Low Overhead Architectures for OMP Compressive Sensing Reconstruction Algorithm

Kulkarni, Amey; Mohsenin, Tinoosh

doi:10.1109/tcsi.2017.2648854

Cited by 75 publications

(23 citation statements)

References 38 publications

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“…The methods used to solve sparse approximation problems are available in a variety of ways. As we present our 1 -norm minimization algorithm in the above context of networks, we would like to compare the performance of our framework for network reconstruction with OMP [53], a greedy algorithm, which can be expressed as…”

Section: Resultsmentioning

confidence: 99%

Roles of Clustering Coefficient for the Network Reconstruction

Han

Mao

2018

Mathematical Problems in Engineering

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It is important to establish relations between the network reconstruction and the topological dynamical structure of networks. In this article, we quantify the effect for two types of network topologies on the performance of network reconstruction. First, we generate two network modes with variable clustering coefficient based on Holme-Kim model and Newman-Watts small-world model, then we reconstruct the artificial networks by using a novel framework called L1-norm minimization algorithm based on a theory called compressive sensing (CS), a framework for recovering sparse signals. The results of the simulation experiment show that the accuracy rate for the network reconstruction is a monotonically increasing function of the clustering coefficient in Holme-Kim model, whereas the opposite occurs in Newman-Watts small-world network. And this yet demonstrates that the larger the network size, the higher the accuracy rate. Morever, we compare the results of CS with orthogonal matching pursuit (OMP), a greedy algorithm. The results show that the accuracy rate of L1-norm minimization method is 10% higher than that of OMP, and OMP yields 1.2 times the computation speed of L1-norm minimization. Our work demonstrates that the topological structure of network has influence on the accurate reconstruction and it is helpful for offering proper method for the network reconstruction.

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Section: Resultsmentioning

confidence: 99%

Roles of Clustering Coefficient for the Network Reconstruction

Han

Mao

2018

Mathematical Problems in Engineering

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“…In Figure 9, our high parallel architecture starts processing from 1024 PEs and arrives to the SAD_64×64, different to the case in [19], that can compute the size of windows differently. The SAD_64×64 is the value of SAD corresponds to the bigger bock 64×64, and generates all its corresponding small SAD simultaneously.…”

Section: Gi=a(i)b(i)mentioning

confidence: 99%

Area & Power Efficient VLSI Architecture of Mode Decision in Integer Motion Estimation for HEVC Video Coding Standard

Abdessamad¹,

Bahri²,

Mansouri³

et al. 2019

IJECE

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<span lang="EN-US">In this paper, we propose a new parallel hardware architecture for the mode decision algorithm, that it is based on the Sum Absolute of the Difference (SAD) for compute the motion estimation, which is the most critical algorithm in the recent video encoding standard HEVC. In fact, this standard introduced new large variable block sizes for the motion estimation algorithm and therefore the SAD requires a more reduced execution time in order to achieve the real time processing even for the ultra-high resolution sequences. The proposed accelerator executes the SAD algorithm in a parallel way for all sub-block prediction units (PUs) and coding unit (CU) whatever their sizes, which turns in a huge improvements in the performances, given that all the block sizes, PUs in each CU, are supported and processed in the same time. The Xilinx Artix-7 (Zynq-7000) FPGA is used for the prototyping and the synthesis of the proposed accelerator. The mode decision for motion estimation scheme is implemented with 32K LUTs, 50K registers and 108Kb BRAMs. The implementation results show that our hardware architecture can achieve 30 frames per second of the 4K (3840 × 2160) resolutions in real time processing at 115.15MHz.</span>

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“…The proposed approach uses fast Fourier transform (FFT) to implement the coefficient selection step which is represented by a correlation operation. Additional efforts for FPGA-based implementation of OMP can be found in [106][107][108][109].…”

Section: Processing Layermentioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

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The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

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Low Overhead Architectures for OMP Compressive Sensing Reconstruction Algorithm

Cited by 75 publications

References 38 publications

Roles of Clustering Coefficient for the Network Reconstruction

Roles of Clustering Coefficient for the Network Reconstruction

Area & Power Efficient VLSI Architecture of Mode Decision in Integer Motion Estimation for HEVC Video Coding Standard

Compressive Sensing-Based IoT Applications: A Review

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