For Libraries For Publication Open Access Downloads About Us Contact Us
Paper Titles
Topology Identification and Adaptive Synchronization of Fractional-Order Complex Networks
p.936
Active Integrated Devices PDN Balanced Truncation Reduction Method
p.939
Under Sampling Stochastic Resonance for Detecting Weak Signal
p.944
An Efficient Sampling Method for the Interpolation/Extrapolation of Electromagnetic Responses in Time and Frequency Domain
p.949
Compressed Sensing Based-On Digital Multi-Channel Sampling for Signal Spectrum Detection
p.953
Design of Compressed Sampling System Based on Digital Pre-Random Filter
p.957
Towards Efficient Task Scheduling on 2D Reconfigurable FPGAs
p.961
Towards Efficient Task Placement Selection on 2D Reconfigurable Devices
p.965
Spectral Residual Based Underwater Animal Detection
p.970

Compressed Sensing Based-On Digital Multi-Channel Sampling for Signal Spectrum Detection

Article Preview

Abstract:

Conventional compressed sensing is accomplished via random modulation in analog domain. In this paper, a compressed sensing method based-on digital multi-channel sampling is proposed, which is used for signal spectrum detection and evaluation. This compressed sensing method is fulfilled by random modulation in digital domain and aliasing signal spectrum to full frequency range, sampling in Nyquist frequency below followed and digital multi-channels associated signal reconstruction. Examples of experiments show this method is useful and some merits are achieved, such as reduction of hardware complexity, saved memory largely and achieved real-time data transmission.

You might also be interested in these eBooks
Advances in Applied Sciences and Manufacturing View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 850-851)

Pages:

953-956

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.850-851.953

Citation:

Cite this paper

Online since:

December 2013

Authors:

Hua Qun Zhan, Juan Ying Zeng, Bin Xu, An Min Wang

Keywords:

Compressed Sensing, Digital Multi-Channels, Sampling, Signal Spectrum

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] C. E. Shannon: Proc. IRE, Vol. 37 (1949) no. 1, pp.10-21.

Google Scholar

[2] E. J. Candes and M. Wakin: IEEE Signal Process. Mag., Vol. 25 (2008) No. 2, pp.21-30.

Google Scholar

[3] Sivan Gleichman and Yonina C. Eldar: IEEE Trans. Inform Theory, Vol. 57 (2011) No. 10, pp.6958-6975.

DOI: 10.1109/tit.2011.2165821

Google Scholar

[4] M A Davenport, P T Boufounos, M B Wakin and R G Baraniuk: IEEE Journal of Selected Topics on Signal Processing, Vol. 4 (2010) No. 2, pp.445-460.

DOI: 10.1109/jstsp.2009.2039178

Google Scholar

[5] David L. Donoho: IEEE Trans. Inform Theory, Vol. 52 (2006) No. 4, pp.1289-1306.

Google Scholar

[6] E. J. Cand`es, J. Romberg and T. Tao: IEEE Trans. Inform Theory, Vol. 52 (2006) No. 2, pp.489-509.

Google Scholar

[7] T Zhang: IEEE Trans. Inform Theory, Vol. 57 (2011) No. 10, pp.6215-6221.

Google Scholar

[8] Peilong Xu. Study on Moving Objects by Video Monitoring System of Recognition and Tracing Scheme, TELKOMNIKA, Vol. 11 (2013), No. 9, p.4847~4853.

DOI: 10.11591/telkomnika.v11i9.2740

Google Scholar

[9] J. Tropp and A. Gilbert: IEEE Trans. Inform Theory, Vol. 53 (2007) No. 12, pp.4655-4666.

Google Scholar

© 2025 Trans Tech Publications Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For open access content, terms of the Creative Commons licensing CC-BY are applied.
Scientific.Net is a registered trademark of Trans Tech Publications Ltd.