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This paper presents the novel approach for retrieving remote sensing images which are based on low level feature extraction like color, texture, and shape. The HSV, Color Correlogram and Color Moments have been used for color feature extraction. Then image has decomposed image using wavelet transform to extract texture feature. Finally, shape feature is extracted using Histogram of oriented gradients (HOG). HOG feature is very useful for finding the local object appearance and shape. KNN classifier is used for training the image features and retrieving similar images. In this paper, UC Merced Land use Land Cover remote sensing Dataset has been used for the experiment. The result of the experiment shows that the combination of wavelet and HOG gives better accuracy and retrieval performance than discrete wavelet transform DWT.

Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation between time series rely on procedures whose validity holds for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative Ljung–Box tests can be significantly distorted. This paper adapts standard correlogram and portmanteau tests to accommodate hidden dependence and nonstationarities involving heteroskedasticity, thereby uncoupling these tests from limiting assumptions that reduce their applicability in empirical work. To enhance the Ljung–Box test for non-i.i.d. data, a new cumulative test is introduced. Asymptotic size of these tests is unaffected by hidden dependence and heteroskedasticity in the series. Related extensions are provided for testing cross-correlation at various lags in bivariate time series. Tests for the i.i.d. property of a time series are also developed. An extensive Monte Carlo study confirms good performance in both size and power for the new tests. Applications to real data reveal that standard tests frequently produce spurious evidence of serial correlation.

Summary In cased-hole acoustic logging, estimating the formation velocity is often problematic when casing is poorly bonded with formation. The overwhelmingly large casing waves dominate the measured waveforms and overlap with the low coherence, weak formation arrival, contributing to the failure of conventional semblance processing method. To tackle this problem, we present a filter frequency semblance array waveform signal processing technique. The multiple filter technique is first used to filter the measured waveforms. We then apply the n-th root stacking method to the filtered signals. Consequently, the coherence of formation signal on the obtained three-dimensional semblance correlogram is significantly enhanced and clearly separated with the casing waves. The advantage of the new method is demonstrated using a field data example.

In this paper, single input--double output linear time-invariant systems are studied. Both components of system's impulse response function (IRF) are supposed to be real-valued and square-integrable. One component is unknown while the second one is controlled. The problem is to estimate the unknown component after observations of the other component. We apply cross-correlating of the outputs given that the input is a standard Wiener process. Weak asymptotic normality of appropriately centred estimators in spaces of continuous functions is proved. This enables us to construct confidence intervals in these spaces. Our results employ techniques related to Gaussian processes and bilinear forms of Gaussian processes., Comment: 25 pages, 11 Postscript figures

Ground target localization is of great importance in UAV aerial image analysis and computer vision, and many researchers have been studied on this topic. In this paper, we focus on the problem of ground target localization using the Multi-feature fusion technology. In this work, visual features are extracted from UAV images, and then visual similarity is calculated, on the other hand. Afterwards, the coordinate in coarse positioning result is estimated by the radiation transformation model and multi-feature fusion. The main innovation of this paper lies in that we effectively integrate different types of image visual features, such as color moments, color histogram, color correlogram, GLOM texture features, and Tamura texture features. Experimental results demonstrate that effectively integrating all these features, the accurate Ground target localization results can be achieved.

The covariance function that forms a variogram is an important measurement for spatial dependence and as a linear kriging interpolation tool. The covariance function requires a definite positive guarantee, this means that not all functions can be used. Therefore, this research explores the correlogram and nonmonoton variogram functions and shows it analytically using the Fourier Transform (Bochner’s theorem). In addition, a simple approach is used to determine definite positivity by paying attention to boundaries. Suppose that C : Rd → R is positive definite if it bounded to exponential which is positive definit. Research shows that Nonmonoton Bessel functions that have Exponential bound are positive definite. Multiplication operations of two covariance functions, C1 and C2 in measured spaces indicate that definite positive properties are fulfilled.

It is noticeable that when flower images are observed, flower’s color signifies people and often people ignore small and isolated sectors of the image. So for retrieving flower images color feature of the images is very important. Different flowers have also different shapes. So for differing flower images from another flower images shape feature is also a significant feature. In this paper, we proposed a method of retrieving flower images using their color and shape feature. We used HSV color histogram, color moments and color auto correlogram descriptors for describing color feature and HOG descriptor for describing shape feature. We calculate the similarities between the query image and the database images by employing a set of distance functions. We evaluated our result for our own dataset of 15 categories flower images.

Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at sub-pixel scales and over short periods of time. Just a few studies have applied a similar approach, employing dense gauge networks, to local-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, we assess spatial uncertainty in observed heavy rainfall events. The network is located in southeastern Austria over an area of 20 km × 15 km with no significant orography. First, the spatial variability of rainfall in the region was characterised using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between wet and dry seasons are apparent and we selected heavy rainfall events, the upper 10 % of wettest days during the wet season, for further analyses because of their high potential for causing hazard risk. Secondly, we investigated uncertainty in estimating mean areal rainfall arising from a limited gauge density. The number of gauges required to obtain areal rainfall with > 20 % accuracy tends to increase roughly following a power law as time scale decreases. Lastly, the impact of spatial aggregation on extreme rainfall was examined using gridded rainfall data with horizontal grid spacings from 0.1° to 0.01°. The spatial scale dependence was clearly observed at high intensity thresholds and high temporal resolutions. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the rational use of the data in relevant applications. Our findings are generalisable to other plain regions in mid-latitudes, however the degree of uncertainty could be affected by regional variations, like rainfall type or topography.

A novel air quality index (AQI) forecasting method based on support vector machine (SVM) and moments is proposed in this paper. By using it, the AQI value of a sky image can be forecasted. In order to improve the forecasting accuracy and stability, color moments, color correlogram and wavelet features are used to extract the image features, which transfers the input image from color space to feature space. We train the SVM with the input of these extracted features. Then the trained SVM is utilized to achieve the AQI forecasting for new images. The experimental results improve that the proposed method has good AQI forecasting results, and the accuracy of the testing dataset is more than 82.3%, with 1300 training images and 200 test images.

An efficient approach for scene classification is necessary for automatically labeling an image as well as for retrieval of desired images from large scale repositories. In this paper machine learning and computer vision techniques have been applied for scene classification. The system is based on feature fusion method with holistic visual color, texture and edge descriptors. Color moments, Color Coherence Vector, Color Auto Correlogram, GLCM, Daubechies Wavelets, Gabor filters and MPEG-7 Edge Direction Histogram have been used in the proposed system to find the best combination of features for this problem. Two state-of- the-art soft computing machine learning techniques: Support vector machine (SVM) and Artificial Neural Networks have been used to classify scene images into meaningful categories. The benchmarked Oliva-Torralba dataset has been used in this research. We report satisfactory categorization performances on a large data set of eight categories of 2688 complex, natural and urban scenes. Using a set of exhaustive experiments our proposed system has achieved classification accuracy as high as 92.5% for natural scenes (OT4) and as high as 86.4% for mixed scene categories (OT8). We also evaluate the system performance by predictive accuracy measures namely sensitivity, specificity, F-score and kappa statistic.

A new geotechnical zoning map for the subsoil of Mexico Valley is presented. This proposal is based on a Geographic Information System for Geotechnical Borings (GIS-GB), which contains over 10000 soil profiles. In addition to the geotechnical information, available topographic and geological data on the studied area were taken into account. Geostatistical techniques were used to assess the spatial distribution of the thickness of the lacustrine clay deposits within the area down to the so-called deep deposits. As a result, a contour map was obtained that was used to update the current geotechnical zoning map for Mexico Valley. It has been proposed to include this new map into the Building Code for the Federal District (Mexico City).

Even under assumption of normality, likelihood-based inferences are often difficult for large and irregularly spaced spatial datasets. Exact calculations of the likelihood for a Gaussian spatial process observed in n locations require O ( n 3 ) operations. Instead of Whittle’s approximation to the Gaussian log likelihood for large spatial datasets, this paper introduces an approximated likelihood function of spatial parameters based on the correlogram, which involves no calculation of determinants and is computationally feasible. The proposed likelihood approximation method for spatial parameter is applied to the estimation of the spatial structure of changes in the average summer temperature based on 30 years of data by using an regional climate model (RCM) with a particular global climate model (GCM) boundary condition. The results verify the benefits and the performance of the proposed method.

Spatial variability of rainfall has been recognised as an important factor controlling the hydrological response of catchments. However, gauged daily rainfall data are often available at scattered locations over the catchments. This paper looks into how to capitalise on the spatial structure of radar rainfall data for improving kriging interpolation of limited gauge data over catchments at the 1-km2 grid scale, using for the case study 117 gauged stations within the 128 km × 128 km region of the Mt Stapylton weather radar field (near Brisbane, Australia). Correlograms were developed using a Fast Fourier Transform method on the Gaussianised radar and gauged data. It is observed that the correlograms vary from day to day and display significant anisotropy. For the radar data, locally varying anisotropy (LVA) was examined by developing the correlogram centred on each pixel and for different radial distances. Cross-validation was carried out using the empirical correlogram tables, as well as different...

Sources, characteristics and seasonal variation of airborne pollutants in China, especially in Beijing, have been massively examined. However, most studies analyze local air quality from an isolated perspective and interactions between local and regional air quality have not been fully considered. This research attempts to evaluate Beijing air quality at the local and regional scale. The weighted cross correlogram spectral matching (CCSM) and convergent cross mapping (CCM) method are employed for similarity and causality analysis respectively. At the local scale, the air quality in Beijing experiences frequent and sudden change, yet changes smoothly across a day's time. At the regional scale, the air quality in Beijing and four neighboring cities is compared. The result suggests that although air quality in Beijing and neighboring cities is of some differences, strong bidirectional coupling exists between the local and regional air quality. The research indicates that air quality in Beijing is better than the general situation in this region, and Tianjin should be a good comparative site for monitoring and evaluating air quality in Beijing. This research provides a feasible methodology for comprehensive analysis of local air quality at multiple scales, which may shed some lights on the forthcoming implementation of local air quality evaluation.

Dams can cause fragmentation of the natural structure and function of fish communities up- and downstream of the dam. In this study, we examined the effects of the Goesan Dam on the longitudinal distribution of fish in the Dal stream, using two computational methods: a self-organizing map (SOM) and spatial autocorrelation. Fish were collected at 16 different study sites in two seasons (spring and autumn). SOM classified the study sites into four clusters (1–4), reflecting differences in the longitudinal distribution patterns of fish, and the effects of the dam on fish community composition. Samples in clusters 1 and 2 mostly originated from tributaries and the upstream section of the dam, whereas samples in cluster 4 originated from downstream study sites of the dam. Cluster 3 included study sites originating directly from up- and downstream of the dam. Species turnover and spatial autocorrelation results confirmed the fish community alteration near the dam. Species turnover was the highest between study sites directly up- and downstream of the dam. The Mantel correlogram was positive when applied to short distances between the study sites. However, it was negative for the farthest distance, including sites near the dam, indicating that the dam caused significant alterations to the fish community. Overall, our results suggest that the habitat alterations (e.g. , fragmentation of longitudinal gradients and disruption of flow regimes) due to dam construction have caused fragmentation of fish communities.

Soil fertility characterised by nitrogen, phosphorus, potassium, calcium, magnesium and sulphur is traditionally measured from soil samples collected from the field. The process is very cumbersome and time intensive. Hyperspectral data available from Hyperion payload of EO 1 was used for facilitating preparation of soil fertility map of Udaipur district of Rajasthan state, India. Hyperion data was pre-processed for band and area sub setting, atmospheric correction and reflectance data preparation. Spectral analysis in the form of SFF and PPI were carried out for selecting the ground truth sites for soil sample collection. Soil samples collected from forty one sites were analysed for analysis of nutrient composition. Generation of correlogram followed by multiple regressions was done for identifying the most important bands and spectral parameters that can be used for nutrient map generation.

Seismology has come a long way in providing insights into Earth's internal structure and dynamics. Among many forward and inverse geophysical techniques developed, full waveform modelling, seismic tomography and receiver-based studies enabled detailed imaging of Earth's subsurface. The invention of ambient noise tomography within the last two decades revolutionized studies of Earth's subsurface based on the cross-correlation of the Earth's ambient noise. That method, in particular, enabled imaging of Earth structure in places where earthquakes or receivers do not exist. At the same time, progress in imaging the Earth's deepest shells has been impeded by the lack of geometric coverage of body waves due to uneven global distribution of seismic sources and receivers and the fact that the ambient noise studies cannot reach deeper than the uppermost Earth's shells, near its surface. In seeking the ways forward, global seismologists started experimenting with cross-correlating the part of the seismograms recorded many hours after the first arrivals of body-waves, the so-called earthquake coda. As in many science disciplines, initial work on this topic resulted in controversies but also led to new realisations and discoveries that all contributed to the rise of a new paradigm – the earthquake coda-correlation wavefield, which is the focus of this review paper. We do not attempt here to provide a review of ambient-noise correlation, although we use some familiar concepts to introduce coda-correlation. Our main goal is to review theoretical and observational work to date that resulted in a better understanding of the coda-correlation wavefield, both as a phenomenon and a powerful method. The features in global correlograms exist due to many cross-terms of reverberating body-waves, a principle fundamentally different from the reconstruction of surface waves in the ambient-noise correlograms. Once the coda-correlation wavefield is fully understood through theoretical developments, the method becomes a powerful way to study Earth's deep structure. Apart from providing a review of the most important results to date, we scrutinise the process of making global correlograms and analyse their characteristics while taking into consideration various aspects, such as the time after the earthquake, the source-receiver geometries, the level of seismicity and the type of earthquake mechanism. Furthermore, we provide practical examples on how to build correlograms and interpret correlogram features using programming language Python. Our review seeks to promote the topic of coda correlation among already experienced researchers as well as students who embark on this interesting research, which may play a central role in global and planetary seismology in the coming decades.

Fourier-transform near infrared spectroscopy (FT-NIR) is a technique used in the compositional and sensory analysis of foodstuffs. In this work, we have measured the main maturity parameters for grape (sugars and acids) using hundreds of intact berry samples to build models for the prediction of these parameters from berries of two very different varieties: &ldquo, Victoria&rdquo, and &ldquo, Autumn Royal&rdquo, Together with the chemical composition in terms of sugar and acidic content, we have carried out a sensory analysis on single berries. Employing the models built for sugars and acids it was possible to learn the sweetness and acidity of each berry before the destructive sensory analysis. The direct correlation of sensory data with FT-NIR spectra is difficult, therefore, spectral data were exported from the spectrometer built-in software and analyzed with R software using a statistical analysis technique (Spearman correlation) which allowed the correlation of berry appreciation data with specific wavelengths that were then related to sugar and acidic content. In this article, we show how it is possible to carry out the analysis of single berries to obtain data on chemical composition parameters and consumer appreciation with a fast, simple, and non-destructive technique with a clear advantage for producers and consumers.

This work provides a general overview and consideration of Box-Jenkins models for temporal data and its extension known as Fourier residual autoregressive moving average models. We examined the modeling and forecasting of malaria incidence rate during pregnancy at Bishop Shannahan Hospital, Nsukka using Autoregressive Integrated Moving Average (ARIMA) Models propounded by Box and Jenkins. We adoptted the Box-Jenkins methodology to build ARIMA model for malaria incidences during pregnancy for a period of 10 years spanning from January 2006 to December 2016. Among the candidate models considered, ARIMA (3,1,1) was identified to be the most robust based on some model performance measures. The model was further improved upon by incorporating Fourier residual modification on the fitted ARIMA model. The Fourier Residual Autoregressive Moving Average (FARIMA) model obtained yielded improved result. Besides, model evaluation criterion such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Mean Bias Error (MBE), Mean Absolute Scaled Error (MASE), were used to access the models. FARIMA Model out performed ARIMA Model. Several time series plots and tests like augmented dickey fuller test, correlogram, Ljung-Box test for serial correlation of the residuals, etc were carried out in this study to test for stationarity, identify the order of ARIMA model and serial correlation residual respectively.

The Image Quality Assessment system based on Color Distribution Information (IQA-CDI) uses descriptors based on the color correlogram in analyzing the distortion types and quality score, for Reduced-Reference (RR) data. IQA-CDI with RR data can predict the perceived image quality scores for real-time digital broadcasting. IQA-CDI is supported image quality prediction by the ensemble of learning machines. However, using the ensemble of learning machine in IQA-CDI may spend much time to data training process that do not suitable for real-time situations. Therefore, our research aims to decrease data training time of IQA-CDI by adapting the input features pattern for reducing the ensemble size. The experimental result shows that a new input features pattern and the reducing ensemble size can reduce processing time, while has the performance comparable to original IQA-CDI.

Objetivou-se avaliar a influência da estruturação espacial e de variáveis ambientais sobre a organização florístico-estrutural do componente arbóreo em uma Floresta Ombrófila Mista Aluvial no sul do Brasil. Para isso, foram alocadas 48 parcelas de 200 m2 no remanescente amostrado, localizado em Lages-SC. Todos os indivíduos dentro das parcelas com diâmetro na altura do peito (DAP) igual ou superior a 5 cm foram identificados. Foram mensuradas variáveis ambientais e espaciais em cada parcela. Os dados foram analisados por meio de Correlograma de Mantel, Particionamento da Variância, Análises de Redundância, análise de Coordenadas Principais de Matrizes Vizinhas e testes de correlação. Foram amostrados 1.462 indivíduos pertencentes a 66 espécies. Conjuntamente, as variáveis ambientais e espaciais explicaram 24,13% da variação total, sendo que a maior parte (15,22%) se encontrou espacialmente estruturada. Dentre as variáveis ambientais, a topografia (desnível máximo) e a fertilidade do solo (soma de bases e P) foram significativas. Conclui-se que o componente arbóreo está organizado na forma de um gradiente florístico-estrutural. Ainda, os resultados sugerem que o regime de inundação do rio e a presença de zonas de maior encharcamento do solo foram relevantes na definição de micro-hábitat, que influenciaram na distribuição das espécies.

The Brazil-Malvinas Confluence Zone (BMCZ) is characterized by high environmental variation, which could be reflected in several optical types of water, from one containing only phytoplankton and sea water to other optically more complex. In this paper, we analyze the spatio-temporal variability of the Chlorophyll-a detected by the ocean color sensor (CHLA(sat)) in BMCZ in order to understand its environmental variability. We use the MODIS-Aqua CHLAsat monthly composites imagery from 2002 to 2015, and applied two statistical methods: the correlogram-based robust periodogram to identify, over a broad spectrum of temporal, the most significant periodicities, and the pixel gradient distribution to study the spatial-temporal gradients within the BMCZ and variations over the continental shelf and coastal waters. Our results point out to the predominance of the annual cycle over most of the investigated area, although an area from latitude 37 degrees S in direction NE, alongshore of Uruguay to Brazil, evidences interannual periodicities, possibly related to variations in the discharge of the Rio de la Plata associated with the El Nino phenomena. The ocean color spectroradiometric signature in terms of pixel gradient presents a relatively high variability (similar to 0.0 to 0.65 mg m(-3)); in particular the high values of the pixel gradient correspond to saline front of the estuarine system of Rio de la Plata, and to the strip of the platform that extends along the isobaths of 80 m (coast of Uruguay), especially in the center and south of the study area. (C) 2017 Institute of Oceanology of the Polish Academy of Sciences.

This research studies the relationship between the climate index and the groundwater level of the lower Chao Phraya basin, in order to forecast the groundwater level in the studied area by using Autoregressive Integrated Moving Average with Explanatory (ARIMAX). The combination of 6 climate indices—Dipole Mode Index, Indian Summer Monsoon Index, Multivariate ENSO Index, Sea Surface Temperature NINO4, Southern Oscillation Index and the Western North Pacific Monsoon Index—were used, along with the groundwater level data from 14 stations during the period 1980–2011 to develop the forecast model and verify it with the data of 2012.The first step was correlation of the ARIMA model with Autocorrelation Function and Partial Autocorrelation Function. The possible model was then selected using BIC statistics. Diagnostic Checking was done to consider the white noise characteristic of estimated residuals by using the statistics of Box and Ljung (Q-statistic). If the selected models were found to be proper, then the Granger Causality Test of the leading parameters or the climate index would be performed as the next step. The results show that there is a relationship between the groundwater level and the climate index. The model could be used to forecast effectively the average RMSE value at 0.6. The last step was to develop the MODFLOW for a conceptual model and synthesize groundwater levels in the study area, which covers around 43,000 km2 and has 8 layers of groundwater, with Bangkok clay on the top. All other boundary values were set to be steady. The calibration was done using the data of 325 observed wells. The normalized RMS value was 9.705%. The results were verified by the data using ARIMAX over the same time periods. To conclude, the simulated results of the monthly groundwater level in 2012 of the wells have a confidence interval of around 95%, which is near the result from the ARIMAX model. The advantages of the ARIMAX model include high accuracy, no requirement for a large amount of data and inexpensive implementation. It is one of the effective tools for the groundwater prediction.

In this empirical work, cognisance has been given to providing a review of literature on the seasonal Box- Jenkins modelling, particularly with reference to a univariate model. Seasonal pattern of Headline Consumer Price Index (HCPI) has been produced for Sierra Leone and with EVIEWS making use of best model selection of (6,0)(0,0). Data were seasonally adjusted with iteration and sufficient diagnostic test outcomes showing that forecast using Static method yielded best outcome, with Year-on-Year inflation over the three monthly period forecasted outcomes. The correlogram of the resultant series revealed very stable outcome of the results, while MAPE for the forecast evaluation revealing marginal error for the outcome, indicating that the model is quite adequate with the chosen methodology. This publication is based on Time Series Econometric forecasting for Inflation in Sierra Leone using Box-Jenkins methodology [ARIMA] Dans ce travail empirique, on a pris connaissance de la revue de la littérature sur les Modélisation de Jenkins, en particulier en référence à un modèle univarié. La tendance saisonnière de l'indice des prix à la consommation global (IPCH) a été produite pour la Sierra Leone et avec EVIEWS en utilisant le meilleur modèle de sélection (6,0) (0,0). Les données ont été corrigées des variations saisonnières avec l'itération et les résultats des tests de diagnostic suffisants montrant que les prévisions utilisant la méthode statique ont donné les meilleurs résultats, avec une inflation d'une année à l'autre sur les trois résultats mensuels prévus. Le corrélogramme de la série résultante a révélé un résultat très stable des résultats, alors que MAPE pour l'évaluation des prévisions révélait une erreur marginale pour le résultat, indiquant que le modèle est tout à fait adéquat avec la méthodologie choisie.

This research investigates the ripeness grading identification of the palm oil FFB using colorfeatures that are color histogram, color moment and color correlogram. Palm is harvestedduring the optimum stage of its ripeness since it improves the FFB oil quality and quantity.Harvesting wrong bunches decreases the oil extraction rate of the palm. A preliminaryresearch on the palm oil FFB grading identification is conducted. Each ripeness stage has itsown unique color. A study on color features is investigated.A new dataset of images of FFB isconstructed. A comparative study between Support Vector Machine (SVM) and Naïve Bayesclassifiers has been performed using the values of color histogram, color moment and colorcorrelogram. The results of the experiments indicate that color moment with SVM produce ahigher palm oil FFB ripeness grading identification accuracy compared to color histogramand color correlogram.Keywords: color features, fresh fruit bunch (FFB), Naïve Bayes, ripeness stage, supportvector machine.

In this paper, we study the periodic fluctuations of connectivity density time series of a wind speed-monitoring network in Switzerland. By using the correlogram-based robust periodogram annual periodic oscillations were found in the correlation-based network. The intensity of such annual periodic oscillations is larger for lower correlation thresholds and smaller for higher. The annual periodicity in the connectivity density seems reasonably consistent with the seasonal meteo-climatic cycle.

We propose a biologically inspired binaural sound localization system for reverberant environments. It uses two 100-channel cochlear models to analyze binaural signals, and each channel of the left cochlea is compared with each channel of the right cochlea in parallel to generate a 2-D instantaneous correlation matrix (correlogram). The correlogram encodes both binaural cues and spectral information in a unified framework. A sound onset detector is used to generate the correlogram only during the sound onsets, and the onset correlogram is analyzed using a linear regression approach as well as an extreme learning machine (ELM). The proposed system is evaluated using experimental data in reverberation environments, and we obtained an average absolute error of 16.5° for linear regression and 12.8° for ELM regression in the −90° to 90° range.

Pitch estimation is frequently used in voice quality analysis. Speech in a noisy environment, the accuracy of pitch extraction is poor due to the effect of noises. This paper presents a simple technique for robust pitch estimation as well as voiced/unvoiced classification based on correlogram of noisy speech signal. Like spectrogram, the short-time autocorrelation outputs can be displayed graphically as another image called correlogram is an alternative to short time spectral analysis. This technique operates frame-by-frame basis on normalized autocorrelation function (NACF) of signal. Initially, the noisy speech signal is low pass filtered within the pitch range 50-500 Hz to obtain the pre-filtered signal. Then a threshold function is derived from the NACF. We use this threshold value for pitch position indicator and voiced/unvoiced classifier. The accurate pitch period is obtained from the weighted correlogram. The proposed pitch estimation and voiced/unvoiced classification algorithm using correlogram is very simple, fast and easily implemented in computer. The performance of the proposed algorithm is compared with recently developed EMD based method. The experimental results show that the proposed one is useful in speech analysis research.

Nowadays the content-based retrieval plays a major role in the area of research in computer vision. Although image or video retrieval is a mature technology, not much work has been done in searching of an object in video sequences. The proposed work proposes a novel method which allows a user to make queries based on visual content properties such as color percentages, layout and texture occurring in frames by using instances of prior matches. Here the author proposes a method that searches representative frames of a digital video sequence containing the required object based on input query provided by the user. The performance measures like color, texture and shape are extracted from the frames of video as well as query image to identify only those relevant frames that are matching. Color correlogram, Gabor filter and morphological operations are used to extract color, texture and shape features, respectively. The proposed work shows a good accuracy of 90% to retrieve the related frames from the video.

This paper presents a speech enhancement approach based on analysis---synthesis framework. An improved multi-band summary correlogram (MBSC) algorithm is proposed for pitch estimation and voiced/unvoiced (V/UV) detection. The proposed pitch detection algorithm achieves a lower pitch detection error compared with the reference algorithm. The denoising autoencoder (DAE) is applied to enhance the line spectrum frequencies (LSFs). The reconstruction loss could be decreased compare with the swallow model. The proposed approach is evaluated using the perceptual evaluation of speech quality (PESQ) and the experimental results show that the proposed approach improves the performance of speech enhancement compared with the conventional speech enhancement approach. In addition, it could be applied to parametric speech coding even at low bit rate and low signal-noise ratio (SNR) environments.

Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the many local spatial variation and spatial dependency while global measurements provide only one set of values that represent the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are also given.

Patterns of fine-scale spatial distribution of multilocus genotypes can provide valuable insights into the biology of forest tree species. Here we tested for the existence of spatial genetic structure (SGS) in a four-oak-species forest with contrasting species abundances and hybridization rates. A total of 483 adult trees were mapped over 8.6 ha and genotyped using 10 highly polymorphic genomic regions. A weak but significant SGS was observed in each of the four oak species, with Quercus frainetto, the species with the lowest density in the sampling plot, exhibiting the strongest SGS. The values of the Sp statistic were 0.0033, 0.0035, 0.0042, and 0.0098 for Q. petraea, Q. robur, Q. pubescens, and Q. frainetto, respectively. The spatial correlogram of the total population was significantly different when hybrids were removed from the analysis, which suggests that hybridization influenced the SGS. Interspecific SGSs were significantly correlated with the rates of hybridization. Implications of the obtained results for the conservation and management of forest genetic resources are discussed.

The presented paper deals with the data processing of correlograms acquired by an electronic speckle pattern interferometry (ESPI). Correlograms are highly noised by the speckle pattern and special care should be taken to denoise these correlograms before calculating the demanded phase map or phase profile. This paper presents a process that utilizes two stage use of wavelet transform (WT) for both denoising and a phase profile retrieval. In the first stage the correlogram is denoised by thresholding of wavelet transform coefficients. Unlike the common use, WT phase retrieval process is not directly applied to the denoised correlogram, but rather to its intensity thresholded image, which is done in the second stage. Proposed algorithm is verified by its application to the artificially generated correlograms and in the final step also to the experimentally acquired correlograms.

The autocorrelation function (acf) and the partial autocorrelation function (pacf) are elementary tools of linear time series analysis. The sensitivity of the conventional sample acf and pacf to outliers is well known. We review robust estimators and evaluate their performances in different data situations considering Gaussian scenarios with and without outliers as well as times series with heavy tails in a simulation study. WIREs Comput Stat 2015, 7:205–222. doi: 10.1002/wics.1351 For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article.

We consider a measurable stationary Gaussian stochastic process. A criterion for testing hypotheses about the covariance function of such a process using estimates for its norm in the space $L_p(\mathbb {T}),\,p\geq1$, is constructed., Comment: Published at http://dx.doi.org/10.15559/15-VMSTA17 in the Modern Stochastics: Theory and Applications (https://www.i-journals.org/vtxpp/VMSTA) by VTeX (http://www.vtex.lt/)

Given a record of daily rainfall over a network of gauges, this paper describes a method of linking the Gauge Wetness Ratio (GWR) on a given day to the joint distribution of the parameters of the anisotropic correlogram defining the spatial statistics of simulated radar-rainfall fields. We generate a large number of Gaussian random fields by sampling from the correlogram parameters conditioned on the GWR and then conditionally merge these fields to the gauge observations transformed into the Gaussian domain. Availability of such a tool allows better spatially distributed hydrological modelling, because good quality ensemble spatial information is required for such work, as it yields uncertainty of the fields so generated. To achieve these ends, correlograms of many Gaussianised daily accumulations of radar images were developed using the Fast Fourier Transform to generate their sample power spectra. Empirical correlograms were fitted using a 2D exponential distribution to yield the 3 key parameters of the correlogram: the range, the anisotropy ratio and the direction of the major axis. It was found that the range follows a Gamma distribution while the anisotropy parameters follow a Loglogistic one; a t5 copula was adequate to capture the bivariate negative dependence structure between the range and ratio. The Radar Wetted Area Ratio (RWAR) drives the parameters of the correlogram, and its link with GWR is modelled by a transition probability matrix. We take each of the generated Gaussian random fields and conditionally merge it with Gaussianised rainfall values at the gauge locations using Ordinary Kriging. The method produces realistic simulated radar images, on a grid chosen to suit the data, which match the gauge observations at their locations. Ensemble simulations of 1000 samples were used to derive the median and the inter-quartile range of the fields; these were found to narrow near the control gauge locations, as expected, emphasising the value of high density gauge networks. Ongoing research is looking towards integration of the presented methodology with a stochastic daily rainfall generator for useful spatial rainfall simulation over catchments with gauged records.

We establish asymptotic expansions of the bias, mean-square deviation, and variance for the correlogram estimator of the unknown covariance function of a Gaussian stationary random noise in the nonlinear regression model with continuous time.