A novel topology identification method based on compressive sensing for multidimensional networks

In real-world scenarios, it is difficult to know about the complete topology of a huge network with different types of links. In this brief, we propose a method to identify the topology of multidimensional networks from information transmission data. We consider information propagating over edges of a two-dimensional (2D) network, where one type of links is known and the other type is unknown. Given the state of all nodes at each unit time, we can transform the topology identification problem into a compressive sensing framework. A modified reconstruction algorithm, called Sparsity Adaptive Matching Pursuit with Mixed Threshold Mechanism (SAMPMTM), is proposed to tackle the problem. Compared with the classical Sparsity Adaptive Matching Pursuit (SAMP) algorithm, the proposed SAMPMTM algorithm can reduce the conflict rate and improve the accuracy of network recovery. We further demonstrate the performance of this improved algorithm through Monte-Carlo simulations under different network models.

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Fast compressive sensing reconstruction algorithm on FPGA using Orthogonal Matching Pursuit

2016 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas.2016.7527217 ◽

2016 ◽

Cited By ~ 5

Author(s):

Zhelun Yu ◽

Jincheng Su ◽

Fan Yang ◽

Yangfeng Su ◽

Xuan Zeng ◽

...

Keyword(s):

Compressive Sensing ◽

Matching Pursuit ◽

Reconstruction Algorithm ◽

Orthogonal Matching Pursuit

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GWRA: grey wolf based reconstruction algorithm for compressive sensing signals

PeerJ Computer Science ◽

10.7717/peerj-cs.217 ◽

2019 ◽

Vol 5 ◽

pp. e217

Author(s):

Ahmed Aziz ◽

Karan Singh ◽

Ahmed Elsawy ◽

Walid Osamy ◽

Ahmed M. Khedr

Keyword(s):

Compressive Sensing ◽

Mean Squared Error ◽

Matching Pursuit ◽

Reconstruction Algorithm ◽

Original Data ◽

Percentage Error ◽

Grey Wolf Optimizer ◽

Signal Acquisition ◽

Grey Wolf ◽

Reconstruction Algorithms

The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error.

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Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

Applied Sciences ◽

10.3390/app11041435 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1435

Author(s):

Xue Bi ◽

Lu Leng ◽

Cheonshik Kim ◽

Xinwen Liu ◽

Yajun Du ◽

...

Keyword(s):

Image Reconstruction ◽

Compressed Sensing ◽

Matching Pursuit ◽

Reconstruction Algorithm ◽

Energy Criterion ◽

Step Size ◽

Relationship Analysis ◽

Support Set ◽

Reconstruction Performance ◽

Sparsity Level

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

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A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions

Geoscientific Model Development ◽

10.5194/gmd-7-1901-2014 ◽

2014 ◽

Vol 7 (5) ◽

pp. 1901-1918 ◽

Cited By ~ 5

Author(s):

J. Ray ◽

V. Yadav ◽

A. M. Michalak ◽

B. van Bloemen Waanders ◽

S. A. McKenna

Keyword(s):

Spatial Variability ◽

Carbon Dioxide Emissions ◽

Fossil Fuel ◽

Matching Pursuit ◽

Regional Scale ◽

Reconstruction Algorithm ◽

Spatiotemporal Variability ◽

Realistic Representation ◽

Non Gaussian ◽

Low Dimensional

Abstract. The characterization of fossil-fuel CO2 (ffCO2) emissions is paramount to carbon cycle studies, but the use of atmospheric inverse modeling approaches for this purpose has been limited by the highly heterogeneous and non-Gaussian spatiotemporal variability of emissions. Here we explore the feasibility of capturing this variability using a low-dimensional parameterization that can be implemented within the context of atmospheric CO2 inverse problems aimed at constraining regional-scale emissions. We construct a multiresolution (i.e., wavelet-based) spatial parameterization for ffCO2 emissions using the Vulcan inventory, and examine whether such a~parameterization can capture a realistic representation of the expected spatial variability of actual emissions. We then explore whether sub-selecting wavelets using two easily available proxies of human activity (images of lights at night and maps of built-up areas) yields a low-dimensional alternative. We finally implement this low-dimensional parameterization within an idealized inversion, where a sparse reconstruction algorithm, an extension of stagewise orthogonal matching pursuit (StOMP), is used to identify the wavelet coefficients. We find that (i) the spatial variability of fossil-fuel emission can indeed be represented using a low-dimensional wavelet-based parameterization, (ii) that images of lights at night can be used as a proxy for sub-selecting wavelets for such analysis, and (iii) that implementing this parameterization within the described inversion framework makes it possible to quantify fossil-fuel emissions at regional scales if fossil-fuel-only CO2 observations are available.

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A Broadband Spectrum Sensing Algorithm in TDCS Based on ICoSaMP Reconstruction

MATEC Web of Conferences ◽

10.1051/matecconf/201817303073 ◽

2018 ◽

Vol 173 ◽

pp. 03073

Author(s):

Liu Yang ◽

Ren Qinghua ◽

Xu Bingzheng ◽

Li Xiazhao

Keyword(s):

Time Use ◽

Matching Pursuit ◽

Reconstruction Algorithm ◽

Transform Domain ◽

Low Snr ◽

System Noise ◽

Broadband Spectrum ◽

Matching Pursuit Algorithm ◽

The Mean ◽

Occupancy Rates

In order to solve the problem that the wideband compressive sensing reconstruction algorithm cannot accurately recover the signal under the condition of blind sparsity in the low SNR environment of the transform domain communication system. This paper use band occupancy rates to estimate sparseness roughly, at the same time, use the residual ratio threshold as iteration termination condition to reduce the influence of the system noise. Therefore, an ICoSaMP(Improved Compressive Sampling Matching Pursuit) algorithm is proposed. The simulation results show that compared with CoSaMP algorithm, the ICoSaMP algorithm increases the probability of reconstruction under the same SNR environment and the same sparse degree. The mean square error under the blind sparsity is reduced.

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