Indian Institute of Technology Kharagpur

About: Indian Institute of Technology Kharagpur is a education organization based out in Kharagpur, India. It is known for research contribution in the topics: Natural rubber & Dielectric. The organization has 16887 authors who have published 38658 publications receiving 714526 citations.

Effect of zinc oxide nanoparticles as cure activator on the properties of natural rubber and nitrile rubber

Abstract: Zinc oxide (ZnO) nanoparticles were synthesized by homogeneous precipitation and calcination method and were then characterized by transmission electron microscopy and X-ray diffraction analysis. Synthesized ZnO was found to have no impurity and had a dimension ranging from 30–70 nm with an average of 50 nm. The effect of these ZnO nanoparticles as cure activator was studied for the first time in natural rubber (NR) and nitrile rubber (NBR) and compared with conventional rubber grade ZnO with special reference to mechanical and dynamic mechanical properties. From the rheograph, the maximum torque value was found to increase for both NR and NBR compounds containing ZnO nanoparticles. ZnO nanoparticles were found to be more uniformly dispersed in the rubber matrix in comparison with the conventional rubber grade ZnO as evident from scanning electron microscopy/X-ray dot mapping analysis. The tensile strength was observed to improve by 80% for NR when ZnO nanoparticles were used as cure activator instead of conventional rubber grade ZnO. An improvement of 70% was observed in the case of NBR. The glass transition temperature (Tg) showed a positive shift by 6°C for both NR and NBR nanocomposites, which indicated an increase in crosslinking density. The swelling ratio was found to decrease in the case of both NR and NBR, and volume fraction of rubber in swollen gel was observed to increase, which supported the improvement in mechanical and dynamic mechanical properties. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007

Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier

Abstract: Auscultation, the technique of listening to heart sounds with a stethoscope can be used as a primary detection system for diagnosing heart valve disorders. Phonocardiogram, the digital recording of heart sounds is becoming increasingly popular as it is relatively inexpensive. In this paper, a technique to improve the performance of the Least Square Support Vector Machine (LSSVM) is proposed for classification of normal and abnormal heart sounds using wavelet based feature set. In the proposed technique, the Lagrange multiplier is modified based on Least Mean Square (LMS) algorithm, which in turn modifies the weight vector to reduce the classification error. The basic idea is to enlarge the separating boundary surface, such that the separability between the clusters is increased. The updated weight vector is used at the time of testing. The performance of the proposed systems is evaluated on 64 different recordings of heart sounds comprising of normal and five different pathological cases. It is found that the proposed technique classifies the heart sounds with higher recognition accuracy than competing techniques.