,
Jinwang Zha,
Jie Ren,
Hui Zhang,
Yadong Li,
Xing Hu*,
Li Xiao,
Gan Wang,
Jian Lan,
Caihong Cao
The compression sensing reconstruction for the 1-d signal can contribute to the communication of autonomous driving, intelligent robots, and fire exploration robots. To address the issue that fully connected layers in the LISTA method lack the ability to extract non-local features, this paper primarily designs a non-local fully connection layer and proposes a novel compressed sensing reconstruction method for audio signals. This paper designs a compression sensing reconstruction method for the 1-d signal. To reconstruct 1-d signal, the deep learning method LISTA is used. Then, the linear full connection layer in LISTA is improved by combining the output of three full connection layer to capture the non-local information. The computing regions of improved non-local full connection layer contain: 1) the full connection before; 2) the current full connection; and 3) the full connection after. Experimental results show the reconstruction results of LISTA and LISTA_nf are both close to the real signal. The MSE of LISTA_nf is reduced by 0.1 than the MSE of ISTA under the same experimental settings. The non-local full connection layer in the LISTA_nf consumes longer computing time. The LISTA_nf increase the computing time by 0.07s than the computing time of the ISTA. Experimental results show the effectiveness of the proposed method.
| Published in | Machine Learning Research (Volume 11, Issue 1) |
| DOI | 10.11648/j.mlr.20261101.11 |
| Page(s) | 1-7 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Compression Sensing Reconstruction, 1-d Signal, LISTA, Non-local Full Connection
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APA Style
Xie, J., Zha, J., Ren, J., Zhang, H., Li, Y., et al. (2026). The Compression Sensing Reconstruction for the 1-d Signal Based on Non-local Full Connection Layer. Machine Learning Research, 11(1), 1-7. https://doi.org/10.11648/j.mlr.20261101.11
ACS Style
Xie, J.; Zha, J.; Ren, J.; Zhang, H.; Li, Y., et al. The Compression Sensing Reconstruction for the 1-d Signal Based on Non-local Full Connection Layer. Mach. Learn. Res. 2026, 11(1), 1-7. doi: 10.11648/j.mlr.20261101.11
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Download@article{10.11648/j.mlr.20261101.11,
author = {Juan Xie and Jinwang Zha and Jie Ren and Hui Zhang and Yadong Li and Xing Hu and Li Xiao and Gan Wang and Jian Lan and Caihong Cao},
title = {The Compression Sensing Reconstruction for the 1-d Signal Based on Non-local Full Connection Layer},
journal = {Machine Learning Research},
volume = {11},
number = {1},
pages = {1-7},
doi = {10.11648/j.mlr.20261101.11},
url = {https://doi.org/10.11648/j.mlr.20261101.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20261101.11},
abstract = {The compression sensing reconstruction for the 1-d signal can contribute to the communication of autonomous driving, intelligent robots, and fire exploration robots. To address the issue that fully connected layers in the LISTA method lack the ability to extract non-local features, this paper primarily designs a non-local fully connection layer and proposes a novel compressed sensing reconstruction method for audio signals. This paper designs a compression sensing reconstruction method for the 1-d signal. To reconstruct 1-d signal, the deep learning method LISTA is used. Then, the linear full connection layer in LISTA is improved by combining the output of three full connection layer to capture the non-local information. The computing regions of improved non-local full connection layer contain: 1) the full connection before; 2) the current full connection; and 3) the full connection after. Experimental results show the reconstruction results of LISTA and LISTA_nf are both close to the real signal. The MSE of LISTA_nf is reduced by 0.1 than the MSE of ISTA under the same experimental settings. The non-local full connection layer in the LISTA_nf consumes longer computing time. The LISTA_nf increase the computing time by 0.07s than the computing time of the ISTA. Experimental results show the effectiveness of the proposed method.},
year = {2026}
}
TY - JOUR T1 - The Compression Sensing Reconstruction for the 1-d Signal Based on Non-local Full Connection Layer AU - Juan Xie AU - Jinwang Zha AU - Jie Ren AU - Hui Zhang AU - Yadong Li AU - Xing Hu AU - Li Xiao AU - Gan Wang AU - Jian Lan AU - Caihong Cao Y1 - 2026/02/20 PY - 2026 N1 - https://doi.org/10.11648/j.mlr.20261101.11 DO - 10.11648/j.mlr.20261101.11 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 1 EP - 7 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20261101.11 AB - The compression sensing reconstruction for the 1-d signal can contribute to the communication of autonomous driving, intelligent robots, and fire exploration robots. To address the issue that fully connected layers in the LISTA method lack the ability to extract non-local features, this paper primarily designs a non-local fully connection layer and proposes a novel compressed sensing reconstruction method for audio signals. This paper designs a compression sensing reconstruction method for the 1-d signal. To reconstruct 1-d signal, the deep learning method LISTA is used. Then, the linear full connection layer in LISTA is improved by combining the output of three full connection layer to capture the non-local information. The computing regions of improved non-local full connection layer contain: 1) the full connection before; 2) the current full connection; and 3) the full connection after. Experimental results show the reconstruction results of LISTA and LISTA_nf are both close to the real signal. The MSE of LISTA_nf is reduced by 0.1 than the MSE of ISTA under the same experimental settings. The non-local full connection layer in the LISTA_nf consumes longer computing time. The LISTA_nf increase the computing time by 0.07s than the computing time of the ISTA. Experimental results show the effectiveness of the proposed method. VL - 11 IS - 1 ER -
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College of Information Technology, Nanchang Vocational University, Nanchang, China;School of Control Science and Engineering, Dalian University of Technology, Dalian, China
College of Information Technology, Nanchang Vocational University, Nanchang, China
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