Electronic Theses and Dissertations

An essential part of robotics research is human-robot collaboration, which enables the use of current and new robots in everyday life and the workforce. This research applies to both parts of human-robot collaboration: physical human-robot interaction (pHRI), as well as non-physical human-robot interaction. The physical interaction uses tactile sensors and a Neuroadaptive Controller (NAC) to allow for the guidance of a robotic arm and its end-effector. The non-physical interaction uses a novel motion similarity metric, the Cartesian Segment Online Dynamic Time-Warping (SODTW), to allow a robot to better adapt to the speed of the user performing the motion during imitation …

Patterning Synthesis Of Lead Halide Perovskites Toward Photonic Application, S. M. Nayeem Arefin

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

Lead halide perovskites (LHPs) are a fascinating class of photonic materials with the potential to revolutionize various optoelectronic applications. Their diverse crystal structures, ranging from 0D to 3D configurations, offer a unique combination of properties, including high tunability and ease of synthesis. However, their inherent instability and the difficulty of patterning them into sophisticated photonic structures using conventional methods present a significant hurdle to their widespread applications. This thesis addresses these challenges by proposing a novel synthesis method that combines soft lithography and self-assembly. By utilizing a patterned template with controlled wettability, precise manipulation of LHP crystal formation is achieved, …

Ultrashort Pulsed Laser Treatment Is Effective At Sterilizing Metal Surfaces For Planetary Protection, Kaleb Mcquillan

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

To prevent forward contamination from microbes aboard spacecraft intended for exploration of solar system bodies there is a need for effective sterilization methods. However, current techniques are both time-consuming and expensive. For example, dry heat sterilization requires removal from the assembly site and several days of treatment. Furthermore, some components such as optics and electronics are not compatible with current sterilization techniques. In this thesis, a novel femtosecond laser surface processing technique for the rapid sterilization of spacecraft hardware is reported. Femtosecond lasers produce extremely high photon fluxes (10²⁹ photons/s*cm², ~0.03 J/cm²) in extremely short …

Development Of An Efficient Multi-Objective Approach For Secure Live Virtual Machine Migration, Venkata Subramanian N

Theses and Dissertations

Cloud computing offers organizations flexibility and cost-efficiency through pay-asyou- go services, allowing them to scale resources according to their needs and reduce expenditures. Cloud as a Service (CaaS) offloads IT management complexities, while Cloud Data Center (CDC) provides infrastructure for on-demand, scalable, and flexible services over the Internet. Virtualization improves operational efficiency by providing simultaneous access to multiple virtual machines, while Live Virtual Machine Migration enhances agility, resilience, resource allocation, and fault tolerance.

However, achieving effective VMM requires forecasting cloud resource utilization, selecting the right target host, and ensuring security. Live VM migration is inevitable for optimizing CDC resource utilization. …

Industria 4.0. Internet De Las Cosas: Ciberseguridad Y Aplicaciones, Jairo Eduardo Márquez Díaz, Arles Prieto Moreno, Luz Jaddy Castañeda Rodríguez, Luis Gonzalo Benavides Ramírez

Ingeniería

Este libro explora cómo la Cuarta Revolución Industrial, también conocida como Industria 4.0, está transformando sectores industriales mediante tecnologías disruptivas como el internet de las cosas (IoT), la inteligencia artificial, el big data y la realidad aumentada, lo que ofrece una mayor eficiencia, productividad y seguridad. Aborda los riesgos de ciberseguridad asociados y propone soluciones basadas en la inteligencia artificial y el blockchain. Además, analiza el uso de drones y tecnologías como el LiDAR en la minería, lo que mejora la exploración, la seguridad y la sostenibilidad de operaciones complejas. Con un enfoque en la minería del carbón en Colombia, …

An Optimization-Based Monte Carlo Method For Estimating The Two-Terminal Survival Signature Of Networks With Two Component Classes, Daniel B. Lopes Da Silva, Kelly M. Sullivan

Industrial Engineering Faculty Publications and Presentations

Evaluating two-terminal network reliability is a classical problem with numerous applications. Because this problem is #𝖯-Complete, practical studies involving large systems commonly resort to approximating or estimating system reliability rather than evaluating it exactly. Researchers have characterized signatures, such as the destruction spectrum and survival signature, which summarize the system's structure and give rise to procedures for evaluating or approximating network reliability. These procedures are advantageous if the signature can be computed efficiently; however, computing the signature is challenging for complex systems. With this motivation, we consider the use of Monte Carlo (MC) simulation to estimate the survival signature of …

Integrated Multi-Omics Analysis Of Cerebrospinal Fluid In Postoperative Delirium, Bridget A. Tripp, Simon T. Dillon, Min Yuan, John M. Asara, Sarinnapha M. Vasunilashorn, Tamara G. Fong, Sharon K. Inouye, Long H. Ngo, Edward R. Marcantonio, Zhongcong Xie, Towia A. Libermann, Hasan H. Otu

Department of Electrical and Computer Engineering: Faculty Publications

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case–no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual …

A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), Ozlem Ece Yurek, Derya Birant

Turkish Journal of Electrical Engineering and Computer Sciences

Predictive maintenance (PdM), a fundamental element of modern industrial systems, employs machine learning to monitor equipment conditions, estimate failure probabilities, and optimize maintenance schedules. Its core objective is to enhance equipment reliability, extend lifespan, and minimize costs through data-driven insights by enabling efficient maintenance scheduling, reducing downtime, and optimizing resource allocation. In this paper, we propose a novel ordinal predictive maintenance with ensemble binary decomposition (OPMEB) method for the PdM domain, considering the hierarchical nature of class labels reflecting the machine's health status, including categories like healthy, low risk, moderate risk, and high risk. The proposed OPMEB method was validated …

Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …

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 …

A New Dynamic Classifier Selection Method For Text Classification, İsmai̇l Terzi̇, Alper Kürşat Uysal

Turkish Journal of Electrical Engineering and Computer Sciences

The primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. …

A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads …

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, …

Adaptable Quantum Education Platform Using Learning Objects, Krishna Puja Anumula

Master's Theses

In recent years, the need to make classroom learning more interactive and engaging has become increasingly important. The lack of workforce in interdisciplinary fields such as quantum networking and quantum internet requires a new approach that addresses every learner’s individual needs. To address this challenge, this thesis introduces an adaptive learning platform rooted in the theory of learning objects and Kolb’s experiential learning model. The platform aids educators and learners in designing and utilizing various learning objects for quantum networking and quantum internet.

The platform enables educators and learners to build their own lessons and lesson plans using learning objects …

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 …

Data Lakes: A Survey Of Concepts And Architectures, Sarah Azzabi, Zakiya Alfughi, Abdelkader Ouda

Electrical and Computer Engineering Publications

This paper presents a comprehensive literature review on the evolution of data-lake technology, with a particular focus on data-lake architectures. By systematically examining the existing body of research, we identify and classify the major types of data-lake architectures that have been proposed and implemented over time. The review highlights key trends in the development of data-lake architectures, identifies the primary challenges faced in their implementation, and discusses future directions for research and practice in this rapidly evolving field. We have developed diagrammatic representations to highlight the evolution of various architectures. These diagrams use consistent notations across all architectures to further …

Reality Capture Technologies For Continuous Inline Quality Monitoring In Construction 3d Printing, Michael J. Martin Ii

LSU Master's Theses

Extrusion-based construction 3D printing (C3DP) is an innovative and emerging technology aiming at the modernization of the construction industry. It uses robots and computer control to reduce the need for human labor and to decrease construction time and cost. C3DP creates elements layer by layer without using formworks, and it is critical to detect layer defects and deformations to ensure that each layer is printed with the desired dimensions. These defects can lead to a variety of issues during or after the construction process, including the collapse of freshly printed elements. In order to detect such deformations and to enable …

Enterprise Systems: Installing And Configuring Erpnext On Macos, Yazan Abbasi

Senior Honors Theses

Enterprise Resource Planning (ERP) systems integrate business processes across organizations onto unified digital platforms through data and workflow consolidation. However, high licensing costs of proprietary ERP solutions like SAP and Oracle limit adoption for small and medium enterprises. This led to the emergence of open-source ERP alternatives like ERPNext which provide sophisticated capabilities at much lower total cost of ownership. However, ERPNext faces documentation gaps that hamper onboarding, customization, and widespread adoption. Accelerating ERPNext implementation by developing a comprehensive installation and configuration guide tailored for developers using Mac environments will be examined furthermore.

The background on ERP systems explores critical …

Development And Evaluation Of An Expedited System For Creation Of Single Walled Carbon Nanotube Platforms, Ivon Acosta Ramirez, Omer Sadak, Wali Sohail, Xi Huang, Yongfeng Lu, Nicole M. Iverson

Department of Electrical and Computer Engineering: Faculty Publications

Single-walled carbon nanotubes (SWNT) have a strong and stable near-infrared (nIR) fluorescence that can be used to selectively detect target analytes, even at the single molecule level, through changes in either their fluorescence intensity or emission peak wavelength. SWNTs have been employed as NIR optical sensors for detecting a variety of analytes. However, high costs, long fabrication times, and poor distributions limit the current methods for immobilizing SWNT sensors on solid substrates. Recently, our group reported a protocol for SWNT immobilization with high fluorescence yield, longevity, fluorescence distribution, and sensor response, unfortunately this process takes 5 days to complete. Herein …

Introduction To Computer Technology: Build Your Own Computer, Michael Deredita, Orit D. Gruber

Open Educational Resources

The students will work on a project to specify, shop, and build a desktop computer (using a budget ~ $1,750), according to their needs and applications. Students will use Newegg (https://www.newegg.com) and/or Microcenter (https://www.microcenter.com/) to browse and select (parts), and will record the specifications of the selected components; including the make/model/item number of each part.