Surface reconstruction from disparate shading: an integration of shape-from-shading and stereopsis

A cooperative integration of stereopsis and shape-from-shading is presented. The integration makes the process of D surface reconstruction better constrained and more reliable. It also obviates the need for surface boundary conditions, and explicit information about the surface albedo and the light source direction, which can now be estimated in an iterative manner

Atmospheric chemistry and climate

Atmospheric chemistry is a branch of atmospheric science where major focus is the composition of the Earth's atmosphere. Knowledge of atmospheric composition is essential due to its interaction with (solar and terrestrial) radiation and interactions of atmospheric species (gaseous and particulate matter) with living organisms. Since atmospheric chemistry covers a vast range of topics, in this article the focus is on the chemistry of atmospheric aerosols with special emphasis on the Indian region. I present a review of the current state of knowledge of aerosol chemistry in India and propose future directions.

A pseudo-time EnKF incorporating shape based reconstruction for diffuse optical tomography

Purpose: The authors aim at developing a pseudo-time, sub-optimal stochastic filtering approach based on a derivative free variant of the ensemble Kalman filter (EnKF) for solving the inverse problem of diffuse optical tomography (DOT) while making use of a shape based reconstruction strategy that enables representing a cross section of an inhomogeneous tumor boundary by a general closed curve. Methods: The optical parameter fields to be recovered are approximated via an expansion based on the circular harmonics (CH) (Fourier basis functions) and the EnKF is used to recover the coefficients in the expansion with both simulated and experimentally obtained photon fluence data on phantoms with inhomogeneous inclusions. The process and measurement equations in the pseudo-dynamic EnKF (PD-EnKF) presently yield a parsimonious representation of the filter variables, which consist of only the Fourier coefficients and the constant scalar parameter value within the inclusion. Using fictitious, low-intensity Wiener noise processes in suitably constructed ``measurement'' equations, the filter variables are treated as pseudo-stochastic processes so that their recovery within a stochastic filtering framework is made possible. Results: In our numerical simulations, we have considered both elliptical inclusions (two inhomogeneities) and those with more complex shapes (such as an annular ring and a dumbbell) in 2-D objects which are cross-sections of a cylinder with background absorption and (reduced) scattering coefficient chosen as mu(b)(a)=0.01mm(-1) and mu('b)(s)=1.0mm(-1), respectively. We also assume mu(a) = 0.02 mm(-1) within the inhomogeneity (for the single inhomogeneity case) and mu(a) = 0.02 and 0.03 mm(-1) (for the two inhomogeneities case). The reconstruction results by the PD-EnKF are shown to be consistently superior to those through a deterministic and explicitly regularized Gauss-Newton algorithm. We have also estimated the unknown mu(a) from experimentally gathered fluence data and verified the reconstruction by matching the experimental data with the computed one. Conclusions: The PD-EnKF, which exhibits little sensitivity against variations in the fictitiously introduced noise processes, is also proven to be accurate and robust in recovering a spatial map of the absorption coefficient from DOT data. With the help of shape based representation of the inhomogeneities and an appropriate scaling of the CH expansion coefficients representing the boundary, we have been able to recover inhomogeneities representative of the shape of malignancies in medical diagnostic imaging. (C) 2012 American Association of Physicists in Medicine. [DOI: 10.1118/1.3679855]

Dependence of climate forcing and response on the altitude of black carbon aerosols

Black carbon aerosols absorb solar radiation and decrease planetary albedo, and thus can contribute to climate warming. In this paper, the dependence of equilibrium climate response on the altitude of black carbon is explored using an atmospheric general circulation model coupled to a mixed layer ocean model. The simulations model aerosol direct and semi-direct effects, but not indirect effects. Aerosol concentrations are prescribed and not interactive. It is shown that climate response of black carbon is highly dependent on the altitude of the aerosol. As the altitude of black carbon increases, surface temperatures decrease; black carbon near the surface causes surface warming, whereas black carbon near the tropopause and in the stratosphere causes surface cooling. This cooling occurs despite increasing planetary absorption of sunlight (i.e. decreasing planetary albedo). We find that the trend in surface air temperature response versus the altitude of black carbon is consistent with our calculations of radiative forcing after the troposphere, stratosphere, and land surface have undergone rapid adjustment, calculated as ``regressed'' radiative forcing. The variation in climate response from black carbon at different altitudes occurs largely from different fast climate responses; temperature dependent feedbacks are not statistically distinguishable. Impacts of black carbon at various altitudes on the hydrological cycle are also discussed; black carbon in the lowest atmospheric layer increases precipitation despite reductions in solar radiation reaching the surface, whereas black carbon at higher altitudes decreases precipitation.

Climate change induced risk in water quality control problems

A modeling framework is presented in this paper, integrating hydrologic scenarios projected from a General Circulation Model (GCM) with a water quality simulation model to quantify the future expected risk. Statistical downscaling with a Canonical Correlation Analysis (CCA) is carried out to develop the future scenarios of hydro-climate variables starting with simulations provided by a GCM. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold quality level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) presented in an earlier study is then used to develop adaptive policies to address the projected water quality risks. Application of the proposed methodology is demonstrated with the case study of Tunga-Bhadra river in India. The results showed that the projected changes in the hydro-climate variables tend to diminish DO levels, thus increasing the future risk levels of LWQ. (C) 2012 Elsevier B.V. All rights reserved.

Practical fully three-dimensional reconstruction algorithms for diffuse optical tomography

We have developed an efficient fully three-dimensional (3D) reconstruction algorithm for diffuse optical tomography (DOT). The 3D DOT, a severely ill-posed problem, is tackled through a pseudodynamic (PD) approach wherein an ordinary differential equation representing the evolution of the solution on pseudotime is integrated that bypasses an explicit inversion of the associated, ill-conditioned system matrix. One of the most computationally expensive parts of the iterative DOT algorithm, the reevaluation of the Jacobian in each of the iterations, is avoided by using the adjoint-Broyden update formula to provide low rank updates to the Jacobian. In addition, wherever feasible, we have also made the algorithm efficient by integrating along the quadratic path provided by the perturbation equation containing the Hessian. These algorithms are then proven by reconstruction, using simulated and experimental data and verifying the PD results with those from the popular Gauss-Newton scheme. The major findings of this work are as follows: (i) the PD reconstructions are comparatively artifact free, providing superior absorption coefficient maps in terms of quantitative accuracy and contrast recovery; (ii) the scaling of computation time with the dimension of the measurement set is much less steep with the Jacobian update formula in place than without it; and (iii) an increase in the data dimension, even though it renders the reconstruction problem less ill conditioned and thus provides relatively artifact-free reconstructions, does not necessarily provide better contrast property recovery. For the latter, one should also take care to uniformly distribute the measurement points, avoiding regions close to the source so that the relative strength of the derivatives for measurements away from the source does not become insignificant. (c) 2012 Optical Society of America

Linearization and Reconstruction of Non-linear Diffuse Optical Tomographic Image

Diffuse optical tomography (DOT) is one of the ways to probe highly scattering media such as tissue using low-energy near infra-red light (NIR) to reconstruct a map of the optical property distribution. The interaction of the photons in biological tissue is a non-linear process and the phton transport through the tissue is modelled using diffusion theory. The inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. The solution of the non-linear problem can be improved by modeling and optimizing the cost functional. The cost functional is f(x) = x(T)Ax - b(T)x + c and after minimization, the cost functional reduces to Ax = b. The spatial distribution of optical parameter can be obtained by solving the above equation iteratively for x. As the problem is non-linear, ill-posed and ill-conditioned, there will be an error or correction term for x at each iteration. A linearization strategy is proposed for the solution of the nonlinear ill-posed inverse problem by linear combination of system matrix and error in solution. By propagating the error (e) information (obtained from previous iteration) to the minimization function f(x), we can rewrite the minimization function as f(x; e) = (x + e)(T) A(x + e) - b(T)(x + e) + c. The revised cost functional is f(x; e) = f(x) + e(T)Ae. The self guided spatial weighted prior (e(T)Ae) error (e, error in estimating x) information along the principal nodes facilitates a well resolved dominant solution over the region of interest. The local minimization reduces the spreading of inclusion and removes the side lobes, thereby improving the contrast, localization and resolution of reconstructed image which has not been possible with conventional linear and regularization algorithm.

Zero-crossing approach to high-resolution reconstruction in frequency-domain optical-coherence tomography

We address the problem of high-resolution reconstruction in frequency-domain optical-coherence tomography (FDOCT). The traditional method employed uses the inverse discrete Fourier transform, which is limited in resolution due to the Heisenberg uncertainty principle. We propose a reconstruction technique based on zero-crossing (ZC) interval analysis. The motivation for our approach lies in the observation that, for a multilayered specimen, the backscattered signal may be expressed as a sum of sinusoids, and each sinusoid manifests as a peak in the FDOCT reconstruction. The successive ZC intervals of a sinusoid exhibit high consistency, with the intervals being inversely related to the frequency of the sinusoid. The statistics of the ZC intervals are used for detecting the frequencies present in the input signal. The noise robustness of the proposed technique is improved by using a cosine-modulated filter bank for separating the input into different frequency bands, and the ZC analysis is carried out on each band separately. The design of the filter bank requires the design of a prototype, which we accomplish using a Kaiser window approach. We show that the proposed method gives good results on synthesized and experimental data. The resolution is enhanced, and noise robustness is higher compared with the standard Fourier reconstruction. (c) 2012 Optical Society of America

Fast image reconstruction for fluorescence microscopy

Real-time image reconstruction is essential for improving the temporal resolution of fluorescence microscopy. A number of unavoidable processes such as, optical aberration, noise and scattering degrade image quality, thereby making image reconstruction an ill-posed problem. Maximum likelihood is an attractive technique for data reconstruction especially when the problem is ill-posed. Iterative nature of the maximum likelihood technique eludes real-time imaging. Here we propose and demonstrate a compute unified device architecture (CUDA) based fast computing engine for real-time 3D fluorescence imaging. A maximum performance boost of 210x is reported. Easy availability of powerful computing engines is a boon and may accelerate to realize real-time 3D fluorescence imaging. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.4754604]

Climate response to changes in atmospheric carbon dioxide and solar irradiance on the time scale of days to weeks

Recent studies show that fast climate response on time scales of less than a month can have important implications for long-term climate change. In this study, we investigate climate response on the time scale of days to weeks to a step-function quadrupling of atmospheric CO2 and contrast this with the response to a 4% increase in solar irradiance. Our simulations show that significant climate effects occur within days of a stepwise increase in both atmospheric CO2 content and solar irradiance. Over ocean, increased atmospheric CO2 warms the lower troposphere more than the surface, increasing atmospheric stability, moistening the boundary layer, and suppressing evaporation and precipitation. In contrast, over ocean, increased solar irradiance warms the lower troposphere to a much lesser extent, causing a much smaller change in evaporation and precipitation. Over land, both increased CO2 and increased solar irradiance cause rapid surface warming that tends to increase both evaporation and precipitation. However, the physiological effect of increased atmospheric CO2 on plant stomata reduces plant transpiration, drying the boundary layer and decreasing precipitation. This effect does not occur with increased solar irradiance. Therefore, differences in climatic effects from CO2 versus solar forcing are manifested within days after the forcing is imposed.

Prior image-constrained l(1)-norm-based reconstruction method for effective usage of structural information in diffuse optical tomography

A novel approach that can more effectively use the structural information provided by the traditional imaging modalities in multimodal diffuse optical tomographic imaging is introduced. This approach is based on a prior image-constrained-l(1) minimization scheme and has been motivated by the recent progress in the sparse image reconstruction techniques. It is shown that the proposed framework is more effective in terms of localizing the tumor region and recovering the optical property values both in numerical and gelatin phantom cases compared to the traditional methods that use structural information. (C) 2012 Optical Society of America

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using l(1)-norm-based linear image reconstruction method

Traditional image reconstruction methods in rapid dynamic diffuse optical tomography employ l(2)-norm-based regularization, which is known to remove the high-frequency components in the reconstructed images and make them appear smooth. The contrast recovery in these type of methods is typically dependent on the iterative nature of method employed, where the nonlinear iterative technique is known to perform better in comparison to linear techniques (noniterative) with a caveat that nonlinear techniques are computationally complex. Assuming that there is a linear dependency of solution between successive frames resulted in a linear inverse problem. This new framework with the combination of l(1)-norm based regularization can provide better robustness to noise and provide better contrast recovery compared to conventional l(2)-based techniques. Moreover, it is shown that the proposed l(1)-based technique is computationally efficient compared to its counterpart (l(2)-based one). The proposed framework requires a reasonably close estimate of the actual solution for the initial frame, and any suboptimal estimate leads to erroneous reconstruction results for the subsequent frames.

Multi-model climate change projections for India under representative concentration pathways

Climate projections for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) are made using the newly developed representative concentration pathways (RCPs) under the Coupled Model Inter-comparison Project 5 (CMIP5). This article provides multi-model and multi-scenario temperature and precipitation projections for India for the period 1860-2099 based on the new climate data. We find that CMIP5 ensemble mean climate is closer to observed climate than any individual model. The key findings of this study are: (i) under the business-as-usual (between RCP6.0 and RCP8.5) scenario, mean warming in India is likely to be in the range 1.7-2 degrees C by 2030s and 3.3-4.8 degrees C by 2080s relative to pre-industrial times; (ii) all-India precipitation under the business-as-usual scenario is projected to increase from 4% to 5% by 2030s and from 6% to 14% towards the end of the century (2080s) compared to the 1961-1990 baseline; (iii) while precipitation projections are generally less reliable than temperature projections, model agreement in precipitation projections increases from RCP2.6 to RCP8.5, and from short-to long-term projections, indicating that long-term precipitation projections are generally more robust than their short-term counterparts and (iv) there is a consistent positive trend in frequency of extreme precipitation days (e.g. > 40 mm/day) for decades 2060s and beyond. These new climate projections should be used in future assessment of impact of climate change and adaptation planning. There is need to consider not just the mean climate projections, but also the more important extreme projections in impact studies and as well in adaptation planning.