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Search Results: 1 - 10 of 58970 matches for " Mingrui Yang "
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New Coherence and RIP Analysis for Weak Orthogonal Matching Pursuit
Mingrui Yang,Frank de Hoog
Mathematics , 2014,
Abstract: In this paper we define a new coherence index, named the global 2-coherence, of a given dictionary and study its relationship with the traditional mutual coherence and the restricted isometry constant. By exploring this relationship, we obtain more general results on sparse signal reconstruction using greedy algorithms in the compressive sensing (CS) framework. In particular, we obtain an improved bound over the best known results on the restricted isometry constant for successful recovery of sparse signals using orthogonal matching pursuit (OMP).
Orthogonal Matching Pursuit with Thresholding and its Application in Compressive Sensing
Mingrui Yang,Frank de Hoog
Computer Science , 2013,
Abstract: Greed is good. However, the tighter you squeeze, the less you have. In this paper, a less greedy algorithm for sparse signal reconstruction in compressive sensing, named orthogonal matching pursuit with thresholding is studied. Using the global 2-coherence , which provides a "bridge" between the well known mutual coherence and the restricted isometry constant, the performance of orthogonal matching pursuit with thresholding is analyzed and more general results for sparse signal reconstruction are obtained. It is also shown that given the same assumption on the coherence index and the restricted isometry constant as required for orthogonal matching pursuit, the thresholding variation gives exactly the same reconstruction performance with significantly less complexity.
Exploiting Distributed Cognition to Make Tacit Knowledge Explicating  [PDF]
Mingrui He, Yongjian Li
Journal of Software Engineering and Applications (JSEA) , 2010, DOI: 10.4236/jsea.2010.33033
Abstract: Distributed cognition is a new development trend of cognitivism, and is also a new research field of knowledge manage- ment. The study discusses that tacit knowledge explicating activity is a distributed cognitive activity, whose success depends on interaction of each of these factors in distributed cognitive system and none of the factor could be neglected. Further, the study exploits distributed cognition to explore how to design these factors in the system so that tacit knowledge explicating can be accomplished successfully.
Compressive hyperspectral imaging via adaptive sampling and dictionary learning
Mingrui Yang,Frank de Hoog,Yuqi Fan,Wen Hu
Computer Science , 2015,
Abstract: In this paper, we propose a new sampling strategy for hyperspectral signals that is based on dictionary learning and singular value decomposition (SVD). Specifically, we first learn a sparsifying dictionary from training spectral data using dictionary learning. We then perform an SVD on the dictionary and use the first few left singular vectors as the rows of the measurement matrix to obtain the compressive measurements for reconstruction. The proposed method provides significant improvement over the conventional compressive sensing approaches. The reconstruction performance is further improved by reconditioning the sensing matrix using matrix balancing. We also demonstrate that the combination of dictionary learning and SVD is robust by applying them to different datasets.
Sparsity based Efficient Cross-Correlation Techniques in Sensor Networks
Prasant Misra,Wen Hu,Mingrui Yang,Marco Duarte,Sanjay Jha
Computer Science , 2015,
Abstract: Cross-correlation is a popular signal processing technique used in numerous localization and tracking systems for obtaining reliable range information. However, a practical efficient implementation has not yet been achieved on resource constrained wireless sensor network platforms. In this paper, we propose: SparseXcorr: cross-correlation via sparse representation, a new framework for ranging based on L1-minimization. The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device, where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework, together with the proposed correlation domain achieved up to two orders of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling. Compared to standard cross-correlation, SparseXcorr uses only 30-40% measurements to obtain precise range estimates with an additional bias of only 2-6 cm for applications with high accuracy requirement; whereas for applications with less constrained accuracy levels, only 5% measurements are adequate to achieve approximately 100cm ranging precision. We also present StructSparseXcorr: cross-correlation via structured sparse representation, an addendum to the proposed computing framework to overcome shortcoming due to dictionary coherence. For cases of high compression factor and low signal-to-noise ratio, we show that StructSparseXcorr improves the performance of SparseXcorr by approximately 40%.
A Deterministic Construction of Projection matrix for Adaptive Trajectory Compression
Rajib Rana,Mingrui Yang,Tim Wark,Chun Tung Chou,Wen Hu
Computer Science , 2013,
Abstract: Compressive Sensing, which offers exact reconstruction of sparse signal from a small number of measurements, has tremendous potential for trajectory compression. In order to optimize the compression, trajectory compression algorithms need to adapt compression ratio subject to the compressibility of the trajectory. Intuitively, the trajectory of an object moving in starlight road is more compressible compared to the trajectory of a object moving in winding roads, therefore, higher compression is achievable in the former case compared to the later. We propose an in-situ compression technique underpinning the support vector regression theory, which accurately predicts the compressibility of a trajectory given the mean speed of the object and then apply compressive sensing to adapt the compression to the compressibility of the trajectory. The conventional encoding and decoding process of compressive sensing uses predefined dictionary and measurement (or projection) matrix pairs. However, the selection of an optimal pair is nontrivial and exhaustive, and random selection of a pair does not guarantee the best compression performance. In this paper, we propose a deterministic and data driven construction for the projection matrix which is obtained by applying singular value decomposition to a sparsifying dictionary learned from the dataset. We analyze case studies of pedestrian and animal trajectory datasets including GPS trajectory data from 127 subjects. The experimental results suggest that the proposed adaptive compression algorithm, incorporating the deterministic construction of projection matrix, offers significantly better compression performance compared to the state-of-the-art alternatives.
SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks
Rajib Rana,Mingrui Yang,Tim Wark,Chun Tung Chou,Wen Hu
Computer Science , 2014,
Abstract: Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using 6 datasets shows that our proposed algorithm can achieve sub-metre accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm show that our algorithm can reduce the error by 10-60 cm for the same compression ratio.
Reliable Multi-path Routing in Selfish Networks with Hidden Information and Actions  [PDF]
Gang Peng, Mingrui Zou, Sammy Chan
Journal of Software Engineering and Applications (JSEA) , 2012, DOI: 10.4236/jsea.2012.512B007
Abstract: In this paper, we propose a novel game-theoretical solution to the multi-path routing problem in wireless ad hoc networks comprising selfish nodes with hidden information and actions. By incorporating a suitable traffic allocation policy, the proposed mechanism results in Nash equilibria where each node honestly reveals its true cost, and forwarding subgame perfect equilibrium in which each node does provide forwarding service with its declared service reliability. Based on the generalised second price auction, this mechanism effectively alleviates the over-payment of the well-known VCG mechanism. The effectiveness of this mechanism will be shown through simulations.
A Hybrid CPU-GPU Accelerated Framework for Fast Mapping of High-Resolution Human Brain Connectome
Yu Wang, Haixiao Du, Mingrui Xia, Ling Ren, Mo Xu, Teng Xie, Gaolang Gong, Ningyi Xu, Huazhong Yang, Yong He
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0062789
Abstract: Recently, a combination of non-invasive neuroimaging techniques and graph theoretical approaches has provided a unique opportunity for understanding the patterns of the structural and functional connectivity of the human brain (referred to as the human brain connectome). Currently, there is a very large amount of brain imaging data that have been collected, and there are very high requirements for the computational capabilities that are used in high-resolution connectome research. In this paper, we propose a hybrid CPU-GPU framework to accelerate the computation of the human brain connectome. We applied this framework to a publicly available resting-state functional MRI dataset from 197 participants. For each subject, we first computed Pearson’s Correlation coefficient between any pairs of the time series of gray-matter voxels, and then we constructed unweighted undirected brain networks with 58 k nodes and a sparsity range from 0.02% to 0.17%. Next, graphic properties of the functional brain networks were quantified, analyzed and compared with those of 15 corresponding random networks. With our proposed accelerating framework, the above process for each network cost 80~150 minutes, depending on the network sparsity. Further analyses revealed that high-resolution functional brain networks have efficient small-world properties, significant modular structure, a power law degree distribution and highly connected nodes in the medial frontal and parietal cortical regions. These results are largely compatible with previous human brain network studies. Taken together, our proposed framework can substantially enhance the applicability and efficacy of high-resolution (voxel-based) brain network analysis, and have the potential to accelerate the mapping of the human brain connectome in normal and disease states.
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Mingrui Xia, Jinhui Wang, Yong He
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0068910
Abstract: The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).
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