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10th International Conference on e-Health 2018, the 11th International Conference on ICT, Society, and Human Beings 2018 and of the 15th International Conference Web Based Communities and Social Media 2018, part of the Multi Conference on computer Science and Information Systems 2018, MCCSIS 2018

作者： Gialelis, John Pavlou, Panos Panagiotou, Chris Koubias, Stavros Pombo, Nuno Garcia, Nuno Applied Electronics Lab Electrical and Computer Engineering Department University of Patras Patras Greece Instituto de Telecomunicações ALLab Assisted Living Computing and Telecommunications Lab Department of Informatics Universidade da Beira Interior Covilhã Portugal Universidade Lusófona de Humanidades e Tecnologias Lisbon Portugal

ISBN: (纸本)9789898533777

Advances in Wearable Devices and Artificial Intelligence upgrade the use of ICT aiming both at improving humans’ life quality as well as safety. This paper presents a system for the detection of mental focus depletion comprising a robust embedded system used for aggregating EEG data and transferring them in real time to a central server where specific purpose software is used for further processing. The performance of the system is tested using experimental data, as well as its accuracy is depicted against a well-known commercial device. © 2018 MCCSIS 2018 - Multi Conference on computer Science and Information Systems;Proceedings of the International Conferences on e-Health 2018, ICT, Society, and Human Beings 2018 and Web Based Communities and Social Media 2018. All rights reserved.

关键词： Social networking (online)

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Optimum design of a tri-band MPA with parasitic elements for CubeSat communications using Genetic Algorithm 6

Optimum design of a tri-band MPA with parasitic elements for...

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6th International Conference on Wireless Networks and Mobile Communications, WINCOM 2018

作者： Bakkali, Mohamed El Idrissi, Najiba El Amrani El Tubbal, Faisel Em Gaba, Gurjot Singh Faculty of Sciences and Technologies Sidi Mohamed Ben Abdellah University Fez30050 Morocco School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia School of Computing Engineering and Mathematics Western Sydney University NSW2522 Australia Technological Projects Department Libyan Center for Remote Sensing and Space Science Tripoli Libya School of Electronics and Electrical Engineering Lovely Professional University Punjab144411 India

ISBN: (纸本)9781538673300

The growth of small satellite communication devices has pushed designers to design compact size antennas. The most prized among miniature antenna choices is the microstrip patch antenna (MPA). These antennas are low cost, lightweight and need for mechanically robust construction as well as ease of installation and aerodynamic profile but have fewer capabilities. In this paper, Genetic Algorithm (GA) optimization method has been utilized for performances optimization of a MPA with parasitic elements (MPA-PE) in order to get a desirable multiband antenna for X/Ku bands CubeSat applications. GAs are capable of handling a large number of design parameters and work for optimization problems that have non-differentiable or discontinuous multi-dimensional solution spaces, making them ideal for antenna optimization. As there is not a unique solution, a GA was applied using an objective function based on the return loss at the desired frequencies. Therefore, the optimized antenna has a compact size, its geometry and characteristics are compatible with all standard structures of CubeSats. This antenna is designed and simulated using the electromagnetic simulator ANSYS HFSS, and CST MWS software is used for re-simulation of the designed antenna and the results are in good agreement. © 2018 IEEE.

关键词： Genetic algorithms

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Editorial

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International Journal of Information and computer Security 2017年第1-2期9卷 1-4页

作者： Gupta, B.B. Agrawal, Dharma P. Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India Department of Electrical Engineering and Computing Systems University of Cincinnati CincinnatiOH45221-0030 United States

Recent advances in wireless and VLSI technologies have led to smart devices both at home and work place that can be connected to form internet of things (IoT). Thus, IoT consists of smart connected devices that have embedded electronics, software, sensors, actuators, and network, which enable these objects to collect and exchange data among themselves. Such a heterogeneous system has components with varying data formats, diverse functionalities and altogether different expectations (Gralla, 2007; Gupta et al., 2016; Agrawal and Zeng, 2016). Some of the elements are static while others are mobile in nature, creating an accurate mobile cloud environment. The diversity in functionalities and diverse range of resources make some easily vulnerable to hackers, while others stay strong against security threats. Given the lax behaviour of users towards device security and having simple channels for data transfer, malware is expected to grow exponentially. These considerations have led to this special issue and security solutions have evolved to detect and prevent attacks, and are able to perform forensic analysis that have significantly complicated user authentication, access control and system security. Specifically, this special issue addresses various security issues in mobile cloud computing and the loT, particularly on advances in mobile computing technologies and related areas (Tewari and Gupta, 2016; Alsmirat et al., 2016; Abolfazli et al., 2016). This special issue contains eight papers dealing with different aspects of security issues in mobile cloud computing, IoT and other related areas (Gupta and Gupta, 2016; Zhang and Gupta, 2016; Li et al., 2015; Whitmore et al., 2015).

关键词： Spyware aluminium Forensic analysis Cloud computing Mobile RANGE RESOURCES Safety Problem computer Hackers Security

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High Gain Patch Antenna Array with Parasitic Elements for CubeSat Applications

High Gain Patch Antenna Array with Parasitic Elements for Cu...

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2018 IEEE Indian Conference on Antennas and Propagation, InCAP 2018

作者： El Bakkali, Mohamed Gaba, Gurjot Singh Tubbal, Faisel Em Amrani El Idrissi, Najiba El Faculty of Sciences and Technologies Sidi Mohamed Ben Abdellah University Fez30050 Morocco School of Electronics and Electrical Engineering Lovely Professional University Punjab144411 India School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia School of Computing Engineering and Mathematics Western Sydney University NSW2522 Australia Technological Projects Department Libyan Center for Remote Sensing and Space Science Tripoli Libya

ISBN: (纸本)9781538670606

In this paper we present a high gain multiband patch antenna array (PAA) for 3U and 6U CubeSats. The parasitic elements (PEs) are incorporated to enhance the antenna total gain and to achieving good impedance matching. We have simulated the proposed antenna in the High Frequency Structure Simulator (HFSS). Our results show that the proposed antenna achieves a high gain of 11.31 dB (> 6 dB) at 8.15 GHz, small reflection coefficient (S11) of -40 dB and -32 dB at operating frequencies 8.15 GHz (X-band) and 13.01 GHz (Ku-band) respectively. © 2018 IEEE.

关键词： Reflection

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Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications

arXiv

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arXiv 2022年

作者： Glanzer, J. Banagiri, S. Coughlin, S.B. Soni, S. Zevin, M. Berry, C.P.L. Patane, O. Bahaadini, S. Rohani, N. Crowston, K. Kalogera, V. Østerlund, C. Trouille, L. Katsaggelos, A. Department of Physics Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Department of Physics and Astronomy Northwestern University 1800 Sherman Ave EvanstonIL60201 United States Northwestern University Information Technology Research Computing Services Northwestern University 1800 Sherman Ave EvanstonIL60201 United States LIGO Massachusetts Institute of Technology CambridgeMA02139 United States Kavli Institute for Cosmological Physics The University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Enrico Fermi Institute The University of Chicago 933 East 56th Street ChicagoIL60637 United States SUPA School of Physics and Astronomy University of Glasgow Kelvin Building University Ave GlasgowG12 8QQ United Kingdom Department of Physics California State University Fullerton Fullerton 800 North State College Blvd CA92831 United States Microsoft Corporation Mountain ViewCA United States Microsoft Corporation RedmondWA United States School of Information Studies Syracuse University 343 Hinds Hall SyracuseNY13210 United States Zooniverse The Adler Planetarium 1300 South DuSable Lake Shore Drive ChicagoIL60605 United States Electrical and Computer Engineering Northwestern University 2145 Sheridan Road EvanstonIL60208 United States

Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of Advanced LIGO. We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory. © 2022, CC BY.

关键词： Gaussian noise (electronic)

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Deterministic subtraction of single photons based on a coupled single quantum dot-cavity system

arXiv

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arXiv 2019年

作者： Du, Jinjin Li, Wenfang Bajcsy, Michal Institute for Quantum Computing University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada

We describe a scheme of deterministic single-photon subtraction in a solid-state system consisting of a charged quantum dot coupled to a bimodal photonic-crystal cavity with a moderate magnetic field applied in a Voigt configuration. We numerically simulate injection of optical pulses into one of the modes of the bimodal cavity and show that the system deterministically transfers one photon into the second cavity mode for input pulses in the form of both Fock states and coherent states. Copyright © 2019, The Authors. All rights reserved.

关键词： Particle beams

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image computing and computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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On the Performance of NOMA-Enabled Spectrally and Energy Efficient OFDM (SEE-OFDM) for Indoor Visible Light Communications

On the Performance of NOMA-Enabled Spectrally and Energy Eff...

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IEEE Conference on Vehicular Technology (VTC)

作者： Galymzhan Nauryzbayev Mohamed Abdallah Hany Elgala Division of Information and Computing Technology Qatar Foundation Doha Qatar Department of Electrical and Computer Engineering State University of New York USA

In this paper, we investigate the performance of spectrally and energy efficient orthogonal frequency-division multiplexing (SEE-OFDM) based visible light communications (VLC) systems. We propose to apply non-orthogonal multiple access (NOMA) for the downlink in SEE- OFDM based VLC network. Moreover, we provide a novel approach in decoding the composite NOMA signal. Next, based on the considered channel conditions, we analyze the impact of power-allocating coefficients on the bit-error rate (BER) performance. Finally, satisfying the target BER, we estimate the data rate regions achievable by the receivers under the derived system parameters and then compare the results with the ones obtainable for orthogonal multiple access.

关键词： NOMA OFDM Optical transmitters Optical receivers Light emitting diodes Downlink

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High-rate and high-capacity measurement-deviceindependent quantum key distribution with Fibonacci matrix coding in free space

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Science China(Information Sciences) 2018年第6期61卷 227-237页

作者： Hong LAI Mingxing LUO Josef PIEPRZYK Jun ZHANG Lei PAN Mehmet A.ORGUN School of Computer and Information Science and Centre for Research and Innovation in Software Engineering (RISE) Southwest University Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong University School of Electrical Engineering and Computer Science Queensland University of Technology Institute of Computer Science Polish Academy of Sciences School of Information Technology Deakin University Department of Computing Macquarie University Faculty of Information Technology Macau University of Science and Technology

This paper proposes a high-rate and high-capacity measurement-device-independent quantum key distribution(MDI-QKD) protocol with Fibonacci-valued and Lucas-valued orbital angular momentum(OAM) entangled states in free space. In the existing MDI-OAM-QKD protocols, the main encoding algorithm handles encoded numbers in a bit-by-bit manner. To design a fast encoding algorithm, we introduce a Fibonacci matrix coding algorithm, by which, encoded numbers are separated into segments longer than one bit. By doing so, when compared to the existing MDI-OAM-QKD protocols, the new protocol can effectively increase the key rate and the coding capacity. This is because Fibonacci sequences are used in preparing OAM entangled states, reducing the misattribution errors(which slow down the execution cycle of the entire QKD) in QKD protocols. Moreover, our protocol keeps the data blocks as small as possible, so as to have more blocks in a given time interval. Most importantly, our proposed protocol can distill multiple Fibonacci key matrices from the same block of data, thus reducing the statistical fluctuations in the sample and increasing the final QKD rate. Last but not the least, the sender and the receiver can omit classical information exchange and bit flipping in the secure key distillation stage.

关键词： measurement-device-independent quantum key distribution Fibonacci-matrix coding free space orbital angular momentum bit flipping misattribution errors

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Indexed fast network proximity querying 44th

Indexed fast network proximity querying

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44th International Conference on Very Large Data Bases, VLDB 2018

作者： Coşkun, Mustafa Grama, Ananth Koyuturk, Mehmet Electrical Engineering and Computer Science Case Western Reserve University ClevelandOH44106 United States Qatar Computing Research Institute HBKU Doha Qatar Department of Computer Science Purdue University West LafayetteIN47906 United States Center for Proteomics and Bioinformatics Case Western Reserve University ClevelandOH44106 United States

Node proximity queries are among the most common operations on network databases. A common measure of node proximity is random walk based proximity, which has been shown to be less susceptible to noise and missing data. Realtime processing of random-walk based proximity queries poses significant computational challenges for larger graphs with over billions of nodes and edges, since it involves solution of large linear systems of equations. Due to the importance of this operation, significant effort has been devoted to developing efficient methods for random-walk based node proximity computations. These methods either aim to speed up iterative computations by exploiting numerical properties of random walks, or rely on computation and storage of matrix inverses to avoid computation during query processing. Although both approaches have been well studied, the speedup achieved by iterative approaches does not translate to real-time query processing, and the storage requirements of inversion-based approaches prohibit their use on very large graph databases. We present a novel approach to significantly reducing the computational cost of random walk based node proximity queries with scalable indexing. Our approach combines domain graph-partitioning based indexing with fast iterative computations during query processing using Chebyshev polynomials over the complex elliptic plane. This approach combines the query processing benefits of inversion techniques with the memory and storage benefits of iterative approache. Using real-world networks with billions of nodes and edges, and top-k proximity queries as the benchmark problem, we show that our algorithm, I-Chopper, significantly outperforms existing methods. Specifically, it drastically reduces convergence time of the iterative procedure, while also reducing storage requirements for indexing. © 2018 VLDB Endowment 21508097/18/4.

关键词： Query processing

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