Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

Performance Evaluation of IEEE 802.11ah Protocol in Wireless Area Network

Abstract: Internet of Things (IoT) can improve the lives of millions of people. In IoT the smart objects are converted from physical to virtual world to connect with Internet. The IoT enables smart objects to observe, identify, and understand the world without intervention of human-being (Machine-to-Machine communication: M2M). Due to the increasing interest of IoT, M2M, and wireless sensor applications the Wireless Local Area Network (WLAN) IEEE802.11 standard has taken interest to define a new standard 802.11ah which focuses mainly on IoT applications. The IEEE 802.11ah exploits 802.11 standard advantages and satisfies the IoT requirements by handling large number of devices and that also with reduced power consumption at node/object level. This standard is still in progress and is expected to be approved by 2016. In this paper we would evaluate the performance of IEEE802.11ah protocol through an analytical model using Markov chain model and then evaluate in MATLAB 2013a.

Intuitionistic Fuzzy Clustering Method with Spatial Information for MRI Image Segmentation

Abstract: Fuzzy c-means is one of the popular clustering technique which has been utilized for medical image analysis. Intuitionistic fuzzy set theory based clustering is an extension of fuzzy c-means which is used for medical image segmentation due to its promising nature for handling the vagueness and uncertainty. The performance of image segmentation is not good in the presence of noise. Many fuzzy and intuitionistic fuzzy set theory based clustering methods have been reported in the literature to handle noise in the segmentation process. In the process of handling noise, most of these methods use smoothing which ignores the important structural information (such as edges and other fine details). In this research work to address this issue, the optimization problem of the proposed IFCM with spatial neighborhood information (IFCMSNI) method is formulated with a novel spatial regularization term which is based on the neighborhood membership value with the advantage of both the intuitionistic fuzzy set theory and a spatial regularization term to handle noise associated with medical images. In the proposed method, the image is represented in the form of Intuitionistic Fuzzy Sets (IFSs) using Sugeno’s negation function. In order to validate the effectiveness of the proposed method, experiments have been carried out on a synthetic image dataset and two publicly available human brain MRI dataset. The segmentation performance of the proposed method is compared with FCM, IFCM, FCMS, FLICM and IIFCM methods in terms of dice score and average segmentation accuracy. The experimental finding endorses the proposed method for image segmentation.