scholarly journals Reconstruction verification for random demodulator based compressed sampling

2014 ◽  
Vol 63 (22) ◽  
pp. 228401
Author(s):  
Zheng Shi-Lian ◽  
Yang Xiao-Niu ◽  
Zhao Zhi-Jin
Keyword(s):  
Compressed Sampling ◽  
Random Demodulator

Review of analog-to-information converters

2019 ◽  
pp. 6-12
Author(s):  
M. N. Polunin ◽  
A. V. Bykova
Keyword(s):  
Spread Spectrum ◽  
Traditional Approach ◽  
Sampling Rate ◽  
Analog To Digital ◽  
Compressed Sampling ◽  
Alternative Approach ◽  
Processing Information ◽  
Random Demodulator ◽  
High Power Consumption ◽  
Modulated Wideband Converter

The implementation of high‑throughput systems with the traditional approach to the discretization of the analog signal according to the Kotelnikov theorem is faced with the problems of high power consumption and the need to store and transfer large amounts of data. An alternative approach to sampling and processing information is based on advances in the compressed sampling theory. The paper provides a brief overview of the main provisions of this theory and considers examples of its use in practice for the implementation of information reading systems – analog‑to‑information converters. The purpose of these devices is to reduce the pressure on conventional analog‑to‑digital converters, to reduce the sampling rate and the amount of output data. The main architectures of analog‑information converters are considered: non‑uniform sampling, random filter, random demodulator, modulated wideband converter, compressive multiplexer, random modulator pre‑integrator, spread spectrum random modulator pre‑integrator.

scholarly journals Sequential Adaptive Compressed Sampling via Huffman Codes

2011 ◽  
Vol 10 (3) ◽  
pp. 231-254
Author(s):  
Akram Aldroubi ◽  
Haichao Wang ◽  
Kourosh Zarringhalam
Keyword(s):  
Huffman Codes ◽  
Compressed Sampling

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

Compressed Sampling of Spectrally Sparse Signals Using Sparse Circulant Matrices

Frequenz ◽  
2014 ◽  
Vol 68 (11-12) ◽  
Author(s):  
Guangjie Xu ◽  
Huali Wang ◽  
Lei Sun ◽  
Weijun Zeng ◽  
Qingguo Wang
Keyword(s):  
Circulant Matrices ◽  
Noise Robustness ◽  
Sparse Signals ◽  
Measurement Matrix ◽  
Compressed Sampling ◽  
Random Measurement ◽  
Compressive Performance ◽  
Generating Sequence ◽  
Periodic Autocorrelation ◽  
Selection Of

AbstractCirculant measurement matrices constructed by partial cyclically shifts of one generating sequence, are easier to be implemented in hardware than widely used random measurement matrices; however, the diminishment of randomness makes it more sensitive to signal noise. Selecting a deterministic sequence with optimal periodic autocorrelation property (PACP) as generating sequence, would enhance the noise robustness of circulant measurement matrix, but this kind of deterministic circulant matrices only exists in the fixed periodic length. Actually, the selection of generating sequence doesn't affect the compressive performance of circulant measurement matrix but the subspace energy in spectrally sparse signals. Sparse circulant matrices, whose generating sequence is a sparse sequence, could keep the energy balance of subspaces and have similar noise robustness to deterministic circulant matrices. In addition, sparse circulant matrices have no restriction on length and are more suitable for the compressed sampling of spectrally sparse signals at arbitrary dimensionality.

Structural Exploration of Water, Nitrate/Water, and Oxalate/Water Clusters with Basin-Hopping Method Using a Compressed Sampling Technique

2014 ◽  
Vol 118 (2) ◽  
pp. 508-516 ◽  
Author(s):  
Yi-Rong Liu ◽  
Hui Wen ◽  
Teng Huang ◽  
Xiao-Xiao Lin ◽  
Yan-Bo Gai ◽  
...  
Keyword(s):  
Water Clusters ◽  
Sampling Technique ◽  
Compressed Sampling ◽  
Basin Hopping

scholarly journals Low Complexity Cyclic Feature Recovery Based on Compressed Sampling

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhuo Sun ◽  
Jia Hou ◽  
Siyuan Liu ◽  
Sese Wang ◽  
Xuantong Chen
Keyword(s):  
Signal Reconstruction ◽  
Low Complexity ◽  
Computation Complexity ◽  
Memory Consumption ◽  
Compressed Sampling ◽  
Communication Signal ◽  
Recovery Performance ◽  
Recovery Approach ◽  
High Processing ◽  
Theoretical Analyses

To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing complexity. In this paper, we propose a new cyclic feature recovery approach based on the reconstruction of autocorrelation sequence from sub-Nyquist samples, which can reduce the computation complexity and memory consumption significantly, while the recovery performance remains well in the same compressive ratio. Through theoretical analyses and simulations, we conducted to show and verify our statements and conclusions.

Generalized Random Demodulator Associated with Fractional Fourier Transform

2018 ◽  
Vol 37 (11) ◽  
pp. 5161-5173 ◽  
Author(s):  
Haoran Zhao ◽  
Liyan Qiao ◽  
Jingchao Zhang ◽  
Ning Fu
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Random Demodulator

Random Acquisition in Compressive Sensing

2021 ◽  
Vol 12 (3) ◽  
pp. 140-165
Author(s):  
Mahdi Khosravy ◽  
Thales Wulfert Cabral ◽  
Max Mateus Luiz ◽  
Neeraj Gupta ◽  
Ruben Gonzalez Crespo
Keyword(s):  
Compressive Sensing ◽  
Sampling Theorem ◽  
Lower Number ◽  
Compressive Sampling ◽  
Minimum Requirement ◽  
Random Modulation ◽  
Random Demodulator ◽  
Modulated Wideband Converter ◽  
Signal Image ◽  
Sparsity Structure

Compressive sensing has the ability of reconstruction of signal/image from the compressive measurements which are sensed with a much lower number of samples than a minimum requirement by Nyquist sampling theorem. The random acquisition is widely suggested and used for compressive sensing. In the random acquisition, the randomness of the sparsity structure has been deployed for compressive sampling of the signal/image. The article goes through all the literature up to date and collects the main methods, and simply described the way each of them randomly applies the compressive sensing. This article is a comprehensive review of random acquisition techniques in compressive sensing. Theses techniques have reviews under the main categories of (1) random demodulator, (2) random convolution, (3) modulated wideband converter model, (4) compressive multiplexer diagram, (5) random equivalent sampling, (6) random modulation pre-integration, (7) quadrature analog-to-information converter, (8) randomly triggered modulated-wideband compressive sensing (RT-MWCS).

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