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Asia-Pacific Conference on Communications

作者： Ryo Yamamoto Takumi Miyoshi Course of Electrical Engineering and Computer Science Graduate School of Engineering Shibaura Institute of Technology Japan Department of Electronic Information Systems College of Systems Engineering Shibaura Institute of Technology Japan

We propose a novel distributed retransmission control method for ad hoc networks. Since ad hoc networks are based on wireless communication to transmit data among terminals, radio interference or obstacles often cause link failures and packet loss. In conventional ad hoc routing protocols, a source terminal must discover and reconstruct a new transmission route when link failures or multiple packet losses are observed on the current route. Such route discovery schemes generally increase network traffic. Various retransmission control mechanisms and multipath routing protocols have been proposed to overcome this problem. In the conventional method, however, the terminal that retransmits the lost packets should be located in the neighborhood of the destination terminal. In other words, only the last hop on the route is retransmittable. Moreover, although multipath routing mechanisms can increase transmission success rate, the terminal overhead increases owing to transmission of extra data stream on the route. The proposed method cooperatively uses neighboring terminals of the route to enable retransmission from any terminal on the route. The effectiveness of our method is demonstrated by computer simulation.

关键词： Ad hoc networks Hardware Parity check codes Decoding Bit error rate Sum product algorithm Joining processes Mice Logic gates Costs

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A Fast K-Means Clustering Algorithm Based on Grid data Reduction

A Fast K-Means Clustering Algorithm Based on Grid Data Reduc...

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IEEE Conference on Aerospace

作者： Daqi Li Junyi Shen Hongmin Chen College of Electronic and Information Xi'an Jiaotong University Xi'an Shanxi China School of Computer Science and Engineering Beijing Institute of Technology Beijing China

K-Means is a popular clustering algorithm to find the clustering easily by iteration. But the computational complexity of the traditional k-means due to accessing the whole data in each cycle of iterative operations is too great to make it fit for very large data set. This paper presents a new clustering algorithm we have developed, fast k-means clustering algorithm based on grid data reduction (GDR-FKM), by which clustering operations can be quickly performed on very large data set. Application of the algorithm to analysis of the data relativity in TT&C has demonstrated its celerity and accuracy.

关键词： Clustering algorithms Iterative algorithms Partitioning algorithms Computational complexity Algorithm design and analysis Educational institutions computer science data engineering Aerospace control data analysis

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Kernel Function Clustering Based on Ant Colony Algorithm

Kernel Function Clustering Based on Ant Colony Algorithm

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International Conference on Natural Computation (ICNC)

作者： Jinjiang Li Hui Fan Da Yuan Caiming Zhang College of Information and Electronic Engineering Shandong Institute of Economic and Technology Yantai China School of Computer Science and Technology Shandong University Jinan China

Cluster analysis is one of the several important tools in modern data analysis, and the clustering can be regarded as an optimization problem. The underlying assumption is that there are natural tendencies of cluster or group structure in the data and the goal is to be able to uncover this structure. In general, traditional clustering algorithms are suitable to implement clustering only if the feature differences of data are large. If the feature differences are small and even cross in the original space, it is difficult for traditional algorithms to cluster correctly. By using kernel functions, the data in the original space was mapped into a high-dimensional feature space, in which more feature of the data were exposed so that clustering could be performed efficiently. Ant colony algorithms are a novel category of evolutionary computing methods for optimization problems. Taking advantage of ant-based clustering algorithm and kernel method, we propose in this paper a kernel function clustering based on ant colony algorithm. The kernel method is extended to ant-based clustering algorithm. In contrast to existing method, AKC has better robustness and convergence. It not only can get better clustering effect at the circumstance of outliers existing, but also can make the final clustering results less sensitive to the number of beforehand clustering data because of relaxed subjection degree condition.

关键词： Kernel Clustering algorithms Partitioning algorithms data engineering data analysis Ant colony optimization Shape Clustering methods Machine learning algorithms computer science

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3D facial recognition with soft computing

3D facial recognition with soft computing

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Lecture Notes in computer science

作者： Lee, Yeung-Hak Han, Chang-Wook Kim, Tae-Sun School of Electrical Engineering and Computer Science Yeungnam University 214-1 Dae-dong Gyongsan Gyongbuk 712-749 Korea Republic of Radio Astronomy Division Korea Astronomy and Space Science Institute 61-1 Hwaam-dong Yuseong-gu Daejeon 305-348 Korea Republic of Department of Digital Electronic Engineering Kyungwoon University 55 Induk-ri Sandong-myun Kumi Kyungbuk 730-852 Korea Republic of

ISBN: (纸本)3540894292

The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. These surface curvature and eigenface, which reduce the data dimensions with less degradation of original information, are collaborated into the proposed 3D face recognition algorithm. The principal components represent the local facial characteristics without loss of the information. Recognition for the eigenface referred from the maximum and minimum curvatures is performed. The normalized facial images are also considered to enhance the recognition rate. To classify the faces, the cascade architectures of fuzzy neural networks, which can guarantee a high recognition rate as well as parsimonious knowledge base, are considered. Experimental results on a 46 persons data set of 3D images demonstrate the effectiveness of the proposed method. © 2008 Springer Berlin Heidelberg.

关键词： Fuzzy neural networks

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Granular computing based data mining in the views of rough set and fuzzy set

Granular computing based data mining in the views of rough s...

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IEEE International Conference on Granular Computing (GRC)

作者： Guoyin Wang Jun Hu Qinghua Zhang Xianquan Liu Jiaqing Zhou Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Electronic Engineering Xidian University Xi'an Shaanxi China School of Information Science and Technology Southwest Jiaotong University Chengdu Sichuan China

Usually, data mining is considered as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. In our data-driven data mining model, knowledge is originally existed in data, but just not understandable for human. data mining is taken as a process of transforming knowledge from data format into some other human understandable format like rule, formula, theorem, etc. In order to keep the knowledge unchanged in a data mining process, the knowledge properties should be kept unchanged during a knowledge transformation process. Many real world data mining tasks are highly constraint-based and domain-oriented. Thus, domain prior knowledge should also be a knowledge source for data mining. The control of a user to a data mining process could also be taken as a kind of dynamic input of the data mining process. Thus, a data mining process is not only mining knowledge from data, but also from human. This is the key idea of Domain- oriented data-driven data Mining (3DM). In the view of granular computing (GrC), a data mining process can be considered as the transformation of knowledge in different granularities. Original data is a representation of knowledge in the finest granularity. It is not understandable for human. However, human is sensitive to knowledge in coarser granularities. So, a data mining process could be considered to be a transformation of knowledge from a finer granularity space to a coarser granularity space. The understanding for data mining of3DM and GrC is consistent to each other. Rough set and fuzzy set are two important computing paradigms of GrC. They are both generalizations of classical set theory for modeling vagueness and uncertainty. Although both of them can be used to address vagueness, they are not rivals. In some real problems, they are even complementary to each other. In this plenary talk, the new understanding for data mining, domain-oriented data-driven data mining (3DM), will be introduced.

关键词： data mining Humans Process control Educational institutions Information science Education Computational modeling

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Veritas: Combining Expert Opinions without Labeled data

Veritas: Combining Expert Opinions without Labeled Data

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Sharath R. Cholleti Sally A. Goldman Avrim Blum David G. Politte Steven Don Department of Computer Science and Engineering Washington University Saint Louis MO USA Google Inc. Department of Computer Science Carnegie Mellon University Pittsburgh PA USA Electronic Radiology Laboratory Mallinckrodt Institute of Radiology Washington University School of Medicine Saint Louis MO USA

We consider a variation of the problem of combining expert opinions for the situation in which there is no ground truth to use for training. Even though we don't have labeled data, the goal of this work is quite different from an unsupervised learning problem in which the goal is to cluster the data into different groups. Our work is motivated by the application of segmenting a lung nodule in a computed tomography (CT) scan of the human chest. The lack of a gold standard of truth is a critical problem in medical imaging. A variety of experts, both human and computer algorithms, are available that can mark which voxels are part of a nodule. The question is, how to combine these expert opinions to estimate the unknown ground truth. We present the Veritas algorithm that predicts the underlying label using the knowledge in the expert opinions even without the benefit of any labeled data for training. We evaluate Veritas using artificial data and real CT images to which a synthetic nodule has been added, providing a known ground truth.

关键词： Computed tomography Biomedical imaging Humans Image segmentation Medical diagnostic imaging Boosting USA Councils Lungs Gold Error analysis

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Solving Sum and Product Riddle via BDD-Based Model Checking

Solving Sum and Product Riddle via BDD-Based Model Checking

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Xiangyu Luo Kaile Su Abdul Sattar Yan Chen Tsinghua National Laboratory for Information Science and Technology Key Laboratory of Security for Information System of Ministry of Education School of Software Tsinghua University Beijing China Department of Computer Science Guilin University of Electronic and Technology Guilin China School of Electronics Engineering and Computer Science Peking University Beijing China Institute for Integrated and Intelligent Systems Griffith University Brisbane QLD Australia

We model the sum and product riddle in public announcement logic, which is interpreted on an epistemic Kripke model. The model is symbolically represented as a finite state program with n agents. A model checking method to the riddle is developed by using the BDD-based symbolic model checking algorithm for logic of knowledge we developed in [7]. The method is implemented by extending the model checker MCTK [7] and then the solution of the riddle is verified successfully.

关键词： data structures Boolean functions IEEE news Intelligent agent Educational technology Automatic logic units Laboratories computer science Information science computer security

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Quick Building of Fuzzy Equivalence Matrix in Granular Computing

Quick Building of Fuzzy Equivalence Matrix in Granular Compu...

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International Conference on Granular Computing

作者： Xianquan Liu Guoyin Wang Qinghua Zhang Jun Hu Jiaqing Zhou School of Information Science and Technology Southwest Jiaotong University Chengdu Sichuan 610031 P.R.China Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 P.R.China Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 P.R.China Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 P.R.China School of Electronic Engineering XiDian University Xi'an Shaanxi 710071 P.R.China

ISBN: (纸本)9781424425129

The fuzzy quotient space model is a hybrid model between quotient space theory and fuzzy set theory, which are two important methods in granular computing models, and it is based on fuzzy equivalence relation. There are many algorithms with high complexity for computing the fuzzy equivalence matrices. A new algorithm is proposed to compute the fuzzy equivalence matrices in this paper, and its time and space complexity is o(n{sup}2). An example also illustrates this algorithm. This result would be very helpful for designing efficient algorithms for huge data processing.

关键词： spatial concept Fuzzy Granular computing Hybrid modes Equivalence relation equivalence space complexities Models, Space Fuzzy Set Theory matrices Quotient

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Variant Rate Based Cross Layer Time Frame Scheduling in Wireless Sensor Networks

Variant Rate Based Cross Layer Time Frame Scheduling in Wire...

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7th Wireless Telecommunications Symposium (WTS 2008)

作者： Wei Wei Yong Qi Wei Wang Ruidong Li Yi Shi Yu Gu Andrew Chen Institute of Computer Software and Theory School of Electronic and Information Engineering Xi''an Jiaotong University Xi'an China Computer and Electronic Engineering Department University of Nebraska Lincoln USA Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Japan Department of Computer Science University of Science and Technology China Department of Computer Science University of California Riverside

In this paper, a novel variable rate based time frame scheduling scheme is proposed to further reduce collisions and improve energy saving in wireless sensor networks. The MAC combines CSMA and TDMA functionalities, while obviating their shortcomings. After being assigned, slot 0 is preserved as the pre-schedule slot to inform neighbor nodes the schedule of the senders. During this pre-schedule slot, each node knows exactly the schedule of other neighbor nodes. Multi-rate and power scaling are adopted to achieve further energy saving by employing an acceptable rate rather than the maximum rate. data rate is dynamically adjusted according to the traffic load of sending nodes in an energy efficient data rate to save energy. Compared with Z-MAC, performance in the present study proves to have better ability of local framing pre-schedule and multi-rate achieves better energy efficiency.

关键词： Rate Based Scheduling Wireless Sensor Networks

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3D Facial recognition with soft computing

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2008年 4650卷 194-205页

作者： Lee, Yeung-Hak Han, Chang-Wook Kim, Tae-Sun School of Electrical Engineering and Computer Science Yeungnam University 214-1 Dae-dong GyongsanGyongbuk712-749 Korea Republic of Radio Astronomy Division Korea Astronomy and Space Science Institute 61-1 Hwaam-dong Yuseong-gu Daejeon305-348 Korea Republic of Department of Digital Electronic Engineering Kyungwoon University 55 Induk-ri Sandong-myun KumiKyungbuk730-852 Korea Republic of

ISBN: (纸本)3540894292

关键词： Fuzzy neural networks

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