Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis-Reference-Cited by-同舟云学术

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Published:2018-10-01 Issue:5 Volume:18 Page:175-182
ISSN:1335-8871
Container-title:Measurement Science Review
language:en
Short-container-title:

Author:

Andráš Imrich¹,Dolinský Pavol¹,Michaeli Linus¹,Šaliga Ján¹

Affiliation:

1. Technical University of Košice, Faculty of Electrical Engineering and Informatics, Department of Electronics and Multimedia Telecommunications, Letna, Košice , Slovakia

Abstract

Abstract This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion. The signal is recovered with minimal information loss from the reduced data record via compressed sensing reconstruction. Several methods of analog to information conversion are described with focus on numerical complexity and implementation in existing embedded devices. Two novel analog to information conversion methods are proposed, distinctive by their computational simplicity - direct subsampling and subsampling with integration. Proposed sensing methods are intended for and evaluated with real water parameter signals measured by a wireless sensor network. Compressed sensing proves to reduce the data transfer rate by >80 % with very little signal processing performed at the sensing side and no appreciable distortion of the reconstructed signal.

Publisher

Walter de Gruyter GmbH

Subject

Instrumentation,Biomedical Engineering,Control and Systems Engineering

Reference33 articles.

1. [1] Donoho, D.L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52 (4), 1289-1306.

2. [2] Andráš, I., Dolinský, P., Michaeli, L., Šaliga, J. (2018). A time domain reconstruction method of randomly sampled frequency sparse signal. Measurement, 127, 68-77.

3. [3] Wu, L., Yu, K., Hu, Y., Wang, Z. (2014). CS-based framework for sparse signal transmission over lossy link. In IEEE International Conference on Mobile Ad Hoc and Sensor Systems, Philadelphia, USA. IEEE, 680-685.

4. [4] Liu, H., Xiao, D., Zhang, R., Zhang., Y., Bai, S. (2016). Robust and hierarchical watermarking of encrypted images based on Compressive Sensing. Signal Processing: Image Communication, 46, 41-51.

5. [5] Xiao, X., He, Q., Fu, Z., Xu, M., Zhang, X. (2016). Applying CS and WSN methods for improving efficiency of frozen and chilled aquatic products monitoring system in cold chain logistics. Food Control, 60, 656-666.

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Detection of security vulnerabilities in cryptographic ICs against fault injection attacks based on compressed sensing and basis pursuit;Journal of Cryptographic Engineering;2023-06-02

2. ECG compressed sensing method with high compression ratio and dynamic model reconstruction;Measurement;2021-10

3. Analog-to-Information Conversion with Random Interval Integration;Sensors;2021-05-19

4. Wideband spectrum sensing in cognitive radio using discrete wavelet packet transform and principal component analysis;Physical Communication;2020-02

5. Метод главных информативных компонент в задачах статистических измерений параметров сигналов (систематизированный обзор);Известия высших учебных заведений. Радиоэлектроника;2019-11-20