Faculty Publications
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Item Experimental investigation of effect of specimen thickness on fracture toughness of Al-TiC composites(Gruppo Italiano Frattura, 2016) Raviraj, M.S.; Sharanaprabhu, C.M.; Mohan Kumar, G.C.In this paper, the macro and micro-mechanical fracture behavior was studied for aluminum (Al6061) alloy matrix, reinforced with various proportions of TiC particles such as 3wt%, 5wt% and 7wt%. The Al6061-TiC metal matrix composites were produced by stir casting method to ensure uniform distribution of the TiC particulates in the Al matrix. The compact tension (CT) specimens were machined according to ASTM E399 specifications to evaluate the fracture toughness for Al6061-TiC metal matrix composites. The CT specimens were machined for crack to width (a/W) ratio of 0.5 and thickness to width (B/W) ratios of 0.2 to 0.7 with an increment of 0.1. Load versus crack mouth opening displacement (CMOD) data was plotted to estimate stress intensity factor KQ for various thicknesses of the specimen. The fracture toughness KIC was obtained by plotting stress intensity factor versus thickness to width ratios of specimen data. The fracture toughness of these composites varied between 16.4-19.2 MPa?m. Scanning Electron Microscope (SEM) studies was made on the fractured surface of the specimens to understand the micro-mechanisms of failure involve in these composites. Void initiation is more significant in the matrix near the interface. The micro-cracks grow from these micro-voids and crack propagates by linking these micro cracks locating the crack path preferentially in the matrix adjacent to the interface indicating ductile fracture. © 2016, Gruppo Italiano Frattura. All rights reserved.Item Perceptually lossless coder for volumetric medical image data(Academic Press Inc. apjcs@harcourt.com, 2017) Chandrika, B.K.; Aparna., P.; Sumam David, S.S.With the development of modern imaging techniques, every medical examination would result in a huge volume of image data. Analysis, storage and/or transmission of these data demands high compression without any loss of diagnostically significant data. Although, various 3-D compression techniques have been proposed, they have not been able to meet the current requirements. This paper proposes a novel method to compress 3-D medical images based on human vision model to remove visually insignificant information. The block matching algorithm applied to exploit the anatomical symmetry remove the spatial redundancies. The results obtained are compared with those of lossless compression techniques. The results show better compression without any degradation in visual quality. The rate-distortion performance of the proposed coders is compared with that of the state-of-the-art lossy coders. The subjective evaluation performed by the medical experts confirms that the visual quality of the reconstructed image is excellent. © 2017Item Additive Manufacturing of Syntactic Foams: Part 1: Development, Properties, and Recycling Potential of Filaments(Minerals, Metals and Materials Society 184 Thorn Hill Road Warrendale PA 15086, 2018) Singh, A.K.; Patil, B.; Hoffmann, N.; Saltonstall, B.; Doddamani, M.; Gupta, N.This work focuses on developing filaments of high-density polyethylene (HDPE) and their hollow particle-filled syntactic foams for commercial three-dimensional (3D) printers based on fused filament fabrication technology. Hollow fly-ash cenospheres were blended by 40 wt.% in a HDPE matrix to produce syntactic foam (HDPE40) filaments. Further, the recycling potential was studied by pelletizing the filaments again to extrude twice (2×) and three times (3×). The filaments were tensile tested at 10?4 s?1, 10?3 s?1, and 10?2 s?1 strain rates. HDPE40 filaments show an increasing trend in modulus and strength with the strain rate. Higher density and modulus were noticed for 2× filaments compared to 1× filaments because of the crushing of some cenospheres in the extrusion cycle. However, 2× and 3× filament densities are nearly the same, showing potential for recycling them. The filaments show better properties than the same materials processed by conventional injection molding. Micro-CT scans show a uniform dispersion of cenospheres in all filaments. © 2018, The Minerals, Metals & Materials Society.Item Multi-Modal Medical Image Fusion with Adaptive Weighted Combination of NSST Bands Using Chaotic Grey Wolf Optimization(Institute of Electrical and Electronics Engineers Inc., 2019) Asha, C.S.; Lal, S.; Gurupur, V.P.; Saxena, P.U.P.Recently, medical image fusion has emerged as an impressive technique in merging the medical images of different modalities. Certainly, the fused image assists the physician in disease diagnosis for effective treatment planning. The fusion process combines multi-modal images to incur a single image with excellent quality, retaining the information of original images. This paper proposes a multi-modal medical image fusion through a weighted blending of high-frequency subbands of nonsubsampled shearlet transform (NSST) domain via chaotic grey wolf optimization algorithm. As an initial step, the NSST is applied on source images to decompose into the multi-scale and multi-directional components. The low-frequency bands are fused based on a simple max rule to sustain the energy of an individual. The texture details of input images are preserved by an adaptively weighted combination of high-frequency images using a recent chaotic grey wolf optimization algorithm to minimize the distance between the fused image and source images. The entire process emphasizes on retaining the energy of the low-frequency band and the transferring of texture features from source images to the fused image. Finally, the fused image is formed using inverse NSST of merged low and high-frequency bands. The experiments are carried out on eight different disease datasets obtained from Brain Atlas, which consists of MR-T1 and MR-T2, MR and SPECT, MR and PET, and MR and CT. The effectiveness of the proposed method is validated using more than 100 pairs of images based on the subjective and objective quality assessment. The experimental results confirm that the proposed method performs better in contrast with the current state-of-the-art image fusion techniques in terms of entropy, VIFF, and FMI. Hence, the proposed method will be helpful for disease diagnosis, medical treatment planning, and surgical procedure. © 2013 IEEE.Item Automated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approach(Institute of Electrical and Electronics Engineers Inc., 2019) Srinidhi, C.L.; Aparna., P.; Rajan, J.Separation of the vascular tree into arteries and veins is a fundamental prerequisite in the automatic diagnosis of retinal biomarkers associated with systemic and neurodegenerative diseases. In this paper, we present a novel graph search metaheuristic approach for automatic separation of arteries/veins (A/V) from color fundus images. Our method exploits local information to disentangle the complex vascular tree into multiple subtrees, and global information to label these vessel subtrees into arteries and veins. Given a binary vessel map, a graph representation of the vascular network is constructed representing the topological and spatial connectivity of the vascular structures. Based on the anatomical uniqueness at vessel crossing and branching points, the vascular tree is split into multiple subtrees containing arteries and veins. Finally, the identified vessel subtrees are labeled with A/V based on a set of hand-crafted features trained with random forest classifier. The proposed method has been tested on four different publicly available retinal datasets with an average accuracy of 94.7%, 93.2%, 96.8%, and 90.2% across AV-DRIVE, CT-DRIVE, INSPIRE-AVR, and WIDE datasets, respectively. These results demonstrate the superiority of our proposed approach in outperforming the state-of-The-Art methods for A/V separation. © 1992-2012 IEEE.Item A fully-automated system for identification and classification of subsolid nodules in lung computed tomographic scans(Elsevier Ltd, 2019) Savitha, G.; Padikkal, P.A fully-automated computer-aided detection (CAD) system is being proposed in this paper for identification and classification of subsolid lung nodules present in Computed Tomography(CT) scans. The system consists of two stages. The first stage aims at detecting locations of the nodules, while the second one classifies the same into the solid and subsolid category. The system performs segmentation of the region of interest (ROI) and extraction of relevant features from the segmented ROI. Graylevel covariance matrix (GLCM) is being used to extract the Feature vectors. Principle component analysis (PCA) algorithm is used to select significant features in the feature space formed by the vectors. The nodule localization is performed using support vector machine, fuzzy C-means, and random forest classification algorithms. The identified nodules are further grouped into solid and subsolid nodules by extracting histogram of gradient (HoG) features adopting K-means and support vector machine (SVM) classifiers. A large number of annotated images from the widely available benchmark database is tested to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using the relevant quantitative measures. The developed CAD system is found to identify subsolid nodules with a high percentage of accuracy. © 2019 Elsevier LtdItem Dynamic impact behavior of syntactic foam core sandwich composites(SAGE Publications Ltd info@sagepub.co.uk, 2020) Breunig, P.; Damodaran, V.; Shahapurkar, K.; Waddar, S.; Doddamani, M.; Jeyaraj, J.; Prabhakar, P.Sandwich composites and syntactic foams independently have been used in many engineering applications. However, there has been minimal effort towards taking advantage of the weight saving ability of syntactic foams in the cores of sandwich composites, especially with respect to the impact response of structures. To that end, the goal of this study is to investigate the mechanical response and damage mechanisms associated with syntactic foam core sandwich composites subjected to dynamic impact loading. In particular, this study investigates the influence of varying cenosphere volume fraction in syntactic foam core sandwich composites subjected to varying dynamic impact loading and further elucidates the extent and diversity of corresponding damage mechanisms. The syntactic foam cores are first fabricated using epoxy resin as the matrix and cenospheres as the reinforcement with four cenosphere volume fractions of 0% (pure epoxy), 20%, 40%, and 60%. The sandwich composite panels are then manufactured using the vacuum assisted resin transfer molding process with carbon fiber/vinyl ester facesheets. Dynamic impact tests are performed on the sandwich composite specimens at two energy levels of 80 J and 160 J, upon which the data are post-processed to gain a quantitative understanding of the impact response and damage mechanisms incurred by the specimens. A qualitative understanding is obtained through micro-computed tomography scanning of the impacted specimens. In addition, a finite element model is developed to investigate the causes for different damage mechanisms observed in specimens with different volume fractions. © The Author(s) 2019.Item Multiple-Coil Magnetic Resonance Image Denoising and Deblurring With Nonlocal Total Bounded Variation(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2020) Holla Kayyar, K.S.; Padikkal, P.; Bini, A.A.One of the complex tasks in image restoration is to restore images under data correlated noise contaminations. In real-time medical imaging scenarios, such as Magnetic Resonance (MR), Ultrasound, Computed Tomography(CT) etc, it is observed that, the data of interest is severely degraded with data dependent noise interventions. A Nonlocal Total Bounded Variation (NLTBV) approach is being proposed in this paper to denoise as well as deblur multiple-coil MR images corrupted by non-central Chi distributed noise and linear Gaussian blur. The energy functional for the restoration model is derived by applying the Maximum A Posteriori (MAP) estimator on the Probability Density Function (PDF) of the non-central Chi distribution. The numerical implementation is performed using the split-Bregman iterative scheme to improve the convergence rate. The proposed model is compared with the other state of the art models in terms of both visual and statistical quantifications to demonstrate it's performance. © 2019, © 2019 IETE.Item A holistic deep learning approach for identification and classification of sub-solid lung nodules in computed tomographic scans(Elsevier Ltd, 2020) Savitha, G.; Padikkal, P.Prompt detection of malignant lung nodules significantly improves the chance of survivability of the affected patients. The lung nodules in their early stages appear as subsolid or part-solid nodules whose identification remains a challenging task. Many of the present lung nodule detection systems fail to identify the nodules in their early stages. Limitations in the feature extraction process lead to significant false-positive rates, which eventually diminish the accuracy aspects of the system. In this study, a sophisticated deep learning approach is employed for feature extraction which improves the nodule localization or identification stage of the system. Further, the false positives sneaking out of the system are drastically reduced by adopting a Conditional Random Framework in the model. The quantitative demonstrations prove the efficiency of the model to detect sub-solid nodules in CT images. Thus the employability of the model for early detection of the nodules is tested and verified. © 2020 Elsevier LtdItem Advantages of cryogenic machining technique over without-coolant and with-coolant machining on SS316(IOP Publishing Ltd, 2021) Karthik, M.; Malghan, R.L.; Shettigar, A.K.; Herbert, M.A.; Rao, S.S.The analysis concentrated towards the influence of speed of the spindle along with a cryogenic (LN2) cooling technique in treating SS316 usingCNC(Computerized numerical control) milling machine. An comparative study path was set and anlyised among three states i.e. Dry (Without coolant), wet (With coolant) and cryogenic (With liquid LN2) machining using coated carbide inserts. The coolant used in case of wet machining was water-soluble, referred to as cutting fluid. The experimental range falls in 3 different levels of spindle speed (SS), such as low level (1000 rpm), medium level (2000 rpm), and high level (3000 rpm), respectively. Meanwhile, feed rate (FR) and depth of cut (DOC) were reserved steadily with 450 mm min-1, 1 mm separately. This vital focus is towards cryogenic (LN2) machining effects and its perception of machinability on SS316, such as tool wear -TW(?m), cutting force-CF (N), cutting temperature-CT (oC) and surface roughness-Ra (?m). The experiments were conducted and documented with cryogenic (LN2) techniques to establish the fairness and practicability of the method to compare with without-coolant (dry) and with-coolant (wet) machining. The attained statistical results in comparison of LN2 method over without-coolant and with-coolant machining concerned to test cases for CF- Fx (N), CT(oC), Ra (?m) andFW(?m) are 53.21%-34.20%, 65.88%-44.51%, 75.43%-44.27%,&59.76%-23.10%, respectively. © 2021 IOP Publishing Ltd.
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