People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets

This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).

Cross-Correlation between Global Temperature and Atmospheric CO2 with a Temperature-Leading Time Lag

The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.

Automated Recovery of the 2012 Sumatera Aftershock Sequence Using Waveform Cross Correlation

We assess the performance of waveform cross correlation as applied to automatic and interactive processing of the aftershock sequence of the April 11, 2012 Sumatera Ms（IDC） = 8.2 earthquake. The XSEL （cross correlation standard event list） is compared to the REB （Reviewed Event Bulletin） issued by the IDC （International Data Centre）. The latter includes 1,181 aftershocks between April 11 and May 25. To automatically recover the sequence, sixteen aftershocks were selected from the IDC SEL3 （Standard Event List） available on April 13 and used as master events. Waveform templates from only seven IMS （International Monitoring System） array stations with the largest SNR （signal-to-noise ratio） for the main shock were used to calculate cross correlation coefficients. Approximately 620,000 arrivals were detected and then used to build events according to the IDC definition. After conflict resolution between event hypotheses with similar arrivals found by more than one master, all survived hypotheses populated the XSEL. The total number of distinct hypotheses was 4,924, with 2,763 hypotheses not matched by the REB, i.e. potentially new REB events. To evaluate the quality of the XSEL, we randomly selected a representative portion （-15%） from 2,763 hypotheses and reviewed them interactively according to standard IDC rules and guidelines. There were 119 new REB events built and 145 XSEL hypotheses rejected. Several frequency and probability density distributions have been constructed for all detections, the associated detections, the XSEL detections, the detections in the new REB, and rejected events.

Composition of high frequency ambient noise from cross-correlation:A case study using a small aperture array

Long-time cross correlation of ambient noise has been proved as a powerful tool to extract Green＇s function between two receivers. The study of composition of ambient noise is important for a better understanding of this method. Previous studies confirm that ambient noise in the long period （3 s and longer） mostly consists of surface wave, and 0.25-2.5 s noise consists more of body waves. In this paper, we perform cross correlation processing at much higher frequency （30-70 Hz） using ambient noise recorded by a small aperture array. No surface waves emerge from noise correlation function （NCF）, but weak P waves emerge. The absence of surface wave in NCF is not due to high attenuation since surface waves are strong from active source, therefore probably the high ambient noise mostly consists of body wave and lacks surface wave. Origin of such high frequency body waves in ambient noise remains to be studied.

Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method

Detection of minor faults in power transformer active part is essential because minor faults may develop and lead to major faults and finally irretrievable damages occur. Sweep Frequency Response Analysis (SFRA) is an effective low-voltage, off-line diagnostic tool used for finding out any possible winding displacement or mechanical deterioration inside the Transformer, due to large electromechanical forces occurring from the fault currents or due to Transformer transportation and relocation. In this method, the frequency response of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformers, the primary reference response is unavailable. So Cross Correlation Co-Efficient (CCF) measurement technique can be a vital process for fault detection in these transformers. In this paper, theoretical background of SFRA technique has been elaborated and through several case studies, the effectiveness of CCF parameter for fault detection has been represented.

An Optimized Cross Correlation Power Attack of Message Blinding Exponentiation Algorithms

The message blinding method is the most efficient and secure countermeasure against first-order differential power analysis(DPA).Although cross correlation attacks(CCAs) were given for defeating message blinding methods,however searching for correlation points is difficult for noise,misalignment in practical environment.In this paper,we propose an optimized cross correlation power attack for message blinding exponentiation algorithms.The attack method can select the more correlative power points of share one operation in the modular multiplication by comparing variances between correlation coefficients.Further we demonstrate that the attack method is more efficient in experiments with hardware implementation of RSA on a crypto chip card.In addition to the proposed CCA method can recovery all 1024 bits secret key and recognition rate increases to 100%even when the recorded signals are noisy.

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.

Teleseismic body waves extracted from ambient noise cross correlation between F-net and Chin Array phase Ⅱ

The vertical-vertical noise cross-correlation functions(NCFs)between two seismic arrays,the Japan F-net and Chin Array phase Ⅱ,are calculated using continuous recordings during 2013-2016.After array interferometry to obtain bin stacked NCFs,clear body waves are retrieved at different period bands.Teleseismic direct P waves for distance 15-40 degrees are observed between short period 3-10 s while core reflected PcP/ScS waves are more obvious for longer period 30-60 s.The signal-to-noise-ratio(SNR)of the short period P waves reaches its highest point with bin widths around 20 km while SNRs of PcP and ScS increase slowly with bin width.All those body waves demonstrate clear directivity with strong signals traveling from the east.The time-lapse SNR variations for the PcP and ScS show correlation with the occurrence of major earthquakes,while the P-wave SNR demonstrates seasonal variations with additional contribution from major earthquakes.The present results suggest teleseismic body waves can be retrieved through bin stacking,though further processing is still necessary to obtain finer waveforms such as P wave triplications.

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.

Walsh Spectral Characteristics and the Auto-Correlation Function Characteristics of Forming Orthomorphic Permutations of Multi-Output Functions

Orthomorphic permutations have good characteristics in cryptosystems. In this paper, by using of knowledge about relation between orthomorphic permutations and multi-output functions, and conceptions of the generalized Walsh spectrum of multi-output functions and the auto-correlation function of multi-output functions to investigate the Walsh spectral characteristics and the auto-correlation function characteristics of orthormophic permutations, several results are obtained.

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.

Biometric feature extraction using local fractal auto-correlation

Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme： （i） using two-dimensional Gabor filter to extract the texture features of biometric images; （ii） calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and （iii） linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach.

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.

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.

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.

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.

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.

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.

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.