A Model for Quantum Stochastic Absorption in Absorbing Disordered Media

Wave propagation in coherently absorbing disordered media is generally modeled by adding a complex part to the real part of the potential. In such a case, it is already understood that the complex potential plays a duel role; it acts as an absorber as well as a reflector due to the mismatch of the phase of the real and complex parts of the potential. Although this model gives expected results for weakly absorbing disordered media, it gives unphysical results for the strong absorption regime where it causes the system to behave like a perfect reflector. To overcome this issue, we develop a model here using stochastic absorption for the modeling of absorption by "fake", or "side", channels obviating the need for a complex potential. This model of stochastic absorption eliminates the reflection that is coupled with the absorption in the complex potential model and absorption is proportional to the magnitude of the absorbing parameter. Solving the statistics of the reflection coefficient and its phase for both the models, we argue that stochastic absorption is a potentially better way of modeling absorbing disordered media.Comment: 5 pages, 4 figure

Significance of thermal fluctuations and hydrodynamic interactions in receptor-ligand mediated adhesive dynamics of a spherical particle in wall bound shear flow

The dynamics of adhesion of a spherical micro-particle to a ligand-coated wall, in shear flow, is studied using a Langevin equation that accounts for thermal fluctuations, hydrodynamic interactions and adhesive interactions. Contrary to the conventional assumption that thermal fluctuations play a negligible role at high P$\acute{e}$clet numbers, we find that for particles with low surface densities of receptors, rotational diffusion caused by fluctuations about the flow and gradient directions aids in bond formation, leading to significantly greater adhesion on average, compared to simulations where thermal fluctuations are completely ignored. The role of wall hydrodynamic interactions on the steady state motion of a particle, when the particle is close to the wall, has also been explored. At high P$\acute{e}$clet numbers, the shear induced force that arises due to the stresslet part of the Stokes dipole, plays a dominant role, reducing the particle velocity significantly, and affecting the states of motion of the particle. The coupling between the translational and rotational degrees of freedom of the particle, brought about by the presence of hydrodynamic interactions, is found to have no influence on the binding dynamics. On the other hand, the drag coefficient, which depends on the distance of the particle from the wall, plays a crucial role at low rates of bond formation. A significant difference in the effect of both the shear force and the position dependent drag force, on the states of motion of the particle, is observed when the P$\acute{e}$let number is small.Comment: The manuscript has been accepted as an article in Physical Review E Journa

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201

An application of Cox regression model to the analysis of grouped pulmonary tuberculosis survival data

The recent statistical literature attests to a considerable current interest in specialised statistical methods for the analysis of time to occurrence or failure time data in medical research. Frequently the primary objective of a failure time Study concerns with the association between certain co-variates Z = (Z1, ..., Zp) and the time T > 0 to the occurrence of a certain event. For example a clinical study may be designed to compare several treatment programmes in respect to the time T to recurrence of a disease or response to treatment of a disease. The regression vector Z would include indicator components for treatment as well as other prognostic factors

Co-variate analysis of tuberculosis data using Cox's regression model

A regression model which allows for analysis of censored survival data adjusting for continuous as well as discrete covariates and varying with time has been proposed by Cox. The hazard rate could be modelled as a function of both time and covariates and the hazard rate could be represented as the product of two terms, the funs representing an unadjusted force of mortality which can be estimated non-parametrically and the second adjusting for the linear combination of a particular covariate profile. In this paper an attempt is made to demonstrate the value of this model with pulmonary tuberculosis data in quantifying the effects of disease, demographic and treatment variables

Maximal expiratory flow rates in South Indian sportsmen.

The maximal Expiratory Flow Volume (MEFV) loop is superior to peak expiratory Flow Rate (PFR) and Forced Expiratory Volume in one second (FEV1) in that it describes total information during Forced Vital Capacity (FVC) test. MEFV loop was utilised to identify ventilatory adaptation in lungs of sportsmen. Twenty non-smoking sportsmen who were active participants in athletics at inter-university and interstate level were selected for the study. After a thorough clinical examination MEFV loop was recorded in the sitting posture using a computerised (P. K. Morgan (U.K.) pulmonary function test equipment and x-y recorder. When the results were analysed, it was found that mean PFR was 7.89 ± 0.29 L/S and flow rates of air at 25 % (V max 25 %) 50 % (V max 50%) and 75% of FVC were 7.12 ± 0.29 L/S, 5.18 ± 0.27 L/S and 2.87 ± 0.24 L/S respectively. Mean Forced Mid Flow (FMF) was 5.09 ± 0.24 L/S. When compared to the predicted values of our laboratory, the mean percentage predicted values of these parameters were as follows : PFR=102.5%, vmax 25% = 107.0%, vmx 50% = 110.7%, vmax 75% = 134.2% and FMF 114.2 %. It is evident from these results that sportsmen have increasingly higher flow rates at terminal part of FVC curve. Mean Flow Volume Loop drawn for the sportsman fails on the right side of the predicted normal curve, indicating thereby that the airways are patent even at every low lung volumes to let the air flow out at faster rate. This may be due to adaptation to habitual ventilatory training on the air ways, especially small airways, in sportsmen

Review of Non-destructive Testing (NDT) Techniques and their applicability to thick walled composites

A tier 1 automotive supplier has developed a novel and unique kinetic energy recovery storage system for both retro-fitting and OEM application for public transport systems where periodic stop start behaviour is paramount. A major component of the system is a composite flywheel spinning at up to 36,000 rpm (600 Hz). Material soundness is an essential requirement of the flywheel to ensure failure does not occur. The component is particularly thick for a composite being up to 30 mm cross section in some places. The geometry, scale and material make-up pose some challenges for conventional NDT systems. Damage can arise in composite materials during material processing, fabrication of the component or in-service activities among which delamination, cracks and porosity are the most common defects. A number of non-destructive testing (NDT) techniques are effective in testing components for defects without damaging the component. NDT techniques like Ultrasonic Testing, X-Ray, Radiography, Thermography, Eddy current and Acoustic Emission are current techniques for various testing applications. Each of these techniques uses different principles to look into the material for defects. However, the geometry, physical and material properties of the component being tested are important factors in the applicability of a technique. This paper reviews these NDT techniques and compares them in terms of characteristics and applicability to composite parts