Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

A HOS-based Blind Signal Extraction Method for Chaotic MIMO Systems

A novel method to extract multiple input and multiple output (MIMO) chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal generators was presented. In order to separate the chaotic source signals only by using the sensor signals at receivers, a blind neural extraction algorithm based on higher-order statistic (HOS) technique was used to recover the primary chaotic signals. Simulation results show that the proposed approach has good performance in separating the primary chaotic signals even under nonideal channel.

Extraction of periodic signals in chaotic secure communication using Duffing oscillators

This paper presents a novel approach to extract the periodic signals masked by a chaotic carrier. It verifies that the driven Duffing oscillator is immune to the chaotic carrier and sensitive to certain periodic signals. A preliminary detection scenario illustrates that the frequency and amplitude of the hidden sine wave signal can be extracted from the chaotic carrier by numerical simulation. The obtained results indicate that the hidden messages in chaotic secure communication can be eavesdropped utilizing Duffing oscillators.

Bicoid Signal Extraction with a Selection of Parametric and Nonparametric Signal Processing Techniques

The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraetion in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.

Acoustic fault signal extraction via the line-defect phononic crystals

Rotating machine fault signal extraction becomes increasingly important in practical engineering applications.However,fault signals with low signal-to-noise ratios(SNRs)are difficult to extract,especially at the early stage of fault diagnosis.In this paper,2D line-defect phononic crystals(PCs)consisting of periodic acrylic tubes with slit are proposed for weak signal detection.The defect band,namely,the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity.The noise can be filtered by the band gap.As a result,fault signals with high SNRs can be obtained for fault feature extraction.The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies.All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals.This work will provide potential for extracting weak signals in many practical engineering applications.

ESPRIT-Based Feature Extraction of Helicopter Acoustic Signal

目的提取直升机声信号谐波频率作为特征用以识别直升机．方法用旋转不变技术估计信号参数法（ＥＳＰＲＩＴ）从实测的直升机声信号中提取谐波频率，算法采用ＳＶＤＴＬＳ．结果ＥＳＰＲＩＴ方法在直升机不同的飞行条件下，用有限长度的数据准确地提取了声信号的谐波频率．结论用ＥＳＰＲＩＴ方法提取直升机声信号谐波频率是十分有效的，将谐波频率作为目标特征用以识别直升机是完全可行的．

A novel dense spectrum correction algorithm for extracting vibration signals in internal combustion engine and its application

The algorithm of dense spectrum correction has been raised and proved based on the correction of discrete spectrum by fast Fourier transform.The result of simulation shows that such algorithm has advantages of high accuracy and small amount of calculation.The algorithm has been successfully applied to the analysis of vibration signals from internal combustion engine.To calculate discrete spectrum,fast Fourier transform has been used to calculate the discrete spectrum by the signals acquired by the sensors on the oil pan,and the signal has been extracted from the mixed signals.

Investigation on the automatic parameters extraction of pulse signals based on wavelet transform

This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis,quantitative methods are needed. To extract the parameters of pulse signals,the prerequisite is to detect the corners of pulse signals correctly. Up to now,the pulse parameters are mostly acquired by marking the pulse corners manually,which is an obstacle to modernize pulse diagnosis. Therefore,a new automatic parameters extraction approach for pulse signals using wavelet transform is presented. The results testified that the method we proposed is feasible and effective and can detect corners of pulse signals accurately,which can be expected to facilitate the modernization of pulse diagnosis.

Seismic signal recognition using improved BP neural network and combined feature extraction method

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.

FEATURE EXTRACTION OF VIBRATION SIGNALS BASED ON WAVELET PACKET TRANSFORM

A method is proposed for the analysis of vibration signals from components ofrotating machines, based on the wavelet packet transformation (WPT) and the underlying physicalconcepts of modulation mechanism. The method provides a finer analysis and better time-frequencylocalization capabilities than any other analysis methods. Both details and approximations are splitinto finer components and result in better-localized frequency ranges corresponding to each node ofa wavelet packet tree. For the punpose of feature extraction, a hard threshold is given and theenergy of the coefficients above the threshold is used, as a criterion for the selection of the bestvector. The feature extraction of a vibration signal is accomplished by computing thereconstruction signal and its spectrum. When applied to a rolling bear vibration signal featureextraction, the proposed method can lead to be very effective.

Small-signal modeling and parameter extraction method for a multigate GaAs pHEMT switch

This paper presents an accurate small-signal model for multi-gate GaAs pHEMTs in switching-mode.The extraction method for the proposed model is developed.A 2-gate switch structure is fabricated on a commercial 0.5μm AlGaAs/GaAs pHEMT technology to verify the proposed model.Excellent agreement has been obtained between the measured and simulated results over a wide frequency range.

Effect of spinal cord extracts after spinal cord injury on proliferation of rat embryonic neural stem cells and Notch signal pathway in vitro

Objective:To investigate the effect of the spinal cord extracts(SCE)after spinal cord injuries(SCIs)on the proliferation of rat embryonic neural stem cells(NSCs)and the expressions of mRNA of Notch1 as well as of Hes1 in this process in vitro.Methods:The experiment was conducted in 4 different mediums:NSCs+PBS(Group A-blank control group),NSCs+SCE with healthy SD rats(Croup B-normal control group),NSCs+SCE with SD rats receiving sham-operation treatment(Croup C-sham-operation group)and NSCs+SCE with SCIs rats(Group D-paraplegic group).Proliferative abilities of 4 different groups were analyzed by MTT chromatometry after co-culture for 1,2,3,4 and 5 d,respectively.The expressions of Notch 1 and Hes1 mRNA were also detected with RT-PCR after co-culture for 24 and 48 h,respectively.Results:After co-culture for 1,2,3,4 and 5 d respectively,the MTT values of group D were significantly higher than those of group A,group B and group C(P<0.05).However,there were no significantly differences regarding MTT values between group A,group B and group C after co-culture for 1,2,3,4 and 5 d,respectively(P>0.05).Both the expressions of Notch1 and Hes1 mRNA of group D were significantly higher than those of other 3 groups after co-culture for 24 h and 48 h as well(P<0.05).But there was no difference oin expressions of Notch1 and Hes1 mRNA among group A,group B and group C after co-culture for 24 h and 48 h(P>0.05).There was no difference in expressions of Notch1and Hes1 mRNA between 24 h and 48 h treatment in group D.Conclusions:SCE could promote the proliferation of NSCs.It is demonstrated that the microenvironment of SCI may promote the proliferation of NSCs.Besides,SCE could increase the expression of Notch1 and Hes1 mRNA of NSC.It can be concluded that the Notch signaling pathway activation is one of the mechanisms that locally injured microenvironment contributes to the proliferation of ENSC after SCIs.This process may be performed by up-regulating the expressions of Notch1 and Hes1 gene.

Background correction in near-infrared spectra of plant extracts by orthogonal signal correction

In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.

PARAMETER IDENTIFICATION IN OFFSHORE PLATFORM USING ARMA MODEL AND TECHNOLOGY OF EXTRACTING FREE VIBRATION SIGNAL

A procedure for identifying the dynamic parameter of offshore platform is presented. The present procedure consists of two key features. First uses random decrement (RD) technology to extract free vibration signal in strong noise environment in which it may not white noise. Second technology which called autoregressive moving average (ARMA) was used to model the data treated by the random decrement method. In order to get rid of the color noise in the output signal response from the offshore platform an imaginary system is added in RD system and make the course of extracting performed under the state of color input by choosing the breakover condition and lead time. For eliminating multi_values of parameters identified, an updating moving average method is used. The dynamic parameters of structure under arbitrary input are identified. Example of the method as applied to a scale_model offshore platform was used to evaluate the technology of efficiency and the value of on_line.

A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal

In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.

Reliability Evaluation on Weak Signal Extraction for Airgun Source Surveys

Based on the data synthesis simulation and the actual processing of the airgun seismic source signal,three quantitative indicators of signal-to-noise ratio,waveform correlation coefficient and phase offset,are superimposed. We systematically evaluate the functions of the following three stack methods including linearity,phase weighting and S-transform in the extraction of weak signals under strong background noise and quantitatively estimate the reliability of the stack results. Through the comprehensive discussion of the above three methods of stack results,the preliminary comparative analysis believes that the linear stack signal-to-noise ratio is low,but the waveform distortion is minimal; the phase-weighted superimposed signal-to-noise ratio is high and the phase offset is small,but the results of the waveform quality and linear stack are larger than the deviation; the S-transform stack has a relatively higher signal-to-noise ratio and a small loss of waveform amplitude,but there is a certain phase shift phenomenon. It is therefore suggested that linear stack technology should be used when the requirements of both waveform quality and time precision are high. However,the selection of the stack method when the airgun source excitation is limited should be emphasized. If high fidelity is required, the S-transform stack method should be selected; if the required time is high,accuracy can be selected by phase-weighted stack method to achieve reasonable extraction of weak signals.

Extraction of Buried Signals in Noise: Correlated Processes

In this paper, we propose extraction of signals correlated with noise in which they are buried. Proposed extraction method uses no a-priori information on the buried signal and works independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Extraction of buried correlated signals is achieved without averaging in the time or frequency domain.

PARTIALLY BLIND EXTRACTION OF CONTINUOUS CHAOTIC SIGNALS FROM A LINEAR MIXTURE

In this paper, we address the problem of blind extraction and separation of a continuous chaotic signal from a linear mixture consisting of some chaotic signal and/or random signals. The problem of blind extraction is firstly formulated as a problem of the synchronization-based parameter estimation. Then an efficient least square based parameter estimation method is introduced to determine the desired extracting vector. The proposed blind signal extraction scheme is applicable to blind separation of chaotic signals by formulating the separation problem as the extraction of each chaotic source. Numerical simulation shows that the proposed approach can blindly extract and separate the desired chaotic signals and it is also robust to measurement noise.

A Direct Parameter-Extraction Method for GaInP/GaAs Heterojunction Bipolar Transistors Small-Signal Model

An accurate and broad-band method for hetero-junction bipolar transistors(HBT) small-signal model parameters-extraction is presented in this paper. An equivalent circuit forthe HBT under a forward-bias condition is proposed for extraction of accessresistance and parasiticinductance. This method differs from previous ones by extracting the c-quivalent circuit parameterswithout using special test structure or global numerical optimization techniques. The mainadvantage of this method is that a unique and physically meaningful set of intrinsic parameters isextracted from impedance and admittance representation of the measured S-pa-rameters in thefrequency range of 1-12 GHz under different bias conditions. The method yields a deviation of lessthan 5% between measured and modeled S-parameters.

Diarylheptanoid-myricanone isolated from ethanolic extract of Myrica cerifera shows anticancer effects on HeLa and PC3 cell lines:signalling pathway and drug-DNA interaction

OBJECTIVE： To test if myricanone （02H2405）, a cyclic diarylheptanoid, has anticancer effects on two different cancer cell lines HeLa and PC3. The present study was conducted with a note on the drug-DNA interaction and apoptotic signalling pathway. METHODS： Several studies like cytotoxicity, nuclear damage, annexin-V-fluorescein isothiocyanate （FITC）/propidium iodide （PI）-Iabelled apoptotic assay and cell cycle arrest, immunoblot and reverse transcriptase-polymerase chain reaction （RT-PCR） were used following standard protocols. Circular dichroism （CD） spectroscopy was also done to evaluate whether myricanone effectively interacted with DNA to bring about conformational changes that could strongly inhibit the cancer cell proliferation. RESULTS： Myricanone showed a greater cytotoxic effect on PC3 cells than on HeLa cells. Myricanone promoted G0/G arrest in HeLa cells and S phase arrest in PC3 cells. Nuclear condensation and annexin V-FITC/PI studies revealed that myricanone promoted apoptotic cell death. CD spectroscopic data indicated that myricanone had an interaction with calf thymus DNA that changed DNA structural conformation. RT-PCR and immunoblot studies revealed that myricanone activated the apoptotic signalling cascades through down-regulation of transcription factors like nuclear factor-KB （NF-KB） （p65）, and signal transducers and activators of transcription 3 （STAT3）; cell cycle regulators like cyclin D1, and survivin and other signal proteins like Bcl-2 and up-regulation of Bax, caspase-9 and caspase-3. CONCLUSION： Myricanone induced apoptosis in both types of cancer cells by triggering caspase activation, and suppression of cell proliferation by down-regulation of NF-KB and STAT3 signalling cascades, which makes it a suitable candidate for possible use in the formulation of therapeutic alent for combatin cancer.