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This paper proposes a new Newton-based adaptive filtering algorithm, namely the Quasi-Newton Least-Mean Fourth (QNLMF) algorithm. The main goal is to have a higher order adaptive filter that usually fits the non-Gaussian signals with an improved performance behavior, which is achieved using the Newton numerical method. Both the convergence analysis and the steady-state performance analysis are derived...
In this paper, we develop a greedy algorithm for sparse learning over a doubly stochastic network. In the proposed algorithm, nodes of the network perform sparse learning by exchanging their individual intermediate variables. The algorithm is iterative in nature. We provide a restricted isometry property (RIP)-based theoretical guarantee both on the performance of the algorithm and the number of iterations...
In this paper, a fractional order calculus based least mean square algorithm is proposed for complex system identification. The proposed algorithm, named as, fractional complex least mean square (FCLMS), successfully deals with the problem of complex error due to negative weights or complex input/output in the FLMS. For the evaluation purpose a complex linear system is considered. The FCLMS algorithm...
Dimensionality reduction techniques play an essential role in data analytics, signal processing, and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations that are well-suited for the intended task is more appealing. This paper proposes...
Compressive sensing (CS) has been shown useful for reducing dimensionality, by exploiting signal sparsity inherent to specific domain representations of data. Traditional CS approaches represent the signal as a sparse linear combination of basis vectors from a prescribed dictionary. However, it is often impractical to presume accurate knowledge of the basis, which motivates data-driven dictionary...
An improved zero-attracting normalized least mean square (IZA-NLMS) algorithm is proposed for sparse channel estimation. The proposed algorithm is realized by using the error sequence to design the step-size of the sparse-aware normalized least mean square (NLMS) algorithms. Also, the computational complexity reduction strategy is used in the proposed IZA-NLMS algorithm. Computer simulations are constructed...
In the teletransmission of images or medical imaging, image reconstruction or enhancement (denoising) is a significant topic. We can consider it as a typical issue of the Blind Source Separation (BSS) which interfere with transmission and cause blurs in images. Denoising and reconstruction refer to the removal of the unknown signals which lead to interference from the signals we intend to receive...
In this work, a novel algorithm named sign regressor least mean mixed-norm (SRLMMN) algorithm is proposed as an alternative to the well-known least mean mixed-norm (LMMN) algorithm. The SRLMMN algorithm is a hybrid version of the sign regressor least mean square (SRLMS) and sign regressor least mean fourth (SRLMF) algorithms. Analytical expressions are derived to describe the convergence, steady-state,...
In this paper, we extend the bi-alternating direction method of multipliers (BiADMM) designed on a graph of two nodes to a graph of multiple nodes. In particular, we optimize a sum of convex functions defined over a general graph, where every edge carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is carefully constructed which naturally...
In handling massive-scale signal processing problems arising from ‘big-data’ applications, key technologies could come from the development of decentralized algorithms. In this context, consensus-based methods have been advocated because of their simplicity, fault tolerance and versatility. This paper presents a new consensus-based decentralized algorithm for a class of non-convex optimization problems...
In this paper we derive a new design of the Convex Variable Step-Size (CVSS) algorithm, based on measurements obtained with LMS algorithm. Computer simulations are provided to support the proposed approach.
This paper provides examples of various synchronous and asynchronous signal processing systems for performing optimization, utilizing the framework and elements developed in a preceding paper. The general strategy in that paper was to perform a linear transformation of stationarity conditions applicable to a class of convex and nonconvex optimization problems, resulting in algorithms that operate...
The conventional FDTD technique utilizes time domain numerical convergence as the criterion for termination; hence, its computational cost is high when accurate results are needed for high Q systems; for low frequencies; or when we have to deal with dispersive media. In this paper, signal processing techniques are employed to reduce the computational cost associated with the FDTD, especially for the...
Allpass filters have found many applications in signal processing areas. This paper describes an algorithm for the design of stable allpass digital filter with equiripple group delay errors. The problem is formulated as an iterative reweighted linear program (LP) problem. An algorithm is derived for solving such a problem. The design examples are given, which demonstrate that the proposed algorithm...
The present contribution deals with the statistical tool of Independent Component Analysis (ICA). The focus is on Fas-tICA, arguably the most popular algorithm in the domain of ICA. Despite its success, it is observed that FastICA occasionally yields outcomes that do not correspond to any solutions of ICA. These outcomes are called spurious solutions. In this work, we give a thorough and rigorous...
The improvised Particle Swarm Optimization (PSO) Algorithm offers better search efficiency than conventional PSO algorithm. It provides an efficient technique to obtain the best optimized result in the search space. This algorithm ensures a faster rate of convergence to the desired solution whose precision can be preset by the user. The inertia parameter is varied linearly with iteration number, which...
In this paper, a novel time recursive implementation of the Sparse Learning via Iterative Minimization (SLIM) algorithm is proposed, in the context of adaptive system identification. The proposed scheme exhibits fast convergence and tracking ability at an affordable computational cost. Numerical simulations illustrate the achieved performance gain in comparison to other existing adaptive sparse system...
This paper deals with a novel class of set-theoretic adaptive sparsity promoting algorithms of linear computational complexity. Sparsity is induced via generalized thersholding operators, which correspond to nonconvex penalties such as those used in a number of sparse LMS based schemes. The results demonstrate the significant performance gain of our approach, at comparable computational cost.
The proportionate normalized least-mean-square (PNLMS) algorithm with individual activation factors (IAFPNLMS) converges fast when the echo path is highly sparse, and has been used in system identification. Unfortunately, it suffers from slow convergence speed after the fast initial process. To solve the problem, in this paper, the idea of mu-law PNLMS (MPNLMS) algorithm is introduced into the IAFPNLMS...
In many analysis based on estimation the parameters of probability distribution functions, the algorithms are developing for unknown probabilities. Some algorithms are derived starting from previous solutions and algorithms. One very popular algorithm is the EM (Expectation-Maximization) algorithm. The EM algorithm is a starting point for developing other advanced algorithms. Features of EM and other...
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