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Intelligent algorithms based on data processing for modular robotic vehicles control

WSEAS Transactions on systems 2014年第1期13卷 242-251页

作者： Papoutsidakis, Michail Piromalis, Dimitrios Neri, Filippo Camilleri, Michel Department of Automation Technological Institute of Piraeus P.Ralli and Thivon 250 Athens Greece Dept. of Electrical Engineering and Information Technology University of Napoli Federico II Via Claudio 21 Naples Italy Dept. of Computer Information Systems University of Malta MSD 2080 Malta

One of the numerous implementations that the Intelligent systems Lab of the Technological Institute of Piraeus, Greece, has recently accomplished is described in this paper. Within the area of hand-on autonomous robotics, the project combines computer science in modern wireless network protocol communication, with microcontroller-based intelligent motion-control tasks. The design of the presented pair of versatile robots is intended for a "predator and prey" application domain. Both robot designs are based on low cost and easy to find equipment, though modern and up-to-date technology. Two different multitasking and flexible algorithms are designed in order to investigate their performance and compare the outcomes. In particular, an intelligent algorithm based on data capturing and processing within a tolerable elapsed time is presented, with two different performance scenarios explained and compared in detail.

关键词： Intelligent systems

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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Malware detection in big data using fast pattern matching: A hadoop based comparison on GPU 2

Malware detection in big data using fast pattern matching: A...

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2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014

作者： Panigrahi, Chhabi Rani Tiwari, Mayank Pati, Bibudhendu Prasath, Rajendra Dept. of Information Technology C.V. Raman College of Engineering Bhubaneswar Odisha752 054 India Dept. of Computer Science and Engineering C.V. Raman College of Engineering Bhubaneswar Odisha752 054 India Business Information Systems University College Cork Cork Ireland

ISBN: (纸本)9783319138169

In big data environment, hadoop stores the data in distributed file systems called hadoop distributed file system and process the data using parallel approach. When the cloud users store unstructured data in cloud storage, it becomes very important for cloud providers to secure those data. To provide malware security, cloud service providers should scan the whole contents of the database, which is a very time intensive job. It may even take days to complete the tasks. The main aim of the proposed work is to reduce the processing time by introducing Graphics Processing Unit (GPU) in hadoop cluster. The proposed work integrates two text pattern matching algorithms with the map-reduce programming model for faster detection of malware in big data. The results of our study indicate that use of GPU decreases the processing time of text pattern matching algorithms in big data hadoop. © Springer International Publishing Switzerland 2014.

关键词： Graphics processing unit

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Biologically inspired hierarchical model for traffic sign recognition 24

Biologically inspired hierarchical model for traffic sign re...

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24th International Conference on computer Theory and Applications, ICCTA 2014

作者： Aziz, Amr Abdel Imam, Ibrahim F. Shoukry, Amin College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Cairo Egypt Computer and Systems Engineering Dept. Faculty of Engineering Alexandria University Alexandria Egypt

ISBN: (纸本)9781479974757

In this research, a study of the Hierarchical Temporal Memory (HTM) model using the Numenta Implementation 'Nupic v1.7' and an investigation of its performance in solving the German traffic sign recognition task using standard benchmark data is conducted. Its performance is compared to that of a state of the art Multi-Column Deep Neural network (MCDNN) that has been proved to exceed the human recognition accuracy for the same task. © 2014 IEEE.

关键词： Image classification

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Fractional frequency reuse for intercell interference mitigation in two-tier underwater multicell OFDM wireless networks 9

Fractional frequency reuse for intercell interference mitiga...

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9th ACM International Conference on Underwater networks and systems, WUWNet 2014

作者： Wang, Ping Zhang, Xi Networking and Information Systems Lab Dept. of Electrical and Computer Engineering Texas AandM University College StationTX77843 United States National Mobile Communications Research Lab Southeast University Nanjing China

ISBN: (纸本)9781450332774

Recently, acoustic communications over underwater multicell OFDM wireless networks have received significant research attention. Due to the severe underwater fading conditions, available bandwidth over underwater wireless networks are much more scarce than terrestrial wireless networks. Thus, various frequency reuse techniques are highly demanded for underwater wireless networks. However, the frequency reuse imposes the new and complicated designing tradeoff among co-channel interference on cell-edge users, the overall network throughput, and spectrum efficiency, which has significant impact on the performance for underwater multicell OFDM wireless networks. To overcome these new challenges, in this paper we propose a novel fractional frequency reuse (FFR) scheme for intercell interference mitigation under the two-tier (interior zone and edge zone) based underwater multicell OFDM wireless networks. Our main objective is to guarantee the edge-user's QoS requirements in terms of SINR and outage probability and to optimize the spectrum efficiency of the entire underwater multicell wireless networks. We also derive the closed-form expressions of outage probability and spectrum efficiency. The extensive simulations validate and evaluate our proposed schemes, showing that our schemes outperform the other existing frequency reuse schemes for underwater wireless networks in terms of signal-to-interference-plus-noise, outage probabilities of cell-edge users, and spectrum efficiency. © 2014 ACM.

关键词： Spectrum efficiency

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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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Complete SIP Message Obfuscation: PrivaSIP over Tor

Complete SIP Message Obfuscation: PrivaSIP over Tor

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International Conference on Availability, Reliability and security, ARES

作者： Georgios Karopoulos Alexandros Fakis Georgios Kambourakis Joint Research Centre Institute for the Protection and Security of the Citizen Dept. of Information and Communication Systems Engineering University of the Aegean

Anonymity on SIP signaling can be achieved either by the construction of a lower level tunnel (via the use of SSL or IPSec protocols) or by employing a custom-tailored solution. Unfortunately, the former category of solutions present significant impediments including the requirement for a PKI and the hop-by-hop fashioned protection, while the latter only concentrate on the application layer, thus neglecting sensitive information leaking from lower layers. To remediate this problem, in the context of this paper, we employ the well-known Tor anonymity system to achieve complete SIP traffic obfuscation from an attacker's standpoint. Specifically, we capitalize on Tor for preserving anonymity on network links that are considered mostly untrusted, i.e., those among SIP proxies and the one between the last proxy in the chain and the callee. We also, combine this Tor-powered solution with PrivaSIP to achieve an even greater level of protection. By employing PrivaSIP we assure that: (a) the first hop in the path (i.e., between the caller and the outbound proxy) affords anonymity, (b) the callee does not know the real identity of the caller, and (c) no real identities of both the caller and the callee are stored in log files. We also evaluate this scheme in terms of performance and show that even in the worst case, the latency introduced is not so high as it might be expected due to the use of Tor.

关键词： Privacy IP networks Encryption Protocols Relays Servers

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PsyBoG: Power spectral density analysis for detecting botnet groups

PsyBoG: Power spectral density analysis for detecting botnet...

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International Conference on Malicious and Unwanted Software (MALWARE)

作者： Jonghoon Kwon Jeongsik Kim Jehyun Lee Heejo Lee Adrian Perrig Dept. of Computer Science and Engineering Korea University Seoul Republic of Korea Institute of Information Security ETH Zurich Zurich Switzerland

Botnets are widely used for acquiring economic profits, by launching attacks such as distributed denial-of-service (DDoS), identification theft, ad-ware installation, mass spamming, and click frauds. Many approaches have been proposed to detect botnet, which rely on end-host installations or operate on network traffic with deep packet inspection. They have limitations for detecting botnets which use evasion techniques such as packet encryption, fast flux, dynamic DNS and DGA. Sporadic botnet behavior caused by disconnecting the power of system or botnet's own nature also brings unignorable false detection. Furthermore, normal user's traffic causes a lot of false alarms. In this paper, we propose a novel approach called PsyBoG to detect botnets by capturing periodic activities. PsyBoG leverages signal processing techniques, PSD (Power Spectral Density) analysis, to discover the major frequencies from the periodic DNS queries of botnets. The PSD analysis allows us to detect sophisticated botnets irrespective of their evasion techniques, sporadic behavior and even the noise traffic generated by normal users. To evaluate PsyBoG, we utilize the real-world DNS traces collected from a /16 campus network including more than 48,046K queries, 34K distinct IP addresses and 146K domains. Finally, PsyBoG caught 19 unknown and 6 known botnet groups with 0.1% false positives.

关键词： Servers IP networks Time series analysis Signal processing Testing Sensors Unsolicited electronic mail

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Distributed gabriel graph construction and meta-information gathering 20

Distributed gabriel graph construction and meta-information ...

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20th IMEKO TC4 Symposium on Measurements of Electrical Quantities: Research on Electrical and Electronic Measurement for the Economic Upturn, Together with 18th TC4 International Workshop on ADC and DCA Modeling and Testing, IWADC 2014

作者： Oliva, Gabriele Setola, Roberto Polycarpou, Marios Complex Systems and Security Laboratory University Campus Bio-Medico Via A. del Portillo 21 Rome00128 Italy Dept. of Electrical and Computer Engineering KIOS Research Center for Intelligent Systems and Networks University of Cyprus 75 Kallipoleios P.O. Box 20537 NicosiaCY-1678 Cyprus

ISBN: (纸本)9789299007327

In this paper we provide a distributed way to transform the network topology underlying a set of agents embedded in a bi-dimensional space into a planar graph, and specifically into a Gabriel graph. Moreover, a distributed algorithm based on synchronized consensus methodologies is provided in order to gather some useful pieces of meta-information (number of faces, average size of the faces, size of the boundary, etc.) and to let each node in the Gabriel graph identify its "role" (i.e., node that links two subgraphs, node on a branch ending with a leaf, node belonging to a face, etc.). The insights obtained by means of the proposed approach may contribute to develop better geographic routing techniques as well as to improve the local decision capability of the agents in distributed environments.

关键词： Graphic methods

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