Destructive aftershocks such as the M w 7.2 Van earthquake on October 23, 2011, and the Hoy (Iran) earthquake with M w 5.9 on February 23, 2020, occurred in the province of Van and its surroundings. In earthquake studies, the issue of examining the distribution and homogeneity of earthquake incidences with Geographic Information Systems (GIS) based via spatial autocorrelation techniques is frequently investigated. Van province and its surroundings are among the areas with high earthquake risk due to its location on the East Anatolian Compressive Tectonic Block. The aim of this study is to analyze the spatial patterns of earthquakes with magnitude M w 4 and above that occurred in the province of Van and its surroundings during the instrumental period and to determine to cluster. Spatial cluster analyses play an important role in examining the distribution of seismicity. The data used in the study have been taken from the database system of the Earthquake Department of the Republic of Turkey Ministry of Interior Disaster and Emergency Management Presidency. Moran’s I and Getis-Ord Gi methods from spatial autocorrelation techniques were preferred on the earthquake data set to be used in this research. It has aimed to determine the dangerous areas by testing the earthquake distributions in clustered regions via spatial autocorrelation techniques.

This paper presents a new simple and accurate frequency estimator of a sinusoidal signal based on the signal autocorrelation function (ACF). Such an estimator was termed as the reformed covariance for half-length autocorrelation (RC-HLA). The designed estimator was compared with frequency estimators well-known from the literature, such as the modified covariance for half-length autocorrelation (MC-HLA), reformed Pisarenko harmonic decomposition for half-length autocorrelation(RPHD-HLA), modified Pisarenko harmonic decomposition for half-length autocorrelation (MPHD-HLA), zero-crossing (ZC), and iterative interpolated DFT (IpDFT-IR) estimators. We determined the samples of the ACF of a sinusoidal signal disturbed by Gaussian noise (simulations studies) and the samples of the ACF of a sinusoidal voltage(experimental studies), calculated estimators based on the obtained samples, and computed the mean squared error(MSE) to compare the estimators. The errorswere juxtaposed with the Cramér–Rao lower bound (CRLB). The research results have shown that the proposed estimator is one of the most accurate, especially for SNR > 25 dB. Then the RC-HLA estimator errors are comparable to the MPHD-HLA estimator errors. However, the biggest advantage of the developed estimator is the ability to quickly and accurately determine the frequency based on samples collected from no more than five signal periods. In this case, the RC-HLA estimator is the most accurate of the estimators tested.

We present computer simulations of a two-way ANOVA gage R&R study to determine the effects on the average speckle width of intensity patterns caused by scattered light reflected from random rough surfaces with different statistical characteristics. We illustrate how to obtain reliable computer data that properly simulate experimental measurements by means of the Fresnel diffraction integral, which represents an accurate analytical model for calculating the propagation of spatially-limited coherent beams that have been phase-modulated after being reflected by the vertical profiles of the generated surfaces. For our description we use four differently generated vertical profiles and five different vertical randomly generated roughness values.

The present study was conducted in the lobbies of 16 Taiwanese urban hospitals to establish what contributes to the degree of noisiness experienced by patients and those accompanying them. Noise level measurements were then conducted by 15 min equivalent sound pressure levels (LAeq, 15m, dB) during daytime hours. The average LAeq itself was found to be poorly related to perceived noisiness. Levels variations were better correlated, more continual noise may actually be perceived as noisier. According to the findings of a multiple linear stepwise regression model (r = 0.91, R2 = 0.83), the 3 independent variables shown to have the largest effects on perceived noisiness were 1) 1/(L5 − L95), 2) effective duration of the normalized autocorrelation function (τe, h), of all LAeq, 15m over 9–17, and 3) percentile loudness, N5, 15m. These results resemble previous studies that had assumed that a larger fluctuation of noise level corresponds to less annoyance experienced for mixed traffic noise studied in a laboratory situation. As an advanced approach, for hospital noise that consisted of 12 audible noise events, subjective noisiness were evaluated by the noise time structure analyzed by autocorrelation with loudness and levels variation.

In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions

Local geometric deviations of free-form surfaces are determined as normal deviations of measurement points from the nominal surface. Different sources of errors in the manufacturing process result in deviations of different character, deterministic and random. The different nature of geometric deviations may be the basis for decomposing the random and deterministic components in order to compute deterministic geometric deviations and further to introduce corrections to the processing program. Local geometric deviations constitute a spatial process. The article suggests applying the methods of spatial statistics to research on geometric deviations of free-form surfaces in order to test the existence of spatial autocorrelation. Identifying spatial correlation of measurement data proves the existence of a systematic, repetitive processing error. In such a case, the spatial modelling methods may be applied to fitting a surface regression model representing the deterministic deviations. The first step in model diagnosing is to examine the model residuals for the probability distribution and then the existence of spatial autocorrelation.

Results of the ab initio molecular dynamics calculations of silicon crystals are presented by means of analysis of the velocity autocorrelation function and determination of mean phonon relaxation time. The mean phonon relaxation time is crucial for prediction of the phonon-associated coefficient of thermal conductivity of materials. A clear correlation between the velocity autocorrelation function relaxation time and the coefficient of thermal diffusivity has been found. The analysis of the results obtained has indicated a decrease of the velocity autocorrelation function relaxation time t with increase of temperature. The method proposed may be used to estimate the coefficient of ther-mal diffusivity and thermal conductivity of the materials based on silicon and of other wide-bandgap semiconductors. The correlation between kinetic energy fluctuations and relaxation time of the velocity autocorrelation function has been calculated with the relatively high coefficient of determination R2 = 0.9396. The correlation obtained and the corresponding approach substantiate the use of kinetic energy fluctuations for the calculation of values related to heat conductivity in silicon-based semiconductors (coefficients of thermal conductivity and diffusivity).