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Articles 1501 - 1530 of 4692

Full-Text Articles in Engineering

Sensing And Measuring The Environment Workshop As Exposure To Engineering Technology For High School Students In A Summer Residential Camp, Vukica M. Jovanovic, Otilia Popescu, Murat Kuzlu, Mujde Erten-Unal, Balŝa Terzić, George Mcleod, Tysha Batts, Cynthia Tomovic Jan 2020

Sensing And Measuring The Environment Workshop As Exposure To Engineering Technology For High School Students In A Summer Residential Camp, Vukica M. Jovanovic, Otilia Popescu, Murat Kuzlu, Mujde Erten-Unal, Balŝa Terzić, George Mcleod, Tysha Batts, Cynthia Tomovic

Engineering Technology Faculty Publications

Summer programs are the latest trend in extracurricular STEM education programs offered by universities. Efforts are made towards residential summer programs, which have the ability to expose students not only to specially designed STEM activities but to the university campus environment and student life, as well. These types of programs are expected to have better success in getting students engaged and to capture their interest in STEM fields. This paper presents one example of designing and implementing a summer residential workshop in order to expose high school students to the field of engineering technology, specifically to electrical circuits, electrical prototyping, …

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Investigating The Numerical Stability Of Using An Impedance Boundary Condition To Model Broadband Noise Scattering With Acoustic Liners, Michelle E. Rodio, Fang Q. Hu, Douglas M. Nark Jan 2020

Investigating The Numerical Stability Of Using An Impedance Boundary Condition To Model Broadband Noise Scattering With Acoustic Liners, Michelle E. Rodio, Fang Q. Hu, Douglas M. Nark

Mathematics & Statistics Faculty Publications

Reducing aircraft noise is a major objective in the field of computational aeroacoustics. When designing next generation quiet aircraft, it is important to be able to accurately and efficiently predict the acoustic scattering by an aircraft body from a given noise source. Acoustic liners are an effective tool for achieving aircraft noise reduction and are characterized by a frequency-dependent impedance value. Converted into the time-domain using Fourier transforms, an impedance boundary condition can be used to simulate the acoustic wave scattering by geometric bodies treated with acoustic liners. A Broadband Impedance Model will be discussed in which the liner impedance …

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Gold/Qds-Embedded-Ceria Nanoparticles: Optical Fluorescence Enhancement As A Quenching Sensor, Nader Shehata, Effat Samir, Ishac Kandas Jan 2020

Gold/Qds-Embedded-Ceria Nanoparticles: Optical Fluorescence Enhancement As A Quenching Sensor, Nader Shehata, Effat Samir, Ishac Kandas

Electrical & Computer Engineering Faculty Publications

This work focuses on improving the fluorescence intensity of cerium oxide (ceria) nanoparticles (NPs) through added plasmonic nanostructures. Ceria nanoparticles are fluorescent nanostructures which can emit visible fluorescence emissions under violet excitation. Here, we investigated different added plasmonic nanostructures, such as gold nanoparticles (Au NPs) and Cadmium sulfide/selenide quantum dots (CdS/CdSe QDs), to check the enhancement of fluorescence intensity emissions caused by ceria NPs. Different plasmonic resonances of both aforementioned nanostructures have been selected to develop optical coupling with both fluorescence excitation and emission wavelengths of ceria. In addition, different additions whether in-situ or post-synthesis have been investigated. We found …

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Prefrontal High Gamma In Ecog Tags Periodicity Of Musical Rhythms In Perception And Imagination, S.A. Herff, C. Herff, A. J. Milne, Garett D. Johnson, J. J. Shih, D. J. Krusienski Jan 2020

Prefrontal High Gamma In Ecog Tags Periodicity Of Musical Rhythms In Perception And Imagination, S.A. Herff, C. Herff, A. J. Milne, Garett D. Johnson, J. J. Shih, D. J. Krusienski

Electrical & Computer Engineering Faculty Publications

Rhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditory phrase segmentation and envelope tracking. Here, we use electrocorticographic (ECoG) recordings from eight human listeners to see whether periodicities in high-gamma activity track the periodicities in the envelope of musical rhythms during rhythm perception and imagination. Rhythm imagination was elicited by instructing participants to imagine the rhythm to continue during pauses of several repetitions. To identify electrodes whose periodicities in high-gamma activity track the periodicities in …

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Degradation Mechanism Due To Water Ingress Effect On The Top Contact Of Cu(In,Ga)Se2 Solar Cells, Deewakar Poudel, Shankar Karki, Benjamin Belfore, Grace Rajan, Sushma Swaraj Atluri, Sina Soltanmohammad, Angus Rockett, Sylvain Marsillac Jan 2020

Degradation Mechanism Due To Water Ingress Effect On The Top Contact Of Cu(In,Ga)Se2 Solar Cells, Deewakar Poudel, Shankar Karki, Benjamin Belfore, Grace Rajan, Sushma Swaraj Atluri, Sina Soltanmohammad, Angus Rockett, Sylvain Marsillac

Electrical & Computer Engineering Faculty Publications

The impact of moisture ingress on the surface of copper indium gallium diselenide (CIGS) solar cells was studied. While industry-scale modules are encapsulated in specialized polymers and glass, over time, the glass can break and the encapsulant can degrade. During such conditions, water can potentially degrade the interior layers and decrease performance. The first layer the water will come in contact with is the transparent conductive oxide (TCO) layer. To simulate the impact of this moisture ingress, complete devices were immersed in deionized water. To identify the potential sources of degradation, a common window layer for CIGS devices—a bilayer of …

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A New Method Of Detecting And Interrupting High Impedance Faults By Specifying The Z-Source Breaker In Dc Power Networks, Sagar Bhatta, Ruiyun Fu, Yucheng Zhang Jan 2020

A New Method Of Detecting And Interrupting High Impedance Faults By Specifying The Z-Source Breaker In Dc Power Networks, Sagar Bhatta, Ruiyun Fu, Yucheng Zhang

Electrical & Computer Engineering Faculty Publications

High impedance faults (HIFs) that cause a relatively smaller current magnitude compared to the traditional low impedance faults are not easily detectable but can cause an extreme threat to electric apparatus and system operation. This paper introduces a new method of detecting and interrupting HIFs in DC power networks by specifying Z-source circuit breakers (ZCBs). The ZCB is a protective device for high power DC branches, with the capabilities of protecting bidirectional power flow and automatic/controllable turnoff function. In this new method, the operational mode of ZCB (i.e., either the detection mode or interruption mode) can be specified. Beyond previous …

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Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning At Jefferson Laboratory, Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin Jan 2020

Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning At Jefferson Laboratory, Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through five passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level rf system configured such that a cavity fault triggers waveform recordings of 17 rf signals for each of the eight cavities in the cryomodule. Subject matter experts are able to analyze the collected time-series data and identify which of the …

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Introducing Coding Into Teacher Education: An Interdisciplinary Robotics Experience For Education And Engineering Students, Jennifer Kidd, Krishna Kaipa, Sam Sacks, Lilian Almeida Jan 2020

Introducing Coding Into Teacher Education: An Interdisciplinary Robotics Experience For Education And Engineering Students, Jennifer Kidd, Krishna Kaipa, Sam Sacks, Lilian Almeida

Teaching & Learning Faculty Publications

Despite nationwide mandates to integrate computer science into P-6 curriculum, most P-6 preservice teachers (PSTs) are not exposed to coding or computational thinking during their professional preparation, and are unprepared to teach these topics. This study, conducted as a part of an NSF-funded project, explores a teacher preparation model designed to increase PSTs’ coding knowledge and coding self-efficacy. PSTs in an educational technology course partnered with engineering undergraduates (EUs) in a computational methods course and worked side-by-side on robotics activities to develop skill and confidence with basic programming concepts and block coding. Students utilized experience gained from these interdisciplinary partnerships …

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"Zooming In" On Robotics During Covid-19: A Preservice Teacher, An Engineering Student, And A 5th Grader Engineer Robotic Flowers Via Zoom, Jennifer Kidd, Krishna Kaipa, Kristie Gutierrez, Pilar Pazos, Orlando Ayala, Stacie Ringleb Jan 2020

"Zooming In" On Robotics During Covid-19: A Preservice Teacher, An Engineering Student, And A 5th Grader Engineer Robotic Flowers Via Zoom, Jennifer Kidd, Krishna Kaipa, Kristie Gutierrez, Pilar Pazos, Orlando Ayala, Stacie Ringleb

Teaching & Learning Faculty Publications

The COVID-19 induced school shutdown dramatically decreased students’ hands-on STEM learning opportunities. An NSF-funded program partnering preservice teachers and undergraduate engineering students to teach robotics to fifth graders was adapted to a virtual format via Zoom. A case study intimately explored one team’s experience as they engineered bio-inspired robots over five weekly sessions. Zoom recordings, written reflections, and lesson slides were analyzed to describe how the virtual context shaped the lesson and influenced the preservice teacher’s experience. All three participants successfully engineered a robotic flower indicating hands-on robotics instruction is feasible in an online format. The virtual context increased the …

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Ethical Decision Making Behind The Wheel – A Driving Simulator Study, Siby Samuel, Sarah Yahoodik, Yusuke Yamani, Krishna Valluru, Donald L. Fisher Jan 2020

Ethical Decision Making Behind The Wheel – A Driving Simulator Study, Siby Samuel, Sarah Yahoodik, Yusuke Yamani, Krishna Valluru, Donald L. Fisher

Psychology Faculty Publications

Over the past several years, there has been considerable debate surrounding ethical decision making in situations resulting in inevitable casualties. Given enough time and all other things being equal, studies show that drivers will typically decide to strike the fewest number of pedestrians in scenarios where there is a choice between striking several versus one or no pedestrians. However, it is unclear whether drivers behave similarly under situations of time pressure. In our experiment in a driving simulator, 32 drivers were given up to 2 s to decide which group of pedestrians to avoid among groups of larger (5) or …

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The Rise, Fall, And Repair Of Trust For Automated Driving Systems, Scott Mishler, Jing Chen Jan 2020

The Rise, Fall, And Repair Of Trust For Automated Driving Systems, Scott Mishler, Jing Chen

Psychology Faculty Publications

The purpose of this study was to investigate how human driver's trust in the automated driving system is built over time and affected by automation failure. The study expanded trust development over time by measuring trust after a practice demonstration ofthe system capabilities and after each of seven unique, sequential drives. The automation performed perfectly on six of the seven drives but made one of three different responses to a critical hazard event in the fourth drive. Depending on the error-type condition, the automation either perfectly avoided the hazard (no error), issued a takeover request (TOR), or failed to notice …

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Distributed Strategy For Power Re-Allocation In High Performance Applications, Vaibhav Sundriyal, Masha Sosonkina Jan 2020

Distributed Strategy For Power Re-Allocation In High Performance Applications, Vaibhav Sundriyal, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to distribute a given power allocation among the cluster nodes assigned to the application while balancing their performance change. The strategy operates in a timeslice-based manner to estimate the current application performance and power usage per node followed by power redistribution across the nodes. Experiments, performed on four nodes (112 cores) of a modern computing platform interconnected with Infiniband showed that even a significant power budget reduction of 20% may result in …

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Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, Chunsheng Xin, Jiang Li, Hongyi Wu Jan 2020

Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, Chunsheng Xin, Jiang Li, Hongyi Wu

Electrical & Computer Engineering Faculty Publications

This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only …

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Advances In Atomic Layer Deposition (Ald) Nanolaminate Synthesis Of Thermoelectric Films In Porous Templates For Improved Seebeck Coefficient, Xin Chen, Helmut Baumgart Jan 2020

Advances In Atomic Layer Deposition (Ald) Nanolaminate Synthesis Of Thermoelectric Films In Porous Templates For Improved Seebeck Coefficient, Xin Chen, Helmut Baumgart

Electrical & Computer Engineering Faculty Publications

Thermoelectrics is a green renewable energy technology which can significantly contribute to power generation due to its potential in generating electricity out of waste heat. The main challenge for the development of thermoelectrics is its low conversion efficiency. One key strategy to improve conversion efficiency is reducing the thermal conductivity of thermoelectric materials. In this paper, the state-of-the-art progresses made in improving thermoelectric materials are reviewed and discussed, focusing on phononic engineering via applying porous templates and ALD deposited nanolaminates structure. The effect of nanolaminates structure and porous templates on Seebeck coefficient, electrical conductivity and thermal conductivity, and hence in …

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Prediction Of Molecular Mutations In Diffuse Low-Grade Gliomas Using Mr Imaging Features, Zeina A. Shboul, James Chen, Khan M. Iftekharrudin Jan 2020

Prediction Of Molecular Mutations In Diffuse Low-Grade Gliomas Using Mr Imaging Features, Zeina A. Shboul, James Chen, Khan M. Iftekharrudin

Electrical & Computer Engineering Faculty Publications

Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include …

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Generation Of Large-Volume High-Pressure Plasma By Spatiotemporal Control Of Space Charge, Shirshak K. Dhali Jan 2020

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 …

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Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin Jan 2020

Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

This guest editorial summarizes the Special Section on Machine Learning in Optics.

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Semi-Supervised Adversarial Domain Adaptation For Seagrass Detection Using Multispectral Images In Coastal Areas, Kazi Aminul Islam, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li Jan 2020

Semi-Supervised Adversarial Domain Adaptation For Seagrass Detection Using Multispectral Images In Coastal Areas, Kazi Aminul Islam, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li

Electrical & Computer Engineering Faculty Publications

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from …

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Bioenergetic Functions In Subpopulations Of Heart Mitochondria Are Preserved In A Non-Obese Type 2 Diabetes Rat Model (Goto-Kakizaki), Nicola Lai, C. M. Kummitha, F. Loy, R. Isola, C. L. Hoppel Jan 2020

Bioenergetic Functions In Subpopulations Of Heart Mitochondria Are Preserved In A Non-Obese Type 2 Diabetes Rat Model (Goto-Kakizaki), Nicola Lai, C. M. Kummitha, F. Loy, R. Isola, C. L. Hoppel

Electrical & Computer Engineering Faculty Publications

A distinct bioenergetic impairment of heart mitochondrial subpopulations in diabetic cardiomyopathy is associated with obesity; however, many type 2 diabetic (T2DM) patients with high-risk for cardiovascular disease are not obese. In the absence of obesity, it is unclear whether bioenergetic function in the subpopulations of mitochondria is affected in heart with T2DM. To address this issue, a rat model of non-obese T2DM was used to study heart mitochondrial energy metabolism, measuring bioenergetics and enzyme activities of the electron transport chain (ETC). Oxidative phosphorylation in the presence of substrates for ETC and ETC activities in both populations of heart mitochondria in …

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Thermoelectric Porous Mof Based Hybrid Materials, Engelbert Redel, Helmut Baumgart Jan 2020

Thermoelectric Porous Mof Based Hybrid Materials, Engelbert Redel, Helmut Baumgart

Electrical & Computer Engineering Faculty Publications

Porous hybrid materials and MOF (Metal-Organic-Framework) films represent modern designer materials that exhibit many requirements of a near ideal and tunable future thermoelectric (TE) material. In contrast to traditional semiconducting bulk TE materials, porous hybrid MOF templates can be used to overcome some of the constraints of physics in bulk TE materials. These porous hybrid systems are amenable for simulation and modeling to design novel optimized electron-crystal phonon-glass materials with potentially very high ZT (figure of merit) numbers. Porous MOF and hybrid materials possess an ultra-low thermal conductivity, which can be further modulated by phonon engineering within their complex porous …

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A Prototype Virginia Ground Station Network, Zach Leffke, Jonathon Black, Kevin Shinpaugh, Ian Harnett, Bryce Clegg, Nick Angle, Chris Goyne, Connor Segal, William 'Trace' Lacour, Mike Mcpherson, Dimitrie Popescu, Samuel Jensen, Jason Harris, Mary Sandy, Mike Miller Jan 2020

A Prototype Virginia Ground Station Network, Zach Leffke, Jonathon Black, Kevin Shinpaugh, Ian Harnett, Bryce Clegg, Nick Angle, Chris Goyne, Connor Segal, William 'Trace' Lacour, Mike Mcpherson, Dimitrie Popescu, Samuel Jensen, Jason Harris, Mary Sandy, Mike Miller

Electrical & Computer Engineering Faculty Publications

This paper provides a detailed technical description of a prototype ground station network, the Virginia Ground Station Network (VGSN), developed for the Virginia Cubesat Constellation (VCC) mission. Virginia Tech (VT), University of Virginia (UVA), and Old Dominion University (ODU) have each constructed ground stations to communicate with their respective VCC spacecraft. Initially, each university was responsible for commanding its own spacecraft via its own ground station. As the mission progressed, it was decided to network the ground stations and operations centers together to provide backup communications capability for the overall mission. The NASA Wallops Flight Facility (WFF) UHF smallsat ground …

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Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos Jan 2020

Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos

Electrical & Computer Engineering Faculty Publications

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer …

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Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li Jan 2020

Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that …

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Context Aware Deep Learning For Brain Tumor Segmentation, Subtype Classification, And Survival Prediction Using Radiology Images, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin Jan 2020

Context Aware Deep Learning For Brain Tumor Segmentation, Subtype Classification, And Survival Prediction Using Radiology Images, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction …

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Real-Time Optimization Of Anti-Reflective Coatings For Cigs Solar Cells, Grace Rajan, Shankar Karki, Robert W. Collins, Nikolas J. Podraza, Sylvain Marsillac Jan 2020

Real-Time Optimization Of Anti-Reflective Coatings For Cigs Solar Cells, Grace Rajan, Shankar Karki, Robert W. Collins, Nikolas J. Podraza, Sylvain Marsillac

Electrical & Computer Engineering Faculty Publications

A new method combining in-situ real-time spectroscopic ellipsometry and optical modeling to optimize the thickness of an anti-reflective (AR) coating for Cu(In,Ga)Se2 (CIGS) solar cells is described and applied directly to fabricate devices. The model is based on transfer matrix theory with input from the accurate measurement of complex dielectric function spectra and thickness of each layer in the solar cell by spectroscopic ellipsometry. The AR coating thickness is optimized in real time to optically enhance device performance with varying thickness and properties of the constituent layers. Among the parameters studied, we notably demonstrate how changes in thickness of …

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Effect Of Layer Thickness On Structural, Morphological And Superconducting Properties Of Nb3Sn Films Fabricated By Multilayer Sequential Sputtering, M. N. Sayeed, U. Pudasaini, C. E. Reece, G. V. Eremeev, H. E. Elsayed-Ali Jan 2020

Effect Of Layer Thickness On Structural, Morphological And Superconducting Properties Of Nb3Sn Films Fabricated By Multilayer Sequential Sputtering, M. N. Sayeed, U. Pudasaini, C. E. Reece, G. V. Eremeev, H. E. Elsayed-Ali

Electrical & Computer Engineering Faculty Publications

Superconducting Nb3Sn films can be synthesized by controlling the atomic concentration of Sn. Multilayer sequential sputtering of Nb and Sn thin films followed by high temperature annealing is considered as a method to fabricate Nb3Sn films, where the Sn composition of the deposited films can be controlled by the thickness of alternating Nb and Sn layers. We report on the structural, morphological and superconducting properties of Nb3Sn films fabricated by multilayer sequential sputtering of Nb and Sn films on sapphire substrates followed by annealing at 950 °C for 3 h. We have investigated the …

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Priority Based Routing And Link Scheduling For Cognitive Radio Networks, Peng Jiang, Mitchell Zhou, Song Wen Jan 2020

Priority Based Routing And Link Scheduling For Cognitive Radio Networks, Peng Jiang, Mitchell Zhou, Song Wen

Electrical & Computer Engineering Faculty Publications

To address the challenges caused by the time-varying rate requirement for multimedia communication sessions, we propose a Priority Based Routing and link Scheduling (PBRS) scheme for multi-hop cognitive radio networks. The objective is to minimize disruption to communication sessions due to channel switching as well as to minimize network resource consumption for multimedia applications based on a prioritized routing and resource allocation scheme. PBRS includes a priority based optimization formulation and an efficient algorithm to solve the problem. The main idea is to allocate the available resource to different types of services with their Quality of Experience (QoE) expectation as …

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Low Temperature Plasma Jets: Characterization And Biomedical Applications, Mounir Laroussi Jan 2020

Low Temperature Plasma Jets: Characterization And Biomedical Applications, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

No abstract provided.

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Efficacy Of Radiomics And Genomics In Predicting Tp53 Mutations In Diffuse Lower Grade Glioma, Zeina A. Shboul, Khan Iftekharuddin Jan 2020

Efficacy Of Radiomics And Genomics In Predicting Tp53 Mutations In Diffuse Lower Grade Glioma, Zeina A. Shboul, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

An updated classification of diffuse lower-grade gliomas is established in the 2016 World Health Organization Classification of Tumors of the Central Nervous System based on their molecular mutations such as TP53 mutation. This study investigates machine learning methods for TP53 mutation status prediction and classification using radiomics and genomics features, respectively. Radiomics features represent patients' age and imaging features that are extracted from conventional MRI. Genomics feature is represented by patients’ gene expression using RNA sequencing. This study uses a total of 105 LGG patients, where the patient dataset is divided into a training set (80 patients) and testing set …

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Deep Learning With Context Encoding For Semantic Brain Tumor Segmentation And Patient Survival Prediction, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin Jan 2020

Deep Learning With Context Encoding For Semantic Brain Tumor Segmentation And Patient Survival Prediction, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of …

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