This paper presents a Modified Error Data Normalized Step Size (MEDNSS) algorithm in which time fluctuating step size relies on standardization of both error and data vector. An Adaptive Noise Canceller (ANC) is utilized to enhance the system performance in the presence of signal leakage components or signal crosstalk. This ANC comprises of three microphones and two adaptive filters that conseq...

One of the most important applications of multi-objective optimization is adjusting parameters ofpractical engineering problems in order to produce a more desirable outcome. In this paper, the decoupled sliding mode control technique (DSMC) is employed to stabilize an inverted pendulum which is a classic example of inherently unstable systems. Furthermore, a new Multi-Objective Particle Swarm O...

A common model for computing the similarity of two strings X and Y of lengths m, and n respectively with m n, is to transform X into Y through a sequence of edit operations which are of three types: insertion, deletion, and substitution of symbols. The model assumes a given weight function which assigns a non-negative real cost to each of these edit operations. The amortized weight for a given ...

The goal of this paper is to investigate the speech signal enhancement using Kernel Affine Projection Algorithm (KAPA ) and Normalized KAPA. The removal of background noise is very important in many applications like speech recognition, telephone conversations, hearing aids, forensic, etc. Kernel adaptive filters shown good performance for removal of noise. If the evaluation of background noise...

Normalized least mean square (NLMS) was considered as one of the classical adaptive system identification algorithms. Because most of systems are often modeled as sparse, sparse NLMS algorithm was also applied to improve identification performance by taking the advantage of system sparsity. However, identification performances of NLMS type algorithms cannot achieve high-identification performan...

Many fast block-matching algorithms reduce computations by limiting the number of checking points. They can achieve high computation reduction, but often result in relatively higher matching error compared with the full-search algorithm. In this letter, a novel fast block-matching algorithm named normalized partial distortion search is proposed. The proposed algorithm reduces computations by us...

The image segmentation problem is to delineate, or segment, a salient feature in an image. As such, this is a bipartition problem with the goal of separating the foreground from the background. An NP-hard optimization problem, the Normalized Cut problem, is often used as a model for image segmentation. The common approach for solving the normalized cut problem is the spectral method which gener...

This paper describes a graph clustering algorithm that aims to minimize the normalized cut criterion and has a model order selection procedure. The performance of the proposed algorithm is comparable to spectral approaches in terms of minimizing normalized cut. However unlike spectral approaches, the proposed algorithm scales to graphs with millions of nodes and edges. The algorithm consists of...

This paper describes a graph clustering algorithm that aims to minimize the normalized cut criterion and has a model order selection procedure. The performance of the proposed algorithm is comparable to spectral approaches in terms of minimizing normalized cut. However, unlike spectral approaches, the proposed algorithm scales to graphs with millions of nodes and edges. The algorithm consists o...