Performance enhancement of online handwritten Tamil symbol recognition with reevaluation techniques

In this article, we aim at reducing the error rate of the online Tamil symbol recognition system by employing multiple experts to reevaluate certain decisions of the primary support vector machine classifier. Motivated by the relatively high percentage of occurrence of base consonants in the script, a reevaluation technique has been proposed to correct any ambiguities arising in the base consonants. Secondly, a dynamic time-warping method is proposed to automatically extract the discriminative regions for each set of confused characters. Class-specific features derived from these regions aid in reducing the degree of confusion. Thirdly, statistics of specific features are proposed for resolving any confusions in vowel modifiers. The reevaluation approaches are tested on two databases (a) the isolated Tamil symbols in the IWFHR test set, and (b) the symbols segmented from a set of 10,000 Tamil words. The recognition rate of the isolated test symbols of the IWFHR database improves by 1.9 %. For the word database, the incorporation of the reevaluation step improves the symbol recognition rate by 3.5 % (from 88.4 to 91.9 %). This, in turn, boosts the word recognition rate by 11.9 % (from 65.0 to 76.9 %). The reduction in the word error rate has been achieved using a generic approach, without the incorporation of language models.

Quantitative Strain and Compositional Studies of InxGa1-xAs Epilayer in a GaAs-based pHEMT Device Structure by TEM Techniques

In GaAs-based pseudomorphic high-electron mobility transistor device structures, strain and composition of the InxGa1 (-) As-x channel layer are very important as they influence the electronic properties of these devices. In this context, transmission electron microscopy techniques such as (002) dark-field imaging, high-resolution transmission electron microscopy (HRTEM) imaging, scanning transmission electron microscopy-high angle annular dark field (STEM-HAADF) imaging and selected area diffraction, are useful. A quantitative comparative study using these techniques is relevant for assessing the merits and limitations of the respective techniques. In this article, we have investigated strain and composition of the InxGa1 (-) As-x layer with the mentioned techniques and compared the results. The HRTEM images were investigated with strain state analysis. The indium content in this layer was quantified by HAADF imaging and correlated with STEM simulations. The studies showed that the InxGa1 (-) As-x channel layer was pseudomorphically grown leading to tetragonal strain along the 001] growth direction and that the average indium content (x) in the epilayer is similar to 0.12. We found consistency in the results obtained using various methods of analysis.

On the Utility of Microscopy and C-Scan Techniques in the Study of Defects Arising From Resin Infusion Process and a Study on the Influence of Voids on the Impact Behavior of CFRP Composites

Carbon Fiber Reinforced Plastic composites were fabricated through vacuum resin infusion technology by adopting two different processing conditions, viz., vacuum only in the first and vacuum plus external pressure in the next, in order to generate two levels of void-bearing samples. They were relatively graded as higher and lower void-bearing ones, respectively. Microscopy and C-scan techniques were utilized to describe the presence of voids arising from the two different processing parameters. Further, to determine the influence of voids on impact behavior, the fabricated +45 degrees/90 degrees/-45 degrees composite samples were subjected to low velocity impacts. The tests show impact properties like peak load and energy to peak load registering higher values for the lower void-bearing case where as the total energy, energy for propagation and ductility indexes were higher for the higher void-bearing ones. Fractographic analysis showed that higher void-bearing samples display lower number of separation of layers in the laminate. These and other results are described and discussed in this report.

Beyond classical dynamic structural plasticity using mesh-free modelling techniques

The problem of modelling the transient response of an elastic-perfectly-plastic cantilever beam, carrying an impulsively loaded tip mass, is,often referred to as the Parkes cantilever problem 25]; The permanent deformation of a cantilever struck transversely at its tip, Proc. R. Soc. A., 288, pp. 462). This paradigm for classical modelling of projectile impact on structures is re-visited and updated using the mesh-free method, smoothed particle hydrodynamics (SPH). The purpose of this study is to investigate further the behaviour of cantilever beams subjected to projectile impact at its tip, by considering especially physically real effects such as plastic shearing close to the projectile, shear deformation, and the variation of the shear strain along the length and across the thickness of the beam. Finally, going beyond macroscopic structural plasticity, a strategy to incorporate physical discontinuity (due to crack formation) in SPH discretization is discussed and explored in the context of tip-severance of the cantilever beam. Consequently, the proposed scheme illustrates the potency for a more refined treatment of penetration mechanics, paramount in the exploration of structural response under ballistic loading. The objective is to contribute to formulating a computational modelling framework within which transient dynamic plasticity and even penetration/failure phenomena for a range of materials, structures and impact conditions can be explored ab initio, this being essential for arriving at suitable tools for the design of armour systems. (C) 2014 Elsevier Ltd. All rights reserved.

Influence of melt pouring temperature and plate inclination on solidification and microstructure of A356 aluminum alloy produced using oblique plate

The preparation of semisolid slurry of A356 aluminum alloy using an oblique plate was investigated. A356 alloy melt undergoes partial solidification when it flows down on an oblique plate cooled from underneath by counter flowing water. It results in continuous formation of columnar dendrites on plate wall. Due to forced convection, these dendrites are sheared off into equiaxed/fragmented grains and then washed away continuously to produce semisolid slurry at plate exit. Melt pouring temperature provides required condition of solidification whereas plate inclination enables necessary shear for producing semisolid slurry of desired quality. Slurry obtained was solidified in metal mould to produce semisolid-cast billets of desired microstructure. Furthermore, semisolid-cast billets were heat treated to improve surface quality. Microstructures of both semisolid-cast and heat-treated billets were analyzed. Effects of melt pouring temperature and plate inclination on solidification and microstructure of billets produced using oblique plate were described. The investigations involved four different melt pouring temperatures (620, 625, 630 and 635 degrees C) associated with four different plate inclinations (30 degrees, 45 degrees, 60 degrees and 75 degrees). Melt pouring temperature of 625 degrees C with plate inclination of 60 degrees shows fine and globular microstructures and it is the optimum.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Two techniques to improve the precision of a demand-driven null-dereference verification approach

The problem addressed in this paper is sound, scalable, demand-driven null-dereference verification for Java programs. Our approach consists conceptually of a base analysis, plus two major extensions for enhanced precision. The base analysis is a dataflow analysis wherein we propagate formulas in the backward direction from a given dereference, and compute a necessary condition at the entry of the program for the dereference to be potentially unsafe. The extensions are motivated by the presence of certain ``difficult'' constructs in real programs, e.g., virtual calls with too many candidate targets, and library method calls, which happen to need excessive analysis time to be analyzed fully. The base analysis is hence configured to skip such a difficult construct when it is encountered by dropping all information that has been tracked so far that could potentially be affected by the construct. Our extensions are essentially more precise ways to account for the effect of these constructs on information that is being tracked, without requiring full analysis of these constructs. The first extension is a novel scheme to transmit formulas along certain kinds of def-use edges, while the second extension is based on using manually constructed backward-direction summary functions of library methods. We have implemented our approach, and applied it on a set of real-life benchmarks. The base analysis is on average able to declare about 84% of dereferences in each benchmark as safe, while the two extensions push this number up to 91%. (C) 2014 Elsevier B.V. All rights reserved.

A unique strategy towards high dielectric constant and low loss with multiwall carbon nanotubes anchored onto graphene oxide sheets

Multiwall carbon nanotubes (MWNTs) were anchored onto graphene oxide sheets (GOs) via diazonium and C-C coupling reactions and characterized by spectroscopic and electron microscopic techniques. The thus synthesized MWNT-GO hybrid was then melt mixed with 50/50 polyamide6-maleic anhydride-modified acrylonitrile-butadiene-styrene (PA6-mABS) blend to design materials with high dielectric constant (30) and low dielectric loss. The phase morphology was studied by SEM and it was observed that the MWNT-GO hybrid was selectively localized in the PA6 phase of the blend. The 30 scales with the concentration of MWNT-GO in the blends, which interestingly showed a very low dielectric loss (< 0.2) making them potential candidate for capacitors. In addition, the dynamic storage modulus scales with the fraction of MWNT-GO in the blends, demonstrating their reinforcing capability as well.

Postsynthetic Exterior Decoration of an Organic Cage by Copper(I)-Catalysed A(3)-Coupling and Detection of Nitroaromatics

A new synthetic protocol based on one-pot, copper(I)-catalysed multicomponent reaction of formaldehyde, secondary amine and terminal alkyne has been employed to postsynthetically modify a self-assembled nanoscopic organic cage. By employing this synthetic strategy, three new cages appended with phenyl-, xylyl-and naphthyl-acetylene moieties have been synthesised. The resulting modified cages were characterised by using a range of spectroscopic techniques. The synthesised cages were fluorescent and thus one of them was tested to explore the potential use of such compounds as chemosensors for the detection of nitroaromatics. Experimental findings suggest a high selective quenching of initial fluorescence intensity in the presence of nitroaromatic compounds. Furthermore, it has been observed that among the various nitroaromatics tested, nitrophenolic compounds have better quenching ability.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Contrasting Effects of Graphene Oxide and Poly(ethylenimine) on the Polymorphism in Poly(vinylidene fluoride)

The nature of interaction between a heteronucleating agent (graphene oxide, GO) and a strongly polar macromolecule (poly(ethylenimine), PEI) with poly(vinylidene fluoride) (PVDF) influencing the crystalline structure and morphology has been systematically investigated in this work. PEI interacts with PVDF via ion-dipole interaction, which helps in lowering the free energy barrier for nucleation thereby promoting faster crystallization. In contrast, besides interacting with PVDF, GO also promotes heteronucleation in PVDF. We observed that both GO and PEI have very different effects on the overall crystalline morphology of PVDF. For instance, the neat PVDF showed a mixture of both alpha and beta phases when cooled from the melt. However, incorporation of 0.1 wt % GO resulted in phase transformation from the stable alpha-phase to polar beta-polymorph in PVDF. In contrast, PEI, which also resulted in faster crystallization in PVDF predominantly, resulted in the stable alpha- phase. Various techniques like Fourier transform infrared spectroscopy, X-ray diffraction, and differential scanning calorimetry were employed to confirm the phase transformations in PVDF. PEI was further grafted onto GO nanosheets to understand the combined effects of both GO and PEI on the polymorphism in PVDF. The PVDF/PEI-GO composite showed a mixture of phases, predominantly rich in a. These phenomenal effects were further analyzed and corroborated with the specific interaction between GO and PEI with PVDF using X-ray photon scattering (XPS) and NMR. In addition, the dielectric permittivity increased significantly in the presence of GO and PEI in the composites. For instance, PVDF/PEI-GO showed the highest permittivity of 39 at 100 Hz.

pi-Electron rich small molecule sensors for the recognition of nitroaromatics

Selective and discriminative detection of -NO2 containing high energy organic compounds such as picric acid (PA), 2,4,6-trinitrotoluene (TNT) and dinitrotoluene (DNT) has become a challenging task due to concerns over national security, criminal investigations and environment protections. Among various known detection methods, fluorescence techniques have gained special attention in recent time. A wide variety of fluorescent chemosensors have been developed for nitroaromatic explosive detection. In this review article, we provide an overview of the recent developments made in small molecule-based turn-off fluorescent sensors for nitroaromatic explosives with special focus on organic and H-bonded supramolecular sensors. The fluorescent sensors discussed in this review are classified and organized according to their functionality and their recognition of nitroaromatics by fluorescence quenching.

On the Structural Stability of Melt Spun Ribbons of Fe95-x Zr (x) B4Cu1 (x=7 and 9) Alloys and Correlation with Their Magnetic Properties

Melt spun ribbons of Fe95-x Zr (x) B4Cu1 with x = 7 (Z7B4) and 9 (Z9B4) alloys have been prepared, and their structure and magnetic properties have been evaluated using XRD, DSC, TEM, VSM, and Mossbauer spectroscopy. The glass forming ability (GFA) of both alloys has been calculated theoretically using thermodynamical parameters, and Z9B4 alloy is found to possess higher GFA than that of Z7B4 alloy which is validated by XRD results. On annealing, the amorphous Z7B4 ribbon crystallizes into nanocrystalline alpha-Fe, whereas amorphous Z9B4 ribbon shows two-stage crystallization process, first partially to bcc solid solution which is then transformed to nanocrystalline alpha-Fe and Fe2Zr phases exhibiting bimodal distribution. A detailed phase analysis using Mossbauer spectroscopy through hyperfine field distribution of phases has been carried out to understand the crystallization behavior of Z7B4 and Z9B4 alloy ribbons. In order to understand the phase transformation behavior of Z7B4 and Z9B4 ribbons, molar Gibbs free energies of amorphous, alpha-Fe, and Fe2Zr phases have been evaluated. It is found that in case of Z7B4, alpha-Fe is always a stable phase, whereas Fe2Zr is stable at higher temperature for Z9B4. (C) The Minerals, Metals & Materials Society and ASM International 2015

A survey on compressed domain video analysis techniques

Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.