The objective of this SpringerBrief is to present security architectures and incentive mechanisms to realize system availability for D2D communications. D2D communications enable devices to communicate directly, improving resource utilization, enhancing user's throughput, extending battery lifetime, etc. However, due to the open nature of D2D communications, there are two substantial technical challenges when applied to large-scale applications, that is, security and availability which is demonstrated in this book. This SpringerBrief proposes a secure data sharing protocol, which merges the advantages of public key cryptography and symmetric encryption, to achieve data security in D2D communications. Furthermore, a joint framework involving both the physical and application layer security technologies is proposed for multimedia service over D2D communications thus the scalable security service can be achieved without changing the current communication framework. Additionally, as the system availability largely depends on the cooperation degree of the users, a graph-theory based cooperative content dissemination scheme is proposed to achieve maximal Quality of Experience (QoE) with fairness and efficiency. This SpringerBrief will be a valuable resource for advanced-level students and researchers who want to learn more about cellular networks.

This book constitutes the thoroughly refereed post-conference proceedings of the First International Joint Conference on Green Communication and Networking (GreeNets 2011), held in Colmar, France, on October 5-7, 2011. The 16 revised full papers presented were carefully selected and reviewed from numerous submissions and explain the scope and challenges of designing, building, and deploying GreeNets. In this regard, the conference aims to establish a forum to bring together research professionals from diverse fields including green mobile networks, system architectures, networking & communication protocols, applications, test-bed and prototype, traffic balance and energy-efficient cooperation transmission, system and application issues related to GreenNets.

This SpringerBrief discusses the most recent research in the field of multimedia QoE evaluation, with a focus on how to evaluate subjective multimedia QoE problems from objective techniques. Specifically, this SpringerBrief starts from a comprehensive overview of multimedia QoE definition, its influencing factors, traditional modeling and prediction methods. Subsequently, the authors introduce the procedure of multimedia service data collection, preprocessing and feature extractions. Then, describe several proposed multimedia QoE modeling and prediction techniques in details. Finally, the authors illustrate how to implement and demonstrate multimedia QoE evaluation in the big data platform. This SpringerBrief provides readers with a clear picture on how to make full use of multimedia service data to realize multimedia QoE evaluation. With the exponential growth of the Internet technologies, multimedia services become immensely popular. Users can enjoy multimedia services from operators or content providers by TV, computers and mobile devices. User experience is important for network operators and multimedia content providers. Traditional QoS (quality of service) can not entirely and accurately describe user experience. It is natural to research the quality of multimedia service from the users' perspective, defined as multimedia quality of experience (QoE). However, multimedia QoE evaluation is difficult, because user experience is abstract and subjective, hard to quantify and measure. Moreover, the explosion of multimedia service and emergence of big data, all call for a new and better understanding of multimedia QoE. This SpringerBrief targets advanced-level students, professors and researchers studying and working in the fields of multimedia communications and information processing. Professionals, industry managers, and government research employees working in these same fields will also benefit from this SpringerBrief.

Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment. The book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision. This edited book provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions. The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.