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Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems that common chaotic mapping has low randomness, sequence elements has strong correlation, and constructing the measurement matrix elements requires interval sampling to satisfy the independence of data statistics, a new composite chaotic system is constructed by cascading quantum Logistic chaotic system and generalized Fibonacci sequence. In terms of information entropy, spatial characteristics and correlation coefficients, different chaotic measurement matrices are quantitatively analyzed to verify that the proposed chaotic system has ergodicity and high chaos. Moreover, the sequence elements have low correlation, which satisfies the independence of data statistics. At the same time, it is proved that the compressed sensing measurement matrix constructed by the proposed chaotic system satisfies the RIP condition. Simulation and discussion on one-dimensional sparse signal and two-dimensional image show that, compared with other measurement matrices, when the sampling rate is 1/2, the proposal increases the success rate of reconstructing one-dimensional sparse signals by 4 %, and increases the SNR rate of reconstructing two-dimensional images by 0. 2dB. It improves the data utilization rate and overcomes the great waste of data resources caused by the interval sampling of other chaotic measurement matrices. [ABSTRACT FROM AUTHOR]

Current research mainly focuses on the case that a single sensor node sends data, and the transmission efficiency is low. Using multiple sensor nodes to send data at the same time can improve the transmission efficiency, but there is interference between user' s data. Compressed sensing (CS) theory is applied to underwater acoustic sensor networks. A large-capacity cooperative communication method was proposed to realize simultaneous transmission of data by multiple nodes. Using the sparse characteristics of sensor nodes, simultaneous transmission of data from multiple source nodes is equivalent to the measurement process in CS. It is proved that the transmission characteristics of multiple nodes can satisfy the restricted isometry property ( RIP) in the CS theory. The destination node can recover the data of multiple concurrent sensor nodes by the reconstruction algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compressive sensing is a new sampling theory which makes use of the sparsity of signals to recover the high-dimension signal from very few measurements. Unfortunately,most of the existing measurement matrices have high correlation between elements,which can not guarantee exact recovery and has greatly restricted their application prospects. Based on this,this paper proposed a novel Chebyshev chaotic measurement sensing matrix based on sampling columnization( SC3M). The sampling columnization and normalization guaranteed the low correlation in column of SC3 M,which optimized the recovery effect. Moreover,it proved that the proposed SC3 M sensing matrix satisfies the RIP with overwhelming probability from the Johnson-Lindenstrauss lemma. Numerical simulations show that the SC3 M is sufficient to guarantee exact recovery,which shows better effect compared to other popular measurement matrixes such as the fully random matrix,Bernoulli matrix and Gaussian sensing matrix. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)