Abstract-Two-dimensional (2D) adaptive filtering is a technique which is applicable to many image, and signal processing applications. This paper extends one-dimensional adaptive filter algorithms to their corresponding 2D counterparts. Based on this extension, a 2D selective partial update APA (2D-SPU-APA) and 2D partial rank affine projection (2D-PRA) are presented. Then, by the combination o...

We investigate exact indexing for high dimensional lp norms based on the 1-Lipschitz property and projection operators. The orthogonal projection that satisfies the 1-Lipschitz property for the lp norm is described. The adaptive projection defined by the first principal component is introduced.

A noise subspace projection (NSP) approach to extraction and subpixel detection of target signatures in an unknown background is presented. The proposed NSP approach is derived from a recently developed subspace orthogonal projection (OSP) method and can be shown to be approximated by an adaptive filter with the optimal weight given by the Wiener–Hopf equation. As a result, the operator resulti...

In this paper, we derive a new normalisation technique for Frequency Domain Adaptive Filtering (FDAF) algorithms. FDAF algorithms are well-known for their low computational complexity and for their decorrelating property that allows the use of di erent step sizes for each adaptive weight, yielding a uniform convergence of all the modes of the input signal. In these algorithms, normalisation is ...

In this paper we present a general formalism for the establishment of the family of selective partial update affine projection algorithms (SPU-APA). The SPU-APA, the SPU regularized APA (SPU-R-APA), the SPU partial rank algorithm (SPU-PRA), the SPU binormalized data reusing least mean squares (SPU-BNDR-LMS), and the SPU normalized LMS with orthogonal correction factors (SPU-NLMS-OCF) algorithms...

Abstract   Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...

We develop an efficient, adaptive locally weighted projection regression (ALWPR) framework for uncertainty quantification (UQ) of systems governed by ordinary and partial differential equations. The algorithm adaptively selects the new input points with the largest predictive variance and decides when and where to add new local models. It effectively learns the local features and accurately qua...

In this paper we will prove saturation estimates for the adaptive hp-finite element method for linear, second order partial differential equations. More specifically we will consider a sequence of nested finite element discretizations where we allow for both, local mesh refinement and locally increasing the polynomial order. We will prove that the energy norm of the error on the finer level can...

For pose-varied color face image, this paper proposed a method of facial feature location and pose estimation based on an unsupervised sphere skin model and fused facial features. Firstly, an adaptive preprocessing, an established unsupervised sphere model and the holes filling technology were presented to extract the face. Then the difference feature and different local binary pattern textural...