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Amorphous localization algorithm based on BP artificial neural network

Amorphous localization algorithm based on BP artificial neur...

International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014

作者： Lin-zhe Zhao Xian-bin Wen Dan Li Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China School of Computer Science and Technology Tianjin University China

Accurate localization of nodes is one of the key issues of wireless sensor network (WSN). Because the disadvantages of the classical Amorphous algorithm will produce large localization error in the process of localization. In order to solve the problem, an improved localization algorithm is proposed in this paper to reduce the localization error. The improved algorithm introduces the received signal strength threshold to modify the minimum hop from the unknown node to the beacon node, then BP algorithm is introduced to optimize the threshold and reduce the localization error. The simulation results show that the improved algorithm is better than other algorithms in the localization accuracy and energy consumption, and it is simple to achieve in the hardware.

关键词： Amorphous localization algorithm BP artificial neural network Wireless Sensor Network (WSN) localization Artificial neural networks algorithms Wireless sensor networks noncrystalline

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A Strategy to Integrate Test Questions from Web Towards a Textbook

A Strategy to Integrate Test Questions from Web Towards a Te...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Biao Ren Yukun Li Shugang Tang Chengyi Xia Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Tianjin University of Technology Tianjin University of Technology Tianjin CN Ministry of Education Tianjin Key Laboratory of Computer Vision and System China

ISBN: (纸本)9781479967162

With the development of network technology, the amount of information on network has been exploded. How to effectively make use of the web information resource has been an important problem which need to be solved. Facing the problem, scholars have some works on web information integration. But these studies mainly pay attention to specific domain information integration, such as news field, movie field, music field, blog field, and so on. There are few studies on information collection towards educational field. In this paper we propose an education-oriented approach for integrating test questions towards a special course. The method is based on a textbook's name and authors' information, and integrate the textbook's catalog and test questions automatically from Web, then computing the similarity between catalog of the textbook and test questions for classification, reaching to the purpose of building test question library by automatic method. Based on the method, we develop a prototype system. The precision and feasibility of the method have been verified by experiments.

关键词： Catalogs Libraries Education Database systems Sections Vectors

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Team of Bayesian optimization algorithms to solve task assignment problems in heterogeneous computing systems

Team of Bayesian optimization algorithms to solve task assig...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jie Li JunQi Zhang Qi Kang ChangJun Jiang Ministry of Education China Key Laboratory of Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai Department of Control Science and Engineering Tongji University Shanghai China

ISBN: (纸本)9781479938414

A Bayesian optimization algorithm (BOA) belongs to estimation of distribution algorithms (EDAs). It is characterized by combining a Bayesian network and evolutionary algorithms to solve nearly decomposable optimization problems. BOA is less popularly applied to solve high dimensionality complex optimization problems. A key reason is that the cost of training all dimensions by BOA becomes expensive with the increase of problem dimensionality. Since data are relatively sparse in a high dimensional space, even though BOA can train all dimensions simultaneously, the interdependent relations between different dimensions are difficult to learn. Its search ability is thus significantly reduced. In this paper, we propose a team of Bayesian optimization algorithms (TBOA) to search and learn dimensionality. TBOA consists of multiple BOAs, in which each BOA corresponds to a dimension of the solution domain and it is responsible for the search of this dimension's value region. The proposed TBOA is used to solve the real problem of task assignment in heterogeneous computing systems. Extensive experiments demonstrate that the computational cost of the overall training in TBOA is decreased very significantly while keeping high solution accuracy.

关键词： Bayes methods Sociology Statistics Optimization Computational efficiency Joints Probability distribution

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A New Class of Learning Automata for Selecting an Optimal Subset

A New Class of Learning Automata for Selecting an Optimal Su...

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IEEE International Conference on systems, Man, and Cybernetics

作者： JunQi Zhang ZeZhou Li Qi Kang MengChu Zhou Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Department of Control Science and Engineering Tongji University Department of Electrical and Computer Engineering New Jersy Institute of Technology

ISBN: (纸本)9781479938414

Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of Learning Automata to select only the optimal action out of a set of actions, this paper considers a multiple-action selection problem and proposes a novel class of Learning Automata for selecting an optimal subset of actions. Their objective is to identify the optimal subset: the top k out of r actions. Based on conventional continuous pursuit and discretized pursuit learning schemes, this paper introduces four pursuit learning schemes for selecting the optimal subset, called continuous equal pursuit, discretized equal pursuit, continuous unequal pursuit and discretized unequal pursuit learning schemes, respectively. In conjunction with a reward-inaction learning paradigm, the above four schemes lead to four versions of pursuit Learning Automata for selecting the optimal subset. The simulation results present a quantitative comparison between them.

关键词： Learning Automata Optimal subset Pursuit algorithm

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Rough Sets and Knowledge technology: 9th International Conference, RSKT 2014, Shanghai, China, October 24-26, 2014, Proceedings 1

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丛书名： Lecture Notes in Computer Science 8818 Lecture Notes in Artificial Intelligence

2014年

作者： Duoqian Miao, Witold Pedrycz, Dominik Ślȩzak, Georg Peters, Qinghua Hu, Ruizhi Wang (eds.)

ISBN: (数字)9783319117409

ISBN: (纸本)9783319117393;9783319117409

This book constitutes the thoroughly refereed conference proceedings of the 9th International Conference on Rough Sets and Knowledge technology, RSKT 2014, held in Shanghai, China, in October 2014. The 70 papers presented were carefully reviewed and selected from 162 submissions. The papers in this volume cover topics such as foundations and generalizations of rough sets, attribute reduction and feature selection, applications of rough sets, intelligent systems and applications, knowledge technology, domain-oriented data-driven data mining, uncertainty in granular computing, advances in granular computing, big data to wise decisions, rough set theory, and three-way decisions, uncertainty, and granular computing.

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An optimized first path detector for UWB ranging using error characteristics

An optimized first path detector for UWB ranging using error...

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IEEE Conference on Wireless Communications and Networking

作者： Hong Cai Tiejun Lv Hui Gao Anzhong Hu Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Singapore University of Technology and Design Singapore

ISBN: (纸本)9781479930845

The key of time of arrival (TOA) estimation in ultra wideband (UWB) ranging is to detect the first path (FP). In this paper, we propose an optimized FP detector with the adaptive threshold in the absence of prior channel state information (CSI). In particular, the error information (EI) set is introduced to guide the threshold adjustment and determine the TOA estimate with a novel iterative algorithm. The EI set captures the characteristics of major errors regarding the inappropriate threshold. After the iterative process, the proposed scheme is shown to achieve the asymptotic optimal threshold without large number of repeated pulses. Therefore, the proposed scheme efficiently improves the TOA estimation accuracy as compare to the traditional schemes. Simulation results validate the effectiveness and superiority of the proposed scheme.

关键词： error analysis iterative methods time-of-arrival estimation ultra wideband technology wireless channels CSI EI set TOA estimation UWB ranging adaptive threshold asymptotic optimal threshold channel state information error characteristic error information iterative process algorithm optimized FP detector optimized first path detector threshold adjustment time of arrival estimation ultrawideband ranging

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Generalized likelihood ratio test multiple-symbol detection for MIMO-UWB: A semidefinite relaxation approach

Generalized likelihood ratio test multiple-symbol detection ...

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IEEE Conference on Wireless Communications and Networking

作者： Chanfei Wang Tiejun Lv Hui Gao Anzhong Hu Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Singapore University of Technology and Design Singapore

ISBN: (纸本)9781479930845

In this paper, semidefinite relaxation (SDR) technology is exploited for the multiple-symbol detection (MSD) over the multiple-input multiple-output (MIMO) ultra-wideband (UWB) systems. The existing scheme generalized likelihood ratio test (GLRT) MSD jointly detect multiple symbols, however, it entails a complexity of O(2 M ), where M is the observation window size. To this end, SDR is employed to reformulate the GLRT detection into a semidefinite programming (SDP) model, and two detectors, randomization-SDR (RSDR) and eigenvector-SDR (ESDR) are proposed on the order of O(M 3.5 ) and O(M 3 ), respectively. Complexity analysis validates that the SDR-MSD strategy is desirable owing to its reduced complexity, compared with the exponential-complexity sphere decoding (SD) MSD. Furthermore, Monte-Carlo simulations demonstrate that the proposed SDR detectors provide the bit error rate (BER) performance almost the same with that of the SD method, and the RSDR outperforms the ESDR at the price of slightly higher complexity.

关键词： Complexity theory Detectors Bit error rate Transmitting antennas Matrix converters Receiving antennas Optimization

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Associating semantic sensor web with domain ontology: The way to obtain meaningful sensor data

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International Journal of u- and e- service, Science and technology 2014年第5期7卷 271-282页

作者： Ji, Chaoqun Liu, Jin Wang, Xiaofeng College of Info. Engineering Shanghai Maritime University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

To realize the sharing and reuse of sensor data and improve interoperability, semantic sensor web(SSW)is proposed to add semantics information to existing sensor networksby utilizing domain, spatial and temporal anthologies and other related semantic *** these is seldom research on how to fully utilize the sensor data through a semantic way such as domain ontology based inference. This paperpresents stateofthe art of SSWin various aspects, and proposesthe method to associate data from semantic sensor web with domain ontology to realize the communication between different domain ontologies and SSW. In addition, this paperalso proposes a new calculation method of semantic similarity amongdifferent entities in different ontology. Experiments show that this method can effectively find the similar entitiesandrealize the knowledge sharing and ontologies reuse. © 2014 SERSC.

关键词： Ontology

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Patra: Parallel tree-reweighted message passing architecture

Patra: Parallel tree-reweighted message passing architecture

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International Conference on Field Programmable Logic and Applications

作者： Wenlai Zhao Haohuan Fu Guangwen Yang Wayne Luk Ministry of Education Key Laboratory for Earth System Modeling Tsinghua University Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computing Imperial College London

Maximum a posteriori probability inference algorithms for Markov Random Field are widely used in many applications, such as computer vision and machine learning. Sequential tree-reweighted message passing (TRW-S) is an inference algorithm which shows good quality in finding optimal solutions. However, the performance of TRW-S in software cannot meet the requirements of many real-time applications, due to the sequential scheme and the high memory, bandwidth and computational costs. This paper proposes Patra, a novel parallel tree-reweighted message passing architecture, which involves a fully pipelined design targeting FPGA technology. We build a hybrid CPU/FPGA system to test the performance of Patra for stereo matching. Experimental results show that Patra provides about 100 times faster than a software implementation of TRW-S, and 12 times faster than a GPU-based message passing algorithm. Compared with an existing design in four FPGAs, we can achieve 2 times speedup in a single FPGA. Moreover, Patra can work at video rate in many cases, such as a rate of 167 frame/sec for a standard stereo matching test case, which makes it promising for many real-time applications.

关键词： Field programmable gate arrays Message passing Algorithm design and analysis Bandwidth Random access memory Accuracy Vectors

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Dynamically tolerating and detecting asymmetric races

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2014年第8期51卷 1748-1763页

作者： Wang, Wenwen Wu, Chenggang Ratanaworabhan, Paruj Yuan, Xiang Wang, Zhenjiang Li, Jianjun Feng, Xiaobing Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Engineering Kasetsart University Bangkok10900 Thailand

Asymmetric races are a common type of data races. They are triggered when a thread accesses a shared variable in a critical section, and another thread accesses the same shared variable not in any critical section, or in a critical section guarded by a different lock. Asymmetric races in multi-threaded programs are usually harmful. To solve the problem introduced by asymmetric races, ARace is proposed. ARace utilizes shared variable protecting and write buffer to dynamically tolerate and detect asymmetric races. Shared variable protecting is used to protect shared variables that are read-only and read-before-write in critical sections, and these shared variables should not be modified out of critical sections;write buffer is used to buffer the writing operations to shared variables in critical sections. ARace can not only tolerate asymmetric races triggered by shared variable accesses in and out of critical sections, but also detect asymmetric races triggered by shared variable accesses in concurrent critical sections. ARace can be directly applied to binary code and requires neither additional compiler support nor hardware support. In addition, an implementation based on dynamic binary instrumentation is also proposed. The experimental results demonstrate that ARace guarantees the tolerance and detection of asymmetric races while incurring acceptable performance and memory overhead.

关键词：非对称数据竞争容忍和检测写缓冲区页保护动态二进制插桩

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