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The so-called electropyroelectric technique uses amplitude modulated electrical current as excitation source of thermal waves in a pyroelectric sensor in contact with a liquid sample. Here it is showed that this technique allows absolute measurements of the thermal effusivity using the quotient of the signal amplitudes obtained from the inverse and the direct configurations. In this way the influence on the experimental results of the frequency dependent instrumental transfer function can be straightforwardly eliminated. Measurements performed in various test liquid samples are presented that demonstrate the good accuracy and usefulness of the method.

The present work proposes a review and comparison of different Nonlocal Means (NLM) methods in the task of digital image filtering. Some different alternatives to change the classical exponential kernel function used in NLM methods are explored. Moreover, some approaches that change the geometry of the neighborhood and use dimensionality reduction of the neighborhood or patches onto principal component analysis (PCA) are also analyzed, and their performance is compared with respect to the classic NLM method. Mainly, six approaches were compared using quantitative and qualitative evaluations, to do this an homogenous framework has been established using the same simulation platform, the same computer, and same conditions for the initializing parameters. One will notice that particularly, the BM3D SAPCA approach gives the best denoising results, but in contrast, the computation times of this method were the longest.

The present work introduces a new alternative to change the classical exponential kernel function used in Nonlocal Means (NLM) methods to deal with digital image filtering. The idea is based on the premise that making a good selection or estimation of the bandwidth parameter h is difficult and there are some other kernels which have another equivalent parameters to be selected into a more easiest way. A First method is obtained, when using an optimal manner proposed in nonparametric estimation literature to estimate h to tune the exponential kernel function. And a second proposed method, is to change the exponential function by the so called Hilbert function where one must to choose a parameter d. This Hilbert function is used for the first time in the NLM framework. Finally, the obtained filtering results reveals, that the NLM Hilbert kernel approach gives similar performance to other approaches according to recent reported results in literature, and the first proposed methodology is restricted by the estimation of h.

In this paper we present a novel algorithm for phase unwrapping where only a subset of data from the wrapped phase map is used to reconstruct the unwrapped phase map as a linear combination of radial basis functions (RBF’s). For noisy phase maps this algorithm gives better results than three reference algorithms based on radial basis functions, Zernike polynomials and path dependent phase unwrapping strategies.

The large number of projections needed for tomographic reconstruction makes prohibitive the use of algebraic methods for fast phase object reconstruction. However, for smooth and continuous phase objects, the reconstruction can be performed with few projections by using an algorithm that approximates the phase as a linear combination of gaussian basis functions. This work presents an accurate algebraic reconstruction of a flame temperature from two independent interferometers using a He-Ne laser (623.8nm).

An alternative search algorithm for a vector quantization codebook is applied as a way to improve the performance of an automatic speaker recognition system. The search algorithm is based on the geometrical properties of the vector space, defining annular and spherical regions instead of a full search method. The speaker recognition system is intended to identify a suspect, among a small group of persons, using low quality recordings, working as a text independent automatic speaker recognition system. Because the rate of recognition required in forensic applications is extremely important, the use of good discrimination algorithms can reduce the risk of bad decisions. The performance of the system under such conditions is reported. Besides using few speaker samples for training, a high recognition rate was obtained, so an improvement of the recognition rate over the full search method was found.