Both habitat heterogeneity and species' life-history traits play important roles in driving population dynamics, yet there is little scientific consensus around the combined effect of these two factors on populations in complex landscapes. Using a spatially explicit agent-based model, we explored how interactions between habitat spatial structure (defined here as the scale of spatial autocorrel...

The Mann-Kendall test is a commonly-used nonparametric test for time trend. However, the standard P-values obtained from it are based on an assumption of independence between observations (since the theory is that of the Kendall correlation). However, observations in time series are often autocorrelated: knowing that one observation is larger than the mean may tell you that the next observation...

One of the most important topics in the sonar sound data processing is proposing a powerful classifier to detect the sound source. In this paper we propose a classifier with proper accuracy. First, proper features should be extracted from sound data; Features could extract from time or frequency domains. Whenever fastness is important, time features are most effective. Otherwise, frequency doma...

Three new algorithms based on minimization of autocorrelation of transmitted symbols and the SLM approach which are computationally less demanding have been proposed. In the first algorithm, autocorrelation of complex data sequence is minimized to a value of 1 that results in reduction of PAPR. Second algorithm generates multiple random sequences from the sequence generated in the first algorit...

Many theories in political science predict the spatial clustering of similar behaviors among neighboring units of observation. This spatial autocorrelation poses implications for both inference and modeling that are distinct from the more familiar serial dependence in time series analysis. In this paper, I examine how political scientists can diagnose and model the spatial dependence that is pr...

One of the main challenges in Binary Offset Carrier (BOC) tracking is the presence of multiple peaks in the signal autocorrelation function. Thus, several tracking algorithms, including Bump-Jump, Double Estimator, Autocorrelation Side-Peak Cancellation Technique and pre-filtering have been developed to fully exploit the advantages brought by BOC signals and mitigate the problem of secondary pe...

Spatial autocorrelation is the correlation among data values, strictly due to the relative location proximity of the objects that the data refer to. This statistical property clearly indicates a violation of the assumption of observation independence a pre-condition assumed by most of the data mining and statistical models. Inappropriate treatment of data with spatial dependencies could obfusca...

We address the problem of autocorrelation-based single-tone frequency estimation. It has been shown that the frequency can be estimated from the phase of the available signal’s autocorrelation with fixed lag. A large lag results in better performance but at the same time limits the estimation range. New methods have been proposed to extend the estimation range. In this paper, a new estimator wh...

The computation of autocorrelation matrix is used heavily in several areas including signal and image processing , where parallel and application-speciic archi-tectures are also being increasingly used. Therefore, an eecient scheme to compute autocorrelation matrix on parallel architectures has tremendous beneets. In this paper, a parallel algorithm for the computation of autocorrelation matrix...

Both intra- and interspecific genomic comparisons have revealed local similarities in the level and frequency of mutational variation, as well as in patterns of gene expression. This autocorrelation between measurements leads to violations of assumptions of independence in many statistical methods, resulting in misleading and incorrect inferences. Here I show that autocorrelation can be due to ...