Computer Engineering Commons^™

The Effects Of Urban Fragmentation / Analysis Of The Causes And Challenges In The City Structure, Seemon Matte Abbo, Inaam Amin Albazzaz

Al-Esraa University College Journal for Engineering Sciences

Fragmentation is a fundamental term in the history of critical urban thought,yet the product of fragmentation is manifested in fragments. Urban fragmentationis used to describe the phenomenon that occurs when the urban environment isbroken up into smaller units or dispersed areas. This means that cities becomefragmented rather than cohesive and integrated. This disintegration can be the resultof various factors such as poor planning, lack of infrastructure, different land uses,and social dispersion. This research addresses the problem of urban fragmentationas one of the challenges facing the urban structure of cities, and clarifies the mostimportant factors affecting the city structure during the …

A Review: Special Types Of Concrete Used In Structures, Reem Hatem Ahmed, Mohammed Mosleh Salman

Al-Esraa University College Journal for Engineering Sciences

The term “special” possesses inherent significance and is utilized in this context to denote the distinctive attributes characteristic of concrete. The term “special” connotes enhanced strength, durability, and solidity in concrete. The production of specialized concrete involves the incorporation of distinct components via specific procedures following specific criteria. Specialized concrete is required to construct unique structures such as power plants, offshore buildings, docks, and airports. Each of these unique structures is essential for survival in harsh conditions, including sulfate, chemical, acid, and chloride attacks, as well as other surface characteristics. Concrete is a versatile material that exhibits good compressive strength …

The Role Of Young Architects As Entrepreneurship Generational Units In Shaping The Future Of Architecture, Attaa H. Al Obaidi, Anwar. S. Al-Qaraghuli

Al-Esraa University College Journal for Engineering Sciences

Youth are the pillar of nations and the secret of their renaissance, thebuilders of their civilization and the line of defense, the main participants inbasic planning processes, and the agents of change to build a world that iscompatible with the aspirations of future generations. This applies to the field ofarchitecture in terms of the importance of young architects in shaping it underthe challenges it faces, which impose interaction in a manner commensuratewith the emerging requirements and needs. This research is based on theconsideration that young architects are an integrated phenomenon thatcontributes to shaping architectural production according to the data of …

Using Artificial Intelligence To Diagnose Demented In The Elderly, Ohood Fadil Alwan

Al-Esraa University College Journal for Engineering Sciences

In addition to possible other symptoms, memory loss and impairment are the hallmarks of Demented or Alzheimer’s disease (AD). Despite the fact that dementia is incurable and has a significant negative impact on patients' lives, an early diagnosis can help start the right treatment and prevent additional brain damage. Over the years, machine learning techniques have been used to classify AD; nevertheless, the efficacy of the results depends on the use of multi-step classifiers and manually created features. Thanks to recent advances in deep learning, patterns may now be classified using neural networks' final stage. In order to diagnose dementia …

Risk Detection Of Gestational Diabetes In Early Pregnancy Depend On Artificial Neural Network, Sanaa Hammad Dhahi

Al-Esraa University College Journal for Engineering Sciences

Early prediction of gestational diabetes mellitus (GDM) risk is particularly important because it may enable more effective interventions and reduce cumulative maternal and fetal injury. The aim of this study is to develop machine learning (ML) models for early prediction of GDM using widely available variables, facilitating early intervention, and enabling the application of prediction models in settings where more complex tests are not accessible. In this paper, an artificial neural network (ANN) was used to detect the risk of gestational diabetes. A global dataset was used to collect clinical and experimental data from pregnant women and people with diabetes …

Impact Of Merging Basalt Fibers With Various Types Of Concrete And The Factors Affecting Them: A Review, Adil M. Jabbar, Lubna S. Dankha

Al-Esraa University College Journal for Engineering Sciences

This paper reviews effecting of blending basalt fibers into concreteon mechanical properties, ductility, and toughness. Based on concretecomponents, the perfect basalt fiber percentage can differ and is influencedby factors like fiber length, w/c ratio, and age. The beneficial basalt fiberspercentage is lower than that of steel fibers. Even with a low concentrationof basalt fibers, concrete tensile and flexural strength can be improved moreeffectively than a higher amount of steel fibers. As the amount of basaltfiber increases, the mix workability decreases. Additionally, the flow abilitymay drop slightly as the length of the fibers increases. Adding basalt fiber toconcrete can minimize drying …

How Cloud, Edge, And Mist Computing Affects Resource Management In Vanets, Andrew Trombly, Izzat Alsmadi

Masters Theses (Archived)

With public interest in automated vehicles as well as self-aware and responsive smart cities the demand for fast and efficient communication and computation will push our current infrastructure beyond its limits. Vehicular networks
(VANETs) look to solve this issue but suffer from extremely dynamic topologies
with unpredictable times and locations of high network and computational de-
minds from highly mobile units. This thesis looks into how current scholarly
works attempt resource management in VANETs and simulate VANETs with
cloud, edge, or mist computing architectures. The data of are compared and
discussed to drive a data-driven understanding and conversation of architecture …

Control Of Fully-Actuated Aerial Manipulators And Omni-Directional Multirotors, Riley M. Mccarthy

Mechanical Engineering ETDs

This thesis details the system modeling, design, control, simulation, construction, and
testing of both a fully-actuated and omni-directional multirotor aerial system created
for the primary purpose of performing active tasks with their environment. This work
verifies the capabilities of both systems through empirical testing, and demonstrates
how through the use of new control methods and physical designs multirotors can
expand their purpose from passive inspection based tasks to active contact based
tasks. These systems take advantage of newly implemented control allocation features present in the PX4 flight control software, version 1.14. The use of which makes designing controllers for such …

Terahertz Permittivity Parameters Of Monoclinic Single Crystal Lutetium Oxyorthosilicate, Sean Knight, Steffen Richter, Alexis Papamichail, Megan Stokey, Rafał Korlacki, Vallery Stanishev, Philipp Kühne, Mathias Schubert, Vanya Darakchieva

Department of Electrical and Computer Engineering: Faculty Publications

The anisotropic permittivity parameters of monoclinic single crystal lutetium oxyorthosilicate, Lu₂SiO₅ (LSO), have been determined in the terahertz spectral range. Using terahertz generalized spectroscopic ellipsometry (THz-GSE), we obtained the THz permittivities along the a, b, and c^⋆ crystal directions, which correspond to the ε_a; ε_b, and ε_c? on-diagonal tensor elements. The associated off diagonal tensor element ε_ac? was also determined experimentally, which is required to describe LSO’s optical response in the monoclinic a–c crystallographic plane. From the four tensor elements obtained in the model fit, we calculate the …

Ai’S Next Frontier — Dermatology? How It May Help Close The Health Gap For Dark-Skinned Patients, Gretchen B. Smail

Capstones

This project examines how engineers are creating artificial intelligence systems to help with dermatology diagnosis. Researchers believe that these systems can help close the health care gap in lower income and Black and brown communities, but dermatologists of color worry that these systems are not being trained to recognize skin conditions on patients of color.

Survey Of Transfer Learning Approaches In The Machine Learning Of Digital Health Sensing Data, Lina Chato, Emma Regentova

Electrical & Computer Engineering Faculty Research

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these …

Turnstile File Transfer: A Unidirectional System For Medium-Security Isolated Clusters, Mark Monnin, Lori L. Sussman

Journal of Cybersecurity Education, Research and Practice

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. …

Adaptive Traction, Power And Torque Control Strategies And Optimization In An All-Electric Powertrain, Aymane Hidara

Theses and Dissertations

Electric and hybrid-electric vehicles lean heavily on intricate control algorithms to provide smooth, reliable, and secure operations under any driving conditions. Three distinct supervisory control strategies have been developed, each aiming to improve reliability and vehicle performance of a dual-motor electric vehicle equipped with an all-wheel-drive, fully electric powertrain. These algorithms are adept at dynamically modulating and constraining the torque provided to the wheels, leveraging two autonomous permanent magnet electric drive units. This study utilizes a vehicle model jointly provided by MathWorks and General Motors in partnership with industry sponsors. The these strategies were implemented in the model and enhanced …

Chronic Kidney Disease Android Application, Paul Le

Computer Science and Engineering Senior Theses

Chronic kidney disease is increasingly recognized as a leading public health problem over the world that affects more than 10 percent of the population worldwide, where electrolytes and wastes can build up in your system. Kidney failure might not be noticeable until more advanced stages where it may then become fatal if not for artificial filtering or a transplant. As a result, it is important to detect kidney disease early on to prevent it from progressing to kidney failure. The current main test of the disease is a blood test that measures the levels of a waste product called creatine …

Finding Diffs Of Pull Request Commits, Chalinee Karawek, John Businge

Undergraduate Research Symposium Posters

Github is a social network that allows developers' projects to be forked for various uses, such as developing the existing repository or use the code to steer development into a new direction. As more forks are made, the more bugs that can occur due to new pull requests not synchronizing with the upstream repository. PaReco is a clone detection tool by Ramkisoen et al. (Ramkisoen et al. 2022) that identifies the changes inside the files inside a pull request by performing diff on every file. However, in this study, we take a different approach to identifying the changes inside the …

Safety-Aware Autonomous Robot Navigation, Mapping And Control By Optimization Techniques, Tingjun Lei

Theses and Dissertations

The realm of autonomous robotics has seen impressive advancements in recent years, with robots taking on essential roles in various sectors, including disaster response, environmental monitoring, agriculture, and healthcare. As these highly intelligent machines continue to integrate into our daily lives, the pressing imperative is to elevate and refine their performance, enabling them to adeptly manage complex tasks with remarkable efficiency, adaptability, and keen decision-making abilities, all while prioritizing safety-aware navigation, mapping, and control systems. Ensuring the safety-awareness of these robotic systems is of paramount importance in their development and deployment. In this research, bio-inspired neural networks, nature-inspired intelligence, deep …

Timeseries Forecasting Of U.S. Housing Price Index Using Machine Learning And Deep Learning Models, Krishna Chaitanya Nunna

Dissertations

Time series forecasting is a promising technique for various applications which predicts future values or patterns by taking historical data as base. Forecasting future trends is very beneficial for different industries to make valuable decisions and strategies. One such industry is housing market; it has biggest influence on U.S. economy. Housing price index (HPI) is a one of the crucial economic indices published by various government funded and private agency to benefit several industries and individuals for better analysis of future trends of housing market.

Several factors influence the HPI, economical, geographical, and demographic features. Development of traditional time series …

Data Science And The Ethics Of Private Information, Faria R. Promi

Publications and Research

This research focuses on the moral questions linked to modern data technologies like Big Data and the Internet of Things. These technologies can be, but they are also about privacy and keeping data secure. We've looked at what experts say about these concerns and discovered that most agree we need to safeguard people's privacy and data. Our research encourages everyone to use these technologies, finding a balance between technological progress and responsible, ethical behavior. This way, we can enjoy the benefits of these technologies while also respecting individuals' privacy and data rights. The study reviews what experts have said about …

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

Cybersecurity In Critical Infrastructure Systems: Emulated Protection Relay, Mitchell Bylak

Computer Science and Computer Engineering Undergraduate Honors Theses

Cyber-attacks on Critical Systems Infrastructure have been steadily increasing across the world as the capabilities of and reliance on technology have grown throughout the 21st century, and despite the influx of new cybersecurity practices and technologies, the industry faces challenges in its cooperation between the government that regulates law practices and the private sector that owns and operates critical infrastructure and security, which has directly led to an absence of eas- ily accessible information and learning resources on cybersecurity for use in public environments and educational settings. This honors research thesis addresses these challenges by submitting the development of an …

A Low-Power Analog Cell For Implementing Spiking Neural Networks In 65 Nm Cmos, John S. Venker, Luke Vincent, Jeff Dix

Computer Science and Computer Engineering Faculty Publications and Presentations

A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, such as speech recognition. The proposed network uses a leaky integrate and fire neuron scheme for computation, interleaved with a Spike Timing Dependent Plasticity (STDP) circuit for implementing synaptic-like weights. The low-power, asynchronous analog neurons and synapses are tailored for the VLSI environment needed to effectively make use of hardware SSN systems. To demonstrate functionality, a feedforward Spiking Neural Network composed of two layers, …

Pollutant Forecasting Using Neural Network-Based Temporal Models, Richard Pike

Masters Theses & Specialist Projects

The Jing-Jin-Ji region of China is a highly industrialized and populated area of the country. Its periodic high pollution and smog includes particles smaller than 2.5 μm, known as PM2.5, linked to many respiratory and cardiovascular illnesses. PM2.5 concentration around Jing-Jin-Ji has exceeded China’s urban air quality safety threshold for over 20% of all days in 2017 through 2020.

The quantity of ground weather stations that measure the concentrations of these pollutants, and their valuable data, is unfortunately small. By employing many machine learning strategies, many researchers have focused on interpolating finer spatial grids of PM2.5, or hindcasting PM2.5. However, …

Ethical Implications Of Ai-Based Algorithms In Recruiting Processes: A Study Of Civil Rights Violations Under Title Vii And The Americans With Disabilities Act, Vanessa Rodriguez

Cyber Operations and Resilience Program Graduate Projects

This research paper analyzes the ethical implications of utilizing artificial intelligence, specifically AI-based algorithms in business selection and recruiting processes, with a focus on potential violations under Title VII of the Civil Rights Act of 1964 and Title 1 of the Americans with Disabilities Act (ADA). Amazon’s attempt at launching AI recruiting tools is examined. This paper will assess the fairness of AI recruiting practices, considering data collection, potential biases, and accuracy concerns in its implementation process. Additionally, the paper will provide an overview of federal civil rights statutes enforced by the U.S. Equal Employment Opportunity Commission (EEOC) and recent …

Recycled Polycarbonate And Polycarbonate/Acrylonitrile Butadiene Styrene Feedstocks For Circular Economy Product Applications With Fused Granular Fabrication-Based Additive Manufacturing, Alessia Romani, Marinella Levi, Joshua M. Pearce

Electrical and Computer Engineering Publications

Distributed recycling and additive manufacturing (DRAM) holds enormous promise for enabling a circular economy. Most DRAM studies have focused on single thermoplastic waste stream. This study takes three paths forward from the previous literature: 1) expanding DRAM into high-performance polycarbonate/ acrylonitrile butadiene styrene (PC/ABS) blends, 2) extending PC/ABS blend research into both recycled materials and into direct fused granular fabrication (FGF) 3-D printing and 3) demonstrating the potential of using recycled PC/ABS feedstocks for new applications in circular economy contexts. A commercial open source large-format FGF 3-D printer was modified and used to assess the different printability and accuracy of …

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …

Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson

Cybersecurity Undergraduate Research Showcase

For this research project I used a Raspberry Pi device and conducted online research to investigate potential security vulnerabilities along with mitigation strategies. I configured the Raspberry Pi by using the proper peripherals such as an HDMI cord, a microUSB adapter that provided 5V and at least 700mA of current, a TV monitor, PiSwitch, SD Card, keyboard, and mouse. I installed the Rasbian operating system (OS). The process to install the Rasbian took about 10 minutes to boot starting at 21:08 on 10/27/2023 and ending at 21:18. 1,513 megabytes (MB) was written to the SD card running at (2.5 MB/sec). …

Are We Day-Dreaming Our Way To The Future?, John O'Connor

Presentations

This paper explores the transformative impact of technology on society, drawing on Marshall McLuhan’s insights. It scrutinizes the consequences of profit-driven technological progress, particularly in VR, brain-computer interfaces, and AI hallucinations. Critiquing the dominance of industry leaders in AI safety discussions, the paper advocates a balanced, inclusive approach.

Philosophical perspectives on AI and VR prompt questions about their impact on human experience. The paper proposes an educational shift to cultivate human attributes alongside technological skills. Examining AI hallucinations and gaming glitches, it raises concerns about the potential blurring of reality and virtuality.

Connecting technological advancements with environmental challenges, the paper …

Early Detection Of Virality In Chinese-Language On Youtube Focus On Comments, Yulin Zeng

Computer Science and Engineering Master's Theses

Predicting the popularity of the YouTube video-sharing site in its early stages is crucial for journalists and researchers, as it helps them uncover disinformation. In addition, research on the number of comments in news and comments has always been an academic topic that has attracted much attention. These Youtube political commentary and News channels that use Chinese as the primary language have a strong influence on the first generation of Chinese immigrants in the United States. However, under our monitoring, we found that it is difficult for these videos to appear in the viral video list defined by YouTube API. …

Resilient, Sustainable, And Secure Systems Support For Ultra-Low-Power Computational Things, Nicole Tobias

All Dissertations

Wireless battery-free and energy-harvesting devices are expanding the reach and vision of the Internet of Things, where trillions of embedded computational things interconnect ubiquitously around us and inform many different aspects of our everyday lives. Designing these systems without batteries and interconnecting wires lowers maintenance, environmental, and economic costs while also extending device lifetime and deployment opportunities. Over the last decade, research on these ultra-low-power embedded sensors and systems has dramatically increased — enabling new and exciting prospects in many different scientific fields, from smart building and health monitoring applications to animal and activity tracking.

These systems are not without …

From Leanstore To Learnedstore: Using A Learned Index To Improve Database Index Search, Sujit Maharjan

Computer Science and Engineering Faculty Publications - Archive

In the realm of database systems, optimizing B+-tree index performance is of paramount importance to overall database performance. LeanStore, a high-performance OLTP storage engine, has extensively optimized its in-memory B+-tree component as well as its B+-tree -indexed database on the disk. However, B+-tree's lookup time increases linearly with the tree height. This is especially problematic when all or part of its lookup path is on the disk. Recently proposed learned index technique has the potential to significantly improve the performance of the B+-tree -based index by predicting location of the search key, instead of the level-by-Ievel path walk. However, this …