Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Deep convolutional neural network (2)
- Domain adaptation (2)
- Gold nanoparticles (2)
- Machine learning (2)
- Materials science (2)
-
- Physics (2)
- Seebeck coefficient (2)
- AR coating (1)
- Adaptive decision threshold (1)
- Alkali (1)
- Annealing (1)
- Artificial intelligence (1)
- Atomic layer deposition (1)
- Augmented reality system (1)
- Bioenergetic (1)
- Biopsy (1)
- Brain tumor (1)
- Brain tumor segmentation (1)
- CIGS (1)
- Cardiac mitochondria (1)
- Ceria (1)
- Ceria nanoparticles (1)
- Chemistry (1)
- Cigs (1)
- Citrate (1)
- Classification (1)
- Cognitive Radio Network (1)
- Cognitive radio (1)
- Comparative studies (1)
- Complex iii (1)
Articles 1 - 28 of 28
Full-Text Articles in Engineering
Three-Event Energy Detection With Adaptive Threshold For Spectrum Sensing In Cognitive Radio Systems, Alexandru Martian, Mahmood Jalal Ahmad Al Sammarraie, Calin Vladeanu, Dimitrie Popescu
Three-Event Energy Detection With Adaptive Threshold For Spectrum Sensing In Cognitive Radio Systems, Alexandru Martian, Mahmood Jalal Ahmad Al Sammarraie, Calin Vladeanu, Dimitrie Popescu
Electrical & Computer Engineering Faculty Publications
Implementation of dynamic spectrum access (DSA) in cognitive radio (CR) systems requires the unlicensed secondary users (SU) to implement spectrum sensing to monitor the activity of the licensed primary users (PU). Energy detection (ED) is one of the most widely used methods for spectrum sensing in CR systems, and in this paper we present a novel ED algorithm with an adaptive sensing threshold. The three-event ED (3EED) algorithm for spectrum sensing is considered for which an accurate approximation of the optimal decision threshold that minimizes the decision error probability (DEP) is found using Newton’s method with forced convergence in one …
Work-In-Progress: Augmented Reality System For Vehicle Health Diagnostics And Maintenance, Yuzhong Shen, Anthony W. Dean, Rafael Landaeta
Work-In-Progress: Augmented Reality System For Vehicle Health Diagnostics And Maintenance, Yuzhong Shen, Anthony W. Dean, Rafael Landaeta
Electrical & Computer Engineering Faculty Publications
This paper discusses undergraduate research to develop an augmented reality (AR) system for diagnostics and maintenance of the Joint Light Tactical Vehicle (JLTV) employed by U.S. Army and U.S. Marine Corps. The JLTV’s diagnostic information will be accessed by attaching a Bluetooth adaptor (Ford Reference Vehicle Interface) to JLTV’s On-board diagnostics (OBD) system. The proposed AR system will be developed for mobile devices (Android and iOS tablets and phones) and it communicates with the JLTV’s OBD via Bluetooth. The AR application will contain a simplistic user interface that reads diagnostic data from the JLTV, shows vehicle sensors, and allows users …
In-Situ Gold-Ceria Nanoparticles: Superior Optical Fluorescence Quenching Sensor For Dissolved Oxygen, Nader Shehata, Ishac Kandas, Effat Samir
In-Situ Gold-Ceria Nanoparticles: Superior Optical Fluorescence Quenching Sensor For Dissolved Oxygen, Nader Shehata, Ishac Kandas, Effat Samir
Electrical & Computer Engineering Faculty Publications
Cerium oxide (ceria) nanoparticles (NPs) have been proved to be an efficient optical fluorescent material through generating visible emission (~530 nm) under violet excitation. This feature allowed ceria NPs to be used as an optical sensor via the fluorescence quenching Technique. In this paper, the impact of in-situ embedded gold nanoparticles (Au NPs) inside ceria nanoparticles was studied. Then, gold–ceria NPs were used for sensing dissolved oxygen (DO) in aqueous media. It was observed that both fluorescence intensity and lifetime were changed due to increased concentration of DO. Added gold was found to enhance the sensitivity of ceria to DO …
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
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 …
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
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 …
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
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 …
Observation Of Reduced Thermal Conductivity In A Metal-Organic Framework Due To The Presence Of Adsorbates, Hasan Babaei, Mallory E. Decoster, Minyoung Jeong, Zeinab M. Hassan, Timur Islamoglu, Helmut Baumgart, Alan J.H. Mcgaughey, Engelbert Redel, Omar K. Farha, Patrick E. Hopkins, Jonathan A. Malen, Christopher E. Wilmer
Observation Of Reduced Thermal Conductivity In A Metal-Organic Framework Due To The Presence Of Adsorbates, Hasan Babaei, Mallory E. Decoster, Minyoung Jeong, Zeinab M. Hassan, Timur Islamoglu, Helmut Baumgart, Alan J.H. Mcgaughey, Engelbert Redel, Omar K. Farha, Patrick E. Hopkins, Jonathan A. Malen, Christopher E. Wilmer
Electrical & Computer Engineering Faculty Publications
Whether the presence of adsorbates increases or decreases thermal conductivity in metal-organic frameworks (MOFs) has been an open question. Here we report observations of thermal transport in the metal-organic framework HKUST-1 in the presence of various liquid adsorbates: water, methanol, and ethanol. Experimental thermoreflectance measurements were performed on single crystals and thin films, and theoretical predictions were made using molecular dynamics simulations. We find that the thermal conductivity of HKUST-1 decreases by 40 – 80% depending on the adsorbate, a result that cannot be explained by effective medium approximations. Our findings demonstrate that adsorbates introduce additional phonon scattering in HKUST-1, …
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
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 …
Low Temperature Plasma Jets: Characterization And Biomedical Applications, Mounir Laroussi
Low Temperature Plasma Jets: Characterization And Biomedical Applications, Mounir Laroussi
Electrical & Computer Engineering Faculty Publications
No abstract provided.
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
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 …
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
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 …
Deep Learning With Context Encoding For Semantic Brain Tumor Segmentation And Patient Survival Prediction, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin
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 …
Mitochondrial Utilization Of Competing Fuels Is Altered In Insulin Resistant Skeletal Muscle Of Non-Obese Rats (Goto-Kakizaki), Nicola Lai, Ciarán E. Fealy, Chinna M. Kummitha, Silvia Cabras, John P. Kirwan, Charles L. Hoppel
Mitochondrial Utilization Of Competing Fuels Is Altered In Insulin Resistant Skeletal Muscle Of Non-Obese Rats (Goto-Kakizaki), Nicola Lai, Ciarán E. Fealy, Chinna M. Kummitha, Silvia Cabras, John P. Kirwan, Charles L. Hoppel
Electrical & Computer Engineering Faculty Publications
Aim: Insulin-resistant skeletal muscle is characterized by metabolic inflexibility with associated alterations in substrate selection, mediated by peroxisome-proliferator activated receptor 𝜹 (PPAR𝜹). Although it is established that PPAR𝜹 contributes to the alteration of energy metabolism, it is not clear whether it plays a role in mitochondrial fuel competition. While nutrient overload may impair metabolic flexibility by fuel congestion within mitochondria, in absence of obesity defects at a mitochondrial level have not yet been excluded. We sought to determine whether reduced PPAR𝜹 content in insulin-resistant rat skeletal muscle of a non-obese rat model of T2DM (Goto-Kakizaki, GK) ameliorate the inhibitory effect …
Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li
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 …
Advances In Atomic Layer Deposition (Ald) Nanolaminate Synthesis Of Thermoelectric Films In Porous Templates For Improved Seebeck Coefficient, Xin Chen, Helmut Baumgart
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 …
Prediction Of Molecular Mutations In Diffuse Low-Grade Gliomas Using Mr Imaging Features, Zeina A. Shboul, James Chen, Khan M. Iftekharrudin
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 …
Generation Of Large-Volume High-Pressure Plasma By Spatiotemporal Control Of Space Charge, Shirshak K. Dhali
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 …
Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin
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.
Gold/Qds-Embedded-Ceria Nanoparticles: Optical Fluorescence Enhancement As A Quenching Sensor, Nader Shehata, Effat Samir, Ishac Kandas
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 …
Thermoelectric Porous Mof Based Hybrid Materials, Engelbert Redel, Helmut Baumgart
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 …
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
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 …
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
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 …
Real-Time Optimization Of Anti-Reflective Coatings For Cigs Solar Cells, Grace Rajan, Shankar Karki, Robert W. Collins, Nikolas J. Podraza, Sylvain Marsillac
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 …
Priority Based Routing And Link Scheduling For Cognitive Radio Networks, Peng Jiang, Mitchell Zhou, Song Wen
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 …
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
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 …
Efficacy Of Radiomics And Genomics In Predicting Tp53 Mutations In Diffuse Lower Grade Glioma, Zeina A. Shboul, Khan Iftekharuddin
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 …
Generative Adversarial Networks For Visible To Infrared Video Conversion, Mohammad Shahab Uddin, Jiang Li, Chiman Kwan (Ed.)
Generative Adversarial Networks For Visible To Infrared Video Conversion, Mohammad Shahab Uddin, Jiang Li, Chiman Kwan (Ed.)
Electrical & Computer Engineering Faculty Publications
Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) …
Distributed Strategy For Power Re-Allocation In High Performance Applications, Vaibhav Sundriyal, Masha Sosonkina
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 …