Analysis on Design of Kohonen-network System Based on Classification of Complex Signals

The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and classification of complex signals is proposed, and both the network design and signal processing are analyzed, including pre-processing of signals, extraction of signal features, classification of signal and network topology, etc.

ASYMPTOTIC PROPERTY STUDY OF THE LEAST SQUARE ESTIMATES OF 2-D EXPONENTIAL SIGNALS VIA COMPLEX SIGNAL PROCESSING APPROACH

By use of the approach of complex random signal processing, the asymptotic statistical properties of the least square estimates of 2-D exponential signals are studied. In doing so it is found that the representation is considerably more intuitive, and is analytically more tractable.

Animal personalities and their implications for complex signaling

Animal personalities have been a major focus of behavioral ecology over the past decade. Consistent individual dif ferences in behavior have been found across taxa, and have been shown to influence a range of ecological processes. The role of personalities in sexual selection has been considered, and examples exist that show selection for personality traits with both assortative and disassortative mating patterns between personality types. One overlooked aspect of the personality and sexual se lection literature is the potential for personalitysignaling interactions, specifically with complex signaling. Complex signaling is a diverse topic in itself, and in short, consists of multiple signals within one or more modalities that interact to elicit a receiver response. Research into complex signaling has been thorough, although at times studies discover complex signaling systems that fail to fit into one of the existing hypotheses in the literature. Here, we argue that personalities may interact with complex signal ing, which should be considered by researchers of both personality and sexual selection and communication. We describe several ways in which personalitycomplex signaling interactions could affect both the signaler and receiver, and the way in which they may drive personalityspecific signals as well as receiver preferences. Finally, we discuss how considering personality in com plex signaling studies may inform theory as well as improve the ability of researchers to accurately describe its function.

Blind source separation algorithm for communication complex signals in communication reconnaissance

Most blind source separation algorithms are only applicable to real signals,while in communication reconnaissance processed signals are complex.To solve this problem,a blind source separation algorithm for communication complex signals is deduced,which is obtained by adopting the Kullback-Leibler divergence to measure the signals’independence.On the other hand,the performance of natural gradient is better than that of stochastic gradient,thus the natural gradient of the cost function is used to optimize the algorithm.According to the conclusion that the signal’s mixing matrix after whitening is orthogonal,we deduce the iterative algorithm by constraining the separating matrix to an orthogonal matrix.Simulation results show that this algorithm can efficiently separate the source signals even in noise circumstances.

A Complex Signal

The removal of Taiwan from the common strategic objectives of the U.S.-Japan alliance could mean much more than a change of wording While U.S.and Japanese top diplomats and defense officials celebrated closer cooperation forged by their annual meeting

A new complex Duffing oscillator used in complex signal detection

In view of the fact that complex signals are often used in the digital processing of certain systems such as digital communication and radar systems,a new complex Duffing equation is proposed.In addition,the dynamical behaviors are analyzed.By calculating the maximal Lyapunov exponent and power spectrum,we prove that the proposed complex differential equation has a chaotic solution or a large-scale periodic one depending on different parameters.Based on the proposed equation,we present a complex chaotic oscillator detection system of the Duffing type.Such a dynamic system is sensitive to the initial conditions and highly immune to complex white Gaussian noise,so it can be used to detect a weak complex signal against a background of strong noise.Results of the Monte-Carlo simulation show that the proposed detection system can effectively detect complex single frequency signals and linear frequency modulation signals with a guaranteed low false alarm rate.

Gamma-aminobutyric acid A receptors in Alzheimer＇s disease： highly localized remodeling of a complex and diverse signaling pathway

Alzheimer＇s disease （AD）, the predominant form of dementia, is a chronic, incurable neurodegenerative disorder presenting with symptoms includ- ing progressive memory loss and disturbed emotional state. It has been estimated that dementia affects over 47 million people worldwide （Prince et al., 2015）, and with 60-80% of cases attributable to AD.

Radar Emitter Signal Recognition Based on Complexity Features

Intra-pulse characteristics of different radar emitter signals reflect on signal waveform by way of changing frequency, phase and amplitude. A novel approach was proposed to extract complexity features of radar emitter signals in a wide range of signal-to-noise ratio (SNR), and radial basis probability neural network (RBPNN) was used to recognize different radar emitter signals. Complexity features, including Lempel-Ziv complexity (LZC) and correlation dimension (CD), can measure the complexity and irregularity of signals, which mirrors the intra-pulse modulation laws of radar emitter signals. In an experiment, LZC and CD features of 10 typical radar emitter signals were extracted and RBPNN was applied to identify the 10 radar emitter signals. Simulation results show that the proposed approach is effective and has good application values because average accurate recognition rate is high when SNR varies in a wide range.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

Arbitrary-Dimensional Frequency Estimation of Complex Sinusoid Signals

The problem of increasing computation in pace with the growth of dimension is discussed for arbitrary dimensional frequency estimation of complex sinusoid signals. The conception of matrix core, the form of which doesnt change with dimension, is put forward. The deduced estimation formula shows that a N dimensional frequency estimation could be obtained by N one dimensional calculations. Obviously, while dimension increases, this method could reduce much computation.

WIDELY LINEAR RLS CONSTANT MODULUS ALGORITHM FOR COMPLEX-VALUED NONCIRCULAR SIGNALS

Based on the constant modulus criterion, a new Widely Linear(WL) blind equalizer and a novel widely linear recursive least square constant modulus algorithm are proposed to improve the blind equalization performance for complex-valued noncircular signals. The new algorithm takes advantage of the WL filtering theory by taking full use of second-order statistical information of the complex-valued noncircular signals. Therefore, the weight vector contains the complete second-order information of the real and imaginary parts to decrease the residual inter-symbol interference effectively. Theoretical analysis and simulation results show that the proposed scheme can significantly improve the equalization performance for complex-valued noncircular signals compared with traditional blind equalization algorithms.

Estimation of the complex frequency of a harmonic signal based on a linear least squares method

In this study, we propose a simple linear least squares estimation method(LLS) based on a Fourier transform to estimate the complex frequency of a harmonic signal. We first use a synthetically-generated noisy time series to validate the accuracy and effectiveness of LLS by comparing it with the commonly used linear autoregressive method(AR). For an input frequency of 0.5 m Hz, the calculated deviations from the theoretical value were 0.004‰and 0.008‰ for the LLS and AR methods respectively; and for an input 5 10 6attenuation,the calculated deviations for the LLS and AR methods were 2.4% and 1.6%. Though the theory of the AR method is more complex than that of LLS, the results show LLS is a useful alternative method. Finally, we use LLS to estimate the complex frequencies of the five singlets of the0S2 mode of the Earth’s free oscillation. Not only are the results consistent with previous studies, the method has high estimation precisions, which may prove helpful in determining constraints on the Earth’s interior structures.

Polyubiquitination and Proteasome Signals in Tubulobulbar Complexes of Rat Late Spermatids

To illustrate the involvement of tubulobulbar complexes (TBC) in ubiquitin-proteasome degradation of unnecessary proteins in the head cytoplasm of late spermatids, the localization of polyubiquitin and proteasome was studied by immunofluorescence and immunoelectron microscopy. Polyubiquitin localized to TBC and proteasome subunit α to dense materials surrounding the TBC in the cytoplasm of Sertoli cell enwrapping sickle-shaped spermatid heads. The results suggest that the TBC is a structural device for ubiquin-proteasome degradation of unnecessary proteins in the cytoplasm of spermatid head during rapid reduction of the head cytoplasm and nuclear compaction of late spermatids.

Mathematical theory of signal analysis vs. complex analysis method of harmonic analysis

We present recent work of harmonic and signal analysis based on the complex Hardy space approach.

基于聚乙烯亚胺还原的金纳米颗粒为基底构建的“signal on”型高灵敏ECL传感器铅离子检测

该实验结合目标物循环与杂交链式反应(HCR)信号放大策略,构建了用于检测Pb^(2+)的"signal on"型ECL生物传感器。该传感器可以通过Pb^(2+)和脱氧核酶的特异识别作用,循环剪切固载在电极表面的发卡型底物链H0,在实现目标物循环的同时使得电极表面越多的H0被剪切为DNA残链,获得的DNA残链继而作为引物打开发夹探针H1和H2引发HCR反应,形成由H1和H2交替杂交形成的长双链聚合物。这些双链聚合物可以通过静电吸附作用固载大量的邻菲罗啉钌配合物(Ru(phen)3^(2+))。利用固载在电极基底的PEIAu复合纳米材料对发光试剂的共反应作用显著放大检测信号,构建了对Pb^(2+)的"signal on"模式ECL检测新方法。实验结果表明,在Pb^(2+)浓度范围为1×10-12mol/L^1×10-8mol/L之间,表现出良好的线性关系,其检出限为3.33×10-13mol/L。

Apoptosis signaling pathways and lymphocyte homeostasis

It has been almost three decades since the term ＂apoptosis＂ was first coined to describe a unique form of cell death that involves orderly, gene-dependent cell disintegration. It is now well accepted that apoptosis is an essential life process for metazoan animals and is critical for the formation and function of tissues and organs. In the adult mammalian body, apoptosis is especially important for proper functioning of the immune system. In recent years, along with the rapid advancement of molecular and cellular biology, great progress has been made in understanding the mechanisms leading to apoptosis. It is generally accepted that there are two major pathways ofapoptotic cell death induction： extrin- sic signaling through death receptors that leads to the formation of the death-inducing signaling complex （DISC）, and intrinsic signaling mainly through mitochondria which leads to the formation of the apoptosome. Formation of the DISC or apoptosome, respectively, activates initiator and common effector caspases that execute the apoptosis process. In the immune system, both pathways operate; however, it is not known whether they are sufficient to maintain lymphocyte homeostasis. Recently, new apoptotic mechanisms including caspase-independent pathways and granzyme-initiated pathways have been shown to exist in lymphocytes. This review will summarize our understanding of the mechanisms that control the homeostasis of various lymphocyte populations.

NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING

Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform （DT-CWT） is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio （SNR） than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals.

Application of Complex Daubechies Wavelet in UHF Partial Discharge Measurements

On-line partial discharge(PD)detection still remains a very challenging task because of the strong electromagnetic interferences.In this paper,a new method of de-noising,using complex Daubechies wavelet(CDW)transform,has been proposed.It is a relatively recent enhancement to the real-valued wavelet transform because of tow important properties,which are nearly shift-invariant and availability of phase information.Those properties give CDW transform superiority over other real-valued wavelet transform,and then the construction algorithm of CDW is introduced in detail.Secondly,based on the real threshold algorithm of real-valued wavelet transform,complex threshold algorithm is devised.This algorithm take the different characteristics of real part and imaginary part of complex wavelet coefficients into account,it modifies the real and imaginary parts of complex wavelet coefficients respectively.Thirdly,to obtain a real de-noised signal,new combined information series is devised.By applying different combination of real part and imaginary part of de-noised complex signal,a real de-noised signal can be restored with higher peak signal-to-noise ratio(PSNR)and less distortion of original signals.Finally,On-site applications of extracting PD signals from noisy background by the optimal de-noising scheme based on CDW are illustrated.The on-site experimental results show that the optimal de-noising scheme is an effective way to suppress white noise in PD measurement.

A NEW QUADRATURE SAMPLING APPROACH FOR ARBITRARY BANDPASS SIGNAL WITHIN BASEBAND SAMPLING RATE

A new quadrature sampling technique for arbitrary bandpass signal within baseband sampling rate is presented. The input bandpass signal whose carrier frequency lies in the A/D baseband sampling rate is first decimated by factor 2 and modulated by (- 1)n, and then is interpolated by a linear phase FIR all-pass filter, finally the modulated complex envelope of bandpass signal can be produced.

Characterisation of Real-World Bus Acceleration and Deceleration Signals

Public transportation by bus is an essential part of mobility. Braking and starting, e.g., approaching a bus stop, are documented as the main reason for non-collision incidents. These situations are evoked by the acceleration forces leading to perturbations of the passenger’s base of support. In laboratory studies perturbations are applied to getting insight into the postural control system and neuromuscular responses. However, bus perturbations diverge from laboratory ones with respect to duration, maximum and shape, and it was shown recently that these characteristics influence the postural response. Thus, results from posturographic studies cannot be generalised and transferred to bus perturbations. In this study, acceleration (ACC) and deceleration (DEC) signals of real traffic situations were examined. A mathematical approach is proposed in order to identify characteristics of these signals and to quantify their similarity and complexity. Typical characteristics (duration, maximum, and shape) of real-world driving manoeuvres concerning start and stop situations could be identified. A mean duration of 13.6 s for ACC and 9.8 s for DEC signals was found which is clearly longer than laboratory perturbations. ACC and DEC signals are more complex than the used signals for platform displacements in the laboratory. The proposed method enables the reconstruction of bus ACC and DEC signals. The data can be used as input for studies on postural control with high ecological validity.