scholarly journals LPI Radar Waveform Recognition Based on Multi-Branch MWC Compressed Sampling Receiver

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 30342-30354 ◽  
Author(s):  
Tao Chen ◽  
Lizhi Liu ◽  
Xiangsong Huang
Keyword(s):  
Compressed Sampling ◽  
Sampling Receiver

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.

A 5-mW 6-Gb/s Quarter-Rate Sampling Receiver With a 2-Tap DFE Using Soft Decisions

2007 ◽  
Vol 42 (4) ◽  
pp. 881-888 ◽  
Author(s):  
Koon-Lun Jackie Wong ◽  
Alexander Rylyakov ◽  
Chih-Kong Ken Yang
Keyword(s):  
Sampling Receiver

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
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