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Recent advances in electromagnetic compatibility of 3D-ICS - Part II

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IEEE Electromagnetic Compatibility Magazine 2016年第1期5卷 65-74页

作者： Sicard, Etienne Jianfei, Wu Shen, Rongjun Li, Er-Ping Liu, En-Xiao Kim, Joungho Cho, Jonghyun Swaminathan, Madhavan INSA GEI 135 Avenue de Rangueil Toulouse31077 France School of Electronic Science and Engineering NUDT Changsha Hunan410073 China Institute of High Performance Computing ASTAR 1 Fusionopolis Way Singapore138632 Singapore Daejeon Korea Republic of John Pippin Department in Electromagnetics School of Electrical and Computer Engineering Interconnect and Packaging Center Georgia Institute of Technology AtlantaGA30332-0250 United States

This second part addresses a selection of topics related to Electromagnetic interference (EMI) issues in three-dimensional integrated components. Details about Through Silicon Via (TSV) technology, modelling and parasitic effects are introduced in the first part, then key concepts such as signal integrity (SI), power integrity (PI), and Electromagnetic Interference are introduced. © 2012 IEEE.

关键词： Signal interference

Software Defined Networking as a Mitigation Strategy for data Communications in Power Systems Critical Infrastructure

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Software Defined Networking as a Mitigation Strategy for Dat...

IEEE Power and Energy Society General Meeting

作者： John O'Raw David M. Laverty D. John Morrow Department of Computing Letterkenny Institute of Technology School of Electronics Electrical Engineering and Computer Science Queen's University

ISBN: (纸本)9781509041695

The frequency and cost of cyber-attacks continues to grow in commercial information systems. In power systems critical infrastructure, any level of breach may be unacceptable. Securing this infrastructure requires new approaches to communications networks which are not readily vulnerable to remote attack. Standard protocols like Ethernet and IP are difficult to secure. Within well-defined domains, SDN may provide mitigation for vulnerabilities which are otherwise intractable. Methodical approaches have been developed and implemented at a test site.

关键词： Smart Grid Critical Infrastructure SDN Smart grids power systems data communication INFRASTRUCTURE critical infrastructures networks(communications) Ethernet networks infraction Networking computer based information systems

Journal of Open Source Software (JOSS): Design and first-year review

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arXiv 2017年

作者： Smith, Arfon M. Niemeyer, Kyle E. Katz, Daniel S. Barba, Lorena A. Githinji, George Gymrek, Melissa Huff, Kathryn D. Madan, Christopher R. Mayes, Abigail Cabunoc Moerman, Kevin M. Prins, Pjotr Ram, Karthik Rokem, Ariel Teal, Tracy K. Guimera, Roman Valls Vanderplas, Jacob T. Data Science Mission Office Space Telescope Science Institute BaltimoreMD United States School of Mechanical Industrial and Manufacturing Engineering Oregon State University CorvallisOR United States National Center for Supercomputing Applications & Department of Computer Science Department of Electrical and Computer Engineering School of Information Sciences University of Illinois at Urbana–Champaign UrbanaIL United States Department of Mechanical & Aerospace Engineering George Washington University WashingtonDC United States KEMRI–Wellcome Trust Research Programme Kilifi Kenya Departments of Medicine & Computer Science and Engineering University of California San DiegoCA United States Department of Nuclear Plasma and Radiological Engineering University of Illinois at Urbana–Champaign UrbanaIL United States School of Psychology University of Nottingham Nottingham United Kingdom Mozilla Foundation TorontoON Canada MIT Media Lab Massachusetts Institute of Technology CambridgeMA United States Trinity Centre for Bioengineering Trinity College University of Dublin Dublin Ireland University of Tennessee Health Science Center MemphisTN United States University Medical Centre Utrecht Utrecht Netherlands Berkeley Institute for Data Science University of California Berkeley BerkeleyCA United States eScience Institute University of Washington SeattleWA United States Data Carpentry DavisCA United States University of Melbourne Centre for Cancer Research University of Melbourne Melbourne Australia

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision;while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI). Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting

Entanglement manipulation and distillability beyond LOCC

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arXiv 2017年

作者： Chitambar, Eric De Vicente, Julio I. Girard, Mark W. Gour, Gilad Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States E-28911 Spain Department of Mathematics and Statistics University of Calgary ABT2N 1N4 Canada Institute for Quantum Computing University of Waterloo ONN2L 3G1 Canada

When a quantum system is distributed to spatially separated parties, it is natural to consider how the system evolves when the parties perform local quantum operations with classical communication (LOCC). However, the structure of LOCC channels is exceedingly complex leaving many important physical problems unsolved. In this paper we consider generalized resource theories of entanglement based on different relaxations to the class of LOCC. The behavior of various entanglement measures is studied under non-entangling channels, as well as the newly introduced classes of dually non-entangling and PPT-preserving channels. In an effort to better understand the nature of LOCC bound entanglement, we study the problem of entanglement distillation in these generalized resource theories. We first show that unlike LOCC, general non-entangling maps can be superactivated, in the sense that two copies of the same non-entangling map can nevertheless be entangling. On the single-copy level, we demonstrate that every NPT entangled state can be converted into an LOCC-distillable state using channels that are both dually non-entangling and having a PPT Choi representation and that every state can be converted into an LOCC-distillable state using operations belonging to any family of polytopes that approximate LOCC. We then turn to the stochastic convertibility of multipartite pure states and show that any two states can be interconverted by any polytope approximation to the set of separable channels. Finally, as an analog to k-positive maps, we introduce and analyze the set of k-non-entangling channels. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

Cumulative prospect theory meets reinforcement learning: Prediction and control 33

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Cumulative prospect theory meets reinforcement learning: Pre...

33rd International Conference on Machine Learning, ICML 2016

作者： Prashanth, L.A. Jie, Cheng Fu, Michael Marcus, Steve Szepesvari, Csaba Institute for Systems Research University of Maryland United States Department of Mathematics University of Maryland United States Robert H. Smith School of Business Institute for Systems Research University of Maryland United States Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland United States Department of Computing Science University of Alberta Canada

ISBN: (纸本)9781510829008

Cumulative prospect theory (CPT) is known to model human decisions well, with substantial empirical evidence supporting this claim. CPT works by distorting probabilities and is more general than the classic expected utility and coherent risk measures. We bring this idea to a risksensitive reinforcement learning (RL) setting and design algorithms for both estimation and control. The RL setting presents two particular challenges when CPT is applied: estimating the CPT objective requires estimations of the entire distribution of the value function and finding a randomized optimal policy. The estimation scheme that we propose uses the empirical distribution to estimate the CPT-value of a random variable. We then use this scheme in the inner loop of a CPT-value optimization procedure that is based on the well-known simulation optimization idea of simultaneous perturbation stochastic approximation (SPSA). We provide theoretical convergence guarantees for all the proposed algorithms and also illustrate the usefulness of CPT-based criteria in a traffic signal control application.

关键词： Estimation

Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network

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arXiv 2017年

作者： Cao, Xiangyong Zhou, Feng Xu, Lin Meng, Deyu Xu, Zongben Paisley, John IEEE IEEE School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China National Laboratory of Radar Signal Processing Xidian University Xi’an China NYU Multimedia and Visual Computing Lab New York University Abu Dhabi United Arab Emirates Department of Electrical Engineering & Data Science Institute Columbia University New YorkNY United States Columbia University China Scholarship Council

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using α-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Spectroscopy

Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks

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arXiv 2017年

作者： Zhang, Shan Zhang, Ning Fang, Xiaojie Yang, Peng Shen, Xuemin Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Department of Computing Science Texas A&M University-Corpus Christi 6300 Ocean Dr. Corpus ChristiTX78412 United States School of Electronics and Information Technology Harbin Institute of Technology Harbin China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed. Copyright © 2017, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Procedural content generation via machine learning (PCGML)

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arXiv 2017年

作者： Summerville, Adam Snodgrass, Sam Guzdial, Matthew Holmgård, Christoffer Hoover, Amy K. Isaksen, Aaron Nealen, Andy Togelius, Julian Department of Computational Media University of California Santa CruzCA95064 United States College of Computing and Informatics Drexel University PhiladelpiaPA19104 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Duck and Cover Games ApS Copenhagen K1311 Denmark College of Arts Media and Design Northeastern University BostonMA02115 United States Department of Computer Science and Engineering New York University BrooklynNY11201 United States

This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement;this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the generated content. Multiple PCGML methods are covered, including neural networks: long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks;Markov models: n-grams and multi-dimensional Markov chains;clustering;and matrix factorization. Finally, we discuss open problems in PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic. Copyright © 2017, The Authors. All rights reserved.

关键词： Knowledge representation

Image segmentation using membrane computing: A literature survey 11th

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Image segmentation using membrane computing: A literature su...

11th International Conference on Bio-inspired computing – Theories and Applications, BIC-TA 2016

作者： Yahya, Rafaa I. Shamsuddin, Siti Mariyam Yahya, Salah I. Hasan, Shafatnnur Al-Salibi, Bisan Al-Khafaji, Ghada Faculty of Computing Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Computer College of Science University of Al-Mustansiriyah Baghdad Iraq UTM Big Data Center Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Software Engineering Koya University Iraq School of Computer Sciences Universiti Sains Malaysia USM GelugorPenang11800 Malaysia Department of Computer Science College of Science Baghdad University Baghdad Iraq

ISBN: (纸本)9789811036101

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Image segmentation