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Resilience Week (RWS)

作者： Anuja S. Bhave Mariesa L. Crow Egemen K. Çetinkaya Department of Electrical and Computer Engineering Intelligent Systems Center Missouri University of Science and Technology Rolla MO USA

The power grid infrastructure is of paramount importance to society. Hence many studies have been undertaken to understand the structural properties of power grids to make them resilient. We study the topological robustness of three power grids - IEEE 300 bus, Western US, and Poland - against centrality-based targeted attacks on nodes and links. Our results indicate that the impact of attacks varies depending on the graph centrality metric being considered.

关键词： Power grids Robustness Topology Resilience Power measurement Internet

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A Cognitive Fault-Detection Design Architecture

A Cognitive Fault-Detection Design Architecture

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International Joint Conference on Neural Networks

作者： Georgios M. Milis Demetrios G. Eliades Christos G. Panayiotou Marios M. Polycarpou KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (纸本)9781509006212

This paper presents a novel architecture for the design of fault-detection schemes, aiming to automate the cognitive process performed by human experts when designing fault detection schemes for certain systems. The work starts with the identification of types of cyber-physical components participating in a fault-detection scheme. These are semantically characterized, adopting a model driven by previous efforts of the World Wide Web Consortium on the semantic composition of Web services. The semantic characterizations of the components are then exploited by a Cognitive Agent with semantic reasoning capabilities, to achieve the configuration of a fault-detection scheme, given a set of specifications and available components. The Cognitive Agent has access to a knowledge representation model and is able to interact with human operators and with the components to enrich its knowledge for making and enforcing decisions about the configuration. The applicability of the architecture and the reasoning steps are demonstrated through the configuration of a water contamination event-detection scheme with learning capabilities within a smart water distribution network.

关键词： Computational modeling Pollution measurement Fault detection Semantics Cognition Feature extraction Algorithm design and analysis Algorithm design and analysis pollution measurement Semantics Defect detection Computational modeling Building design Feature extraction W3C XML Standards Cognition Web Service Knowledge Representation semiotics cognitive process

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Self-Powered Triboelectric Inertial Sensor Ball for IoT and Wearable Applications

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Journal of Physics: Conference Series 2018年第1期1052卷

作者： Qiongfeng Shi Hao Wang Tianyiyi He Chengkuo Lee Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Center for Intelligent Sensors and MEMS National University of Singapore E6 #05-11F 5 Engineering Drive 1 Singapore 117608 Hybrid-Integrated Flexible (Stretchable) Electronic Systems Program National University of Singapore E6 #05-4 5 Engineering Drive 1 Singapore 117608

Motion monitoring can provide useful information for rehabilitation or diagnostics purpose. Recently, triboelectric nanogenerator (TENG) has developed rapidly as a promising technology for energy harvesting and self-powered sensing. However, research effort of using TENG for multi-axis acceleration sensing is very limited. What is more, rotation sensing has not been achieved by TENG to date. Here we propose a 3-dimensional (3D) symmetric triboelectric nanogenerator based inertial sensor ball (TENG-Ball) for self-powered multi-axis acceleration and rotation sensing. The TENG-Ball has great potential in motion monitoring, internet-of-things (IoT) and wearable applications.

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Static Impairment-Aware Multicast Session Provisioning in Metro Optical Networks

Static Impairment-Aware Multicast Session Provisioning in Me...

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Mediterranean Electrotechnical Conference

作者： Tania Panayiotou Konstantinos Manousakis Georgios Ellinas KIOS Research Center for Intelligent Systems and Networks and the Department of Electrical and Computer Engineering University of Cyprus Nicosia 1678 Cyprus.

ISBN: (纸本)9781509000593

This work investigates the static impairment-aware multicast routing and wavelength assignment (IA-MC-RWA) problem for transparent Wavelength Division Multiplexing (WDM) metro networks. The IA-MC-RWA problem, known to be NP-complete, is first formulated mathematically as an integer linear program (ILP) taking into account a number of impairments encountered by the multicast connections at the physical layer. Efficient heuristics are also developed to solve the multicast routing problem with practical network size parameters. To evaluate the heuristics, their optimality is first examined by comparing their performance results to the results of the ILP for a small-sized network. Their performance is subsequently evaluated over a practical-sized problem. The proposed Q-based Steiner Tree (QBST) heuristic that takes the physical layer impairments (PLIs) into consideration during the provisioning phase of the multicast tree, outperforms the rest of the multicast routing heuristic algorithms developed for comparison purposes and provides results closer to the optimal compared to the ILP.

关键词： RWA ILP WDM

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An Efficient Regularized NLMF Algorithm

An Efficient Regularized NLMF Algorithm

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International Conference on intelligent and Advanced systems

作者： S. M. Umar Talha Hammad Hussain Muhammad Moinuddin Department of Telecommunication Engineering Sir Syed University of Engineering and Technology (SSUET) Electrical & Computer Engineering Department Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah

ISBN: (纸本)9781509008469

Least-Mean Square algorithm, or simply the LMS, is generally one of the most commonly used techniques for optimized solution in adaptive schemes especially in the Gaussian environment. However, least mean fourth (LMF) algorithm and its variants, such as normalized LMF (NLMF), perform better in the non-Gaussian environment. The conventional LMF algorithms usually diverge in non-Gaussian environment with dynamic input. Conventionally, regularization is archived by using a small constant compensation term in the denominator of the learning rate to protect the algorithm from divergence. This paper introduces an efficient time-varying regularized normalized least mean fourth (R-NLMF) algorithm. In the proposed algorithm, the regularization term is made time varying and gradient adaptive according to steepest descent approach. Thus the proposed algorithm adapts its learning rate according to the environment and the input signal dynamics. A similar approach, namely Generalized Normalized Gradient Descent (GNGD) algorithm, has been previously applied to the normalized least mean-square (NLMS) algorithm in the Gaussian environment. However, due to its dependence on NLMS, the GNGD algorithm performance degrades in non-Gaussian environment. In order to overcome this problem, an efficient regularized NLMF algorithm for non-Gaussian environment is proposed. The algorithm shows promising results and achieves faster convergence while maintaining lesser steady-state miss-adjustment. Simulations are carried out to support the theoretical development.

关键词： Adaptive filtering NLMF Algorithm Gradient adaptive learning rate Regularized NLMF Channel estimation

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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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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Distributed adaptive task allocation for energy conservation in Camera Sensor Networks 15

Distributed adaptive task allocation for energy conservation...

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9th International Conference on Distributed Smart Cameras, ICDSC 2015

作者： Kyrkou, Christos Theocharides, Theocharis Panayiotou, Christos Polycarpou, Marios Department of Electrical and Computer Engineering KIOS Research Center for Intelligent Systems and Networks University of Cyprus Cyprus

ISBN: (纸本)9781450336819

Camera Sensor Networks (CSNs) have a large and diverse application spectrum ranging from security and safety-critical applications, to industrial monitoring, and augmented reality. Cameras in such networks are equipped with real-time multitasking processors and communication infrastructure, which enables them to perform various computer vision tasks in a distributed and collaborative manner. In many cases, the cameras in the network operate under limited or unreliable power sources. Therefore in order to extend the CSN lifetime it is important to manage the energy consumption of the cameras, which is related to the workload of the vision tasks they perform. Hence by managing and assigning vision tasks to cameras in an energy-aware manner it is possible to extend the network lifetime. In this paper we address this problem by proposing a distributed market-based solution where cameras bid for tasks using an energy-aware utility function. An additional novelty of the proposed solution is that as the cameras can adapt their bidding strategy based on their remaining energy levels. The results for different CSN configurations and setups show that the proposed methodology can increase network lifetime by 10%-30% while improving the number of dynamic and static tasks being monitored by 30-50%. © 2015 ACM.

关键词： Cameras

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A camera uncertainty model for collaborative Visual Sensor Network applications 15

A camera uncertainty model for collaborative Visual Sensor N...

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9th International Conference on Distributed Smart Cameras, ICDSC 2015

作者： Kyrkou, Christos Christoforou, Eftychios Theocharides, Theocharis Panayiotou, Christos Polycarpou, Marios Department of Electrical and Computer Engineering KIOS Research Center for Intelligent Systems and Networks University of Cyprus Cyprus

ISBN: (纸本)9781450336819

Visual Sensor Networks (VSNs) exploit the processing and communication capabilities of modern smart cameras to handle a variety of applications such as security and surveillance, industrial monitoring, and critical infrastructure protection. The performance of VSNs can be severely degraded because of errors in the detection module. As a result, the performance of the higher-level application such as activity recognition, tracking, etc., also suffers due to the fact that in most cases the decision making process in VSNs assumes ideal detection capabilities for the cameras. Realizing that it is necessary to introduce robustness in the decision process this paper presents results towards uncertainty-aware VSNs. Specifically, we introduce a flexible uncertainty model that can be used to study the behaviour of missed detections in a camera network. We also show how to utilize the model to develop uncertainty-aware coordination and decision making solutions to improve the efficiency of VSNs. Our experimental results in an active vision application indicate that the proposed solution is able to improve the robustness and reliability of VSNs. © 2015 ACM.

关键词： Cameras

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Active tactile object exploration with Gaussian processes

Active tactile object exploration with Gaussian processes

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： Zhengkun Yi Roberto Calandra Filipe Veiga Herke van Hoof Tucker Hermans Yilei Zhang Jan Peters Intelligent Autonomous Systems Lab Department of Computer Science Technische Universitat Darmstadt Hochschulstr. 10 64289 Germany School of Mechanical and Aerospace Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 University of Utah Robotics Center School of Computing University of Utah USA

ISBN: (纸本)9781509037636

Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy to efficiently reduce the surface geometry uncertainty by leveraging a probabilistic representation of object surface. In particular, we model the object surface using a Gaussian process and use the associated uncertainty information to efficiently determine the next point to explore. We validate the resulting method for tactile object surface modeling using a real robot to reconstruct multiple, complex object surfaces.

关键词： Gaussian processes Touch tactile sensors grid point

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