NDLI: Chaotic analogue-to-information conversion with chaotic state modulation

Please wait, while we are loading the content...

A single-input multiple-output transceiver architecture to ‘Blindly’ null unknown interference for block-based single-carrier transmission with an insufficient guard interval

Algorithm of efficient computation of generalised discrete Hartley transform based on cyclic convolutions

Approach for fast time–frequency analysis

Chaotic analogue-to-information conversion with chaotic state modulation

Dispersed chirp-z transform-based spectrum sensing and utilisation in cognitive radio networks

Efficient design of very large-scale DFT modulated filter banks using Mth band condition

Efficient estimation of signal parameters via rotational invariance technique-based algorithm with automatic pairing for two-dimensional angle and polarisation estimation using crossed dipoles

Enhanced spectrum utilisation in dynamic cognitive radios with adaptive sensing

Error probability upper bound for perfect sequences implemented with super-structured fibre Bragg gratings

Linear fractional order system identification using adjustable fractional order differentiator

Quasi-maximum-likelihood estimator of polynomial phase signals

Robust Bayesian partition for extended target Gaussian inverse Wishart PHD filter

Spectrum trading in cognitive radio networks with uncertainty in primary service requirements

Volume 8 (2014) : Issue 3, May

Volume 8 (2014) : Issue 2, Apr

Volume 8 (2014) : Issue 1, Feb

IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

Chaotic analogue-to-information conversion with chaotic state modulation

Content Provider	IET Digital Library
Author	Chen, Sheng Yao Xi, Feng Liu, Zhong
Abstract	Chaotic compressive sensing is a non-linear framework for compressive sensing. Along the framework, this study proposes a chaotic analogue-to-information converter, ‘chaotic modulation’, to acquire and reconstruct band-limited sparse analogue signals at sub-Nyquist rate. In the chaotic modulation, the sparse signal is randomised through state modulation of continuous-time chaotic system and one state output is sampled as compressive measurements. The reconstruction is achieved through the estimation of the sparse coefficients with the principle of chaotic impulsive synchronisation and lp -norm regularised non-linear least squares. The concept of supreme local Lyapunov exponents (SLLE) is introduced to study the reconstructablity. It is found that the sparse signals are reconstructable, if the largest SLLE of the error dynamic system is negative. As examples, the Lorenz system and the Liu system excited by sparse multi-tone signals are taken to illustrate the principle and the performance.
Starting Page	373
Ending Page	380
Page Count	8
ISSN	17519675
Volume Number	8
e-ISSN	17519683
Issue Number	Issue 4, Jun (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/8/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0171
Journal	IET Signal Processing
Publisher Date	2013-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	A/D And D/A Convertor Analogue-Digital Conversion Band-limited Sparse Analogue Signal Reconstruction Chaos Chaotic Analogue-to-information Conversion Chaotic Compressive Sensing Chaotic Impulsive Synchronisation Chaotic State Modulation Compressed Sensing Compressive Measurement Continuous-time Chaotic System Digital Signal Processing Error Dynamic System Interpolation And Function Approximation Least Squares Approximation Liu System Lorenz System Lp-norm Regularised Nonlinear Least Squares Lyapunov Method Modulation Modulation And Coding Method Numerical Analysis Signal Processing And Detection Signal Reconstruction SLLE Sparse Coefficient Sparse Multitone Signal SubNyquist Rate Supreme Local Lyapunov Exponents
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

LS-CS-Residual (LS-CS): Compressive Sensing on Least Squares Residual

Adaptive sparsity-aware parameter vector reconstruction with application to compressed sensing

Compressive wideband spectrum sensing based on single channel

Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements

A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework

Cooperative wideband spectrum sensing in cognitive radio based on sparse real-valued fast Fourier transform

Signal pre-processing method suitable for compressive sensing based on frequency modulation

Iteratively reweighted least squares for block-sparse recovery

Continuous structure based Bayesian compressive sensing for sparse reconstruction of time-frequency distributions

Chaotic analogue-to-information conversion with chaotic state modulation

Similar Documents

LS-CS-Residual (LS-CS): Compressive Sensing on Least Squares Residual

Adaptive sparsity-aware parameter vector reconstruction with application to compressed sensing

Compressive wideband spectrum sensing based on single channel

Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements

A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework

Cooperative wideband spectrum sensing in cognitive radio based on sparse real-valued fast Fourier transform

Signal pre-processing method suitable for compressive sensing based on frequency modulation

Iteratively reweighted least squares for block-sparse recovery

Continuous structure based Bayesian compressive sensing for sparse reconstruction of time-frequency distributions

Chaotic analogue-to-information conversion with chaotic state modulation