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检索条件"任意字段=3rd International Symposium on Information Processing in Sensor Networks"

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NS3 simulation studies for optimized neighbour discovery in 6LoWPAN networks 3

NS3 simulation studies for optimized neighbour discovery in ...

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3rd IEEE international symposium on Wireless Systems within the IEEE international Conferences on Intelligent Data Acquisition and Advanced Computing Systems, IDAACS-SWS 2016

作者： Prasad, S G Raghavendra Vivek, R. Mungara, Jitendranath Sebastian, E Jubin Department of Information Science and Engineering R v College of Engineering Bengaluru560059 India Dept of ISE New Horizon College of Engineering Bangalore560103 India Department of Electronics and Communication Engineering Vimal Jyothi Engineering College Chemperi Kerala India

ISBN: (纸本)9781509043163

The main motivation behind the 6LoWPAN standard is that every sensor node has to be connected to Internet and participate in Internet of Things (IoT). The RFC 6775 provides optimized Neighbor Discovery (ND) procedure which is supposedly provides better performance when compared to traditional ICMPv6 neighbor discovery process. The main optimization is obtained due to reduced multicast messages host initiated router interactions that support both sleeping nodes and lesser time spent in message transmission. This work presents the implementation details of important features in this optimized ND process. This work is done in the form of an NS3 model where in ICMPv6 model is implemented considering the ND optimizations. The simulation results are compared against the performance of basic ND process for IPv6 described in RFC 4861. Our results show that optimized ND for 6LoWPAN reduces the exchange of control messages by 50 to 70 percent. © 2016 IEEE.

关键词： Internet of things

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On the information Capacity of Hinge Functions 3

On the Information Capacity of Hinge Functions

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3rd international symposium on information Theory and Its Applications (ISITA)

作者： Alirezaei, Gholamreza Mathar, Rudolf Rhein Westfal TH Aachen Inst Theoret Informat Technol D-52056 Aachen Germany

ISBN: (纸本)9784885523090

So called hinge functions play an important role in many applications, e.g., deep learning, support vector machines, regression, classification and others. A thorough theory, which explains why some of these applications arc so successful for their respective purpose, is still missing. This paper aims at filling a knowledge gap by answering the question of how much information can be conveyed across a neural node, e.g., which uses the hinge loss function on weighted agglomerated information from precedent nodes. We hope that insight from artificial neural networks may also have implications for understanding biological information processing. As key results in this paper, an elegant representation of mutual information is derived and, furthermore, some structural properties of a channel, that consists of a certain input signal which is overlaid by additive noise and is filtered by a hinge function, are investigated. Determining the capacity of this channel in an explicit form, although an important fundamental problem, seems to be extremely difficult and unsolved as of today. Thus, necessary and sufficient conditions for an optimal input signal along with upper bounds on the capacity are deduced. Furthermore, we conjecture that exponentially distributed input signals are asymptotically capacity-achieving in the high SNR regime.

关键词： Additive noise

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Deep Long Short-Term Memory networks for Speech Recognition 10

Deep Long Short-Term Memory Networks for Speech Recognition

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10th international symposium on Chinese Spoken Language processing (ISCSLP)

作者： Chien, Jen-Tzung Misbullah, Alim Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu Taiwan

ISBN: (纸本)9781509042937

Speech recognition has been significantly improved by applying acoustic models based on deep neural network which could be realized as the feedforward NN (FNN) or the recurrent NN (RNN). In general, FNN is feasible to project the observations onto a deep invariant feature space while RNN is beneficial to capture the temporal information in a sequential data for speech recognition. RNN based on long short-term memory (LSTM) is capable of storing inputs over a long time period and thus exploiting a self-learned mechanism for long-range temporal context. Considering the complimentary FNN and RNN in their modeling capabilities, this paper presents a deep model which is constructed by stacking LSTM and FNN. Through the cascade of LSTM cells and fully-connected feedforward units, we explore the temporal patterns and summarize the long history of previous inputs in a deep learning machine. The experiments on 3rd CHiME challenge and Aurora-4 show that the stacks of hybrid model with FNN post-processor outperform stand-alone FNN and LSTM and the other hybrid models for robust speech recognition.

关键词： speech recognition acoustic modeling hybrid neural network long short-term memory

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Smart sensor-based geospatial architecture for dike monitoring 9

Smart sensor-based geospatial architecture for dike monitori...

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9th symposium of the international Society for Digital Earth, ISDE 2015

作者： Herle, S. Becker, R. Blankenbach, J. Geodetic Institute Department of Computing in Civil Engineering and Geo Information Systems RWTH Aachen University Aachen Germany

Artificial hydraulic structures like dams or dikes used for water level regulations or flood prevention are continuously under the influence of the weather and variable river regimes. Thus, ongoing monitoring and simulation is crucial in order to determine the inner condition. Potentially life-threatening situations, in extreme case a failure, must be counteracted by all available means. Nowadays flood warning systems rely exclusively on water level forecast without considering the state of the structure itself. Area-covering continuous knowledge of the inner state including time dependent changes increases the capability of recognizing and locating vulnerable spots for early treatment. In case of a predicted breach, advance warning time for alerting affected citizens can be extended. Our approach is composed of smart sensors integrated in a service-oriented geospatial architecture to monitor and simulate artificial hydraulic structures continuously. The sensors observe the inner state of the construction like the soil moisture or the stress and deformation over time but also various external influences like water levels or wind speed. They are interconnected in distributed network architecture by a so-called sensor bus system based on lightweight protocols like Message Queue Telemetry Transport for sensor networks (MQTT-SN). These sensor data streams are transferred into an OGC sensor Web Enablement (SWE) data structure providing high-level geo web services to end users. Bundled with 3rd party geo web services (WMS etc.) powerful processing and simulation tools can be invoked using the Web processing Service (WPS) standard. Results will be visualized in a geoportal allowing user access to all information.

关键词： Web services

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Proceedings - 3rd IEEE/ACM international Conference on Big Data Computing, Applications and Technologies, BDCAT 2016

Proceedings - 3rd IEEE/ACM International Conference on Big D...

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3rd IEEE/ACM international Conference on Big Data Computing, Applications and Technologies, BDCAT 2016

ISBN: (纸本)9781450346177

The proceedings contain 39 papers. The topics discussed include: H2F: a hierarchical Hadoop framework for big data processing in geo-distributed environments;performance characterization of Hadoop workloads on SR-IOV-enabled virtualized InfiniBand clusters;a visual analytics approach to author name disambiguation;applying big data warehousing and visualization techniques on PingER data;efficient service discovery in decentralized online social networks;towards longitudinal analysis of a population's electronic health records using factor graphs;disease gene discovery of single-gene disorders based on complex network;identifying patient experience from online resources via sentiment analysis and topic modelling;a study of factuality, objectivity and relevance: three desiderata in large-scale information retrieval;on exploiting data locality for iterative MapReduce applications in hybrid clouds;spatial and temporal analysis of urban space utilization with renewable wireless sensor network;spatial big data for designing large scale infrastructure: a case-study of electrical road systems;a real-time big data analysis framework on a CPU/GPU heterogeneous cluster: a meteorological application case study;a benchmarking platform for analyzing corpora of traces: the recognition of the users' involvement in fields of competencies;and not too late to identify potential churners: early churn prediction in telecommunication industry.

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The impact of network sampling on relational classification 3

The impact of network sampling on relational classification

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3rd Annual international symposium on information Management and Big Data, SIMBig 2016

作者： Berton, Lilian Vega-Oliveros, Didier A. Valverde-Rebaza, Jorge Da Silva, Andre Tavares De Andrade Lopes, Alneu Department of Computer Science ICMC University of São Paulo São Carlos - SP13560-970 Brazil Technological Sciences Center University of Santa Catarina State Joinville - SC89219-710 Brazil

Many real-world networks, such as the Internet, social networks, biological networks are massive in size, which difficult different processing and analysis tasks. For this reason, it is necessary to apply a sampling process to reduce the network size without losing relevant network information. In this paper, we propose a new and intuitive sampling method based on exploiting the following centrality measures: degree, k-core, clustering, eccentricity and structural holes. For our experiments, we delete 30% and 50% of the vertices from the original network and evaluate our proposal on six real-world networks on relational classification task using six different classifiers. Classification results achieved on sampled graphs generated from our proposal are similar to those obtained on the entire graphs. In most cases, our proposal reduced the original graphs by up to 50% of its original number of edges. Moreover, the execution time for learning step of the classifier is shorter on the sampled graph.

关键词： Complex networks

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Simulation and analysis of classification optimization model of temperature sensing big data in intelligent building 2016

Simulation and analysis of classification optimization model...

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3rd Multidisciplinary international Social networks Conference, MISNC 2016, 5th ASE international Conference on Social Informatics, SocialInformatics 2016 and 7th ASE international Conference on Data Science, DS 2016

作者： Zhang, Fuquan Mao, Zijing Ding, Gangyi School of software Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Department of Electrical Engineering University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States School of software Beijing Institute of TechnologyBeijing 10008 1 China Software Building Beijing Institute of Technology 5 South Zhongguancun Street Haidian Beijing100081 China

ISBN: (纸本)9781450341295

The temperature sensor network in intelligent building classified collection of big data processing has the problem of big data redundancy interference, which results in unable to determine the fixed filter thresholds. This paper proposed Chaos differential disturbance based fuzzy C-means clustering model for big temperature sensing data classification tasks. It requires to analyze temperature sensor in the intelligent building big distributed structure model of data in a database storage system, the big data information flow feature fusion and time series analysis. Based on traditional fuzzy c-means clustering processing, we introduced chaos disturbance to avoid the classification into local convergence and local optimum, and therefore improve the performance of data clustering. The testing results show that our proposed classification method effectively reduces the error rate for classification tasks of temperature data in intelligent building and have achieved the best performance among the existing algorithms. © 2016 ACM.

关键词： Temperature sensors

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Compact Trip Representation over networks 23rd

Compact Trip Representation over Networks

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23rd international symposium on String processing and information Retrieval (SPIRE)

作者： Brisaboa, Nieves R. Farina, Antonio Galaktionov, Daniil Rodriguez, M. Andrea Univ A Coruna Database Lab La Coruna Spain Univ Concepcion Dept Comp Sci Concepcion Chile

ISBN: (纸本)9783319460499;9783319460482

We present a new Compact Trip Representation (CTR) that allows us to manage users' trips (moving objects) over networks. These could be public transportation networks (buses, subway, trains, and so on) where nodes are stations or stops, or road networks where nodes are intersections. CTR represents the sequences of nodes and time instants in users' trips. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array (CSA), which provides both a compact representation and interesting indexing capabilities. We also represent the temporal component of the trips, that is, the time instants when users visit nodes in their trips. We create a sequence with these time instants, which are then self-indexed with a balanced Wavelet Matrix (WM). This gives us the ability to solve range-interval queries efficiently. We show how CTR can solve relevant spatial and spatio-temporal queries over large sets of trajectories. Finally, we also provide experimental results to show the space requirements and query efficiency of CTR.

关键词： Indexing (of information)

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\ Data Validation and Reconstruction for Performance Enhancement and Maintenance of Water networks

\ Data Validation and Reconstruction for Performance Enhance...

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3rd IFAC Workshop on Advanced Maintenance Engineering, Services and Technology (AMEST)

作者： Quevedo, J. Pascual, J. Espin, S. Roquet, J. BarcelonaTech UPC Ctr Supervis Safety & Automat Control CS2AC Campus Terrassa Barcelona 08222 Spain ATLL Concessionaria Generalitat Catalunya SA St Marti de lErm 30 Barcelona 08970 Spain

In a real water network, a telecontrol system must periodically acquire, store and validate data gathered by sensor measurements in order to achieve accurate monitoring of the whole network in real time. For each sensor measurement, data are usually represented by one-dimensional time series. These values, known as raw data, need to be validated before further use to assure the reliability of the results obtained when using them. In real operation, problems affecting the communication system, lack of reliability of sensors, or other inherent errors often arise, generating missing or false data during certain periods of time. These wrong data must be detected and replaced by estimated data. Thus, it is important to provide the data system with procedures that can detect such problems and assist the user in monitoring and processing the incoming data. Data validation is an essential step to improve data reliability. The validated data represent measurements of the variables in the required form where unnecessary information from raw data has been removed. In this paper, a methodology for data validation and reconstruction of sensor data in a water network is used to analyze the performance of the sectors of a water network. Finally, from this analysis several indicators of the components (sensors, actuators and pipes) and of the sectors themselves can be derived in order to organize useful plans for performance enhancement and maintenance. Nice practices have been developed during a large period in the water network of the company ATLL Concessionaria de la Generalitat de Catalunya, S.A. (C) 2016, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Data Analytics Data Validation Data Estimation Water networks Performance Index Maintenance

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3rd international Conference on Future Data and Security Engineering, FDSE 2016

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3rd international Conference on Future Data and Security Engineering, FDSE 2016

ISBN: (纸本)9783319480565

The proceedings contain 29 papers. The special focus in this conference is on Big Data Analytics, Cloud Data Management, Internet of Things, Security, Privacy Engineering, Data Protection, Data Hiding, Context-Based Data Analysis, Emerging Data Management Systems and Applications. The topics include: Incorporating trust, certainty and importance of information into knowledge processing systems - an approach;incremental parallel support vector machines for classifying large-scale multi-class image datasets;a large-scale two-level clustering similarity search with MapReduce;immune approach to the protection of IoT devices;heuristic-guided verification for fast congestion detection on wireless sensor networks;security risk management in the aviation turnaround sector;a novel encryption mechanism for door lock;information and identity theft Without ARP spoofing in LAN environments;a watermarking framework for outsourced and distributed relational databases;face quality measure for face authentication;using graph database for evidence correlation on android smartphones;a secure token-based communication for authentication and authorization servers;an enhancement of the Rew-XAC model for workflow data access control in healthcare;trust and risk-based access control for privacy preserving threat detection systems;fine grained attribute based access control model for privacy protection;automatic extraction of semantic relations from text documents;the present and future of large-scale systems modeling and engineering;non-disjoint multi-agent scheduling problem on identical parallel processors and an evaluative model to assess the organizational efficiency in training corporations.

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