R. Raja Kumar

A VANET - Vehicular Adhoc Network, que tem ve culos como n s comunicantes, uma mudan a de paradigma na liga o em rede com ve culos em movimento r pido que colocam desafios t cnicos como a varia o das caracter sticas do canal devido varia o din mica da velocidade dos ve culos. A VANET facilita o congestionamento do tr fego, evitando as vias de tr fego intenso e minimizando tamb m os acidentes. Neste trabalho, dedic mo-nos a analisar o conceito de VANET utilizando uma ferramenta de software, incentivando o leitor a escolher qualquer ferramenta em fun o das suas necessidades. Compar mos a VANET como um tipo espec fico de MANET (Mobile Adhoc Network). A abordagem Blockchain tamb m utilizada para a VANET. A VANET a antecessora da tecnologia V2X do 5G.

VANET - Vehicular Adhoc Network, che ha come nodi comunicanti i veicoli, un cambiamento paradigmatico nel networking con veicoli in rapido movimento che pongono sfide tecniche come la variazione delle caratteristiche del canale dovuta alla velocit dinamica dei veicoli. VANET facilita la congestione del traffico, evitando le corsie ad alta densit di traffico e riducendo al minimo gli incidenti. In questo lavoro ci siamo soffermati ad analizzare il concetto di VANET utilizzando uno strumento software, incoraggiando il lettore a scegliere qualsiasi strumento in base alle proprie esigenze. Abbiamo confrontato le VANET come un tipo specifico di MANET (Mobile Adhoc Network). L'approccio blockchain viene utilizzato anche per le VANET. VANET il predecessore della tecnologia V2X del 5G.

Superposition codes are used for reliable communication over the additive white Gaussian noise (AWGN) channel at rates approaching the channel capacity. In this work, we develop a communication scheme that codes data using superposition coding scheme in such a way, users in noisy channels can recover a part of the data while users with sufficient signal to noise ratio (SNR) can recover the entire data using subtractive decoding process. Simulation results show that between the complexity of dependencies in auxiliary codeword generation and that of the function, maps them into transmitted codeword.With billions of users worldwide, vying for communication networks (both wired as well as wireless), networking has undergone a paradigm shift. Traditionally optical communication systems are considered as extremely high bandwidth systems. But now with so many users competing for spectrum, even an optical communication system has to be classified as 'Bandwidth limited communication system'only So the demands made by the modern user on networking while he is on the move, is also equally exorbitant. To improve the spectral efficiency, current designs adopt universal frequency reuse.

The tremendous growth of data due to Internet and electronic commerce has created serious challenges to the researches in pattern recognition. There is a need of processing and analysing data. Advances in data mining and knowledge discovery provide the requirement of new approaches to reduce the data. The reduction of data is an important problem that attracts the eyes of researches in pattern recognition. The reduction of data is the core problem in classifiers especially for Nearest Neighbor Classifier since it stores the entire training set for classifying the query patterns and also the classifier needs to compute the distances between the query pattern and each and every pattern from the stored training set. Hence the time and space requirements are high for Nearest Neighbor Classifier. In this book, methods are proposed to overcome the computational requirements of Nearest Neighbor Classifier. This book explores some of the possible remedies to overcome the problems with Nearest Neighbor based classifiers. The main disadvantages of Nearest Neighbor Classifier can be avoided using the proposed methods in this book.

Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage.Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.Topics Covered:The many academic areas covered in this publication include, but are not limited to:Data visualizationDistributed Computing SystemsOpinion MiningPrivacy and securityRisk analysisSocial Network AnalysisText Data AnalyticsWeb Data Analytics