Determining the effective elastic thickness through cross-correlation between isostatic disturbances

The elastic thickness parameter was estimated using the mobile correlation technique between the observed isostatic disturbance and the gravity disturbance calculated through direct gravimetric modeling. We computed the vertical flexure value of the crust for a specific elastic thickness using a given topographic dataset. The gravity disturbance due to the topography was determined after the calculation. A grid of values for the elastic thickness parameter was generated. Then, a moving correlation was performed between the observed gravity data(representing actual surface data) and the calculated data from the forward modeling. The optimum elastic thickness of the particular point corresponded to the highest correlation coefficient. The methodology was tested on synthetic data and showed that the synthetic depth closely matched the original depth, including the elastic thickness value. To validate the results, the described procedure was applied to a real dataset from the Barreirinhas Basin, situated in the northeastern region of Brazil. The results show that the obtained crustal depth is highly correlated with the depth from known models. Additionally, we noted that the elastic thickness behaves as expected, decreasing from the continent towards the ocean. Based on the results, this method has the potential to be employed as a direct estimate of crustal depth and elastic thickness for any region.

Three-dimensional color particle image velocimetry based on a cross-correlation and optical flow method

Rainbow particle image velocimetry(PIV)can restore the three-dimensional velocity field of particles with a single camera;however,it requires a relatively long time to complete the reconstruction.This paper proposes a hybrid algorithm that combines the fast Fourier transform(FFT)based co-correlation algorithm and the Horn–Schunck(HS)optical flow pyramid iterative algorithm to increase the reconstruction speed.The Rankine vortex simulation experiment was performed,in which the particle velocity field was reconstructed using the proposed algorithm and the rainbow PIV method.The average endpoint error and average angular error of the proposed algorithm were roughly the same as those of the rainbow PIV algorithm;nevertheless,the reconstruction time was 20%shorter.Furthermore,the effect of velocity magnitude and particle density on the reconstruction results was analyzed.In the end,the performance of the proposed algorithm was verified using real experimental single-vortex and double-vortex datasets,from which a similar particle velocity field was obtained compared with the rainbow PIV algorithm.The results show that the reconstruction speed of the proposed hybrid algorithm is approximately 25%faster than that of the rainbow PIV algorithm.

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis.

Effects of signal modulation and coloured cross-correlation of coloured noises on the diffusion of a harmonic oscillator

The diffusion in a harmonic oscillator driven by coloured noises ξ（t） and η（t） with coloured cross-correlation in which one of the noises is modulated by a biased periodic signal is investigated. The exact expression of diffusion coefficient d as a function of noise parameter, signal parameter, and oscillator frequency is derived. The findings in this paper are as follows. 1） The curves of d versus noise intensity D and d versus noises cross-correlation time z3 exist as two different phases. The transition between the two phases arises from the change of the cross-correlation coefficient A of the two Ornstein-Uhlenbeck （O-U） noises. 2） Changing the value of T3, the curves of d versus Q, the intensity of colored noise that is modulated by the signal, can transform from a phase having a minimum to a monotonic phase. 3） Changing the value of signal amplitude A, d versus Q curves can transform from a phase having a minimum to a monotonic phase. The above-mentioned results demonstrate that a like noise-induced transition appears in the model.

Reconstruction of sub cross-correlation cancellation technique for unambiguous acquisition of BOC(kn, n) signals

In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is proposed.In this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation function.The method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation function.Finally,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed.The simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to BPSK-like.For BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.For BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.

Estimation of Cloud Motion Using Cross-Correlation

This paper describes the estimation of cloud motion using lag cross-correlation. In order to compute the lag cross correlation, the Bayes Decision method is used first to identify cloud and surface of earth. Then cloud motion vectors are retrieved at a subset of points through multiple applications of a cross-correlation analysis. An objective analysis is used to define displacement at every satellite pixel throughout the domain and smooth the local inconsistencies. Cloud motions are then produced with a backward trajectory technique by using these displacement vectors.

Detrended cross-correlation analysis of electroencephalogram

In the paper we use detrended cross-correlation analysis （DCCA） to study the electroencephalograms of healthy young subjects and healthy old subjects. It is found that the cross-correlation between different leads of a healthy young subject is larger than that of a healthy old subject. It was shown that the cross-correlation relationship decreases with the aging process and the phenomenon can help to diagnose whether the subject＇s brain function is healthy or not.

Public cloud computing for seismological research:Calculating large-scale noise cross-correlations using ALIYUN

The amount of seismological data is rapidly increasing with accumulating observational time and increasing number of stations, requiring modern technique to provide adequate computing power. In present study, we proposed a framework to calculate large-scale noise crosscorrelation functions(NCFs) using public cloud service from ALIYUN. The entire computation is factorized into small pieces which are performed parallelly on specified number of virtual servers provided by the cloud. Using data from most seismic stations in China, five NCF databases are built. The results show that, comparing to the time cost using a single server, the entire time can be reduced over two orders of magnitude depending number of evoked virtual servers. This could reduce computation time from months to less than 12 hours. Based on obtained massive NCFs, the global body waves are retrieved through array interferometry and agree well with those from earthquakes. This leads to a solution to process massive seismic dataset within an affordable time and is applicable to other large-scale computing in seismological researches.

RESEARCH ON THE ALGORITHM OF BINARY IMAGE CROSS-CORRELATION FOR UNSTEADY FLOW FIELD MEASUREMENT

The algorithm of Binary Image Cross-Correlation (BICC) was developed to measure the unsteady flow field. A vortex flow field was used to test the algorithm by numerical simulation. The results show that BICC is an effective algorithm for particle identification from consecutive images, the accurate velocity vector field can be obtained. The real velocity field in a valve chamber was measured by BICC in this study. From the full-field velocity information, the pressure and vorticity fields were also extracted by post-processing. (Edited author abstract) 6 Refs.

Model-data-driven P-wave impedance inversion using ResNets and the normalized zero-lag cross-correlation objective function

Model-driven and data-driven inversions are two prominent methods for obtaining P-wave impedance,which is significant in reservoir description and identification.Based on proper initial models,most model-driven methods primarily use the limited frequency bandwidth information of seismic data and can invert P-wave impedance with high accuracy,but not high resolution.Conventional data-driven methods mainly employ the information from well-log data and can provide high-accuracy and highresolution P-wave impedance owing to the superior nonlinear curve fitting capacity of neural networks.However,these methods require a significant number of training samples,which are frequently insufficient.To obtain P-wave impedance with both high accuracy and high resolution,we propose a model-data-driven inversion method using Res Nets and the normalized zero-lag cross-correlation objective function which is effective for avoiding local minima and suppressing random noise.By using initial models and training samples,the proposed model-data-driven method can invert P-wave impedance with satisfactory accuracy and resolution.Tests on synthetic and field data demonstrate the proposed method’s efficacy and practicability.

Time dependence of entropy flux and entropy production for a dynamical system driven by noises with coloured cross-correlation

This paper shows the Fokker-Planck equation of a dynamical system driven by coloured cross-correlated white noises in the absence and presence of a small external force. Based on the Fokker-Planck equation and the definition of Shannon＇s information entropy, the time dependence of entropy flux and entropy production can be calculated. The present results can be used to explain the extremal behaviour of time dependence of entropy flux and entropy production in view of the dissipative parameter γ of the system, coloured cross-correlation time τ and coloured cross-correlation strength λ.

Simulation of distributed optical fiber disturbance detection system based on Sagnac/Mach-Zehnder interferometer and cross-correlation location

A distributed optical fiber disturbance detection system consisted of a Sagnac interferometer and a Mach-Zehnder interferometer is demonstrated. Two interferometers outputs are connected to an electric band-pass filter via a detector respectively. The central frequencies of the two filters are selected adaptively according to the disturbance frequency. The disturbance frequency is obtained by either frequency spectrum of the two interferometers outputs. An alarm is given out only when the Sagnac interferometer output is changed. A disturbance position is determined by calculating a time difference with a cross-correlation method between the filter output connected to the Sagnac interferometer and derivative of the filter output connected to the Mach-Zehnder interferometer. The frequency spectrum, derivative and cross-correlation are obtained by a signal processing system. Theory analysis and simulation results are presented. They show that the system structure and location method are effective, accurate, and immune to environmental variations.

Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix

This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet(PST)of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion(NIC),in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations(ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable(which is also the desired solution), and all others are(unstable)saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method.

Time correction of the ocean bottom seismometers deployed at the southwest Indian ridge using ambient noise cross-correlation

Seismic monitoring using ocean bottom seismometers（OBS） is an efficient method for investigating earthquakes in mid-ocean ridge far away from land. Clock synchronization among the OBSs is difficult without direct communication because electromagnetic signals cannot propagate efficiently in water. Time correction can be estimated through global positioning system（GPS） synchronization if clock drift is linear before and after the deployment. However, some OBSs in the experiments at the southwest Indian ridge（SWIR） on the Chinese DY125-34 cruise had not been re-synchronized from GPS after recovery. So we attempted to estimate clock drift between each station pairs using time symmetry analysis（TSA） based on ambient noise cross-correlation. We tested the feasibility of the TSA method by analyzing daily noise cross-correlation functions（NCFs） that extract from the data of another OBS experiment on the Chinese DY125-40 cruise with known clock drift and the same deployment site. The results suggest that the NCFs＇ travel time of surface wave between any two stations are symmetrical and have an opposite growing direction with the date. The influence of different band-pass filters,different components and different normalized methods was discussed. The TSA method appeared to be optimal for the hydrophone data within the period band of 2–5 s in dozens of km-scale interstation distances. A significant clock drift of ~2 s was estimated between OBSs sets through linear regression during a 108-d deployment on the Chinese cruise DY125-34. Time correction of the OBS by the ambient noise cross-correlation was demonstrated as a practical approach with the appropriate parameters in case of no GPS re-synchronization.

AN IMPROVED CROSS-CORRELATION METHOD FOR(DIGITAL)PARTICLE IMAGE VELOCIMETRY

An improved method that brings enhancement in accuracy for the interrogation of (digital) PIV images is described in this paper. This method is based on cross-correlation with discrete window offset, which makes use of a translation of the second interrogation window and rebuilds it considering rotation and shear. The displacement extracted from PIV images is predicted and corrected by means of an iterative procedure. In addition, the displacement vectors are validated at each intermediate of the iteration process. The present improved cross-correlation method is compared with the conventional one in accuracy by interrogation of synthetic and real (digital) PIV images and the interrogation results are discussed.

USING CROSS-CORRELATION THEORY TO EXTRACT MODAL PARAMETERS IN FREQUENCY- DOMAIN

Conventional modal parameter identifications are usually based on frequencyresponse functions, which require measurements of both the input force and the resulting response.However, in many cases, only response data are available while the actual excitations (such aswind/wave load) are not measurable. Modal parameters estimation must base itself on response-onlydata. Over the past years, many time-domain modal parameter identification techniques fromoutput-only are proposed. A poly-reference frequency-domain modal identification scheme onresponse-only is presented. It is based on coupling the cross-correlation theory with conventionalfrequency-domain modal parameter extraction. An experiment using an airplane model is performed toverify the proposed method.

Eigenvalue Modeling and Cross-Correlation Analysis for 6. 0- 6. 4 GHz MIMO Channel

In Multiple-Input Multiple-Output( MIMO) system, the number of positive channel matrix eigenvalues is directly related to system performance. In order to characterize and model channel matrix eigenvalues,channel measurements at 6. 0- 6. 4GHz by using 4 × 4 MIMO structure were conducted in a typical classroom environment. Based on measured data, the eigenvalues were modeled as Log-Normal distributed random variables and parameterized. Furthermore, Cross-Correlation( CC) coefficients of eigenvalues were estimated. The measurement results show that,under both Light-Of-Sight( LOS) and NonLight-Of-Sight( NLOS) scenarios,eigenvalues are highly de-correlated so that CC can be ignored for eigenvalue model.

Cross-correlation analysis of crude oil and new energy markets: A new perspective based on carbon emission market

Taking the return series of the EU carbon allowance price, WTI crude oil price, the European renewable energy index and Shenzhen carbon emission price, Daqing crude oil price, the China securities new energy index as sample data, the multifractal detrend cross-correlation analysis method(MF-DCCA)is used to research the dynamic cross-correlation relationships among the carbon emission market, crude oil market and the new energy market in Europe and China and the source of the multifractality. The empirical analysis shows that the cross-correlations among the carbon emission market, crude oil market and new energy market in Europe and China have all significant multifractal characteristics. Moreover, the multifractal strength of cross-correlation between the carbon emission market and crude oil market is less than that between the carbon emission market and new energy market in Europe. The Chinese market is the opposite. In addition, the multifractal strength of cross-correlation between the crude oil market and new energy market in Europe is more than that between the crude oil market and new energy market in China. It is also found that the long-range correlation of the sequences themselves and the fat-tailed distribution in fluctuations are the common causes of the multifractality, and the fat-tailed in fluctuations distribution contributes more to the multifractals of the series.

Cross-correlation matrix analysis of Chinese and American bank stocks in subprime crisis

In order to study the universality of the interactions among different markets, we analyze the cross-correlation matrix of the price of the Chinese and American bank stocks. We then find that the stock prices of the emerging market are more correlated than that of the developed market. Considering that the values of the components for the eigenvector may be positive or negative, we analyze the differences between two markets in combination with the endogenous and exogenous events which influence the financial markets. We find that the sparse pattern of components of eigenvectors out of the threshold value has no change in American bank stocks before and after the subprime crisis. However, it changes from sparse to dense for Chinese bank stocks. By using the threshold value to exclude the external factors, we simulate the interactions in financial markets.

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.