Pseudorandom binary sequences with high linear complexity and low correlation function values are sought in many applications of modern communication systems. A new family of pseudorandom binary sequences, cascaded GMW sequences, is constructed. These sequences are shown to share many desirable correlation properties with the GMW sequences of Gordon, Mills, and Welch, for example high shifted a...

in this study digital image correlation (dic) technique combined with a high speed video system was used to predict movement of particles in a water model. comparing with particle-image velocimetry (piv) technique, it provides a low cost alternative approach to visualize flow fields and was successfully employed to predict the movement of particles in a water model at different submergence dept...

In this paper, a novel method is proposed to recognize the type of mesh pattern synthesized in a binary text image. Since the pixels of mesh pattern are regularly arranged, it is intuitive to identify the type of mesh pattern by analyzing the regularity of black pixels in the considered image. To achieve the recognition goal, the coefficient of correlation between the input image and the mesh p...

The unitary-correlation-operator method (UCOM) is formulated for the description of lambda hypernuclei. The method is applied to 17 Λ O. A lambda-nucleon effective interaction is derived, taking the coupling of the sigma-nucleon channel into account. The lambda single-particle energies are calculated for the 0s1/2, 0p3/2 and 0p1/2 states employing the Nijmegen soft-core (NSC), Jülich model-Ã (J...

The accuracy of forward models for electroencephalography (EEG) partly depends on head tissues geometry and strongly affects the reliability of the source reconstruction process, but it is not yet clear which brain regions are more sensitive to the choice of different model geometry. In this paper we compare different spherical and realistic head modeling techniques in estimating EEG forward so...

The complexity of searches and the volume of genomic data make sequence alignment one of bioinformatics most active research areas. New alignment approaches have incorporated digital signal processing techniques. Among these, correlation methods are highly sensitive. This paper proposes a novel sequence alignment method based on 2-dimensional images, where each nucleic acid base is represented ...

This paper proposes a new correlation analysis method for nonstationary and energy-limited continual signals, which works out a formula similar to time correlation theory; that is to say when the operator, , meets a certain condition, its auto-correlation property of Fourier transform can be presented by Fourier transform of its energy function, f 2 ) ( t f , revealing its correlation of the Fo...

The classic supervised classification algorithms are efficient, but time-consuming, complicated and not interpretable, which makes it difficult to analyze their results that limits the possibility to improve them based on real observations. In this paper, we propose a new and a simple classifier to predict a sentiment label of a short text. This model keeps the capacity of human interpret-abili...

1. Canonical correlation analysis (CCA) is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine (Vapnik, 1998) is an efficient approach to improve such a linear method. In this study, we investigate the effectiveness of...