Improved subject-independent acoustic-to-articulatory inversion

In subject-independent acoustic-to-articulatory inversion, the articulatory kinematics of a test subject are estimated assuming that the training corpus does not include data from the test subject. The training corpus in subject-independent inversion (SII) is formed with acoustic and articulatory kinematics data and the acoustic mismatch between training and test subjects is then estimated by an acoustic normalization using acoustic data drawn from a large pool of speakers called generic acoustic space (GAS). In this work, we focus on improving the SII performance through better acoustic normalization and adaptation. We propose unsupervised and several supervised ways of clustering GAS for acoustic normalization. We perform an adaptation of acoustic models of GAS using the acoustic data of the training and test subjects in SII. It is found that SII performance significantly improves (similar to 25% relative on average) over the subject-dependent inversion when the acoustic clusters in GAS correspond to phonetic units (or states of 3-state phonetic HMMs) and when the acoustic model built on GAS is adapted to training and test subjects while optimizing the inversion criterion. (C) 2014 Elsevier B.V. All rights reserved.

Independent Positioning of Magnetic Nanomotors

There is considerable interest in powering and maneuvering nanostructures remotely in fluidic media using noninvasive fuel-free methods, for which small homogeneous magnetic fields are ideally suited. Current strategies include helical propulsion of chiral nanostructures, cilia-like motion of flexible filaments, and surface assisted translation of asymmetric colloidal doublets and magnetic nanorods, in all of which the individual structures are moved in a particular direction that is completely tied to the characteristics of the driving fields. As we show in this paper, when we use appropriate magnetic field configurations and actuation time scales, it is possible to maneuver geometrically identical nanostructures in different directions, and subsequently position them at arbitrary locations with respect to each other. The method reported here requires proximity of the nanomotors to a solid surface, and could be useful in applications that require remote and independent control over individual components in microfluidic environments.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Boron-doped carbon nanoparticles: Size-independent color tunability from red to blue and bioimaging applications

Here, we report the hydrothermal synthesis of boron-doped CNPs (B-CNPs) with different size/atomic percentage of doping and size-independent color tunability from red to blue. The variation of size/atomic percentage of B is achieved by simply varying the reaction time, while the color tunability is obtained by diluting the solution. With dilution, the luminescence spectra are not only blue-shifted, the intensity increases as well. The huge blue-shift in the emission energy (similar to 1 eV) is believed to be due to the increase in the interparticle distance. The quantum yield with optimum dilution is found to increase with boron doping though it is very low as compared to CNPs and nitrogen-doped CNPs. Finally, we show that B-CNPs with a quantum yield of 0.5% can be used for bioimaging applications. (C) 2015 Elsevier Ltd. All rights reserved.