Jadavpur University

About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Fuzzy logic. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.

High P–T Polymetamorphism, Dehydration Melting, and Generation of Migmatites and Granites in the Higher Himalayan Crystalline Complex, Sikkim, India

Abstract: The Higher Himalayan Crystalline Complex (HHC) in Sikkim, India, consists of pelitic migmatites interlayered with calc-silicate rocks and minor metabasites. Microstructural relationships between the mineral phases and deformational fabric elements and zoning characteristics of garnet indicate a prolonged and complex polymetamorphic history for the HHC. The pelitic rocks in the upper part of the HHC contain the assemblage plagioclase + quartz ± garnet + K-feldspar + biotite + sillimanite and are devoid of muscovite. Most of the mineral phases grew syn- to post-tectonically. Mineral growth coincided with development of a pervasive fabric (S 2 ) during prograde metamorphism (M 2 ), in the early stages of the collisional event. Some garnet grains display texturally distinct cores and rims, which are separated by calcic plagioclase. This texture suggests an earlier metamorphic episode (M l ). M may represent pre-Himalayan metamorphism and decompression of the HHC. Later, the collisional event led to renewed burial of the HHC and M 2 reactions. M 2 is reflected by dehydration melting of muscovite and biotite to form granitic melts, which either crystallized in situ to form leucosomes, or migrated from their source regions to form larger granitic bodies. Geothermobarometric estimates for peak M 2 conditions indicate P=10-12 kbar, T=800-850°C. A subsequent metamorphic event (M 3 ) occurred because of 5 kbar of decompression. M 3 is recorded by the breakdown of porphyroblastic garnet in all HHC lithologies. Higher temperature and pressure estimates come from progressively higher structural levels of the complex. The thermal gradient of 5.5°C/km is anomalous, and may be a consequence of thermal buffering during melting. However, the pressure gradient of 0.25 kbar/km resembles a normal lithostatic gradient, which suggests that the HHC in Sikkim represents an inverted Barrovian sequence. This inverted zonation of the HHC is probably the result of large-scale structural inversion and/or tectonic juxtaposition because of ductile shearing.

On the Design of Provably Secure Lightweight Remote User Authentication Scheme for Mobile Cloud Computing Services

Abstract: Secure and efficient lightweight user authentication protocol for mobile cloud computing becomes a paramount concern due to the data sharing using Internet among the end users and mobile devices. Mutual authentication of a mobile user and cloud service provider is necessary for accessing of any cloud services. However, resource constraint nature of mobile devices makes this task more challenging. In this paper, we propose a new secure and lightweight mobile user authentication scheme for mobile cloud computing, based on cryptographic hash, bitwise XOR, and fuzzy extractor functions. Through informal security analysis and rigorous formal security analysis using random oracle model, it has been demonstrated that the proposed scheme is secure against possible well-known passive and active attacks and also provides user anonymity. Moreover, we provide formal security verification through ProVerif 1.93 simulation for the proposed scheme. Also, we have done authentication proof of our proposed scheme using the Burrows-Abadi-Needham logic. Since the proposed scheme does not exploit any resource constrained cryptosystem, it has the lowest computation cost in compare to existing related schemes. Furthermore, the proposed scheme does not involve registration center in the authentication process, for which it is having lowest communication cost compared with existing related schemes.

Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms

Abstract: Selection of the optimal values of different process parameters, such as pulse duration, pulse frequency, duty factor, peak current, dielectric flow rate, wire speed, wire tension, effective wire offset of wire electrical discharge machining (WEDM) process is of utmost importance for enhanced process performance The major performance measures of WEDM process generally include material removal rate, cutting width (kerf), surface roughness and dimensional shift Although different mathematical techniques, like artificial neural network, gray relational analysis, simulated annealing, desirability function, Pareto optimality approach, etc have already been applied for searching out the optimal parametric combinations of WEDM processes, but in most of the cases, sub-optimal or near-optimal solutions have been arrived at In this paper, an attempt is made to apply six most popular population-based non-traditional optimization algorithms, ie genetic algorithm, particle swarm optimization, sheep flock algorithm, ant colony optimization, artificial bee colony and biogeography-based optimization for single and multi-objective optimization of two WEDM processes The performance of these algorithms is also compared and it is observed that biogeography-based optimization algorithm outperforms the others

Obstructive sleep apnoea detection using convolutional neural network based deep learning framework.

Abstract: This letter presents an automated obstructive sleep apnoea (OSA) detection method with high accuracy, based on a deep learning framework employing convolutional neural network. The proposed work develops a system that takes single lead electrocardiography signals from patients for analysis and detects the OSA condition of the patient. The results show that the proposed method has some advantages in solving such problems and it outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the detection of OSA. The proposed network performs both feature learning and classifies the features in a supervised manner. The scheme is computation-intensive, but can achieve very high degree of accuracy-on an average a margin of more than 9% compared to other published literature till date. The method also has a good immunity to the contamination of the signals by noise. Even with pessimistic signal to noise ratio values considered here, the methods already reported are not able to outshine the present method. The software for the algorithm reported here can be a good contender to constitute a module that can be integrated with a portable medical diagnostic system.