Electrical and Electronics Commons^™

Gpu Based Monte Carlo Estimation Of Eddy Current Losses In Electromagnetic Coil-Core System, Adwaith Ravichandran

Theses and Dissertations

A novel parallelizable probabilistic approach to model eddy currents in AC electromagnets is presented in this research. Consequently, power loss associated with the formation of these eddy currents is estimated and validated using experimental data. Furthermore, predicting the effect of ferromagnetic alternating field enhancement on power loss in the source excitation winding has been an active area of research. Unlike a stationary field, an alternating sinusoidal field diffuses partially into the ferromagnetic material leading to a predictably sub-optimal field enhancement. To model these physics, finite element techniques employ nonlinear iterative solvers which are time consuming. A novel method is developed …

Improving Cellphone-Based Bio-Imaging Technique For Fluorescence Detection, Erteza T. Efaz

LSU Master's Theses

The research presented in this thesis focuses on the design, development, and evaluation of a fluorescence detection system. The system is implemented primarily as an Android application, Auto Camera, which leverages smartphone cameras to capture and analyze fluorescent images. The application provides a user-friendly interface with some configurable features like exposure time, ISO speed, and storage limit; as well as defining detection thresholds and setting acquisition intervals. This study begins with the architectural framework of the Android application, which is written in Java using Android Studio. The API compatibility is set to version 33, and users are prompted to grant …

Mathematical Model Of An Industrial Robot Built On The Basis Of Linear Motion Mechatron Modules, Matyokubov Nurbek Rustamovich, Rakhimov Temurbek Omonboyevich

Chemical Technology, Control and Management

The use of Industry 4.0 technologies in modern production serves to increase production efficiency. Robots and robotic complexes with innovative intelligent control are widely used in industrial production. However, studying their mechanical part, linear motion mechatronic modules and control system is a complex object. The variety and complexity of control methods and operating modes negatively affects the operator's working time and production productivity. In such cases, it is necessary to improve control methods and develop technologies used in industry, including robots and robotic manipulators, which allow to perform short movements with high accuracy and speed. In the article, the mathematical …

Design Of Class F-Based Doherty Power Amplifier For S-Band Applications, Kyle Chang

Master's Theses

Modern RF and millimeter-wave communication links call for high-efficiency front end systems with high output power and high linearity to meet minimum transmission requirements. Advanced modulation techniques, such as orthogonal frequency-division multiplexing (OFDM) require a large power amplifier (PA) dynamic range due to the high peak-to-average power ratio (PAPR). This thesis provides the analysis, design, and experimental verification of a high-efficiency, high-linearity S-band Doherty power amplifier (DPA) based on the Class F PA. Traditional Class F PAs use harmonically tuned output matching networks to obtain up to 88.4% power-added efficiency (PAE) theoretically, however the amplifier experiences poor linearity performance due …

Characterization Of Low Power Hfo2 Based Switching Devices For In-Memory Computing, Aseel Zeinati

Theses

Oxide based Resistive Random Access Memory (RRAM) devices are investigated as one of the promising non-volatile memories to be used for in-memory computing that will replace the classical von Neumann architecture and reduce the power consumption. These applications required multilevel cell (MLC) characteristics that can be achieved in RRAM devices. One of the methods to achieve this analog switching behavior is by performing an optimized electrical pulse. The RRAM device structure is basically an insulator between two metals as metal-insulator-metal (MIM) structure. Where one of the primary challenges is to assign an RRAM stack with both low power consumption and …

Multiple Output Power Supply Using Toroidal Transformers For Medium Voltage Active Gate Drivers, Samhitha Venkata Machireddy

Graduate Theses and Dissertations

When operating in high power applications, power devices dissipate tens or hundreds of watts of power in the form of heat. The ability of the power devices to withstand power and dissipation of heat across the power devices becomes a prominent requirement in designing the power converter. This challenge demands a power converter design to be more effective and consistent which factors in size, cost, weight, power density and reliability. This study aims to propose a gate driver isolated power supply design that can be used in medium voltage applications (e.g., up to 10 kV) while respecting the principle of …

Effect On Focusing Fields By Ferromagnetic Cell Cores In Linear Induction Accelerators, Cooper Guillaume

Senior Honors Theses

In the Los Alamos National Laboratories DARHT facility, there are two perpendicular linear induction accelerators, LIAs. The LIAs’ solenoids produce magnetic fields which focus the electron beam. Simultaneously, the accelerating pulse creates a magnetic field. These two field intensities act upon a ferromagnetic material in the cells to enhance magnetic flux density. Due to the nonlinearity of the material, this flux density will reach a saturation point. In turn, the magnetic field intensity of the axial solenoidal magnetic field will be affected and slightly altered. The width of the electron beam will increase, causing a decrease in effectiveness. Through simulation, …

Evaluation Of The Dynamic Vision Sensor’S Photoreceptor Circuit For Infrared Event-Based Sensing, Zinah M. Alsaad

Electrical and Computer Engineering ETDs

For space surveillance applications, neuromorphic imaging is being studied as it may perform sensing and tracking tasks with less power and downstream datalink demand. The read-out of the event-based camera is made to only be sensitive to changes in the signals it receives from the photodetector, which results in a datastream of events indicating where and when changes in illumination occur. This is in contrast to the conventional framing camera, which produces images by essentially counting the electrons produced by light incident on each pixel’s photodetector. These cameras are commercially available with siliconbased detectors for applications involving visible wavelengths. However, …

Processes & Toolchain For Automation Of Hybrid Direct-Write 3d Printing, Gilbert Thomas Carranza

Open Access Theses & Dissertations

Hybrid 3D printing has evolved from a means to rapidly prototype devices to a fully viable means of manufacturing final products. In particular, electronic and electromagnetic devices have been a focus of hybrid 3D printing multi material structures. However, while the hardware capabilities have been around for years, the software capabilities have only begun to catch up. A process and toolchain for hybrid 3D printing is critically needed.This work details a process and toolchain for hybrid 3D printing metal dielectric structures. In it, a basic slicing algorithm is shown along with off-axis printing and conformal printing for arbitrary curvatures. These …

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 …

Design And Analysis Of A Discrete, Pcb-Level Low-Power, Microwave Cross-Coupled Differential Lc Voltage-Controlled Oscillator, Pavin Singh Virdee

Master's Theses

Radio Frequency (RF) and Microwave devices are typically implemented in Integrated Circuit (IC) form to minimize parasitics, increase precision and tolerances, and minimize size. Although IC fabrication for students and independent engineers is cost-prohibitive, an abundance of low-cost, easily accessible printed circuit board (PCB) and electronic component manufacturers allows affordable PCB fabrication.

While nearly all microwave voltage-controlled oscillator (VCO) designs are IC-based, this study presents a discrete PCB-level cross-coupled, differential LC VCO to demonstrate this more affordable and accessible approach. This thesis presents a 65 mW, discrete component VCO PCB with industry-comparable RF performance. A phase noise of -103.7 dBc/Hz …

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 …

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 …

X-Band Rf Transmitter Design For Multi-Purpose Small Satellite Communication Operations, Omer F. Gumus

Master's Theses

This thesis provides a description of the analysis, design, and tests of an X-band RF Transmitter communication system for small satellites. X-band transmitter systems are becoming popular in the upcoming deep space missions. Most of the deep-space ground stations have been using X-band frequencies to receive or transmit signals. The X-band (<10 GHz) can offer lower atmospheric losses and up to a couple of Mbps data rates for multiple satellite operations. Nowadays, many small satellites have been using frequency bands such as VHF, UHF, L, and S-band frequencies for communication. From deep space to the ground station, the low-frequency ranges are inadequate in providing Mbps level data rates and enough bandwidth for deep space missions.

The main focus of this thesis was the development of the subsystems such as gain block amplifier, Mixer, Bandpass Filter, and RF power amplifier. The subsystems were designed separately, then they were connected together to perform an end-to-end system test. One of the thesis aims is to design …

Second-Order Enhanced Coupling Strength Grating Out-Couplers For A Monolithic Laser-Electro-Absorption Modulator, Maryam Dezfuli

Electrical Engineering Theses and Dissertations

Conventional grating out-couplers in III-V waveguides typically require lengths of several hundreds of microns to outcouple 50 to 90% of the incident optical power. Enhanced coupling strength (ECS) gratings, which reduce the out-coupler grating length to tens of microns, have a large relative permittivity difference between the materials on either side of the grating boundary along with a high index cover layer. With appropriately chosen permittivity and layer thicknesses, the addition of a cover layer “pulls” the peak of the optical mode towards the grating region, resulting in a significant increase in the grating confinement factor. The resulting ECS out-coupler …

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 …

Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position, and velocity information using mechanization equations. In this work, we describe a novel deep-learning-based methodology, using Convolutional Neural Networks (CNN), to reduce errors from MEMS IMU sensors. We develop a CNN-based approach that can learn from the responses of a particular inertial sensor …

A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran

Electrical and Computer Engineering Faculty Publications

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep …

A Progressive Learning Strategy For Large-Scale Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, the worldwide temperature increase has resulted in rapid deglaciation and a higher risk of glacier-related natural hazards such as flooding and debris flow. Due to the severity of these hazards, continuous observation and detailed analysis of glacier fluctuations are crucial. Many such analyses require an accurately delineated glacier boundary. However, the complexity and heterogeneity of glaciers, particularly debris-covered glaciers (DCGs), poses a challenge for glacier mapping when using conventional remote sensing or machine-learning techniques. Some examples exist about small-scale automated glacier mapping, but large or regional-scale mapping is challenging. Previously, a deep-learning-based approach named GlacierNet2 had been …

Meltpondnet: A Swin Transformer U-Net For Detection Of Melt Ponds On Arctic Sea Ice, Ivan Sudakow, Vijayan K. Asari, Ruixu Liu, Denis Demchev

Electrical and Computer Engineering Faculty Publications

High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune, and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and …

Circuit Optimization Techniques For Efficient Ex-Situ Training Of Robust Memristor Based Liquid State Machine, Alex Henderson, Christopher Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan

Electrical and Computer Engineering Faculty Publications

Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several …

Smart Uv-C Disinfectant Module, Nicole Baldy, Luke Rogers, Haitham Saleh

Williams Honors College, Honors Research Projects

The Smart UV Disinfectant device shall sanitize objects which are 18”x14”x8” or smaller and less than 20 lbs. using UV-C light. This device should contain many safety measures to prevent human and animal exposure to the UV-C light and have no public touchpoints to operate the interface. In order to achieve the first objective, this device shall contain a "sanitizing chamber" which completely encloses the object to be sanitized to prevent outside exposure with detection of any lifeforms inside of the chamber; for the second objective, it will contain a wireless interface to an Android application which can be used …

Wave Engineering In Time Modulated, Nonlinear, And Anisotropic Metamaterials, Ahmed Mekawy

Dissertations and Theses

Leveraging wave matter interactions is central to a myriad of electromagnetic wave-based applications. During the past decades, research on extreme wave manipulation has been revolutionized by artificially engineered materials (metamaterials) and by adding new aspects to the wave-matter interactions that showed intriguing results inaccessible in conventional linear, time invariant (LTI), passive and isotropic media. In this work, I will explore, numerically and experimentally, the possibility of realizing devices that perform beyond or close to their fundamental LTI limitations by adding periodic modulation, nonlinearity, and gain. I will demonstrate these concepts at radio frequencies (RF) and at optical frequencies. Specifically, at …

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya

Electrical and Computer Engineering Faculty Publications

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …

Sigesn Light-Emitting Devices: From Optical To Electrical Injection, Yiyin Zhou

Graduate Theses and Dissertations

Si photonics is a fast-developing technology that impacts many applications such as data centers, 5G, Lidar, and biological/chemical sensing. One of the merits of Si photonics is to integrate electronic and photonic components on a single chip to form a complex functional system that features compact, low-cost, high-performance, and reliability. Among all building blocks, the monolithic integration of lasers on Si encountered substantial challenges. Si and Ge, conventional epitaxial material on Si, are incompetent for light emission due to the indirect bandgap. The current solution compromises the hybrid integration of III-V lasers, which requires growing on separate smaller size substrates …

Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli

Electrical and Computer Engineering Faculty Publications

Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR …

A Unified Framework Of Deep Learning-Based Facial Expression Recognition System For Diversified Applications, Sanoar Hossain, Saiyed Umer, Vijayan K. Asari, Ranjeet Kumar Rout

Electrical and Computer Engineering Faculty Publications

This work proposes a facial expression recognition system for a diversified field of appli- cations. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution …

The Effect Of A Ferrite-Core Relay Vs. An Air-Core Relay On The Output Power Characteristics Of A Three-Coil Wireless Power Transfer System, Jakob L. White

University Honors Theses

The purpose of this thesis is to determine the effect of using a ferrite-core relay on the output power characteristics of a three-coil, parallel-tuned, domino-resonator wireless power transfer (WPT) system in comparison to the effect of using an air-core relay in such a system. First, a general mathematical model is presented to describe both the ferrite-core-relay system and the air-core-relay system and to calculate their output power characteristics for seven different resistive loads at each of five different distance configurations between the coils. Next, experimental results are analyzed and compared to the mathematical results to confirm model accuracy. Finally, the …

Computational Modeling Of Black Phosphorus Terahertz Photoconductive Antennas Using Comsol Multiphysics With Experimental Comparison Against A Commercial Lt-Gaas Emitter, Jose Isaac Santos Batista

Graduate Theses and Dissertations

This thesis presents computational models of terahertz (THz) photoconductive antenna (PCA) emitter using COMSOL Multiphysics commercial package. A comparison of the computer simulated radiated THz signal against that of an experimentally measured signal of commercial reference LT-GaAs emitter is presented. The two-dimensional model (2D) aimed at calculating the photoconductivity of a black phosphorus (BP) PCA at two laser wavelengths of 780 nm and 1560 nm. The 2D model was applied to the BP PCA emitter and the LT-GaAs devices to compare their simulated performance in terms of the photocurrent and radiated THz signal pulse. The results showed better performance of …