Engineering Commons^™

An Analytical Solution Of Fractional Order Pi Controller Design For Stable/Unstable/Integrating Processes With Time Delay, Erdal Çökmez, İbrahi̇m Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

This paper aims to put forward an analytical solution for tuning parameters of a fractional order PI (FOPI) controller for stable, unstable, and integrating processes with time delay. Following this purpose, the analytical weighted geometrical center (AWGC) method has been extended to the design of fractional order PI controllers. To apply AWGC, the stability equations of the closed-loop system are written in terms of process and fractional order PI controller parameters. With the proposed method, the centroid can be calculated analytically, and the controller parameters can be easily calculated without the need of repetitive drawings of the stability boundary regions. …

More Wifi For Everyone: Increasing Spectral Efficiency In Wifi6 Networks Using A Distributed Obss/Pd Mechanism, Ali̇ Karakoç, Hüseyi̇n Bi̇rkan Yilmaz, Mehmet Şükrü Kuran

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose a distributed algorithm that determines effective Overlapping Basic Service Set/Preamble Detection (OBSS/PD) threshold levels in each WiFi6 device to maximize the total throughput by increasing the spectral efficiency. Within WiFi6 standard, OBSS/PD mechanism is introduced to increase the overall efficiency of WiFi networks by tuning the receiver sensitivity as well as the transmission power. In a nutshell, the proposed algorithm, RACEBOT, tunes the hearing (i.e. reception) and speaking (i.e. transmission) parameters of each WiFi device individually for the betterment of the WiFi experience of all WiFi networks in a neighborhood. WiFi experience is not only …

Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …

Analysis And Implementation Of A New High-Buck Dc-Dc Converter With Interleaved Output Inductors And Soft Switching Capability, Sajad Ghabeli Sani, Mohamad Reza Banaei, Seyed Hossein Hosseini

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes an innovative structure for DC-DC converters with high buck gain by using a lower number of elements. The converter provides highly efficient output power and an extended output voltage range. In addition, the distribution of output current between two inductors and the soft-switching capability of the power switches have made the converter suitable for applications that require high output current. All power switches accomplish the ZVZCS (zero-voltage and zero-current switching) condition with the aid of a small auxiliary inductor (Lx), which charges and discharges parallel capacitors of main switches to provide soft-switching conditions. Thus, the switching losses …

An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇

Turkish Journal of Electrical Engineering and Computer Sciences

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images …

Protecting The Infrastructure Of Michigan: Analyzing And Understanding Internet Infrastructure, Samuel Blaser, Travis Munyer, Damian Ramirez, Lester Juarez, Jackson Servant

Theses/Capstones/Creative Projects

The Michigan Army National Guard DCOE is hoping to increase their understanding of the physical, electrical, protocol, and logical topography of internet service. In order to understand the infrastructure of the internet, information must be collected on its pieces. By studying, describing, and illustrating the infrastructure of the global internet we can develop hardening tactics, improve user training, and develop contingency plans in the case of an attack. The research is focused on where data lives, locating data centers in the region, identifying global infrastructure and who owns it, and potential for hardening. An interactive map has been created in …

The Effects Of Social Media On Mental Health And Career Planning, Spencer A. Rowan

Honors Theses

Social media use is prevalent and necessary in society—nearly anything can be accomplished with a mobile device or smartphone. Among the US population, two thirds of American adults admit to using social media (Perrin, 2015) and in 2022, Georgiev (2023) found Americans spent an average of two and a half hours daily on social media. Furthermore, social media use is tied to mental well-being, work confidence levels, and feelings of being an imposter (Johnson et al., 2020; Uram & Skalski, 2022; Hernandez & Chalk, 2021; Myers, 2021; Ramm, 2019).

This project examined the role of social media use among college …

Modeling And Visualization Of Competing Escalation Dynamics: A Multilayer Multiagent Network Approach, Josh Allen

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Recent advances in military technology, such as hypersonic missiles, which can travel at more than five times the speed of sound and descend quickly into the atmosphere, give world nuclear superpowers a new edge. These advances up the game for nuclear superpowers with an extremely rapid, intense burst of military striking capability to secure upfront gains before encountering potentially overwhelming military confrontation. However, this so-called fait accompli has not been systematically studied by the United States in the perspective of the escalation philosophies of nuclear power competitors, or the mathematical modeling and visualization of multi-modal escalation dynamics. This gap may …

Leveraging Aruco Fiducial Marker System For Bridge Displacement Estimation Using Unmanned Aerial Vehicles, Mohamed Aly

School of Computing: Dissertations, Theses, and Student Research

The use of unmanned aerial vehicles (UAVs) in construction sites has been widely growing for surveying and inspection purposes. Their mobility and agility have enabled engineers to use UAVs in Structural Health Monitoring (SHM) applications to overcome the limitations of traditional approaches that require labor-intensive installation, extended time, and long-term maintenance. One of the critical applications of SHM is measuring bridge deflections during the bridge operation period. Due to the complex remote sites of bridges, remote sensing techniques, such as camera-equipped drones, can facilitate measuring bridge deflections. This work takes a step to build a pipeline using the state-of-the-art computer …

Sim-To-Real Reinforcement Learning Framework For Autonomous Aerial Leaf Sampling, Ashraful Islam

School of Computing: Dissertations, Theses, and Student Research

Using unmanned aerial systems (UAS) for leaf sampling is contributing to a better understanding of the influence of climate change on plant species, and the dynamics of forest ecology by studying hard-to-reach tree canopies. Currently, multiple skilled operators are required for UAS maneuvering and using the leaf sampling tool. This often limits sampling to only the canopy top or periphery. Sim-to-real reinforcement learning (RL) can be leveraged to tackle challenges in the autonomous operation of aerial leaf sampling in the changing environment of a tree canopy. However, trans- ferring an RL controller that is learned in simulation to real UAS …

Roboretrieve--In A Dual Role As A Hand-Held Surgical Robot And A Collaborative Robot End-Effector To Perform Spillage-Free Specimen Retrieval In Laparoscopy, Siqin Dong

Mechanical & Aerospace Engineering Theses & Dissertations

Recent advances in surgical robotics attempt to overcome limitations of manual surgery by augmenting the surgeon’s capabilities while performing suturing, incision, retraction, and retrieval tasks. This dissertation presents novel approaches for spillage-free specimen retrieval in confined spaces, targeted toward the surgical domain of minimally invasive robotic surgery. The retrieval task involves extraction of a resected specimen, residing in the abdominal cavity, completely outside of the patient’s body. A major challenge in this context is the spillage of content being retrieved, which may cause dissemination of malignancy. To address this challenge, this dissertation develops RoboRetrieve, a portable hand-held robot that …

Opensim-Based Musculoskeletal Modeling: Foundation For Interactive Obstetric Simulator, Bahador Dodge

Electrical & Computer Engineering Theses & Dissertations

The use of mathematical and computational models to understand complex biological systems, such as the human birth process, is a rapidly growing field in medicine. These models can be used to optimize and personalize medical treatments for individual patients, enhance training, and aid in educational efforts. While recent advancements in healthcare, particularly in obstetrics, have improved care for mothers and babies, studies and government reports indicate a rising rate of maternal mortality in the United States.

Despite this rising trend, there is a lack of detailed studies concerning the use of modeling and simulation to develop an interactive obstetrics simulator …

The Rapid Increase Of Ransomware Attacks Over The 21st Century And Mitigation Strategies To Prevent Them From Arising, Sanjay Jacob

Senior Honors Theses

Cyber-attacks have continued to become more common throughout the past century as more people are exposed to the Internet. Every year, various studies, reports, and scholarly research is done to emphasis the rapid increase of attacks. In this honors thesis, the student sought to gather further information about the rise of ransomware attacks, various cyber threats, discuss the psychological manipulation that exist, and provided the reader with an ethical complement of cyber-attacks. Additionally, case studies from previous research have been analyzed and mitigation strategies have been explained to provide the reader with practical application. This research emphasizes in on key …

Iot Health Devices: Exploring Security Risks In The Connected Landscape, Abasi-Amefon Obot Affia, Hilary Finch, Woosub Jung, Issah Abubakari Samori, Lucas Potter, Xavier-Lewis Palmer

School of Cybersecurity Faculty Publications

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of …

2023 (Spring) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the spring of 2023.

Ott Subscriber Churn Prediction Using Machine Learning, Needhi Devan Senthil Kumar

Electronic Theses, Projects, and Dissertations

Subscriber churn is a critical issue for companies that rely on recurring revenue from subscription-based services like the OTT platform. Machine Learning algorithms can be used to predict churn and develop targeted retention strategies to address the specific needs and concerns of at-risk subscribers. The research questions are 1) What Machine Learning algorithms are used to overcome subscriber churn? 2) How to predict subscribers’ churn in the OTT platform using Machine Learning? 3) How to retain subscribers and improve customer targeting? The dataset was collected from the Kaggle repository and implemented it into the various prediction algorithms used in previous …

Analyzing The Impact Of Automation On Employment In Different Us Regions: A Data-Driven Approach, Thejaas Balasubramanian

Electronic Theses, Projects, and Dissertations

Automation is transforming the US workforce with the increasing prevalence of technologies like robotics, artificial intelligence, and machine learning. As a result, it is essential to understand how this shift will impact the labor market and prepare for its effects. This culminating experience project aimed to examine the influence of computerization on jobs in the United States and answer the following research questions: Q1. What factors affect how likely different jobs will be automated? Q2. What are the possible effects of automation on the US workforce across states and industries? Q3. What are the meaningful predictors of the likelihood of …

Niche: A Curated Dataset Of Engineered Machine Learning Projects In Python, Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This …

Deep Learning-Based Turkish Spelling Error Detection With A Multi-Class False Positive Reduction Model, Burak Aytan, Cemal Okan Şakar

Turkish Journal of Electrical Engineering and Computer Sciences

Spell checking and correction is an important step in the text normalization process. These tasks are more challenging in agglutinative languages such as Turkish since many words can be derived from the root word by combining many suffixes. In this study, we propose a two-step deep learning-based model for misspelled word detection in the Turkish language. A false positive reduction model is integrated into the system to reduce the false positive predictions originating from the use of foreign words and abbreviations that are commonly used in Internet sharing platforms. For this purpose, we create a multi-class dataset by developing a …

Lignin Copolymer Property Prediction Using Machine Learning, Collin Larsen

Chemical Engineering Undergraduate Honors Theses

Lignin, an abundant biopolymer, is a waste byproduct of the paper and pulp industry. Despite its renewable nature and potential applicability in various products, such as plastics and composites, the development of lignin-based materials has been impeded by the cumbersome, Edisonian process of trial and error. This research proposes a novel approach to forecasting the properties of lignin-based copolymers by utilizing a recurrent neural network (RNN) based on the Keras models previously created by Tao et al. Example units of modified lignin were synthesized via esterification and amination functional group modifications. To increase the efficiency and accuracy of the prediction …

Radic Voice Authentication: Replay Attack Detection Using Image Classification For Voice Authentication Systems, Hannah Taylor

Undergraduate Honors Theses

Systems like Google Home, Alexa, and Siri that use voice-based authentication to verify their users’ identities are vulnerable to voice replay attacks. These attacks gain unauthorized access to voice-controlled devices or systems by replaying recordings of passphrases and voice commands. This shows the necessity to develop more resilient voice-based authentication systems that can detect voice replay attacks.

This thesis implements a system that detects voice-based replay attacks by using deep learning and image classification of voice spectrograms to differentiate between live and recorded speech. Tests of this system indicate that the approach represents a promising direction for detecting voice-based replay …

Government Aid Portal, Darshan Togadiya

Electronic Theses, Projects, and Dissertations

In today’s world, contacting government officials seems a big task when it comes to reporting small concerns. There are many authorities and officials which makes it very difficult for ordinary people to figure out who they should contact to resolve their daily issues. To address this problem, I have developed an application which can act as intermediary between citizens and government authorities. This portal will enable locals to submit complaints regarding personal or general issues through a complaint form, which will then be routed to the appropriate government department. Once a complaint is filed, government teams are immediately alerted and …

A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang

Electronic Theses, Projects, and Dissertations

Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, we try to find a suitable long-term predictor for the funds market by testing different kinds of neural network models, including the Long Short-Term Memory(LSTM) model with different layers, the Gated Recurrent Units(GRU) model with different layers, and the combination …

Bridging The Gap Between Public Organizaions And Cybersecurity, Christopher Boutros

Electronic Theses, Projects, and Dissertations

Cyberattacks are a major problem for public organizations across the nation, and unfortunately for them, the frequency of these attacks is constantly growing. This project used a case study approach to explore the types of cybersecurity public organization agencies face and how those crimes can be mitigated. The goal of this paper is to understand how public organization agencies have prepared for cyberattacks and discuss additional suggestions to improve their current systems with the current research available This research provides an analysis of current cyber security systems, new technologies that can be implemented, roadblocks public agencies face before and during …

Pillow Based Sleep Tracking Device Using Raspberry Pi, Venkatachalam Seviappan

Electronic Theses, Projects, and Dissertations

Almost half of all people have sleep interruptions at some point in their lives, making sleep disorders a common issue that affects a sizeable section of the population. Both their physical and emotional well-being may suffer as a result of this.Insomnia, which is a prevalent sleep disorder, is identified by symptoms including insufficient sleep duration and quality, trouble initiating sleep, multiple nighttime awakenings, early morning awakenings, and non-restorative sleep. It is essential to employ sleep monitoring systems to detect sleeping disorders as soon as possible for prompt diagnosis and treatment. To avoid sleep related health issues, there are plenty of …

Meat Quality Prediction Using Machine Learning, Rohit Buddiga

Electronic Theses, Projects, and Dissertations

Meat quality is an essential aspect of the food industry. However, traditional methods of meat quality prediction have limitations in terms of accuracy, cost, and time efficiency. This project focused on utilizing advanced Deep learning and Machine learning algorithms to develop- machine learning models that could predict the freshness (or spoilage) of meat with a 100% accuracy, based on image data. In addition to accuracy, this study emphasizes the significance of speed and time in selecting the optimal machine learning model. The research questions are: Q1. What hybrid neural networks should be used to predict freshness? Q2. How do hybrid …

Laying The Foundation For A Miniatuairzed Scada Testbed To Be Built At Csusb, Ryan Perera

Electronic Theses, Projects, and Dissertations

This culminating experience sought to lay the foundation for a miniaturized physical SCADA testbed to be built at California State University San Bernardino to enable students to apply the cybersecurity knowledge, skills and abilities in a fun and engaging environment while learning about what SCADA is, how it works, and how to improve the security of it. This project was conducted in response to a growing trend of cybersecurity attacks that have targeted our critical infrastructure systems through SCADA systems which are legacy systems that manage critical infrastructure systems within the past 10 years. Since SCADA systems require constant availability, …

Leveraging Blockchain Technology For Sla Enforcement In Health Care Cloud Partnerships, Shivani Uday Jahagirdar

Electronic Theses, Projects, and Dissertations

The healthcare industry is rapidly adopting cloud-based solutions to improve operational efficiency and patient outcomes. However, healthcare cloud partnerships often face challenges related to the lack of scalability, trust, and Service Level Agreement (SLA) enforcement, and has a notable impact on consumer care quality. To address this issue, the study proposed leveraging blockchain technology to enhance SLA enforcement by using smart contracts in health care cloud partnerships for small and medium-sized facilities. The research questions were: Q.1 What are the current challenges facing small to medium sized healthcare facilities in enforcing SLAs in cloud partnerships? Q.2 How can BC-based smart …

Digital Simulations Of Memristors Towards Integration With Reconfigurable Computing, Ivris Raymond

Computer Science and Computer Engineering Undergraduate Honors Theses

The end of Moore’s Law has been predicted for decades. Demand for increased parallel computational performance has been increased by improvements in machine learning. This past decade has demonstrated the ever-increasing creativity and effort necessary to extract scaling improvements in CMOS fabrication processes. However, CMOS scaling is nearing its fundamental physical limits. A viable path for increasing performance is to break the von Neumann bottleneck. In-memory computing using emerging memory technologies (e.g. ReRam, STT, MRAM) offers a potential path beyond the end of Moore’s Law. However, there is currently very little support from industry tools for designers wishing to incorporate …