We present a new implementation of the commonly used Box-fitting Least Squares (BLS) algorithm, for detection transiting exoplanets in photometric data. Unlike BLS, our - Sparse BLS (SBLS), does not use binning data into phase bins, nor it any kind grid. Thus, its efficiency depend on transit phase, and is therefore slightly better than that BLS. For sparse data, also significantly faster It pe...

Two groups of pattern-recognition algorithms for hybrid optical-digital computer processing are theoretically and experimentally compared. The first group is based on linear mapping, while the second group is based on feature extraction and eigenvector analysis. We study the relations among various linear-mapping-based algorithms by formulating a more general unified pseudoinverse algorithm. We...

in the present work we study the use of fourier transform near infrared spectroscopy (ftnirs)technique to analysis the calcium (ca), phosphorus (p) and copper (cu) contents offish meal. the regression methods employed were partial least squares (pls) and kernelpartial least squares (kpls). the results showed that the efficiency of kpls was better thanpls. as a whole, the application of ft-nirs ...

We present a novel multidimensional seismic trace interpolator that works on constant-frequency slices. It performs completion on Hankel tensors whose order is twice the number of spatial dimensions. Completion is done by fitting a PARAFAC model using an Alternating Least Squares algorithm. The new interpolator runs quickly and can better handle large gaps and high sparsity than existing comple...

The Algebraic Reconstruction Technique (ART), based on the well known algorithm proposed by S. Kaczmarz in 1937, is one of the most important class of solution methods for image reconstruction problems. But unfortunately, almost all the methods from the ART class give satisfactory results only in the case of consistent problems. In the inconsistent case (and unfortunately this is what happens i...

We consider an application of the least squares piecewise monotonic data approximation method to the problem of locating significant extrema in univariate observations that are contaminated by random errors. The piecewise monotonic approximation method makes the smallest change to the data such that the first differences of the smoothed values change sign a prescribed number of times, but the p...

In this paper, the interpolation mechanism of functional networks is discussed. A kind of fourlayer (with 1 input and 1 output unit) and a five-layer (with double input and single output unit) functional network are designed. Meanwhile, a learning algorithm based on Minimizing a least squares error function with a unique minimum has been proposed for the purpose of approximating function. Exper...

Scattered data fitting is a big issue in numerical analysis. In many applications, some of the data are contaminated by noise and some are not. It is not appropriate to interpolate the noisy data, and the traditional least squares method may lose accuracy at the points which are not contaminated. In this paper, we present least squares with interpolation method to solve this problem. The existe...