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Physical layer security jamming: Theoretical limits and practical designs in wireless networks

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arXiv 2017年

作者： Cumanan, Kanapathippillai Xing, Hong Xu, Peng Zheng, Gan Dai, Xuchu Nallanathan, Arumugam Ding, Zhiguo Karagiannidis, George K. Department of Electronics University of York YO10 5DD United Kingdom School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom Department of Informatics King's College London United Kingdom Department of Electronic Engineering and Information Science University of Science and Technology of China China Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University United Kingdom Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

Physical layer security has been recently recognized as a promising new design paradigm to provide security in wireless networks. In addition to the existing conventional cryptographic methods, physical layer security exploits the dynamics of fading channels to enhance secured wireless links. In this approach, jamming plays a key role by generating noise signals to confuse the potential eavesdroppers, and significantly improves quality and reliability of secure communications between legitimate terminals. This article presents theoretical limits and practical designs of jamming approaches for physical layer security. In particular, the theoretical limits explore the achievable secrecy rates of user cooperation based jamming whilst the centralized, and game theoretic based precoding techniques are reviewed for practical implementations. In addition, the emerging wireless energy harvesting techniques are exploited to harvest the required energy to transmit jamming signals. Future directions of these approaches, and the associated research challenges are also briefly outlined. Copyright © 2017, The Authors. All rights reserved.

关键词： Jamming

Overview of the DESI Milky Way Survey

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arXiv 2022年

作者： Cooper, Andrew P. Koposov, Sergey E. Prieto, Carlos Allende Manser, Christopher J. Kizhuprakkat, Namitha Myers, Adam D. Dey, Arjun Gänsicke, Boris T. Li, Ting S. Rockosi, Constance Valluri, Monica Najita, Joan Deason, Alis Raichoor, Anand Wang, Mei-Yu Ting, Yuan-Sen Kim, Bokyoung Carrillo, Andreia Wang, Wenting Silva, Leandro Beraldo e Han, Jiwon Jesse Ding, Jiani Sánchez-Conde, Miguel Aguilar, Jessica N. Ahlen, Steven Bailey, Stephen Belokurov, Vasily Brooks, David Cunha, Katia Dawson, Kyle de la Macorra, Axel Doel, Peter Eisenstein, Daniel J. Fagrelius, Parker Fanning, Kevin Font-Ribera, Andreu Forero-Romero, Jaime E. Gaztañaga, Enrique Gontcho, Satya Gontcho A. Guy, Julien Honscheid, Klaus Kehoe, Robert Kisner, Theodore Kremin, Anthony Landriau, Martin Levi, Michael E. Martini, Paul Meisner, Aaron M. Miquel, Ramon Moustakas, John Nie, Jundan Palanque-Delabrouille, Nathalie Percival, Will J. Poppett, Claire Prada, Francisco Rehemtulla, Nabeel Schlafly, Edward Schlegel, David Schubnell, Michael Sharples, Ray M. Tarlé, Gregory Wechsler, Risa H. Weinberg, David H. Zhou, Zhimin Zou, Hu Institute of Astronomy Department of Physics National Tsing Hua University 101 Kuang-Fu Rd. Sec. 2 Hsinchu30013 Taiwan Center for Informatics and Computation in Astronomy NTHU 101 Kuang-Fu Rd. Sec. 2 Hsinchu30013 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Institute of Astronomy University of Cambridge Madingley Rd. CambridgeCB3 0HA United Kingdom Instituto de Astrofísica de Canarias C/ Vía Láctea s/n La Laguna TenerifeE-38205 Spain Universidad de La Laguna Dpto. Astrofísica La Laguna TenerifeE-38206 Spain Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Department of Physics & Astronomy University of Wyoming 1000 E. University Dept. 3905 LaramieWY82071 United States NSF’s NOIRLab 950 N. Cherry Ave. TucsonAZ85719 United States Department of Physics University of Warwick Gibbet Hill Rd. CoventryCV4 7AL United Kingdom Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Department of Astronomy and Astrophysics University of California Santa Cruz 1156 High St. Santa CruzCA95065 United States University of California Observatories 1156 High St. Santa CruzCA95065 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States Institute for Computational Cosmology Department of Physics Durham University South Rd. DurhamDH1 3LE United Kingdom Lawrence Berkeley National Laboratory 1 Cyclotron Rd. BerkeleyCA94720 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Department of Astronomy School of Physics and Astronomy Shanghai Jiao Tong Universi

We describe the Milky Way Survey (MWS) that will be undertaken with the Dark Energy Spectroscopic Instrument (DESI) on the Mayall 4m telescope at the Kitt Peak National Observatory. Over the next 5 yr DESI MWS will observe approximately seven million stars at Galactic latitudes |b| > 20◦, with an inclusive target selection scheme focused on the thick disk and stellar halo. MWS will also include several high-completeness samples of rare stellar types, including white dwarfs, low-mass stars within 100 pc of the Sun, and horizontal branch stars. We summarize the potential of DESI to advance understanding of Galactic structure and stellar evolution. We introduce the final definitions of the main MWS target classes and estimate the number of stars in each class that will be observed. We describe our pipelines for deriving radial velocities, atmospheric parameters, and chemical abundances. We use ' 500, 000 spectra of unique stellar targets from the DESI Survey Validation program (SV) to demonstrate that our pipelines can measure radial velocities to ' 1 km s−1 and [Fe/H] accurate to ' 0.2 dex for typical stars in our main sample. We find the stellar parameter distributions from ≈ 100 deg2 of SV observations with & 90% completeness on our main sample are in good agreement with expectations from mock catalogs and previous surveys. © 2022, CC BY.

关键词： Pipelines

Energy efficient data caching in wireless sensor networks: A case of precision agriculture 6th

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Energy efficient data caching in wireless sensor networks: A...

6th International Conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2014

作者： Musaazi, Kizito Patrick Bulega, Tonny Lubega, Stephen Mutaawe Department of Networks School of Computing and Informatics Technology Makerere University Kampala Uganda

ISBN: (纸本)9783319168852

Implementation of information and control technologies is essential for application areas such as precision agriculture, where Wireless Sensor Networks are deployed. In wireless sensor networks (WSNs), data is collected from the source nodes and routed to the sink. And from the sink, this data is sent to the internet. Energy conservation is paramount during the communication phase in order to prolong the lifetime of the sensor nodes. The main method deployed to conserve energy consists of switching off the nodes transceiver when not transmitting nor receiving packets. In this project, we introduce a Data Caching Algorithm (DCAL) that optimises the sleep/wake up periods of wireless sensor nodes to achieve low energy consumption and latency. The algorithm was used to analyse data in order to avoid situations of continuously transmitting the same information from the source node to the sink. This approach exploits the duty cycling scheme and operating on top of Media Access Control (MAC).We implement a Data Caching Algorithm (DCAL) in MIXIM (Mixed simulator for wireless andmobile networks using OMNET++ simulation engine) running on OMNET++ (Objective Modular Network Testbed in C++) simulator where different network sizes and simulation times were varied. We compared the performance of our algorithm with Data gathering MAC (DMAC) and Sensor-MAC (SMAC) and results showed that DCAL scheme significantly performs better in energy conservation and achieved a better latency than the DMAC protocol. Further still the DCAL approach performed extremely well in large, random networks. © Institute for Computer Sciences, Social informatics and Telecommunications Engineering 2015.

关键词： Medium access control

Real-time 3D tumor tracking based on 2D projections of implanted markers 31

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Real-time 3D tumor tracking based on 2D projections of impla...

31st International Conference on Computers and Their Applications, CATA 2016

作者： Mao, Songan Wu, Huanmei Lu, Minghui Cheng, Chee-Wai Dept. of Electrical and Computer Engineering Purdue University IndianapolisIN46202 United States School of Engineering and Technology IUPUI School of Informatics and Computing IUPUI IndianapolisIN46202 United States PerkinElmer Medical Imaging Inc. Santa ClaraCA95054 United States UH Case Medical Center 11100 Euclid Ave ClevelandOH44106 United States

ISBN: (纸本)9781943436026

In cancer radiation therapy, the treatment effectiveness is degraded greatly by the tumor respiratory motion during treatment delivery, induced by patient free breathing and organ motions. Advance treatment delivery techniques with image guidance helps to address the tumor motion challenges. However, real-time tumor localization is still a big concern as the respiratory motion dynamically changes its motion velocities, directions, and amplitude during treatment delivery. In this project, an optimized internal marker 3D localization method was proposed by combining the use of onboard kiovoltage (kV) beam and megavoltage (MV) imaging systems. Two sets of 2D projection images from any pair of kV-kV, kV-MV or MV-MV with different gantry rotation angles will be utilized to derive the 3D tumor positions. This approach transforms the projection 2D images to the original 3D location without requirements of the two imaging systems to be orthogonal. The accuracy of the proposed method was validated by several evaluation metrics. The average 3D Euclidean difference is between 0.21 mm to 0.56 mm, which is within the range of clinical applications. Further study will be carried out to evaluate the system comprehensively and integrate to the existing treatment delivery system for better communications among different systems. It potentially will improve the treatment accuracy and provide better health care to cancer patients. Copyright ISCA.

关键词： Tumors

Rpyfmm: Parallel adaptive fast multipole method for rotne-prager-yamakawa tensor in biomolecular hydrodynamics simulations

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arXiv 2017年

作者： Guan, W. Cheng, X. Huang, J. Huber, G. Li, W. McCammon, J.A. Zhang, B. Department of Mathematics University of North Carolina Chapel HillNC27599-3250 United States Medicinal Chemistry and Pharmacognosy College of Pharmacy Ohio State University ColumbusOH43210 United States School of Transportation and Vehicle Engineering Shandong University of Technology ZiboShandong China Department of Chemistry and Biochemistry Department of Pharmacology University of California at San Diego La JollaCA92093-0365 United States Center for Research in Extreme Scale Technologies School of Informatics Computing and Engineering Indiana University BloomingtonIN47404 United States

RPYFMM is a software package for the efficient evaluation of the potential field governed by the Rotne-Prager-Yamakawa (RPY) tensor interactions in biomolecular hydrodynamics simulations. In our algorithm, the RPY tensor is decomposed as a linear combination of four Laplace interactions, each of which is evaluated using the adaptive fast multipole method (FMM) [1] where the exponential expansions are applied to diagonalize the multipole-to-local translation operators. RPYFMM offers a unified execution on both shared and distributed memory computers by leveraging the DASHMM library [2, 3]. Preliminary numerical results show that the interactions for a molecular system of 15 million particles (beads) can be computed within one second on a Cray XC30 cluster using 12, 288 cores, while achieving approximately 54% strong-scaling efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Tensors

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge

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arXiv 2018年

作者： Veta, Mitko Heng, Yujing J. Stathonikos, Nikolas Bejnordi, Babak Ehteshami Beca, Francisco Wollmann, Thomas Rohr, Karl Shah, Manan A. Wang, Dayong Rousson, Mikael Hedlund, Martin Tellez, David Ciompi, Francesco Zerhouni, Erwan Lanyi, David Viana, Matheus Kovalev, Vassili Liauchuk, Vitali Phoulady, Hady Ahmady Qaiser, Talha Graham, Simon Rajpoot, Nasir Sjöblom, Erik Molin, Jesper Paeng, Kyunghyun Hwang, Sangheum Park, Sunggyun Jia, Zhipeng Chang, Eric I-Chao Xu, Yan Beck, Andrew H. Van Diest, Paul J. Pluim, Josien P.W. Medical Image Analysis Group Eindhoven University of Technology Eindhoven Netherlands Department of Pathology Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology University Medical Center Utrecht Utrecht Netherlands Diagnostic Image Analysis Group Radboud University Medical Center Nijmegen Netherlands Department of Pathology Stanford University School of Medicine Biomedical Computer Vision Group University of Heidelberg BIOQUANT IPMB DKFZ Heidelberg Germany Harker School San Jose United States ContextVision AB Linköping Sweden Foundations of Cognitive Computing IBM Research Zürich Rüschlikon Switzerland Visual Analytics and Insights IBM Research Brazil Saõ Paulo Brazil Biomedical Image Analysis Department United Institute of Informatics Minsk Belarus Department of Computer Science and Engineering University of South Florida TampaFL United States Department of Computer Science University of Warwick Warwick United Kingdom Research Sectra Linköping Sweden Lunit Inc. Seoul Korea Republic of Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China Microsoft Research Beijing China Biology and Medicine Engineering Beihang University Beijing China IBM Research Brazil

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a realworld scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task. Copyright © 2018, The Authors. All rights reserved.

关键词： Diseases

Reducing Redundant Search in Parallel Graph Mining Using Exceptions

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Reducing Redundant Search in Parallel Graph Mining Using Exc...

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Shingo Okuno Tasuku Hiraishi Hiroshi Nakashima Masahiro Yasugi Jun Sese Graduate School of Informatics Kyoto University Academic Center for Computing and Media Studies Kyoto University Department of Artificial Intelligence Kyushu Institute of Technology National Institute of Advanced Industrial Science and Technology

ISBN: (纸本)9781509036837

This paper proposes an implementation of a parallel graph mining algorithm using a task-parallel language having exception handling features. The performance of a straightforward task-parallel implementation is poor for many practical backtrack search algorithms due to pruning employed in them, a worker may prune a useless subtree, which is pruned before traversal in the sequential search algorithms for search space reduction, after another worker starts the traversal of it resulting in a large amount of redundant search. Such redundancy will be significantly reduced by letting a worker know that the subtree which it is traversing is pruned so that it aborts the traversal. This abortion can be implemented elegantly and efficiently using the task-parallel language that has a mechanism for exception handling by which all running parallel tasks in a try block with an exception are automatically aborted. We applied this abort mechanism to the graph mining algorithm called COPINE, which is practically used for drug discovery, using the task-parallel language Tascell. As a result, we reduced the search space by 31.9% and the execution time by 27.4% in a 28-worker execution.

关键词： Itemsets Heuristic algorithms Electronic mail Drugs Redundancy Search problems Load management

Limited individual attention and online virality of low-quality information

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Nature Human Behaviour 2017年第7期1卷 1-7页

作者： Qiu, Xiaoyan Oliveira, Diego F.M. Sahami Shirazi, Alireza Flammini, Alessandro Menczer, Filippo School of Economics and Management Shanghai Institute of Technology 100 Haiquan Road Fengxian District Shanghai 201418 China Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University 919 East 10th Street Bloomington 47408 IN United States Yahoo Research 701 1st Avenue Sunnyvale 94089 CA United States Indiana University Network Science Institute 1001 IN-45 Bloomington 47408 IN United States

Social media are massive marketplaces where ideas and news compete for our attention 1. Previous studies have shown that quality is not a necessary condition for online virality 2 and that knowledge about peer choices can distort the relationship between quality and popularity 3. However, these results do not explain the viral spread of low-quality information, such as the digital misinformation that threatens our democracy 4. We investigate quality discrimination in a stylized model of an online social network, where individual agents prefer quality information, but have behavioural limitations in managing a heavy flow of information. We measure the relationship between the quality of an idea and its likelihood of becoming prevalent at the system level. We find that both information overload and limited attention contribute to a degradation of the market's discriminative power. A good tradeoff between discriminative power and diversity of information is possible according to the model. However, calibration with empirical data characterizing information load and finite attention in real social media reveals a weak correlation between quality and popularity of information. In these realistic conditions, the model predicts that low-quality information is just as likely to go viral, providing an interpretation for the high volume of misinformation we observe online.

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Time-Bin-Encoded Boson Sampling with a Single-Photon Device

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Physical Review Letters 2017年第19期118卷 190501-190501页

作者： Yu He X. Ding Z.-E. Su H.-L. Huang J. Qin C. Wang S. Unsleber C. Chen H. Wang Y.-M. He X.-L. Wang W.-J. Zhang S.-J. Chen C. Schneider M. Kamp L.-X. You Z. Wang S. Höfling Chao-Yang Lu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS-Alibaba Quantum Computing Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Technische Physik Physikalisches Instität and Wilhelm Conrad Röntgen-Center for Complex Material Systems Universitat Würzburg Am Hubland D-97074 Wüzburg Germany State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences 865 Changning Road Shanghai 200050 China SUPA School of Physics and Astronomy University of St Andrews St Andrews KY16 9SS United Kingdom

Boson sampling is a problem strongly believed to be intractable for classical computers, but can be naturally solved on a specialized photonic quantum simulator. Here, we implement the first time-bin-encoded boson sampling using a highly indistinguishable (∼94%) single-photon source based on a single quantum-dot-micropillar device. The protocol requires only one single-photon source, two detectors, and a loop-based interferometer for an arbitrary number of photons. The single-photon pulse train is time-bin encoded and deterministically injected into an electrically programmable multimode network. The observed three- and four-photon boson sampling rates are 18.8 and 0.2 Hz, respectively, which are more than 100 times faster than previous experiments based on parametric down-conversion.

关键词： Optical tests of quantum theory Quantum algorithms Quantum optics Computational complexity