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Identity recognition of plantar pressure image based on compressed sensing

Identity recognition of plantar pressure image based on comp...

引用

International Conference on Mechatronic Science, Electric Engineering and Computer

作者： Yan Zhang Dong Liang Ming Zhu Yining Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education AHU Hefei China Anhui University AHU Hefei China School of Electronics and Information Engineering Anhui University AHU Hefei China Institute of Intelligent Machines Chinese Academy of Sciences CAS Hefei China

ISBN: (纸本)9781479925667

Herein, a new identity recognition method of plantar pressure image (PPI) was investigated based on compressed sensing. During the process of identity recognition, the PPIs were collected with platform system in normal walking speed. The sparse representation of PPI was then obtained according to the sparse basis (i.e., wavelet basis). Finally, measurement vectors were calculated by the Topelitz measurement matrix and the PPI was recognized by compressed sensing classifier. The results showed that the accuracy of identity recognition of PPI based on compressed sensing exceeded 97.76%, demonstrating the effectiveness and stability of the Topelitz-compressed sensing algorithm. Meanwhile, the method used in this study reduced the data storage amount and increased the real-time recognition during the PPI process.

关键词： Accuracy Compressed sensing Sparse matrices Classification algorithms Foot Image recognition Support vector machine classification

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TriggerCas: Enabling wireless consrucive collisions

TriggerCas: Enabling wireless consrucive collisions

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yin Wang Yuan He Dapeng Cheng Yunhao Liu Xiang-yang Li MOE Key Laboratory for Information System Security School of Software TNLIST Tsinghua University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Key Laboratory of Intelligent Information Processing Universities of Shandong China Department of Computer Science Illinois Institute of Technology USA

Constructive Interference (CI) proposed in the existing work (e.g., A-MAC [1], Glossy [2]) may degrade the packet reception performance in terms of Packet Reception Ratio (PRR) and Received Signal Strength Indication (RSSI). The packet reception performance of a set of nodes transmitting simultaneously might be no better than that of any single node transmitting individually. In this paper, we redefine CI and propose TriggerCast, a practical wireless architecture which ensures concurrent transmissions of an identical packet to interfere constructively rather than to interfere non-destructively. CI potentially allows orders of magnitude reductions in energy consumption and improvements in link quality. Moreover, we for the first time present a theoretical sufficient condition for generating CI with IEEE 802.15.4 radio: concurrent transmissions with an identical packet should be synchronized at chip level. Meanwhile, co-senders participating in concurrent transmissions should be carefully selected, and the starting instants for the concurrent transmissions should be aligned. Based on the sufficient condition, we propose practical techniques to effectively compensate propagation and radio processing delays. TriggerCast has 95 th percentile synchronization errors of at most 250ns. Extensive experiments in practical testbeds reveal that TriggerCast significantly improves PRR (from 5% to 70% with 7 concurrent senders, from 50% to 98.3% with 6 senders) and RSSI (about 6dB with 5 senders).

关键词： Delays Synchronization Signal to noise ratio Receivers Wireless sensor networks IEEE 802.15 Standards

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Effectively constructing reliable data for cross-domain text classification 1

引用

7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

Traditional classification algorithms often fail when the independent and identical distributed (i.i.d.) assumption does not hold, and the cross-domain learning emerges recently is to deal with this problem. Actually, we observe that though the trained model from training data may not perform well over all test data, it can give much better prediction results on a subset of the test data with high prediction confidence. Also this subset of data from test data set may have more similar distribution with the test data. In this study, we propose to construct the reliable data set with high prediction confidence, and use this reliable data as training data. Furthermore, we develop an EM algorithm to refine the model trained from the reliable data. The extensive experiments on text classification verify the effectiveness and efficiency of our methods. It is worth to mention that the model trained from the reliable data achieves a significant performance improvement compared with the one trained from the original training data, and our methods outperform all the baseline algorithms. © 2012 IFIP International Federation for information processing.

关键词： Forecasting

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Chinese named entity recognition and disambiguation based on wikipedia

Chinese named entity recognition and disambiguation based on...

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1st CCF Conference on Natural Language processing and Chinese computing, NLPCC 2012

作者： Miao, Yu Yajuan, Lv Qun, Liu Jinsong, Su Hao, Xiong Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642344558

This paper presents a method for named entity recognition and disambiguation based on Wikipedia. First, we establish Wikipedia database using open source tools named JWPL. Second, we extract the definition term from the first sentence of Wikipedia page and use it as external knowledge in named entity recognition. Finally, we achieve named entity disambiguation using Wikipedia disambiguation pages and contextual information. The experiments show that the use of Wikipedia features can improve the accuracy of named entity recognition. © 2012 Springer-Verlag.

关键词： Computers

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A propositional family deontic logic

A propositional family deontic logic

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2012 International Conference on Artificial Intelligence, ICAI 2012

作者： Cao, Cungen Sui, Yuefei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)1601322178

A propositional family deontic logic is introduced, which is a combination of the propositional deontic logic, dynamic logic and ambient logic. A three-layered possible world semantics is given, where a possible world is a set of families which is used to define the semantics for action modality [a];a subpossible world is a family used to define the semantics for family modality [m], and a subsubpossible world is used to define the semantics for the deontic modality OB. An axiomatic system of the logic will be given, and the normal form theorem, the soundness theorem and completeness theorem of the axiomatic system will be proved.

关键词： Semantics

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A fast and robust pedestrian detection framework based on static and dynamic information

A fast and robust pedestrian detection framework based on st...

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2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012

作者： Xu, Tao Liu, Hong Qian, Yueliang Wang, Zhe Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467316590

With the powerful development of pedestrian detection technique based on sliding-window and machine-learning, detection-based tracking systems have become increasingly popular. Most of these systems rely on existing static pedestrian detectors only despite the obvious potential motion information for people detection. This paper proposes a novel pedestrian detection framework fusing static and dynamic features. Motion cue is firstly used to detect potential pedestrian regions. Secondly, static detector scans potential regions to get candidate pedestrian detections. Final detection results are improved by removing false detections based on their motion distribution. The proposed framework significantly raises detection speed and detection performance. Static detector of pedestrian in this paper is trained by AdaBoost with simplified HOG feature (1HOG). Additionally, we introduce a detection-window-pyramid based scanning strategy for quickly extracting 1HOG features. The experimental results on several public data sets show the effectiveness of the proposed approach. © 2012 IEEE.

关键词： Adaptive boosting

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The possibility of using simple neuron models to design brain-like computers 1

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5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012

作者： Hu, Hong Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100190 China

ISBN: (数字)9783642315619

ISBN: (纸本)9783642315602

IBM Research and five leading universities are partnering to create computing systems that are expected to simulate and emulate the brain's abilities. Although this project has achieved some successes, it meets great difficulties in the further research. The main difficulty is that it is almost impossible to analyze the dynamic character of neural networks in detail, when more than ten thousands neurons of complex nonlinear neural models are piled up. So it is nature to present such question: in order to simplify the design of brain-like computers, can we use simple neuron models to design brain-like computers or can we find a simplest neuron model which can simulate most neuron models with arbitrary precision? In this paper, we proved that almost all neural models found by neural scientists nowadays can be simulated by Hopfield neural networks. So it is possible to use simple neuron model to design Brain-like computers. © 2012 Springer-Verlag.

关键词： Neurons

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Reasoning theory for D3L with compositional bridge rules 1

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7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Zhao, Xiaofei Tian, Dongping Chen, Limin Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Research Institute of China Unicom Beijing China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

The semantic mapping in Distributed Dynamic Description Logics (D3L) allows knowledge to propagate from one ontology to another. The current research for knowledge propagation in D3L is only for a simplified case when only two ontologies are involved. In this paper we study knowledge propagation in more complex cases. We find in the case when more than two ontologies are involved and bridge rules form chains, knowledge does not always propagate along chains of bridge rules even if we would expect it. Inspired by Package-based description Logics, we extend the original semantics of D3L by imposing so called compositional consistency condition on domain relations in D3L interpretations. Under this semantics knowledge propagates along chains of bridge rules correctly. Furthermore we provide a distributed Tableaux reasoning algorithm for deciding satisfiability of concepts which is decidable in D3L under compositional consistency. Compared with original one, the extended D3L provides more reasonable logic foundation for distributed, dynamic system such as the information integration system and the Semantic Web. © 2012 IFIP International Federation for information processing.

关键词： Ontology

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Evaluating structure of complex networks by navigation entropy

Evaluating structure of complex networks by navigation entro...

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2012 8th International Conference on Semantics, Knowledge and Grids, SKG 2012

作者： Sun, Xiaoping Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769547947

Complex networks are extensively studied in various areas such as social networks, biological networks, Internet and WWW. Those networks have many characters such as small-diameter, higher cluster and power-law degree distribution. Small-world is evolved for efficient information transformation and navigation. Thus, navigation is an important functional character of networks. Previous researches mostly focus on understanding the navigability of small-world networks by analyzing the diameter and the routing efficiency. In this paper, we use the navigability to model the basic structural complexity of a network. That is, given a network topology, we need a model to evaluate how complex the topology is. Some network complexity models have been proposed but none of them consider the navigability factor of the network systems. We believe that using the navigability factor to evaluate the network structural complexity is a feasible and reasonable way. We use the adjacent matrix to build a navigation transition matrix and evaluate the randomness of random walks on the transition matrix by defining navigation entropy on it. We use the iteration of the random walk matrix to evaluate the navigability of a network and the complexity of network. That is, the higher the navigation entropy, the higher the randomness of a network. The lower the navigation entropy, the higher the structure of a network. We apply the navigation entropy model on a set of structural and random network topologies to show how the model can show the different complexity of networks. © 2012 IEEE.

关键词： Entropy

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Parallel decision tree with application to water quality data analysis 1

引用

9th International Symposium on Neural Networks, ISNN 2012

作者： He, Qing Dong, Zhi Zhuang, Fuzhen Shang, Tianfeng Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (数字)9783642313622

ISBN: (纸本)9783642313615

Decision tree is a popular classification technique in many applications, such as retail target marketing, fraud detection and design of telecommunication service plans. With the information exploration, the existing classification algorithms are not good enough to tackle large data set. In order to deal with the problem, many researchers try to design efficient parallel classification algorithms. Based on the current and powerful parallel programming framework - MapReduce, we propose a parallel ID3 classification algorithm(PID3 for short). We use water quality data monitoring the Changjiang River which contains 17 branches as experimental data. As the data are time series, we process the data to attribute data before using the decision tree. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 Springer-Verlag.

关键词： MapReduce

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