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.

Stochastic resonance in a gain-noise model of a single-mode laser driven by pump noise and quantum noise with cross-correlation between real and imaginary parts under direct signal modulation

Stochastic resonance （SR） is studied in a gain-noise model of a single-mode laser driven by a coloured pump noise and a quantum noise with cross-correlation between real and imaginary parts under a direct signal modulation. By using a linear approximation method, we find that the SR appears during the variation of signal-to-noise ratio （SNR） separately with the pump noise self-correlation time τ, the noise correlation coefficient between the real part and the imaginary part of the quantum noise λq, the attenuation coefficient γ＇ and the deterministic steady-state intensity I0. In addition, it is found that the SR can be characterized not only by the dependence of SNR on the noise variables of and λq, but also by the dependence of SNR on the laser system variables of γ and I0. Thus our investigation extends the characteristic quantity of SR proposed before.

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.

Two-Dimensional Direction Finding via Sequential Sparse Representations

The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method.

DOA estimation method for wideband signals by block sparse reconstruction

For the direction of arrival（DOA） estimation,traditional sparse reconstruction methods for wideband signals usually need many iteration times.For this problem,a new method for two-dimensional wideband signals based on block sparse reconstruction is proposed.First,a prolate spheroidal wave function（PSWF） is used to fit the wideband signals,then the block sparse reconstruction technology is employed for DOA estimation.The proposed method uses orthogonalization to choose the matching atoms,ensuring that the residual components correspond to the minimum absolute value.Meanwhile,the vectors obtained by iteration are back-disposed according to the corresponding atomic matching rules,so the extra atoms are abandoned in the course of iteration,and the residual components of current iteration are reduced.Thus the original sparse signals are reconstructed.The proposed method reduces iteration times comparing with the traditional reconstruction methods,and the estimation precision is better than the classical two-sided correlation transformation（TCT）algorithm when the snapshot is small or the signal-to-noise ratio（SNR） is low.

宽带水平场形电视发射天线的解决方案

本文介绍了一种电视宽带水平场形发射天线的解决方案,该天线可安装于任意截面的塔桅组阵,也可安装于米波VHF及FM天线的夹角或边缘处,可获得良好的宽带覆盖场形,有效地解决了发展UHF新业务与天线塔桅空间紧张的矛盾。

真菌竹黄双向调节TNF-α诱导的L929细胞凋亡过程

真菌竹黄是我国特有的菌类药物,应用前景广阔,具有抗肿瘤和抗炎功效,但作用机理仍不十分清楚。我们以TNF-α诱导小鼠成纤维细胞(L929细胞株)为模型,深入探讨竹黄发挥药效的作用机理。研究发现,竹黄可以双向调节TNF-α诱导的L929细胞凋亡过程,高浓度竹黄可以促TNF-α诱导的细胞凋亡,低浓度竹黄具有抗TNF-α诱导的细胞凋亡作用。在竹黄抗凋亡过程中,信号网络中所涉及的关键蛋白COX-2表达下降,BCL-2表达上升,而NF-κB基本无明显变化。我们推测竹黄中含有药理作用相反的物质,通过调节信号网络中关键蛋白COX-2和BCL-2等的变化,进而改变信号网络平衡,发挥抗肿瘤与抗炎功效。相关研究有利于深入理解竹黄抗肿瘤与抗炎的药效作用,为运用生物活性导向和天然药物提取分离技术,进行相关新药的研发提供理论依据。

DOA estimation via sparse recovering from the smoothed covariance vector

A direction of arrival（DOA） estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector（SCV） is established, which is constructed using the lower left diagonals of the covariance matrix. DOA estimation is then achieved from the SCV by sparse recovering, where two distinguished error limit estimation methods of the constrained optimization are proposed to make the algorithms more robust. The algorithm shows robust performance on DOA estimation in a uniform array, especially for coherent signals. Furthermore, it significantly reduces the computational load compared with those algorithms based on multiple measurement vectors（MMVs）. Simulation results validate the effectiveness and efficiency of the proposed algorithm.

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.

Robust wide-band weighted focusing method to estimate direction of arrival of signals

A robust weighted focusing matrix is proposed for CSM (Coherent Signal-Subspace Method). An explicit expression is deduced to estimate the SNR (Signal-to-Noise Ratio) by studying the characteristics of covariance matrices and then a weighted focusing matrix, which can improve the performance of the original CSM in application, is formed. The results of computer simulation, water tank experiment and ship noise data analysis show the efficiency of the method.

A coherent method for finding arrival directions of speech signals and its application for noise reduction in microphone array

The research on finding the arrival directions of speech signals by microphone arrny is proposed. We first analyze the uniform microphone array and give the design for microphone array applied in the hand-free speech recognition. Combining the traditional direction finding technique of MUltiple SIgnal Classification (MUSIC) with the focusing matrix method, we improve the resolving power of the microphone array for multiple speech sources.As one application of finding Direction of Arrival (DOA), a new microphone-array system for noise reduction is proposed. The new system is based on maximum likelihood estimate technique which reconstruct superimposed signals from different directions by using DOA information. The DOA information is got in terms of focusing MUSIC method which has been proven to have high performance than conventional MUSIC method on speaker localization[1].

Transmission of 20 Gb/s PAM-4 signal over 20 km optical fiber using a directly modulated tunable DBR laser

We report 20 Gb/s transmission of four-level pulse amplitude modulation （PAM） signal using a directly modulated tunable distributed Bragg reflector （DBR） laser. Transmission distance over 20 km was achieved without using optical amplifiers and optical dispersion compensation modules. A wavelength tuning range of 11.5 nm and a 3 dB bandwidth greater than 10 GHz over the entire wavelength tuning range were obtained.

Directly Modulated 10 Gb/s Signal Transmission over 320 km of Negative Dispersion Fiber for Regional Metro Network

We demonstrate the transmission of directly modulated 10-Gb/s WDM signals over 320 km of negative dispersion fiber (dispersion: -2.5 ps/km/nm @1550 nm) without dispersion compensation. The results indicate that a regional metro WDM network could be implemented cost-effectively by using the proposed negative dispersion fiber and direct modulated lasers.

Research on direct-path interference suppression for bistatic radar based on GNSS reflected from the ocean surface

The use of global navigation satellite system signals reflected by the sea surface as a remote sensing tool has received considerable attention for over a decade. However, in signal detection processing, the direct-path interference can potentially introduce a bigger obstacle to ocean surface detection than other interferences, such as clutter and radio signals. In order to improve retrieval of the ocean surface parameters, the direct-path interference signals have to be suppressed. The interference suppression based on the improved subspace projection algorithm is put forward in this paper. First of all, the interferential signal is acquired and tracked, then according to the acquired interferential signal parameter, the corresponding disturbance subspace is constructed. Finally, the interference is eliminated using the improved subspace block projection. This method can not only reduce the influence of interferential performance by the tracking error but also decrease computation greatly. It suits the changeable environment and the multi-data sample situation. The analysis of the simulated results has confirmed the validity of this method and provided a basis for the following system optimization design.

Source localization using a non-cocentered orthogonal loop and dipole (NCOLD) array

A uniform array of scalar-sensors with intersensor spacings over a large aperture size generally offers enhanced resolution and source localization accuracy,but it may also lead to cyclic ambiguity.By exploiting the polarization information of impinging waves,an electromagnetic vector-sensor array outperforms the unpolarized scalar-sensor array in resolving this cyclic ambiguity.However,the electromagnetic vector-sensor array usually consists of cocentered orthogonal loops and dipoles（COLD）,which is easily subjected to mutual coupling across these cocentered dipoles/loops.As a result,the source localization performance of the COLD array may substantially degrade rather than being improved.This paper proposes a new source localization method with a non-cocentered orthogonal loop and dipole（NCOLD）array.The NCOLD array contains only one dipole or loop on each array grid,and the intersensor spacings are larger than a half-wavelength.Therefore,unlike the COLD array,these well separated dipoles/loops minimize the mutual coupling effects and extend the spatial aperture as well.With the NCOLD array,the proposed method can effciently exploit the polarization information to offer high localization precision.