1,721,564 research outputs found
EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations
No full textIn this paper, we present an approximate data gathering technique, called EDGES, for sensor networks that utilizes temporal and spatial correlations. The goal of EDGES is to efficiently obtain the sensor reading within a certain error bound. To do this, EDGES utilizes the multiple model Kalman filter, which is for the non-linear data distribution, as an approximation approach. The use of the Kalman filter allows EDGES to predict the future value using a single previous sensor reading in contrast to the other statistical models such as the linear regression and multivariate Gaussian. In order to extend the lifetime of networks, EDGES utilizes the spatial correlation. In EDGES, we group spatially close sensors as a cluster. Since a cluster header in a network acts as a sensor and router. a cluster header wastes its energy severely to send its own reading and/or data coming from its children. Thus, we devise a redistribution method which distributes the energy consumption of a cluster header using the spatial correlation. In some previous works, the fixed routing topology is used or the roles of nodes are decided at the base station and this information propagates through the whole network. But, in EDGES, the change of a cluster is notified to a small portion of the network. Our experimental results over randomly generated sensor networks with synthetic and real data sets demonstrate the efficiency of EDGES. (C) 2009 Elsevier Inc. All rights reserved
AMID: Approximation of MultI-measured Data using SVD
No full textApproximate query answering has recently emerged as an effective method for generating a viable answer. Among various techniques for approximate query answering, wavelets have received a lot of attention. However, wavelet techniques minimizing the root squared error (i.e., the L(2) norm error) have several problems such as the poor quality of reconstructed data when the original data is biased. In this paper, we present AMID (Approximation of Multi-measured Data using SVD) for multi-measured data. In AMID, we adapt the singular value decomposition (SVD) to compress multi-measured data. We show that SVD guarantees the root squared error, and also drive an error bound of SVD for an individual data value, using mathematical analyses. In addition, in order to improve the accuracy of approximated data, we combine SVD and wavelets in AMID. Since SVD is applied to a fixed matrix, we use various properties of matrices to adapt SVD to the incremental update environment. We devise two variants of AMID for the incremental update environment: incremental AMID and local AMID. To the best of our knowledge, our work is the first to extend SVD to incremental update environments. (c) 2009 Elsevier Inc. All rights reserved
Peregrine: A Flexible Hardware Accelerator for LSTM with Limited Synaptic Connection Patterns
No full text1
An efficient XML encoding and labeling method for query processing and updating on dynamic XML data
No full textIn this paper, we propose an efficient encoding and labeling scheme for XML, called EXEL, which is a variant of the region labeling scheme using ordinal and insert-friendly bit strings. We devise a binary encoding method to generate the ordinal bit strings, and an algorithm to make a new bit string inserted between bit strings without any influences on the order of preexisting bit strings. These binary encoding method and bit string insertion algorithm are the bases of the efficient query processing and the complete avoidance of re-labeling for updates. We present query processing and update processing methods based on EXEL. In addition, the Stack-Tree-Desc algorithm is used for an efficient structural join, and the String B-tree indexing is utilized to improve the join performance. Finally, the experimental results show that EXEL enables complete avoidance of re-labeling for updates while providing fairly reasonable query processing performance. (C) 2008 Elsevier Inc. All rights reserved
An efficient encoding and labeling for dynamic XML data
No full textInternational Conference of Database Systems for Advanced Applications DASFAA2007, 9-12 April 07, Bangkok THAILANDInformation Technology Research Cente
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Wideband, Flexible and High Performance EMI Shielding Thin Film based on Graphene-Nanotube-Iron
No full textGeCo: Classification Restricted Boltzmann Machine Hardware for On-chip Learning
No full textWe present a Classification Restricted Boltzmann Machine (ClassRBM) hardware for embedded machines with on-chip learning capability. The RBM is a kind of the generative model, and has been used as one of the most popular feature extractors and image preprocessors. The ClassRBM is a variant of the RBM that is adapted to classification tasks. We propose the multi-Neuron-Per-Class (multi-NPC) voting scheme for improving accuracy of ClassRBM. We also show that the Contrastive Divergence (CD), which is one of the most popular algorithms to train RBM, has limitations in multi-NPC ClassRBM learning and propose a modified CD algorithm to overcome the limitation. Experimental results on FPGA flatform for MNIST datasets confirm that classification accuracy of the proposed algorithm is∼ 2.12% higher than the conventional CD.1
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