J.C. Nieto-Borge

TL;DR: In this article, the structure of the different contributions to the image spectrum derived by the three-dimensional Fourier decomposition of sea clutter time series measured by ordinary X-band marine radars is analyzed.

TL;DR: A novel hybrid approach for feature selection in two different relevant problems for marine energy applications: significant wave height (Hm0) and wave energy flux (P) prediction is proposed, in such a way that the GGA searches for several subsets of features, and the ELM provides the fitness of the algorithm by means of its accuracy on Hm0 or P prediction.

TL;DR: This paper reviews those used in wave energy applications, both in the resource estimation and in the design and control of wave energy converters, and illustrates the potential of hybridizing a Coral Reefs Optimization algorithm with an Extreme Learning Machine to tackle the problem of significant wave height reconstruction.

TL;DR: Nonlinear signal processing techniques based on neural networks (NNs) are used in the proposed clutter reduction system, showing promising results characterized by different subjective and objective results.

TL;DR: This paper tackles the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations, and proposes a genetic algorithm hybridized with an extreme learning machine that selects a subset of wave parameters that minimizes the Hs reconstruction error.