Fingerspelling Recognition by Hand Shape Using Higher-Order Local Auto-Correlation Features

The fingerspelling recognition by hand shape is an important step for developing a human-computer interaction system. A method of fingerspelling recognition by hand shape using HLAC （higher-order local auto-correlation） features is proposed. Furthermore, in order to use HLAC features more effectively, the use of image processing techniques： reducing an image resolution, dividing an image, and image pre-processing techniques, is also proposed. The experimental results show that the proposed method is promising.

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.

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.

SPEECH ENHANCEMENT BASED ON DYNAMIC NOISE ESTIMATION WITHIN AUTO-CORRELATION DOMAIN

Most noise suppression algorithms of single channel use the mean of noisy segments to estimate the characteristics of noise spectrum, ignoring the estimation of noise in speech segments. Therefore, when the energy level of noise varies with the time, the performance of removing noise will be degraded. To solve this problem, a speech enhancement approach based on dynamic noise estimation within correlation domain was proposed. This method exploits the characteristics that noise energy mainly concentrates on 0 th order correlation coefficients, signal is auto correlated but signal and noise, noise and noise are uncorrelated, then estimates and decomposes the noise, thus helps to solve the above mentioned problem. The results of recognition experiments on speech signals of 15 Chinese cities’ names corrupted by noise of exhibition hall shows, this approach is better than SS (Spectral Subtraction) method, adapts better to the variances of energy levels of speech signal corrupted by noise, has some practicability to improve the robustness of recognition systems under noisy environment.

Chrestenson Spectrum and Auto-Correlation Function of Inverse Permutations of Quick Trickle Permutations

In this paper, a sufficient and necessary condition of quick trickle permutations is given from the point of inverse permutations. The bridge is built between quick trickle permutations and m-value logic functions. By the methods of the Chrestenson spectrum of m-value logic functions and the auto-correlation function of m-value logic functions to investigate the Chrestenson spectral characteristics and the auto-correlation function charac- teristics of inverse permutations of quick trickle permutations, a determinant arithmetic of quick trickle permutations is given. Using the results, it becomes easy to judge that a permutation is a quick trickle permutation or not by using computer. This gives a new pathway to study constructions and enumerations of quick trickle permutations.

Cyclic-Auto-Correlation Based Timing Estimation Algorithm for Time-Frequency Overlapping Multi-Carrier Signals

In recent years,the time-frequency overlapping multi-carrier signal has been a novel and valuable topic in blind signal processing,especially in the non-cooperative receiving field.But there is little related research in public published papers.This paper proposes two timing estimation algorithms,which are non-data-aided and based on the cyclic auto-correlation function.In order to evaluate the performance of the proposed algorithms,the theoretical bound of the timing estimation is derived.According to the analyses and simulation results,the effectiveness of the proposed algorithms has been demonstrated.It shows that MethodⅠhas better performance than MethodⅡ.However,MethodⅡdoes not need prior information,so it has a wider range of applications.

Monitoring Bridge Deformation Using Auto-Correlation Adjustment Technique for Total Station Observations

Bridges are omnipresent in every society and they affect its human, social, ecological, economical and cultural aspects. This is why a durable and safe usage of bridges is an imperative goal of structural management. Measurement and monitoring have an essential role in structural management. The benefits of the information obtained by monitoring are apparent in several domains. In deformation analysis, the functional relationship between the acting forces and the resulting deformations should be established. If time depending observations are given, a regression could be used as a functional model. In case of stochastic model uncorrelated observations with identical variance are assumed. Due to the high sampling rate, a small time difference arises between two observations. Thus the assumed stochastic model is not suitable. The calculation has to be effected by means of auto-correlated observations. This paper investigates an integrated monitoring system for the estimation of the deformation (i.e., static, quasi-static) behavior of bridges from total station observations and studies the effect of autocorrelation technique on the accuracy of the estimated parameters and variances. The results have shown that autocorrelation technique is reduced the standard deviation of X&Y-direction about 6.7% to 29.4% and 6.5% to 15.5% of the original value, respectively, but the situation was differ in Z direction;the standard deviation in vertical component Z was increased.

Prediction of the Helix/Sheet Content of Proteins from Their Primary Sequences by Neural Network Method

The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by using the independent non-homologous protein database. It is shown that the average absolute errors for resubstitution test are 0.070 and 0.068 with the standard deviations 0.049 and 0.047 for the prediction of the content of α-helix and β-sheet respectively. For cross-validation test, the average absolute errors are 0.075 and 0.070 with the standard deviations 0.050 and 0.049 for the prediction of the content of α-helix and β-sheet respectively. Compared with the other methods currently available, the BP neural network method combined with the amino acid composition and the biased auto-correlation function features can effectively improve the prediction accuracy.

Generalizations of Several Identities Related to Mercier's

The purpose of this paper is to establish sev eral identities containing Gaussian binomial coefficient. These results generali ze several Mercier's results. Key words:Gaussian binomial coefficient; identity; the functio n A(T,T 2,...,T n)culating eige nvalues of auto-correlation matrix of the physical control force of actuators. T he optimization algorithm calculating the optimal actuator placement is then put forward via the minimization of an energy criterion, which is chosen as the con trol index. Numerical examples show the effectiveness of the proposed method.

Evaluation of Phonon-level Density of UO2 by Molecular Dynamics Simulation

Molecular dynamics calculation of UO2 in a wide temperature range are presented and dis- cussed. The calculated lattice parameters, mean square displacements, and dynamic prop- erty of phonon-level density of the velocity auto-correlation functions for UO2 are provided. The Morelon potential and the Basak potential are employed. It confirms that the cal- culated lattice parameters using the Basak potential are in nearly perfect agreement with the reported values. The models successfully predict mean square displacement and Bredig transition. Furthermore, the phonon-level density of uranium dioxide are discussed. The intensity of phonon-level density increases with temperature, and the properties of UO2 are characterized by large thermal vibrations rather than extensive disorder.

Parallel Spectral-Domain Optical Coherence Tomography for Non-Scattering Object Imaging

The parallel spectral-domain optical coherence tomography（PSDOCT） is described for highspeed optical coherence tomography（OCT） without lateral scanning. In this setup, the self-elimination of auto-correlation（AC） interference algorithm was used for eradicating the AC interference and ghost images. However, when performed in free space OCT, this algorithm still generated a weak DC component. The algorithm was improved by adding the background intensity part to compensate for the mutual interference between object and reference arms. The results demonstrate that the DC component can be eradicated. Compared with conventional QCT and complex Fourier-domain optical coherence to- mography, the advantages of PSDOCT with the improved algorithm in free space are that it has no moving parts to generate consecutive phase shift, the structure of the object can be reconstructed immediately and automatically, and the speed is approximately 16 times faster than those of the other two in the same case.

The Relationship between the Wintertime Blocking over Greenland and the Sea Ice Distribution over North Atlantic

The sea-ice concentration in the Northern Hemisphere, 500 hPa height, sea-level pressure and 1000-500 hPa thickness of monthly mean data are examined for the period 1953-1989, with emphasis on the winter season.Relationships between large-scale patterns of atmospheric variability and sea-ice variability are investigated, making use of the correlation method. The analysis is conducted for the Atlantic sectors. In agreement with earlier studies based upon monthly mean data on sea-ice concentration, the strongest sea-ice pattern is composed of a dipole with opposing centers of action in the Davis Straits / Labrador Sea region and the Greenland and Barents Seas. Its temporal variability is strongly coupled to the atmospheric North Atlantic Oscillation (NAO). The relationship between the two patterns is strongest with the atmosphere leading the ocean. The polarity of the NAO is associated with Greenland blocking episodes, during which the influence of the atmosphere is strong enough to temporarily halt the climatological mean advance of the ice edge in some regions and substantially accelerate it in others.The relationships between the fields are indicative of local forcing of sea-ice in most regions, with wind stress and thermodynamic fluxes at the air-sea interface both contributing.

ESDA-GIS Analysis of Spatial-temporal Disparity in Rural Economic Development of Guangxi

On the basis of spatial-temporal perspective,by using the data of farmers' net income per capita from 1996 to 2007 in counties of Guangxi coupled with the global and local spatial auto-correlation analysis of ESDA and GIS technology,we conduct the empirical research on the rural economic developmental disparity of counties in Guangxi and the evolving characteristics of local spatial heterogeneity.The results show that the rural economic developmental disparity of counties in Guangxi from 1996 to 2007 varies infinitesimally on the whole,and the regions with similar rural economic developmental level have spatial concentrated distribution.Based on these,the local MORAN'S I scatter diagram and LISA concentration diagram are drawn.In comparison with the traditional analytical method,the spatial analytical method of ESDA-GIS can explain the problem of spatial heterogeneity of rural economic development clearly,and have direct visual effect.

Wave Energy Estimation by Using A Statistical Analysis and Wave Buoy Data near the Southern Caspian Sea

Statistical analysis was done on simultaneous wave and wind using data recorded by discus-shape wave buoy. The area is located in the southern Caspian Sea near the Anzali Port. Recorded wave data were obtained through directional spectrum wave analysis. Recorded wind direction and wind speed were obtained through the related time series as well. For 12-month measurements（May 25 2007-2008）, statistical calculations were done to specify the value of nonlinear auto-correlation of wave and wind using the probability distribution function of wave characteristics and statistical analysis in various time periods. The paper also presents and analyzes the amount of wave energy for the area mentioned on the basis of available database. Analyses showed a suitable comparison between the amounts of wave energy in different seasons. As a result, the best period for the largest amount of wave energy was known. Results showed that in the research period, the mean wave and wind auto correlation were about three hours. Among the probability distribution functions, i.e Weibull, Normal, Lognormal and Rayleigh, ＂Weibull＂ had the best consistency with experimental distribution function shown in different diagrams for each season. Results also showed that the mean wave energy in the research period was about 49.88 k W/m and the maximum density of wave energy was found in February and March, 2010.

Design for Vibrator Field Experiment Based-on Vibrator-earth System

Source-generated energy in seismic vibrator records high frequency harmonic behavior. Conventional vibrator-earth coupling model was set up on the linear system. Some assumptions in the application of linear theory to the vibrator problem play an insignificant role in the overall coupling structure. Obviously, non-linear behaviors can be modeled using a “hard-spring” form of the Duffing equation. Model dedicates that a qualitatively similar harmonic component is present for a broad range of possible mathematical descriptions. After some qualitative analysis about the non-linear system, some conclusion can be drawn. Firstly, The design of the vibrator weight should be abided by two points as followed: In order to avoid decoupling for the vibrator to the earth, the weight should be greater than the peak of the driving force amplitude as to keep the resultant force pointing to the earth’s core. On the other hand, for the limited energy output, the vibrator overweight may damage the system high-frequency ability.Secondly, as the driving force frequency approaching to the ground hard-spring inherent frequency, the energy transmission was found to climb its peak from the system energy absorbed curve. At last, due to the non-linear coupling model system, its load curve would come into unstable frequency range, which might limit the application of the Vibroseis conventional sweeping pattern-linear sweep. A new sweeping pattern was listed: the driving signal was the pseudo-random sequence modulated by a fixed frequency cosine signal satisfying with the exploration precision and absorbing efficiency. The synthesized signal was ready to be realized by the electromagnetic driven system. Even the side-lobes noise of its auto-correlation function was restrained well. The theory coming from the Vibrator-earth coupling model was applied to the design of the Portable High-frequency Vibrator System (PHVS), and the good result was obtained. By the analysis of the vibrator base plate signal, the model was proved to be true. The exploration research on PHVS made a first step on its following optimal design.

Blind channel estimation for redundant precoded OFDM systems

Considering that channel estimation can play a crucial role in coherent detection of the information symbols in each data block, a blind channel estimation approach is proposed for redundant precoded orthogonal frequency-division multiplexing （OFDM） systems. A redundant linear frequency-domain precoder is applied to each pair of blocks before they enter the OFDM system. Because of the introduced structure, the frequency-selective channel can be identified at the receiver based on autocorrelation operations, singular value decomposition （SVD）, and by resolving the scalar ambiguity. The proposed channel estimation method has low computational complexity and requires no prior statistical information on channel or noise. And the proposed blind method has high spectral efficiency owing to exploiting no training sequence. Computer simulations confirm that this proposed blind channel estimation method can identify the frequency-selective channels perfectly and obtain a good performance.

Spatio-temporal Distribution Characteristics and Environmental Impact Factors of Lung Cancer Mortality: A Case Study of Yuhui District in Bengbu City, China

Among cancers, lung cancer is the most common cause of death in China. For the prevention and control of lung cancer, it is necessary to investigate the spatial and temporal distribution of lung cancer mortality, as well as the changes in the trend and the affecting mechanism. Based on statistics and auto-correlation analysis, this paper studied the spatial and temporal distribution of lung cancer mortality in Yuhui District, Bengbu, Huaihe River Basin, from 2017 to 2020. In addition, Spearman’s Rank Correlation Assessment Model and Geographic Detector Model were used to examine the relationship between environmental factors and lung cancer mortality to identify impact factors and their mechanisms. The findings indicated that: 1) from the characteristics of temporal distribution, the number of lung cancer deaths exhibited a linear growth tendency, with the highest mortality in winter;2) from the characteristics of spatial distribution, lung cancer mortality showed a strong spatial agglomeration form, concentrating on two clustering areas, located in the old city and the central city of Bengbu, near the Huaihe River;3) from the point of view of the whole research area, there were 15 impact factors with significant correlation in the built and natural environment factors. The significant impacting factors in the built environment included land use, road traffic, spatial form and blue-green space, which could indirectly affect lung cancer mortality, while air pollution and temperature constituted the significant impacting factors in the natural environment;4) the influence of screened environmental factors on lung cancer mortality was different. Spatial stratified heterogeneity assessment, the interaction among environmental factors demonstrated statistical significance, it was found that the interaction between environmental factors in pairs had a significant enhancement effect on lung cancer mortality. To some extent, urban planning and policies could reduce lung cancer mortality.

ROLLING BEARING FAULT DETECTION USING CORRELATION TECHNIQUE

It's known that auto-correlation technique is effective in extracting periodical signals from random noises.In the case of fault monitoring of rolling element bearing,we can't acquire the fault information directly from the original signal because of the difference of signal phases.And the signal is shown as the wide band random signal in auto-correlation function.In this paper,the signal is pre-processed and the results are proved effective.Moreover,by taking the auto-correlation function we can obtain the determined and comparable samples.This is very important for establishing the data base of running condition and for detecting the faults.

Extraction of failure characteristic of rolling element bearing based on wavelet transform under strong noise

There has been a lot of research has been performed regarding diagnosing rolling element bearing faults using wavelet analysis, but almost all methods are not ideal for picking up fault signal characteristics under strong noise. Therefore, this paper proposes auto-correlation, cross-correlation and weighted average fault diagnosis methods based on wavelet transform (WT) de-noising which combine correlation analysis with WT for the first time. These three methods compute the auto-correlation, the cross-correlation and the weighted average of the measured vibration signals, then de-noise by thresholding and computing the auto-correlation of de-noised coefficients of WT and FFT of energy sequence. The simulation results indicate that all methods enhance the capabilities of fault diagnosis of rolling bearings and pick up the fault characteristics effectively.

Blind channel estimation through redundant linear precoding for OFDM systems

A novel approach of blind channel estimation through redundant linear precoding for orthogonal fre-quency-division multiplexing （OFDM） is proposed. A redundant linear frequency-domain preceder is ap-plied to each pair of blocks before they enter the OFDM system. With the aid of the introduced structure, the frequency-selective channel can be identified at the receiver through auto-correlation operations, sin-gular value decomposition （SVD） and scalar ambiguity resolution. The proposed blind channel estimation method has low computation complexity and requires no prior statistical information of channel or noise. The redundant linear frequency-domain precoder is employed to identify the frequency-selective fading channels. And the proposed blind channel estimation method has high spectral efficiency because it re-quires no training sequence. Computer simulations have proved that this proposed blind channel estima- tion method can identify the frequency-selective channels perfectly and have a good performance.