Physics Commons^™

Developing Positive Thermal Coefficient (Ptc) Heaters For Solar Electric Cooking, Katarina Ivana Brekalo, Andrew Shepherd

Physics

Positive Thermal Coefficients, PTCs, are materials that abruptly change in resistance in response to changes in temperature. The purpose of this experiment is to explore the viability of using the switching type ceramic PTC thermistor as a replacement for current resistive heaters. These types of PTCs have a nonlinear change in resistance with increases in temperature. This device will be used as a temperature-controlling heating element intended to power an Insulated Solar Electric Cooker (ISEC). The ISEC is designed to cook meals throughout the day for impacted communities as an alternative cooking method that doesn’t require biofuel as an energy …

Low-Cost Compact Optical Spectroscopy And Novel Spectroscopic Algorithm For Point-Of-Care Real-Time Monitoring Of Nanoparticle Delivery In Biological Tissue Models, Zahid Hasan, Jing Yan, Zhongchao Yi, Madison O. Korfhage, Sheng Tong, Caigang Zhu

Markey Cancer Center Faculty Publications

Objective: Real-time monitoring of nanoparticle delivery in biological models is essential to optimize nanoparticle- mediated therapies. However, few techniques are available for convenient real-time monitoring of nanoparticle concentrations in tissue samples. This work reported novel optical spectroscopic approaches for low-cost point-of-care real-time quantification of nanoparticle concentrations in biological tissue samples.

Methods: Fiber probe measured diffuse reflectance can be described with a simple analytical model by introducing an explicit dependence on the reduced scattering coefficient. Relying on this, the changes on the inverse of diffuse reflectance are proportional to absorption change when the scattering perturbation is negligible. We developed this model …

An Inexpensive Maximum Power Point Tracking System For An Insulated Solar Electric Cooker Using An Off-The-Shelf Buck Converter, Andrew A. Perez

Physics

A specialized control circuit using an off-the-shelf buck converter is built for an Insulated Solar Electric Cooker (ISEC). Cost and efficient power delivery are the focus. An ISEC is synonymous to a direct load heat resistor, allowing a specific maximum power point tracking (MPPT) algorithm and fewer components. Only a microcontroller, voltage sensor, and digital-to-analog converter are used with the buck converter to maximize the power delivered by a 100W solar panel for the 3.3Ω load.

Performance Analysis Of The Dominant Mode Rejection Beamformer, Enlong Hu

Dissertations

In array signal processing over challenging environments, due to the non-stationarity nature of data, it is difficult to obtain enough number of data snapshots to construct an adaptive beamformer (ABF) for detecting weak signal embedded in strong interferences. One type of adaptive method targeting for such applications is the dominant mode rejection (DMR) method, which uses a reshaped eigen-decomposition of sample covariance matrix (SCM) to define a subspace containing the dominant interferers to be rejected, thereby allowing it to detect weak signal in the presence of strong interferences. The DMR weight vector takes a form similar to the adaptive minimum …

Falcon Neuro: An Event-Based Sensor On The International Space Station, Matthew G. Mcharg, Richard L. Balthazor, Matthew P. Borg, Brian J. Mcreynolds, David H. Howe, Colin J. Maloney, Daniel O'Keefe, Rayomand Bam, Gabriel Wilson, Paras Karki, Alexandre Marcireau, Gregory Cohen

Student Publications

We report on the Falcon neuro event-based sensor (EBS) instrument that is designed to acquire data from lightning and sprite phenomena and is currently operating on the International Space Station. The instrument consists of two independent, identical EBS cameras pointing in two fixed directions, toward the nominal forward direction of flight and toward the nominal Nadir direction. The payload employs stock DAVIS 240C focal plane arrays along with custom-built control and readout electronics to remotely interface with the cameras. To predict the sensor’s ability to effectively record sprites and lightning, we explore temporal response characteristics of the DAVIS 240C and …

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 …

Characterization Of Electrophoretic Deposited Zinc Oxide Nanopartices For The Fabrication Of Next-Generation Nanoscale Electronic Applications, Fawwaz Abduh A. Hazzazi

LSU Doctoral Dissertations

Several reports state that it is crucial to analyze nanoscale semiconductor materials and devices with potential benefits to meet the need for next-generation nanoelectronics, bio, and nanosensors. The progress in the electronics field is as significant now, with modern technology constantly evolving and a greater focus on more efficient robust optoelectronic applications. This dissertation focuses on the study and examination of the practicality of Electrophoretic Deposition (EPD) of zinc oxide (ZnO) nanoparticles (NPs) for use in semiconductor applications.

The feasibility of several synthesized electrolytes, with and without surfactants and APTES surface functionalization, is discussed. The primary objective of this study …

Design And Commissioning Of An E-Beam Irradiation Beamline At The Upgraded Injector Test Facility At Jefferson Lab, Xi Li, Helmut Baumgart, Charles Bott, Gianluigi Ciovati, Shaun Gregory, Fay Hannon, Mike Mccaughan, Robert Pearce, Matthew Poelker, Hannes Vennekate, Shaoheng Wang

Electrical & Computer Engineering Faculty Publications

The Upgraded Injector Test Facility (UITF) at Jefferson Lab is a continuous-wave superconducting linear accelerator capable of providing an electron beam with energy up to 10 MeV. A beamline for electron-beam irradiation has been designed, installed and successfully commissioned at this facility, aimed at the degradation study of 1,4-dioxane and per- and polyfluoroalkyl substances (PFAS) in wastewater treatment. A solenoid with a peak axial magnetic field of up to 0.28 T and a set of raster coils were used to obtain a Gaussian beam profile with a transverse standard deviation of ∼15.0 mm at the target location. Monte-Carlo simulations using …

Noncontact Liquid Crystalline Broadband Optoacoustic Sensors, Hengky Chandrahalim, Michael T. Dela Cruz

AFIT Patents

An optoacoustic sensor includes a liquid crystal (LC) cell formed between top and bottom plates of transparent material. A transverse grating formed across the LC cell that forms an optical transmission bandgap. A CL is aligned to form a spring-like, tunable Bragg grating that is naturally responsive to external agitations providing a spectral transition regime, or edge, in the optical transmission bandgap of the transverse grating that respond to broadband acoustic waves. The optoacoustic sensor includes a narrowband light source that is oriented to transmit light through the top plate, the LC cell, and the bottom plate. The optoacoustic sensor …

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 …

Hinged Temperature-Immune Self-Referencing Fabry–Pérot Cavity Sensors, Jeremiah C. Williams, Hengky Chandrahalim

AFIT Patents

A passive microscopic Fabry-Pérot Interferometer (FPI) sensor includes a three-dimensional microscopic optical structure formed on a cleaved tip of the optical fighter using a two-photon polymerization process on a photosensitive polymer by a three-dimensional micromachining device. The three-dimensional microscopic optical structure having a hinged optical layer pivotally connected to a distal portion of a suspended structure. A reflective layer is deposited on a mirror surface of the hinged optical layer while in an open position. The hinged optical layer is subsequently positioned in the closed position to align the mirror surface to at least partially reflect a light signal back …

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 …

The Surface Conditions Of Spacecraft Panels May Significantly Affect Spacecraft Survivability, Trace Taylor

Research on Capitol Hill

USU junior Trace grew up in Brigham City and studies physics and electrical engineering. The majority of spacecraft failure is caused by electron charging on the outer surfaces of the craft. Additionally, contaminants on the craft can cause a film over surface panels, increasing the problem. Trace is studying how roughness on panels can mitigate this contamination as it affects the charging that can lead to craft failure. This research will help determine what optimal panel materials should be used in future spacecraft construction. Trace started research almost as soon as he came to campus in his freshman year, and …

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 …

Development Of A Fluxgate Magnetometer Model, Eleonora Olsmats

Honors Theses and Capstones

As a part of the UNH SWFO-L1 mission to monitor space weather and the sun’s behavior, the fluxgate magnetometer is an important component to measure external magnetic fields. The basic principle of a fluxgate magnetometer is to detect changes in the ambient magnetic field by inducing a magnetic field in a ferromagnetic material via a drive winding. Each magnetometer is unique due to the ferromagnetic properties of the core material which can be seen in the hysteresis loop which is a relationship between the magnetic field strength (H) and the induced magnetic field (B). Measuring the hysteresis of a fluxgate …

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 …

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 …

Interplay Between The Lattice And Spin Degrees Of Freedom In Magnetoelectric And Magnetic Materials, Temuujin Bayaraa

Graduate Theses and Dissertations

This dissertation contains several investigations on the cross-coupling between structural and spin degrees of freedom in multiferroic and ferrimagnetic compounds by means of first-principles calculations and ab-initio-based Monte-Carlo simulations. We start with the reviews of magnetoelectricity, ferrimagnetism, strain engineering, followed by a brief introduction to first-principles computational methods, magnetic effective Hamiltonians, and other techniques that are utilized here. The results section of the dissertation can be divided into two parts. The first half focuses on magnetoelectric effects arising from different sources, while the second half is about the ferrimagnetic nature of materials. In the first part, we examine the epitaxial …

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 …

Colloidal Quantum Dot (Cqd) Based Mid-Wavelength Infrared Optoelectronics, Shihab Bin Hafiz

Dissertations

Colloidal quantum dot (CQD) photodetectors are a rapidly emerging technology with a potential to significantly impact today’s infrared sensing and imaging technologies. To date, CQD photodetector research is primarily focused on lead-chalcogenide semiconductor CQDs which have spectral response fundamentally limited by the bulk bandgap of the constituent material, confining their applications to near-infrared (NIR, 0.7-1.0 um) and short-wavelength infrared (SWIR, 1-2.5 um) spectral regions. The overall goal of this dissertation is to investigate a new generation of CQD materials and devices that advances the current CQD photodetector research toward the technologically important thermal infrared region of 3-5 ?m, known as …

Efficient, Dual-Particle Directional Detection System Using A Rotating Scatter Mask, Robert Olesen, Bryan V. Egner, Darren E. Holland, Valerie Martin, James E. Bevins, Larry W. Burggraf, Buckley E. O'Day Iii

AFIT Patents

A directional radiation detection system and an omnidirectional radiation detector. The omnidirectional radiation detector detects radiation comprising at least one of: (i) gamma rays; and (ii) neutron particles. A radiation scatter mask (RSM) of the radiation detection system includes a rotating sleeve received over the omnidirectional radiation detector and rotating about a longitudinal axis. The RSM further includes: (i) a fin extending longitudinally from one side of the rotating sleeve; and (ii) a wall extending from the rotating sleeve and spaced apart from the fin having an upper end distally positioned on the rotating sleeve spaced apart or next to …

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 …

On-Chip Nanoscale Plasmonic Optical Modulators, Abdalrahman Mohamed Nader Abdelhamid

Theses and Dissertations

In this thesis work, techniques for downsizing Optical modulators to nanoscale for the purpose of utilization in on chip communication and sensing applications are explored. Nanoscale optical interconnects can solve the electronics speed limiting transmission lines, in addition to decrease the electronic chips heat dissipation. A major obstacle in the path of achieving this goal is to build optical modulators, which transforms data from the electrical form to the optical form, in a size comparable to the size of the electronics components, while also having low insertion loss, high extinction ratio and bandwidth. Also, lap-on-chip applications used for fast diagnostics, …

Design And Characterization Of Standard Cell Library Using Finfets, Phanindra Datta Sadhu

Master's Theses

The processors and digital circuits designed today contain billions of transistors on a small piece of silicon. As devices are becoming smaller, slimmer, faster, and more efficient, the transistors also have to keep up with the demands and needs of the daily user. Unfortunately, the CMOS technology has reached its limit and cannot be used to scale down due to the transistor's breakdown caused by short channel effects. An alternative solution to this is the FinFET transistor technology, where the gate of the transistor is a three dimensional fin that surrounds the transistor and prevents the breakdown caused by scaling …