Bøger af Vikrant Sharma

Neste trabalho de estudo, centramo-nos nos problemas de desempenho dos protocolos de encaminhamento, Optimized Link State Routing (OLSR), Ad Hoc On Demand Distance Vetor (AODV), Dynamic Source Routing (DSR) e Temporally Ordered Routing Algorithm (TORA) em redes ad-hoc móveis e autónomas. Apresentamos o Ad-hoc On Demand Distance Vetor Routing (AODV), um novo algoritmo para o funcionamento de tais redes ad-hoc. O nosso novo algoritmo de encaminhamento é bastante adequado para uma rede dinâmica de arranque automático, tal como exigido pelos utilizadores que pretendem utilizar redes ad-hoc. Mostramos que o nosso algoritmo se adapta a grandes populações de nós móveis que pretendem formar redes ad-hoc. Também incluímos uma metodologia de avaliação e resultados de simulação para verificar o funcionamento do nosso algoritmo.

In this study work, we focus on performance issues of routing protocols, Optimized Link State Routing (OLSR), Ad Hoc On Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Temporally Ordered Routing Algorithm (TORA) in mobility and standalone ad-hoc networks. We present Ad-hoc On Demand Distance Vector Routing (AODV), a novel algorithm for the operation of such ad-hoc networks. Our new routing algorithm is quite suitable for a dynamic self starting network, as required by users wishing to utilize ad-hoc networks.We show that our algorithm scales to large populations of mobile nodes wishing to form ad-hoc networks. We also include an evaluation methodology and simulation results to verify the operation of our algorithm.

Breast cancer is believed to be one in all most far reaching causes of death among women and second highest reason for deaths among humans. Today millions of women are suffering from breast cancer. It is difficult to detect breast cancer in the early stages due to its dormant nature and very few signs and symptoms. Therefore the main reason behind the diagnosis of breast cancer is to decrease the death rate by achieving accurate results. Hence there is a need to automate the diagnostic process to improve the sensitivity and accuracy of the tests. An artificial intelligence based hierarchical fuzzy expert system is developed which consists of risk parameters, subjective parameters, mammograms and cancerous cell images to diagnose breast cancer with precise results.