74 resultados para Histogram Stretch
Resumo:
We investigate the correlation between the band propagation property and the nature and amplitude of serrations in the Portevin-Le Chatelier effect within the framework of the Ananthakrishna model. Several significant results emerge. First, we find that spatial and temporal correlations continuously increase with strain rate from type C to type A bands. Consequently, the nature of the bands also changes continuously from type C to A bands, and so do the changes in the associated serrations. Second, even the smallest extent of propagation induces small amplitude serrations. The spatial extent of band propagation is directly correlated with the duration of small amplitude serrations, a result that is consistent with recent experiments. This correspondence allows one to estimate the spatial extent of band propagation by just measuring the temporal stretch of small amplitude serrations. Therefore, this should be of practical value when only stress versus strain is recorded. Third, the average stress drop magnitude of the small amplitude serrations induced by the propagating bands remains small and nearly constant with strain rate. As a consequence, the fully propagating type A bands are in a state of criticality. We rationalize the increasing levels of spatial and temporal correlations found with increasing strain rates. Lastly, the model also predicts several band morphologies seen in experiments including the Luders-like propagating band. (C) 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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We carry out an extensive and high-resolution direct numerical simulation of homogeneous, isotropic turbulence in two-dimensional fluid films with air-drag-induced friction and with polymer additives. Our study reveals that the polymers (a) reduce the total fluid energy, enstrophy, and palinstrophy; (b) modify the fluid energy spectrum in both inverse-and forward-cascade regimes; (c) reduce small-scale intermittency; (d) suppress regions of high vorticity and strain rate; and (e) stretch in strain-dominated regions. We compare our results with earlier experimental studies and propose new experiments.
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Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).
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17 independent crystal structures of family I uracil-DNA glycosylase from Mycobacterium tuberculosis (MtUng) and its complexes with uracil and its derivatives, distributed among five distinct crystal forms, have been determined. Thermodynamic parameters of binding in the complexes have been measured using isothermal titration calorimetry. The two-domain protein exhibits open and closed conformations, suggesting that the closure of the domain on DNA binding involves conformational selection. Segmental mobility in the enzyme molecule is confined to a 32-residue stretch which plays a major role in DNA binding. Uracil and its derivatives can bind to the protein in two possible orientations. Only one of them is possible when there is a bulky substituent at the 50 position. The crystal structures of the complexes provide a reasonable rationale for the observed thermodynamic parameters. In addition to providing fresh insights into the structure, plasticity and interactions of the protein molecule, the results of the present investigation provide a platform for structure-based inhibitor design.
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If a deuterated molecule containing strong intramolecular hydrogen bonds is placed in a hydrogenated solvent, it may preferentially exchange deuterium for hydrogen. This preference is due to the difference between the vibrational zero-point energy for hydrogen and deuterium. It is found that the associated fractionation factor (I) is correlated with the strength of the intramolecular hydrogen bonds. This correlation has been used to determine the length of the H-bonds (donor-acceptor separation) in a diverse range of enzymes and has been argued to support the existence of short low-barrier H-bonds. Starting with a potential energy surface based on a simple diabatic state model for H-bonds, we calculate (I) as a function of the proton donor-acceptor distance R. For numerical results, we use a parameterization of the model for symmetric 0-H. ``.0 bonds R. H. McKenzie, Chem. Phys. Lett. 535, 196 (2012)]. We consider the relative contributions of the 0-H stretch vibration, O-H bend vibrations (both in plane and out of plane), tunneling splitting effects at finite temperature, and the secondary geometric isotope effect. We compare our total (I) as a function of R with NMR experimental results for enzymes, and in particular with an earlier model parametrization (D(R), used previously to determine bond lengths. (C) 2015 AIP Publishing LLC.
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Zircon has been recognized as the unaltered part of the Earth's history which preserves nearly 4 billion year record of earth's evolution. Zircon preserves igneous and metamorphic processes during its formation and remains unaffected by sedimentary processes and crustal recycling. U-Pb and Lu-Hf in zircon work as geochronometer and geochemical tracer respectively. Zircon provide valuable information about the source composition of the rocks and the intrinsic details of an unseen crust-mantle processes. The world wide data of U-Pb and Lu-Hf isotope systems in zircon reveal crustal evolution through geological history. Moreover, the U-Pb age pattern of zircons show distinct peaks attributed to preservation of crustal rocks or mountain building during supercontinent assembly. The histogram of continental crust preservation shows that nearly one-third of continental crust was formed during the Archean, almost 20% was formed during Paleoproterozoic and 14% in last 400 Ma.
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Active biological processes like transcription, replication, recombination, DNA repair, and DNA packaging encounter bent DNA. Machineries associated with these processes interact with the DNA at short length (<100 base pair) scale. Thus, the study of elasticity of DNA at such length scale is very important. We use fully atomistic molecular dynamics (MD) simulations along with various theoretical methods to determine elastic properties of dsDNA of different lengths and base sequences. We also study DNA elasticity in nucleosome core particle (NCP) both in the presence and the absence of salt. We determine stretch modulus and persistence length of short dsDNA and nucleosomal DNA from contour length distribution and bend angle distribution, respectively. For short dsDNA, we find that stretch modulus increases with ionic strength while persistence length decreases. Calculated values of stretch modulus and persistence length for DNA are in quantitative agreement with available experimental data. The trend is opposite for NCP DNA. We find that the presence of histone core makes the DNA stiffer and thus making the persistence length 3-4 times higher than the bare DNA. Similarly, we also find an increase in the stretch modulus for the NCP DNA. Our study for the first time reports the elastic properties of DNA when it is wrapped around the histone core in NCP. We further show that the WLC model is inadequate to describe DNA elasticity at short length scale. Our results provide a deeper understanding of DNA mechanics and the methods are applicable to most protein-DNA complexes.
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An action is typically composed of different parts of the object moving in particular sequences. The presence of different motions (represented as a 1D histogram) has been used in the traditional bag-of-words (BoW) approach for recognizing actions. However the interactions among the motions also form a crucial part of an action. Different object-parts have varying degrees of interactions with the other parts during an action cycle. It is these interactions we want to quantify in order to bring in additional information about the actions. In this paper we propose a causality based approach for quantifying the interactions to aid action classification. Granger causality is used to compute the cause and effect relationships for pairs of motion trajectories of a video. A 2D histogram descriptor for the video is constructed using these pairwise measures. Our proposed method of obtaining pairwise measures for videos is also applicable for large datasets. We have conducted experiments on challenging action recognition databases such as HMDB51 and UCF50 and shown that our causality descriptor helps in encoding additional information regarding the actions and performs on par with the state-of-the art approaches. Due to the complementary nature, a further increase in performance can be observed by combining our approach with state-of-the-art approaches.
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In this paper, we have proposed an anomaly detection algorithm based on Histogram of Oriented Motion Vectors (HOMV) 1] in sparse representation framework. Usual behavior is learned at each location by sparsely representing the HOMVs over learnt normal feature bases obtained using an online dictionary learning algorithm. In the end, anomaly is detected based on the likelihood of the occurrence of sparse coefficients at that location. The proposed approach is found to be robust compared to existing methods as demonstrated in the experiments on UCSD Ped1 and UCSD Ped2 datasets.
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Interactions of turbulence, molecular transport, and energy transport, coupled with chemistry play a crucial role in the evolution of flame surface geometry, propagation, annihilation, and local extinction/re-ignition characteristics of intensely turbulent premixed flames. This study seeks to understand how these interactions affect flame surface annihilation of lean hydrogen-air premixed turbulent flames. Direct numerical simulations (DNSs) are conducted at different parametric conditions with a detailed reaction mechanism and transport properties for hydrogen-air flames. Flame particle tracking (FPT) technique is used to follow specific flame surface segments. An analytical expression for the local displacement flame speed (S-d) of a temperature isosurface is considered, and the contributions of transport, chemistry, and kinematics on the displacement flame speed at different turbulence-flame interaction conditions are identified. In general, the displacement flame speed for the flame particles is found to increase with time for all conditions considered. This is because, eventually all flame surfaces and their resident flame particles approach annihilation by reactant island formation at the end of stretching and folding processes induced by turbulence. Statistics of principal curvature evolving in time, obtained using FPT, suggest that these islands are ellipsoidal on average enclosing fresh reactants. Further examinations show that the increase in S-d is caused by the increased negative curvature of the flame surface and eventual homogenization of temperature gradients as these reactant islands shrink due to flame propagation and turbulent mixing. Finally, the evolution of the normalized, averaged, displacement flame speed vs. stretch Karlovitz number are found to collapse on a narrow band, suggesting that a unified description of flame speed dependence on stretch rate may be possible in the Lagrangian description. (C) 2015 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
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This article is aimed to delineate groundwater sources in Holocene deposits area in the Gulf of Mannar Coast from Southern India. For this purpose 2-D electrical resistivity tomography (ERT), hydrochemical and granulomerical studies were carried out and integrated to identify hydrogeological structures and portable groundwater resource in shallow depths which in general appears in the coastal tracts. The 2-D ERT was used to determine the two-dimensional subsurface geological formations by multicore cable with Wenner array. Low resistivity of 1-5 Omega m for saline water appeared due to calcite at the depth of about 5 m below the ground level (bgl). Sea water intrusion was observed around the maximum resistivity as 5 Omega m at the 8 m depth, bgl in the calcite environs, but the calcareous sandstone layer shows around 15-64 Omega m at the 6 m depth, bgl. The hydrochemical variation of TDS, HCO3-, Cl-, Na+, K+, Ca2+, and Mg2+ concentrations was observed for the saline and sea water intrusion in the groundwater system. The granulometic analysis shows that the study area was under the sea between 5400 and 3000 year ago. The events of ice melting an unnatural ice-stone rain/hail among 5000-4000 years ago resulted in the inundation of sea over the area and deposits of late Holocene marine transgression formation up to Puthukottai quartzite region for a stretch of around 17 km.
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Human detection is a complex problem owing to the variable pose that they can adopt. Here, we address this problem in sparse representation framework with an overcomplete scale-embedded dictionary. Histogram of oriented gradient features extracted from the candidate image patches are sparsely represented by the dictionary that contain positive bases along with negative and trivial bases. The object is detected based on the proposed likelihood measure obtained from the distribution of these sparse coefficients. The likelihood is obtained as the ratio of contribution of positive bases to negative and trivial bases. The positive bases of the dictionary represent the object (human) at various scales. This enables us to detect the object at any scale in one shot and avoids multiple scanning at different scales. This significantly reduces the computational complexity of detection task. In addition to human detection, it also finds the scale at which the human is detected due to the scale-embedded structure of the dictionary.
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A new approach for rapid resonance assignments in proteins based on amino acid selective unlabeling is presented. The method involves choosing a set of multiple amino acid types for selective unlabeling and identifying specific tripeptides surrounding the labeled residues from specific 2D NMR spectra in a combinatorial manner. The methodology directly yields sequence specific assignments, without requiring a contiguously stretch of amino acid residues to be linked, and is applicable to deuterated proteins. We show that a 2D N-15,H-1]HSQC spectrum with two 2D spectra can result in approximate to 50% assignments. The methodology was applied to two proteins: an intrinsically disordered protein (12kDa) and the 29kDa (268 residue) -subunit of Escherichia coli tryptophan synthase, which presents a challenging case with spectral overlaps and missing peaks. The method can augment existing approaches and will be useful for applications such as identifying active-site residues involved in ligand binding, phosphorylation, or protein-protein interactions, even prior to complete resonance assignments.
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Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.