Security and Privacy Issues in IoT Devices and Sensor Networks by
Security and Privacy Issues in IoT Devices and Sensor Networks investigates security breach issues in IoT and sensor networks, exploring various solutions. The book follows a two-fold approach, first focusing on the fundamentals and theory surrounding sensor networks and IoT security. It then explores practical solutions that can be implemented to develop security for these elements, providing case studies to enhance understanding. Machine learning techniques are covered, as well as other security paradigms, such as cloud security and cryptocurrency technologies. The book highlights how these techniques can be applied to identify attacks and vulnerabilities, preserve privacy, and enhance data security. This in-depth reference is ideal for industry professionals dealing with WSN and IoT systems who want to enhance the security of these systems. Additionally, researchers, material developers and technology specialists dealing with the multifarious aspects of data privacy and security enhancement will benefit from the book's comprehensive information.
Elements of Radio Frequency Energy Harvesting and Wireless Power Transfer Systems by
This book specifically focusses on Radio Frequency (RF) energy harvesting and comprehensively introduces the circuit design for RF energy harvesting and wireless power transfer applications. It focusses on systematic overview of key components for RF energy harvesting and wireless transfer applications. The book serves as a single point of reference for the practicing engineer and researchers for referencing potential sources and elements for RF energy harvesting, to provide rapid training and design guidelines in this area including: Different sensing elements used in RF energy harvesting (RFEH) and Wireless Power Transfer (WPT). Illustration of some design examples using simulation software. Inclusions of mathematical expressions and design problems.
Optical Sensing in Power Transformers by
A cutting-edge, advanced level, exploration of optical sensing application in power transformers Optical Sensing in Power Transformers is filled with the critical information and knowledge on the optical techniques applied in power transformers, which are important and expensive components in the electric power system. Effective monitoring of systems has proven to decrease the transformer lifecycle cost and increase a high level of availability and reliability. It is commonly held that optical sensing techniques will play an increasingly significant role in online monitoring of power transformers. In this comprehensive text, the authors--noted experts on the topic--present a scholarly review of the various cutting-edge optical principles and methodologies adopted for online monitoring of power transformers. Grounded in the authors' extensive research, the book examines optical techniques and high-voltage equipment testing and provides the foundation for further application, prototype, and manufacturing. The book explores the principles, installation, operation, condition detection, monitoring, and fault diagnosis of power transformers. This important text: Provides a current exploration of optical sensing application in power transformers Examines the critical balance and pros and cons of cost and quality of various optical condition monitoring techniques Presents a wide selection of techniques with appropriate technical background Extends the vision of condition monitoring testing and analysis Treats condition monitoring testing and analysis tools together in a coherent framework Written for researchers, technical research and development personnel, manufacturers, and frontline engineers, Optical Sensing in Power Transformers offers an up-to-date review of the most recent developments of optical sensing application in power transformers.
Content-Based Image Classification by
"Content-Based Image Classification Efficient Machine Learning using Robust Feature Extraction Techniques is a comprehensive guide to initiate and excel in researching with invaluable image data. Social Science Research Network has revealed the fact that sixty five percent of us are visual learners. Research data provided by Hyerle (2000) has clearly shown ninety percent of information in our brain is visual. Thus, it is no wonder that processing of visual information in brain is 60,000 times faster than text based information (3M Corporation, 2001). Recent times have witnessed significant surge in conversing with images with popularity of social networking platforms. The other reason of embracing extensive usage of image data is easy availability of image capturing devices in the form of high resolution cell phone cameras. Extensive application of image data in diversified application areas including, medical science, media, sports, remote sensing and so on has stimulated the requirement of further research in optimizing archival, maintenance and retrieval of appropriate image content to leverage data driven decision making. This book has demonstrated several techniques of image processing to represent image data in desired format for information identification. It has discussed the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Different Open Access Image Datasets to start your Machine Learning Journey Image Feature Extraction with Novel Handcrafted Techniques (Traditional Feature Extraction) Image Feature Extraction with Automated Techniques (Representation Learning with CNNs) Significance of Fusion Based Approaches in enhancing Classification Accuracy Matlab Codes for implementing the Techniques Use of Open Access Data Mining tool Weka for multiple tasks The book is intended for budding researchers, technocrats, engineering students and machine learning / deep learning enthusiasts who are willing to start their computer vision journey with content based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means of insight generation. The book will make the reader adept with coding tricks necessary to propose novel mechanisms and also to enhance state-of-the-art with disruptive approaches. The Weka guide provided in the book can prove itself beneficial for those who are not comfortable with coding for application of machine learning algorithm. The Weka tool will assist the learner to implement machine learning algorithms with the click of a button. Thus, the book is going to be your stepping stone for your machine learning journey. You may visit the author's website to get in touch for any further guidance required (Website: https://www.rikdas.com/)"--
Multi-Agent Coordination by
Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.
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