Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Old Dominion University

Discipline
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 841 - 870 of 1996

Full-Text Articles in Computer Sciences

The Effect Of Touch Simulation In Virtual Reality Shopping, Ha Kyung Lee, Namhee Yoon, Dooyoung Choi Jan 2022

The Effect Of Touch Simulation In Virtual Reality Shopping, Ha Kyung Lee, Namhee Yoon, Dooyoung Choi

STEMPS Faculty Publications

This study aims to explore the effect of touch simulation on virtual reality (VR) store satisfaction mediated by VR shopping self-efficacy and VR shopping pleasure. The moderation effects of the autotelic and instrumental need for touch between touch simulation and VR store satisfaction are also explored. Participants wear a head-mounted display VR device (Oculus Go) in a controlled laboratory environment, and their VR store experience is recorded as data. All participants’ responses (n = 58) are analyzed using SPSS 20.0 for descriptive statistics, reliability analysis, exploratory factor analysis, and the Process macro model analysis. The results show that touch simulation …

Go to article

Formal Modeling And Verification Of A Blockchain-Based Crowdsourcing Consensus Protocol, Hamra Afzaal, Muhammad Imran, Muhammad Umar Janjua, Sarada Prasad Gochhayat Jan 2022

Formal Modeling And Verification Of A Blockchain-Based Crowdsourcing Consensus Protocol, Hamra Afzaal, Muhammad Imran, Muhammad Umar Janjua, Sarada Prasad Gochhayat

VMASC Publications

Crowdsourcing is an effective technique that allows humans to solve complex problems that are hard to accomplish by automated tools. Some significant challenges in crowdsourcing systems include avoiding security attacks, effective trust management, and ensuring the system’s correctness. Blockchain is a promising technology that can be efficiently exploited to address security and trust issues. The consensus protocol is a core component of a blockchain network through which all the blockchain peers achieve an agreement about the state of the distributed ledger. Therefore, its security, trustworthiness, and correctness have vital importance. This work proposes a Secure and Trustworthy Blockchain-based Crowdsourcing (STBC) …

Go to article

Healthcare 5.0 Security Framework: Applications, Issues And Future Research Directions, Mohammad Wazid, Ashok Kumar Das, Noor Mohd, Youngho Park Jan 2022

Healthcare 5.0 Security Framework: Applications, Issues And Future Research Directions, Mohammad Wazid, Ashok Kumar Das, Noor Mohd, Youngho Park

VMASC Publications

Healthcare 5.0 is a system that can be deployed to provide various healthcare services. It does these services by utilising a new generation of information technologies, such as Internet of Things (IoT), Artificial Intelligence (AI), Big data analytics, blockchain and cloud computing. Due to the introduction of healthcare 5.0, the paradigm has been now changed. It is disease-centered to patient-centered care where it provides healthcare services and supports to the people. However, there are several security issues and challenges in healthcare 5.0 which may cause the leakage or alteration of sensitive healthcare data. This demands that we need a robust …

Go to article

Development Of A Framework To Support Informed Shipbuilding Based On Supply Chain Disruptions, Katherine Smith, Rafael Diaz, Yuzhong Shen Jan 2022

Development Of A Framework To Support Informed Shipbuilding Based On Supply Chain Disruptions, Katherine Smith, Rafael Diaz, Yuzhong Shen

VMASC Publications

In addition to stresses induced by the Covid-19 pandemic, supply chains worldwide have been growing more complex while facing a continuous onslaught of disruptions. This paper presents an analysis and extension of a graph based model for modeling and simulating the effects of such disruptions. The graph based model combines a Bayesian network approach for simulating risks with a network dependency analysis approach for simulating the propagation of disruptions through the network over time. The initial analysis provides evidence supporting extension to for using a multi-layered approach allowing for the inclusion of cyclic features in supply chain models. Initial results …

Go to article

An Exploration On Apts In Biocybersecurity And Cyberbiosecurity, Xavier-Lewis Palmer, Lucas Potter, Saltuk Karahan Jan 2022

An Exploration On Apts In Biocybersecurity And Cyberbiosecurity, Xavier-Lewis Palmer, Lucas Potter, Saltuk Karahan

School of Cybersecurity Faculty Publications

Novel and complex digital threats that are increasingly interwoven with means and products of biology that can affect society. Much work in Biocybersecurity/Cyberbiosecurity (BCS/CBS) discuss vulnerabilities, but few deeply address malicious actor varieties as attacks at this intersection are new. The path to those attacks remains mostly theoretical, presenting considerable difficulty to accomplish in practical scenarios. In terms of advanced persistent threats (APTs) this of course needs to change as biomanufacturing facilities are at risk, considering Covid-19 and other potential pandemics. Further attacks are not out of reach and thus we must start to imagine how BCS APTs may appear. …

Go to article

Trust In Human-Robot Interaction Within Healthcare Services: A Review Study, Dedra Townsend, Amirhossein Majidirad Jan 2022

Trust In Human-Robot Interaction Within Healthcare Services: A Review Study, Dedra Townsend, Amirhossein Majidirad

Mechanical & Aerospace Engineering Faculty Publications

There has always been a dilemma of the extent to which human can rely on machines in different activities of daily living. Ranging from riding on a self-driving car to having an iRobot vacuum clean the living room. However, when it comes to healthcare settings where robots are intended to work next to human, making decision gets difficult because repercussions may jeopardize people’s life. That has led scientists and engineers to take one step back and think out of the box. Having concept of trust under scrutiny, this study helps deciphering complex human-robot interaction (HRI) attributes. Screening essential constituents of …

Go to article

Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das, Sachin Shetty, Muhammad Khurram Khan, Joel J.P.C. Rodrigues Jan 2022

Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das, Sachin Shetty, Muhammad Khurram Khan, Joel J.P.C. Rodrigues

VMASC Publications

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be …

Go to article

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos Jan 2022

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos

VMASC Publications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …

Go to article

Human Ergonomic Simulation To Support The Design Of An Exoskeleton For Lashing/De-Lashing Operations Of Containers Cargo, Francesco Longo, Antonio Padovano, Vittorio Solina, Virginia D' Augusta, Stefan Venzl, Roberto Calbi, Michele Bartuni, Ornella Anastasi, Rafael Diaz Jan 2022

Human Ergonomic Simulation To Support The Design Of An Exoskeleton For Lashing/De-Lashing Operations Of Containers Cargo, Francesco Longo, Antonio Padovano, Vittorio Solina, Virginia D' Augusta, Stefan Venzl, Roberto Calbi, Michele Bartuni, Ornella Anastasi, Rafael Diaz

VMASC Publications

Lashing and de-lashing operations of containers cargo on board containerships are considered as quite strenuous activities in which operators are required to work continuously over a 6 or 8 hours shift with very limited break. This is mostly because containerships need to leave the port as soon as possible and containers loading and unloading operations must be executed with very high productivity (stay moored in a port is a totally unproductive time for a ship and a loss-making business for a shipping company). Operators performing lashing and de-lashing operations are subjected to intense ergonomic stress and uncomfortable working postures. To …

Go to article

Core Point Pixel-Level Localization By Fingerprint Features In Spatial Domain, Xueyi Ye, Yuzhong Shen, Maosheng Zeng, Yirui Liu, Huahua Chen, Zhijing Zhao Jan 2022

Core Point Pixel-Level Localization By Fingerprint Features In Spatial Domain, Xueyi Ye, Yuzhong Shen, Maosheng Zeng, Yirui Liu, Huahua Chen, Zhijing Zhao

Computational Modeling & Simulation Engineering Faculty Publications

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve …

Go to article

Attitudes About Cybersecurity Articulation Agreements And Transfer Students: A Statewide Survey Of Faculty Members And Advisors, Brian K. Payne, Tracy Vandecar-Burdin, Daniela Cigularova Jan 2022

Attitudes About Cybersecurity Articulation Agreements And Transfer Students: A Statewide Survey Of Faculty Members And Advisors, Brian K. Payne, Tracy Vandecar-Burdin, Daniela Cigularova

Sociology & Criminal Justice Faculty Publications

In this study, cybersecurity faculty and academic advisors from community colleges and 4-year universities in the southeast region of the United States completed a survey assessing attitudes about and support for articulation agreements and related transfer policies. Hypothesizing that professional structures shape attitudes and experiences, the researchers conducted an exploratory quantitative study with primarily descriptive analyses. The results reveal differences in attitudes between community college and 4-year stakeholders and between faculty and academic advisors. The results of this study are discussed in relation to faculty and advisor training and communication.

Go to article

Civiic: Cybercrime In Virginia: Impacts On Industry And Citizens Final Report, Randy Gainey, Tancy Vandecar-Burdin, Jay Albanese, Thomas Dearden, James Hawdon, Katalin Parti Jan 2022

Civiic: Cybercrime In Virginia: Impacts On Industry And Citizens Final Report, Randy Gainey, Tancy Vandecar-Burdin, Jay Albanese, Thomas Dearden, James Hawdon, Katalin Parti

Sociology & Criminal Justice Faculty Publications

[First paragraph] Victimization from cybercrime is a major concern in Virginia, the US, and the world. As individuals and businesses spend more time online, it becomes increasingly important to understand cybercrime and how to protect against it. Such an understanding is dependent on valid and reliable baseline data that identifies the specific nature, extent, and outcomes of cybercrime activity. A better understanding of cybercrime activity is needed to target and prevent it more effectively, minimize its consequences, and provide support for both individual and corporate victims. Before that can occur, however, better baseline data are required, and this project was …

Go to article

Precursors Of Email Response To Cybersecurity Scenarios: Factor Exploration And Scale Development, Miguel A. Toro-Jarrin, Pilar Pazos-Lago, Miguel Padilla Jan 2022

Precursors Of Email Response To Cybersecurity Scenarios: Factor Exploration And Scale Development, Miguel A. Toro-Jarrin, Pilar Pazos-Lago, Miguel Padilla

Engineering Management & Systems Engineering Faculty Publications

In the last decade, information security research has further expanded to include human factors as key elements of the organization's cybersecurity infrastructure. Numerous factors from several theories have been explored to explain and predict the multitude of information security-related behaviors in organizations. Lately, there has been a call for the study of specific cybersecurity behaviors in contextualized scenarios that reflect specific and realistic situations of a potential cyber-attack. This paper focuses on precursors of email response in situations that can be the origin of cybersecurity incidents in organizations (i.e., phishing attacks, ransomware, etc.). This study explores participants' intentions to follow …

Go to article

Can I Touch The Clothes On The Screen? The Touch Effect In Online Shopping, Ha Kyung Lee, Dooyoung Choi Jan 2022

Can I Touch The Clothes On The Screen? The Touch Effect In Online Shopping, Ha Kyung Lee, Dooyoung Choi

STEMPS Faculty Publications

We examined the interplay effects of device types (touch vs. non-touch) and the tactile sensitivity (fur vs. woven) on the product attitudes mediated by the mental simulation for touch. The participants from MTurk were randomly assigned to one of two tactile conditions. Responses from those who used tablets (n=83, touch device) and laptops (n=96, non-touch device) were included in the analysis. The main effects of device types and tactile-sensitivity on the mental simulation for touch were significant. The interaction effect of device types and tactile sensitivity was also significant. Those participants seeing the less tactile-sensitive product showed greater mental simulation …

Go to article

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 …

Go to article

A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways, Sarp Salih, Kuzlu Murat, Zhao Yanxiao, Cetin Mecit Jan 2022

A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways, Sarp Salih, Kuzlu Murat, Zhao Yanxiao, Cetin Mecit

Engineering Technology Faculty Publications

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show …

Go to article

Bfv-Based Homomorphic Encryption For Privacy-Preserving Cnn Models, Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu Jan 2022

Bfv-Based Homomorphic Encryption For Privacy-Preserving Cnn Models, Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu

Engineering Technology Faculty Publications

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers …

Go to article

Augmented Reality Integrated Welder Training For Mechanical Engineering Technology, Aditya Akundi, Hamid Eisazadeh, Mona Torabizadeh Jan 2022

Augmented Reality Integrated Welder Training For Mechanical Engineering Technology, Aditya Akundi, Hamid Eisazadeh, Mona Torabizadeh

Engineering Technology Faculty Publications

The shortage of welders is well documented and projected to become more severe for various industries such as shipbuilding in coming years. It is mainly because welding training is a critical and often costly endeavor. This study examines the training potential using augmented reality technology as a critical part of welder training for mechanical engineering technology students. This study assessed the performance of two groups of MET students trained with two different methods. One group received training with the traditional method in three sessions. The second group acquired training initially with an augmented reality welding system for three sessions. Then, …

Go to article

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 …

Go to article

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 …

Go to article

Development Of Experiential Learning Experiences For K-12 Students Focusing On Smart Cities, Murat Kuzlu, Vukica Jovanovic, Nathan Puryear, Patrick J. Martin, Sherif Abdelwahed, Özgür Güler Jan 2022

Development Of Experiential Learning Experiences For K-12 Students Focusing On Smart Cities, Murat Kuzlu, Vukica Jovanovic, Nathan Puryear, Patrick J. Martin, Sherif Abdelwahed, Özgür Güler

Engineering Technology Faculty Publications

The main objective of this paper is to describe a project focused on the development of experiential learning experiences for undergraduate and graduate students focusing on smart cities. The future workforce needs students with various data analytics skills, service reliability, and sustainability. The team of researchers from Old Dominion University and Virginia Commonwealth University is developing a virtual smart city lab environment at both universities and collaborating on multiple research projects. The main purpose of this virtual labs is to provide a testbed that can be used for students who are interested in careers related to cyber-physical systems (CPS). These …

Go to article

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu Jan 2022

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q2 and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

Go to article

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. …

Go to article

Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu Jan 2022

Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu

Electrical & Computer Engineering Faculty Publications

Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in K consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of K . Our …

Go to article

Coupled Dynamics Of Spin Qubits In Optical Dipole Microtraps: Application To The Error Analysis Of A Rydberg-Blockade Gate, L. V. Gerasimov, R. R. Yusupov, A. D. Moiseevsky, I. Vybornyi, K. S. Tikhonov, S. P. Kulik, S. S. Straupe, Charles I. Sukenik, D. V. Kupriyanov Jan 2022

Coupled Dynamics Of Spin Qubits In Optical Dipole Microtraps: Application To The Error Analysis Of A Rydberg-Blockade Gate, L. V. Gerasimov, R. R. Yusupov, A. D. Moiseevsky, I. Vybornyi, K. S. Tikhonov, S. P. Kulik, S. S. Straupe, Charles I. Sukenik, D. V. Kupriyanov

Physics Faculty Publications

Single atoms in dipole microtraps or optical tweezers have recently become a promising platform for quantum computing and simulation. Here we report a detailed theoretical analysis of the physics underlying an implementation of a Rydberg two-qubit gate in such a system—a cornerstone protocol in quantum computing with single atoms. We focus on a blockade-type entangling gate and consider various decoherence processes limiting its performance in a real system. We provide numerical estimates for the limits on fidelity of the maximally entangled states and predict the full process matrix corresponding to the noisy two-qubit gate. We consider different excitation geometries and …

Go to article

Online Deep Learning From Doubly-Streaming Data, Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He Jan 2022

Online Deep Learning From Doubly-Streaming Data, Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He

Computer Science Faculty Publications

This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. A plausible idea to deal with such data streams is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional multimedia data with complex feature interplay, which suffers a tradeoff between onlineness, which biases shallow …

Go to article

Deeppose: Detecting Gps Spoofing Attack Via Deep Recurrent Neural Network, Peng Jiang, Hongyi Wu, Chunsheng Xin Jan 2022

Deeppose: Detecting Gps Spoofing Attack Via Deep Recurrent Neural Network, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this …

Go to article

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification At Jefferson Laboratory, Lasitha Vidyaratne, Adam Carpenter, Tom Powers, Chris Tennant, Khan M. Iftekharuddin, Md. Monibor Rahman, Anna S. Shabalina Jan 2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification At Jefferson Laboratory, Lasitha Vidyaratne, Adam Carpenter, Tom Powers, Chris Tennant, Khan M. Iftekharuddin, Md. Monibor Rahman, Anna S. Shabalina

Electrical & Computer Engineering Faculty Publications

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of …

Go to article

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 …

Go to article

Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski Jan 2022

Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski

Electrical & Computer Engineering Faculty Publications

This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging.

Go to article

First Prev Next Last