Mariane R. Petraglia

Adaptive FIR filter structure based on the generalized subband decomposition of FIR filters

An adaptive structure based on a generalized structural subband decomposition of FIR (finite-impulse-response) filters is presented. The proposed structure implements an adaptive FIR filter of length N as a parallel connection of L branches, with each branch composed of a cascade of a fixed interpolator and a sparse adaptive subfilter containing at least L nonzero coefficients. There is no sampling rate alteration in this structure, and therefore no problems with aliasing occur. The interpolators are implemented by a computationally efficient transform (e.g., DCT, DFT). The proposed structure presents superior convergence performance for colored input signals when compared to the conventional direct-form LMS (least-mean-square) structure, with a very small increase in the number of operations. The advantages of using the subband structure in the adaptive line enhancer, acoustic echo canceller, and channel equalizer applications are shown through computer simulations. >

Frequency Domain FIR Filter Design Using Fuzzy Adaptive Simulated Annealing

An alternative approach to digital filter design is presented. Although it is used in this paper to fit FIR filters to frequency domain specifications, the method is suitable to application in other problems of digital filter design, where the matter under study can be stated as finding the global minimum of a numerical function of filter parameters. The adopted numerical optimization algorithm is based upon the well-known simulated annealing paradigm and its implementation known as fuzzy adaptive simulated annealing. The overall design method is as follows: starting from frequency domain constraints and a parameterized expression of the filter family under adaptation, a corresponding training set is created, an error function synthesized and a global minimization process executed. At the end, the point that minimizes globally the particular cost function at hand determines the optimal filter.

A delayless alias-free subband adaptive filter structure

Adaptive subband techniques have been developed to solve complexity and slow convergence problems of the traditional fullband high-order adaptive filters. However, the effect of aliasing associated with the multirate structure, which is a source of error in the modeling of the unknown, system, and the delay introduced in the signal path, are some of the disadvantages often encountered in most of the proposed structures. In this paper, we present two delayless maximally decimated structures where the optimal channel filters are related to the wideband system in a closed form. They make use of a special DFP analysis filter bank where the polyphase components of the prototype filter represent fractional delays such that aliasing is cancelled with no need of adaptive cross-filters.

Fault tolerant adaptive filter structure based on the generalized subband decomposition of FIR filters

This paper investigates the use of the generalized structural subband implementation of adaptive FIR filters in high-speed fault tolerant adaptive filtering. The convergence behavior of the proposed fault tolerant adaptive structure, after a coefficient fault has occurred, is analyzed. The problems of identifying the occurrence of a fault and the faulty coefficient are examined. The convergence behavior of the proposed subband structure during a fault occurrence is compared to the behavior of previously reported structures.<<ETX>>

Stereo vision system for real time inspection and 3D reconstruction

This work presents a three-dimensional vision system for inspection activities of installations by remotely operated vehicles. A real-time stereo vision system is used for the acquisition of stereo pairs of images that, after preprocessing, are submitted to a reconstruction procedure in order to obtain three-dimensional coordinates, to perform dimensioning of objects in the acquired images.

Transient and steady-state MSE analysis of the IMPNLMS algorithm

Abstract Several techniques have been proposed in the literature to accelerate the convergence of adaptive algorithms for the identification of sparse impulse responses (i.e., with energy concentrated in a few coefficients). Among these techniques, the improved μ-law proportionate normalized least mean squares (IMPNLMS) algorithm is one of the most effective. This paper presents an accurate transient analysis of this algorithm and derives an estimate of its steady-state MSE, without requiring the assumption of white Gaussian input signals.

Convergence analysis of a subband adaptive filter structure

This paper investigates the convergence properties of an adaptive FIR subband structure, where the input signal is decomposed by an analysis filter bank and each of the subband signals is modified by shorter adaptive filters operating at a lower rate. We consider here a subband structure with down-sampling factor smaller than the number of subbands. A convergence analysis is presented, where it is shown that the non-ideal filter bank characteristics cause a degradation in the adaptation speed. Such degradation increases with the number of subbands and calls for the use of more sophisticated adaptive algorithms than the LMS algorithm. Computer simulation results are presented with the objective of verifying the convergence analysis.

Transient analysis of l0-LMS and l0-NLMS algorithms

Sparsity-aware adaptive algorithms present some advantages over standard ones, specially due to the fact that they have faster convergence rate. This paper proposes a stochastic model for both l0-LMS and l0-NLMS algorithms, and carries out an accurate transient analysis of these algorithms without requiring the input signal to be white. Some previously unreported learning properties of these algorithms are revealed by the proposed framework, which are confirmed by experimental evidence. HighlightsAn accurate transient analysis of both l0-LMS and l0-NLMS algorithms is advanced.A stochastic model for predicting their MSD and MSE learning behaviors is proposed.The analysis employs weaker conditions, e.g., input signal whiteness is not required.Learning properties of these algorithms are revealed by the proposed framework.Steady-state results can be predicted as limiting cases of the transient analysis.

New adaptive algorithms for identification of sparse impulse responses — Analysis and comparisons

The convergence of the classical adaptive filtering algorithms becomes slow when the number of coefficients is very large. However, in many applications, such as digital network and acoustical echo cancelers, the system being modeled presents sparse impulse response, that is, most of its coefficients have small magnitudes. In order to improve the convergence for these applications, several algorithms have been proposed recently, which employ individual step-sizes for the updating of the different coefficients. The adaptation step-sizes are made larger for the coefficients with larger magnitudes, resulting in a faster convergence for the most significant coefficients. In this paper, we give an overview of the most important adaptive algorithms developed for the fast identification of systems with sparse impulse responses. Their convergence rates are compared through computer simulations for the identification of the channel impulse responses in a digital network echo cancellation application. A theoretical analysis of an improved version of the PNLMS algorithm is presented.

Subband adaptive filtering with critical sampling using the data selective affine projection algorithm

Subband adaptive filtering techniques have been recently developed for a number of applications, such as acoustic echo cancellation and wideband active noise control. Such applications require adaptive filters with hundreds of taps, resulting in high computational complexity and low convergence rate for LMS based algorithms. For fullband system, a variety of adaptive algorithm, which improve the adaptation convergence rate, have been developed. Most of them (such as the affine projection algorithm), however, present larger complexity than the conventional LMS algorithm. Such computational load can be reduced by making use of subband processing techniques. Considering these matters, we apply the affine projection algorithm (APA) in a recently proposed subband adaptive filter structure.