In this paper, we propose a novel method for recognizing objects in images in a way that is invariant to blur and affine transformation of the images. The method is based on a set of rotated and log-log sampled phase-only bispectrum slices of the images and their phase correlation. The method is invariant to centrally symmetric blur, such as linear motion or out of focus blur. Because of the no...

Obtaining consensual solutions is an important issue in decision making processes. It depends on several factors such as experts’ opinions, principles, knowledge, experience, etc. In the literature we can find a considerable amount of consensus measurement from different research areas (from a Social Choice perspective: Alcalde-Unzu and Vorsatz [1], Alcantud, de Andrés Calle and Cascón [2] and ...

In biomedical analytics correlation is one of the major criteria for the characterization of similarities between measured data items. It is demonstrated how the formal derivative of Pearson correlation can be used for gradient-based optimization of data models. Firstly, individual data attributes can be rated according to their impact on pairwise data relationships, analogous to the variance m...

This study investigated the relationship between language anxiety and English speaking performance among Taiwanese college students. The participants were 59 students attending a one-semester course entitled Speaking Communication at university in central Taiwan. They underwent computer-based oral proficiency tests completed Foreign Language Classroom Anxiety Scale. Data analyzed using descript...

Knowledge about low flow statistics is essential for effective water resource planning and management in ungauged orpoorly gauged catchment areas, especially in arid and semi-arid regions such as Iran. We employed a data set of 20 riverflow time-series from the Iranian Central Plateau River Basin, Iran to evaluate the low-flow series using several frequencyanalysis methods and compared the resu...

Principal component analysis (PCA) guided clustering approach is widely used in high dimensional data to improve the efficiency of Kmeans cluster solutions. Typically, Pearson correlation is used in PCA to provide an eigenanalysis to obtain the associated components that account for most of the variations in the data. However, PCA based Pearson correlation can be sensitive on non-Gaussian distr...