Computer Engineering Commons^™

Lightweight Driver Face Object Detection Algorithm Based On Yolov8-Df, Mingyu Li, Jiaquan Lin

Journal of System Simulation

Abstract: The YOLOv8n detection algorithm has a large amount of computation and parameters in the driving environment. To address this issue, a lightweight driver facial object detection algorithm YOLOv8-DF was proposed. A lightweight multi-scale convolution module (LMCM) was proposed to replace the Conv module in the network, and the dual-channel design could reduce the computation and parameter quantity of the algorithm; the multi-scale design could enrich the feature information inside the network. The lightweight convolutional GhostConv, Fasterblock module, and C2f module were fused, and a dual-channel lightweight convolution module (DLCM) was fused with the SPPF module. The experimental results show …

Optimal Scheduling Of An Integrated Energy System Considering Demand Response And Two-Stage P2g, Xinhui Duan, Zelong Cheng, Dongchao Zhang, Xiaochong Duan

Journal of System Simulation

Abstract: In the context of carbon peaking and carbon neutrality goals, this study aims to improve the energy utilization rate and further explore the role of user-side flexible loads and P2G equipment in energy saving and emission reduction. An optimal scheduling model for integrated energy systems considering demand response and two-stage P2G was proposed. A regional integrated energy system coupled with electricity, heating, cooling, gas, storage, and hydrogen was taken as the research object. Models for system equipment and two-stage P2G were established. Based on load characteristics, a multi-load demand response model for electricity, heating, and cooling was constructed using …

Early Detection Of Oak Wilt Using Unmanned Aerial Vehicles (Uav) & Computer Vision, Muttaki I. Bismoy

Masters Theses

Forests are critical ecosystems, delivering services such as biodiversity conservation, climate regulation, timber production, and recreation. However, they face increasing threats from pathogens like Bretziella fagacearum, which causes Oak Wilt, a lethal disease that disrupts water transport in oak trees, leading to canopy dieback and eventual death. Traditional detection methods rely on manual ground surveys, which are labor-intensive, time-consuming, and prone to error, particularly in early disease stages.

This research presents an automated, scalable, high-precision Oak Wilt detection system using Unmanned Aerial Vehicles (UAVs) combined with deep learning-based computer vision. Expanding on earlier work with a lightweight CNN achieving an …

Re-Engaging Driver Attention Using Chatgpt: A Multimodal Study On Stress Impact And Driving Performance, Alaa Zaki Elfiqi

Theses and Dissertations

Driving is considered a complex task that requires continuous focus and attention from the driver. Driving performance can also be impacted by changes in the driver’s stress level. However, limited research explores methods to address this challenge. With the current advances in Large Language Models (LLMs) and their ability to engage in human-like interaction, this study investigates the potential of using ChatGPT, designed to speak Egyptian Arabic, in re-engaging driver attention under different driving scenarios for low-stress and high-stress conditions within a virtual reality (VR) environment driving simulator. Drivers were asked to follow a leading car into two scenarios with …

Depaul Digest

DePaul Magazine

College of Communication faculty Matthew Ragas and Ron Culp mentor students on gaining access to executive-level administration. News briefs on exciting developments at DePaul University’s 10 colleges and schools, from nursing students studying public health protocols in Prague to a new DePaul-hosted conference exploring AI in filmmaking. DePaul alumni volunteers share their experiences spreading the Vincentian mission nationwide.

Multi-Layer Support For Component-Based Cyber-Physical Systems Applications, Oren Bell

McKelvey School of Engineering Graduate Student Theses & Dissertations

Component-based design is a paradigm meant to aid in development of software applications by modularizing different functionalities of a system. This building-block approach is used extensively in cyber–physical and robotic systems. Common and established solutions to specific problems, such as perception nodes, state estimators, and motion planners, can be developed, verified, and reused as off-the-shelf modules. These modules may be integrated atop today’s heterogeneous hardware platforms, where an application can be distributed across GPUs, FPGAs, and CPU cores. When heterogeneous computational devices share the workload of a collection of components, the very act of integration may inject timing uncertainty. For …

Design And Development Of Deep Learning Based Generic Platform For Promoting Precision Agriculture, Srilakshmi A

Theses and Dissertations

Precision agriculture also referred as precision farming or smart farming, is an innovative approach to agricultural management that leverages technology and data to optimize various aspects of the farming process. This approach aims to make farming more effective, sustainable, and profitable by affording farmers with the application tools and information they need to make more informed decisions.

Precision agriculture combines elements of agriculture, technology, and data science to enhance crop production, and resource utilization. Precision agriculture techniques can be highly effective in leaf disease detection within crop fields. Machine learning has been developed incredibly across multiple domains and shown it …

Encountering And Mitigating Selfish Mining In Bitcoin Mining Pools, Jeyasheela Rakkini M J

Theses and Dissertations

Blockchain, an innovative decentralized distributed, disrupting programming paradigm embodies key principles such as decentralization, data provenance, immutability, and transparency. At its core blockchain begins with the genesis block and progresses with each subsequent block containing the hash of the previous block, Merkle root, timestamp, a coin base transaction address, and a nonce. Miners compete to discover a target hash value (hash value of the previous block and nonce) for the current block, that is less than or equal to the difficulty value set by the system, a process known as mining.

This work encounters selfish mining attacks in bitcoin mining …

Ai Based Early Detection Of Hormonal Imbalance And Poly-Cystic Ovary Syndrome In Young Women, Reka S

Theses and Dissertations

A hormonal disorder, Poly-Cystic Ovary Syndrome (PCOS) usually affects women during the reproductive age. It is characterised by imbalances in hormones, particularly a rise in the female body's androgen level (male hormone) and enlarged ovaries with small cysts. PCOS can cause ovarian cysts, weight gain, acne, excessive hair growth, insulin resistance, and irregular menstrual cycles along with other health problems. While the exact origin of PCOS is uncertain and its symptoms are unclear, diagnosing PCOS in real-world conditions is a difficult task. Therefore, prompt and precise PCOS diagnosis is essential for efficient treatment and for averting long-term issues.

Clinicians typically …

Investigation Of Dependency Parsing Techniques For Digital Document Analysis Through Deep Learning Approach, Rekah D Ms

Theses and Dissertations

Digital document dependency parsing is a significant task in natural language processing. Dependency parsing supports verification of the grammatical correctness of a sentence besides enabling extraction of relevant documents. Inappropriate extraction of features may result in falsely parsing a document, leading to decreased accuracy. Machine learning methods have been employed to perform feature extraction. However, selecting pertinent features was never achieved which minimizes the time consumption and overhead.

Hence, novel machine learning and deep learning techniques have been designed in our work for accurate and computationally efficient digital document analytics through dependency parsing. Four different contributions have been proposed for …

Cyclone Intensity Prediction In The Bay Of Bengal Using Deep Learning Methods, Senthil Kumar J

Theses and Dissertations

The Bay of Bengal region's coastlines have been badly devastated by tropical cyclones, as the region experiences an average of five to six cyclones per year, with about two to three of these intensifying into tropical storms or severe cyclones. Thus it necessitates to study the accurate and efficient forecasting of their intensity to improve preparedness and response to natural disasters. The present study compares and examines three distinct approaches to cyclone intensity prediction using historical datasets from 1998 to 2020: hybrid optimisation, deep learning-based, and empirical approaches.The predicted accuracy, computational effectiveness, and feasibility for real-time scenarios of each model …

Design Of An Integrated Lightweight Cryptographic Algorithm For Device Level Security And Fraternal Cryptographic Algorithm For Communication Security In Medical Cyber Physical Systems, Vimala Devi P

Theses and Dissertations

Healthcare involves detecting symptoms, diagnosing conditions and giving treatment to patients. It is one of the fundamental human rights, and it might be difficult to provide healthcare to those with chronic illnesses, elderly people with disabilities and those under distant observation. The World Health Organisation (WHO) states that Cardio Vascular Disease (CVD) is the leading cause of death worldwide.

According to the prediction, CVD-related causes such as heart attacks and strokes, could result in 23.3 million deaths by 2030. In addition, the number of people with diabetes will reach 246 million; thereby, the prevalence of CVD patients and diabetics will …

Investigations Of Secure Memory For Vlsi Based Crypto System, Vijay Sai R Mr

Theses and Dissertations

Semiconductor technology is growing very rapidly in their architectural developments, involving the usage of processor and memory. Presence of memory, in general is a vital commodity in various devices which are almost embedded into human activity, from robust work stations to handy mobile phones. Security of data stored in memory is very important, and hence, observation must be made that these valuable data should not be thwarted by malicious means. Security in cache memory is a major issue in memory related applications such as smart cards and bio-metric implementations.

Cache, is a small and limited memory located between central processing …

Learning Structure With Multivariate Information Bottleneck And Exploration Of New Methods In Sequential Decision Making, Volodymyr Makarenko

Master's Theses

Research in useful information extraction has been motivated by the increasing demand to extract insights from unstructured data, and by the need to store and transmit great volumes of information, often originating in unstructured data such as videos. Research in rate distortion and information bottleneck paved the path for understanding and guiding the design of lossy encoders, capable of extracting relevant information. Independently, research in deep representation learning has enabled numerous applications for unstructured high-dimensional data such as images. However, the interpretability of the deep learning methods remained limited. Several desired properties of learned representations have been suggested, including disentanglement. …

Beyond Single Metrics: A Holistic Benchmarking Framework For Low-Power Embedded Systems, Hassan Adam

UNLV Theses, Dissertations, Professional Papers, and Capstones

Modern embedded systems encounter a notable challenge in evaluation. While devices may meet traditional benchmarks, they often underperform in real-world applications due to neglected interactions at the system level. Current benchmarking suites, such as MLPerf Tiny and EEMBC ULPMark, evaluate specific metrics including computational throughput, energy efficiency, and memory usage. However, they do not consider the complex interdependencies that affect real-world performance. This thesis presents a benchmarking framework that concurrently evaluates multiple performance dimensions under realistic workloads, revealing system behaviors that are often hidden in conventional benchmarks.Through the comprehensive evaluation of three representative algorithms: Fast Fourier Transform, quantized neural network …

Autonomous Uav Swarm Formation Utilizing Gradient-Driven Contour Mapping For Radiation Source Localization, Edgar Amalyan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis presents a drone swarm for radiation mapping to aid source localization. The Department of Energy advocates employing UAVs for this task, but existing approaches remain inefficient and impractical in real-world scenarios. Three custom drones are built and flight-tested. A control algorithm to follow a contour, a constant-intensity path, is designed using a gradient fit. By knowing the source’s direction, the drone swarm can fly in the optimal trajectory at every step, leaving nothing to assumption. A program is created that implements formation flight and autonomous navigation. It is tested via a software-in-the-loop simulation utilizing radiation sources and detectors …

Investigating Information Extraction And Language Models In Medical Domain Text Processing, Pouyan Nahed

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation demonstrates that carefully adapted language-model pipelines can transform unstructured clinical-trial and pharmacological prose into reliable, low-latency structured data. Four interconnected studies support this claim.Tri-AL platform. An open-source dashboard ingests all 440 k+ ClinicalTrials.gov records—including every historical revision—into a normalized schema and parses the 20 GB XML archive over 10x faster than a BeautifulSoup baseline, while exposing hooks for demographic analytics and supporting integration of user-defined modules. Clinical trial summarization. An encoder–decoder model is trained on 57k description–summary pairs to condense clinical trials into a few sentences. ROUGE evaluation shows a 20% improvement over the baseline, while graph-based evaluation …

The Dropbot: Design And Development Of A Custom Drone For Precision Water Drop Penetration Time (Wdpt) Testing, Mugundan Prakash

UNLV Theses, Dissertations, Professional Papers, and Capstones

Assessing the hydrophobic characteristics of soil is vital for understanding soil wettability or soil-water interactions, particularly in post-wildfire environments where water repellency can significantly impact ecosystem recovery, water infiltration, and erosion control. One key metric in soil wettability studies is the Water Drop Penetration Time (WDPT) test, which evaluates the hydrophobicity of soil and guides land treatment strategies. This thesis presents the design and development of DropBot, a custom-built drone platform engineered for the precise delivery and analysis of water droplets in WDPT tests.The DropBot, a custom drone, integrates a lightweight, 3D-printed frame with a self-leveling platform, enabling consistent droplet …

Csa-Xai: Channel–Spatial Attention And Explainable Ai In A Modular Multi-Backbone Framework For Lung Cancer Classification, Omar Ibrahim Obaid, Abdulbasit Alazzawi

Iraqi Journal for Computer Science and Mathematics

Computed tomography (CT) scans require precise and early lung cancer detection to produce better clinical results. High accuracy in deep learning approaches (DL) poses an existing challenge to interpret their functionality effectively. This research presents an innovative modular multi-backbone structure that combines channel-spatial attention together with explainable AI (XAI) methods for three-class lung cancer diagnosis (Normal, Benign, and Malignant). Research was carried out to evaluate six pre-trained CNN backbones (ResNet-50, VGG19, Inception-V3, EfficientNet-B0, MobileNet-V2, DenseNet-121) which received hybrid attention enhancement on the IQ-OTH/NCCD dataset. The experimental data showed four pre-trained models reaching perfect accuracy at 100 percent whereas the others …

Class Weighting In Imbalanced Data For Leukocyte Classification Using Fine-Tuning Inception-V3, Wahyudi Setiawan, Meidya Koeshardianto, Eka Mala Sari Rochman, Aeri Rachmad, Tutut Herawan

Iraqi Journal for Computer Science and Mathematics

This study investigates the classification of leukocyte images in an imbalanced dataset using deep learning techniques. The dataset consists of 14,514 images, categorized into five leukocyte types: basophils (301), neutrophils (8,891), lymphocytes (3,461), monocytes (795), and eosinophils (1,066). To address class imbalance, we applied class weighting alongside transfer learning and fine-tuning using the Inception-v3 architecture. The dataset was split into 80% for training and 20% for testing, and 5-fold cross-validation was conducted to evaluate model robustness. Hyperparameters were set with a learning rate of 0.0001, batch size of 32, and 30 training epochs, optimized using the Adam optimizer. Fine-tuning was …

Symmetric Toeplitz Matrices For A Class Of New Subclass Functions And Bazilevič Functions, Nihad H. Shehab, Abdul Rahman S. Juma

Iraqi Journal for Computer Science and Mathematics

In this work, we describe and investigate a novel collection of analytic functions, including the new functions and the Bazilevič functions. An important component of analytic functions, Bazilevič functions have numerous uses in both pure and practical mathematics. For this class of functions, we concentrate on building Toeplitz matrices, examining their structural characteristics, and evaluating their eigenvalues and trends. We utilise these studies to draw sophisticated mathematical conclusions on the stability and convergence characteristics of Bazilevič functions, as well as possible uses in geometry and differential equations. This work aims to determine coefficient estimates for the functions in this family …

Orbital Maneuvers And Interplanetary Trajectory Design Via Reinforcement Learning, Roberto Cuéllar Rangel

Doctoral Dissertations and Master's Theses

This dissertation investigates the application of reinforcement learning (RL) to the design and optimization of low-thrust spacecraft trajectories, with an emphasis on autonomy, adaptability, and robustness in the presence of system uncertainties and unmodeled perturbations. Classical approaches to low-thrust trajectory design are predominantly grounded in optimal control theory, which relies on the availability of precise dynamical models and often requires problem-specific reformulation and solver tuning. While optimal control methods offer high accuracy under deterministic conditions, their sensitivity to stochastic disturbances and computational limitations in highly nonlinear or uncertain environments pose significant challenges for future autonomous space missions.

To address these …

Understanding And Evaluating Genomic Language Models, Aadit Kapoor

Master's Theses

Large Language Models (LLMs) have shown remarkable capabilities in interpreting complex patterns across various domains, yet their application to genomic data remains limited. We see great potential in leveraging LLMs for vital biological tasks, such as predicting transcription factor binding sites and identifying antibiotic-resistant genes. This emergent behavior positions LLMs as powerful tools for enhancing our understanding of intricate biological language. LLMs trained specifically on genomic data, such as DNA sequences, operate distinctly compared to those trained on natural language. This difference is evident not only in the architectural landscape of the models but also in the methodologies employed by …

Accelerating Gnn Inference On Multi-Core Systems, Binglin Ji

McKelvey School of Engineering Graduate Student Theses & Dissertations

Graph Neural Networks (GNNs) are becoming increasingly popular, with their applications expanding across diverse domains. As the scale of graph data continues to grow, including larger numbers of nodes, edges, and higher embedding dimensions, standardized libraries such as DGL and PyG have been developed to facilitate GNN computation. However, with the rapid increase in the number of processor cores and the evolution of multi-core architectures, these libraries often show poor scalability and fail to execute GNN inference efficiently on the latest multi-core systems, particularly those with upwards of a hundred cores. To address this limitation, we present FGI, a Fast …

Retracted: Mathematical Properties And Simulations Of The Neutrosophic Gompertz-Inverse Burr-X Distribution With Application To Under-Five Mortality, Mustafa Hassan Jumaa, Asmaa S. Qaddoori, Sara A. Khalaf, Nooruldeen A. Noori, Mundher A. Khaleel

Iraqi Journal for Computer Science and Mathematics

Despite significant progress in the development of statistical distributions, there remain clear gaps in modelling complex datasets, such as those involving uncertainty or requiring flexible representations of multidimensional variables. This study introduces a new distribution the Neutrosophic Gompertz-Inverse Burr-X (NGoIB-X) distribution to address these challenges. The model is based on the Neutrosophic Gompertz family (NGo-G), which itself employs the T-X method in its formulation. Characterised by four Neutrosophic parameters and a Neutrosophic random variable, the NGoIB-X distribution offers enhanced flexibility for representing and analysing uncertain data. The theoretical properties of the NGoIB-X distribution are explored, including its Neutrosophic probability density …

A New Lightweight Encryption Method Based On The Dna-Rc4 Substitution For Resource-Constrained Iot Devices, Athraa J. H. Witwit, Ahmed Fanfakh, Ali Kadhum Idrees

Iraqi Journal for Computer Science and Mathematics

Technology's impact on daily life necessitates increased data protection. Cryptographic systems improve security, ensuring confidentiality and legitimacy of Internet of Things systems. However, resource-constrained devices face challenges like memory, battery life, processing power, and small size. In light of this, lightweight cryptography (LWC) provides techniques specifically tailored to the constraints of resource-constrained Internet of Things devices. However, the presence of a fixed S-Box in some LWC algorithms, such as Advanced Encryption Standard, or the absence of one in others, such as Speck and Tiny Encryption Algorithm, renders them more susceptible to attacks. In this paper, we suggest a new lightweight …

Streamlining The Cleaning And Analysis Of Eye Tracking Data For Cross-Recurrence Analysis, Michael V. Rosalia

McNair Summer Research Program

The use of multimodal data to understand and support collaborative learning has grown in popularity recently as it allows researchers to study complex learning tasks from different facets. However, multimodal analysis is often computationally complex and often requires a strong set of technical skills and mathematical understanding to clean and process the data, creating barriers that restrict researchers without advanced programming skills from conducting these analyses. There are existing software packages that can help with this process, but they are often piecemeal requiring the user to understand how to put them together and what best practices may entail. This can …

Exploring The Role Of Accessibility In Enhancing User Experience: Trends, Challenges, And Opportunities Across Digital Platforms, Adiba Khan

Harrisburg University Dissertations and Theses

Accessibility was a fundamental aspect of enhancing user experience (UX) across digital platforms, ensuring equitable access to information and services for a diverse range of users, including individuals with visual, auditory, cognitive, or motor impairments. As digital products became increasingly central to education, commerce, and communication, the need for inclusive design grew. This study explored the intersection of accessibility and UX, with a specific focus on how accessibility-driven design improved usability, user satisfaction, engagement, and the overall quality of digital interactions. Grounded in Agile project management principles, the research examined how early integration of accessibility into the software development lifecycle …

Towards Applying Artificial Intelligence To Solve Np-Complete Problems Using Quantum Computing, Andrew Robert Haverly

Theses and Dissertations

This dissertation explores the methodology for more thoroughly entangling artificial intelligence and quantum computing. This is explored through a background search of problems solved using Grover’s quantum algorithm, a new quantum protein folding and drug discovery algorithm, using Grover’s algorithm to train quantum artificial neural networks, using quantum artificial neural networks for reinforcement learning, mapping classical assembly instructions to quantum circuits to make quantum programming easier, and using prompt engineering to get a classical artificial intelligence agent to solve an NP-Complete problem using a Grover’s algorithm. This method not only simplifies the creation of algorithms but also opens new avenues …

Kernel Principal Component Analysis And Convolutional Neural Network-Based Approach For Obstructive Sleep Apnoea Detection Using Electrocardiogram, Aida Noor Indrawati, Nuryani Nuryani, Wiharto Wiharto, Diah Kurnia Mirawati, Trio Pambudi Utomo, Nanang Wiyono

Iraqi Journal for Computer Science and Mathematics

Obstructive Sleep Apnoea (OSA) is a prevalent sleep disorder characterised by repeated episodes of partial or complete upper airway obstruction during sleep, primarily due to the relaxation and collapse of soft tissues in the throat. These interruptions lead to disrupted sleep patterns and reduced oxygen saturation, increasing the risk of cardiovascular complications. Although Polysomnography (PSG) is considered the gold standard for diagnosing OSA, it is often uncomfortable for patients due to the extensive use of sensors and prolonged monitoring duration. As a result, there is a growing need for alternative diagnostic methods that are more efficient, comfortable, and cost-effective. This …