Propagation of quasi-periodic random sound pulse sequence signals in shallow-water waveguides

It is an efficient method to model ship radiated noise as quasi-periodic random sound pulse sequences. Based on the model, this paper discusses the characteristics change of ship noise after through shallow-water waveguides. Theoretical analysis and numerical simulation show that random waveguides and multi-path effects can bring much additional transmission loss for the noise line spectra, demonstrating the instability of ship radiated noise line spectra to a certain extent, and providing some theoretical support for advanced studies of ship radiated noise.

Approach to blind estimation of the PN sequence in DS-SS signals with residual carrier

This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.

Neural network approach to blindestimation of PN spreading sequence in lower SNR DS/SS signals

An approach based on discrete Karhunen-Loeve transformation of the DS/SS signals is proposed to estimate PN sequence in lower S/N ratio DS/SS signals. Characteristics of self-organization and principle components extraction of unsupervised neural networks are exploited adequately. Theoretical analysis and experimental results are provided to show that this approach can work well on the lower S/N ratio input signals.

A Neural Network Approach to Blind Estimation of PN Spreading Sequence in DS/SS Signals

In this paper, a new approach is proposed to estimate pseudo noise(PN) sequence in the lower SNR DS/SS signals blindly. This method utilizes the characteristics of self-organization, principal components analysis and extraction of unsupervised neural networks adequately, in addition to its higher-speed operation ability, successfully solve the difficult problem about PN sequence blind estimation. The theoretic analysis and experimental results show that this approach can work very well on lower SNR input signals.

Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

An idea of estimating the direct sequence spread spectrum（DSSS） signal pseudo-noise（PN） sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map（SOFM） neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise（SNR） and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm＇s bit error rate（BER） and complexity are lower.

Knowledge-based potentials in bioinformatics: From a physicist's viewpoint

Biological raw data are growing exponentially, providing a large amount of information on what life is. It is believed that potential functions and the rules governing protein behaviors can be revealed from analysis on known native structures of proteins. Many knowledge-based potentials for proteins have been proposed. Contrary to most existing review articles which mainly describe technical details and applications of various potential models, the main foci for the discussion here are ideas and concepts involving the construction of potentials, including the relation between free energy and energy, the additivity of potentials of mean force and some key issues in potential construction. Sequence analysis is briefly viewed from an energetic viewpoint.

Subspace decomposition-based correlation matrix multiplication

The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.

Design and implementation of the NaI(Tl)/CsI(Na) detectors output signal generator

We designed and implemented a signal generator that can simulate the output of the NaI（Tl）/CsI（Na）detectors＇pre-amplifier onboard the Hard X-ray Modulation Telescope（HXMT）.Using the development of the FPGA（Field Programmable Gate Array）with VHDL language and adding a random constituent,we have finally produced the double exponential random pulse signal generator.The statistical distribution of the signal amplitude is programmable.The occurrence time intervals of the adjacent signals contain negative exponential distribution statistically.