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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Yu-Ting Liu Shang-Lin Wu Kuang-Pen Chou Yang-Yin Lin Jie Lu Guangquan Zhang Wen-Chieh Lin Chin-Teng Lin Brain Research Center Institute of Electrical Control Engineering Institute of Computer Science and Engineering Electronic Systems Research Division National Chung-Shan Institute of Science and Technology (NCSIST) Taoyuan Taiwan FacuIty of Engineering and Information Technology University of Technology Sydney Sydney Australia Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9781509006274

We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-the-arts models.

关键词： Electroencephalography Fatigue Real-time systems Vehicles Fuzzy neural networks Signal processing algorithms Integrated circuits

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Target detection and recognition in SAR imagery based on KFDA

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Journal of systems engineering and electronics 2015年第4期26卷 720-731页

作者： Fei Gao Jingyuan Mei Jinping Sun Jun Wang Erfu Yang Amir Hussain School of Electronic and Information Engineering Beihang University Space Mechatronic Systems Technology Laboratory Department of Design Manufacture and Engineering Management University of Strathclyde Cognitive Signal-Image and Control Processing Research Laboratory School of Natural SciencesUniversity of Stirling

Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.

关键词： synthetic aperture radar （SAR） target detection ker-nel fisher discriminant analysis （KFDA） target recognition imageEuclidean distance （IMED） support vector machine （SVM）.

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Guest Editorial for Special Issue on Cyber-Physical systems

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IEEE/CAA Journal of Automatica Sinica 2015年第3期2卷 233-234页

作者： Youxian Sun Xinping Guan Jiming Chen Yilin Mo Zhejiang University the Chinese Academy of Engineering the Institute of Industrial Process Control and the National Engineering Research Center of Industrial Automation Zhejiang University Department of Automation Shanghai Jiao Tong University University Research Management Office Shanghai Jiao Tong University the Key Laboratory of Systems Control and Information Processing Ministry of Education of China the prestigious Innovative Research Team of the National Natural Science Foundation of China(NSFC) Department of Control Science and Engineering group of Networked Sensing and Control in the State Key laboratory of Industrial Control Technology Institute of Industrial Process Control at Zhejiang University the School of Electrical and Electronic Engineering at Nanyang Technological University

Ⅰ.Introduction CYBER-PHYSICAL system is a system of collaborating computational elements to control physical *** coordination and the tight link between computational,virtual and physical resources in cyber-physical system will have a pervasive effect on our everyday *** development of cyber-physical system will create new opportunities for the introduction of services that will enhance the quality of life

关键词： for WSAN Guest Editorial for Special Issue on Cyber-Physical systems of into on WSN is

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Bus Priority System Using Radio Frequency Identification (RFID) Detection in Singapore

Bus Priority System Using Radio Frequency Identification (RF...

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2015年南京第十四届亚太智能交通论坛

作者： Tatsuya Higuchi Lincoln Tan Musthafa Ibrahim Joanne Cheong Traffic Control Systems Engineering Section Electronic Control Technology Department ICT Solution He Traffic Control & Transportation Division Mitsubishi Heavy Industries Engine System Asia Pie LtdSin Intelligent Transport Systems Development Land Transport AuthoritySingapore City219428Singapore

Making public transport a choice mode is one of the three key strategic thrusts listed in Land Transport Master Plan (LTMP) *** achieve that, it is important to make the public transport system attractive for use by the *** of such application identified is to provide signal priority for busses at traffic light *** scan and assessment of various detection technologies have indicated that RFID is the appropriate, open standards and economical technology for the bus *** is a wireless non-contact system that uses radio-frequency electromagnetic fields to transfer data from a tag attached to an object, for the purposes of automatic identification and *** tags has been widely used in various industries, including transportation for payment services such as for transit fares, highway tolls and parking fares, as well as in the areas of vehicle identification and access control in many *** the cost of RFID tags has decreased significantly over time, its detection capability has also improved to allow longer range *** this, a concerted initiative between the local authorities and the industry to conduct a technical trial for traffic light priority for buses using radio frequency identification technology (RFID) was executed at selected test locations in *** paper discusses on the technical trial conducted to assess the accuracy and reliability of Bus Priority System using RFID in providing traffic light priority for buses in Singapore.

关键词： ITS signal priority technical trial radio frequency identification

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Search for Gravitational Waves Emitted from SN 2023ixf

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The Astrophysical Journal 2025年第2期985卷 183-183页

作者： A. G. Abac R. Abbott I. Abouelfettouh F. Acernese K. Ackley S. Adhicary N. Adhikari R. X. Adhikari V. K. Adkins D. Agarwal M. Agathos M. Aghaei Abchouyeh O. D. Aguiar I. Aguilar L. Aiello A. Ain T. Akutsu S. Albanesi R. A. Alfaidi A. Al-Jodah C. Alléné A. Allocca S. Al-Shammari P. A. Altin S. Alvarez-Lopez A. Amato L. Amez-Droz A. Amorosi C. Amra A. Ananyeva S. B. Anderson W. G. Anderson M. Andia M. Ando T. Andrade N. Andres M. Andrés-Carcasona T. Andrić J. Anglin S. Ansoldi J. M. Antelis S. Antier M. Aoumi E. Z. Appavuravther S. Appert S. K. Apple K. Arai A. Araya M. C. Araya J. S. Areeda L. Argianas N. Aritomi F. Armato N. Arnaud M. Arogeti S. M. Aronson G. Ashton Y. Aso M. Assiduo S. Assis de Souza Melo S. M. Aston P. Astone F. Attadio F. Aubin K. AultONeal G. Avallone S. Babak F. Badaracco C. Badger S. Bae S. Bagnasco E. Bagui J. G. Baier L. Baiotti R. Bajpai T. Baka M. Ball G. Ballardin S. W. Ballmer S. Banagiri B. Banerjee D. Bankar P. Baral J. C. Barayoga B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta A. M. Bartoletti M. A. Barton I. Bartos S. Basak A. Basalaev R. Bassiri A. Basti D. E. Bates M. Bawaj P. Baxi J. C. Bayley A. C. Baylor P. A. Baynard II M. Bazzan V. M. Bedakihale F. Beirnaert M. Bejger D. Belardinelli A. S. Bell V. Benedetto W. Benoit J. D. Bentley M. Ben Yaala S. Bera M. Berbel F. Bergamin B. K. Berger S. Bernuzzi M. Beroiz D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge N. Bevins R. Bhandare U. Bhardwaj R. Bhatt D. Bhattacharjee S. Bhaumik S. Bhowmick A. Bianchi I. A. Bilenko G. Billingsley A. Binetti S. Bini O. Birnholtz S. Biscoveanu A. Bisht M. Bitossi M.-A. Bizouard J. K. Blackburn L. A. Blagg C. D. Blair D. G. Blair F. Bobba N. Bode G. Boileau M. Boldrini G. N. Bolingbroke A. Bolliand L. D. Bonavena R. Bondarescu F. Bondu E. Bonilla M. S. Bonilla A. Bonino R. Bonnand P. Booker A. Borchers V. Boschi S. Bose V. Bossilkov V. Boudart A. Boudon A. Bozzi C. Bradaschia P. R. Brady M. Braglia A. Branch M. Branchesi J. Brandt I. Braun M. Breschi T. B Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-14476 Potsdam Germany LIGO Laboratory California Institute of Technology Pasadena CA 91125 USA LIGO Hanford Observatory Richland WA 99352 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli I-80126 Napoli Italy University of Warwick Coventry CV4 7AL UK The Pennsylvania State University University Park PA 16802 USA University of Wisconsin-Milwaukee Milwaukee WI 53201 USA Louisiana State University Baton Rouge LA 70803 USA Université catholique de Louvain B-1348 Louvain-la-Neuve Belgium Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Queen Mary University of London London E1 4NS UK Department of Physics and Astronomy Sejong University 209 Neungdong-ro Gwangjin-gu Seoul 143-747 Republic of Korea Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Stanford University Stanford CA 94305 USA Università di Roma Tor Vergata I-00133 Roma Italy INFN Sezione di Roma Tor Vergata I-00133 Roma Italy Cardiff University Cardiff CF24 3AA UK Universiteit Antwerpen 2000 Antwerpen Belgium Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy SUPA University of Glasgow Glasgow G12 8QQ UK OzGrav University of Western Australia Crawley Western Australia 6009 Australia University Savoie Mont Blanc CNRS Laboratoire d’Annecy de Physique des Particules - IN2P3 F-74000 Annecy France Università di Napoli “Federico II ” I-80126 Napoli Italy OzGrav Australian National University C

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19, during the LIGO–Virgo–KAGRA 15th engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered ∼14% of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz, where we assume the gravitational-wave emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy 1 × 10⁻⁴ M_⊙c² and luminosity 2.6 × 10⁻⁴ M_⊙c² s⁻¹ for a source emitting at 82 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as 1.08, at frequencies above 1200 Hz, surpassing past results.

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A new algorithm for the weighted sum rate maximization in MIMO interference networks

A new algorithm for the weighted sum rate maximization in MI...

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2015 IEEE Wireless Communications and Networking Conference, WCNC 2015

作者： Li, Xing You, Seungil Chen, Lijun Liu, An Liu, Youjian Eugene College of Engineering and Applied Science University of Colorado at Boulder United States Department of Control and Dynamical Systems California Institute of Technology United States Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781479984060

MIMO interference network optimization is important for increasingly crowded wireless communication networks. This paper presents a new algorithm, named Dual Link Algorithm, for weighted sum-rate maximization where the interference is efficiently managed. We consider general MIMO interference channels with Gaussian input and a total power constraint. Two of the previous state-of-the-art algorithms are the WMMSE algorithm and the polite water-filling (PWF) algorithm. The WMMSE algorithm is provably convergent, while the PWF algorithm takes the advantage of the optimal transmit signal structure and converges the fastest in most situations but is not guaranteed to converge in all situations. Therefore, it is highly desirable to design an algorithm that has the advantages of both algorithms. The proposed dual link algorithm is such an algorithm. Its fast and guaranteed convergence is important to distributed implementation and time varying channels. In addition, the technique and a scaling invariance property used in the convergence proof may find applications in other non-convex problems in communication networks. © 2015 IEEE.

关键词： MIMO systems

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Applicability of Extra-Local Methods for Assessing Community Resilience to Disasters: A Case of Sri Lanka

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Journal of Environmental Assessment Policy and Management 2016年第2期18卷

作者： Abenayake, Chethika Yoshiki, Mikami Marasinghe, Ashu Takashi, Yukawa Masahiro, Iwahashi Information Science and Control Engineering Graduate School of Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata 940-2188 Japan Department of Management and Information Systems Science Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata 940-2188 Japan Department of Electrical Electronic and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata 940-2188 Japan

This paper is focused on a practice that has been widely noted in South Asia, i.e. employing extra-local methods to assess community resilience with no or minimal attempts on localising. The objective of this paper is to assess the consistency/inconsistency and concordance/discordance of resilience levels computed by different extra-local assessment methods with reference to a Sri Lankan case. Community resilience levels were computed for 40 localities with three methods utilising secondary data. Statistical analysis revealed neither strong correlation (r≥0.574), (p<0.01) or strong reliability (ICC =0.481) among the computed resilience levels, indicating inconsistency and discordance. The findings explained how the randomly selected extra-local assessment methods could produce different values. Hence, can the resilience building decisions based on such assessment methods also be inconsistent? This study suggests decision makers should revisit the practice of employing non-localised extra-local assessment methods without addressing the context-specific resilience building needs profoundly. © 2016 World Scientific Publishing Europe Ltd.

关键词： Assessment methods climate-related disasters community resilience localising Sri Lanka

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A comparative study on D converter based on control schemes of maximum extracted power

A comparative study on D converter based on control schemes ...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Ahmad El Khateb M.N. Uddin N. A. Rahim Barry W. Williams Automatic Control and Human-Machine Systems LAMIH UMR/CNRS Valenciennes France Electronic Engineering Department Tsinghua University Tsinghua National Laboratory for Information Science and Technology (TNList) Beijing P. R. China Shenzhen City Key Laboratory of Digital TV System Valeo Powertrain Systems Amiens France National Engineering Lab. for DTV(Beijing) Beijing P. R. China

ISBN: (纸本)9781479983988

This paper presents analysis and control of D converter, which has continuous input and output current to obtain maximum power transfer from a photovoltaic (PV) system. Examining the maximum power involves optimization and control techniques that ensure maximum power extraction enhancement. Traditional converters have rather high input ripple current that originates considerable loss of power extraction when linked to photovoltaic panels. Operating with zero input ripple current enlarges the power obtained from the PV panel without employing electrolytic capacitor. Experiment and simulation of the converter performance support and extol the system concept and establish a balanced view among several types of used controllers as well.

关键词： Inverters Capacitors Photovoltaic systems Radiation effects control systems Fuel cells

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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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