Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning,
2022
University of Dayton
Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram …
Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning,
2022
University of New Orleans
Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar
LSU New Orleans Theses and Dissertations
Parallel computing plays a crucial role in processing large-scale graph data. Complex network analysis is an exciting area of research for many applications in different scientific domains e.g., sociology, biology, online media, recommendation systems and many more. Graph mining is an area of interest with diverse problems from different domains of our daily life. Due to the advancement of data and computing technologies, graph data is growing at an enormous rate, for example, the number of links in social networks is growing every millisecond. Machine/Deep learning plays a significant role for technological accomplishments to work with big data in modern …
Ocean Wave Prediction And Characterization For Intelligent Maritime Transportation,
2022
University of New Orlenas
Ocean Wave Prediction And Characterization For Intelligent Maritime Transportation, Pujan Pokhrel
LSU New Orleans Theses and Dissertations
The national Earth System Prediction (ESPC) initiative aims to develop the predictions
for the next generation predictions of atmosphere, ocean, and sea-ice interactions in the scale of days to decades. This dissertation seeks to demonstrate the methods we can use to improve the ESPC models, especially the ocean prediction model. In the application side of the weather forecasts, this dissertation explores imitation learning with constraints to solve combinatorial optimization problems, focusing on the weather routing of surface vessels. Prediction of ocean waves is essential for various purposes, including vessel routing, ocean energy harvesting, agriculture, etc. Since the machine learning approaches …
Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping,
2022
University of Dayton
Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras
Electrical and Computer Engineering Faculty Publications
In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed ob-servations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an …
Act5 Eit System : A Multiple-Source Electrical Impedance Tomography System,
2022
University at Albany, State University of New York
Act5 Eit System : A Multiple-Source Electrical Impedance Tomography System, Omid Rajabi Shishvan
Legacy Theses & Dissertations (2009 - 2024)
This dissertation describes the design and implementation of Adaptive Current Tomograph 5 (ACT5) with a focus on the digital processing and data ow in the instrument. ACT5 is an electrical impedance tomography (EIT) instrument that produces images of the complex impedivity distribution within the body by injecting currents through and measuring voltages on electrodes applied to the skin. ACT5 is a parallel-drive EIT system with a dedicated current source for each electrode, capable of driving all electrodes simultaneously and measuring the induced voltages on them. It can support up to 48 electrodes with a frequency range of 5 kHz to …
Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning,
2022
East Tennessee State University
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 …
A Digital Healthcare Application For Patient Monitoring And Assessment,
2022
Clemson University
A Digital Healthcare Application For Patient Monitoring And Assessment, Brandon Shumin
All Theses
The COVID-19 pandemic strained our healthcare resources and exacerbated the existing issues of primary care shortages and burnout rates for healthcare professionals. Due in part to these factors, telehealth has seen more wide-spread use during this time. However, current asynchronous telehealth applications require stable Internet to function fully. Since many medically underserved populations in the United States lack Internet access in their homes, an application that offers patient monitoring and assessment could extend their access to medical resources. This work proposes such a digital healthcare application for iOS devices and evaluates it based on the system requirements of availability, data …
A Memory-Centric Customizable Domain-Specific Fpga Overlay For Accelerating Machine Learning Applications,
2022
University of Arkansas, Fayetteville
A Memory-Centric Customizable Domain-Specific Fpga Overlay For Accelerating Machine Learning Applications, Atiyehsadat Panahi
Graduate Theses and Dissertations
Low latency inferencing is of paramount importance to a wide range of real time and userfacing Machine Learning (ML) applications. Field Programmable Gate Arrays (FPGAs) offer unique advantages in delivering low latency as well as energy efficient accelertors for low latency inferencing. Unfortunately, creating machine learning accelerators in FPGAs is not easy, requiring the use of vendor specific CAD tools and low level digital and hardware microarchitecture design knowledge that the majority of ML researchers do not possess. The continued refinement of High Level Synthesis (HLS) tools can reduce but not eliminate the need for hardware-specific design knowledge. The designs …
Personalizing Student Graduation Paths Using Expressed Student Interests,
2022
Missouri University of Science and Technology
Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Charles Dobbins
Masters Theses
"This work proposes an intelligent recommender approach to facilitate personalized education and help students in planning their path to graduation. The original research contribution of this work is to develop a recommender approach that pervasively personalizes and optimizes a student’s path to graduation by accounting for the student’s career interests and academic background. The approach is a multi-objective optimization problem, subject to institutional constraints, with the goal of optimizing the graduation path with respect to one or more criteria, such as time-to-graduation, credit hours taken, and alignment with student’s career interests. The efficacy of the approach is illustrated and verified …
Securing Information On A Web Application System To Facilitate Online Blood Donation Booking,
2022
University of South Carolina - Columbia
Securing Information On A Web Application System To Facilitate Online Blood Donation Booking, Hrishitva Patel
Faculty Publications
Blood donation has saved many lives in the past. According to statistics presented by the American Red Cross, a patient is in need of a blood transfusion every two seconds. There are many benefits that arise from blood donation to both the donor and the blood recipients. With blood donation, cancer patients, people involved in accidents, or those battling diseases that require blood donation have access to enough blood to sustain their survival. There is a need to digitize the blood donation booking to facilitate blood donation across the United States, and ensure patients in need of blood, receive their …
Collaborative Robotics Strategies For Handling Non-Repetitive Micro-Drilling Tasks Characterized By Low Structural Mechanical Impedance,
2022
Old Dominion University
Collaborative Robotics Strategies For Handling Non-Repetitive Micro-Drilling Tasks Characterized By Low Structural Mechanical Impedance, Xiangyu Wang
Mechanical & Aerospace Engineering Theses & Dissertations
Mechanical micro-drilling finds widespread use in diverse applications ranging from advanced manufacturing to medical surgery. This dissertation aims to develop techniques that allow programming of robots to perform effective micro-drilling tasks. Accomplishing this goal is faced with several challenges. Micro-drills suffer from frequent breakage caused from variations in drill process parameters. Micro-drilling tasks afford extremely low feed rates and almost zero tolerance for any feed rate variations. The accompanying robot programming task is made difficult as mathematical models that capture the micro-drilling process complexities and sensitive variations in micro-drill parameters are highly difficult to obtain. Therefore, an experimental approach is …
Integrating Plcs With Robot Motion Control In Engineering Capstone Courses,
2022
Old Dominion University
Integrating Plcs With Robot Motion Control In Engineering Capstone Courses, Sanjeevi Chitikeshi, Shirshak K. Dhali, Vukica Jovanovic
Engineering Technology Faculty Publications
Robotic motion control methods and Programmable Logic Controllers (PLCs) are critical in engineering automation and process control applications. In most manufacturing and automation processes, robots are used for moving parts and are controlled by industrial PLCs. Proper integration of external I/O devices, sensors and actuating motors with PLC input and output cards is very important to run the process smoothly without any faults and/or safety concerns. Most traditional electrical and computer engineering (ECE) programs offer high level of motion theory and controls but little hands-on exposure to PLCs which are the main industrial controllers. This paper provides a framework for …
Algorithm Optimization And Hardware Acceleration For Machine Learning Applications On Low-Energy Systems,
2022
Clemson University
Algorithm Optimization And Hardware Acceleration For Machine Learning Applications On Low-Energy Systems, Jianchi Sun
All Dissertations
Machine learning (ML) has been extensively employed for strategy optimization, decision making, data classification, etc. While ML shows great triumph in its application field, the increasing complexity of the learning models introduces neoteric challenges to the ML system designs. On the one hand, the applications of ML on resource-restricted terminals, like mobile computing and IoT devices, are prevented by the high computational complexity and memory requirement. On the other hand, the massive parameter quantity for the modern ML models appends extra demands on the system's I/O speed and memory size. This dissertation investigates feasible solutions for those challenges with software-hardware …
Snap : A Software-Defined & Named-Data Oriented Publish-Subscribe Framework For Emerging Wireless Application Systems,
2022
Clemson University
Snap : A Software-Defined & Named-Data Oriented Publish-Subscribe Framework For Emerging Wireless Application Systems, Manveen Kaur
All Dissertations
The evolution of Cyber-Physical Systems (CPSs) has given rise to an emergent class of CPSs defined by ad-hoc wireless connectivity, mobility, and resource constraints in computation, memory, communications, and battery power. These systems are expected to fulfill essential roles in critical infrastructure sectors. Vehicular Ad-Hoc Network (VANET) and a swarm of Unmanned Aerial Vehicles (UAV swarm) are examples of such systems. The significant utility of these systems, coupled with their economic viability, is a crucial indicator of their anticipated growth in the future. Typically, the tasks assigned to these systems have strict Quality-of-Service (QoS) requirements and require sensing, perception, and …
Comparative Study And Design Optimization Of A Dual-Mechanical-Port Electric Machine For Hybrid Electric Vehicle Applications,
2022
Chalmers University of Technology
Comparative Study And Design Optimization Of A Dual-Mechanical-Port Electric Machine For Hybrid Electric Vehicle Applications, Hao Chen, Ayman M. El-Refaie, Yuefei Zuo, Shun Cai, Libing Cao, Christopher H. T. Lee
Electrical and Computer Engineering Faculty Research and Publications
A new dual-mechanical-port (DMP) electric machine for hybrid electric vehicle applications, particularly in the power-split continuously variable transmission systems, is proposed in this paper. In order to comprehensively and quantitatively evaluate the pros and cons of the proposed machine, a comparative study of four DMP electric machines with different topologies is conducted. These four investigated DMP electric machines include a conventional DMP machine, a DMP machine with spoke-type permanent magnets, a DMP machine with reluctance rotor, and a DMP machine with open slots which is the proposed machine in this paper. Even though these four machines have similar topologies, they …
Evaluation Of Generative Models For Predicting Microstructure Geometries In Laser Powder Bed Fusion Additive Manufacturing,
2022
Old Dominion University
Evaluation Of Generative Models For Predicting Microstructure Geometries In Laser Powder Bed Fusion Additive Manufacturing, Andy Ramlatchan
Computer Science Theses & Dissertations
In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex …
Cyber Deception For Critical Infrastructure Resiliency,
2022
Old Dominion University
Cyber Deception For Critical Infrastructure Resiliency, Md Ali Reza Al Amin
Computational Modeling & Simulation Engineering Theses & Dissertations
The high connectivity of modern cyber networks and devices has brought many improvements to the functionality and efficiency of networked systems. Unfortunately, these benefits have come with many new entry points for attackers, making systems much more vulnerable to intrusions. Thus, it is critically important to protect cyber infrastructure against cyber attacks. The static nature of cyber infrastructure leads to adversaries performing reconnaissance activities and identifying potential threats. Threats related to software vulnerabilities can be mitigated upon discovering a vulnerability and-, developing and releasing a patch to remove the vulnerability. Unfortunately, the period between discovering a vulnerability and applying a …
Hyperspectral Image Analysis Of Food For Nutritional Intake,
2022
University of Nevada, Las Vegas
Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani
UNLV Theses, Dissertations, Professional Papers, and Capstones
The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers …
Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing,
2022
Old Dominion University
Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque
Electrical & Computer Engineering Theses & Dissertations
Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …
Feature Analysis Of Indus Valley And Dravidian Language Scripts With Similarity Matrices,
2022
University of Nebraska-Lincoln
Feature Analysis Of Indus Valley And Dravidian Language Scripts With Similarity Matrices, Sarat Sasank Barla, Sai Surya Sanjay Alamuru, Peter Revesz
School of Computing: Conference and Workshop Papers
This paper investigates the similarity between the Indus Valley script and the Kannada, Malayalam, Tamil, and Telugu scripts that are used to write Dravidian languages. The closeness of these scripts is determined by applying a feature analysis of each sign of these scripts and creating similarity matrices that describe the similarity of any pair of signs from two different scripts. The feature list that we use for the analysis of these Dravidian language-related scripts includes six new features beyond the thirteen features that were used for the study of Minoan Linear A and related scripts by Revesz. These new features …
