In this paper we propose an approach for feature selection in a problem of significant wave height prediction, to improve the exploitation of marine energy. The method that we present, a Grouping Genetic Algorithm — Extreme Learning Machine approach (GGA-ELM), mainly tries to improve the prediction performance of the regressors, providing more effective predictors and good performance in the final significant wave height prediction. In this method, the GGA looks for several subsets of features, and the ELM provides the fitness of the algorithm, through its accuracy on significant wave height prediction. The GGA is able to evolve different groups of features in parallel, which may improve the performance of the prediction obtained. After the feature selection process with the GGA-ELM, the final results are obtained by applying an ELM and also by a Support Vector Regressor algorithm, both working on the best GGA groups of features previously evolved. In the experimental part of the paper, we show the performance of the proposed approach in a real problem of significant wave height prediction at the West Coast of the USA, using variables directly obtained from several measuring buoys.

Hearing loss may disqualify many people from leading a normal life, though the majority do not make use of hearing aids. This is because most hearing aids on the market cannot automatically adapt to the changing acoustical environment the user faces daily. This paper focuses on the development of an automatic sound classifier for digital hearing aids that aims to enhance listening comprehension when the user goes from one sound environment to another. Given the strong complexity constraints of these devices, reducing the number of signal-describing features which feed the automatic classifier is of great importance and becomes a challenging topic. Thus, the use of genetic algorithms with restricted search is explored for the mentioned feature selection. In an effort to evaluate its performance, the algorithm is compared with a standard unconstrained genetic algorithm and with sequential methods. The restricted search driven by the implemented genetic algorithm performs better than both the sequential methods and unconstrained genetic algorithms. It thus allows a subset of signal-describing features with lower cardinality to be selected. This may permit these selected features to be programmed on the digital signal processor that the hearing aid is based on, and to make efficient use of its limited computational facilities.

This paper discusses the convenience of using two-dimensional (2-D) coding techniques for the compression of electrocardiogram (ECG) signals. These signals present a very clear periodicity that can be exploited by the use of a 2-D time/frequency transform to decorrelate it as much as possible. A brief theoretical approach is given to justify the use of this technique, and a comparison is made between a 2-D and a one-dimensional (1-D) uniform quantization scenarios. The influence of the error as well as the frame size on the estimation of the fundamental period is studied.

This letter presents a method for estimating the quantization noise introduced by a nonuniform quantizer, like those used in the family of MPEG-2/4 AAC audio coders. The method is generalized for the case of estimating the mean squared or the maximum value of the quantization noise. Its use will allow the bit-allocation algorithm to be adapted to different coding scenarios, depending on the available number of bits. Using the proposed method, it is possible to implement a loopless bit-allocation algorithm, without the need for using any kind of iteration loops. This helps to dramatically reduce the computational complexity of the bit-allocation algorithm, making it easier to implement in real-time applications where computational power is limited.