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"This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such as security, and network selection. Machine learning based methods are applied to solve these issues. This book also includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues. This book will also help readers understand how to use machine learning to address the security and communication challenges in VANETs. Vehicular ad-hoc networks (VANETs) support vehicle-to-vehicle communications and vehicle-to-infrastructure communications to improve the transmission security, help build unmanned-driving, and support booming applications of onboard units (OBUs). The high mobility of OBUs and the large-scale dynamic network with fixed roadside units (RSUs) make the VANET vulnerable to jamming. The anti-jamming communication of VANETs can be significantly improved by using unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help relay the OBU message to improve the signal-to-interference-plus-noise-ratio of the OBU signals, and thus reduce the bit-error-rate of the OBU message, especially if the serving RSUs are blocked by jammers and/or interference, which is also demonstrated in this book.This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues."

"Cyber warfare explores the battlefields, participants and tools and techniques used during today's digital conflicts. The concepts discussed in this book will give those involved in information security at all levels a better idea of how cyber conflicts are carried out now, how they will change in the future and how to detect and defend against espionage, hacktivism, insider threats and non-state actors like organized criminals and terrorists. Every one of our systems is under attack from multiple vectors, our defenses must be ready all the time and our alert systems must detect the threats every time."

"Segmentation is basically a process of pixel classification, the picture is segmented into subsets by assigning the individual pixel to classes. In this case when segmentation by thresholding it have to do classifying the pixels into " dark" and "light" classes, in an attempt to distinguish dark object from their light background.Similarly in edge detection pixels is classified into "edge" and "not edge" by thresholding the response of some difference operator that has high values when the rate of change of gray level is high.This thesis shows segmentation techniques base on gray level values, and some experiments process correspond to segmentation techniques."