Computer Engineering Commons^™

Unrealvision: A Synthetic Dataset Generator For Human-Pose Estimation And Behavior Analysis, Thinh Lu

Master's Theses

For over a decade, computer vision (CV) has become an indispensable component of numerous camera surveillance applications as well as intelligent autonomous systems. Thanks to new advances in AI, Edge Computing, and IoT technologies, there is now a rapidly growing number of smart camera devices, industrial and consumer robots that are using computer vision for various applications - from human tracking and analysis, object classification, to visual inspection and anomaly detection. For most common use cases, building vision-based applications can be a straightforward and affordable task thanks to the increasing number of publicly accessible datasets and research publications. However, it …

Automatic Temperature Control System Using African Vultures Optimization Algorithm, Mostafa Abdulghafoor Mohammed, Muntadher Khamees, Dina Hassan Abbas

Iraqi Journal for Computer Science and Mathematics

One of the most important tasks in control engineering is tuning a PID controller for maximum efficiency. However, without a great deal of practice, manual adjustment of PID settings might result in erroneous results. Using met heuristic algorithms is one method for tweaking the PID controller. These algorithms, which are inspired bythe laws of nature, can effectively find the sweet spot for the PID settings. Therefore, instead of manually tweaking the PID controller, using met heuristic methods can greatly enhance the system's performance while decreasing the related expenses. A reliable temperature control system is crucial to the production of a …

Embedded Deep Learning To Improve The Performance Of Approaches For Extinct Heritage Images Denoising, Ali Salim Rasheed, Alaa Hamza Omran

Iraqi Journal for Computer Science and Mathematics

Many advanced deep convolutional neural network (DCNN) methods have proven their efficacy in reconstructing the texture of super-resolution images (SR) from low-resolution images (LR). Nevertheless, the objective of achieving super-resolution (SR) reconstruction using Deep Convolutional Neural Networks (DCNN) becomes difficult when the input image is distorted by noise. Photographs captured at the inception of the camera are presently regarded as acultural heritage that chronicles an important periodin human history; however, they are marred by low resolution and noise as a result of obsolescence and the primitive nature of the technology that captured them, in contrast to the technological advances that …

An Integrative Computational Intelligence For Robust Anomaly Detection In Social Networks, Helina Rajini Suresh, K R. Harsavarthini, R Mageswaran, Hirald Dwaraka Praveena, C Gnanaprakasam, C.Sakthi Lakshmi Priya

Iraqi Journal for Computer Science and Mathematics

Anomaly detection is one of the most important tasks for maintaining the integrity, security, and trustworthiness of online communities in a social network. This paper proposes AdaptoDetect, which represents a new framework; it discusses a new anomaly detection approach called Pufferfish Optimization Technique for feature selection, together with a Graph Embedding Autoencoder for identifying anomalies. What makes AdaptoDetect special is that, with the use of POT, it has a distinctive capability in dynamic adaptation against network changes by selecting only the most relevant features in social network data. The technique for optimization underlines the important attributes for anomaly detection so …

Improving Security In The 5g-Based Medicalinternet Of Things Toimprove The Qualityofpatient Services, Israa Ibraheem Al_Barazanchi, Kholood J. Moulood, Muneer Sameer Gheni Mansoor, Jamal Fadhil Tawfeq

Iraqi Journal for Computer Science and Mathematics

The Internet of Medical Things (IoMT) is like a tech upgrade that benefits patients by reducing healthcare costs, making medical care more accessible, and improving the quality of treatment. To make IoMT devices smart and capable, they need super-fast 5G support. However, there are security concerns when using IoMT devices that can put a patient's data and privacy at risk. For instance, someone could eavesdrop on your medical data due to weak network access management and data encryption.Many systems use encryption methods to protect data, but these methods often fall short when it comes to the high security standards required …

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 …

Constructing An Interpretable Deep Learning Framework Utilizing Variational Autoencoder Latent Space For Part-Prototype Learning, Shiska Raut

Computer Science and Engineering Theses - Archive

What visual attributes do cats have in common, and what features set them apart from dogs? How are we able to tell the difference between the two? While we do not fully understand the mechanism humans use for object detection, one popular theory suggests that it boils down to identifying distinct visual features specific to each object. For example, all cats have vertical slit-shaped pupils when their eyes are constricted, which is something we do not see in dogs. These slit-shaped pupils are a feature ‘prototypical’ to cats. Object classification is a computer vision task that involves identifying and categorizing …

Nonlinear Guidance And Control Of Unmanned Aerial Manipulators For Delivering A Payload On A Moving Platform, Ravi Gyawali

Mechanical and Aerospace Engineering Theses - Archive

Unmanned Aerial manipulators (UAMs) are a class of Unmanned Aerial Vehicles (UAVs) equipped with a manipulator. By combining the aerial mobility of a UAV with a manipulator's dexterity, these hybrid systems can perform a wide range of complex tasks while reducing risks and costs. As a result, they are increasingly being utilized for military, industrial, and agricultural applications.

The thesis presents a novel approach for a multi-UAM system to collaboratively deliver a payload on a stationary or maneuvering platform. A sliding-mode-based guidance law, sourced from existing literature, is integrated with a combined control technique for the UAVs and their respective …

Adaptive Beyond Von-Neumann Computing Devices And Reconfigurable Architectures For Edge Computing Applications, Mousam Hossain

Graduate Thesis and Dissertation 2023-2024

The Von-Neumann bottleneck, a major challenge in computer architecture, results from significant data transfer delays between the processor and main memory. Crossbar arrays utilizing spin-based devices like Magnetoresistive Random Access Memory (MRAM) aim to overcome this bottleneck by offering advantages in area and performance, particularly for tasks requiring linear transformations. These arrays enable single-cycle and in-memory vector-matrix multiplication, reducing overheads, which is crucial for energy and area-constrained Internet of Things (IoT) sensors and embedded devices.

This dissertation focuses on designing, implementing, and evaluating reconfigurable computation platforms that leverage MRAM-based crossbar arrays and analog computation to support deep learning and error …

Addressing Challenges In Utilizing Gpus For Accelerating Privacy-Preserving Computation, Ardhi Wiratama Baskara Yudha

Graduate Thesis and Dissertation 2023-2024

Cloud computing increasingly handles confidential data, like private inference and query databases. Two strategies are used for secure computation: (1) employing CPU Trusted Execution Environments (TEEs) like AMD SEV, Intel SGX, or ARM TrustZone, and (2) utilizing emerging cryptographic methods like Fully Homomorphic Encryption (FHE) with libraries such as HElib, Microsoft SEAL, and PALISADE. To enhance computation, GPUs are often employed. However, using GPUs to accelerate secure computation introduces challenges addressed in three works.

In the first work, we tackle GPU acceleration for secure computation with CPU TEEs. While TEEs perform computations on confidential data, extending their capabilities to GPUs …

Internet-Of-Things Privacy In Wifi Networks: Side-Channel Leakage And Mitigations, Mnassar Alyami

Graduate Thesis and Dissertation 2023-2024

WiFi networks are susceptible to statistical traffic analysis attacks. Despite encryption, the metadata of encrypted traffic, such as packet inter-arrival time and size, remains visible. This visibility allows potential eavesdroppers to infer private information in the Internet of Things (IoT) environment. For example, it allows for the identification of sleep monitors and the inference of whether a user is awake or asleep.

WiFi eavesdropping theoretically enables the identification of IoT devices without the need to join the victim's network. This attack scenario is more realistic and much harder to defend against, thus posing a real threat to user privacy. However, …