Computer Engineering Commons^™

Virtual Control: A Comparison Of Methods For Hand-Tracking Implementation, Nathan Roberts

Honors Program: Senior Projects (Public)

This thesis examines the design philosophy of modern virtual reality applications that utilize hand-tracking as a primary form of user input. The analysis presented hopes to provide ideas for future implementations of this technology so that more immersive experiences are developed. This analysis starts with the discussion of a modern example of successful hand-tracking implementation, then comparing that implementation to a recent senior design project. This comparison is primarily based on each experience’s ability to create interactivity and immediacy. Interactivity is the degree to which the user can quickly and reliably make changes to their virtual environment, while immediacy is …

General Purpose Gpu Benchmarks For Neural Networks, Jose Maria Granados

Open Access Theses & Dissertations

Neural networks are a field of computing experiencing a rise in popularity in recent years due to the utilization of graphics processing units as their computational centerpiece. The lack of neural network benchmarks for the open-source Nyuzi architecture, a developing general-purpose processor with graphical processing capabilities, is the focus of this thesis. This work aims to determine whether Nyuziâ??s performance counters and traceable events suffice for performance tuning of neural network implementations. Given the mathematical intensity of neural networks, a strong emphasis is placed on events related to arithmetic instructions. Experimenting with neural network implementations in C and C++, existent …

Populations Digitally Excluded From Education: Issues, Factors, Contributions And Actions For Policy, Practice And Research In A Post-Pandemic Era, Don Passey, Jean Gabin Ntebutse, Manal Yazbak Abu Ahmad, Janet Cochrane, Simon Collin, Asmaa Ganayem, Elizabeth Langran, Sadaqat Mulla, Ma. Mercedes T. Rodrigo, Toshinori Saito, Miri Shonfeld, Saunand Somasi

Department of Information Systems & Computer Science Faculty Publications

This conceptual paper draws on a wide range of research and policy literature, providing a contemporary view of issues, factors and practices that affect education for digitally excluded populations. Concern for how education for digitally excluded populations can be supported is focal to this paper, with different sections offering key related perspectives. From an analysis of issues, factors and practices, actions for policy, practice and research are identified. Given a key finding that power issues can have major effects on plans, implementation processes and outcomes when addressing needs of education for digitally excluded populations, the paper concludes by offering frameworks …

Heterogeneous Collaborative Robotics: Multi-Robot Navigation In Dynamic Environments, Tyler Nicholas Raettig

Theses and Dissertations

Abstract: The challenges of multi-robot navigation in dynamic environments, focusing on uncertainties in obstacle complexities, partial observation, and the transition of policies from simulations to the real world. The proposed approach utilizes a deep reinforcement learning (DRL) framework enabling a Light Detection and Ranging (LiDAR)-equipped robot to communicate with a camera-equipped robot to achieve optimal paths despite their different sensors. The key contributions include the development of a cooperative architecture for information exchange between robots, a DRL-based framework for learning navigation policies, and a training mechanism based on dynamic randomization for enhanced real-world adaptability. Experimental validation using Gazebo simulations demonstrates …

An Analysis Of Security Risks Posed By Text-Based Generative Ai And Corporate Security Weaknesses Leading To Data Leaks, Tashya Rakshana Byreddy

Electronic Theses, Projects, and Dissertations

ABSTRACT

Generative AI (GenAI) has become a fundamental part of modern life, influencing how we work, learn, and interact with technology. This project focuses specifically on text-based GenAI, which is widely used for tasks such as information gathering, code improvement, and content creation. Despite its benefits, it presents significant security risks that are often underestimated by users. This project investigates these risks and the corporate security gaps that lead to unintentional data leaks. The project also provides a brief overview of Large Language Models (LLMs), which are based on the deep learning technique known as Transformer architecture, used for performing …

Autism Spectrum Disorder, Vidhya Lakshmi Jeevarathinam

Electronic Theses, Projects, and Dissertations

Autism Spectrum Disorder (ASD) diagnosis requires an integrative approach that combines behavioral, biomedical, and computational methodologies for enhanced accuracy. This study introduces a comprehensive framework that employs machine learning (ML) and deep learning (DL) techniques alongside linear regression to model relationships between behavioral traits, biomedical markers, and ASD likelihood. Behavioral inputs, such as social interaction patterns, repetitive behaviors, and communication characteristics, are analyzed using linear regression to identify significant predictors of ASD. Simultaneously, a Convolutional Neural Network (CNN) is trained on image datasets to detect visual cues, such as facial expressions, associated with ASD. Advanced techniques, including transfer learning and …

Project Tracking With Mobile Devices, Mike Son

Electronic Theses, Projects, and Dissertations

This innovative project tracking with mobile devices provides comprehensive access to project information, modernized communication, and effective work management from any device. Through a simple interface, it enables real-time collaboration, work delegation, and progress monitoring, making project management simpler everywhere. A standout feature of this application is its provision of a dedicated API (Web Programming Interface) for mobile devices, enabling seamless integration and synchronization between the web application and mobile platform apps. This guarantees a consistent and integrated user experience across all devices, allowing for quick task updates, and information sharing. The web application emphasizes security, with secure encryption and …

Optimizing Compression Efficiency With Adaptive Quantization Bit Depths, Carson Sisk

All Theses

Large-scale scientific instruments and applications generate massive amounts of data, lead- ing to significant challenges in data transfer and storage for analysis. This constitutes a major

bottleneck to workflow efficiency and scientific throughput. Lossy compression offers a solution to

these storage challenges in increasingly complex systems and services. Error-bounded lossy compression allows users to limit the error introduced during the compression process according to a user-defined metric and achieves significantly higher compression ratios than lossless compression for floating-point data. However, certain data types and compression configurations hinder the attainment of large compression ratios. To address the need for improved compression …

Application Of Lossy And Lossless Compression To Dicom Files, Yizhe Yang

All Theses

The Digital Imaging and Communications in Medicine (DICOM) standard is widely utilized for the management, storage, and transfer of medical images. However, the substantial file sizes associated with DICOM data present challenges in terms of storage and data transmission. Data reduction techniques help address these challenges by minimizing the size of the data while preserving its integrity. This thesis examines various compression methods aimed at reducing the size of DICOM files. We evaluate five lossless compressors and four lossy compressors on DICOM data to compare and assess their performance. Through an analysis of each compressor’s compression efficiency and resulting image …

Co-Emulation Of Robotics And Software-Defined Radio Based 5g Wireless Communications., Bhaskara Venkata Raju Garuda

Electronic Theses and Dissertations

The convergence of robotics and 5G wireless communication technologies has opened new avenues for real-time, dynamic robotic applications. This dissertation introduces a novel framework that integrates the Robot Operating System (ROS), Software-Defined Radios (SDRs), and 5G wireless networks to achieve seamless coemulation of robotic systems. The research emphasizes the unique features of 5G, such as ultra-low latency and high throughput, which enable critical applications like remote surgery, industrial automation, and autonomous vehicles. The methodology combines ROS for robotic control, SDRs for programmable communication channels, and 5G testbeds for high-speed, reliable data transmission. The experimental evaluation focuses on both position-based and …

End-To-End Learning For A Low-Cost Robotics Arm, Abhishek Chothani

Theses and Dissertations

Robotic manipulation is a cornerstone of automation, with the ultimate goal of developing versatile systems capable of executing a wide range of real-world tasks autonomously. Traditional robotics approaches, while reliable and widely adopted in industrial settings, often struggle with adaptability, perception, and dynamic task execution. This thesis explores the evolution from classical robotics techniques to modern learning-based approaches, leveraging advancements in artificial intelligence to overcome these limitations.

Initially, the thesis presents a pick-and-place pipeline built using the Drake robotics framework and the KUKA iiwa robotic arm. This system employs a pseudoinverse controller for inverse kinematics to perform structured tasks like …

Integrating Deep Traffic Prediction And Environmental Impact Assessment Using Noisy Real-World Data In Las Vegas, Tarek Bin Zahid

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis introduces an integrated framework for advanced traffic prediction and real-time emission estimation, designed to aid urban planning and environmental monitoring. Utilizing a graph-based transformer model, it predicts traffic conditions across the Las Vegas road network, drawing on spatial and temporal data from a large-scale sensor network. The study significantly expands the dataset from 26 to approximately 900 sensors, enhancing predictive accuracy and regional coverage. Inspired by masking techniques and strategies tailored to incomplete datasets, the model effectively handles real-world, noisy data without relying on resource-intensive imputation. Innovative training approaches enable robust traffic flow predictions despite missing or imperfect …

Enhancing Home Energy Efficiency: Web And Cloud Integration For Sustainable Electricity Monitoring, Kyle Aaron Coloma, King Harold A. Recto

Electronics, Computer, and Communications Engineering Faculty Publications

This paper demonstrates how sustainability can be integrated to technology by developing a cloud-based web application that monitors the use of energy in a residential setting. In the development of the minimum viable product (MVP), frontend tools were utilized to ensure that the platform runs on most types of devices. Moreover, backend tools were also used to ascertain efficient handling of data while maintaining security for the users. The project which has guaranteed fundamental functionality and a measure of security has been deployed successfully for early users. For future improvements, it is recommended to prioritize the optimization of user interface …

Tracking Joint Movement Using Optical Flow, Isabella Paperno

UNLV Theses, Dissertations, Professional Papers, and Capstones

We developed an algorithm that aims to move us closer to detecting early signs of arthritis. The program processes and analyzes X-ray videos using coyote and dog cadavers as models to examine the range of motion around the hip and connecting joints using optical flow techniques that track motion and velocity. We focus on how optical flow techniques track embedded metal markers and verify accuracy through comparisons with XMALab (X-ray motion analysis lab). Once proven as an accurate alternative, the focus will switch to markerless tracking and become a proof-of-concept for optical flow to be used in place of XMALab, …

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

Graduate Theses and Dissertations (2019 - present)

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

Quantum Visual Feature Encoding Revisited, Xuan-Bac Nguyen, Hoang-Quan Nguyen, Hugh Churchill, Samee U. Khan, Khoa Luu

Computer Science and Computer Engineering Faculty Publications and Presentations

Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantum machine learning. Investigating the root cause, we uncover that the existing quantum encoding design fails to ensure information preservation of the visual features after the encoding process, thus complicating the learning process of the quantum machine learning models. In particular, the problem, termed the “Quantum Information Gap” (QIG), leads to an information gap between classical and corresponding quantum features. We provide theoretical proof and practical examples with visualization …

Protocol Transformations Across Osi Network Stack Layers For Attack, Evasion, And Defense, Nathan Tusing

All Dissertations

Network endpoints frequently contend with errors and deviations within protocols. Many factors account for these deviations including noise, tampering, and algorithm implementations. Intermediate nodes are expected to modify instantiated protocols and not guarantee correctness. This ability to modify traffic enables all sides of network security to alter security and performance properties of protocols, and we define this intermediary modification of an instantiated protocol as a transformation. Protocol transformations traverse layers of the OSI reference model and changes a protocol's time series byte sequence. Within this thesis, we show that this framework applies to multiple domains and protocols. Common examples of …

An Automated Design Flow From Synchronous Rtl To Optimized Layout Using Commercial Eda Tools For Multi-Threshold Null Convention Logic Circuits, Cole Harrington Sherrill

Graduate Theses and Dissertations

This work presents the first automated design flow from synchronous RTL to highly optimized layout for Multi-Threshold NULL Convention Logic (MTNCL) circuits. The developed synthesis flow overcomes many of the drawbacks of existing attempts and leverages the advanced optimization features provided by modern synthesis tools. The remaining timing race conditions native to the MTNCL architecture have been identified and thoroughly explored. Two sets of novel timing constraints were devised: the first responds to these race conditions, yielding highly reliable MTNCL circuits; the second directly targets the critical paths within MTNCL circuits, allowing the designer to optimize the target circuit for …

Improving Robustness Of Learning-Based Approaches In Autonomous Systems And Engineering Education, Godwyll Aikins

Theses and Dissertations

This dissertation advances the development of robust learning-based approaches across two complementary domains: engineering education and autonomous systems. Through four studies, this research addresses critical challenges in preparing data-proficient engineers and developing reliable autonomous systems that can operate under uncertainty and incomplete information. The engineering education study examines how mechanical and aerospace engineering undergraduates conceptualize and develop data proficiency skills essential for modern engineering practice. Through interviews with 27 students, the research employs the How People Learn framework to analyze student perspectives on information literacy, data interpretation, and computational thinking. The findings inform pedagogical strategies for developing data proficiency in …

Work-Stealing Scheduler For Parallel Cache-Adaptive Algorithms, Chuqi Jiang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Modern computing systems with hierarchical memory structures, such as multiple cache levels, main memory, and external storage, pose significant challenges in optimizing memory usage for algorithm efficiency. Traditional models like the Disk Access Model (DAM) and advancements such as cache-oblivious algorithms have focused on minimizing memory transfers without requiring explicit knowledge of memory hierarchy parameters. However, these approaches assume fixed memory sizes and exclusive cache access, limiting their applicability in real-world, shared-memory environments where memory allocations fluctuate. To address these limitations, cache-adaptive algorithms were developed to dynamically adjust to changing memory profiles, enabling near-optimal performance even in multi-process systems. While …

Tac-It An Affective Computing User Interface Design, Andrew Biron

Theses and Dissertations

TAC-IT affective computing user-interface design is an independent computer peripheral that is a tool to be utilized to obtain a user’s self-reported emotional state in real-time. Doctor Rosalind Picard first coined and used the term affective computing in her paper Affective Computing [Picard, R. (1995)]. Affective Computing is defined as the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. Since that time, areas of research have expanded exponentially, and areas of interest include how to trigger emotions in a test …

E-Learning Management Technology Integration For Laboratory Usage, Mohammed Khaleel Hussein, Mohammed Ahmed Subhi, Saleh Mahdi Mohammed, Mayasah Al-Khateeb

Iraqi Journal for Computer Science and Mathematics

E-learning systems have transformed educational sectors and more interestingly, the use of these educational platforms for laboratory-based practices has been trending in the last couple of years. Laboratory-usage e-learning system has various tremendous benefits such as the skill of accessibility utilization and practical experimentation simulation. Several intelligent tasks still need to be tackled to make the most of it. One of the biggest problems is how to recreate hands-on experience in a virtual or remote environment. Some e-learning systems offer simulations and virtual experiments but may also require additional tactile feedback through interaction with physical equipment for this challenge, it …

The Significance Of Cannibalism, Panic, And Sanctuary In The Interactions Between Prey And Predator With A Stage Structure, Ahmed Sami Abdulghafour, Raid Kamel Naji

Iraqi Journal for Computer Science and Mathematics

This paper analyzes a novel prey-predator model that takes into account the predator's stage structure, cannibalism within the predator population, panicky behavior, and the existence of a sanctuary where the prey might hide from the predator. The Holling type II functional response is used in the predation process. The behavior of the identified fixed points of the proposed system has been closely analyzed. The analysis focuses on the local stability and potential bifurcations that could happen close to the system's fixed points. The Lyapunov function approach is used to investigate the fixed-point stability zone globally. Numerical simulations were run to …

Juegos De Rol En El Desarrollo De Projectpipe: Una Inmersión En El Uso De Metodologías De Diseño, Julieth A. Gómez Hernández, Cristian C. Orozco Ospina, Nicolas Restrepo Henao

Journal of Roleplaying Studies and STEAM

En el entorno digital actual, la evolución del concepto de juegos de rol ha llevado al desarrollo de aplicaciones web interactivas y educativas. En sintonía con este planteamiento, se presenta la siguiente propuesta, que adopta una metodología integrada para aprovechar la versatilidad del método de Bruno Munari y el Diseño Centrado en el Usuario (DCU). La metodología de Munari descompone la gestión macro del proyecto en pasos manejables; mientras que el DCU se enfoca en la creación de una interfaz intuitiva y funcional mediante prototipos iterativos y pruebas continuas de usabilidad. Como complemento a estas estrategias se integra la metodología …

Benchmarking Pretrained Models For Speech Emotion Recognition: A Focus On Xception, Ahmed Hassan, Tehroom Masood, Hassan A. Ahmed, H. M. Shahzad, Hafiz Muhammad T. Khushi

Business Faculty Publications

Speech emotion recognition (SER) is an emerging technology that utilizes speech sounds to identify a speaker’s emotional state. Computational intelligence is receiving increasing attention from academics, health, and social media applications. This research was conducted to identify emotional states in verbal communication. We applied a publicly available dataset called RAVDEES. The data augmentation process involved adding noise, applying time stretching, shifting, and pitch, and extracting the features zero cross rate (ZCR), chroma shift, Mel-Frequency Cepstral Coefficients (MFCC), and a spectrogram. In addition, we used many pretrained deep learning models, such as VGG16, ResNet50, Xception, InceptionV3, and DenseNet121. Out of all …

Secure Blind Medical Image Watermarking Using Hybrid Feature Extraction Techniques, Sawsan D. Mahmood, Yassine Aribi, Fadoua Drira, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

Watermarking offers great potential for medical images by embedding identifiable information that ensures secure and authenticated sharing of patient data while maintaining both integrity and diagnostic quality. In this paper, we present an innovative framework for blind medical image watermarking that harnesses advanced feature extraction techniques, including K-Means clustering, BRISK (Binary Robust Invariant Scalable Key-points), GFTT (Good Features to Track), and chaotic systems algorithms.

We conducted extensive experiments on the Ocular Disease Intelligent Recognition (ODIR) dataset, focusing specifically on Retinal Optical Coherence Tomography (OCT) images. The results highlight the framework's ability to preserve image quality and diagnostic utility, with minimal …

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

Cybersecurity Undergraduate Research Showcase

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

Identifying Redundant Audio Content Over Cloud Environment Using Deduplication Techniques, Venkatesh K

Theses and Dissertations

Cloud computing has become an integral part of modern internet-based services, with users relying heavily on cloud environments as primary storage solutions. However, the exponential growth in data volume presents a challenge (i.e) the proliferation of duplicated content within cloud repositories. Deduplication techniques provide a promising approach to mitigate this issue. This research focuses on detecting redundant audio content within a cloud environment, specifically targeting the sharing of extensive audio files, such as those in Waveform Audio File Format (WAV). The study proposes the Refined Super Subset Identification Algorithm (RSSIA) to efficiently identify redundant content and segments within existing audio …

Convolutional Neural Networks For Dementia Severity Classification: Ordinal Versus Regular Methods, Ambresh Bhadrashetty, P. Sandhya

Iraqi Journal for Computer Science and Mathematics

Dementia, a chronic neurodegenerative disorder, progressively impairs cognitive functions such as memory, reasoning, learning, and recall, placing a significant burden on patients and healthcare systems. Early and accurate classification of dementia severity is crucial for personalized care and intervention. This study introduces a novel Convolutional Neural Network (CNN) designed to classify dementia into four ordinal severity levels (None, Very Mild, Mild, and Moderate) based on MRI brain scans. Utilizing the extensive Open Access Series of Imaging Studies (OASIS) dataset, which includes 86,437 MRI scans (67,222 ‘none,’ 13,725 ‘very mild,’ 5,002 ‘mild,’ and 488 ‘moderate’), our model addresses severe class imbalance …

New Three-Parameter Exponentiated Benini Distribution: Properties And Applications, Elif Yıldırım, Gamze Özel, Christophe Chesneaul, Farrukh Jamal, Ahmed M. Gemeay

Iraqi Journal for Computer Science and Mathematics

Modeling of some rarely occurring environmental events, obtaining and analyzing accurate predictions is quite important in determining the nature of such events and taking measures accordingly. Modeling these rarely occurring environmental events with the statistical distributions in the literature may cause some problems. For this reason, different statistical distributions are needed for modeling this type of rare data. In this paper, a new three-parameter exponentiated Benini distribution based on the exponential family is proposed as a new lifetime distribution. It is called the three-parameter exponentiated Benini distribution. Although new distributions are derived by different methods in the literature, there is …