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EEG-based fatigue classification by using parallel hidden Markov model and pattern classifier combination 1

19th International Conference on Neural Information Processing, ICONIP 2012

作者： Sun, Hui Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE Key Laboratory of Systems Biomedicine Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (数字)9783642344787

ISBN: (纸本)9783642344770

Fatigue is the most important reason leading to traffic accidents. In order to ensure traffic safety, various methods based on electroencephalogram (EEG) are proposed. But most of them, either regression or classification, are focused on the relationship between feature space and observation values, so the changing patterns of features are ignored or discarded. In this paper, we propose a new fatigue classification method by using parallel hidden-Markov-model and pattern classifier combination techniques, where each model represents a particular fatigue-high-related feature. In the experiment, subjects are asked to accomplish some simple tasks, and both their fatigue states and their EEG signals are recorded simultaneously. Experimental results indicate that the mean error rate obtained by using our new method are 11.15% for classifying 3 states and 16.91% for classifying 4 states, respectively, while the existing approach can only reach 16.45% and 23.55% under the same condition. © 2012 Springer-Verlag.

关键词： Electroencephalography

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Advances in Intelligent and Soft computing: Preface

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Advances in Intelligent and Soft computing 2012年 152 AISC卷 V-VI页

作者： Vittorini, Pierpaolo Gennari, Rosella Marenzi, Ivana De La Prieta, Fernando Corchado Rodríguez, Juan M. Department of Internal Medicine and Public Health University of L'Aquila Italy Computer Science Faculty Free University of Bozen-Bolzano Bolzano Italy L3S Research Center Leibniz University of Hanover Hannover Germany Department of Computing Science University of Salamanca Salamanca Spain

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A segmentation and data association annotation system for laser-based multi-target tracking evaluation

A segmentation and data association annotation system for la...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Chien-Chen Weng Chieh-Chih Wang Jennifer Healey Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering Graduate Institute of Networking and Multimedia and the Intel-NTU Connected Context Computing Center National Taiwan University Taipei Taiwan INTEL Research Laboratory Santa Clara CA USA

2D laser scanners are now widely used to accomplish robot perception tasks such as SLAM and multi-target tracking (MTT). While a number of SLAM benchmarking datasets are available, only a few works have discussed the issues of collecting multi-target tracking benchmarking datasets. In this work, a segmentation and data association annotation system is proposed for evaluating multi-target tracking using 2D laser scanners. The proposed annotation system uses the existing MTT algorithm to generate initial annotation results and uses camera images as the strong hints to assist annotators to recognize moving objects in laser scans. The annotators can draw the object's shape and future trajectory to automate segmentation and data association and reduce the annotation task loading. The user study results show that the performance of the proposed annotation system is superior in the V-measure vs. annotation speed tests and the false positive and false negative rates.

关键词： Trajectory Shape Accuracy Lasers Cameras Image segmentation Benchmark testing

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Sparse MEG Source Localization on Parameterized Cortical Medial Surface

Sparse MEG Source Localization on Parameterized Cortical Med...

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Iranian Conference of Biomedical Engineering

作者： Hamid Khosravi Mahsa Mohammadkhani Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran Department of Computing Science University of Alberta Edmonton Alberta (AB) Canada

ISBN: (纸本)9781467331289

The inverse mapping in MagnetoEncephaloGraphy involves localizing neuronal activity of the gray matter tissue from measured signals around the head. The search space of the inverse mapping is conventionally represented in 3-D Cartesian coordinates including the cortical volume, or alternatively, 2-D manifold describing the cortical tissue. 3-D Cartesian coordinates does not suit sparse regularization of the inverse mapping in multi-resolution source domains. On the other hand, boundary surface of the cortex, represented as triangulated mesh, is used as the search space in some other inverse mapping techniques. However, this surface does not represent depth of the cortex. Moreover, to represent neuronal activity in embedded signal dictionaries, convoluted manifold should be gridded. In this paper we propose to use medial surface as the best surface descriptor of the cortical tissue. Furthermore, manifold gridding is constructed by defining bijective mapping between a flattened parameterized domain and convoluted surface. Signal bases are defined conventionally in the parameterized domain and mapped to the cortical volume by means of the bijective mapping. Medial surface of the cortex tissue, represented as triangulated mesh, is extracted by gradient-based search algorithm. Source localization, for simulated neuronal activity, is performed in real head model extracted from magnetic resonance images. Sparse estimated source of the inverse mapping are compared on volume, boundary surface, and medial surface domains. Simulation results confirm advantages of surface over volume domain. Performnace measures of source localization proves the medial surface as the best surface describing the cortex volume.

关键词： Magneto-encephalography (MEG) Inverse mapping Sparse regularization Multi-resolution Medial surface Mesh parameterization

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A Mixed Deterministic Model for Coreference Resolution 12

A Mixed Deterministic Model for Coreference Resolution

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Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

作者： Bo Yuan Qingcai Chen Yang Xiang Xiaolong Wang Liping Ge Zengjian Liu Meng Liao Xianbo Si Intelligent Computing Research Center Key Laboratory of Network Oriented Intelligent Computation Computer Science and technology Department Harbin Institute of Technology Shenzhen graduate School Shenzhen Guangdong 518055 China

ISBN: (纸本)9781627484046

This paper presents a mixed deterministic model for coreference resolution in the CoNLL-2012 shared task. We separate the two main stages of our model, mention detection and coreference resolution, into several sub-tasks which are solved by machine learning method and deterministic rules based on multi-filters, such as lexical, syntactic, semantic, gender and number information. We participate in the closed track for English and Chinese, and also submit an open result for Chinese using tools to generate the required features. Finally, we reach the average F1 scores 58.68, 60.69 and 61.02 on the English closed task, Chinese closed and open tasks.

关键词： Coreference Resolution deterministic English English language Chinese Chinese philosophy Chinese art

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Combination of fuzzy clustering algorithms for change detection in remote sensing images

Combination of fuzzy clustering algorithms for change detect...

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International Conference on Emerging Applications of Information Technology (EAIT)

作者： Niladri Shekhar Mishra Susmita Ghosh Ashish Ghosh Department of Electronics and Communication Engineering Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Center for Soft Computing Research Indian Statistical Institute Kolkata India

Here we propose a methodology to combine the output of fuzzy clusterings to detect changes in remote sensing images. In this regard we select two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson Kessel clustering (GKC). For clustering purpose various image features are extracted using the neighborhood information of pixels from the difference image (DI). To assign a pixel-pattern to either of the two groups (for changed and unchanged regions of the DI) maximum of the two membership-values (given by FCM and by GKC for the same pattern for the same cluster) is considered. It has been observed experimentally that the changesare detected more efficiently using the proposed ensemble-based procedure. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. Results are compared with those of existing stand-alone fuzzy clustering based techniques, Markov random field (MRF) & neural network based algorithms and found to be superior.

关键词： Remote sensing Clustering algorithms Satellites Shape Change detection algorithms Neural networks visualization

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Isosurface extraction from hybrid unstructured grids containing pentahedral elements

Isosurface extraction from hybrid unstructured grids contain...

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International Conference on computer Graphics Theory and Applications, GRAPP 2012 and International Conference on Information visualization Theory and Applications, IVAPP 2012

作者： Narayan, Akshay Sreevalsan-Nair, Jaya Gaither, Kelly Hamann, Bernd International Institute of Information Technology - Bangalore 26/C Electronics City Hosur Road Bangalore 560100 India Texas Advanced Computing Center University of Texas at Austin 10100 Burnet Road Austin TX 78758 United States Institute for Data Analysis and Visualization Department of Computer Science University of California Davis One Shields Avenue Davis CA 95616 United States

ISBN: (纸本)9789898565020

Grid-based computational simulations often use hybrid unstructured grids consisting of various types of elements. Most commonly used elements are tetrahedral, pentahedral (namely, square pyramids and right triangular prisms) and hexahedral elements. Extracting isosurfaces of scalar fields defined on such hybrid unstructured grids is often done using indirect methods, such as, (a) subdividing all non-tetrahedral cells into tetrahedra and computing the triangulated isosurfaces using the marching tetrahedra algorithm, or (b) triangulating intersection points of edges of cells and computing the isosurface using a standard triangulation algorithm. Using the basic ideas underlying the well-established marching cubes and marching tetrahedra algorithms, which are applied to hexahedral and tetrahedral elements, respectively, we generate look-up tables for extracting isosurfaces directly from pentahedral elements. By using appropriate look-up tables, it is possible to process nearly all types of hybrid unstructured grids used in practical applications without the need for indirect methods. We construct look-up tables for square pyramidal and triangular prismatic cells with accurate topological considerations.

关键词： Table lookup

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Collaborative authoring of Walden's paths

Collaborative authoring of Walden's paths

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2nd International Conference on Theory and Practice of Digital Libraries, TPDL 2012

作者： Li, Yuanling Bogen II, Paul Logasa Pogue, Daniel Furuta, Richard Shipman, Frank Center for the Study of Digital Libraries Department of Computer Science and Engineering Texas A and M University College Station TX United States Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge United States User Experience and Interaction Design Team Production Enhancement Halliburton Energy Services Houston TX United States

ISBN: (纸本)9783642332890

This paper presents a prototype of an authoring tool to allow users to collaboratively build, annotate, manage, share and reuse collections of distributed resources from the World Wide Web. This extends on the Walden's Path project's work to help educators bring resources found on the World Wide Web into a linear contextualized structure. The introduction of collaborative authoring feature fosters collaborative learning activities through social interaction among participants, where participants can coauthor paths in groups. Besides, the prototype supports path sharing, branching and reusing;specifically, individual participant can contribute to the group with private collections of knowledge resources;paths completed by group can be shared among group members, such that participants can tailor, extend, reorder and/or replace nodes to have sub versions of shared paths for different information needs. © 2012 Springer-Verlag.

关键词： World Wide Web

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Structure-preserving retargeting of irregular 3D architecture

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2012年第2期24卷 146-148页

作者： Lin, Jinjie Cohen-Or, Daniel Zhang, Hao Liang, Cheng Sharf, Andrei Deussen, Oliver Chen, Baoquan Visual Computing Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Computer Science Tel Aviv University Tel Aviv Israel Computer Science Simon Fraser University Vancouver Canada Department of Computer Science Ben-Gurion University Beer-Sheva Israel Department of Computer and Information Science University of Konstanz Konstanz Germany

We present an algorithm for interactive structure-preserving retargeting of irregular 3D architecture models, offering the modeler an easy-to-use tool to quickly generate a variety of 3D models that resemble an input piece in its structural style. Working on a more global and structural level of the input, our technique allows and even encourages replication of its structural elements, while taking into account their semantics and expected geometric interrelations such as alignments and adjacency. The algorithm performs automatic replication and scaling of these elements while preserving their structures. Instead of formulating and solving a complex constrained optimization, we decompose the input model into a set of sequences, each of which is a 1D structure that is relatively straightforward to retarget. As the sequences are retargeted in turn, they progressively constrain the retargeting of the remaining sequences. We demonstrate interactivity and variability of results from our retargeting algorithm using many examples modeled after real-world architectures exhibiting various forms of irregularity.

关键词： Constrained optimization

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Quantitative Trait Locus Analysis Using a Partitioned Linear Model on a GPU Cluster

Quantitative Trait Locus Analysis Using a Partitioned Linear...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Peter E. Bailey Tapasya Patki Gregory M. Striemer Ali Akoglu David K. Lowenthal Peter Bradbury Matt Vaughn Liya Wang Stephen Goff Department of Computer Science University of Arizona Tucson USA Department of Electrical and Computer Engineering University of Arizona Tucson USA USDA/ARS and Cornell University USA Texas Advanced Computing Center USA Cold Spring Harbor Laboratory USA IPlant Collaborative USA

Quantitative Trait Locus (QTL) analysis is a statistical technique that allows understanding of the relationship between plant genotypes and the resultant continuous phenotypes in non-constant environments. This requires generation and processing of large datasets, which makes analysis challenging and slow. One approach, which is the subject of this paper, is Partitioned Linear Modeling (PLM), lends itself well to parallelization, both by MPI between nodes and by GPU within nodes. Large input datasets make this parallelization on the GPU non-trivial. This paper compares several candidate integrated MPI/GPU parallel implementations of PLM on a cluster of GPUs for varied data sets. We compare them to a naive implementation and show that while that implementation is quite efficient on small data sets, when the data set is large, data-transfer overhead dominates an all-GPU implementation of PLM. We show that an MPI implementation that selectively uses the GPU for a relative minority of the code performs best and results in a 64 improvement over the MPI/CPU version. As a first implementation of PLM on GPUs, our work serves as a reminder that different GPU implementations are needed, depending on the size of the working set, and that data intensive applications are not necessarily trivially parallelizable with GPUs.

关键词： Graphics processing unit Instruction sets Random access memory Kernel Genetics Registers Memory management

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