2 resultados para Spectrograms

em Universidad Politécnica de Madrid


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We demonstrate a simple self-referenced single-shot method for simultaneously measuring two different arbitrary pulses, which can potentially be complex and also have very different wavelengths. The method is a variation of cross-correlation frequency-resolved optical gating (XFROG) that we call double-blind (DB) FROG. It involves measuring two spectrograms, both of which are obtained simultaneously in a single apparatus. DB FROG retrieves both pulses robustly by using the standard XFROG algorithm, implemented alternately on each of the traces, taking one pulse to be ?known? and solving for the other. We show both numerically and experimentally that DB FROG using a polarization-gating beam geometry works reliably and appears to have no nontrivial ambiguities.

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In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.