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Jamming-assisted proactive eavesdropping over two suspicious communication links

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arXiv 2019年

作者： Zhang, Haiyang Duan, Lingjie Zhang, Rui Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

This paper studies a new and challenging wireless surveillance problem where a legitimate monitor attempts to eavesdrop two suspicious communication links simultaneously. To facilitate concurrent eavesdropping, our multi-antenna legitimate monitor employs a proactive eavesdropping via jamming approach, by selectively jamming suspicious receivers to lower the transmission rates of the target links. In particular, we are interested in characterizing the achievable eavesdropping rate region for the minimum-mean-squared-error (MMSE) receiver case, by optimizing the legitimate monitor's jamming transmit covariance matrix subject to its power budget. As the monitor cannot hear more than what suspicious links transmit, the achievable eavesdropping rate region is essentially the intersection of the achievable rate region for the two suspicious links and that for the two eavesdropping links. The former region can be purposely altered by the monitor's jamming transmit covariance matrix, whereas the latter region is fixed when the MMSE receiver is employed. Therefore, we first analytically characterize the achievable rate region for the two suspicious links via optimizing the jamming transmit covariance matrix and then obtain the achievable eavesdropping rate region for the MMSE receiver case. In addition, as the achievable rate region for suspicious links is in general non-convex, we also propose a time-sharing based jamming strategy to enlarge this region and characterize the corresponding achievable eavesdropping rate region for the MMSE receiver case. Furthermore, we also extend our study to the MMSE with successive interference cancellation (MMSE-SIC) receiver case and characterize the corresponding achievable eavesdropping rate region by jointly optimizing the time-sharing factor between different decoding orders. Finally, numerical results are provided to corroborate our analysis and examine the eavesdropping performance. Copyright © 2019, The Authors. All rights reserv

关键词： Jamming

An Explicit Time Domain Finite Element Boundary Integral Method with Element Level Domain Decomposition for Electromagnetic Scattering Analysis

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An Explicit Time Domain Finite Element Boundary Integral Met...

European Conference on Antennas and Propagation, EuCAP

作者： Ming Dong Ping Li Hakan Bagci Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia Key Laboratory of Ministry of Education of Design and Electromagnetic Compatibility of High-Speed Electronic Systems Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9788831299008

ISBN: (纸本)9781728137124

A numerical scheme, which hybridizes the element level dual field time domain finite element domain decomposition method (ELDFDD/TDFEM) and time domain boundary integral (TDBI) method to accurately and efficiently analyze open-region transient electromagnetic scattering problems, is proposed. The element level decomposition permits an efficient leapfrog-like explicit marching scheme to be used to integrate Maxwell equations in time. The hybridization with TDBI method ensures that an accurate solution can be obtained in the smallest computation domain possible. The accuracy and applicability of the proposed hybrid method is demonstrated by numerical experiments.

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Society for Cardiovascular Magnetic Resonance recommendations toward environmentally sustainable cardiovascular magnetic resonance

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Journal of Cardiovascular Magnetic Resonance 2025年第1期27卷 101840页

作者： Hanneman, Kate Picano, Eugenio Campbell-Washburn, Adrienne E Zhang, Qiang Browne, Lorna Kozor, Rebecca Battey, Thomas Omary, Reed Saldiva, Paulo Ng, Ming Rockall, Andrea Law, Meng Kim, Helen Lee, Yoo Jin Mills, Rebecca Ntusi, Ntobeko Bucciarelli-Ducci, Chiara Markl, Michael Department of Medical Imaging University of Toronto Toronto ON Canada University Clinical Center of Serbia Cardiology Division University of Belgrade Serbia Cardiovascular Branch Division of Intramural Research National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States RDM Division of Cardiovascular Medicine & NDPH Big Data Institute University of Oxford Oxford United Kingdom Dept of Radiology Division of Pediatric Radiology Children's Hospital Colorado University of Colorado School of Medicine United States University of Sydney and Royal North Shore Hospital Sydney Australia Department of Radiology and Medical Imaging University of Virginia Health System Charlottesville VA United States Departments of Radiology & Biomedical Engineering Vanderbilt University Nashville TN United States Department of Pathology University of Sao Paulo School of Medicine Sao Paulo Brazil Department of Diagnostic Radiology School of Clinical Medicine Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Dept of Surgery and Cancer Faculty of Medicine Imperial College London United Kingdom Departments of Neuroscience Electrical and Computer Systems Engineering Monash University Australia Department of Radiology University of Washington WA United States Department of Radiology and Biomedical Engineering UCSF San Francisco CA United States University of Oxford Centre for Clinical Magnetic Resonance Research Oxford United Kingdom Groote Schuur Hospital Department of Medicine University of Cape Town Cape Town South Africa Royal Brompton and Harefield Hospitals Guys’ & St Thomas NHS Trust London United Kingdom Department of Radiology Feinberg School of Medicine Northwestern University Chicago IL United States Greenwell Project Nashville TN United States Department of Radiology Alfred Health Melbourne Australia School of Biomedical Engineering and Imaging Sciences Faculty of Life Sciences and Medicine King's College U

Delivery of health care, including medical imaging, generates substantial global greenhouse gas emissions. The cardiovascular magnetic resonance (CMR) community has an opportunity to decrease our carbon footprint, mitigate the effects of the climate crisis, and develop resiliency to current and future impacts of climate change. The goal of this document is to review and recommend actions and strategies to allow for CMR operation with improved sustainability, including efficient CMR protocols and CMR imaging workflow strategies for reducing greenhouse gas emissions, energy, and waste, and to decrease reliance on finite resources, including helium and waterbody contamination by gadolinium-based contrast agents. The article also highlights the potential of artificial intelligence and new hardware concepts, such as low-helium and low-field CMR, in achieving these aims. Specific actions include powering down magnetic resonance imaging scanners overnight and when not in use, reducing low-value CMR, and implementing efficient, non-contrast, and abbreviated CMR protocols when feasible. Data on estimated energy and greenhouse gas savings are provided where it is available, and areas of future research are highlighted. © 2025 The Authors

关键词： Climate change Energy consumption Greenhouse gas emission Planetary health Sustainability

EEG microstates: Functional significance and short-term test-retest reliability

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Neuroimage: Reports 2022年第2期2卷 100089-100089页

作者： Antonova, Elena Holding, Martin Suen, Ho Chak Sumich, Alex Maex, Reinoud Nehaniv, Chrystopher Division of Psychology Department of Life Sciences College of Health Medicine and Life Sciences & Centre for Cognitive Neuroscience Brunel University London Kingston Lane Uxbridge UB8 3PH United Kingdom Department of Psychology Institute of Psychiatry Psychology & Neuroscience King's College London De Crespigny Park London SE5 8AF United Kingdom Department of Psychology School of Social Sciences Nottingham Trent University 50 Shakespeare Street Nottingham NG1 4FQ United Kingdom Royal Society Wolfson Biocomputation Research Laboratory Centre for Computer Science and Informatics Research University of Hertfordshire College Lane Hatfield AL10 9AB United Kingdom Departments of Systems Design Engineering and Electrical & Computer Engineering University of Waterloo 200 University Avenue W Waterloo N2L 3G1 ON Canada

EEG microstates, reflecting discrete topographical organization of the EEG signal power, may have clinical relevance;however, their functional significance and test-retest reliability remain unclear. To investigate the functional significance of the canonical EEG microstate classes and their pairwise transitions, and to establish their within-session test-retest reliability, we recorded 36-channel EEGs in 20 healthy volunteers during three eyes-closed conditions: mind-wandering, verbalization (silently repeating the word ‘square’ every 2 s), and visualization (visualizing a square every 2 s). Each condition lasted 3 min and the sequence of three conditions was repeated four times (two runs of two sequence repetitions). The participants' alertness and their sense of effort during the experiment were rated using visual-analogue scales. The EEG data were 2–20 Hz bandpass-filtered and analysed into the four canonical microstate classes: A, B, C, and D. EEG microstate classes C and D were persistently more dominant than classes A and B in all conditions. Of the first-order microstate parameters, average microstate duration was most reliable. The duration of class D microstate was longer during mind-wandering (106.8 ms) than verbalization (102.2 ms) or visualization (99.8 ms), with a concomitantly higher coverage (36.4% vs. 34.7% and 35.2%), but otherwise there was no clear association of the four microstate classes to particular mental states. The test-retest reliability was higher at the beginning of each run, together with higher average alpha power and subjective ratings of alertness. Only the transitions between classes C and D (from C to D in particular) were significantly higher than what would be expected from the respective microstates' occurrences. The transition probabilities, however, did not distinguish between conditions, and their test-retest reliability was overall lower than that of the first-order parameters such as duration and coverage. Further studies

关键词： Biomarkers EEG microstates Mind-wandering Neuroimaging Resting-state EEG Resting-state networks

A robust Lyapunov criterion for non-oscillatory behaviors in biological interaction networks

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arXiv 2020年

作者： Angeli, David Al-Radhawi, M. Ali Sontag, Eduardo D. The Department of Electrical & Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Dipartimento di Ingegneria dell'Informazione University of Florence Florence Italy Department of Electrical and Computer Engineering Department of Bioengineering Northeastern University BostonMA02115 United States The Departments of Chemical Engineering and Mathematics Northeastern University The Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States

We introduce the notion of non-oscillation, propose a constructive method for its robust verification, and study its application to biological interaction networks (also known as, chemical reaction networks). We begin by revisiting Muldowney's result on the non-existence of periodic solutions based on the study of the variational system of the second additive compound of the Jacobian of a nonlinear system. We show that exponential stability of the latter rules out limit cycles, quasi-periodic solutions, and broad classes of oscillatory behavior. We focus then on nonlinear equations arising in biological interaction networks with general kinetics, and we show that the dynamics of the aforementioned variational system can be embedded in a linear differential inclusion. We then propose algorithms for constructing piecewise linear Lyapunov functions to certify global robust non-oscillatory behavior. Finally, we apply our techniques to study several regulated enzymatic cycles where available methods are not able to provide any information about their qualitative global behavior. Copyright © 2020, The Authors. All rights reserved.

关键词： Piecewise linear techniques

COVID-19 impact on global maritime mobility

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arXiv 2020年

作者： Millefiori, Leonardo M. Braca, Paolo Zissis, Dimitris Spiliopoulos, Giannis Marano, Stefano Willett, Peter K. Carniel, Sandro NATO STO Centre for Maritime Research and Experimentation Research Department La Spezia19126 Italy University of the Aegean Department of Product and Systems Design Engineering Syros84100 Greece MarineTraffic Athens115 25 Greece FiscianoSA84084 Italy University of Connecticut Department of Electrical and Computer Engineering Storrs06269 United States

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80 % of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local vessel density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June, when the most severe restrictions were in force. We quantify a variation of mobility between −5.62 % and −13.77 % for container ships, between +2.28 % and −3.32 % for dry bulk, between −0.22 % and −9.27 % for wet bulk, and between −19.57 % and −42.77 % for passenger traffic. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50 000 ships, a figure that closely parallels the documented size of the world merchant

关键词： Ships

COVID-19 Impact on Global Maritime Mobility

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Research Square

Research Square 2020年

作者： Millefiori, Leonardo M. Braca, Paolo Zissis, Dimitris Spiliopoulos, Giannis Marano, Stefano Willett, Peter K. Carniel, Sandro NATO STO Centre for Maritime Research and Experimentation Research Department La Spezia19126 Italy University of the Aegean Department of Product and Systems Design Engineering Syros 84100 Greece MarineTraffic Athens115 25 Greece SA Fisciano84084 Italy University of Connecticut Department of Electrical and Computer Engineering Storrs 06269 United States

To prevent the outbreak of the Coronavirus disease (COVID-19), numerous countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data, collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and the containment measures had on the shipping industry, which accounts alone for more than 80 % of the world trade. We introduce the notion of a "maritime mobility index", a synthetic composite index, to quantitatively assess ship mobility in a given unit of time. The mobility index calculation used in this study, has a worldwide extent and is based on the computation of Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. The reduced activity is observable from March to June, when the most severe restrictions were in force, producing a variation of mobility quantified between −5.62 % and −13.77 % for container ships, between 2.28 % and −3.32 % for dry bulk, between −0.22 % and −9.27 % for wet bulk, and between −19.57 % and −42.77 % for passenger shipping. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50 000 ships, a figure that closely parallels the documented size of the world merchant fleet. © 2020, CC BY.

关键词： COVID-19

Latency Modeling of Hyperledger Fabric for Blockchain-enabled IoT Networks

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arXiv 2021年

作者： Lee, Sungho Kim, Minsu Lee, Jemin Hsu, Ruei-Hau Kim, Min-Soo Quek, Tony Q.S. 333 Techno Jungang-daero Dalseong-gun Daegu42988 Korea Republic of DGIST Daegu42998 Korea Republic of Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Suwon16419 Korea Republic of National Sun Yat-sen University 70 Lienhai Rd. Kaohsiung80424 Taiwan 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

Hyperledger Fabric (HLF), one of the most popular private blockchain platforms, has recently received attention for blockchain-enabled Internet of Things (BC-IoT) networks. However, for IoT devices handling latency-critical tasks, the additional time spent in HLF has emerged as a new challenge in BC-IoT networks. In this paper, therefore, we develop an HLF latency model using the probability distribution fitting method for HLF-based IoT networks. We first explain the architecture and the transaction flow in HLF, and the structure of an HLF-based IoT network. After implementing real HLF, we capture the latencies that each transaction experiences for various HLF environments, and then show that the total latency of HLF can be modeled as a Gamma distribution. Our HLF latency model is also validated by conducting a goodness-of-fit test, i.e., KS test. Furthermore, we explore the impacts of three HLF parameters including transaction generation rate, block size, and block-generation timeout on the HLF latency. As a result, some HLF design insights on minimizing the latency are provided for HLF-based IoT networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Probability distributions

Densenet for breast tumor classification in mammographic images

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arXiv 2021年

作者： Gaona, Yuliana Jiménez Rodriguez-Alvarez, María José Morató, Hector Espinó Malla, Darwin Castillo Lakshminarayanan, Vasudevan Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja San Cayetano Alto s/n LojaCP1101608 Ecuador Instituto de Instrumentación para la Imagen Molecular I3M Universitat Politécnica de Valencia ValenciaE-46022 Spain Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada

Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. The common approaches of screening for women are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload by pathologists, who are prone to diagnostic errors. Thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to features selection and extraction;and the classification was carried out by DenseNet architecture. Finally, the precision and accuracy of the model is evaluated by cross validation matrix and AUC curve. To summarize, the findings of this study may provide a helpful to improve the diagnosis and efficiency in the automatic tumor localization through the medical image classification. © 2021, CC BY.

关键词： Convolutional neural networks