Modeling and analysis of MOS capacitor controlled by independent double gates

This paper, for the first time, explores the charcatersictics of MOS capacitor controlled by independent double gates by numerical simulation and analytical modeling for its possible use in RF circuit design as a varactor. By numerical simulation it is shown how the quasi-static and non-quasi-static characteristics of the first gate capacitance could be tuned by the second gate biases. Effect of body doping and energy quantization are also discussed in this regard. A semi-empirical quasi-static model is also developed by using the existing incomplete Poisson solution of independent double gate transistors. Proposed model, which is valid from accumulation to inversion, is shown to have excellent agreement with numerical simulation for practical bias conditions.

Threshold-Independent QRS Detection Using the Dynamic Plosion Index

Detection of QRS serves as a first step in many automated ECG analysis techniques. Motivated by the strong similarities between the signal structures of an ECG signal and the integrated linear prediction residual (ILPR) of voiced speech, an algorithm proposed earlier for epoch detection from ILPR is extended to the problem of QRS detection. The ECG signal is pre-processed by high-pass filtering to remove the baseline wandering and by half-wave rectification to reduce the ambiguities. The initial estimates of the QRS are iteratively obtained using a non-linear temporal feature, named the dynamic plosion index suitable for detection of transients in a signal. These estimates are further refined to obtain a higher temporal accuracy. Unlike most of the high performance algorithms, this technique does not make use of any threshold or differencing operation. The proposed algorithm is validated on the MIT-BIH database using the standard metrics and its performance is found to be comparable to the state-of-the-art algorithms, despite its threshold independence and simple decision logic.

Phenotypic characters of rice landraces reveal independent lineages of short-grain aromatic indica rice

Rice landraces are lineages developed by farmers through artificial selection during the long-term domestication process. Despite huge potential for crop improvement, they are largely understudied in India. Here, we analyse a suite of phenotypic characters from large numbers of Indian landraces comprised of both aromatic and non-aromatic varieties. Our primary aim was to investigate the major determinants of diversity, the strength of segregation among aromatic and non-aromatic landraces as well as that within aromatic landraces. Using principal component analysis, we found that grain length, width and weight, panicle weight and leaf length have the most substantial contribution. Discriminant analysis can effectively distinguish the majority of aromatic from non-aromatic landraces. More interestingly, within aromatic landraces long-grain traditional Basmati and short-grain non-Basmati aromatics remain morphologically well differentiated. The present research emphasizes the general patterns of phenotypic diversity and finds out the most important characters. It also confirms the existence of very unique short-grain aromatic landraces, perhaps carrying signatures of independent origin of an additional aroma quantitative trait locus in the indica group, unlike introgression of specific alleles of the BADH2 gene from the japonica group as in Basmati. We presume that this parallel origin and evolution of aroma in short-grain indica landraces are linked to the long history of rice domestication that involved inheritance of several traits from Oryza nivara, in addition to O. rufipogon. We conclude with a note that the insights from the phenotypic analysis essentially comprise the first part, which will likely be validated with subsequent molecular analysis.

Continuity equation based nonquasi-static charge model for independent double gate MOSFET

Using the numerical device simulation we show that the relationship between the surface potentials along the channel in any double gate (DG) MOSFET remains invariant in QS (quasistatic) and NQS (nonquasi-static) condition for the same terminal voltages. This concept along with the recently proposed `piecewise charge linearization' technique is then used to develop the intrinsic NQS charge model for a Independent DG (IDG) MOSFET by solving the governing continuity equation. It is also demonstrated that unlike the usual MOSFET transcapacitances, the inter-gate transcapacitance of a IDG-MOSFET initially increases with the frequency and then saturates, which might find novel analog circuit application. The proposed NQS model shows good agreement with numerical device simulations and appears to be useful for efficient circuit simulation.

Incoherence-based optimal selection of independent measurements in diffuse optical tomography

An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The proposed method was compared with the recently established data-resolution matrix-based approach for optimal choice of independent measurements and shown, using simulated and experimental gelatin phantom data sets, to be superior as it does not require an optimal regularization parameter for providing the same information. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

On the Maximum Number of Linearly Independent Cycles and Paths in a Network

Central to network tomography is the problem of identifiability, the ability to identify internal network characteristics uniquely from end-to-end measurements. This problem is often underconstrained even when internal network characteristics such as link delays are modeled as additive constants. While it is known that the network topology can play a role in determining the extent of identifiability, there is a lack in the fundamental understanding of being able to quantify it for a given network. In this paper, we consider the problem of identifying additive link metrics in an arbitrary undirected network using measurement nodes and establishing paths/cycles between them. For a given placement of measurement nodes, we define and derive the ``link rank'' of the network-the maximum number of linearly independent cycles/paths that may be established between the measurement nodes. We achieve this in linear time. The link rank helps quantify the exact extent of identifiability in a network. We also develop a quadratic time algorithm to compute a set of cycles/paths that achieves the maximum rank.

Frequency and Temperature-Independent Electrical Transport Properties of 2BaO-0.5Na(2)O-2.5Nb(2)O(5)-4.5B(2)O(3) Glass-Ceramics

The temperature (300-973K) and frequency (100Hz-10MHz) response of the dielectric and impedance characteristics of 2BaO-0.5Na(2)O-2.5Nb(2)O(5)-4.5B(2)O(3) glasses and glass nanocrystal composites were studied. The dielectric constant of the glass was found to be almost independent of frequency (100Hz-10MHz) and temperature (300-600K). The temperature coefficient of dielectric constant was 8 +/- 3ppm/K in the 300-600K temperature range. The relaxation and conduction phenomena were rationalized using modulus formalism and universal AC conductivity exponential power law, respectively. The observed relaxation behavior was found to be thermally activated. The complex impedance data were fitted using the least square method. Dispersion of Barium Sodium Niobate (BNN) phase at nanoscale in a glass matrix resulted in the formation of space charge around crystal-glass interface, leading to a high value of effective dielectric constant especially for the samples heat-treated at higher temperatures. The fabricated glass nanocrystal composites exhibited P versus E hysteresis loops at room temperature and the remnant polarization (P-r) increased with the increase in crystallite size.

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.

Independent component analysis of microarray data in the study of endometrial cancer.

Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.