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The signal detection in chaotic background has gradually become one of the research focuses in recent years. Previous research showed that the measured signals were often unavoidable to be contaminated by the chaotic noise, such as the radar signal detection from sea clutter wave,signal source extraction from chaotic secure communication and ECG/EEG abnormal signal detection,etc.At present,there are two methods to detect the target signal from the chaotic background.One is to detect the target signals by using the difference in geometric structure between the chaotic signal and the target signal,and the other is to regard the chaotic signal as the noise,and the target signal is extracted from the chaotic background by the time frequency analysis method,such as wavelet transform and empirical mode decomposition. The first kind of method can detect the target signal well,but it needs to characterize the chaotic system and reconstruct the phase space,which is difficult in the practical applications.The second kind of method does not need to reconstruct the chaotic phase space and can effectively extract the target signal from the chaotic background.However,the wavelet transform lacks adaption and how to select the optimal wavelet basis and decomposition layers is a difficult problem.In the empirical mode decomposition there exists the mode mixing that is very sensitive to the noise.The synchrosqueezed wavelet transform (SST) effectively improves the mixing of mode by compressing the continuous wavelet coefficients in the frequency direction,but also it has good robustness to noise.Therefore,the SST can extract the harmonic signal well from the chaotic background.In the present algorithm of abstracting harmonic signal from chaotic background by SST,the harmonic signals are extracted by using single accumulation frequency range SST (SAFR-SST) based on wavelet ridge detection.If the target signal is stable harmonic signal,whose frequency does not change with time,the SAFR-SST method can have a high abstraction precision.But if the target signal is the non-stable harmonic signal whose frequency changes with time,the SAFR-SST method is not enough nor can obtain high abstraction precision.In order to overcome the shortcomings of the SST in extracting the non-stationary harmonic signal from the chaotic background, an improved SST extracting method is proposed which is based on the adaptive optimal cumulative frequency range. Firstly,the formulas of calculating the adaptive cumulative frequency range in SST extraction are deduced according to the relationship between the wavelet coefficient of non-stationary harmonic and the interval of supporting wavelet bases.Then,the optimal values of the parameters in the adaptive cumulative frequency range formula are calculated by the minimum energy error criterion according to the integrity and orthogonality of the intrinsic mode types function.Finally,the SST adaptive extraction of the non-stationary harmonic signal is realized according to the SST inverse transform.In experiment,the different types of non-stationary harmonics are extracted from the Lorenz and Duffing chaotic background.The experimental results show that the proposed method can effectively extract the non-stationary harmonic from the noisy chaotic background.Compared with the classical SST method with single cumulative frequency range,the proposed method has good performance in both mean square error and correlation coefficient.And when the chaotic background contains different-intenity Gauss white noises,the proposed method can also effectively abstract the non-stationary harmonic from the chaos and noise interference.So,the proposed method has a good practice value.

Extracting the harmonic signal from the chaotic interference background is very important for theory and practical application. The wavelet transform and empirical mode decomposition (EMD) have been widely applied to harmonic extraction from chaotic interference, but because the wavelet and EMD both present the mode mixing and are sensitive to noise, the harmonic signal often cannot be precisely separated out. The synchrosqueezing wavelet transform (SST) is based on the continuous wavelet transform, through compressing the time-frequency map of wavelet transform in the frequency domain, the highly accurate time-frequency curve is obtained. The time-frequency curve of SST which does not exist between cross terms, can better improve the mode mixing. The SST has also good robustness against noise. When the signal is a mixed strong noise, the SST can still obtain the clear time-frequency curve and approximate invariant decomposition results. In this paper, the SST is applied to the multiple harmonic signal extraction from chaotic interference background, and a new harmonic extracting method is proposed based on the SST. First, the signal obtained by mixing chaotic and harmonic signals is decomposed into intrinsic mode type function (IMTF) by the SST. Then using the Hilbert transform the frequency of each IMTF is analyzed, and the harmonic signals are separated from the mixed signal. Selecting the Duffing signal as the chaotic interference signal, the extracting ability of the proposed method for multiple harmonic signals is analyzed. The different harmonic extraction experiments are conducted by using the proposed SST method for different frequency intervals and different noise intensity multiple harmonic signals. And the experimental results are compared with those from the classical EMD method. When the chaotic interference signal is not contained by noise, the harmonic signal extraction effect is seriously affected by the frequency interval between harmonic signals. If the harmonic frequency interval between harmonic signals is relatively narrow, each harmonic signal cannot be accurately extracted by the EMD method. However, the harmonic extraction precision of SST method is not seriously influenced by the change of harmonic frequency interval, and when the frequency interval between harmonic signals is small the SST method can still accurately extract each harmonic signal from chaotic interference. When the noise contains a chaotic interference signal, the harmonic extraction effect of EMD method significantly decreases with noise intensity increasing. When the noise level reaches 80%, the extracted harmonic signal from the EMD method is seriously distorted, the correlation coefficient of the extracted harmonic signal with original harmonic signal is only about 0.6. With the increase of noise intensity, the harmonic extraction effect of SST method has also a declining trend. But as the noise intensity is within 120%, the harmonic extraction effect of SST method does not greatly change and the extracted harmonic signal precision is still higher, which shows that the harmonic extraction method based on the SST has good robustness against noise. The comprehensive experimental results show that the proposed SST method has high extracting precision for multiple harmonic signals of different frequency intervals, and the SST method has better robustness against Gauss white noise. The extracted results of harmonic signal are better than those from the classical empirical mode decomposition method.

According to the characteristics of empirical mode decomposition and denoise of independent component analysis, an adaptive denoising method of chaotic signal is proposed based on independent component analysis and empirical mode decomposition. First, the chaotic signal is decomposed into a set of intrinsic mode functions by empirical mode decomposition; then, the multi-dimensional input vectors are constructed based on the translation invariant empirical mode decomposition, and the noise of each intrinsic mode function is removed through the constructed multi-dimensional input vectors and the independent component analysis; finally, the denoisied chaotic signal is obtained by accumulating and reconstructing all the processed intrinsic mode functions. Both the chaotic signal generated by Lorenz map with different level Gaussian noises, and the observed monthly series of sunspots are respectively used for noise reduction using the proposed method. The results of numerical experiments show that the proposed method is efficient. It can better correct the positions of data points in phase space and approximate the real chaotic attractor trajectories more closely．

Laser-induced breakdown plasma is produced by using Q-switched Nd: YAG laser operating at 532 nm, which interacts with the Al alloy sample target in air. The spectral lines in the 230—440 nm wavelength range have been identified, and based on the calibration-free method, the mass concentration of Al alloy are obtained, which is in good agreement with the standard value of the sample.

In this paper, the Ne 1s photoelectron spectrum and the corresponding Auger decay processes have been investigated theoretically by using the GRASP92, RATIP and the recently developed RERR06 codes based on the multi-configuration Dirac-Fock method. Good overall agreement was found between the present calculations and the previous theoretical and experimental results. By analyzing the Auger decay processes of the photoionization satellite state Ne+1s-12p-1（1,3Ｐ）3p, it was found that the spectator Auger decay is the dominant decay mode and the Auger intensity of the Ne+1s-12p-1（1Ｐ）3p is greater than that of the Ne+1s-12p-1（3Ｐ）3p as a whole. Meanwhile, the relative populations of the final ions Ne+, Ne2+and Ne3+produced by the photoionization satellite state Ne+1s-12p-1（1,3Ｐ）3p were predicted to be 0.02, 0.58 and 0.41, respectively.