216 resultados para Super-Real Fields
Resumo:
A network of ship-mounted real-time Automatic Weather Stations integrated with Indian geosynchronous satellites Indian National Satellites (INSATs)] 3A and 3C, named Indian National Centre for Ocean Information Services Real-Time Automatic Weather Stations (I-RAWS), is established. The purpose of I-RAWS is to measure the surface meteorological-ocean parameters and transmit the data in real time in order to validate and refine the forcing parameters (obtained from different meteorological agencies) of the Indian Ocean Forecasting System (INDOFOS). Preliminary validation and intercomparison of analyzed products obtained from the National Centre for Medium Range Weather Forecasting and the European Centre for Medium-Range Weather Forecasts using the data collected from I-RAWS were carried out. This I-RAWS was mounted on board oceanographic research vessel Sagar Nidhi during a cruise across three oceanic regimes, namely, the tropical Indian Ocean, the extratropical Indian Ocean, and the Southern Ocean. The results obtained from such a validation and intercomparison, and its implications with special reference to the usage of atmospheric model data for forcing ocean model, are discussed in detail. It is noticed that the performance of analysis products from both atmospheric models is similar and good; however, European Centre for Medium-Range Weather Forecasts air temperature over the extratropical Indian Ocean and wind speed in the Southern Ocean are marginally better.
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We address the issue of stability of recently proposed significantly super-Chandrasekhar white dwarfs. We present stable solutions of magnetostatic equilibrium models for super-Chandrasekhar white dwarfs pertaining to various magnetic field profiles. This has been obtained by self-consistently including the effects of the magnetic pressure gradient and total magnetic density in a general relativistic framework. We estimate that the maximum stable mass of magnetized white dwarfs could be more than 3 solar mass. This is very useful to explain peculiar, overluminous type Ia supernovae which do not conform to the traditional Chandrasekhar mass-limit.
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An earlier superfluid aether model pairing fermionic and antifermionic fields is invoked to explain Rauch's time dependent neutron interference results which now suggest that microobjects are waves and particles simultaneously. The covariant superfluid provides a medium which carries real Einstein-de Broglie “pilot” waves. Further consequences are discussed.
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The surface instability of a semi-infinite plasma immersed in a high frequency field is investigated. When the natural Langmuir frequency of the surface is nearly equal to the frequency of the high frequency field, the dispersion relation predicts build-up of oscillations with a growth rate comparable with the real part of the frequency. Threshold values above which the instability is possible are derived.
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This paper addresses the problem of detecting and resolving conflicts due to timing constraints imposed by features in real-time and hybrid systems. We consider systems composed of a base system with multiple features or controllers, each of which independently advise the system on how to react to input events so as to conform to their individual specifications. We propose a methodology for developing such systems in a modular manner based on the notion of conflict-tolerant features that are designed to continue offering advice even when their advice has been overridden in the past. We give a simple priority-based scheme forcomposing such features. This guarantees the maximal use of each feature. We provide a formal framework for specifying such features, and a compositional technique for verifying systems developed in this framework.
Resumo:
To perform super resolution of low resolution images, state-of-the-art methods are based on learning a pair of lowresolution and high-resolution dictionaries from multiple images. These trained dictionaries are used to replace patches in lowresolution image with appropriate matching patches from the high-resolution dictionary. In this paper we propose using a single common image as dictionary, in conjunction with approximate nearest neighbour fields (ANNF) to perform super resolution (SR). By using a common source image, we are able to bypass the learning phase and also able to reduce the dictionary from a collection of hundreds of images to a single image. By adapting recent developments in ANNF computation, to suit super-resolution, we are able to perform much faster and accurate SR than existing techniques. To establish this claim, we compare the proposed algorithm against various state-of-the-art algorithms, and show that we are able to achieve b etter and faster reconstruction without any training.
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We propose an algorithmic technique for accelerating maximum likelihood (ML) algorithm for image reconstruction in fluorescence microscopy. This is made possible by integrating Biggs-Andrews (BA) method with ML approach. The results on widefield, confocal, and super-resolution 4Pi microscopy reveal substantial improvement in the speed of 3D image reconstruction (the number of iterations has reduced by approximately one-half). Moreover, the quality of reconstruction obtained using accelerated ML closely resembles with nonaccelerated ML method. The proposed technique is a step closer to realize real-time reconstruction in 3D fluorescence microscopy. Microsc. Res. Tech. 78:331-335, 2015. (c) 2015 Wiley Periodicals, Inc.
Resumo:
In this paper, we propose a super resolution (SR) method for synthetic images using FeatureMatch. Existing state-of-the-art super resolution methods are learning based methods, where a pair of low-resolution and high-resolution dictionary pair are trained, and this trained pair is used to replace patches in low-resolution image with appropriate matching patches from the high-resolution dictionary. In this paper, we show that by using Approximate Nearest Neighbour Fields (ANNF), and a common source image, we can by-pass the learning phase, and use a single image for dictionary. Thus, reducing the dictionary from a collection obtained from hundreds of training images, to a single image. We show that by modifying the latest developments in ANNF computation, to suit super resolution, we can perform much faster and more accurate SR than existing techniques. To establish this claim we will compare our algorithm against various state-of-the-art algorithms, and show that we are able to achieve better and faster reconstruction without any training phase.
Resumo:
Here we extend the exploration of significantly super-Chandrasekhar magnetized white dwarfs by numerically computing axisymmetric stationary equilibria of differentially rotating magnetized polytropic compact stars in general relativity (GR), within the ideal magnetohydrodynamic regime. We use a general relativistic magnetohydrodynamic (GRMHD) framework that describes rotating and magnetized axisymmetric white dwarfs, choosing appropriate rotation laws and magnetic field profiles (toroidal and poloidal). The numerical procedure for finding solutions in this framework uses the 3 + 1 formalism of numerical relativity, implemented in the open source XNS code. We construct equilibrium sequences by varying different physical quantities in turn, and highlight the plausible existence of super-Chandrasekhar white dwarfs, with masses in the range of 2-3 solar mass, with central (deep interior) magnetic fields of the order of 10(14) G and differential rotation with surface time periods of about 1-10 s. We note that such white dwarfs are candidates for the progenitors of peculiar, overluminous Type Ia supernovae, to which observational evidence ascribes mass in the range 2.1-2.8 solar mass. We also present some interesting results related to the structure of such white dwarfs, especially the existence of polar hollows in special cases.
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Recently, efficient scheduling algorithms based on Lagrangian relaxation have been proposed for scheduling parallel machine systems and job shops. In this article, we develop real-world extensions to these scheduling methods. In the first part of the paper, we consider the problem of scheduling single operation jobs on parallel identical machines and extend the methodology to handle multiple classes of jobs, taking into account setup times and setup costs, The proposed methodology uses Lagrangian relaxation and simulated annealing in a hybrid framework, In the second part of the paper, we consider a Lagrangian relaxation based method for scheduling job shops and extend it to obtain a scheduling methodology for a real-world flexible manufacturing system with centralized material handling.
Resumo:
A beam-column resting on continuous Winkler foundation and discrete elastic supports is considered. The beam-column is of variable cross-section and the variation of sectional properties along the axis of the beam-column is deterministic. Young's modulus, mass per unit length and distributed axial loadings of the beam-column have a stochastic distribution. The foundation stiffness coefficient of the Winkler model, the stiffnesses of discrete elastic supports, stiffnesses of end springs and the end thrust, are all considered as random parameters. The material property fluctuations and distributed axial loadings are considered to constitute independent, one-dimension uni-variate homogeneous real stochastic fields in space. The foundation stiffness coefficient, stiffnesses of the discrete elastic supports, stiffnesses of end springs and the end thrust are considered to constitute independent random variables. Static response, free vibration and stability behaviour of the beam-column are studied. Hamilton's principle is used to formulate the problem using stochastic FEM. Sensitivity vectors of the response and stability parameters are evaluated. Using these statistics of free vibration frequencies, mode shapes, buckling parameters, etc., are evaluated. A numerical example is given.
Resumo:
High end network security applications demand high speed operation and large rule set support. Packet classification is the core functionality that demands high throughput in such applications. This paper proposes a packet classification architecture to meet such high throughput. We have implemented a Firewall with this architecture in reconflgurable hardware. We propose an extension to Distributed Crossproducting of Field Labels (DCFL) technique to achieve scalable and high performance architecture. The implemented Firewall takes advantage of inherent structure and redundancy of rule set by using our DCFL Extended (DCFLE) algorithm. The use of DCFLE algorithm results in both speed and area improvement when it is implemented in hardware. Although we restrict ourselves to standard 5-tuple matching, the architecture supports additional fields. High throughput classification invariably uses Ternary Content Addressable Memory (TCAM) for prefix matching, though TCAM fares poorly in terms of area and power efficiency. Use of TCAM for port range matching is expensive, as the range to prefix conversion results in large number of prefixes leading to storage inefficiency. Extended TCAM (ETCAM) is fast and the most storage efficient solution for range matching. We present for the first time a reconfigurable hardware implementation of ETCAM. We have implemented our Firewall as an embedded system on Virtex-II Pro FPGA based platform, running Linux with the packet classification in hardware. The Firewall was tested in real time with 1 Gbps Ethernet link and 128 sample rules. The packet classification hardware uses a quarter of logic resources and slightly over one third of memory resources of XC2VP30 FPGA. It achieves a maximum classification throughput of 50 million packet/s corresponding to 16 Gbps link rate for the worst case packet size. The Firewall rule update involves only memory re-initialization in software without any hardware change.
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In this work, two families of asymptotic near-tip stress fields are constructed in an elastic-ideally plastic FCC single crystal under mode I plane strain conditions. A crack is taken to lie on the (010) plane and its front is aligned along the [(1) over bar 01] direction. Finite element analysis is first used to systematically examine the stress distributions corresponding to different constraint levels. The general framework developed by Rice (Mech Mater 6:317-335, 1987) and Drugan (J Mech Phys Solids 49:2155-2176, 2001) is then adopted to generate low triaxiality solutions by introducing an elastic sector near the crack tip. The two families of stress fields are parameterized by the normalized opening stress (tau(A)(22)/tau(o)) prevailing in the plastic sector in front of the tip and by the coordinates of a point where elastic unloading commences in stress space. It is found that the angular stress variations obtained from the analytical solutions show good agreement with finite element analysis.
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This paper presents real-time simulation models of electrical machines on FPGA platform. Implementation of the real-time numerical integration methods with digital logic elements is discussed. Several numerical integrations are presented. A real-time simulation of DC machine is carried out on this FPGA platform and important transient results are presented. These results are compared to simulation results obtained through a commercial off-line simulation software.
Resumo:
High end network security applications demand high speed operation and large rule set support. Packet classification is the core functionality that demands high throughput in such applications. This paper proposes a packet classification architecture to meet such high throughput. We have Implemented a Firewall with this architecture in reconfigurable hardware. We propose an extension to Distributed Crossproducting of Field Labels (DCFL) technique to achieve scalable and high performance architecture. The implemented Firewall takes advantage of inherent structure and redundancy of rule set by using, our DCFL Extended (DCFLE) algorithm. The use of DCFLE algorithm results In both speed and area Improvement when It is Implemented in hardware. Although we restrict ourselves to standard 5-tuple matching, the architecture supports additional fields.High throughput classification Invariably uses Ternary Content Addressable Memory (TCAM) for prefix matching, though TCAM fares poorly In terms of area and power efficiency. Use of TCAM for port range matching is expensive, as the range to prefix conversion results in large number of prefixes leading to storage inefficiency. Extended TCAM (ETCAM) is fast and the most storage efficient solution for range matching. We present for the first time a reconfigurable hardware Implementation of ETCAM. We have implemented our Firewall as an embedded system on Virtex-II Pro FPGA based platform, running Linux with the packet classification in hardware. The Firewall was tested in real time with 1 Gbps Ethernet link and 128 sample rules. The packet classification hardware uses a quarter of logic resources and slightly over one third of memory resources of XC2VP30 FPGA. It achieves a maximum classification throughput of 50 million packet/s corresponding to 16 Gbps link rate for file worst case packet size. The Firewall rule update Involves only memory re-initialiization in software without any hardware change.