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Partial Discharge Mitigation In Power Modules Using An Automation-Driven Design Rule Development Method, Shilpi Mukherjee

Graduate Theses and Dissertations

Power modules used for the conversion and conditioning of electrical power for applications like electric vehicles, more-electric aircraft, the power grid, etc., are largely designed manually by engineers. Design automation of power modules is starting to gain recognition as a timely and necessary alternative to intuitive manual design and fabrication. With increasing need for wide bandgap materials that can operate at higher voltages, and the need to make modules more compact, hazards like electrical breakdown are more likely. Partial discharge (PD) is a silent and invisible precursor to electrical breakdown. It is compounded with compaction, creating a potential for electrical …

A Real-Time Anpc Inverter Digital Twin With Integrated Design-For-Trust, Paulo Vitor Do Amaral Custodio

Graduate Theses and Dissertations

The demand for renewable energy has increased over the last few years, and so has the demand for greater expectations within the energy market. This increasing trend has been accompanied by more significant usage of internet-connected devices (IoT), leading to critical electrical infrastructure being connected to the internet. Implementing internet connectivity with such devices and systems provides benefits such as improving the system's performance, facilitating irregularity and anomaly mitigation, and providing additional situational awareness for enhanced decision-making. However, enhancing the connected system with IoT introduces a drawback – a greater vulnerability to cyber-attacks. Cyber-attacks targeting critical infrastructure in the electrical …

A Portable, Low Power Radiation Detection And Identification System For High Count Rate, Long Term Monitoring, Samuel J. Murray

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

This dissertation presents the design of a novel radiation detection and identification system that can operate continuously over a period of 8 days while detecting at 30,000 counts per second, consuming a total of 11 mW. The entire system is highly integrated, containing a gamma ray detector, a high voltage detector power supply, and a multichannel analyzer (MCA) system-on-a-chip (SoC), which are all combined into a compact form using a multi-level, configurable printed circuit board design. The MCA SoC, fabricated using a 65 nm CMOS technology, features two enabling resources to allow low power detections at high count rates for …

Electromagnetic Simulations Of The Lansce Chopper Structure, Yusuf Sahin

Electrical and Computer Engineering ETDs

Particle accelerators are important for basic research as well as in applications such as ion implantation and cancer therapy. One important accelerator is the Los Alamos Neutron Science Center (LANSCE) located at Los Alamos National Laboratory (LANL) that primarily produces neutrons for research. The beam chopper simulation described here is a study to provide better modeling of this subsystem of LANSCE. A chopper is a device in which particles are intermittently sliced and directed to a target and/or experiment. The simulations in this thesis are used to analyze the correct operating parameters of the chopper. A good chopper model can …

Speaker Diarization Of Noisy Classrooms From A Single Microphone Based On An Array Of Virtual Microphones And Machine Learning, Richard Briggs

Electrical and Computer Engineering ETDs

Speaker diarization from a single microphone is extremely challenging in noisy classroom environments. A new method based on simulating a microphone array has shown promising results, while requiring very little training. It used a minimum distance classifier to identify the speaker among a list of possible speakers. This thesis investigates machine learning methods for determining the speaker. The AOLME dataset that was used contains 758 samples totaling 894.4 seconds. Each is taken from a noisy classroom environment, focusing on five speakers - any one of whom could be active in a given sample - lasting an average of 1.2 seconds. …

Miniature, Submersible Electromagnetic Pumps Of Molten Lead And Sodium For Gen-Iv Nuclear Reactors Development, Ragai M. Altamimi

Nuclear Engineering ETDs

Heavy metals and alkali Liquid Metals are suitable coolants for Generation IV terrestrial nuclear reactors for operating at elevated temperatures for achieving plant thermal efficiency more than 40% and the thermochemical generation of hydrogen fuel. In addition, the low vapor pressure of these liquids eliminates the need for a pressure vessel and instead operates slightly below ambient pressure. A primary issue with the uses of these coolants is their compatibility with nuclear fuel, cladding and core structure materials at elevated temperatures more than 500oC. Therefore, in pile and out-of-pile test loops have been constructed or being considered for quantifying the …

Digital Twins And Artificial Intelligence For Applications In Electric Power Distribution Systems, Deborah George

All Theses

As modern electric power distribution systems (MEPDS) continue to grow in complexity, largely due to the ever-increasing penetration of Distributed Energy Resources (DERs), particularly solar photovoltaics (PVs) at the distribution level, there is a need to facilitate advanced operational and management tasks in the system driven by this complexity, especially in systems with high renewable penetration dependent on complex weather phenomena.

Digital twins (DTs), or virtual replicas of the system and its assets, enhanced with AI paradigms can add enormous value to tasks performed by regulators, distribution system operators and energy market analysts, thereby providing cognition to the system. DTs …

Tunable Linewidth Chip-Level Lasers Using Hybrid Integration Methods, Charles Porter

All Theses

Photonic integrated circuits (PICs) have significant value in today’s world of research. They enable devices to experience large reductions in size, weight, operation power, and cost (SWAPc), making photonic technology more accessible than ever. The functionality of PICs is greatly enhanced due to the realization of both active and passive devices within a single device structure. This is made possible through hybrid integration, which utilizes various coupling methods to transfer light from an active chip to a passive chip. Hybrid integration technology tremendously expands the capabilities of PICs, leading to a plethora of applications. One significant application is that of …

Spectral Broadening Effects On Pulsed-Source Digital Holography, Steven A. Owens, Mark F. Spencer, Glen P. Perram

Faculty Publications

Using a pulsed configuration, a digital-holographic system is setup in the off-axis image plane recording geometry, and spectral broadening via pseudo-random bit sequence is used to degrade the temporal coherence of the master-oscillator laser. The associated effects on the signal-to-noise ratio are then measured in terms of the ambiguity and coherence efficiencies. It is found that the ambiguity efficiency, which is a function of signal-reference pulse overlap, is not affected by the effects of spectral broadening. The coherence efficiency, on the other hand, is affected. As a result, the coherence efficiency, which is a function of effective fringe visibility, is …

Empowering 5g Mmwave: Leveraging Kml Placemarks For Enhanced Rf Design And Deployment Efficiency, Gustavo A. Fernandez

School of Mathematical & Statistical Sciences Faculty Publications

This publication explores the significance of Keyhole Markup Language (KML) in telecommunications, particularly in the context of 5G mmWave RF design and planning. With the advent of 5G mmWave technology, the demand for seamless and efficient network deployments has never been greater. The deployment of small cells and repeaters for 5G mmWave necessitates utmost precision in location accuracy and rapid information exchange during site surveys and evaluations. The challenges of mmWave frequencies, including their limited range and susceptibility to attenuation, intensify the complexity and criticality of this process. Network operators must ensure that the chosen location is devoid of obstacles …

Deep Learning For Multi-Structured Javanese Gamelan Note Generator, Arik Kurniawati, Eko Mulyanto Yuniarno, Yoyon Kusnendar Suprapto

Knowledge Engineering and Data Science

Javanese gamelan, a traditional Indonesian musical style, has several song structures called gendhing. Gendhing (songs) are written in conventional notation and require gamelan musicians to recognize patterns in the structure of each song. Usually, previous research on gendhing focuses on artistic and ethnomusicological perspectives, but this study is to explore the correlation between gendhing as traditional music in Indonesia and deep learning technology that replaces the task of gamelan composers. This research proposes CNN-LSTM to generate notation of ricikan struktural instruments as an accompaniment to Javanese gamelan music compositions based on balungan notation, rhythm, song structure, and gatra …

Band Alignments Of Metal/Oxides-Water Interfaces Using Ab Initio Molecular Dynamics, Yong-Bin Zhuang, Jun Cheng

Journal of Electrochemistry

Band alignments of electrode-water interfaces are of crucial importance for understanding electrochemical interfaces. In the scenario of electrocatalysis, applied potentials are equivalent to the Fermi levels of metals in the electrochemical cells; in the scenario of photo(electro)catalysis, semiconducting oxides under illumination have chemical reactivities toward redox reactions if the redox potentials of the reactions straddle the conduction band minimums (CBMs) or valence band maximums (VBMs) of the oxides. Computational band alignments allow us to obtain the Fermi level of metals, as well as the CBM and VBM of semiconducting oxides with respect to reference electrodes. In this tutorial, we describe …

Machine Learning-Based Drone And Aerial Threat Detection For Increased Turret Gunner Survivability, Nikolas Koutsoubis

Theses and Dissertations

The introduction of aerial drones on the modern battlefield has transformed combat operations, posing a significant threat to ground-based military operations. Detecting drones in safety scenarios is crucial. However, modern machine learning (ML)-based object detectors struggle to detect small objects like drones. This thesis presents three main contributions: (a) data and algorithmic modifications to improve small object detection in YOLO to aid in drone detection, (b) the development of a benchmark drone detection dataset called DyViR, and (c) the implementation of explainable artificial intelligence (XAI) to ensure transparent and trustworthy decision-making. To boost the performance of small object detection, we …

Design Of Asic Based Electrical Impedance Tomography Microendoscopic System For Prostate Cancer Surgical Marginal Assessment, Mohsen Shahghasemi

Dartmouth College Ph.D Dissertations

Prostate cancer is the second most common cancer in the United States. It is typically treated by surgically excising the cancerous section of the prostate. Because there is not always a visible distinction between the healthy and cancerous sections, surgery often leaves some cancerous tissue behind. This is referred to as a positive surgical margin and it requires adjuvant treatment with adverse side effects. Electrical impedance tomography (EIT) is a low-cost low-form-factor method that can be used to assess surgical marginal intraoperatively to ensure that no cancerous tissue is left behind. EIT-based surgical margin assessment works on the principle that …

Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive Iot Communication In Lora Networks, Jui Mhatre

Master of Science in Computer Science Theses

We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices have voracious energy demand. Hence there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access of channels, collision management is an important task. Moreover, in …

Adaptive Gps Antenna Array Beam Nulling Effectiveness Under Varying Antenna Element Positioning, Aadesh Neel

Electrical and Computer Engineering ETDs

Global Positioning System (GPS) is an essential part of modern life but is susceptible to same frequency jamming. GPS jamming can add excessive noise to a received low power signal and have the capability to change or completely distort information being sent through the GPS signal. Adaptive antenna arrays have long since been a solution to mitigating GPS jamming via beamnulling algorithms. However, there is little research on the effectiveness of these beamnulling algorithms under varying element positioning. In this work, an adaptive antenna array, consisting of Right-Hand Circularly Polarized (RHCP) nearly square GPS antenna elements, was constructed and tested …

A Machine Learning Approach For The Early Detection Of Bronchopulmonary Dysplasia (Bpd) In Preterm Infants Using Inter Hypoxemia Intervals, Ratri Mukherjee

Electrical Engineering Theses

Preterm birth is a significant global public health concern, affecting millions of babies yearly. Despite advancements in medical care that have improved the survival rates of preterm infants, preterm birth remains a leading cause of neonatal morbidity and mortality worldwide. It has both short-term and long-term health consequences that can profoundly impact the child's growth and development, as well as their family and society.

One of the challenges preterm infants face is their underdeveloped immune system, which makes them more vulnerable to infections and other health problems. Their delicate condition requires specialized care, often provided in a Neonatal Intensive Care …

A Natural Organic Artificial Synaptic Device Made From A Honey And Carbon Nanotube Admixture For Neuromorphic Computing, Md Mehedi Hasan Tanim, Zoe Templin, Kaleb Hood, Jun Jiao, Feng Zhao

Electrical and Computer Engineering Faculty Research & Creative Works

Artificial synaptic devices are the essential hardware component in emerging neuromorphic computing systems by mimicking biological synapse and brain functions. When made from natural organic materials such as protein and carbohydrate, they have potential to improve sustainability and reduce electronic waste by enabling environmentally friendly disposal. In this paper, a new natural organic memristor based artificial synaptic device is reported with the memristive film processed by a honey and carbon nanotube (CNT) admixture, that is, honey-CNT memristor. Optical microscopy, scanning electron microscopy, and micro-Raman spectroscopy are employed to analyze the morphology and chemical structure of the honey-CNT film. The device …

A Unified Switched Nonlinear Dynamic Model Of An Electric Vehicle For Performance Evaluation, Dibyendu Khan, Kuntal Mandal, Anjan Kumar Ray, Abdelali El Aroudi

Department of Electrical and Computer Engineering: Faculty Publications

The advanced modeling and estimation of overall system dynamics play a vital role in electric vehicles (EVs), as the selection of components in the powertrain and prediction of performance are the key market qualifiers. The state-space averaged model and small-signal transfer function model are useful for assessing long-term behavior in system-level analysis and for designing the controller parameters, respectively. Both models take less computation time but ignore the high-frequency switching dynamics. Therefore, these two models could be impractical for the development and testing of EV prototypes. On the other hand, the multi-domain model in available simulation tools gives in-depth information …

Is There A “Nationwide System” In Field Of Science And Technology In Japan?—Survey Of Semiconductor Technology, Huimin Li, Rongping Mu, Yue Hao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Different scholars interpret the connotations and characteristics of the “Nationwide System” in quite different ways and fail to reach a consensus about whether some measures that Japan implements to tackle key problems are attributed to a “Nationwide System”. On this basis, the work started with the connotations and characteristics of the “Nationwide System” in the field of science and technology, conducted a case study of measures taken by Japan to tackle key technical problems in the semiconductor technology, and analyzed whether these measures embody relevant characteristics of the “Nationwide System” from an empirical perspective, so as to demonstrate whether there …

Systematic Characterization Of Power Side Channel Attacks For Residual And Added Vulnerabilities, Aurelien Tchoupou Mozipo

Dissertations and Theses

Power Side Channel Attacks have continued to be a major threat to cryptographic devices. Hence, it will be useful for designers of cryptographic systems to systematically identify which type of power Side Channel Attacks their designs remain vulnerable to after implementation. It’s also useful to determine which additional vulnerabilities they have exposed their devices to, after the implementation of a countermeasure or a feature. The goal of this research is to develop a characterization of power side channel attacks on different encryption algorithms' implementations to create metrics and methods to evaluate their residual vulnerabilities and added vulnerabilities. This research studies …

Field Studies Of Perc And Al-Bsf Pv Module Performance Loss Using Power And I-V Timeseries, Alan J. Curran, Xuanji Yu, Jiqi Liu, Laura S. Bruckman, Roger H. French

Faculty Scholarship

We have studied the degradation of both full-sized modules and minimodules with PERC and Al-BSF cell variations in fields while considering packaging strategies. We demonstrate the implementations of data-driven tools to analyze large numbers of modules and volumes of timeseries data to obtain the performance loss and degradation pathways. This data analysis pipeline enables quantitative comparison and ranking of module variations, as well as mapping and deeper understanding of degradation mechanisms. The best performing module is a half-cell PERC, which shows a performance loss rate (PLR) of −0.27 ± 0.12% per annum (%/a) after initial losses have stabilized. Minimodule studies …

Ecg Recordings As Predictors Of Very Early Autism Likelihood: A Machine Learning Approach, Deepa Tilwani, Jessica Bradshaw, Amit Sheth, Christian O'Reilly

Publications

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker …

Generation Of Vector Vortex Wave Modes In Cylindrical Waveguides, Md Khadimul Islam, Pawan Gaire, Arjuna Madanayake, Shubhendu Bhardwaj

Department of Electrical and Computer Engineering: Faculty Publications

In this paper, we propose a method to generate Vector Vortex Modes (VVM) inside a metallic cylindrical waveguide at microwave frequencies and demonstrate the experimental validation of the concept. Vector vortex modes of EM waves can carry both spin and orbital angular momentum as they propagate within a tubular medium. The existence of such waves in tubular media can be beneficial to wireless communication in such structures. These waves can carry different orbital angular momentum and spin angular momentum, and therefore, they feature the ability to carry multiple orthogonal modes at the same frequency due to spatial structure of the …

Optimizing High-Performance Computing Design: The Impacts Of Bandwidth And Topology Across Workloads For Distributed Shared Memory Systems, Jonathan A. Milton

Electrical and Computer Engineering ETDs

With the complexity of high-performance computing designs continuously increasing, the importance of evaluating with simulation also grows. One of the key design aspects is the network architecture; topology and bandwidth greatly influence the overall performance and should be optimized. This work uses simulations written to run in the Structural Simulation Toolkit software framework to evaluate a variety of architecture configurations, identify the optimal design point based on expected workload, and evaluate the changes with increased scale. The results show that advanced topologies outperform legacy architectures justifying the additional design complexity; and that after a certain point increasing the bandwidth provides …

Data-Driven Porosity Prediction For Directed Energy Deposition, Georgia E. Kaufman

Electrical and Computer Engineering ETDs

Stochastic flaw formation leading to poor print quality is a major obstacle to the utility of directed energy deposition (DED), a laser and metal powder-based additive manufacturing technology for construction and repair of custom metal parts. While melt pool temperature variability is known to be a major factor in flaw formation, control schemes to decrease flaw formation are limited by a lack of physics-based models that fully and accurately describe DED. In this work, a stochastic reachability analysis with a data-driven model based on thermal images of the melt pool was conducted to determine the likelihood of violating melt pool …

Well-Conditioned T-Matrix Formulation For Scattering By A Dielectric Obstacle, Murat Enes Hati̇poğlu, Fati̇h Di̇kmen

Turkish Journal of Electrical Engineering and Computer Sciences

The classic formulation of the extended boundary condition method is revisited to inject the regularization operators for the unknown coefficients of the eigen-function expansions for the travelling and standing waves throughout the dielectric scatterer. It is shown that, using the new definitions, the existing algorithm of the scattering field calculation can be kept the same for its well-conditioned version. This is exemplified for scalar 2D problems for both TM and TE polarization under illumination of a line source. The condition numbers of the matrix operators in the new version of the algorithm are drastically reduced when the regularization interfaces are …

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …

A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç

Turkish Journal of Electrical Engineering and Computer Sciences

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …

Improving Unet Segmentation Performance Using An Ensemble Model In Images Containing Railway Lines, Mehmet Sevi̇, İlhan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to make sense of the autonomous system and the railway environment for railway vehicles. For this purpose, by determining the railway line, information about the general condition of the line can be obtained along the way. In addition, objects such as pedestrian crossings, people, cars, and traffic signs on the line will be extracted. The rails and the rail environment in the images will be segmented with a semantic segmentation network. In order to ensure the safety of rail transport, computer vision, and deep learning-based methods are increasingly used to inspect railway tracks and surrounding objects. In …