Computer Engineering Commons^™

Immersive Framework For Designing Trajectories Using Augmented Reality, Joseph Anderson, Leo Materne, Karis Cooks, Michelle Aros, Jaia Huggins, Jesika Geliga-Torres, Kamden Kuykendall, David Canales, Barbara Chaparro

Publications

The intuitive interaction capabilities of augmented reality make it ideal for solving complex 3D problems that require complex spatial representations, which is key for astrodynamics and space mission planning. By implementing common and complex orbital mechanics algorithms in augmented reality, a hands-on method for designing orbit solutions and spacecraft missions is created. This effort explores the aforementioned implementation with the Microsoft Hololens 2 as well as its applications in industry and academia. Furthermore, a human-centered design process and study are utilized to ensure the tool is user-friendly while maintaining accuracy and applicability to higher-fidelity problems.

Uncovering Weaknesses In Autonomous Driving: A Formal Approach To Adversarial Scenario Generation And Falsification, Carlos O. Hernandez

Master's Theses

Autonomous vehicles utilize advanced safety features like proactive driving assistance and pre-collision alerts to minimize the risk of accidents. However, evaluating the correct functionality of these systems is complex. First, safety systems are highly sophisticated, integrating software, networking, and hardware components, many of which rely on advanced artificial intelligence and machine learning algorithms. Second, an array of dynamic factors, including numerous actors and physical variables, can influence the performance of safety mechanisms during critical scenarios. Each actor’s unique behavior introduces unpredictability, making it difficult to anticipate future states and outcomes. This thesis presents a comprehensive framework for testing autonomous vehicle …

Resource Allocation And Edge Computing For Dual Hop Communication In Satellite Assisted Uavs Enabled Vanets, Ahmed Adil Nafea, Mustafa Maad Hamdi, Sami Abduljabbar Rashid

Iraqi Journal for Computer Science and Mathematics

VANETs are highly attractive and isused in maximum of the applications of cross-regional communication. To increase the coverage of the vehicular network, Unmanned Arial Vehicles (UAVs) are introduced, and they getconnected with the satellite networks to perform heterogeneous communication. With the help of this connectivity,the communication quality of ground level to air medium is increased.Currently the vehicle usage is highly increased and as aresults of communication link failure, improper resource allocation are arises whither abruptly assumesa stability about a network with that increasesan energy consumption and communication delay in the heterogeneous networks. In these conditions, thus study is idea of …

A Review Of Optimization Techniques: Applications And Comparative Analysis, Ahmed Hasan Alridha, Fouad H. Abd Alsharify, Zahir Al-Khafaji

Iraqi Journal for Computer Science and Mathematics

Optimization algorithms exist to find solutions to various problems and then find out the optimal solutions. These algorithms are designed to reach desired goals with high accuracy and low error, as well as improve performance in various fields, including machine learning, operations research, physics, chemistry, and engineering. As technology continues to advance, optimization algorithms are increasingly needed to address complex real-world challenges and drive innovation across all disciplines. Quantitative leaps have been achieved in improving the efficiency of optimization algorithms through the diversity of sources of information feeding these algorithms according to the type of optimization problem, based on scientific …

The Sensory Accommodation Framework For Technology: Bridging Sensory Processing To Social Cognition, Louanne Boyd

Engineering Faculty Books and Book Chapters

This book provides a thorough introduction to the many facets of designing technologies for autism, with a particular focus on optimizing visual attention frameworks. This book is designed to provide a detailed overview of several aspects of technology for autism. Each Chapter illustrates different parts of the Sensory Accommodation Framework and provides examples of relevant available technologies. The books first discusses a variety of skills that make up human development as well as a history of autism as a diagnosis and the birth of the neurodiversity movement. It goes on to detail individual types of therapy and how they interact …

Identification Of Faulty Sensor Nodes In Wban Using Genetically Linked Artificial Neural Network, Haider Rasheed Abdulshaheed, Mayasa M. Abdulrahman, Israa Ibraheem Al_Barazanchi, Jamal Fadhil Tawfeq

Iraqi Journal for Computer Science and Mathematics

Wireless Body Area Networks (WBANs) have risen as a promising innovation for checking human physiological parameters in real time. Be that as it may, the unwavering quality and precision of WBANs depend on the right working of sensor hubs. The distinguishing proof of defective sensor hubs is vital for guaranteeing the quality of information collected by WBANs. In this paper, we propose a novel approach to recognizing faulty sensor hubs in WBAN employing a hereditarily linked artificial neural organize (GLANN). The GLANN is preparedto employa crossbreedfuzzy-genetic calculation to optimize its execution in distinguishing defective sensor hubs. The proposed approach is …

A Framework-Based Cross-Institutional Cpd For Academic Staff In Gen-Ai Literacy, Critical Inquiry And Authentic Assessment, Roisin Donnelly, Ita Kennelly

Books/Book Chapters

Generative-Artificial Intelligence (Gen-AI) has emerged as a transformative force profoundly influencing, if not revolutionizing the way we now teach and how students learn in higher education (HE). Despite the initial flurry of early research studies following the raising of public awareness of Gen-AI (and in particular ChatGPT), enduring pragmatic questions remain for academic staff on how best to protect and promote student learning, how to meaningfully support assessment integrity from a curriculum perspective, and additionally how to effectively use Gen-AI technologies to aid learning and foster deeper critical thinking.

Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu

Browse all Theses and Dissertations

Sickle Cell Disease (SCD) is one of the most prevalent genetic blood disorders affecting millions of people worldwide. It is often accompanied by acute and/or chronic pain leading to increased healthcare costs and adverse outcomes. Effective management of SCD requires an understanding of the diverse physiological profiles. This study employs unsupervised machine learning, specifically K-means clustering to categorize the patients suffering with SCD into different clusters based on their vital signs. The main aim is to identify the groups that reflect similarities in physiological and pain profiles, allowing an in-depth analysis to reveal distinctive features distinguishing patient clusters. The project …

The Trouble With Technology, John O'Connor

Conference Papers

In contemporary education, technology is either hailed as the panacea for affordable mass education or dreaded as a threat to our humanity. At the European Culture and Technology Laboratory, technology is understood in the context of the Ancient Greek origin of the word: technē—meaning a system or a method of making or doing, an art or a craft; a technique or a practice, even a way of thinking. The tools humans use are not merely a means of intervention in our environment but also a way of becoming human and thus, technology has a fundamental impact on our identity and …

Bert-Based Detection Of Ai-Generated Text For Content Verification, Soham Biren Katlariwala

2024 REYES Proceedings

With advancements in AI-driven natural language generation, distinguishing between AI-generated and human-written text has become imperative for ensuring content authenticity across industries. This study explores the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) in addressing this classification challenge. Utilizing a diverse dataset and robust preprocessing techniques, BERT achieved a peak F1-score of 0.94364, outperforming traditional models such as Logistic Regression and Support Vector Machines. The results underscore the potential of transformer-based models in addressing real-world con- tent verification problems. Future enhancements include fine-tuning and expanding datasets for greater generalizability.

Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques, Muhammad Faisal Yaqub

2024 REYES Proceedings

Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost …

Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi

Browse all Theses and Dissertations

Graph Neural Networks (GNNs) have increasingly gained popularity as tools for analyzing graph data in areas like biology, knowledge-graphs, social networks, biology, and recommendation systems. However, their vulnerability to adversarial attacks - small, targeted manipulations of graph structures or node features - raises serious concerns about their reliability in real-world applications. Existing defense strategies, such as adversarial training, edge filtering, low-rank approximations, and randomization-based methods, often suffer from high computational costs, scalability issues, or reduced clean-data performance. Unlike these methods, the proposed approach integrates multi-hop relationships, applies adaptive regularization, and maintains a balance between feature-based and structural embeddings, ensuring improved …

Meta-Learning-Based Model Stacking Framework For Hardware Trojan Detection In Fpga Systems, Mani Rupak Gurram

Browse all Theses and Dissertations

In today's technological landscape, hardware devices are integral to critical applications such as industrial automation, autonomous vehicles, and medical equipment, relying on advanced platforms like FPGAs for core functionalities. However, the multi-stage manufacturing process, often distributed across various foundries, introduces substantial security risks, notably the potential for hardware Trojan insertion. These malicious modifications compromise the reliability and safety of hardware systems. This research addresses the detection of hardware Trojans through side-channel analysis, utilizing power and electromagnetic signal data, combined with meta-learning techniques, specifically model stacking. By employing diverse base models and a meta-model to consolidate predictions, this non-invasive approach effectively …

Secure Similar Patients Query With Homomorphically Evaluated Thresholds, Mounika Pratapa, Aleksander Essex

Electrical and Computer Engineering Publications

Patient-centric precision medicine requires the analysis of large volumes of genomic data to tailor treatments and medications based on individual-level characteristics. Because the amount of data held by a single institution is limited, researchers may want access to genomic data held by other institutions. Owing to the inherent privacy implications of genomic data, performing comparisons on encrypted data is preferable in certain settings. The Similar patient query (SPQ) is an application that enables a secure search across genomic databases for patients with similar genetic makeup. Query results can be used to draw meaningful conclusions regarding suitable therapies.

However, existing protocols …

Deep Transfer Learning For Detection Of Upper And Lower Body Movements: Transformer With Convolutional Neural Network, Kyle Lacroix, Davoud Gholamiangonabadi, Ana Luisa Trejos, Katarina Grolinger

Electrical and Computer Engineering Publications

When humans repeat the same motion, the tendons, muscles, and nerves can be damaged, causing Repetitive Stress Injuries (RSI). If the repetitive motions that lead to RSI are recognized early, actions can be taken to prevent these injuries. As Human Activity Recognition (HAR) aims to identify activities employing wearable or environment sensors, HAR is the first step toward identifying repetitive motions. Deep learning models, such as Convolutional Neural Networks (CNNs), have seen great success in recognizing activities for participants whose data are used in the model training; however, their accuracy drops for new participants as people move in different ways. …

Federated Learning For Sentiment Analysis In Presence Of Non-Iid Data: Sensitivity Of Deep Learning Models, Davoud Gholamiangonabadi, Katarina Grolinger

Electrical and Computer Engineering Publications

In sentiment analysis, data are commonly distributed across many devices, and traditional machine learning requires transferring these data to a central location exposing data to security and privacy risks. Federated Learning (FL) avoids this transfer by training a model without requiring the clients/devices to share their local data; however, FL performance drops when data are not Independent and Identically Distributed (non-IID), such as when label distribution or data size vary across clients. Although techniques for non-IID data have been proposed primarily in the image domain, the sensitivity of various deep learning models to non-IID data needs to be examined. Consequently, …

Low-Cost Open-Source Melt Flow Index System For Distributed Recycling And Additive Manufacturing, Dawei Liu, Aditi Basdeo, Catalina Suescun Gonzalez, Alessia Romani, Hakim Boudaoud, Cécile Nouvel, Fabio A. Cruz Sanchez, Joshua M. Pearce

Electrical and Computer Engineering Publications

The increasing adoption of distributed recycling via additive manufacturing (DRAM) has facilitated the revalorization of materials derived from waste streams for additive manufacturing. Recycled materials frequently contain impurities and mixed polymers, which can degrade their properties over multiple cycles. This degradation, particularly in rheological properties, limits their applicability in 3D printing. Consequently, there is a critical need for a tool that enables the rapid assessment of the flowability of these recycled materials. This study presents the design, development, and manufacturing of an open-source melt flow index (MFI) apparatus. The open-source MFI was validated with tests on virgin polylactic acid pellets, …

Case Studies Of Restapi And Graphql Architectures, Janni Daniel Balraj, Gary Clynch

Academic Poster Collection

The adoption of microservices architecture has experienced significant growth, driven by its appeal in terms of modularity, scalability, and deployment ease. However, this proliferation of microservices has intensified the demand for efficient data exchange among them. While REST API has long been the standard protocol for microservices communication, its limitations in flexibility and performance have spurred the ascent of GraphQL as a more efficient alternative, as highlighted by studies comparing their performance. As the number of microservices continues to rise, traditional methods like REST APIs prove challenging, leading to issues such as over-fetching or under-fetching of data. GraphQL addresses these …

The Role Of Ai, Big Data And Predictive Analytics In Mitigating Unemployment Insurance Fraud, Siddikur Rahman, Md Abu Sayem, Shariar Emon Alve, Md Shahidul Islam, Muhammad Mahmudul Islam, Arifa Ahmed, Mohammed Kamruzzaman

Finance, Economics, and Data Analytics

The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud …

Ai And Workforce Development: A Comparative Analysis Of Skill Gaps And Training Needs In Emerging Economies, Gursahildeep Singh Sidhu, Md Abu Sayem, Nazifa Taslima, Ahmed Selim Anwar, Fariba Chowdhury, Manataka Rowshon

Finance, Economics, and Data Analytics

AI is developing quickly and offers considerable prospects for economic development; nevertheless, it presents crucial challenges for instance, skills development and its effect on employee's jobs in developing economies. This research will seek to establish the current state of AI related skills deficits and training in these economies to establish how governments and organizations can close the gaps by preparing the workforce for the future AI revolution. In line with the research questions, the following emerges as the research problem: To what extent have emerging market companies embraced AI? What key skills are scarce both in the internal and external …