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International Conference on Microelectronics, ICM

作者： Oussama Kerdjidj Khalida Ghanem Abbes Amira Farid Harizi Fatima Chouireb Lab Telecommunications Signals and Systems University of Amar Telidji-Laghouat Wireless Communication Telecom Centre for Development of Advanced Technologies Algeria School of Computing University of the West of Scotland Paisley Scotland

Compressed Sensing (CS) is a promising method for signal recovery using fewer measurement samples than the ordinarily amount imposed by Shannon's sampling theorem. Many fields may need CS technique, but herein we are solely interested in applying it to the physiological signal acquisition systems, particularly the Electrocardiogram (ECG) biosignal. Since this signal is sparse, it is a perfect candidate for CS processing. This paper investigates the application of two greedy algorithms on ECG raw data, namely the matching pursuit (MP) and the orthogonal matching pursuit (OMP) algorithms. Several tests on various ECG data sets are carried out to make the best choice of the parameters (type of dictionary, maximum number of iterations,etc) that allow a good reconstruction of the original signal. Moreover, we propose to concatenate different dictionaries which is shown to enhance the accuracy by 14 dB. The novelty lies in the choice of the dictionaries and the application of the windowing on the original data that allows to significantly reduce the size of the dictionary.

关键词： Matching pursuit algorithms Dictionaries PSNR Electrocardiography Accuracy Discrete cosine transforms Compressed sensing

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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A Range and Scaling Study of an FPGA-Based Digital Wireless Channel Emulator

A Range and Scaling Study of an FPGA-Based Digital Wireless ...

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Annual IEEE Symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Scott Buscemi Will Kritikos Ron Sass Advanced Communication Technologies Lab SPAWAR Systems Center Atlantic Charleston SC USA Reconfigurable Computing Systems Lab University of North Carolina Charlotte Charlotte NC USA

A Digital Wireless Channel Emulator (DWCE) is a system that is capable of emulating the RF environment for a group of wireless devices. A major issue with current designs is that they do not scale to a large enough number of nodes to emulate meaningful network. A reason for this lack of scalability is the large amount of computations and network capacity required for such a system. Previously documented DWCE systems implement a hub-and-spoke configuration that inhibits them from simply adding additional hardware to scale. This paper investigates the use of a FPGA cluster configured as a distributed system to provide the computational and network structure to scale a DWCE to support 1250 wireless devices. This scale is approximately two orders of magnitude larger than any other previously documented system. This paper presents multiple FPGA cluster configurations that use currently available hardware and describes the algorithms used to route the signals through the network and place the computational hardware on each FPGA. The low level VHDL Signal Path Component (SPC) is synthesized and mapped under different parameters to interpolate is resource utilization. One example FPGA build with enough SPCs to fill 80% of the FPGA resources is successfully run through the Xilinx tool-chain to determine the maximum FPGA system clock speed. Finally, the scaling results are presented that detail the maximum sample frequency of various sized DWCE systems which could be used to examine a variety of wireless devices.

关键词： Field programmable gate arrays Wireless communication Hardware Clocks Ad hoc networks Transceivers Equations

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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A Self-Adaptive Scheduling (SAS) Solution for Enhancing VoIP Service Quality in OFDM-based Mobile Networks

A Self-Adaptive Scheduling (SAS) Solution for Enhancing VoIP...

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IEEE International Conference on communications

作者： Tammy Chang Yang Wang Anpeng Huang Department of Electrical Engineering Stanford University California United States Institute of Network Computing and Information Systems Peking University Beijing China State Key Lab of Advanced Optical Communication Systems and Networks Peking University Beijing China

To save signaling overhead, Serai-Persistent Scheduling (SPS) is the standard for delivering Voice-over-IP (VoIP) in OFDM-based mobile networks. But uncompressed or variable-size compressed VoIP packets are problematic for SPS because they spill over the assigned semi-persistent resource whenever the ROHC (Robust Header Compression) mechanism is in an initialization or reset period, propagating system latency and accumulating unnecessary signaling overhead. In response, we propose a Self-Adaptive Scheduling (SAS) algorithm, where dynamic scheduling is used as an adaptive mechanism to assist in scheduling oversized packets, triggered by buffer information already known to the system, without requiring dedicated signaling. To analyze the performance of our proposal in theory, we created a novel theoretical model to describe VoIP traffic considering the uncompressed voice state. This is, to our best knowledge, the first effort to model the effect of uncompressed packets on VoIP performance in mobile networks from the system-level point of view. Theoretical analysis and simulation experiments validate our proposal, demonstrating its ability to enhance the VoIP user experience for a minor signaling overhead cost.

关键词： Decision support systems Quality of service Reliability

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Design and utilization of an FPGA cluster to implement a digital wireless channel emulator

Design and utilization of an FPGA cluster to implement a dig...

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22nd International Conference on Field Programmable Logic and Applications, FPL 2012

作者： Buscemi, Scott Sass, Ron Advanced Communication Technologies Lab. SPAWAR Systems Center Atlantic Charleston SC United States Reconfigurable Computing Systems Lab. University of North Carolina at Charlotte Charlotte NC United States

ISBN: (纸本)9781467322560

A Digital Wireless Channel Emulator (DWCE) is a system that is capable of emulating the RF environment for a group of radios. A major issue with current designs is that they do not scale to a large enough number of nodes to emulate meaningful network due to the amount of computations required for such a system. This paper proposes a novel DWCE design based on a cluster of FPGA devices. By decentralizing the signal processing it is possible to increase the available computational resources to the point where they are no longer the limiting factor to scalability. It demonstrates feasibility by analyzing the computational and network requirements of the application and showing how the proposed design meets those needs. The paper also describes the physical design of a new 64 node FPGA cluster that is being manufactured. An implementation on prototype nodes of this cluster demonstrates the machine will support a high fidelity emulation for 32 radios or a lower-fidelity emulation for 128 radios. © 2012 IEEE.

关键词： Field programmable gate arrays (FPGA)

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QoS-driven service selection for multi-tenant SaaS

QoS-driven service selection for multi-tenant SaaS

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2012 IEEE 5th International Conference on Cloud computing, CLOUD 2012

作者： He, Qiang Han, Jun Yang, Yun Grundy, John Jin, Hai Faulty of Information and Communication Technologies Swinburne University of Technology Melbourne VIC Australia Services Computing Technology and Systems Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769547558

Cloud-based software applications (Software as a Service - SaaS) for multi-tenant provisioning have become a major development paradigm in Web engineering. Instead of serving a single end-user, a multi-tenant SaaS provides multiple end-users with the same functionality but with potentially different quality-of-service (QoS) values. The service selection for such a SaaS is a complex decision-making process which involves a number of stakeholders with different QoS requirements. SaaS developers need to compose services with different QoS values to meet end-users' different multidimensional QoS constraints for the SaaS. Furthermore, they also need to satisfy SaaS providers' optimisation goals for the SaaS, such as least resource cost and best system performance. Existing QoS-aware service selection approaches are oriented at a single tenant. They do not consider the characteristics of multi-tenant SaaS and hence are ineffective and inefficient when applied to compose multi-tenant SaaS. In this paper, we introduce a novel QoS-driven approach for helping SaaS developers select the services for composing multi-tenant SaaS, which achieves SaaS providers' optimisation goals while fulfilling the end-users' different levels of QoS constraints. The proposed approach is evaluated using an example SaaS synthetically generated based on a dataset of real-world Web services. Experimental results show that our approach significantly outperforms existing approaches in terms of both effectiveness and performance. © 2012 IEEE.

关键词： Quality of service

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INSC: An iterative negotiation approach for service compositions

INSC: An iterative negotiation approach for service composit...

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2012 IEEE 9th International Conference on Services computing, SCC 2012

作者： He, Qiang Yang, Yun Yan, Jun Jin, Hai Faculty of Information and Communication Technologies Swinburne University of Technology Melbourne VIC 3122 Australia School of Information Systems and Technology University of Wollongong Wollongong NSW Australia Services Computing Technology and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769547534

The service-oriented paradigm offers support for engineering service-based systems (SBSs) based on service compositions. The selection of services with the aim to fulfil the quality constraints for SBSs and to achieve the optimisation goals is a critical and challenging issue. In particular, when the quality-of-service (QoS) constraints for a SBS are severe, it is often difficult to find an optimal solution for the SBS. Exploiting the competition among service providers can help SBS developers obtain favourable QoS offers for the component services of SBSs and increase the possibility of finding optimal solutions for the SBSs. In this paper, we present a novel joint optimisation and negotiation approach named Iterative Negotiation for Service Composition (INSC) that supports effective and efficient QoS-aware service selection for SBSs. We evaluate INSC experimentally using example SBSs that are synthetically generated based on a real-world Web service dataset. The experimental results show that INSC can significantly and efficiently increase the possibility of finding optimal solutions in severe service composition scenarios. © 2012 IEEE.

关键词： Quality of service

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Irregular community discovery for cloud service improvement

Irregular community discovery for cloud service improvement

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作者： Liu, Jin Zhou, Jing Wang, Junfeng Zhang, Feng Liu, Fei State Key Lab. of Software Engineering Wuhan University Wuhan 430072 China Lab. of Complex Systems and Intelligence Science Institute of Automation CAS Beijing 100190 China State Key Lab. for Novel Software Technology Nanjing University Nanjing China School of Computer Science Communication University of China Beijing 100024 China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100190 China College of Computer Science Sichuan University Chengdu 610064 China Software School Sun Yat-sen University Guangzhou 510006 China

Utility services provided by cloud computing rely on virtual customer communities forming spontaneously and evolving continuously. Clarifying the explicit boundaries of these communities is thus essential to the quality of utility services in cloud computing. Communities with overlapping features or prominent peripheral vertexes are usually typical irregular communities. Traditional community identification algorithms are limited in discovering irregular topological structures from CR networks, whereas these irregular shapes typically play an important role in finding prominent customers which are ignored in social CRM otherwise. We present a novel method of discovering irregular communities. It firstly finds and merges primitive maximal cliques and the irregular features of overlapping and prominent sparse vertices are further considered. An empirical case and a methodology comparison confirm the feasibility and efficiency of our approach. © 2012 Springer Science+Business Media, LLC.

关键词： Cloud computing

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A Cloud Portal with a Cloud Service Search Engine

A Cloud Portal with a Cloud Service Search Engine

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2011 International Conference on Information and Intelligent computing（ICIIC 2011）

作者： Jaeyong Kang Kwang Mong Sim Multiagent and Cloud Computing Systems Lab. Department of Information and Communication Gwangju Institute of Science and Technology (GIST) Gwangju Korea

Cloud computing has become another buzzword after Web 2.0. Many companies, such as Amazon, Google, Microsoft and so on, accelerate their paces in developing Cloud computing systems. However, there is no study that focuses on search engine and web portal for Cloud computing system. Hence, this paper presents Cloud portal with various service categories and Cloud service search engine for Cloud computing system. In Cloud service search engine, we use Cloud ontology to semantically define the relationship among Cloud services. It contains a set of Cloud concepts, individuals of those concepts, and the relationship among those individuals. It is used for determining the similarity among Cloud services with three kinds of reasoning methods (1) concept similarity reasoning, (2) object property similarity reasoning, and (3) datatype property similarity reasoning. The interface and features of the Cloud portal are briefly presented. Finally a proof-of-concept-example demonstrates the features and functionalities of our Cloud portal and Cloud search engine.

关键词： Cloud computing Cloud Portal Search Engines Cloud Ontology

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