Computer Engineering Commons^™

Improving Inference Speed Of Perception Systems In Autonomous Unmanned Ground Vehicles, Bradley Selee

All Theses

Autonomous vehicle (AV) development has become one of the largest research challenges in businesses and research institutions. While much research has been done, autonomous driving still requires extensive amounts of research due to its immense, multi-factorial difficulty. Autonomous vehicles rely on many complex systems to function, make accurate decisions, and, above all, provide maximum safety. One of the most crucial components of autonomous driving is the perception system.

The perception system allows the vehicle to identify its surroundings and make accurate, but safe, decisions through the use of computer vision techniques like object detection, image segmentation, and path planning. Due …

Adversarial Deep Learning And Security With A Hardware Perspective, Joseph Clements

All Dissertations

Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep …

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 …

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 …

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 …

Dynamic Scene Understanding: Pedestrian Tracking From Aerial Devices., Abdelhamid Bouzid

Electronic Theses and Dissertations

Multiple Object Tracking (MOT) is the problem that involves following the trajectory of multiple objects in a sequence, generally a video. Pedestrians are among the most interesting subjects to track and recognize for many purposes such as surveillance, and safety. In the recent years, Unmanned Aerial Vehicles (UAV’s) have been viewed as a viable option for monitoring public areas, as they provide a low-cost method of data collection while covering large and difficult-to-reach areas. In this thesis, we present an online pedestrian tracking and re-identification from aerial devices framework. This framework is based on learning a compact directional statistic distribution …

Guided Data Augmentation For Improved Semi-Supervised Image Classification In Low Data Regime., Fadoua Khmaissia

Electronic Theses and Dissertations

Deep learning models have achieved state of the art performances, especially for computer vision applications. Much of the recent successes can be attributed to the existence of large, high quality, labeled datasets. However, in many real-world applications, collecting similar datasets is often cumbersome and time consuming. For instance, developing robust automatic target recognition models from infrared images still faces major challenges. This is mainly due to the difficulty of acquiring high resolution inputs, sensitivity to the thermal sensors' calibration, meteorological conditions, targets' scale and viewpoint invariance. Ideally, a good training set should contain enough variations within each class for the …

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 …

User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor, Didih Rizki Chandranegara, Anzilludin Ashari, Zamah Sari, Hardianto Wibowo, Wildan Suharso

Makara Journal of Technology

Mouse dynamics authentication is a method for identifying a person by analyzing the unique pattern or rhythm of their mouse movement. Owing to its distinctive properties, such mouse movements can be used as the basis for security. The development of technology is followed by the urge to keep private data safe from hackers. Therefore, increasing the accuracy of user classification and reducing the false acceptance rate (FAR) are necessary to improve data security. In this study, we propose to combine the K-nearest neighbor method and simple random sampling and obtain a sample from a dataset to improve the classification of …

Infrastructure-As-Code: Automating The Deployment On Aws Using Terraform, Srikar Pratap

Masters Projects

In my master’s project, I used Terraform to create a scalable infrastructure on Amazon Web Services (AWS) for my personal website. Terraform is an open-source infrastructure-as-code (IAC) tool that allows you to create, manage and provision infrastructure resources, such as virtual machines, storage accounts, networks, and more, across multiple cloud providers and on-premises data centers using a declarative configuration language. A scalable infrastructure is important because it enables a system or application to handle increasing amounts of traffic or workload without experiencing performance issues or downtime. It ensures that the system remains responsive, available, and reliable as an organization grows …

Ransomware: Evaluation Of Mitigation And Prevention Techniques, Juanjose Rodriguez-Cardenas

Symposium of Student Scholars

Ransomware is classified as one of the main types of malware and involves the design of exploitations of new vulnerabilities through a host. That allows for the intrusion of systems and encrypting of any information assets and data in order to demand a sum of payment normally through untraceable cryptocurrencies such as Monero for the decryption key. This rapid security threat has put governments and private enterprises on high alert and despite evolving technologies and more sophisticated encryption algorithms critical assets are being held for ransom and the results are detrimental, including the recent Colonial Pipeline ransomware attack in 2021 …

Detection Optimization Of Rare Attacks In Software-Defined Network Using Ensemble Learning, Ahmed M. El-Shamy, Nawal A. El-Fishawy, Gamal M. Attiya, Mokhtar Ahmed

Mansoura Engineering Journal

Software-defined networking (SDN) is a highly flexible architecture that automates and facilitates network configuration and management. Intrusion detection systems (IDS) are becoming essential components in the network to detect malicious attacks and suspicious activities by continuously monitoring network traffic. Integration between SDN and machine learning (ML) techniques is extensively used to build an effective IDS against all potential cyber-attacks that aim at breaking the network security policy and stealing valuable data. Implementing an IDS based on SDN and ML has the advantage of managing traffic dynamically and fully autonomously to provide high protection against security threats. The main objective of …

Ransomware: What Is Ransomware, And How To Prevent It, Brandon Chambers

Cybersecurity Undergraduate Research Showcase

This research paper answers the question, “What is Ransomware, and How to prevent it?”. This paper will discuss what ransomware is, its history about ransomware, how ransomware attacks Windows systems, how to prevent ransomware, how to handle ransomware once it is already on the network, ideas for training professionals to avoid ransomware, and how anti-virus helps defend against ransomware. Many different articles, case studies, and professional blogs will be used to complete the research on this topic.

Region-Specified Inverse Design Of Absorption And Scattering In Nanoparticles By Using Machine Learning, Alex Vallone, Nooshin M. Estakhri, Nasim Mohammadi Estrakhri

Engineering Faculty Articles and Research

Machine learning provides a promising platform for both forward modeling and the inverse design of photonic structures. Relying on a data-driven approach, machine learning is especially appealing for situations when it is not feasible to derive an analytical solution for a complex problem. There has been a great amount of recent interest in constructing machine learning models suitable for different electromagnetic problems. In this work, we adapt a region-specified design approach for the inverse design of multilayered nanoparticles. Given the high computational cost of dataset generation for electromagnetic problems, we specifically investigate the case of a small training dataset, enhanced …

Game Based Learning: Engaging Students And Measuring Their Progress, Jonathan Stover, Siegwart Mayr

Campus Research Month

Digital natives are constantly surrounded by technology. Therefore, traditional methods of teaching are becoming obsolete and increasingly creative solutions are required to keep students engaged. Among these solutions is a concept called game based learning. Game based learning is a unique educational experience that incorporates the engagement factors of video games with education. Even though it is still in an early stage of adoption, game based learning has been proven to be a surprisingly effective tool for providing students with a valuable educational experience. Even with this evidence in mind, current game based learning programs still hold the potential to …

Machine Learning Based Software Fault Prediction Models, Gurmeet Kaur, Jyoti Pruthi, Parul Gandhi

Karbala International Journal of Modern Science

The study aims to identify soft-computing-based software fault prediction models that assist in resolving issues related to the quality, reliability, and cost of the software projects. It proposes models for implementation of software fault prediction using decision-tree regression and the K-nearest neighbor technique of machine learning. The proposed models have been designed and implemented in Python using designed metric suites as input, and the predicted-faults as output, for the real-time, wider dataset from the Promise repository. By comparing the prediction and validation results of the proposed models for the same dataset, it has been concluded that the decision-tree regression-based fault …

Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha

LSU Doctoral Dissertations

Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …

The Fashion Visual Search Using Deep Learning Approach, Smita V. Bhoir, Sunita R. Patil

Library Philosophy and Practice (e-journal)

In recent years, the World Wide Web (WWW) has established itself as a popular source of information. Using an effective approach to investigate the vast amount of information available on the internet is essential if we are to make the most of the resources available. Visual data cannot be indexed using text-based indexing algorithms because it is significantly larger and more complex than text. Content-Based Image Retrieval, as a result, has gained widespread attention among the scientific community (CBIR). Input into a CBIR system that is dependent on visible features of the user's input image at a low level is …

Enhancing Cyberspace Monitoring In The United States Aviation Industry: A Multi-Layered Approach For Addressing Emerging Threats, Matthew Janson

Doctoral Dissertations and Master's Theses

This research project examined the cyberspace domain in the United States (U.S.) aviation industry from many different angles. The research involved learning about the U.S. aviation cyberspace environment, the landscape of cyber threats, new technologies like 5G and smart airports, cybersecurity frameworks and best practices, and the use of aviation cyberspace monitoring capabilities. The research looked at how vulnerable the aviation industry is from cyber-attacks, analyzed the possible effects of cyber-attacks on the industry, and suggests ways to improve the industry's cybersecurity posture. The project's main goal was to protect against possible cyber-attacks and make sure that the aviation industry …

The Exigency And How To Improve And Implement International Humanitarian Legislations More Advantageously In Times Of Both Cyber-Warfare And Cyberspace, Shawn J. Lalman

Doctoral Dissertations and Master's Theses

This study provides a synopsis of the following topics: the prospective limiters levied on cyber-warfare by present–day international legislation; significant complexities and contentions brought up in the rendering & utilization of International Humanitarian Legislation against cyber-warfare; feasible repercussions of cyber-warfare on humanitarian causes. It is also to be contended and outlined in this research study that non–state actors can be held accountable for breaches of international humanitarian legislation committed using cyber–ordnance if sufficient resources and skill are made available. It details the factors that prosecutors and investigators must take into account when organizing investigations into major breaches of humanitarian legislation …

Para Cima Y Pa’ Abajo: Building Bridges Between Hci Research In Latin America And In The Global North, Pedro Reynolds-Cuéllar, Marisol Wong-Villacres, Karla A. Badillo-Urquiola, Mayra Donaji Barrera-Machuca, Franceli L. Cibrian, Marianela Ciolfi Felice, Carolina Fuentes, Laura Sanely Gaytán-Lugo, Vivian Genaro Motti, Monica Perusquía-Hernández, Oscar A. Lemus

Engineering Faculty Articles and Research

The Human-computer Interaction (HCI) community has the opportunity to foster the integration of research practices across the Global South and North to begin overcoming colonial relationships. In this paper, we focus on the case of Latin America (LATAM), where initiatives to increase the representation of HCI practitioners lack a consolidated understanding of the practices they employ, the factors that influence them, and the challenges that practitioners face. To address this knowledge gap, we employ a mixed-methods approach, comprising a survey (66 respondents) and in-depth interviews (19 interviewees). Our analyses characterize a set of research perspectives on how HCI is practiced …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Low-Power Redundant-Transition-Free Tspc Dual-Edge-Triggering Flip-Flop Using Single-Transistor-Clocked Buffer, Zisong Wang, Peiyi Zhao, Tom Springer, Congyi Zhu, Jaccob Mau, Andrew Wells, Yinshui Xia, Lingli Wang

Engineering Faculty Articles and Research

In the modern graphics processing unit (GPU)/artificial intelligence (AI) era, flip-flop (FF) has become one of the most power-hungry blocks in processors. To address this issue, a novel single-phase-clock dual-edge-triggering (DET) FF using a single-transistor-clocked (STC) buffer (STCB) is proposed. The STCB uses a single-clocked transistor in the data sampling path, which completely removes clock redundant transitions (RTs) and internal RTs that exist in other DET designs. Verified by post-layout simulations in 22 nm fully depleted silicon on insulator (FD-SOI) CMOS, when operating at 10% switching activity, the proposed STC-DET outperforms prior state-of-the-art low-power DET in power consumption by 14% …

Reference Frames In Human Sensory, Motor, And Cognitive Processing, Dongcheng He

Electronic Theses and Dissertations

Reference-frames, or coordinate systems, are used to express properties and relationships of objects in the environment. While the use of reference-frames is well understood in physical sciences, how the brain uses reference-frames remains a fundamental question. The goal of this dissertation is to reach a better understanding of reference-frames in human perceptual, motor, and cognitive processing. In the first project, we study reference-frames in perception and develop a model to explain the transition from egocentric (based on the observer) to exocentric (based outside the observer) reference-frames to account for the perception of relative motion. In a second project, we focus …

Procedural Model Of The Monitoring System, Fayzullajon Botirov

Chemical Technology, Control and Management

This article discusses the process of monitoring information security incidents and factors affecting the effectiveness of monitoring. A procedural model of the functioning of the information security incident monitoring system is constructed, based on the study of its structure, the principles of operation of individual components and literature data, the analysis of individual stages.

Comprehensive Wind Speed Forecasting-Based Analysis Of Stacked Stateful & Stateless Models, Swayamjit Saha, Amogu Uduka, Hunter Walt, James Lucore

Graduate Student Research Symposium

Wind speed is a powerful source of renewable energy, which can be used as an alternative to the non-renewable resources for production of electricity. Renewable sources are clean, infinite and do not impact the environment negatively during production of electrical energy. However, while eliciting electrical energy from renewable resources viz. solar irradiance, wind speed, hydro should require special planning failing which may result in huge loss of labour and money for setting up the system. In this poster, we discuss four deep recurrent neural networks viz. Stacked Stateless LSTM, Stacked Stateless GRU, Stacked Stateful LSTM and Statcked Stateful GRU which …

Author Gender Identification Considering Gender Bias, Manuela N. Jeyaraj, Sarah Jane Delany

Conference papers

Writing style and choice of words used in textual content can vary between men and women both in terms of who the text is talking about and who is writing the text. The focus of this paper is on author gender prediction, identifying the gender of who is writing the text. We compare closed and open vocabulary approaches on different types of textual content including more traditional writing styles such as in books, and more recent writing styles used in user generated content on digital platforms such as blogs and social media messaging. As supervised machine learning approaches can reflect …