Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Inverse modeling of heat transfer with application to solidification and quenching(2002) Prabhu, K.N.; Ashish, A.A.The inverse modeling of heat transfer involves the estimation of boundary conditions from the knowledge of thermal history inside a heat conducting body. Inverse analysis is extremely useful in modeling of contact heat transfer at interfaces of engineering surface during materials processing. In the present work, the one-dimensional transient heat conduction equation was inversely modeled in both cartesian as well as cylindrical coordinates. The model is capable of estimating heat flux transients, chill surface temperature, and total heat flow from the source to the sink for an input of thermal history inside the sink. The methodology was adopted to solve boundary heat transfer problems inversely during solidification and quenching. The response of the inverse solution to measured sensor data was studied by carrying out numerical experiments involving the use of varying grid size and time steps, future temperatures, and regularization techniques.Item Steganalysis: Using the blind deconvolution to retrieve the hidden data(2011) Jidesh, P.; George, S.Steganography has gained a substantial attention due to its application in wide areas. Steganography as it literally mean is hiding the information (stego data) inside the data (communication data) so that the receiver can only extract the desired information from the data. Steganalysis is the reverse process of steganography in which the information about the original data is hardly available, from the received data the extractor needs to identify the original data. Since this belong to a class of inverse problems it is hard to find the approximate match of the original data from the received one. In most of the cases this will fall under the category of ill-posed problems. The stego-data that has been embedded into the communication data can be considered as linear bounded operator operating on the input data and the reverse process (the Steganalysis) can be thought like a deconvolution problem by which we can extract the original data. Here we are assuming the watermarking as a linear operation with a bounded linear operator K : X→Y where X and Y are spaces of Bounded Variation (BV). The forward problem (the Steganography) is a direct convolution and the reverse (backward) problem (steganalysis) is a de-convolution procedure. In this work we are embedding a Gaussian random variable array with zero mean and with a specific variance into the data and we show how the original data can be extracted using the regularization method. The results are shown to substantiate the ability of the method to perform steganalysis. © 2011 IEEE.Item Finite dimensional realization of a Guass-Newton method for ill-posed hammerstein type operator equations(2012) Erappa, M.E.; George, S.Finite dimensional realization of an iterative regularization method for approximately solving the non-linear ill-posed Hammerstein type operator equations KF(x) = f, is considered. The proposed method is a combination of the Tikhonov regularization and Guass-Newton method. The advantage of the proposed method is that, we use the Fr chet derivative of F only at one point in each iteration. We derive the error estimate under a general source condition and the regularization parameter is chosen according to balancing principle of Pereverzev and Schock (2005). The derived error estimate is of optimal order and the numerical example provided proves the efficiency of the proposed method. © 2012 Springer-Verlag.Item Non-local Gradient Fidelity Model for Multiplicative Gamma Noise Removal(Institute of Electrical and Electronics Engineers Inc., 2018) Banothu, B.; Jidesh, P.In this paper a non-local gradient vector flow model is designed for restoration of images corrupted with Gamma distributed (speckle) noise and linear blurring artefacts. The filter effectively preserves edges and finer details in the course of its evolution due to the presence of the non-local TV based diffusion term and the piecewise linear approximation is reduced considerably by the gradient fidelity term present in the model. The model is found suitable for restoration of various images from the field of satellite and clinical imaging. The experimental results are shown and compared for different image data sets both visually and qualitatively using various statistical measures. © 2017 IEEE.Item A Graph Spectral Approach for Restoring Images Corrupted by Shot-Noise(Springer Science and Business Media Deutschland GmbH, 2021) Jidesh, P.; Bini, A.A.Image restoration is a fundamental problem in image processing. Usually, images gets deteriorated while storing or transmitting them. Image restoration is an ill-posed inverse problem, wherein one has to restore the original data with a priori information or assumption regarding the degradation model and its characteristics. The literature is too elaborate for various restorations under different assumptions on the degradation-architecture. This paper introduces a strategy based on graph spectral theory to restore images with non-local filters controlled by a loss function. The non-local similarity-based weight function controls the restoration process resulting in the preservation of local image features considerably well. The parameter controlled adaptive fidelity term helps to re-orient the diffusion to handle data correlated shot-noise following a Poisson distribution, which is pretty common in many medical and telescopic imaging applications. Experimental results are conforming to the fact that the proposed model performs well in restoring images of the different intensity distributions. © 2021, Springer Nature Singapore Pte Ltd.