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检索条件"主题词=Pursuit Algorithms"

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Learning shift-invariant sparse representation of actions

Learning shift-invariant sparse representation of actions

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Li Cornelia Fermuller Yiannis Aloimonos Hui Ji Computer Vision Laboratory University of Maryland College Park MD USA Department of Mathematics National University of Singapore Singapore

A central problem in the analysis of motion capture (MoCap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should facilitate the recognition, synthesis, and characterization of actions. We propose an unsupervised learning algorithm for automatically decomposing joint movements in human motion capture (MoCap) sequences into shift-invariant basis functions. Our formulation models the time series data of joint movements in actions as a sparse linear combination of short basis functions (snippets), which are executed (or “activated”) at different positions in time. Given a set of MoCap sequences of different actions, our algorithm finds the decomposition of MoCap sequences in terms of basis functions and their activations in time. Using the tools of L 1 minimization, the procedure alternately solves two large convex minimizations: Given the basis functions, a variant of Orthogonal Matching pursuit solves for the activations, and given the activations, the Split Bregman Algorithm solves for the basis functions. Experiments demonstrate the power of the decomposition in a number of applications, including action recognition, retrieval, MoCap data compression, and as a tool for classification in the diagnosis of Parkinson (a motion disorder disease).

关键词： Matching pursuit algorithms Motion analysis Minimization methods Character recognition Unsupervised learning Humans pursuit algorithms Information retrieval Data compression Parkinson's disease

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Message passing Bayesian Compressed Sensing for UWB pulse acquisition

Message passing Bayesian Compressed Sensing for UWB pulse ac...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Depeng Yang Husheng Li Gregory D. Peterson Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA

The theory of compressed sensing has been applied to UWB systems for acquisition of UWB signals at a sub-Nyquist sampling rate. The UWB echo signal has lots of template redundancy which can be exploited to make the sparse UWB signal even sparser. This paper proposes a message passing Bayesian Compressed Sensing (BCS) algorithm to utilize the template redundancy for reducing the number of measurements and improving the capability of combating noise. Simulation results show the proposed massage passing BCS algorithm outperforms the original BCS and Orthogonal Matching pursuit (OMP) algorithms.

关键词： Message passing Bayesian methods Compressed sensing Pulse compression methods Sampling methods Matching pursuit algorithms Signal reconstruction Noise measurement Noise reduction pursuit algorithms

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A new multi-agent reinforcement learning approach

A new multi-agent reinforcement learning approach

引用

International Conference on Information and Automation (ICIA)

作者： Jun Li Qishu Pan Bingrong Hong School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang China

A new multi-agent reinforcement learning approach is proposed to learn the optimal behaviors among cooperative agent teams. The approach combines advantages of the integer programming, single agent learning and repeated game in a multi-agent framework. The integer programming is used to build cooperative teams in order to prevent the curse of dimensionality. Every cooperative team learns independently, whose members take the best response actions in the light of other agents actions in the same condition, after many repeated games, the aim root could be found. Because of other agents influence, the process of learning is supervised periodically, then through changing the learning rate to gain the right learning results. Simulation results on pursuit problem show that the proposed learning approach overcomes the divergence and improves learning speed obviously.

关键词： Learning Multiagent systems Linear programming Collaborative work Automation Computer science State-space methods pursuit algorithms Intestines Artificial intelligence

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Compressive sampling hardware reconstruction

Compressive sampling hardware reconstruction

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Avi Septimus Raphael Steinberg VLSI Laboratory-Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel

Compressive Sampling reconstruction techniques require computationally intensive algorithms, often using L 1 optimization to reconstruct a signal that was originally sampled at a sub-Nyquist rate. In this work we present a VLSI implementation of a computationally efficient algorithm named Orthogonal Matching pursuit. We further optimize the algorithm to meet typical hardware constraints and describe the different block units of our design. We synthesize our design for the Xilinx Virtex 5 FPGA and give timing and area results. We summarize our work with a short discussion of the possible uses for our system.

关键词： Sampling methods Hardware Matching pursuit algorithms Very large scale integration pursuit algorithms Constraint optimization Design optimization Algorithm design and analysis Signal synthesis Field programmable gate arrays

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On the compression of ECG records employing triangular elements and analysis-by-synthesis modeling

On the compression of ECG records employing triangular eleme...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Fellipe dos Santos Guimarães Lisandro Lovisolo Manuel Blanco-Velasco Fernando Cruz-Roldán Dept. of Electronics and Telecommunications Engineering State University of Rio de Janeiro (UERJ) Rio de Janeiro RJ Brazil Department of Signal Theory and Communications Universidad de Alcalá Alcalá de Henares (Madrid) Spain

In this work, non-traditional coding of ECG signals using greedy decompositions is analyzed. For this purpose one starts from a dictionary of triangular waveforms, and using the Matching pursuit algorithm one searches the dictionary for the elements that will be employed for representing a given signal. In this way, compression is performed together with signal analysis. This analysis models the signal utilizing a subset of elements from the dictionary, which may be overlapping and non-orthogonal. The extracted triangles are coded in terms of their centers and their bases' lengths and asymmetries. One of the benefits of the discussed approach is that it provides a flexible signal model rather than just a plain waveform coding or the coarse-detail approximation model of wavelet based compression systems. Therefore, it can be used for pattern characterization of different signal blocks, providing an efficient way to find pathological phenomena, i.e. to analyze the signal, without reconstructing it. The proposed compressor is evaluated in terms of PRD versus bit rate.

关键词： Electrocardiography Dictionaries Signal analysis Encoding Matching pursuit algorithms Signal design Signal processing pursuit algorithms Pathology Pattern analysis

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Sparse recovery and Kronecker products

Sparse recovery and Kronecker products

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Annual Conference on Information Sciences and Systems (CISS)

作者： Sadegh Jokar Department of Mathematics Technical University Berlin Berlin Germany

In this note will consider sufficient conditions for sparse recovery such as Spark, coherence, restricted isometry property (RIP) and null space property (NSP). Then we will discuss the solution of underdetermined linear equations when the matrix is the Kronecker product of matrices. Specially we will explain how NSP behave in the case where the matrix is the Kronecker product of matrices.

关键词： Sparse matrices Sparks Linear systems Sufficient conditions Null space Compressed sensing Equations Vectors Minimization methods pursuit algorithms

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Worst configurations (instantons) for Compressed Sensing over reals: A channel coding approach

Worst configurations (instantons) for Compressed Sensing ove...

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IEEE International Symposium on Information Theory

作者： Shashi Kiran Chilappagari Michael Chertkov Bane Vasic Marvell Semiconductor Inc. Santa Clara CA USA Theoretical Division & CNLS LANL and New Mexico Consortium Los Alamos NM USA Department of ECE University of Arizona Tucson Tucson AZ USA

ISBN: (纸本)9781424478903

We consider the Linear Programming (LP) solution of the Compressed Sensing (CS) problem over reals, also known as the Basis pursuit (BasP) algorithm. The BasP allows interpretation as a channel-coding problem, and it guarantees error-free reconstruction with a properly chosen measurement matrix and sufficiently sparse error vectors. In this manuscript, we examine how the BasP performs on a given measurement matrix and develop an algorithm to discover the sparsest vectors for which the BasP fails. The resulting algorithm is a generalization of our previous results on finding the most probable error-patterns degrading performance of a finite size Low-Density Parity-Check (LDPC) code in the error-floor regime. The BasP fails when its output is different from the actual error-pattern. We design a CS-Instanton Search Algorithm (ISA) generating a sparse vector, called a CS-instanton, such that the BasP fails on the CS-instanton, while the BasP recovery is successful for any modification of the CS-instanton replacing a nonzero element by zero. We also prove that, given a sufficiently dense random input for the error-vector, the CS-ISA converges to an instanton in a small finite number of steps. The performance of the CS-ISA is illustrated on a randomly generated 120 × 512 matrix. For this example, the CS-ISA outputs the shortest instanton (error vector) pattern of length 11.

关键词： Compressed sensing Channel coding Sparse matrices Parity check codes Linear programming pursuit algorithms Vectors Performance evaluation Degradation Algorithm design and analysis

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Channel estimation for OFDM systems with subspace persuit algorithm

Channel estimation for OFDM systems with subspace persuit al...

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International Conference on Green Circuits and Systems (ICGCS)

作者： Wen-Rong Wu Ruey-Ching Chiueh Fan-Shuo Tseng Institute of Communication Engineering National Chiao Tung University Hsinchu Taiwan

Signal recovery techniques for compressive sampling, such as matching pursuit (MP), orthogonal MP (OMP), and linear programming (LP), have been developed and applied to the spare channel estimation problem in orthogonal-frequency-division-multiplexed (OFDM) systems. In this paper, we first propose using a newly developed algorithm, subspace pursuit (SP), for the channel estimation problem. It is shown that the SP algorithm can outperform the MP, OMP, and LP algorithms. However, the SP algorithm will fail if the number of the pilots is small. We then propose a decision-feedback method, referred to as the pseudo-pilot assisted (PA) SP, which can be used in low pilot-density scenarios. Simulations show that the proposed PASP can significantly improve the performance of channel estimation when the number of pilots is small.

关键词： Channel estimation OFDM Matching pursuit algorithms Sampling methods pursuit algorithms Bandwidth Computational complexity Linear programming Wireless communication Delay

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Extension of replica analysis to MAP estimation with applications to compressed sensing

Extension of replica analysis to MAP estimation with applica...

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IEEE International Symposium on Information Theory

作者： Sundeep Rangan Alyson K. Fletcher Vivek K Goyal Polytechnic Institute New York University USA University of California Berkeley USA Massachusetts Institute of Technology USA

ISBN: (纸本)9781424478903

The replica method is a non-rigorous but widely-accepted technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems. This paper applies the replica method to analyze non-Gaussian maximum a posteriori (MAP) estimation. The main result is a counterpart to Guo and Verdú's replica analysis of minimum mean-squared error estimation. The replica MAP analysis can be readily applied to many estimators used in compressed sensing, including basis pursuit, lasso, linear estimation with thresholding, and zero norm-regularized estimation. Among other benefits, the replica method provides a computationally-tractable method for exactly computing various performance metrics including mean-squared error and sparsity pattern recovery probability.

关键词： Compressed sensing Physics Noise measurement Distributed computing Error analysis Algorithm design and analysis Coherence pursuit algorithms Vectors Convergence

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Message passing algorithms for compressed sensing: I. motivation and construction

Message passing algorithms for compressed sensing: I. motiva...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： David L. Donoho Arian Maleki Andrea Montanari Department of Statistics University of Stanford USA Department of Electrical Engineering University of Stanford USA Department of Electrical Engineering and Department of Statistics University of Stanford USA

ISBN: (纸本)9781424463725

In a recent paper, the authors proposed a new class of low-complexity iterative thresholding algorithms for reconstructing sparse signals from a small set of linear measurements. The new algorithms are broadly referred to as AMP, for approximate message passing. This is the first of two conference papers describing the derivation of these algorithms, connection with the related literature, extensions of the original framework, and new empirical evidence. In particular, the present paper outlines the derivation of AMP from standard sum-product belief propagation, and its extension in several directions. We also discuss relations with formal calculations based on statistical mechanics methods.

关键词： Message passing Compressed sensing Iterative algorithms pursuit algorithms Statistics Belief propagation Vectors Equations Electric variables measurement Noise reduction

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