Double-path multiscale adaptive compressed sensing network for electronic data
Abstract The progression in integrated circuit technology has necessitated advanced solutions for the storage and rapid transmission of extensive data generated by electronic modules. Compared to traditional signal compression and transmission techniques, compressed sensing (CS) transcends the limit...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
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| Online Access: | https://doi.org/10.1186/s13634-025-01220-z |
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| _version_ | 1849331595571363840 |
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| author | Lubin Yu Yongsheng Huang Yiqiang Cheng Qiliang Du Zhenwei Zhou Lianfang Tian Shilie He Honghui Liu |
| author_facet | Lubin Yu Yongsheng Huang Yiqiang Cheng Qiliang Du Zhenwei Zhou Lianfang Tian Shilie He Honghui Liu |
| author_sort | Lubin Yu |
| collection | DOAJ |
| description | Abstract The progression in integrated circuit technology has necessitated advanced solutions for the storage and rapid transmission of extensive data generated by electronic modules. Compared to traditional signal compression and transmission techniques, compressed sensing (CS) transcends the limitations of the Shannon–Nyquist sampling theorem by enabling low-frequency signal sampling, thereby becoming a extensively utilized approach in the signal processing. Recent advancements in Artificial Intelligence have further propelled the reconstruction efficacy of deep learning-based CS methods, mitigating certain constraints inherent in traditional CS approaches. Nonetheless, the existing deep learning-based CS methods are not optimally efficient for electronic data processing. In this regard, this study proposes a novel double-path multiscale adaptive compressed sensing network (DMA-CS). This network is structured around four key modules: signal compression, preprocessing, initial reconstruction, and secondary reconstruction. The signal compression module samples the signal for compression, the preprocessing module prepares the sampled signal for subsequent processing, the initial reconstruction module employs a double-path complementary network comprising a multiscale residual module fused with a multihead attention module and an inverse residual module for the initial reconstruction. Then, the secondary reconstruction module uses the adaptive dilated convolution residual module to adaptively adjust the size of the convolution kernel to ensure the high-quality reconstruction of different signals and combines it with the tree-like structure residual block for enhanced reconstruction. Our experimental evaluation on the P2020 module fault signal dataset and NASA Lithium Battery dataset demonstrates that our scheme attains the lowest percentage root-mean-square difference and the highest signal-to-noise ratio, demonstrating substantial enhancement in reconstruction performance and robustness. |
| format | Article |
| id | doaj-art-2e6f8f65d2b54483b60edf8f87e8ea70 |
| institution | Kabale University |
| issn | 1687-6180 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj-art-2e6f8f65d2b54483b60edf8f87e8ea702025-08-20T03:46:29ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802025-07-012025113110.1186/s13634-025-01220-zDouble-path multiscale adaptive compressed sensing network for electronic dataLubin Yu0Yongsheng Huang1Yiqiang Cheng2Qiliang Du3Zhenwei Zhou4Lianfang Tian5Shilie He6Honghui Liu7China Electronic Product Reliability and Environmental Testing Research InstituteChina Electronic Product Reliability and Environmental Testing Research InstituteChina Electronic Product Reliability and Environmental Testing Research InstituteSchool of Automation Science and Engineering, South China University of TechnologyChina Electronic Product Reliability and Environmental Testing Research InstituteSchool of Automation Science and Engineering, South China University of TechnologyChina Electronic Product Reliability and Environmental Testing Research InstituteChina Electronic Product Reliability and Environmental Testing Research InstituteAbstract The progression in integrated circuit technology has necessitated advanced solutions for the storage and rapid transmission of extensive data generated by electronic modules. Compared to traditional signal compression and transmission techniques, compressed sensing (CS) transcends the limitations of the Shannon–Nyquist sampling theorem by enabling low-frequency signal sampling, thereby becoming a extensively utilized approach in the signal processing. Recent advancements in Artificial Intelligence have further propelled the reconstruction efficacy of deep learning-based CS methods, mitigating certain constraints inherent in traditional CS approaches. Nonetheless, the existing deep learning-based CS methods are not optimally efficient for electronic data processing. In this regard, this study proposes a novel double-path multiscale adaptive compressed sensing network (DMA-CS). This network is structured around four key modules: signal compression, preprocessing, initial reconstruction, and secondary reconstruction. The signal compression module samples the signal for compression, the preprocessing module prepares the sampled signal for subsequent processing, the initial reconstruction module employs a double-path complementary network comprising a multiscale residual module fused with a multihead attention module and an inverse residual module for the initial reconstruction. Then, the secondary reconstruction module uses the adaptive dilated convolution residual module to adaptively adjust the size of the convolution kernel to ensure the high-quality reconstruction of different signals and combines it with the tree-like structure residual block for enhanced reconstruction. Our experimental evaluation on the P2020 module fault signal dataset and NASA Lithium Battery dataset demonstrates that our scheme attains the lowest percentage root-mean-square difference and the highest signal-to-noise ratio, demonstrating substantial enhancement in reconstruction performance and robustness.https://doi.org/10.1186/s13634-025-01220-zCompressed sensingDouble-path complementary networkAdaptive dilated convolution |
| spellingShingle | Lubin Yu Yongsheng Huang Yiqiang Cheng Qiliang Du Zhenwei Zhou Lianfang Tian Shilie He Honghui Liu Double-path multiscale adaptive compressed sensing network for electronic data EURASIP Journal on Advances in Signal Processing Compressed sensing Double-path complementary network Adaptive dilated convolution |
| title | Double-path multiscale adaptive compressed sensing network for electronic data |
| title_full | Double-path multiscale adaptive compressed sensing network for electronic data |
| title_fullStr | Double-path multiscale adaptive compressed sensing network for electronic data |
| title_full_unstemmed | Double-path multiscale adaptive compressed sensing network for electronic data |
| title_short | Double-path multiscale adaptive compressed sensing network for electronic data |
| title_sort | double path multiscale adaptive compressed sensing network for electronic data |
| topic | Compressed sensing Double-path complementary network Adaptive dilated convolution |
| url | https://doi.org/10.1186/s13634-025-01220-z |
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