On the design of bandpass and bandstop digital filters from lowpass analog prototypes

Presently, the transformation of lowpass analog prototypes constitutes an useful method of design for recursive digital filters. Although the involved bilinear and frequency band transformations are well known, the best criterion to choose the parameters of the transformations has not yet been investigated. This paper deals with the determination of such parameters in order to require the lowest order for the analog prototype.Peer ReviewedPostprint (published version

Transmisión de imágenes de TV por métodos digitales: las técnicas diferenciales

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Plant identification via adaptive combination of transversal filters

For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least-squares (RLS) filters.Publicad

On generalized sampling expansions for deterministic signals

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Some new results in sampling deterministic signals

Whittaker's (or Shannon 's) Sampling Theorem is a well-known interpolation formula that has been extended in many directions. In this paper, we introduce two new formulations: -The first follows Papoulis' Generalized Sampling Expansion for reconstructing a signal from regular samples of N(linear, time-invariant) functionals of it, taking the samples at 1/N the Nyquist rate; but generalizing it for including linear T- periodically time-varying systems. This way is in close relation with works that extend sampling in other directions. -The second generalizes Linden's proof of Kohlenberg's sampling for a bandpass signal, in order to maintain the minimum sampling rate (in the average) and to obtain a separate interpolation of the in-phase and quadrature components of the signal. This follows Grace- Pitt-Brown's theory of bandpass sampling.Peer ReviewedPostprint (published version

Sample selection via clustering to construct support vector-like classifiers

This paper explores the possibility of constructing RBF classifiers which, somewhat like support vector machines, use a reduced number of samples as centroids, by means of selecting samples in a direct way. Because sample selection is viewed as a hard computational problem, this selection is done after a previous vector quantization: this way obtaining also other similar machines using centroids selected from those that are learned in a supervised manner. Several forms of designing these machines are considered, in particular with respect to sample selection; as well as some different criteria to train them. Simulation results for well-known classification problems show very good performance of the corresponding designs, improving that of support vector machines and reducing substantially their number of units. This shows that our interest in selecting samples (or centroids) in an efficient manner is justified. Many new research avenues appear from these experiments and discussions, as suggested in our conclusions.Publicad

An adaptive design of an all-zero spectral estimator

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ARMA model maximum entropy power spectral estimation

Peer ReviewedPostprint (published version

Support vector method for robust ARMA system identification

This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals andSVM-ARMA coefficients allows the linking of the fundamentals of SVM with several classical system identification methods. Additionally, the effect of outliers can be cancelled. Application examples show the performance of SVM-ARMA algorithm when it is compared with other system identification methods.Publicad