288 resultados para Twin Peaks


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Sn4+-doped In2O3 (ITO) is a benchmark transparent conducting oxide material. We prepared ligand-free but colloidal ITO (8nm, 10% Sn4+) nanocrystals (NCs) by using a post-synthesis surface-modification reaction. (CH3)(3)OBF4 removes the native oleylamine ligand from NC surfaces to give ligand-free, positively charged NCs that form a colloidal dispersion in polar solvents. Both oleylamine-capped and ligand-free ITO NCs exhibit intense absorption peaks, due to localized surface plasmon resonance (LSPR) at around =1950nm. Compared with oleylamine-capped NCs, the electrical resistivity of ligand-free ITO NCs is lower by an order of magnitude (approximate to 35mcm(-1)). Resistivity over a wide range of temperatures can be consistently described as a composite of metallic ITO grains embedded in an insulating matrix by using a simple equivalent circuit, which provides an insight into the conduction mechanism in these systems.

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Electrical conductivity and dielectric relaxation studies on SO4 (2-) doped modified molybdo-phosphate glasses have been carried out over a wide range of composition, temperature and frequency. The d.c. conductivities which have been measured by both digital electrometer (four-probe method) and impedance analyser are comparable. The relaxation phenomenon has been rationalized using electrical modulus formalism. The use of modulus representation in dielectric relaxation studies has inherent advantages viz., experimental errors arising from the contributions of electrode-electrolyte interface capacitances are minimized. The relaxation observed in the present study is non-Debye type. The activation energies for relaxation were determined using imaginary parts of electrical modulus peaks which were close to those of the d.c. conductivity implying the involvement of similar energy barriers in both the processes. The enhanced conductivity in these glasses can be attributed to the migration of Na+, in expanded structures due to the introduction of SO4 (2-) ions.

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Acoustic feature based speech (syllable) rate estimation and syllable nuclei detection are important problems in automatic speech recognition (ASR), computer assisted language learning (CALL) and fluency analysis. A typical solution for both the problems consists of two stages. The first stage involves computing a short-time feature contour such that most of the peaks of the contour correspond to the syllabic nuclei. In the second stage, the peaks corresponding to the syllable nuclei are detected. In this work, instead of the peak detection, we perform a mode-shape classification, which is formulated as a supervised binary classification problem - mode-shapes representing the syllabic nuclei as one class and remaining as the other. We use the temporal correlation and selected sub-band correlation (TCSSBC) feature contour and the mode-shapes in the TCSSBC feature contour are converted into a set of feature vectors using an interpolation technique. A support vector machine classifier is used for the classification. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora in a five-fold cross validation setup. The average correlation coefficients for the syllable rate estimation turn out to be 0.6761, 0.6928 and 0.3604 for three corpora respectively, which outperform those obtained by the best of the existing peak detection techniques. Similarly, the average F-scores (syllable level) for the syllable nuclei detection are 0.8917, 0.8200 and 0.7637 for three corpora respectively. (C) 2016 Elsevier B.V. All rights reserved.