Other Electrical and Computer Engineering Commons^™

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% …

Evaluation Of Lidar Uncertainty And Applications Towards Slam In Off-Road Environments, Zachary D. Jeffries

Dissertations, Master's Theses and Master's Reports

Safe and robust operation of autonomous ground vehicles in all types of conditions and environment necessitates complex perception systems and unique, innovative solutions. This work addresses automotive lidar and maximizing the performance of a simultaneous localization and mapping stack. An exploratory experiment and an open benchmarking experiment are both presented. Additionally, a popular SLAM application is extended to use the type of information gained from lidar characterization, demonstrating the performance gains and necessity to tightly couple perception software and sensor hardware. The first exploratory experiment collects data from child-sized, low-reflectance targets over a range from 15 m to 35 m. …

Comparing Importance Of Knowledge And Professional Skill Areas For Engineering Programming Utilizing A Two Group Delphi Survey, John F. Hutton

Theses and Dissertations

All engineering careers require some level of programming proficiency. However, beginning programming classes are challenging for many students. Difficulties have been well-documented and contribute to high drop-out rates which prevent students from pursuing engineering. While many approaches have been tried to improve the performance of students and reduce the dropout rate, continued work is needed. This research seeks to re-examine what items are critical for programming education and how those might inform what is taught in introductory programming classes (CS1). Following trends coming from accreditation and academic boards on the importance of professional skills, we desire to rank knowledge and …

A Robust Platform For Mobile Robotics Teaching And Developing Using Arduino’S Integrated Development Environment (Ide) For Programming The Arduino Mega 2560, Sajjad Alhassan

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

In light of the rapid pace at which development happens with modern technology, mobile robots play an important role in our daily lives. This is due to their great importance in facilitating the affairs of life in various economic, commercial, industrial, scientific, and many other fields. In this research and project, we have restructured the microcontroller and system for one of the mobile robots (CEENBOT) that was designed by the University of Nebraska and replaced it with an Arduino Mega 2560.

The purpose of using the Arduino Mega 2560 robot is to provide alternative programming for the CEENBOT platform to …

A Stacking-Based Misbehavior Detection System In Vehicular Communication Networks, Troy Green

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

Over the past few decades communication systems for vehicles have continued to advance. Communications between these vehicles can be classified into safety related and non safety related messages. An example of a safety related message would be one vehicle warning others of an icy road it encountered, where a non safety related communication would be a passenger streaming a movie. In either case it's important to secure the communications so that the system continues to behave as expected. In this thesis we propose a Misbehavior Detection System (MDS), which is a system that monitors messages sent between vehicles, and detects …

Low Power Multi-Channel Interface For Charge Based Tactile Sensors, Samuel Hansen

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

Analog front end electronics are designed in 65 nm CMOS technology to process charge pulses arriving from a tactile sensor array. This is accomplished through the use of charge sensitive amplifiers and discrete time filters with tunable clock signals located in each of the analog front ends. Sensors were emulated using Gaussian pulses during simulation. The digital side of the system uses SAR (successive approximation register) ADCs for sampling of the processed sensor signals.

Adviser: Sina Balkır

A Low-Power, Low-Area 10-Bit Sar Adc With Length-Based Capacitive Dac, Zhili Pan

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

A 2.5 V single-ended 10-bit successive-approximation-register analog-to-digital converter (SAR ADC) based on the TSMC 65 nm CMOS process is designed with the goal of achieving low power consumption (33.63 pJ/sample) and small area (2874 µm^2 ). It utilizes a novel length-based capacitive digital-to-analog converter (CDAC) layout to achieve low total capacitance for power efficiency, and a custom static asynchronous logic to free the dependence on a high-frequency external clock source. Two test chips have been designed and the problems found through testing the first chip are analyzed. Multiple improved versions of the ADC with minor variations are implemented on the …

Dual-Band Circularly Polarized Shared Aperture Antenna For Cubesat Communication System, Syed Salman Kabir

Graduate Theses and Dissertations (2019 - present)

For space exploration, a special class of small satellite known as CubeSat, has become popular in academia and industry in recent years. Due to its low cost and short development time, researchers prefer CubeSat over large-scale satellites wherever possible. In academia, CubeSat has major applications in the field of space atmosphere, space-to-ground communication, weather forecasting, etc. Like other spacecrafts, CubeSat uses multiple antennas in different frequency bands to establish a communication link with ground stations. Recent CubeSat missions are generating a large amount of data which cannot be downlinked using conventional antennas. The demand for a compact antenna system that …

A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on …

Compilation Optimizations To Enhance Resilience Of Big Data Programs And Quantum Processors, Travis D. Lecompte

LSU Doctoral Dissertations

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), …

Evaluating Serverless Computing, Charitra Maharjan

LSU Master's Theses

Function as a Service (FaaS) is gaining admiration because of its way of deploying the computations to serverless backends in the different clouds. It transfers the complexity of provisioning and allocating the necessary resources for an application to the cloud providers. The cloud providers also give an illusion of always availability of resources to the users. Among the cloud providers, AWS serverless platform offers a new paradigm for developing cloud applications without worrying about the underlying hardware infrastructure. It manages not only the resource provisioning and scaling of an application but also provides an opportunity to reimagine the cloud infrastructure …

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 …

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 …

Femtosecond Laser Surface Processing To Create Self-Organized Micro- And Nano-Scale Features On Composite And Ceramic Materials, Nate Koeppe

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

Femtosecond laser surface processing (FLSP) is applied to a range of materials in this thesis. The materials studied were a carbon fiber reinforced polymer (CFRP), a thermosetting polymer, silicon nitride (Si₃N₄), and ceramic alumina. The CFRP is a composite material consisting of a thermosetting polymer and carbon fibers. The CFRP are referred to as a composite and the thermosetting polymer is referred to as a resin in this thesis. Alumina can exist in many different forms. The alumina used is 0.5 mm thick nonporous alumina sheets purchased from McMaster-Carr, and will be referred to as alumina …

A Novel Testbed For Evaluation Of Operational Technology Communications Protocols And Their On-Device Implementations, Matthew Boeding

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

Operational Technology (OT) and Infrastructure Technology (IT) systems are converging with the rapid addition of centralized remote management in OT systems. Previously air-gapped systems are now interconnected through the internet with application-specific protocols. This has led to systems that had limited access points being remotely accessible. In different OT sectors, legacy protocols previously transmitted over serial communication were updated to allow internet communication with legacy devices. New protocols such as IEC-61850 were also introduced for monitoring of different OT resources. The IEC-61850 standard’s Generic Object Oriented Substation Event (GOOSE) protocol outlines the representation and communication of a variety of different …

Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors And Machine Learning Algorithms, Rahul Soangra, Yuxin Wen, Hualin Yang, Marybeth Grant-Beuttler

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe …

One-Bit Algorithm Considerations For Sparse Pmcw Radar, Ethan Triplett

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

Phase Modulated Continuous Wave (PMCW) radar an emerging technology for autonomous cars. It is more flexible than the current frequency modulated systems, offering better detection resolution, interference mitigation, and future development opportunities. The issue preventing PMCW adoption is the need for high sample-rate analog to digital converters (ADCs). Due to device limits, a large increase in cost and power consumption occurs for every added resolution bit for a given sampling rate. This thesis explores radar detection techniques for few-bit and 1-bit ADC measurements. 1-bit quantization typically results in poor amplitude estimation, which can limit detections if the target signals are …

Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay

Electrical and Computer Engineering Faculty Publications

Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study, we proposed a novel and computationally efficient deep learning approach to leverage small data for learning generalizable and domain invariant representations in different medical imaging applications such as malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets …

A Nano-Drone Safety Architecture, Connor J. Sexton

Master's Theses

As small-form factor drones grow more intelligent, they increasingly require more sophisticated capabilities to record sensor data and system state, ensuring safe and improved operation. Already regulations for black boxes, electronic data recorders (EDRs), for determining liabilities and improving the safety of large-form factor autonomous vehicles are becoming established. Conventional techniques use hardened memory storage units that conserve all sensor (visual) and system operational state; and N-way redundant models for detecting uncertainty in system operation. For small-form factor drones, which are highly limited by weight, power, and computational resources, these techniques become increasingly prohibitive. In this paper, we propose a …

Mars Prototype Rover Environmental Measurement System, James A. Renick

Computer Engineering

In my senior project, the problem I am trying to solve is how to efficiently design, create, and install an original library onto a Mars prototype rover operating system and to further use that library to integrate a new weather measurement sensor device into the rover system with the necessary software and hardware implementations. This is an important and highly valued problem as many aerospace and other engineering companies utilize rovers and other autonomous systems for important research, explorations, and reconnaissance missions and goals. In solving this problem, I utilized many resources that were available to me such as advisors, …

Smartphone Control Of Rc Cars, Weston R. Fitzgerald

Electrical Engineering

The smartphone-controlled RC (remote-controlled) car is an inexpensive remote-controlled car designed to be fast and portable. Instead of manufacturing, packaging, and shipping a separate controller, the remote control is implemented in a phone application, which saves time and money in both the design process and the manufacturing process. Utilizing the user’s smartphone is more cost-effective since mobile devices are a common recurrence, and packaging fewer devices results in overall better portability of the product.

This smartphone-controlled car is speedy and intuitive to learn for typical smartphone users. The user can change the car’s speed and direction wirelessly using their phone; …

Project Metamorphosis: Designing A Dynamic Framework For Converting Musical Compositions Into Paintings, Rao Hamza Ali, Grace Fong, Erik Linstead

Engineering Faculty Articles and Research

The authors present an automated, rule-based system for converting piano compositions into paintings. Using a color-note association scale presented by Edward Maryon in 1919, which correlates 12-tone scale with 12 hues of the color circle, the authors present a simple approach for extracting colors associated with each note played in a piano composition. The authors also describe the color extraction and art generation process in detail, as well as the process for creating “moving art,” which imitates the progression of a musical piece in real time. They share and discuss artworks generated for four well-known piano compositions.

Chapter 27: Bloch Sphere And Single-Qubit Arbitrary Unitary Gate, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Chapter 28: Quantum Phase Estimation, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Chapter 3: Basis, Basis Vectors, And Inner Product, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Identification Of Orthologous Gene Groups Using Machine Learning, Dillon Burgess

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

Identification of genes that show similarity between different organisms, a.k.a orthologous genes, is an open problem in computational biology. The purpose of this thesis is to create an algorithm to group orthologous genes using machine learning. Following an optimization step to find the best characterization based on training data, we represented sequences of genes or proteins with kmer vectors. These kmer vectors were then clustered into orthologous groups using hierarchical clustering. We optimized the clustering phase with the same training data for the method and parameter selection. Our results indicated that use of protein sequences with k=2 and scaling the …

Cognality Vr: Exploring A Mobile Vr App With Multiple Stakeholders To Reduce Meltdowns In Autistic Children, Louanne E. Boyd, Espen Garner, Ian Kim, Gianna Valencia

Engineering Faculty Articles and Research

Many autistic children can have difficulty communicating, understanding others, and interacting with new and unfamiliar environments. At times they may suffer from a meltdown. The major contributing factor to meltdowns is sensory overwhelm. Technological solutions have shown promise in improving the quality of life for autistic children-however little exists to manage meltdowns. In this work with stakeholders, we design and deploy a low cost, mobile VR application to provide relief during sensory discomfort. Through the analysis of surveys from 88 stakeholders from a variety of groups (i.e., autistic adults, children with autism, parents of autistic individuals, and medical practitioners), we …

Combat Robot, Wayne Lambert, Elijah Harris, Brian Eiseman, Jordan Meyer

ONU Student Research Colloquium

The senior capstone project that was tasked to the team was the decision of choosing a challenge within a national robotics competition. The group decided to compete at the National Robotics Challenge in Marion, Ohio. The idea was to participate in the combat robot competition at this NRC event. Once this decision had been made the next steps were to get an idea of what the rules and requirements of the competition were and to try and to sketch a very rough drawing of what the ideal robot should look like. From there it was decided to start a timeline …

Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha

Electrical and Computer Engineering Faculty Publications

Background Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI). Methods In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and …