Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

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.

Observation 20-s periodic signals on Mars from InSight,Sols 800-1,000

Seismometers of the InSight probe(Interior Exploration using Seismic Investigation,Geodesy and Heat Transport)currently operating on Mars have recorded not only seismic events but also high-frequency non-seismic periodic signals that appear to have been induced by variations in the Martian environment and the hardware.Here,we report an observation of a long-period signal with a dominant period of~20 s from Martian solar days(Sol)800 to Sol 1,000.This 20-s signal is detected mostly at quiet nighttime—from22:00 to 04:00 LMST(Local Mean Solar Time)—at the InSight landing site.The measurement of the particle motion suggests that this linearly polarized signal focuses on the horizontal plane with an angle of~30°from the north.By examining the temporal variation of the signal’s amplitude and polarization angle and its times of occurrence in relation to the planet’s atmospheric data,we suggest that this20-s signal may be relevant to wind and temperature variations on Mars.Furthermore,we study the possible influence of this 20-s signal on the noise autocorrelation and find that the stacked autocorrelograms can be quite different when the 20-s signal is present.

Research and application of regular phenomenon between periodic signals

The phase change between periodic signals is regular. Research on the regular phenomenon between periodic signals is helpful to improve the precision of some measurements and develop some new measurement methods. So it is necessary to analyze the characteristics of the greatest common factor frequency and the least common multiple period universally existing in periodic signals. The regulation of the quantitative phase shift resolution between periodic signals is presented.The cause of difference in phase characteristics between periodic signals is explained well. In this paper we propose different application prospects based on the regular phenomenon between periodic signals, with focusing on the methods for high precision frequency measurement and transient stability measurement. The experimental results are satisfactory.

Stochastic resonance in a time-delayed asymmetric bistable system with mixed periodic signal

This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A general approximate Fokker-Planck equation and the expression of the signal-to-noise ratio are derived through the small time delay approximation at both fundamental harmonics and mixed harmonics. The effects of the additive noise intensity Q, multiplicative noise intensity D, static asymmetry r and delay time T on the signal-to-noise ratio are discussed. It is found that the higher mixed harmonics and the static asymmetry r can restrain stochastic resonance, and the delay time τ can enhance stochastic resonance. Moreover, the longer the delay time τ is, the larger the additive noise intensity Q and the multiplicative noise intensity D are, when the stochastic resonance appears.

Influences of a periodic signal on a noisy synthetic gene network

The effects of noise and a periodic signal on a synthetic gene network have been investigated. By tuning the distance of a parameter from the Hopf bifurcation point, both implicit internal signal stochastic resonance and explicit internal signal stochastic resonance can be induced by noise. Furthermore, a switch process can also be elicited. When a periodic signal is coupled to the gene network, two interesting phenomena occur with the modulation of the frequency of the signal: the effect of noise amplifying cellular signal can be inhibited or even destroyed, and "locked" coherence resonance occurs.

DETECTING TECHNIQUE OF WEAK PERIODIC PULSE SIGNAL VIA SYNTHESIS OF CROSS-CORRELATION AND CHAOTIC SYSTEM

In this letter, with the synthesis of usual cross-correlation detecting method andchaotic detecting method, a new detecting system for the weak periodic pulse signal is constituted,in which the two methods can play respective preponderance. Theoretical analyses and simulationstudies have shown that the detecting system is very sensitive to the periodic pulse signal understrong noise background and has exceedingly powerful capability of suppressing complex noise.

Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises （even white noise）. In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

Periodic electromagnetic signals as potential precursor for seismic activity

Electromagnetic signals may be a promising precursor to seismic activity which has been observed in many case studies in past decades.However,the correlation and causation between the electromagnetic signals and the seismic activity are still unclear without intensive observation network.In order to find seismoelectromagnetic phenomenon,we deployed AETA(acoustic and electromagnetic testing all-in-one system),a high-density multi-component seismic monitoring system in the China Earthquake Science Experiment site(CESE,in Sichuan Province and Yunnan Province,China)and the capital circle(areas with a distance which is≤200 km from Beijing),to record electromagnetic and geo-acoustic data across 0.1 Hz−10 kHz.In the course of data collection,we discovered an electromagnetic waveform that occurs on a daily basis.Because the signal generally coincides with sunrise and sunset,we named this phenomenon the SRSS(Sunrise-Sunset)waveform.After conducting three statistical tests based on seismicity and SRSS,we determined that the SRSS waveform is roughly correlated with the onset of seismic activity.It generally occurs at the regions where seismicity occurs.This discovery might have significant implications with respect to the future of earthquake prediction.

Detection of different-time-scale signals in the length of day variation based on EEMD analysis technique

Scientists pay great attention to different-time-scale signals in the lengllh of day （LOD） variations △LOD, which provide signatures of the Earth＇s interior structure, couplings among different layers, and potential excitations of ocean and atmosphere. In this study, based on the ensemble empirical mode decomposition （EEMD）, we analyzed the latest time series of △LOD data spanning from January 1962 to March 2015. We observed the signals with periods and amplitudes of about 0.5 month and 0.19 ms, 1.0 month and 0.19 ms, 0.5 yr and 0.22 ms, 1.0 yr and 0.18 ms, 2.28 yr and 0.03 ms, 5.48 yr and 0.05 ms, respectively, in coincidence with the results of predecessors. In addition, some signals that were previously not definitely observed by predecessors were detected in this study, with periods and amplitudes of 9.13 d and 0.12 ms, 13.69 yr and 0.10 ms, respectively. The mechanisms of the LOD fluctuations of these two signals are still open.

North Atlantic Abrupt Climate Signals during the Last Glacial Period in Central Asia：Evidences from Aeolian Loess Sediments

Objective Climate fluctuations over suborbital or millennial timescale display significant instability during the last glacial period,which are often superimposed upon the orbital periodicity.They triggered some abrupt climate events,

The phenomenon of stochastic resonance in a single-mode laser system with an input periodical signal

Using the linear approximation method, we study a single-mode laser system driven by colored pump noise and quantum noise with coupling between the real and imaginary parts when the laser is operated well above threshold. The steady state mean intensity fluctuation C(0) and signal-to-noise ratio (SNR) are calculated. It is found that there is a maximum in SNR when there is a minimum in the fluctuation of laser system if the coupling coefficient between real and imaginary parts of the quantum noise equals zero.

Effects of Electric Stimulations Applied During Absolute Refractory Period on Ventricular Muscle from Rabbits

Objectives To investigate the influences of electric signals applied during absolute refractory period (ARP) on the contractility of isolated papillary muscle from rabbits. Methods Papillary muscle was exercised from the right ventricle and was paced at 1 Hz. Biphasic square wave current pulse was delivered during the absolute refractory period (called CCM) in isolated, superfused, isometrically contractility rabbit papillary muscle. The peak tension (PT) of papillary muscle, as well as maximum positive tension change ( + dT/dtmax), were observed. Results Compared with the baseline, both PT and + dT/dtmax significantly increased during CCM stimulation by 18.2% and 21.4% respectively (P < 0. 05) . In addition, PT increased significantly with one or two beats following CCM signal application and reached a. new steady state level after a few beats. Once the CCM signals were turned off, the PT returned to the approximately baseline level ( P < 0. 05). Moreover, the effect of CCM on PT was dose - response to voltage. The obvious effect was at higher voltage. No effect was observed at lower voltage. Conclusions Electric signals delivered during the absolute refractory period can rapidly enhance the contractility of myocardium, which suggests that CCM signal is a novel potent method for contractility modulation.

Adaptive Optimization with Periodic Dither Signals

Optimization methods in cyber-physical systems do not involve parameter uncertainties in most existing literature.This paper considers adaptive optimization problems in which searching for optimal solutions and identifying unknown parameters must be performed simultaneously.Due to the dual roles of the input signals on achieving optimization and providing persistent excitation for identification,a fundamental conflict arises.In this paper,a method of adding a small deterministic periodic dither signal to the input is deployed to resolve this conflict and provide sufficient excitation for estimating the unknown parameters.The designing principle of the dither is discussed.Under dithered inputs,the authors show that simultaneous convergence of parameter estimation and optimization can be achieved.Convergence properties and convergence rates of parameter estimation and optimization variable updates are presented under the scenarios of uncertainty-free observations and systems with noisy observation and unmodeled components.The fundamental relationships and tradeoff among updating step sizes,dither magnitudes,parameter estimation errors,optimization accuracy,and convergence rates are further investigated.

Vibration Feature Abstraction and Classification of Diesel Fault

ion and classification is put forward for periodic cyclic nonstationary vibration signal. The proposed method is applied to experimental data of an ISUZU C240 diesel engine. Experiment results show the effectiveness of the proposed method in classification of engine faults.