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Articles 901 - 930 of 8897

Full-Text Articles in Physical Sciences and Mathematics

Fft Enabled Ecc For Wsn Nodes Without Hardware Multiplier Support, Utku Gülen, Selçuk Baktir Jan 2022

Fft Enabled Ecc For Wsn Nodes Without Hardware Multiplier Support, Utku Gülen, Selçuk Baktir

Turkish Journal of Electrical Engineering and Computer Sciences

ECC is a popular cryptographic algorithm for key distribution in wireless sensor networks where power efficiency is desirable. A power efficient implementation of ECC without using hardware multiplier support was proposed earlier for wireless sensor nodes. The proposed implementation utilized the number theoretic transform to carry operands to the frequency domain, and conducted Montgomery multiplication, in addition to other finite field operations, in that domain. With this work, we perform in the frequency domain only polynomial multiplication and use the fast Fourier transform to carry operands between the time and frequency domains. Our ECC implementation over $GF((2^{13}-1)^{13})$ on the MSP430 …

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Improving Collaborative Recommendation Based On Item Weight Link Prediction, Sahraoui Kharroubi, Youcef Dahmani, Omar Nouali Jan 2022

Improving Collaborative Recommendation Based On Item Weight Link Prediction, Sahraoui Kharroubi, Youcef Dahmani, Omar Nouali

Turkish Journal of Electrical Engineering and Computer Sciences

There is a continuous information overload on the Web. The problem treated is how to have relevant items (documents, products, services, etc.) at time and without difficulty. Filtering system also called recommender systems are widely used to recommend items to users by similarity process such as Amazon, MovieLens, Cdnow, etc. In the literature, to predict a link in a bipartite network, most methods are based either on a binary history (like, dislike) or on the common neighbourhood of the active user. In this paper, we modelled the recommender system by a weighted bipartite network. The bipartite topology offers a bidirectional …

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Using Vertical Areas In Finite Set Model Predictive Control Of A Three-Level Inverter Aimed At Computation Reduction, Alireza Jaafari, Alireza Davari, Cristian Garcia, Jose Rodriguez Jan 2022

Using Vertical Areas In Finite Set Model Predictive Control Of A Three-Level Inverter Aimed At Computation Reduction, Alireza Jaafari, Alireza Davari, Cristian Garcia, Jose Rodriguez

Turkish Journal of Electrical Engineering and Computer Sciences

In power electronics applications, finite set model predictive control (FS-MPC) has proven to be a viable strategy. However, due to the high processing power required, using this technology in multilevel converters is difficult. This strategy, which is based on predicting the behavior of the system for all conceivable states, has an issue with a numerous of possible switching states. A recent and useful strategy for dealing with the problem is the limiting of calculations based on triangle regions. Despite its success, this method has several limitations, including the computation required to locate the right triangle and the boundary modes. In …

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A Futuristic Approach To Generate Random Bit Sequence Using Dynamic Perturbedchaotic System, Sathya Krishnamoorthi, Premalatha Jayapaul, Vani Rajasekar, Rajesh Kumar Dhanaraj, Celestine Iwendi Jan 2022

A Futuristic Approach To Generate Random Bit Sequence Using Dynamic Perturbedchaotic System, Sathya Krishnamoorthi, Premalatha Jayapaul, Vani Rajasekar, Rajesh Kumar Dhanaraj, Celestine Iwendi

Turkish Journal of Electrical Engineering and Computer Sciences

Most of the web applications require security which in turn requires random numbers. Pseudo-random numbers are required with good statistical properties and efficiency. Use of chaotic map to dynamically perturb another chaotic map that generates the random bit output is introduced in this work. Perturbance is introduced to improvise the chaotic behaviour of a base map and increase the periodicity. PRNG with this architecture is devised to generate random bit sequence from initial keyspace. The statistical properties of newly constructed PRNG are tested with NIST SP 800-22 statistical test suite and were shown to have good randomness. To ensure its …

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Application Of Long Short-Term Memory (Lstm) Neural Network Based On Deeplearning For Electricity Energy Consumption Forecasting, Mehmet Bi̇lgi̇li̇, Ni̇yazi̇ Arslan, Ali̇i̇hsan Şekerteki̇n, Abdulkadi̇r Yaşar Jan 2022

Application Of Long Short-Term Memory (Lstm) Neural Network Based On Deeplearning For Electricity Energy Consumption Forecasting, Mehmet Bi̇lgi̇li̇, Ni̇yazi̇ Arslan, Ali̇i̇hsan Şekerteki̇n, Abdulkadi̇r Yaşar

Turkish Journal of Electrical Engineering and Computer Sciences

Electricity is the most substantial energy form that significantly affects the development of modern life, work efficiency, quality of life, production, and competitiveness of the society in the ever-growing global world. In this respect, forecasting accurate electricity energy consumption (EEC) is fairly essential for any country?s energy consumption planning and management regarding its growth. In this study, four time-series methods; long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with subtractive clustering (SC), ANFIS with fuzzy cmeans (FCM), and ANFIS with grid partition (GP) were implemented for the short-term one-day ahead EEC prediction. Root mean square error (RMSE), …

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Evaluating The English-Turkish Parallel Treebank For Machine Translation, Onur Görgün, Olcay Taner Yildiz Jan 2022

Evaluating The English-Turkish Parallel Treebank For Machine Translation, Onur Görgün, Olcay Taner Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

This study extends our initial efforts in building an English-Turkish parallel treebank corpus for statistical machine translation tasks. We manually generated parallel trees for about 17K sentences selected from the Penn Treebank corpus. English sentences vary in length: 15 to 50 tokens including punctuation. We constrained the translation of trees by (i) reordering of leaf nodes based on suffixation rules in Turkish, and (ii) gloss replacement. We aim to mimic human annotator?s behavior in real translation task. In order to fill the morphological and syntactic gap between languages, we do morphological annotation and disambiguation. We also apply our heuristics by …

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A Simple Dual-Band Quasi-Yagi Antenna With Defected Ground Structures, Göksel Turan, Hayretti̇n Odabaşi Jan 2022

A Simple Dual-Band Quasi-Yagi Antenna With Defected Ground Structures, Göksel Turan, Hayretti̇n Odabaşi

Turkish Journal of Electrical Engineering and Computer Sciences

In this article, a dual-band compact quasi-Yagi antenna with defected ground structure (DGS) is proposed. The proposed antenna has a simple feeding mechanism consists of a microstrip and transmission line. Half of the driver and director elements are printed on the opposite side of the substrate to ensure good coupling between the antenna elements and achieve a stable radiation pattern. The ground plane is modified with one rectangular slot below the microstrip line to form dual-band operation. Also rectangular slots placed on the sides of the ground plane to improve the matching. The proposed antenna works at $f_{1}=3.35$ and $f_{2}=6.15$ …

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Shape Investigations Of Structures Formed By The Self-Assembly Of Aromaticamino Acids Using The Density-Based Spatial Clustering Of Applications With Noise Algorithm, Mehmet Gökhan Habi̇boğlu, Helen W. Hernandez, Şahi̇n Uyaver Jan 2022

Shape Investigations Of Structures Formed By The Self-Assembly Of Aromaticamino Acids Using The Density-Based Spatial Clustering Of Applications With Noise Algorithm, Mehmet Gökhan Habi̇boğlu, Helen W. Hernandez, Şahi̇n Uyaver

Turkish Journal of Electrical Engineering and Computer Sciences

Tyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using density-based spatial clustering of applications with noise (DBSCAN) to measure the shapes of …

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Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach, Meli̇h Öder, Şükrü Eraslan, Yeli̇z Yesi̇lada Jan 2022

Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach, Meli̇h Öder, Şükrü Eraslan, Yeli̇z Yesi̇lada

Turkish Journal of Electrical Engineering and Computer Sciences

Eye-tracking studies typically collect enormous amount of data encoding rich information about user behaviours and characteristics on the web. Eye-tracking data has been proved to be useful for usability and accessibility testing and for developing adaptive systems. The main objective of our work is to mine eye-tracking data with machine learning algorithms to automatically detect users' characteristics. In this paper, we focus on exploring different machine learning algorithms to automatically classify whether users are familiar or not with a web page. We present our work with an eye-tracking data of 81 participants on six web pages. Our results show that …

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Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs, Ausnain Naveed, Şahi̇n Sönmez, Saffet Ayasun Jan 2022

Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs, Ausnain Naveed, Şahi̇n Sönmez, Saffet Ayasun

Turkish Journal of Electrical Engineering and Computer Sciences

With the extensive usage of open communication networks, time delays have become a great concern in load frequency control (LFC) systems since such inevitable large delays weaken the controller performance and even may lead to instabilities. Electric vehicles (EVs) have a potential tool in the frequency regulation. The integration of a large number of EVs via an aggregator amplifies the adverse effects of time delays on the stability and controller design of LFC systems. This paper investigates the impacts of the EVs aggregator with communication time delay on the stability. Primarily, a graphical method characterizing stability boundary locus is implemented. …

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Security Concerns On Machine Learning Solutions For 6g Networks In Mmwave Beam Prediction, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Devrim Unal Jan 2022

Security Concerns On Machine Learning Solutions For 6g Networks In Mmwave Beam Prediction, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Devrim Unal

Engineering Technology Faculty Publications

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial …

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Defensive Distillation-Based Adversarial Attack Mitigation Method For Channel Estimation Using Deep Learning Models In Next-Generation Wireless Networks, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler Jan 2022

Defensive Distillation-Based Adversarial Attack Mitigation Method For Channel Estimation Using Deep Learning Models In Next-Generation Wireless Networks, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those …

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Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao Jan 2022

Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the …

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Inferential Statistics And Information Theoretical Measures: An Approach To Interference Detection In Radio Astronomy, Morgan R. Dameron Jan 2022

Inferential Statistics And Information Theoretical Measures: An Approach To Interference Detection In Radio Astronomy, Morgan R. Dameron

Graduate Theses, Dissertations, and Problem Reports

In a time when technology is rapidly growing, radio observatories are now able to expand their computational power to achieve higher receiver sensitivity power and a more flexible realtime computing approach to probe the universe for its composition and study new astronomical phenomena. This allows searches to go deeper into the universe, and results in the recording of massive quantities of observed data. At the same time, this increases the amount of radio frequency interference (RFI) found in the obtained observatory data. The high power of RFI easily masks the low power of extraterrestrial signals, making them hard to detect …

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An Analysis On Adversarial Machine Learning: Methods And Applications, Ali Dabouei Jan 2022

An Analysis On Adversarial Machine Learning: Methods And Applications, Ali Dabouei

Graduate Theses, Dissertations, and Problem Reports

Deep learning has witnessed astonishing advancement in the last decade and revolutionized many fields ranging from computer vision to natural language processing. A prominent field of research that enabled such achievements is adversarial learning, investigating the behavior and functionality of a learning model in presence of an adversary. Adversarial learning consists of two major trends. The first trend analyzes the susceptibility of machine learning models to manipulation in the decision-making process and aims to improve the robustness to such manipulations. The second trend exploits adversarial games between components of the model to enhance the learning process. This dissertation aims to …

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Modeling, Fabrication, And Characterization Of Rf-Based Passive Wireless Sensors Composed Of Refractory Semiconducting Ceramics For High Temperature Applications, Kavin Sivaneri Varadharajan Idhaiam Jan 2022

Modeling, Fabrication, And Characterization Of Rf-Based Passive Wireless Sensors Composed Of Refractory Semiconducting Ceramics For High Temperature Applications, Kavin Sivaneri Varadharajan Idhaiam

Graduate Theses, Dissertations, and Problem Reports

Real-time health monitoring of high temperature systems (>500oC) in harsh environments is necessary to prevent catastrophic events caused by structural failures, varying pressure, and chemical reactions. Conventional solid-state temperature sensors such as resistance temperature detectors (RTDs) and thermocouples are restricted by their operating environments, sensor dimensions and often require external power sources for their operation. The current work presents the research and development of RF-based passive wireless sensing technology targeting high temperatures and harsh environmental conditions. Passive wireless devices are generally classified as near-field and far-field devices based on the interrogation distance. Near-field sensors are placed at …

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A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin Jan 2022

A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. …

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Qu-Brats: Miccai Brats 2020 Challenge On Quantifying Uncertainty In Brain Tumor Segmentation - Analysis Of Ranking Scores And Benchmarking Results, Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard Mckinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-Han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-Min Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh Mchugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicholas Boutry, Alexis Huard, Lasitha Vidyaratne, Md. Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel Jan 2022

Qu-Brats: Miccai Brats 2020 Challenge On Quantifying Uncertainty In Brain Tumor Segmentation - Analysis Of Ranking Scores And Benchmarking Results, Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard Mckinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-Han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-Min Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh Mchugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicholas Boutry, Alexis Huard, Lasitha Vidyaratne, Md. Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

Electrical & Computer Engineering Faculty Publications

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. …

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Bitcoin Selfish Mining Modeling And Dependability Analysis, Chencheng Zhou, Liudong Xing, Jun Guo, Qisi Liu Jan 2022

Bitcoin Selfish Mining Modeling And Dependability Analysis, Chencheng Zhou, Liudong Xing, Jun Guo, Qisi Liu

Electrical & Computer Engineering Faculty Publications

Blockchain technology has gained prominence over the last decade. Numerous achievements have been made regarding how this technology can be utilized in different aspects of the industry, market, and governmental departments. Due to the safety-critical and security-critical nature of their uses, it is pivotal to model the dependability of blockchain-based systems. In this study, we focus on Bitcoin, a blockchain-based peer-to-peer cryptocurrency system. A continuous-time Markov chain-based analytical method is put forward to model and quantify the dependability of the Bitcoin system under selfish mining attacks. Numerical results are provided to examine the influences of several key parameters related to …

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Hybridization From Guest-Host Interactions Reduces The Thermal Conductivity Of Metal-Organic Frameworks, Mallory E. Decoster, Hasan Babaei, Sangeun S. Jung, Zeinab M. Hassan, John T. Gaskins, Ashutosh Giri, Emma M. Tiernan, John A. Tomko, Helmut Baumgart, Pamela M. Norris, Alan J.H. Mcgaughey, Christopher E. Wilmer, Engelbert Redel, Gaurav Giri, Patrick E. Hopkins Jan 2022

Hybridization From Guest-Host Interactions Reduces The Thermal Conductivity Of Metal-Organic Frameworks, Mallory E. Decoster, Hasan Babaei, Sangeun S. Jung, Zeinab M. Hassan, John T. Gaskins, Ashutosh Giri, Emma M. Tiernan, John A. Tomko, Helmut Baumgart, Pamela M. Norris, Alan J.H. Mcgaughey, Christopher E. Wilmer, Engelbert Redel, Gaurav Giri, Patrick E. Hopkins

Electrical & Computer Engineering Faculty Publications

We experimentally and theoretically investigate the thermal conductivity and mechanical properties of polycrystalline HKUST-1 metal–organic frameworks (MOFs) infiltrated with three guest molecules: tetracyanoquinodimethane (TCNQ), 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4-TCNQ), and (cyclohexane-1,4-diylidene)dimalononitrile (H4-TCNQ). This allows for modification of the interaction strength between the guest and host, presenting an opportunity to study the fundamental atomic scale mechanisms of how guest molecules impact the thermal conductivity of large unit cell porous crystals. The thermal conductivities of the guest@MOF systems decrease significantly, by on average a factor of 4, for all infiltrated samples as compared to the uninfiltrated, pristine HKUST-1. This reduction in thermal conductivity goes in …

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Runtime Power Allocation Based On Multi-Gpu Utilization In Gamess, Masha Sosonkina, Vaibhav Sundriyal, Jorge Luis Galvez Vallejo Jan 2022

Runtime Power Allocation Based On Multi-Gpu Utilization In Gamess, Masha Sosonkina, Vaibhav Sundriyal, Jorge Luis Galvez Vallejo

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 maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy …

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Grand Challenges In Low Temperature Plasmas, Xinpei Lu, Peter J. Bruggeman, Stephan Reuter, George Naidis, Annemie Bogaerts, Mounir Laroussi, Michael Keidar, Eric Robert, Jean-Michel Pouvesle, Dawei Liu, Kostya (Ken) Ostrikov Jan 2022

Grand Challenges In Low Temperature Plasmas, Xinpei Lu, Peter J. Bruggeman, Stephan Reuter, George Naidis, Annemie Bogaerts, Mounir Laroussi, Michael Keidar, Eric Robert, Jean-Michel Pouvesle, Dawei Liu, Kostya (Ken) Ostrikov

Electrical & Computer Engineering Faculty Publications

Low temperature plasmas (LTPs) enable to create a highly reactive environment at near ambient temperatures due to the energetic electrons with typical kinetic energies in the range of 1 to 10 eV (1 eV = 11600K), which are being used in applications ranging from plasma etching of electronic chips and additive manufacturing to plasma-assisted combustion. LTPs are at the core of many advanced technologies. Without LTPs, many of the conveniences of modern society would simply not exist. New applications of LTPs are continuously being proposed. Researchers are facing many grand challenges before these new applications can be translated to practice. …

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Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li Jan 2022

Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li

Electrical & Computer Engineering Faculty Publications

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t1 together with low-resolution images at a …

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Real-Time Cavity Fault Prediction In Cebaf Using Deep Learning, Md. M. Rahman, K. Iftekharuddin, A. Carptenter, T. Mcguckin, C. Tennant, L. Vidyaratne, Sandra Biedron (Ed.), Evgenya Simakov (Ed.), Stephen Milton (Ed.), Petr M. Anisimov (Ed.), Volker R.W. Schaa (Ed.) Jan 2022

Real-Time Cavity Fault Prediction In Cebaf Using Deep Learning, Md. M. Rahman, K. Iftekharuddin, A. Carptenter, T. Mcguckin, C. Tennant, L. Vidyaratne, Sandra Biedron (Ed.), Evgenya Simakov (Ed.), Stephen Milton (Ed.), Petr M. Anisimov (Ed.), Volker R.W. Schaa (Ed.)

Electrical & Computer Engineering Faculty Publications

Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply …

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Empirical Comparison Of Machine Learning Methods For Wind Power Predictions, Sidny M. Stewart Jan 2022

Empirical Comparison Of Machine Learning Methods For Wind Power Predictions, Sidny M. Stewart

EWU Masters Thesis Collection

No abstract provided.

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Experimental And Computational Studies Of Functionalized Carbon Nanotubes For Use In Energy Storage Devices And Membranes, Emine S. Karaman Dec 2021

Experimental And Computational Studies Of Functionalized Carbon Nanotubes For Use In Energy Storage Devices And Membranes, Emine S. Karaman

Dissertations

Electrolytes with good interfacial stability are a crucial component of any electrochemical device. The development of novel gel polymer electrolytes (GEs) with good interface stability and better manufacturability is important for the development of the next generation electrochemical devices. Gel electrolytes are hybrid electrolyte materials, combining benefits of both liquid and solid systems. Compared with liquid and solid electrolytes, GEs open new design opportunities and do not require rigorous encapsulation methods. In this dissertation, studies on functionalized carbon nanotubes (fCNTs) and graphene oxide (GO) doped polyvinyl alcohol (PVA) based gel electrolytes (GEs) are reported. The ionic conductivity and mechanical strength …

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Structure Analysis Of Pemfc Cathode Catalyst Layer, Rui-Qing Wang, Sheng Sui Dec 2021

Structure Analysis Of Pemfc Cathode Catalyst Layer, Rui-Qing Wang, Sheng Sui

Journal of Electrochemistry

The sluggish oxygen reduction reaction (ORR) on the cathode of the proton exchange membrane fuel cell (PEMFC) has always been one of the key factors limiting its commercialization. The optimization of the cathode catalytic layer structure plays an important role in improving fuel cell performance and reducing production costs. In this paper, two different catalysts (platinum nanoparticles (Pt-NPs) and platinum nanowires (Pt-NWs)) were prepared by using catalyst coated substrate (CCS) method. By constructing a double-layer catalytic layer structure, we analyzed the effect of different catalytic layer structures by performing a single cell test. The results showed that the dense platinum …

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Photoelectrochemical Sensing Based On Zr-Mofs For Homocysteine Detection, Wen-Xia Dong, Guang-Ming Wen, Bin Liu, Zhong-Ping Li Dec 2021

Photoelectrochemical Sensing Based On Zr-Mofs For Homocysteine Detection, Wen-Xia Dong, Guang-Ming Wen, Bin Liu, Zhong-Ping Li

Journal of Electrochemistry

Due to the independent form of the light source and detection system, photoelectrochemical (PEC) sensor has the advantages of low background, high sensitivity and simple operation. So far, PEC systems have been widely used in the fields including the actual detection of metal ions, biological antibodies or antigens in environmental pollutants. When the photosensitive material is irradiated by a light source with an energy being equal to or greater than its band gap, electrons (e-) transition occurs from the valence band to the conduction band, leaving a hole (h+), at the same time, the generated electron-hole …

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Copper Nanoparticles In-Situ Anchored On Nitrogen-Doped Carbon For High-Efficiency Oxygen Reduction Reaction Electrocatalyst, Hui-Fang Yuan, Yue Zhang, Xing-Wu Zhai, Li-Bing Hu, Gui-Xian Ge, Gang Wang, Feng Yu, Bin Dai Dec 2021

Copper Nanoparticles In-Situ Anchored On Nitrogen-Doped Carbon For High-Efficiency Oxygen Reduction Reaction Electrocatalyst, Hui-Fang Yuan, Yue Zhang, Xing-Wu Zhai, Li-Bing Hu, Gui-Xian Ge, Gang Wang, Feng Yu, Bin Dai

Journal of Electrochemistry

Compared with noble metal platinum (Pt)-based catalysts, inexpensive non-noble metal electrocatalysts have attracted extensive attention for oxygen reduction reaction (ORR). Herein, chitosan as a kind of biomass resource rich in nitrogen and carbon was used to prepare nitrogen-doped carbon (N-C) and N-C in-situ anchored by copper nanoparticles (Cu/N-C). The as-obtained N-C and Cu/N-C nanoparticles were successfully used as non-noble eletrocatalysts tested for ORR. Compared with the N-C, the Cu/N-C showed the high surface area of 607.3 m 2·g-1 with the mean pore size of 2.5 nm and the pore volume of 0.40 cm3·g-1 …

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Nitrogen-Sulfur Co-Doped Porous Carbon Preparation And Its Application In Lithium-Sulfur Batteries, Zhao Gui-Xiang, Hafiz Zaki Ahmed Wail, Zhu Fu-Liang Dec 2021

Nitrogen-Sulfur Co-Doped Porous Carbon Preparation And Its Application In Lithium-Sulfur Batteries, Zhao Gui-Xiang, Hafiz Zaki Ahmed Wail, Zhu Fu-Liang

Journal of Electrochemistry

In recent years, lithium-sulfur (Li-S) batteries have been considered as a promising candidate for the next generation of energy storage system due to their ultrahigh theoretical capacity (1675 mAh·g-1) and energy density (2600 Wh·kg-1). However, the practical application of Li-S batteries is seriously limited by their insulating nature of sulfur, the shuttle effect of polysulfides (LiPSs), and volume expansion during charging and discharging. To overcome those disadvantages, one of the commonly methods is to infiltrate sulfur into porous conductive carbon framework, such as porous carbon, hollow carbon spheres, graphene, carbon nanotubes and some composites of the …

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