Floating Point Implementation of the Improved QRD and OMP for Compressive Sensing Signal Reconstruction

Radhika, Alahari; Kodati, Satya Prasad; Kalitkar, Kishan Rao

doi:10.1007/s11220-022-00389-z

Cited by 3 publications

(5 citation statements)

References 27 publications

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“…To address the uncertainty of sparsity (Alahari et al, 2022), we propose the SIU-CoSaMP algorithm, an improvement of the traditional CoSaMP algorithm. This algorithm obtains more accurate sparsity through iterative updates while ensuring complete signal reconstruction, thus improving the quality of the reconstructed image.…”

Section: Discussionmentioning

confidence: 99%

A Reconstruction Algorithm for Compression Perception Based on Local Learning Dictionary and Sparsity Iterative Update

2024

Sage Open

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The global sparse representation method based on compressed sensing fails to capture the local texture and detail structure of an image. To address this, a local dictionary learning method based on the wavelet domain is proposed. The wavelet high-frequency subband sub-block classification and local dictionary learning are implemented using the FCM clustering method and K-L method, respectively. At the reconstruction end, a compressed sampling Matching pursuit algorithm based on iterative update of sparsity is proposed. This adaptive iteration can effectively reconstruct the original image under unknown sparsity conditions. Simulation experiments show that compared to existing reconstruction methods, this approach has the advantages of simple computation, strong adaptive sparse representation ability for the original image, and superior reconstructed image performance.

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Section: Discussionmentioning

confidence: 99%

A Reconstruction Algorithm for Compression Perception Based on Local Learning Dictionary and Sparsity Iterative Update

2024

Sage Open

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“…Thus, the data samples in the dictionary matrix D are labeled samples, as shown in Figures 4 and 5. Based on Equation (22), the underlying principle of fault diagnosis is that the testing sample z can be represented by the linear combination of the atoms (the labeled samples with different patterns) in the dictionary D. However, by using the atoms whose pattern are the same as the testing sample z, the number of atoms selected for representation can be minimized, because of the feature similarity. Thus, the classification problem is regarded as an optimization problem, and the object function is:…”

Section: Sparse-representation-based Classification and Fault Diagnosismentioning

confidence: 99%

“…The procedures in sparse vector calculation are repeatedly executed for n Iter times, and n SV • n Iter nonzero elements in the sparse vector are obtained. Finally, these nonzero elements are filled into the sparse vector in accordance with the vector of indices rPos : rn DS •p×1 : rr Pos = r(r Pos ) = rElement Compared to the traditional OMP algorithm [22], the number of iterations of BMP is 1/n SV times of OMP, which reduces the computational burden significantly. Thus, for the edge computing platform and on-site fault diagnosis, the BMP algorithm provides a more efficient solution.…”

Section: Sparse-representation-based Classification and Fault Diagnosismentioning

confidence: 99%

“…In the data reconstruction, a discrete Fourier transform (DFT) matrix was employed as the dictionary matrix, and its size was 960 960 × . A traditional OMP algorithm was then used to calculate the sparse vector [22]. Here, we take the 1st vibration signal of horizontal direction as an example.…”

Section: Data Reconstruction and Analysismentioning

confidence: 99%

“…In this case study, the compression rate was 0.33: we therefore saved 66.67% storage and computational resources. In the data reconstruction, a discrete Fourie form (DFT) matrix was employed as the dictionary matrix, and its size was 960 × traditional OMP algorithm was then used to calculate the sparse vector [22]. Here, w the 1st vibration signal of horizontal direction as an example.…”

Section: Data Reconstruction and Analysismentioning

confidence: 99%

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Fault Diagnosis of Rotating Machinery Based on Two-Stage Compressed Sensing

You

Deng

et al. 2023

Machines

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Intelligent on-site fault diagnosis and professional vibration analysis are essential for the safety and stability of rotating machinery operation. This paper represents a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based on compressed data and data reconstruction for professional vibration analysis. In the 1st stage, the triaxial vibration signals are compressed using a pre-designed hybrid measurement matrix; these compressed data can be used both for time-frequency transform and for vibration data reconstruction. In the 2nd stage, the frequency spectra of the triaxial vibration signals are fused and further compressed using another pre-designed joint measurement matrix, which inhibits the high-frequency noises simultaneously. Finally, the fused spectra are employed as feature vectors in sparse-representation-based classification, where the proposed batch matching pursuit (BMP) algorithm is utilized to calculate the sparse vectors. The two-stage compression scheme and the BMP algorithm minimize the computational cost of on-site fault diagnosis, which is suitable for edge computing platforms. Meanwhile, the compressed vibration data can be reconstructed, which provides evidence for professional vibration analysis. The method proposed in this study is validated by two practical case studies, in which the accuracies are 99.73% and 96.70%, respectively.

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FPGA enabled ECG signal reconstruction based on an enhanced orthogonal matching pursuit algorithm

Reddy,

Kumar

2025

Integration

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Floating Point Implementation of the Improved QRD and OMP for Compressive Sensing Signal Reconstruction

Cited by 3 publications

References 27 publications

A Reconstruction Algorithm for Compression Perception Based on Local Learning Dictionary and Sparsity Iterative Update

A Reconstruction Algorithm for Compression Perception Based on Local Learning Dictionary and Sparsity Iterative Update

Fault Diagnosis of Rotating Machinery Based on Two-Stage Compressed Sensing

FPGA enabled ECG signal reconstruction based on an enhanced orthogonal matching pursuit algorithm

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