Recognition of Kannada characters extracted from scene images

In this paper, we describe a method for feature extraction and classification of characters manually isolated from scene or natural images. Characters in a scene image may be affected by low resolution, uneven illumination or occlusion. We propose a novel method to perform binarization on gray scale images by minimizing energy functional. Discrete Cosine Transform and Angular Radial Transform are used to extract the features from characters after normalization for scale and translation. We have evaluated our method on the complete test set of Chars74k dataset for English and Kannada scripts consisting of handwritten and synthesized characters, as well as characters extracted from camera captured images. We utilize only synthesized and handwritten characters from this dataset as training set. Nearest neighbor classification is used in our experiments.

Continuous beam of laser-cooled Yb atoms

We demonstrate the launching of laser-cooled Yb atoms in a continuous atomic beam. The continuous cold beam has significant advantages over the more-common pulsed fountain, which was also demonstrated by us recently. The cold beam is formed in the following steps: i) atoms from a thermal beam are first Zeeman-slowed to a small final velocity; ii) the slowed atoms are captured in a two-dimensional magneto-optic trap (2D-MOT); and iii) atoms are launched continuously in the vertical direction using two sets of moving-molasses beams, inclined at +/- 15 degrees to the vertical. The cooling transition used is the strongly allowed S-1(0) -> P-1(1) transition at 399 nm. We capture about 7x10(6) atoms in the 2D-MOT, and then launch them with a vertical velocity of 13m/s at a longitudinal temperature of 125(6) mK. Copyright (C) EPLA, 2013

Detection of the closure-burst transitions of stops and affricates in continuous speech using the plosion index

Automatic and accurate detection of the closure-burst transition events of stops and affricates serves many applications in speech processing. A temporal measure named the plosion index is proposed to detect such events, which are characterized by an abrupt increase in energy. Using the maxima of the pitch-synchronous normalized cross correlation as an additional temporal feature, a rule-based algorithm is designed that aims at selecting only those events associated with the closure-burst transitions of stops and affricates. The performance of the algorithm, characterized by receiver operating characteristic curves and temporal accuracy, is evaluated using the labeled closure-burst transitions of stops and affricates of the entire TIMIT test and training databases. The robustness of the algorithm is studied with respect to global white and babble noise as well as local noise using the TIMIT test set and on telephone quality speech using the NTIMIT test set. For these experiments, the proposed algorithm, which does not require explicit statistical training and is based on two one-dimensional temporal measures, gives a performance comparable to or better than the state-of-the-art methods. In addition, to test the scalability, the algorithm is applied on the Buckeye conversational speech corpus and databases of two Indian languages. (C) 2014 Acoustical Society of America.

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.

Risk-sensitive control of continuous time Markov chains

We study risk-sensitive control of continuous time Markov chains taking values in discrete state space. We study both finite and infinite horizon problems. In the finite horizon problem we characterize the value function via Hamilton Jacobi Bellman equation and obtain an optimal Markov control. We do the same for infinite horizon discounted cost case. In the infinite horizon average cost case we establish the existence of an optimal stationary control under certain Lyapunov condition. We also develop a policy iteration algorithm for finding an optimal control.

Estimation of voice-onset time in continuous speech using temporal measures

This paper proposes an automatic acoustic-phonetic method for estimating voice-onset time of stops. This method requires neither transcription of the utterance nor training of a classifier. It makes use of the plosion index for the automatic detection of burst onsets of stops. Having detected the burst onset, the onset of the voicing following the burst is detected using the epochal information and a temporal measure named the maximum weighted inner product. For validation, several experiments are carried out on the entire TIMIT database and two of the CMU Arctic corpora. The performance of the proposed method compares well with three state-of-the-art techniques. (C) 2014 Acoustical Society of America

A Continuous Electrical Conductivity Model for Monolayer Graphene From Near Intrinsic to Far Extrinsic Region

We present a closed-form continuous model for the electrical conductivity of a single layer graphene (SLG) sheet in the presence of short-range impurities, long-range screened impurities, and acoustic phonons. The validity of the model extends from very low doping levels (chemical potential close to the Dirac cone vertex) to very high doping levels. We demonstrate complete functional relations of the chemical potential, polarization function, and conductivity with respect to both doping level and temperature (T), which were otherwise developed for SLG sheet only in the very low and very high doping levels. The advantage of the continuous conductivity model reported in this paper lies in its simple form which depends only on three adjustable parameters: the short-range impurity density, the long-range screened impurity density, and temperature T. The proposed theoretical model was successfully used to correlate various experiments in the midtemperature and moderate density regimes.

Tailoring the dispersion of multiwall carbon nanotubes in co-continuous PVDF/ABS blends to design materials with enhanced electromagnetic interference shielding

Highly conducting composites were derived by selectively localizing multiwall carbon nanotubes (MWNTs) in co-continuous PVDF/ABS (50/50, wt/wt) blends. The electrical percolation threshold was obtained between 0.5 and 1 wt% MWNTs as manifested by a dramatic increase in the electrical conductivity by about six orders of magnitude with respect to the neat blends. In order to further enhance the electrical conductivity of the blends, the MWNTs were modified with amine terminated ionic liquid (IL), which, besides enhancing the interfacial interaction with PVDF, facilitated the formation of a network like structure of MWNTs. This high electrical conductivity of the blends, at a relatively low fraction (1 wt%), was further explored to design materials that can attenuate electromagnetic (EM) radiation. More specifically, to attenuate the EM radiation by absorption, a ferroelectric phase was introduced. To accomplish this, barium titanate (BT) nanoparticles chemically stitched onto graphene oxide (GO) sheets were synthesized and mixed along with MWNTs in the blends. Intriguingly, the total EM shielding effectiveness (SE) was enhanced by ca. 10 dB with respect to the blends with only MWNTs. In addition, the effect of introducing a ferromagnetic phase (Fe3O4) along with IL modified MWNTs was also investigated. This study opens new avenues in designing materials that can attenuate EM radiation by selecting either a ferroelectric (BT-GO) or a ferromagnetic phase (Fe3O4) along with intrinsically conducting nanoparticles (MWNTs).

Continuous vapour adsorption cooling system with three adsorber beds

In this paper, the design of a new solar operated adsorption cooling system with two identical small and one large adsorber beds, which is capable of producing cold continuously, has been proposed. In this system, cold energy is stored in the form of refrigerant in a separate refrigerant storage tank at ambient temperature. Silica gel water is used as a working pair and system is driven by solar energy. The operating principle is described in details and its thermodynamic transient analysis is presented. Effect of COP and SCE for different adsorbent mass and adsorption/desorption time of smaller beds are discussed. Recommended mass and number of cycles of operation for smaller beds to attain continuous cooling with average COP and SCE of 0.63 and 337.5 kJ/kg, respectively are also discussed, at a generation, condenser and evaporator temperatures of 368 K, 303 K and 283 K, respectively. (C) 2015 Elsevier Ltd. All rights reserved.

Continuous synthesis of high quality CdSe quantum dots in supercritical fluids

We demonstrate in here a powerful scalable technology to synthesize continuously high quality CdSe quantum dots (QDs) in supercritical hexane. Using a low cost, highly thermally stable Cd-precursor, cadmium deoxycholate, the continuous synthesis is performed in 400 mu m ID stainless steel capillaries resulting in CdSe QDs having sharp full-width-at-half-maxima (23 nm) and high photoluminescence quantum yields (45-55%). Transmission electron microscopy images show narrow particles sizes distribution (sigma <= 5%) with well-defined crystal lattices. Using two different synthesis temperatures (250 degrees C and 310 degrees C), it was possible to obtain zinc blende and wurtzite crystal structures of CdSe QDs, respectively. This synthetic approach allows achieving substantial production rates up to 200 mg of QDs per hour depending on the targeted size, and could be easily scaled to gram per hour.

Risk-Sensitive Ergodic Control of Continuous Time Markov Processes With Denumerable State Space

In this article, we study risk-sensitive control problem with controlled continuous time Markov chain state dynamics. Using multiplicative dynamic programming principle along with the atomic structure of the state dynamics, we prove the existence and a characterization of optimal risk-sensitive control under geometric ergodicity of the state dynamics along with a smallness condition on the running cost.

A Perspective to Adopt Continuous Dynamic Cognition for Malicious Node Detection in Heterogeneous Networks

The current day networks use Proactive networks for adaption to the dynamic scenarios. The use of cognition technique based on the Observe, Orient, Decide and Act loop (OODA) is proposed to construct proactive networks. The network performance degradation in knowledge acquisition and malicious node presence is a problem that exists. The use of continuous time dynamic neural network is considered to achieve cognition. The variance in service rates of user nodes is used to detect malicious activity in heterogeneous networks. The improved malicious node detection rates are proved through the experimental results presented in this paper. (C) 2015 The Authors. Published by Elsevier B.V.

PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds

Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous dataflows due to the variations in the input data rates and the need for sustained throughput. The elastic resource provisioning of cloud infrastructure is valuable to meet the changing resource needs of such continuous applications. However, multi-tenant cloud resources introduce yet another dimension of performance variability that impacts the application's throughput. In this paper we propose PLAStiCC, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based lookahead approach. It not only addresses variations in the input data rates but also the underlying cloud infrastructure. In addition, we also propose several simpler static scheduling heuristics that operate in the absence of accurate performance prediction model. These static and adaptive heuristics are evaluated through extensive simulations using performance traces obtained from Amazon AWS IaaS public cloud. Our results show an improvement of up to 20% in the overall profit as compared to the reactive adaptation algorithm.

Improved recognition of aged Kannada documents by effective segmentation of merged characters

In optical character recognition of very old books, the recognition accuracy drops mainly due to the merging or breaking of characters. In this paper, we propose the first algorithm to segment merged Kannada characters by using a hypothesis to select the positions to be cut. This method searches for the best possible positions to segment, by taking into account the support vector machine classifier's recognition score and the validity of the aspect ratio (width to height ratio) of the segments between every pair of cut positions. The hypothesis to select the cut position is based on the fact that a concave surface exists above and below the touching portion. These concave surfaces are noted down by tracing the valleys in the top contour of the image and similarly doing it for the image rotated upside-down. The cut positions are then derived as closely matching valleys of the original and the rotated images. Our proposed segmentation algorithm works well for different font styles, shapes and sizes better than the existing vertical projection profile based segmentation. The proposed algorithm has been tested on 1125 different word images, each containing multiple merged characters, from an old Kannada book and 89.6% correct segmentation is achieved and the character recognition accuracy of merged words is 91.2%. A few points of merge are still missed due to the absence of a matched valley due to the specific shapes of the particular characters meeting at the merges.

Computationally efficient models for simulation of non-ideal DC-DC converters operating in continuous and discontinuous conduction modes

This paper discusses dynamic modeling of non-isolated DC-DC converters (buck, boost and buck-boost) under continuous and discontinuous modes of operation. Three types of models are presented for each converter, namely, switching model, average model and harmonic model. These models include significant non-idealities of the converters. The switching model gives the instantaneous currents and voltages of the converter. The average model provides the ripple-free currents and voltages, averaged over a switching cycle. The harmonic model gives the peak to peak values of ripple in currents and voltages. The validity of all these models is established by comparing the simulation results with the experimental results from laboratory prototypes, at different steady state and transient conditions. Simulation based on a combination of average and harmonic models is shown to provide all relevant information as obtained from the switching model, while consuming less computation time than the latter.