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This paper is concerned with the problem of analyzing turbulent flow signals that are irregularly sampled by a laser Doppler velocimeter. The temporal irregularity of the sampling is the main problem addressed due to the difficulties it introduces in the use of traditional analysis techniques. The main contribution of this paper is to assess the adequateness of introducing a signal-modeling strategy by means of temporal delay-based artificial neural networks (ANNs) that are adaptively and continuously trained online on the irregularly sampled real data using an evolutionary-based strategy called the multilevel Darwinist brain. The networks are used to model the signal and thus are able to produce a regularly sampled signal straight from the irregular one. It is important to note that they are being trained to model the signal and not just interpolate it; they are able to generate spectral signatures that are very close to that of the original signal. [ABSTRACT FROM AUTHOR]

In this paper, impedance spectroscopy, gene expression programming (GEP), and genetic algorithms are combined to perform sensor characterization. The process presented is useful when there is no knowledge of the sensor equivalent circuit, and a set of impedance responses can be obtained for different measurand values. These responses are used by the algorithm to determine a suitable equivalent circuit and choose a circuit component that describes the measurand values. From this component, interpolation is used to infer the measurand value from the measured frequency responses. Improvements on the application of GEP to impedance characterization are presented. The method is validated through its application to numerical results of a humidity sensor and measurement results of a viscosity sensor. [ABSTRACT FROM AUTHOR]