Knowledge Institute of Technology

About: Knowledge Institute of Technology is a based out in . It is known for research contribution in the topics: Wireless sensor network & Encryption. The organization has 178 authors who have published 164 publications receiving 1368 citations.

A comprehensive review on performance, combustion and emission characteristics of biodiesel fuelled diesel engines

Abstract: Direct injection diesel engines are more popular in the automotive sector than spark ignition (SI) engines due to its fuel lean operation However, the demand of fossil fuel is rising day by day and hence the major fuel source of diesel engine, the petroleum based fuel, is depleting rapidly Many countries depend mainly on imported fossil fuels due to lack of fuel reserves and it has great impact on the economy In addition to this, the major concerns of diesel engine are its oxides of nitrogen and smoke emissions Therefore, for the past several decades extensive efforts are being made to search for alternate fuels to overcome the dependence on fossil fuel and environment pollution In this regard, several alternate fuels namely hydrogen, oxygenated fuels like alcohol fuels, dimethyl ether and biodiesel fuels etc, have been extensively analysed Recent studies show that biodiesel is one of the most promising alternate fuels for diesel engines because of its biodegradable, oxygenated, sulphur free and renewable characteristics Hence, it is getting the attention of researchers all over the world The blends of biodiesel with fossil diesel have many benefits like reduction in emissions, lower engine wear, lesser engine oil consumption and comparable thermal efficiency vis-a-vis diesel fuel Exhaustive experimental works have been carried out to analyse the suitability of biodiesel fuel as alternate fuel and to explore their advantages in diesel engines Hence, this paper is attempted to present a comprehensive review on the performance, combustion and emission characteristics of some important biodiesel fuels on diesel engines This comprehensive review on the published literature will be helpful to the researchers to understand the state-of-the-art technology of the biodiesel fuelled compression ignition engine

Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain

Abstract: This paper does the qualitative comparison of Fuzzy C-means (FCM) and k-Means segmentation, with histogram guided initialization, on tumor edema complex MR images. The accuracy of any segmentation scheme depends on its ability to distinguish different tissue classes, separately. Hence, there is a serious pre-requisite to evaluate this ability before employing the segmentation scheme on medical images. This paper evaluates the ability of FCM and k-Means to segment Gray Matter (GM), White Matter (WM), Cerebro-Spinal Fluid (CSF), Necrotic Focus of Glioblastoma Multiforme (GBM) and the perifocal vasogenic edema from pre-processed T1 contrast axial plane MR images of tumor edema complex. The experiment reveals that FCM identifies the vasogenic edema and the white matter as a single tissue class and similarly gray matter and necrotic focus, also. k-Means is able to characterize these regions comparatively better than FCM. FCM identifies only three tissue classes whereas; k-Means identifies all the six classes. The experimental evaluation of k-Means and FCM, with histogram guided initialization is performed in Matlab®.