arXiv subjects

Subrata Sarkar

Publications and source records attributed to Subrata Sarkar.

6 recordsLinked to original sources

arXiv2023

Aero-thermal analysis of a laminar separation bubble subjected to varying free-stream turbulence: Large Eddy Simulation

A quantitative analysis illustrating salient features of a Laminar Separation Bubble (LSB), its transition forming coherent structures, and associated heat transfer has been performed on a flat plate for varying free stream turbulence (fst) between 1.2% to 10.3%. A well-resolved Large Eddy Simulation (LES) developed in-house is used for the purpose. Flow separation has been induced by imposing an adverse pressure gradient on the upper boundary of a Cartesian domain. Isotropic perturbations are introduced at the inlet to mimic grid turbulence. With an increase of fst, an upstream shift in the mean reattachment point has been observed while the onset of separation remains almost invariant, shrinking the bubble length significantly. The transition of the shear layer is triggered by the Kelvin-Helmholtz (K-H) instability for fst of less than 3.3%, while Klebanoff modes (Kmodes) dictate the flow transition at fst greater than 6.5%. Further, a mixed mode, i.e., both K-H and K-modes, contribute to the flow transition at a moderate level of fst, lying between 3.3% and 6.5%. Thus, the roll-up of the shear layer appears in the second half of the bubble shedding large-scale vortices that keep their identity far downstream at low fst levels. On the contrary, the streamwise streaks via K-modes prior to the separation are found to interact with the LSB, resulting in an earlier breakdown of the shear layer with abundant small-scale vortices downstream at the moderate to high fst levels. However, higher surface-normal heat flux is associated with large-scale energetic coherent vortices.

physics.flu-dyn↗

arXiv2020

Malaria detection from RBC images using shallow Convolutional Neural Networks

The advent of Deep Learning models like VGG-16 and Resnet-50 has considerably revolutionized the field of image classification, and by using these Convolutional Neural Networks (CNN) architectures, one can get a high classification accuracy on a wide variety of image datasets. However, these Deep Learning models have a very high computational complexity and so incur a high computational cost of running these algorithms as well as make it hard to interpret the results. In this paper, we present a shallow CNN architecture which gives the same classification accuracy as the VGG-16 and Resnet-50 models for thin blood smear RBC slide images for detection of malaria, while decreasing the computational run time by an order of magnitude. This can offer a significant advantage for commercial deployment of these algorithms, especially in poorer countries in Africa and some parts of the Indian subcontinent, where the menace of malaria is quite severe.

eess.IV↗

arXiv2020

MRI Image Recovery using Damped Denoising Vector AMP

Motivated by image recovery in magnetic resonance imaging (MRI), we propose a new approach to solving linear inverse problems based on iteratively calling a deep neural-network, sometimes referred to as plug-and-play recovery. Our approach is based on the vector approximate message passing (VAMP) algorithm, which is known for mean-squared error (MSE)-optimal recovery under certain conditions. The forward operator in MRI, however, does not satisfy these conditions, and thus we design new damping and initialization schemes to help VAMP. The resulting DD-VAMP++ algorithm is shown to outperform existing algorithms in convergence speed and accuracy when recovering images from the fastMRI database for the practical case of Cartesian sampling.

cs.IT↗

arXiv2019

Bilinear Recovery using Adaptive Vector-AMP

We consider the problem of jointly recovering the vector $\boldsymbol{b}$ and the matrix $\boldsymbol{C}$ from noisy measurements $\boldsymbol{Y} = \boldsymbol{A}(\boldsymbol{b})\boldsymbol{C} + \boldsymbol{W}$, where $\boldsymbol{A}(\cdot)$ is a known affine linear function of $\boldsymbol{b}$ (i.e., $\boldsymbol{A}(\boldsymbol{b})=\boldsymbol{A}_0+\sum_{i=1}^Q b_i \boldsymbol{A}_i$ with known matrices $\boldsymbol{A}_i$). This problem has applications in matrix completion, robust PCA, dictionary learning, self-calibration, blind deconvolution, joint-channel/symbol estimation, compressive sensing with matrix uncertainty, and many other tasks. To solve this bilinear recovery problem, we propose the Bilinear Adaptive Vector Approximate Message Passing (BAd-VAMP) algorithm. We demonstrate numerically that the proposed approach is competitive with other state-of-the-art approaches to bilinear recovery, including lifted VAMP and Bilinear GAMP.

cs.IT↗

arXiv2018

Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis

Estimating a vector $\mathbf{x}$ from noisy linear measurements $\mathbf{Ax}+\mathbf{w}$ often requires use of prior knowledge or structural constraints on $\mathbf{x}$ for accurate reconstruction. Several recent works have considered combining linear least-squares estimation with a generic or "plug-in" denoiser function that can be designed in a modular manner based on the prior knowledge about $\mathbf{x}$. While these methods have shown excellent performance, it has been difficult to obtain rigorous performance guarantees. This work considers plug-in denoising combined with the recently-developed Vector Approximate Message Passing (VAMP) algorithm, which is itself derived via Expectation Propagation techniques. It shown that the mean squared error of this "plug-and-play" VAMP can be exactly predicted for high-dimensional right-rotationally invariant random $\mathbf{A}$ and Lipschitz denoisers. The method is demonstrated on applications in image recovery and parametric bilinear estimation.

cs.IT↗

arXiv2018

On the Convergence of Approximate Message Passing with Arbitrary Matrices

Approximate message passing (AMP) methods and their variants have attracted considerable recent attention for the problem of estimating a random vector $\mathbf{x}$ observed through a linear transform $\mathbf{A}$. In the case of large i.i.d. zero-mean Gaussian $\mathbf{A}$, the methods exhibit fast convergence with precise analytic characterizations on the algorithm behavior. However, the convergence of AMP under general transforms $\mathbf{A}$ is not fully understood. In this paper, we provide sufficient conditions for the convergence of a damped version of the generalized AMP (GAMP) algorithm in the case of quadratic cost functions (i.e., Gaussian likelihood and prior). It is shown that, with sufficient damping, the algorithm is guaranteed to converge, although the amount of damping grows with peak-to-average ratio of the squared singular values of the transforms $\mathbf{A}$. This result explains the good performance of AMP on i.i.d. Gaussian transforms $\mathbf{A}$, but also their difficulties with ill-conditioned or non-zero-mean transforms $\mathbf{A}$. A related sufficient condition is then derived for the local stability of the damped GAMP method under general cost functions, assuming certain strict convexity conditions.

cs.IT↗