Browsing by Author "Mishra, D."

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    An investigation on effects of wire-EDT machining parameters on surface roughness of INCONEL 718
    (Elsevier Ltd, 2019) Naik, G.M.; Anjan, B.N.; Badiger, R.I.; Bellubbi, S.; Mishra, D.
    This paper studied the effects of machining parameters on surface roughness of wire EDT of INCONEL 718 super alloy. The investigated machining parameters were rotational speed, pulse-on time, pulse-off time, servo voltage, wire feed rate and flushing pressure. Analysis of variance (ANOVA) technique was used to find out the most significant parameters affecting the surface roughness. Results from ANOVA show that pulse-on time is significant variables to surface roughness of wire-EDT INCONEL 718 alloy. The surface roughness of the test specimen increased as these variables increased. Lastly, regression model was developed using a regression method to formulate the machining parameters to the surface roughness. The developed model was validated with an optimal setting parameters and the maximum prediction error of the model was less than 8%. © 2019 Elsevier Ltd.
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    Effective denoising with non-local means filter for reliable unwrapping of digital holographic interferometric fringes
    (2018) Aparna, P.L.; Waghmare, R.G.; Mishra, D.; Sai, Subrahmanyam, Gorthi, R.K.
    Estimation of phase from the complex interference field has become an emerging area of research for last few decades. The phase values obtained by using arctan function are limited to the interval (-?, ?]. Such phase map is known as wrapped phase. The unwrapping process, which produces continuous phase map from the wrapped phase, becomes tedious in presence of noise. In this paper, we propose a preprocessing technique that removes the noise from the interference field, thereby improving the performance of naive unwrapping algorithms. For de-noising of the complex field, real part and imaginary parts of the field are processed separately. Real-valued images (real and imaginary parts) are processed using non-local means filter with non-Euclidian distance measure. The de-noised real and imaginary parts are then combined to form a clean interference field. MATLAB�s unwrap function is used as unwrapping algorithm to get the continuous phase from the cleaned interference field. Comparison with the Frost�s filter validates the applicability of proposed approach for processing the noisy interference field. � 2018, Springer Nature Singapore Pte Ltd.
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    Effective denoising with non-local means filter for reliable unwrapping of digital holographic interferometric fringes
    (Springer Verlag service@springer.de, 2018) Aparna, P.L.; Waghmare, R.G.; Mishra, D.; Sai Subrahmanyam Gorthi, R.K.
    Estimation of phase from the complex interference field has become an emerging area of research for last few decades. The phase values obtained by using arctan function are limited to the interval (-π, π]. Such phase map is known as wrapped phase. The unwrapping process, which produces continuous phase map from the wrapped phase, becomes tedious in presence of noise. In this paper, we propose a preprocessing technique that removes the noise from the interference field, thereby improving the performance of naive unwrapping algorithms. For de-noising of the complex field, real part and imaginary parts of the field are processed separately. Real-valued images (real and imaginary parts) are processed using non-local means filter with non-Euclidian distance measure. The de-noised real and imaginary parts are then combined to form a clean interference field. MATLAB’s unwrap function is used as unwrapping algorithm to get the continuous phase from the cleaned interference field. Comparison with the Frost’s filter validates the applicability of proposed approach for processing the noisy interference field. © 2018, Springer Nature Singapore Pte Ltd.
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    Neighbor embedding based super-resolution using residual luminance
    (2015) Mishra, D.; Majhi, B.; Sa, P.K.
    Resolution plays a crucial role for study of information in an image. Therefore to enhance the resolution of an image, there are so many techniques have been proposed with respect to the reference images. In this paper, we proposed a new scheme for single image super-resolution based on the neighbor embedding method. Many feature selection methods have been proposed for the learning based super-resolution using manifold learning. Here a new feature selection has been proposed by combining first-order gradient and residual of the luminance component, inspired by Gaussian pyramid. In this Neighbor Embedding based Super-Resolution using the Residual Luminance (NESRRL) method the high resolution targeted image is estimated by the training image pairs. This approach imposes the local compatibility and smoothness constraints between patches in the estimated high resolution image. The experimental results show the comparisons of qualitative performance of proposed method with different existing methods using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). � 2014 IEEE.
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    Neighbor embedding based super-resolution using residual luminance
    (Institute of Electrical and Electronics Engineers Inc., 2015) Mishra, D.; Majhi, B.; Sa, P.K.
    Resolution plays a crucial role for study of information in an image. Therefore to enhance the resolution of an image, there are so many techniques have been proposed with respect to the reference images. In this paper, we proposed a new scheme for single image super-resolution based on the neighbor embedding method. Many feature selection methods have been proposed for the learning based super-resolution using manifold learning. Here a new feature selection has been proposed by combining first-order gradient and residual of the luminance component, inspired by Gaussian pyramid. In this Neighbor Embedding based Super-Resolution using the Residual Luminance (NESRRL) method the high resolution targeted image is estimated by the training image pairs. This approach imposes the local compatibility and smoothness constraints between patches in the estimated high resolution image. The experimental results show the comparisons of qualitative performance of proposed method with different existing methods using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). © 2014 IEEE.
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    Neural Pooling for Graph Neural Networks
    (Springer Science and Business Media Deutschland GmbH, 2024) Harsha, S.S.; Mishra, D.
    Tasks such as graph classification, require graph pooling to learn graph-level representations from constituent node representations. In this work, we propose two novel methods using fully connected neural network layers for graph pooling, namely Neural Pooling Method 1 and 2. Our proposed methods have the ability to handle variable number of nodes in different graphs, and are also invariant to the isomorphic structures of graphs. In addition, compared to existing graph pooling methods, our proposed methods are able to capture information from all nodes, collect second-order statistics, and leverage the ability of neural networks to learn relationships among node representations, making them more powerful. We perform experiments on graph classification tasks in the bioinformatics and social network domains to determine the effectiveness of our proposed methods. Experimental results show that our methods lead to an increase in graph classification accuracy over previous works and a general decrease in standard deviation across multiple runs indicating greater reliability. Experimental results also indicate that this improvement in performance is consistent across several datasets. © Springer Nature Switzerland AG 2024.
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    Restoration of space variant motion blurred images using adaptive particle filter techniques
    (2015) Thakur, A.; Benny, J.; Butola, B.S.; Mishra, D.
    Many of the images taken in real world suffer from imperfections in the imaging and capturing process and thus represent a degraded version of the original scene. Therefore the restoration of degraded images is an important task in image processing. In this paper we concentrated on space variant motion blurring methods for removing blur and noise from the recorded images. The proposed method uses adaptive particle filter techniques. The performance of algorithm depends on number of particles used for restoration. The performance of proposed algorithm is verified using peak signal to noise ratio (PSNR). � 2015 IEEE.
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    Restoration of space variant motion blurred images using adaptive particle filter techniques
    (Institute of Electrical and Electronics Engineers Inc., 2015) Thakur, A.; Benny, J.; Butola, B.S.; Mishra, D.
    Many of the images taken in real world suffer from imperfections in the imaging and capturing process and thus represent a degraded version of the original scene. Therefore the restoration of degraded images is an important task in image processing. In this paper we concentrated on space variant motion blurring methods for removing blur and noise from the recorded images. The proposed method uses adaptive particle filter techniques. The performance of algorithm depends on number of particles used for restoration. The performance of proposed algorithm is verified using peak signal to noise ratio (PSNR). © 2015 IEEE.