<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>LOT wireless sensor nodes are limited by physical factors, usually have weak computing power and endurance, and wireless communication methods are very vulnerable to information theft. Therefore, it is of great significance to ensure the safe and efficient transmission of images in new application scenarios. In view of the need for an efficient image transmission, this paper combines compressed sensing technology with p-tensor product theory, applies the above theory to distributed wireless sensor networks, and uses the correlation of adjacent sensor nodes in wireless sensor networks to propose an improved a joint sparse model for measurement matrices and reduction algorithms. The feasibility is verified by simulation experiments, and the comparison between joint reconstruction and single reconstruction, and the application of various algorithms in other algorithms is carried out, and the actual completion time and storage capacity are analysed. The minimum completion time for wavelet transform is 1.29, the sparse estimated time for the selection of preliminary P waves is 0.07 and the compressed sensing time is 0.20. The maximum completion time for wavelet transform was 1.32, for sparse estimation, it is 0.62, for preliminary P-wave selection, it is 0.17, and for compressed sensing, it is 0.88. The processing time is no >3 s and the runtime is only 0.22–0.88 s. The results show that compared with the compressed sensing of a single node, the joint sparse model based on distributed compressed sensing has a smaller reconstruction error, and can achieve high-precision signal reconstruction when the measurement value is small.</p>
</abstract>

LOT wireless sensor nodes are limited by physical factors, usually have weak computing power and endurance, and wireless communication methods are very vulnerable to information theft. Therefore, it is of great significance to ensure the safe and efficient transmission of images in new application scenarios. In view of the need for an efficient image transmission, this paper combines compressed sensing technology with p-tensor product theory, applies the above theory to distributed wireless sensor networks, and uses the correlation of adjacent sensor nodes in wireless sensor networks to propose an improved a joint sparse model for measurement matrices and reduction algorithms. The feasibility is verified by simulation experiments, and the comparison between joint reconstruction and single reconstruction, and the application of various algorithms in other algorithms is carried out, and the actual completion time and storage capacity are analysed. The minimum completion time for wavelet transform is 1.29, the sparse estimated time for the selection of preliminary P waves is 0.07 and the compressed sensing time is 0.20. The maximum completion time for wavelet transform was 1.32, for sparse estimation, it is 0.62, for preliminary P-wave selection, it is 0.17, and for compressed sensing, it is 0.88. The processing time is no >3 s and the runtime is only 0.22–0.88 s. The results show that compared with the compressed sensing of a single node, the joint sparse model based on distributed compressed sensing has a smaller reconstruction error, and can achieve high-precision signal reconstruction when the measurement value is small.

Application of matrix multiplication in signal sensor image perception

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Application of matrix multiplication in signal sensor image perception"}

LOT wireless sensor nodes are limited by physical factors, usually have weak computing power and endurance, and wireless communication methods are very...

Application of matrix multiplication in signal sensor image perception

Published Online: Dec 23, 2022

Page range: 601 - 614

Received: Jul 08, 2022

Accepted: Oct 09, 2022

DOI: https://doi.org/10.2478/amns.2021.2.00276

Keywords
Matrix multiplication, Wireless sensor networks, Measurement matrices, Distributed compressed sensing

© 2023 Lihua Dai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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Application of matrix multiplication in signal sensor image perception

Published Online: Dec 23, 2022

Page range: 601 - 614

Received: Jul 08, 2022

Accepted: Oct 09, 2022

DOI: https://doi.org/10.2478/amns.2021.2.00276

KeywordsMatrix multiplication, Wireless sensor networks, Measurement matrices, Distributed compressed sensing

© 2023 Lihua Dai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Keywords
Matrix multiplication, Wireless sensor networks, Measurement matrices, Distributed compressed sensing