The major drawback of watershed transformation is over-segmentation. It also has a significant advantage: very good edge extraction. Thresholding methods usually utilize only global information such as an image histogram; however, they have the ability to group pixels into clusters by their value. The method presented in this paper combines the advantages of watershed segmentation and multileve...

the speech enhancement techniques are often employed to improve the quality and intelligibility of the noisy speech signals. this paper discusses a novel technique for speech enhancement which is based on singular value decomposition. this implementation utilizes a genetic algorithm based optimization method for reducing the effects of environmental noises from the singular vectors as well as t...

Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed determine the singular values their associated vectors of a given matrix. Although these are promising, required subroutines resources too costly on near-term devices. In this work, we propose variational algorithm for (VQSVD). By exploiting principles Ky ...

We study extensions of compressive sensing and low rank matrix recovery (matrix completion) to the recovery of low rank tensors of higher order from a small number of linear measurements. While the theoretical understanding of low rank matrix recovery is already well-developed, only few contributions on the low rank tensor recovery problem are available so far. In this paper, we introduce versi...

A wavelet optimized finite difference (WOFD) method is presented for adaptively solving a class of singularly perturbed elliptic and parabolic problems. The method is based on an interpolating wavelet transform using polynomial interpolation on dyadic grids. Adaptive feature is performed automatically by thresholding the wavelet coefficients. Numerical examples for elliptic and parabolic proble...

A QR based technique is presented for estimating the approximate numerical rank and corresponding signal subspace of a matrix together with the subspace projection of the least squares weights. Theoretical difficulties associated with conventional QR factorisation are overcome by applying the technique of RowZeroing QR to the covariance matrix. Thresholding is simplified compared with the use o...