Arturo Serrano

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The first step to detect when a vineyard has any type of deficiency, pest or disease is to observe its stems, its grapes and/or its leaves. To place a sensor in each leaf of every vineyard is obviously not feasible in terms of cost and deployment. We should thus look for new methods to detect these symptoms precisely and economically. In this paper, we(More)
This paper presents a new procedure for learning mixtures of independent component analyzers. The procedure includes non-parametric estimation of the source densities, supervised-unsupervised learning of the model parameters, incorporation of any independent component analysis (ICA) algorithm into the learning of the ICA mixtures, and estimation of residual(More)
Student working groups or tasks forces established to comply with short term assignments or projects require a high level of communication and interactivity as well as a space or platform for collaborative work. The availability of 3G mobile technology provides broadband internet and voice connectivity allowing the access to content and tools in an(More)
The problem of acoustic detection and recognition is of particular interest in surveillance applications, especially in noisy environments with sound sources of different nature. Therefore, we present a multiple energy detector (MED) structure which is used to extract a new set of features for classification, called frequency MED (FMED) and combined MED(More)
This paper presents a novel procedure to classify data from mixtures of independent component analyzers. The procedure includes two stages: learning the parameters of the mixtures (basis vectors and bias terms) and clustering the ICA mixtures following a bottom-up agglomerative scheme to construct a hierarchy for classification. The approach for the(More)
We study the application of artificial neural networks (ANNs) to the classification of spectra from impact-echo signals. In this paper we focus on analyses from experiments. Simulation results are covered in paper I. Impact-echo is a procedure from Non-Destructive Evaluation where a material is excited by a hammer impact which produces a response from the(More)
Standard energy detectors (ED) are optimum to detect unknown signals in presence of uncorrelated Gaussian noise. However, in real applications the signal duration and bandwidth are unpredictable and this fact can considerably degrade the detection performance if the appropriate observation vector length is not correctly selected. Therefore, a multiple(More)
We investigate the application of artificial neural networks (ANNs) to the classification of spectra from impact-echo signals. In this paper we provide analyses from simulated signals and the second part paper details results of lab experiments. The data set for this research consists of sonic and ultrasonic impact-echo signal spectra obtained from 100(More)