bullet Sensors & Transducers Journal

    (ISSN: 2306-8515, e-ISSN 1726-5479)


2013 Global Impact Factor


2008 e-Impact Factor

25 Top Downloaded Articles

Best Selling Articles 2012

Journal Subscription 2014

Editorial Calendar

Submit an Article

Editorial Board

Current Issue

S&T journal's cover

Sensors & Transducers Journal 2011

Sensors & Transducers Journal 2010

Sensors & Transducers Journal 2009

Sensors & Transducers Journal 2008

Sensors & Transducers Journal 2007

2000-2002 S&T e-Digest Contents

2003 S&T e-Digest Contents

2004 S&T e-Digest Contents

2005 S&T e-Digest Contents

2006 S&T e-Digest Contents


Best Articles 2011




Vol. 170, Issue 5, May 2014, pp. 48-53




Random Sampling and Signal Reconstruction Based on Compressed Sensing

Caiyun Huang

School of Electrical & Information Engineering, Hunan International Economics University, Changsha, 410205, China
Tel.: 86-0731-88760386

E-mail: matlab_wjf@126.com


Received: 25 March 2014 /Accepted: 30 April 2014 /Published: 31 May 2014

Digital Sensors and Sensor Sysstems


Abstract: Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure and content of the information in the signal. In this paper, the signal is the sparse in the Fourier transform and random sparse sampling is advanced by programming random observation matrix for peak random base. The signal is successfully restored by the use of Bregman algorithm. The signal is described in the transform space, and a theoretical framework is established with a new signal description and processing. By making the case to ensure that the information loss, signal is sampled at much lower than the Nyquist sampling theorem requiring rate, but also the signal is completely restored in high probability. The random sampling has following advantages: alias-free, sampling frequency need not obey the Nyquist limit, and higher frequency resolution. So the random sampling can measure the signals which their frequencies component are close, and can implement the higher frequencies measurement with lower sampling frequency.


Keywords: Compressed sensing, Random sampling, Nonuniformly sampling, Sparse sampling, Signal reconstruction.


Acrobat reader logo Click <here> or title of paper to download the full pages article in pdf format



Subscribe the full-page Sensors & Transducers journal in print (paper) or pdf formats

(shipping cost by standard mail for paper version is included)

(25 % discount for IFSA Members)




Alternatively we accept a money transfer to our bank account. Please contact for details: sales@sensorsportal.com



Download <here> the Library Journal Recommendation Form






1999 - 2014 Copyright , International Frequency Sensor Association (IFSA) Publishing, S.L. All Rights Reserved.

Home - News - Links - Archives - Tools - Voltage-to-Frequency Converters - Standardization - Patents - Marketplace - Projects - Wish List - e-Shop - Sensor Jobs - Membership - Videos - Publishing - Site Map - Subscribe - Search

 Members Area -Sensors Portal -Training Courses - S&T Digest - For advertisers - Bookstore - Forums - Polls - Submit Press Release - Submit White Paper - Testimonies - Twitter - Facebook - LinkedIn