检索结果-内蒙古大学图书馆

IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Yu, Zongxin Wang, Rui Zhang, Haiyan Jin, Yanliang Fu, Yixing Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781479947171

distributed compressed sensing (DCS) is able to exploit both intra-and inter-signal correlation structures of multi-signal ensemble. This paper proposes a DCS scheme for image signal compression and reconstruction. The key idea is to exploit the inter-correlation of the blocks that split from the image. Significantly, joint sparse model was employed to compress the intra-and inter-redundancy of the image signal. Moreover, our scheme allocates more sensing resources to common component while fewer measurements for innovation component. In order to improve the performance, we also utilize variable sizes method to replace the uniform size approach for image split. Experimental results on natural images validate that the proposed DCS scheme validly improves the reconstructed image quality with fewer measurements compared to the existing CS schemes.

关键词： compressed sensing distributed compressed sensing Joint Sparse Signal Model Image Compression

来源：评论

学校读者我要写书评

暂无评论

distributed compressed sensing Reconstruction Algorithm Based on Attention Mechanism and GRU 19

Distributed Compressed Sensing Reconstruction Algorithm Base...

引用

19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Li, Xiaodong Gao, Yulong Wang, Gang Harbin Inst Technol Dept Commun Engn Harbin Peoples R China

ISBN: (纸本)9798350333398

The purpose of this paper is to solve the reconstruction problem of sparse vectors in distributed compressed sensing with Multiple Measurement Vectors (MMV). Many traditional methods are based on certain sparse conditions, such as assuming that signals of different channels are joint sparse. This paper relaxes this restriction and assumes some correlation between the signals of different channels. We use Gated Recurrent Units (GRU) to capture this correlation and reconstruct the signal. Meanwhile, the previous reconstruction algorithms treat the signals of different channels equally, but the influence of different channels on the reconstruction results may be different. To solve this problem, we design an attention mechanism that adaptively learns and adjusts the influence factors of different channel signals, and optimizes the reconstruction results. The method proposed in this paper is only for the decoder, without any additional requirements for the encoder, so the encoder can use the conventional compression sampling method. Extensive experiments indicate the efficiency and robustness of the proposed method in the case of simulated data and real data.

关键词： distributed compressed sensing Deep learning Attention mechanism GRU Sparse reconstruction

来源：评论

学校读者我要写书评

暂无评论

A new backtracking-based sparsity adaptive algorithm for distributed compressed sensing

引用

Journal of Central South University 2015年第10期22卷 3946-3956页

作者：徐勇张玉洁邢婧李宏伟 School of Mathematics and Physics China University of Geosciences Institute of Statistics Hubei University of Economics Hubei Subsurface Multi-scale Imaging Key Laboratory China University of Geosciences

A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing(DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero(significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.

关键词： distributed compressed sensing sparsiy backtracking soft thresholding

来源：评论

学校读者我要写书评

暂无评论

A survey on distributed compressed sensing： theory and applications

引用

Frontiers of Computer Science 2014年第6期8卷 893-904页

作者： Hongpeng YIN Jinxing LI Yi CHAI Simon X. YANG College of Automation Chongqing University Chongqing 400030 China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing 400030 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing 400030 China School of Engineering University of Guelph Guelph N1G 2W1 Canada

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.

关键词： compressed sensing distributed compressed sensing sparse representation measurement matrix joint reconstruction joint sparsity model

来源：评论

学校读者我要写书评

暂无评论

Inner-Outer Support Set Pursuit for distributed compressed sensing

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第11期66卷 3024-3039页

作者： Huang, Kaiyu Liu, Jing Xi An Jiao Tong Univ Sch Elect & Informat Engn Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Shaanxi Peoples R China

We address the distributed compressed sensing problem of reconstructing a sequence of jointly sparse signals under the condition of an inaccurate and insufficient estimate of the common support set. Correlations between the jointly sparse signals are modeled by the joint sparsity model 1 (JSM-1). We propose a novel algorithm, namely inner-outer support set pursuit (IOSSP), which removes the impact of the estimated common support set, to reduce the computing time as well as the reconstruction error. The IOSSP algorithm divides the support set of each individual sparse signal into two disjoint sets: an inner support set and an outer support set. Two separate procedures, the outer support set pursuit and inner support set pursuit, are then utilized to estimate the outer and inner support sets. We provide the convergence performance of the IOSSP algorithm in both the noiseless and noisy conditions. For the noiseless case, we derive the condition on the estimated common support set, which guarantees an exact reconstruction. For the noisy case, a bound of the reconstruction error is provided, and the derivation process is inspired by the compressive sampling matching pursuit algorithm. The experimental and simulation results demonstrate that the proposed IOSSP algorithm is superior on reconstruction error and computing time over the conventional algorithms for JSM-1, e.g., the parallel pursuit with side information and joint recovery strategy algorithms.

关键词： distributed compressed sensing joint sparsity model 1 (JSM-1) inner-outer support set model inner-outer support set pursuit algorithm

来源：评论

学校读者我要写书评

暂无评论

Backtracking-based matching pursuit method for distributed compressed sensing

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2017年第13期76卷 14691-14710页

作者： Zhang, Yujie Qi, Rui Zeng, Yanni China Univ Geosci Sch Math & Phys Wuhan Peoples R China Univ Windsor Sch Comp Sci Windsor ON Canada Naval Univ Engn Sch Sci Wuhan Peoples R China Hubei Univ Econ Fac Stat Wuhan Peoples R China

In this paper, we study a distributed compressed sensing (DCS) problem in which we need to recover a set of jointly sparse vectors from the measurements. A Backtracking-based Adaptive Orthogonal Matching Pursuit (BAOMP) method to approximately sparse solutions for DCS is proposed. It is an iterative approach where each iteration consists of consecutive forward selection to adaptively choose several atoms and backward removal stages to detect the previous chosen atoms' reliability and then delete the unreliable atoms at each iteration. Also, unlike its several predecessors, the proposed method does not require the sparsity level to be known as a prior which makes it a potential candidate for many practical applications, when the sparsity of signals is not available. We demonstrate the reconstruction ability of the proposed algorithm on both synthetically generated data and image using Normal and Binary sparse signals, and the real-life electrocardiography (ECG) data, where the proposed method yields less reconstruction error and higher exact recovery rate than other existing DCS algorithms.

关键词： distributed compressed sensing Greedy algorithm Sparse signal reconstruction Iterative algorithm Orthogonal matching pursuit

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Approximate Message Passing for distributed compressed sensing

引用

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2018年第5期12卷 857-870页

作者： Hannak, Gabor Perelli, Alessandro Goertz, Norbert Matz, Gerald Davies, Mike E. Vienna Univ Technol Inst Telecommun A-1040 Vienna Austria Alexander Graham Bell AGB Inst Digital Commun IDCOM Edinburgh EH9 3FG Midlothian Scotland

Bayesian approximate message passing (BAMP) is an efficient method in compressed sensing that is nearly optimal in the minimum mean squared error (MMSE) sense. Multiple measurement vector (MMV)-BAMP performs joint recovery of multiple vectors with identical support and accounts for correlations in the signal of interest and in the noise. In this paper, we show how to reduce the complexity of vector BAMP via a simple joint decorrelation (diagonalization) transform of the signal and noise vectors, which also facilitates the subsequent performance analysis. We prove that the corresponding state evolution is equivariant with respect to the joint decorrelation transformand preserves diagonality of the residual noise covariance for the Bernoulli-Gauss prior. We use these results to analyze the dynamics and the mean squared error (MSE) performance of BAMP via the replica method, and thereby understand the impact of signal correlation and number of jointly sparse signals. Finally, we evaluate an application of MMV-BAMP for single-pixel imaging with correlated color channels and thereby explore the performance gain of joint recovery compared to conventional BAMP reconstruction as well as group lasso.

关键词： Approximate message passing distributed compressed sensing replica analysis

来源：评论

学校读者我要写书评

暂无评论

Common-Innovation Subspace Pursuit for distributed compressed sensing in Wireless Sensor Networks

引用

IEEE SENSORS JOURNAL 2019年第3期19卷 1091-1103页

作者： Liu, Jing Huang, Kaiyu Yao, Xianghua Xi An Jiao Tong Univ Sch Elect & Informat Engn Key Lab Intelligent Networks & Network Secur Minist Educ Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China

We study the sparse signal reconstruction problem in a wireless sensor network (WSN) using distributed compressed sensing. The sparse signals from multiple sensors are modeled by the mixed-support set model that characterizes the inter-correlation of the signals by the common support set and represents the individual features by the innovation support sets. A novel algorithm, namely common-innovation subspace pursuit (CISP), is proposed to estimate the common and innovation support sets separately, to reduce the reconstruction error and computing time. The proposed algorithm consists of two stages, the eigen-value decomposition-based common subspace pursuit and projection-based innovation subspace pursuit. We analyze the convergence performance of the CISP algorithm, where a hound of reconstruction error is provided. Furthermore, a range of simulations and experiments are conducted to evaluate the performance of the proposed algorithm. The results of simulations on a hierarchical clustering-based WSN show that the proposed CISP algorithm is superior over the orthogonal matching pursuit and distributed parallel pursuit algorithms in terms of both reconstruction error and runtime, in the scenario of a large-scale sensor network. Moreover, the experiments on the real data from the temperature and humidity sensors deployed in the Intel Berkeley Lab verify that, the proposed CISP algorithm is capable of reconstructing the multiple sparse signals accurately and efficiently, in the real-life application.

关键词： Wireless sensor network distributed compressed sensing mixed-support set model common support set estimation

来源：评论

学校读者我要写书评

暂无评论

Probability-based pilot allocation for MIMO relay distributed compressed sensing channel estimation

引用

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2018年第1期2018卷 1-14页

作者： Akbarpour-Kasgari, Abbas Ardebilipour, Mehrdad KN Toosi Univ Technol Dept Elect & Comp Engn Shariati St Tehran Iran

Multiple-Input Multiple-Output (MIMO) relay communication systems are used as an efficient system in spectral efficiency and power allocation view point. In these systems, some of the facilities need channel state information (CSI). Besides, new estimation methods based on compressed sensing (CS) are well known for their spectral efficiency and accuracy. In this paper, we have used a distributed CS-based channel estimation method to improve the accuracy and spectral efficiency of channel estimation for MIMO-Orthogonal Frequency Division Multiplexing relay network. Specifically, using Least Squares (LS) estimation increases the accuracy of well-known Compressive Sampling Matching Pursuit (CoSaMP) algorithm and proposes Block-verified CoSaMP (B-vCoSaMP). To improve the accuracy of estimation, we are encountered with a combinatorial optimization which is dealt with probability-based approaches in this paper. More particularly, three probability-based optimization methods have been proposed to optimize the mutual coherence of measurement matrix called Sequential Cross-Entropy (SCE), Extended Estimating of Distribution Algorithm (EEDA), and Parallel Cross-Entropy (PCE). All these methods are based on sampling from a Probability Density Function (PDF) which is updated in each iteration using elite samples of the population. The simulation results represent the accuracy and speed of the proposed methods, and the comparison is expressed as well.

关键词： Block-verified compressive sampling matching pursuit Cross-entropy distributed compressed sensing MIMO-OFDM relay channel estimation Pilot allocation

来源：评论

学校读者我要写书评

暂无评论

Forward-backward pursuit method for distributed compressed sensing

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2017年第20期76卷 20587-20608页

作者： Zhang, Yujie Qi, Rui Zeng, Yanni China Univ Geosci Sch Math & Phys Wuhan 430074 Peoples R China Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Naval Univ Engn Sch Sci Wuhan 430033 Hubei Peoples R China Hubei Univ Econ Fac Stat Wuhan 430070 Hubei Peoples R China

In this paper, a forward-backward pursuit method for distributed compressed sensing (DCSFBP) is proposed. In contrast to existing distributed compressed sensing (DCS), it is an adaptive iterative approach where each iteration consists of consecutive forward selection and backward removal stages. And it not needs sparsity as prior knowledge and multiple indices are identified at each iteration for recovery. These make it a potential candidate for many practical applications, when the sparsity of signals is not available. Numerical experiments, including recovery of random sparse signals with different nonzero coefficient distributions in many scenarios, in addition to the recovery of sparse image and the real-life electrocardiography (ECG) data, are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing DCS algorithms.

关键词： distributed compressed sensing Forward-backward pursuit Sparsity Sparse signal reconstruction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：