Engineering Commons^™

Rail-To-Rail Operational In Low-Power Reconfigurable Analog Circuitry, Jared Dale Baker

Graduate Theses, Dissertations, and Problem Reports

Analog signal processing (ASP) can be used to decrease energy consumption by several orders of magnitude over completely digital applications. Low-power field programmable analog arrays (FPAA) have been previously used by analog designers to decrease energy consumption. Combining ASP with an FPAA, energy consumption of these systems can be further reduced. For ASP to be most functional, it must achieve rail-to-rail operation to maintain a high dynamic range. This work strives to further reduce power consumption in reconfigurable analog circuitry by presenting a novel data converter that utilizes ASP and rail-to-rail operation. Rail-to-Rail operation is achieved in the data converter …

Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan

All Graduate Theses, Dissertations, and Other Capstone Projects

The detection of switching faults of power converters or the Circuit Under Test (CUT) is real-time important for safe and efficient usage. The CUT is a single-phase inverter. This thesis presents two unique methods that rely on backpropagation principles to solve classification problems with a two-layer network. These mathematical algorithms or proposed networks are able to diagnose single, double, triple, and multiple switching faults over different iterations representing range of frequencies. First, the fault detection and classification problems are formulated as neural network-based classification problems and the neural network design process is clearly described. Then, neural networks are trained over …

Developing A Uas-Deployable Methane Sensor Using Low-Cost Modular Open-Source Components, Gavin Demali

Williams Honors College, Honors Research Projects

This project aimed to develop a methane sensor for deployment on an unmanned aerial system (UAS), or drone, platform. This design is centered around low cost, commercially available modular hardware components and open source software libraries. Once successfully developed, this system was deployed at the Bath Nature Preserve in Bath Township, Summit County Ohio in order to detect any potential on site fugitive methane emissions in the vicinity of the oil and gas infrastructure present. The deliverables of this project (i.e. the data collected at BNP) will be given to the land managers there to better inform future management and …

Zips Racing Electric Can Communications, Andrew Jordan, Adam Long, Susanah Kowalewski, Rami Nehme

Williams Honors College, Honors Research Projects

The CAN protocol has been a standard of electronic communication networks of automotive vehicles since the early 2000s due to its robust reliability in harsh environments. For the 2020 competition year, the Zips Racing Electric design team will be building an entirely new, fully-electric vehicle with CAN communication implemented rather than communicating via pure analog signals. Hardware and software can be utilized to read analog electrical signals from a source, such as accelerator and brake sensors, and encode them into a digital message that meets the CAN 2.0B communication protocol standard. Likewise, software can be used to extract data from …

Smart Collar, Gretchen T. Woodling, Sean Moran, Justen Bischoff, Jacob Sindelar

Williams Honors College, Honors Research Projects

The Smart Collar is a universal pet tracker, designed to be small and exceedingly comfortable for any pet to wear. GPS technology is used to locate the device, allowing the user to track their pet, via a smart phone application. This application can be used to program the device, view maps of their pet’s location and history of travel. Operating primarily on Long Range Wide Area Network (LoRaWAN) for data transfer, the device consumes very little power, allowing for several days of run-time per charge of the battery. Boasting no monthly service fees, The Smart Collar provides pet owner’s an …

Kettlebell Ultra, Elissa Peters, Kathryn Wegman, Daniel Basch, Mason Pastorius

Williams Honors College, Honors Research Projects

This project will consist of an attachment to an average kettlebell that will track the number of repetitions that the user has performed. The device will send this data over Bluetooth to a smart phone application so the user can track their workout accurately.

Digital, Automated Reactive Target System, Nicholas Haas, Saipranay Vellala, Trandon Ware, Thomas Martin

Williams Honors College, Honors Research Projects

In this era, technology is woven into almost every facet of our leisure activities. Although technology has innovated hobbies ranging from chess to soccer, the art of shooting has been neglected. Unnecessary insufficiency such as bullet ricochets off of mechanical steel targets, ineffective progress tracking, and general inaccessibility to outdoor training facilities are all improvable areas of this sport. The Dynamic Automated Reactive Target (D.A.R.T) System aims to fill some of these gaps and help modernize recreational marksmanship. Modeling the system after a dueling tree will optimize the use of the system and allow for different training models to challenge …

Statistical Methods To Unravel Cortical Mechanism Of Perception And Response To Auditory Stimuli, Ladan Moheimanian

Legacy Theses & Dissertations (2009 - 2024)

Behavioral responses to auditory stimuli have a critical role in our daily activities. The perception of these stimuli and the generation of appropriate behavioral responses requires the interaction of thousands of neurons in the auditory-motor pathways in the brain. Despite their importance, still many neuroscientific questions about these interactions are remained to be answered. This may result from the limitations of brain recordings as well as statistical methods to analyze brain recordings. In this dissertation, I investigated underlying mechanisms that govern these neural interactions in the auditory-motor pathways using novel statistical techniques applied to the brain recordings from the surface …

Multi-Modal Natural Frequency Response Of Utility Transmission Tapered Wood Poles Under Various Soil Foundation Conditions: Natural Frequency Response Under Various Soil Conditions, Ramani Ayakannu, Zia Razzaq

Civil & Environmental Engineering Faculty Publications

Studied herein is the multi-modal natural frequency response of utility transmission tapered wood poles under various soil foundation conditions. Strong winds and hurricanes in various parts of the world have resulted in collapse of such utility poles and have resulted in the disruption of electrical distribution systems in addition to creating hazardous conditions for the public. To avoid the development of resonance under such dynamic loading, the multi-modal natural vibration of the utility poles first needs to be understood in the presence of practical soil foundation conditions. To capture the soil-structure interaction effects on the multi-modal frequencies, a SAP2000 dynamic …

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

In the above article [1], Figure 2 was incorrect. Unfortunately, we mixed the color label of "CONV $\to $ BN $\to $ ReLu" and "Unpooling" in the CNN structure section of Figure 2. The color label of "CONV $\to $ BN $\to $ ReLu" should be orange while the color label of "Unpooling" should be green. Also, the word "Decoder" is misspelled. That same figure with the same error is also used for the graphic abstract. The corrected figure is given here. None of the sections in the figure is modified. The only change is in the color label of …

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Utilizing Machine Learning For Respiratory Rate Detection Via Radar Sensor, Anwar Elhadad

Graduate College Dissertations and Theses

In this research, we investigate a data processing method to capture the respiratory rate of a person by utilizing a doppler radar to monitor their body movement during respiration. We utilize a machine learning algorithm with a radar sensor to capture the chest movement of a person while breathing and determine the respiratory rate according to that movement. We are using a Random Forest classifier to distinguish between different classes of pulses. After that, the algorithm constructs a sinusoidal signal representing the breathing rate of the sample. By applying this technique, we can detect the breathing rate accurately for different …

Software Defined Radio Based Frequency Modulated Continuous Wave Ground Penetrating Radar, Patrick Fiske

Graduate College Dissertations and Theses

Frequency modulated continuous wave (FMCW) radar allows for a wide range of research applications. One primary use of this technology and what is explored in this thesis, is imaging in the form of ground penetrating radar. To generate proper results, spectral wide-band reconstruction has been developed to overcome hardware limitations allowing for high resolution radar. Requiring complex reconstruction algorithms, the proposed method benefits greatly in terms of performance and implementation compared to other radar systems.

This thesis develops a wideband linearly frequency modulated radar leveraging a software-defined radio (SDR). The modular system is capable of a tunable wideband bandwidth up …

Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb

Electrical and Computer Engineering Faculty Publications

Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as …

Generation Of Large-Volume High-Pressure Plasma By Spatiotemporal Control Of Space Charge, Shirshak K. Dhali

Electrical & Computer Engineering Faculty Publications

Any attempt to scale pressure and volume of nonthermal plasma usually leads to instabilities due to the formation of localized space charge. The control of the plasma is limited by the discharge geometry, type of excitation, and gas composition. This article explores the possibility of controlling the space charge in a discharge with a spatially and temporally varying electric field. It is shown that a phase-staggered sinusoidal excitation to a set of conformal azimuthal electrodes in a cylindrical geometry leads to a traveling electric field. Simulations show that in space charge dominated transport, the charged species are dispersed both in …

Control Of Voltage-Source Converters Considering Virtual Inertia Dynamics, Tri Nguyen

Electronic Theses and Dissertations

Controlling power-electronic converters in power systems has significantly gained more attention due to the rapid penetration of alternative energy sources. This growth in the depth of penetration also poses a threat to the frequency stability of modern power systems. Photovoltaic and wind power systems utilizing power-electronic converters without physical rotating masses, unlike traditional power generations, provide low inertia, resulting in frequency instability. Different research has developed the control aspects of power-electronic converters, offering many control strategies for different operation modes and enhancing the inertia of converter-based systems. The precise control algorithm that can improve the inertial response of converter-based systems …

Hybrid Rocket Engine Ignition And Control, Benjamin Letourneau, Trevor Blampied, Megan Johnson, Thomas Pham

Honors Theses and Capstones

Control of a hybrid rocket engine is dependent upon a robust system capable of executing commands at precise times. In order to accomplish this, hardware systems must be in place to control the flow of a pressurized gas and provide feedback to launch site personnel. Through the use of solenoid valves and wireless transceivers, control over the thrust of a rocket can be accomplished. In order to understand this information and provide a user-friendly interface to complete this, a launch control module is used. Through the combined capabilities of the two system it becomes possible to test and launch a …

Phase-Locked Loop Control In Low-Inertia Grid-Connected Voltage-Source Converters, Ifechukwude Gideon Odogwu

Electronic Theses and Dissertations

As the integration of renewable energy on the grid increases, the number of voltage-source converters (VSC) installed also does. VSC controls both switch turn-on and turn-off, allowing a dc voltage source to be switched between phases. For the converter to accurately synchronize with the grid, a phase-locked loop (PLL) is used for the frequency measurements of the grid. However, the implementation of PLL with measurement delay introduces harmonics, noise, high frequency, and voltage oscillation to the system due to its dynamics. The dynamics introduced to the grid can be ignored under stiff grid conditions, but power from renewable sources decreases …

End-To-End Prediction Of Weld Penetration In Real Time Based On Deep Learning, Wenhua Jiao

Theses and Dissertations--Electrical and Computer Engineering

Welding is an important joining technique that has been automated/robotized. In automated/robotic welding applications, however, the parameters are preset and are not adaptively adjusted to overcome unpredicted disturbances, which cause these applications to not be able to meet the standards from welding/manufacturing industry in terms of quality, efficiency, and individuality. Combining information sensing and processing with traditional welding techniques is a significant step toward revolutionizing the welding industry. In practical welding, the weld penetration as measured by the back-side bead width is a critical factor when determining the integrity of the weld produced. However, the back-side bead width is difficult …

The Incidence Of Inverter Incidents: Understanding And Quantifying Contributions To Risk In Systems With Large Amounts Of Inverter-Based Resources, Caroline Rose Popiel

Graduate College Dissertations and Theses

Renewable energy is an important and growing percentage of the total power supply. Additionally, non-wires alternatives, which are meant to substitute for the construction of more transmission lines, are increasing in quantity as the demand for electrical power increases. Many non-wires alternatives take the form of renewable energy resources and batteries, and are distributed over short distances through neighborhoods and communities. Inverters are used to connect these DC resources to the AC grid.

However, there is growing industry concern that the disconnect function that is inherent to interconnection standards for inverter-based resources has the potential to result in a cascading …

Synthesis Of Graphene Using Plasma Etching And Atmospheric Pressure Annealing: Process And Sensor Development, Andrew Robert Graves

Graduate Theses, Dissertations, and Problem Reports

Having been theorized in 1947, it was not until 2004 that graphene was first isolated. In the years since its isolation, graphene has been the subject of intense, world-wide study due to its incredibly diverse array of useful properties. Even though many billions of dollars have been spent on its development, graphene has yet to break out of the laboratory and penetrate mainstream industrial applications markets. This is because graphene faces a ‘grand challenge.’ Simply put, there is currently no method of manufacturing high-quality graphene on the industrial scale. This grand challenge looms particularly large for electronic applications where the …

Interoperability Of Ip-Based Cameras, Faythe C. Maston

Graduate Theses, Dissertations, and Problem Reports

In this day and age of advancing technology and increasing crime, more and more citizens are investing in technology to increase their personal security. One such technology is the use of home-based IP security systems. These systems are comprised of one or more IP-based security cameras with owners preferring to have a way to view all camera feeds at once. Since not all security cameras are made to interact with each other, it is necessary to find a program that allows a user to view all security cameras at once, regardless of what brand of cameras they use. After researching, …

Reference Governors For Time-Varying Systems And Constraints, Collin Freiheit

Graduate College Dissertations and Theses

Control systems are often subject to constraints imposed by physical limitations or safety considerations, and require means of constraint management to ensure the stability and safety of the system. For real-time implementation, constraint management schemes must not carry a heavy computational burden; however many of the current solutions are computationally unattractive, especially those with robust formulations. Thus, the design of constraint management schemes with low computational loads is an important and practical problem for control engineers. Reference Governor (RG) is an efficient constraint management scheme that is attractive for real-time implementation due to its low computational complexity and ease of …

Reference Governors: From Theory To Practice, Joycer Osorio

Graduate College Dissertations and Theses

Control systems that are subject to constraints due to physical limitations, hardware

protection, or safety considerations have led to challenging control problems that have

piqued the interest of control practitioners and theoreticians for many decades. In

general, the design of constraint management schemes must meet several stringent

requirements, for example: low computational burden, performance, recovery mechanisms

from infeasibility conditions, robustness, and formulation simplicity. These

requirements have been particularly difficult to meet for the following three classes

of systems: stochastic systems, linear systems driven by unmodeled disturbances,

and nonlinear systems. Hence, in this work, we develop three constraint management

schemes, based …

Open Source Quantitative Stress Prediction Leveraging Wearable Sensing And Machine Learning Methods, Blake Hewgill

Graduate College Dissertations and Theses

The ability to monitor physiological parameters in an individual is paramount for the evaluation of physical health and the detection of many ailments. Wearable technologies are being introduced on a widening scale to address the absence of low-cost and non-invasive health monitoring as compared to medical grade equipment and technologies. By leveraging wearable technologies to supplement or replace traditional gold-standard measurement techniques, the research community can develop a deeper multifaceted understanding of the relationship between specific physiological parameters and particular health conditions. One particular research area in which wearable technologies are beginning to see application is the quantification of physical …