Computer Engineering Commons^™

Modeling The Impact Of The Covid-19 Pandemic On Speeding At Rural Roadway Facilities In Maine Using Short-Term Speed And Traffic Count Data., Amirhossein Shahlaeegilan

Electronic Theses and Dissertations

The COVID-19 pandemic caused a significant change in traffic operations and safety. For instance, various U.S. states reported an increase in the rate of fatal and severe injury crashes over this duration. In April and May 2020, the comprehensive stay-at-home orders were issued across the country, including in Maine. These orders resulted in drastic reductions in traffic volume,switching working policies of noticeable number of corporations and educational administrations like universities to the remote working, closure of many organizations during the lockdown period, and people avoidance to public places to suppress the possible exposure to the virus were among the most …

Iot In Smart Communities, Technologies And Applications., Muhammad Zaigham Abbas Shah Syed

Electronic Theses and Dissertations

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing …

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Role Of Machine Learning In Early Diagnosis Of Kidney Diseases., Mohamed Nazih Mohamed Ibrahim Shehata

Electronic Theses and Dissertations

Machine learning (ML) and deep learning (DL) approaches have been used as indispensable tools in modern artificial intelligence-based computer-aided diagnostic (AIbased CAD) systems that can provide non-invasive, early, and accurate diagnosis of a given medical condition. These AI-based CAD systems have proven themselves to be reproducible and have the generalization ability to diagnose new unseen cases with several diseases and medical conditions in different organs (e.g., kidneys, prostate, brain, liver, lung, breast, and bladder). In this dissertation, we will focus on the role of such AI-based CAD systems in early diagnosis of two kidney diseases, namely: acute rejection (AR) post …

Developing Takeover Request Warning System To Improve Takeover Time And Post-Takeover Performance In Level 3 Automated Drivin, Niloy Talukder

Electronic Theses and Dissertations

The automotive industry is shifting towards partial (level 3) or fully automated vehicles. An important research question in level 3 automated driving is how quickly drivers can take over the vehicle control in response to a critical event. In this regard, this study develops an integrated takeover request (TOR) system which provides visual and auditorial TOR warning in both vehicle interface and personal portable device (e.g., tablet). The study also evaluated the effectiveness of the integrated TOR system in reducing the takeover time and improving post-takeover performance. For these purposes, 44 drivers participated in the driving simulator experiment where they …

Classification Of Electropherograms Using Machine Learning For Parkinson’S Disease, Soroush Dehghan

Electronic Theses and Dissertations

Parkinson’s disease (PD) is a neurodegenerative movement disorder that progresses gradually over time. The onset of symptoms in people who are suffering from PD can vary from case to case, and it depends on the progression of the disease in each patient. The PD symptoms gradually develop and exacerbate the patient’s movements throughout time. An early diagnosis of PD could improve the outcomes of treatments and could potentially delay the progression of this disorder and that makes discovering a new diagnostic method valuable. In this study, I investigate the feasibility of using a machine learning (ML) approach to classify PD …

A Water-Surface Self-Leveling Landing Platform For Small-Scale Uavs, Mbidi Santos

Electronic Theses and Dissertations

Because many of the most widely used UAVs, such as the Vertical Take-Off and Landing (VTOL), cannot land securely on sloped or fast-changing surfaces, there is a need to design better deployment and landing stations. This document proposes an approach to design a water-surface self-leveling landing platform by implementing the best concept to be used as a safe ground for UAVs to land and deploy on open waters. After conceptualizing multiple design ideas, these options were laid out in a decision matrix with four criteria: degrees of freedom, mechanical complexity, manufacturing, and cost. The chosen concept was the spherical parallel …

Hardware Implementations Of Spiking Neural Networks And Artificially Intelligent Systems, Alexander J. Leigh

Electronic Theses and Dissertations

Artificial spiking neural networks are gaining increasing prominence due to their potential advantages over traditional, time-static artificial neural networks. Custom hardware implementations of spiking neural networks present many advantages over other implementation mediums. Two main topics are the focus of this work. Firstly, digital hardware implementations of spiking neurons and neuromorphic hardware are explored and presented. These implementations include novel implementations for lowered digital hardware requirements and reduced power consumption.

The second section of this work proposes a novel method for selectively adding sparsity to a spiking neural network based on training set images for pattern recognition applications, thereby greatly …

Using Long Short-Term Memory Networks To Make And Train Neural Network Based Pseudo Random Number Generator, Aditya Harshvardhan

Electronic Theses and Dissertations

Neural Networks have been used in many decision-making models and been employed in computer vision, and natural language processing. Several works have also used Neural Networks for developing Pseudo-Random Number Generators [2, 4, 5, 7, 8]. However, despite great performance in the National Institute of Standards and Technology (NIST) statistical test suite for randomness, they fail to discuss how the complexity of a neural network affects such statistical results. This work introduces: 1) a series of new Long Short- Term Memory Network (LSTM) based and Fully Connected Neural Network (FCNN – baseline [2] + variations) Pseudo Random Number Generators (PRNG) …

Study On Performance Of Pruned Cnn-Based Classification Models, Mengling Deng

Electronic Theses and Dissertations

Convolutional Neural Network (CNN) is a neural network developed for processing image data. CNNs have been studied extensively and have been used in numerous computer vision tasks such as image classification and segmentation, object detection and recognition, etc. [1] Although, the CNNs-based approaches showed humanlevel performances in these tasks [2], they require heavy computation in both training and inference stages, and the models consist of millions of parameters. This hinders the development and deployment of CNN-based models for real world applications. Neural Network Pruning and Compression techniques have been proposed [3, 4] to reduce the computation complexity of trained CNNs …