The energy transition initiated in recent years has enabled the growing integration of renewable production into the energy mix. Microgrids make it possible to maximize the efficiency of energy transmission from source to consumer by bringing the latter together geographically and by reducing losses linked to transport. However, the lack of inertia and the micro-grid support system makes it weak, and energy storage is necessary to ensure its proper functioning. Current storage technologies do not make it possible to provide both a large capacity of energy and power at the same time. Hybrid storage is a solution that combines the advantages of several technologies and reduces their disadvantages. Modeling and Control of Static Converters for Hybrid Storage Systems covers the modeling, control theorems, and optimization techniques that solve many scientific problems for researchers in the field of power converter control for renewable energy hybrid storage and places particular emphasis on the modeling and control of static converters for hybrid storage systems. Covering topics ranging from energy storage to power generation, this book is ideal for automation engineers, electrical engineers, mechanical engineers, professionals, scientists, academicians, master's and doctoral students, and researchers in the disciplines of electrical and mechanical engineering.

This book contains selected papers presented at the First International Symposium on Sustainable Energy and Technological Advancements (ISSETA 2021), which was organized by the Department of Electrical Engineering, NIT Meghalaya, Shillong, India, during September 24–25, 2021. The topics covered in the book mainly focuses on the cutting-edge research domain with respect to sustainable energy technologies, smart building, integration, and application of multiple energy sources; advanced power converter topologies and their modulation techniques; and information and communication technologies for smart microgrids.

The chasm between the physical capabilities of Intelligent Robotics and Autonomous Systems (IRAS) and their cognitive potential presents a formidable challenge. While these machines exhibit astonishing strength, precision, and speed, their intelligence and adaptability lag far behind. This inherent limitation obstructs the realization of autonomous systems that could reshape industries, from self-driving vehicles to industrial automation. The solution to this dilemma is unveiled within the pages of Modeling, Simulation, and Control of AI Robotics and Autonomous Systems. Find within the pages of this book answers for the cognitive deficit within IRAS. While these systems boast remarkable physical capabilities, their potential for intelligent decision-making and adaptation remains stunted, thereby bringing innovation to a halt. Solving this issue would mean the re-acceleration of multiple industries that could utilize automation to prevent humans from needing to do work that is dangerous, and could revolutionize transportation, and more.

Experts continue to struggle with developing methods to effectively navigate the intricate landscape of the Internet of Things (IoT). As the IoT landscape continues to expand and influence various industries, from healthcare to smart cities and beyond, scholars often find themselves facing an absence of comprehensive guidance in navigating this evolving technological landscape. The challenges are multifaceted and include the need for intelligent modeling techniques, the intricacies of managing IoT applications, and the relentless pace of technological advancements. This issue of staying well-informed and equipped to address these challenges demands an insightful solution. To tackle these challenges, Scalable Modeling and Efficient Management of IoT Applications emerges as a valuable resource, offering a multitude of effective solutions to address these concerns. This is a book that was meticulously crafted to empower scholars with the knowledge and tools they need. By tackling the scarcity of guidance on intelligent modeling techniques, the book equips readers with a profound understanding of the fundamental concepts, algorithms, and methodologies crucial for designing and managing intelligent IoT systems.

Power Electronics Converters and their Control for Renewable Energy Applications provides information that helps to solve common challenges with power electronics converters, including loss by switching, heating of power switches, management of switching time, improvement of the quality of the signals delivered by power converters, and improvement of the quality of energy produced by renewable energy sources. This book is of interest to academics, researchers, and engineers in renewable energy, power systems, electrical engineering, electronics, and mechanical engineering. - Includes important visual illustrations and imagery of concise circuit schematics and renewable energy applications - Features a templated approach for step-by-step implementation of the new MPPT algorithm based on recent and intelligent techniques - Provides methods for optimal harnessing of energy from renewable energy sources and converter topology synthesis

As wireless services rapidly expand, the inefficient use of limited spectrum resources poses a critical challenge. The conventional approach to spectrum allocation, based on fixed assignments, could be more effective in meeting the escalating demand for wireless devices and systems. Cognitive radio technology offers a transformative solution by reimagining the spectrum as a multidimensional space, enabling opportunistic access to underutilized bands. However, the field of cognitive radio is still in its early stages, needing more in-depth analyses and descriptions of crucial processes. Spectrum and Power Allocation in Cognitive Radio Systems addresses this pressing need by offering a comprehensive guide for academic scholars, researchers, and industry professionals. This book delves into cognitive radio technology's foundations, organization, and challenges, providing insights into dynamic spectrum access, networking protocols, hardware architecture, and emerging applications. It presents advanced topics such as spectrum sensing algorithms, cooperative spectrum sensing, and multi-user access, offering practical solutions to enhance spectrum efficiency.

In the age of immediate technical expansion, our world faces a multifaceted challenge: ensuring the sustainability of our digital transformation. Governments and organizations have wholeheartedly embraced innovative technologies such as artificial intelligence, blockchain, and e-governance, but in doing so, they have encountered a complex web of issues. These range from cybersecurity concerns in an increasingly digitalized world to the need for intelligent systems capable of managing automation infrastructure and interconnected environments. Sustainable Development in AI, Blockchain, and E-Governance Applications offers a forward-thinking approach that harnesses the synergy between intelligent systems, machine learning, deep learning, and blockchain methods. It explores data-driven decision-making, automation infrastructure, autonomous transportation, and the creation of connected buildings, all aimed at crafting a sustainable digital future. By delving into topics like machine learning for smart parking, disease classification through neural networks, and the Internet of Things (IoT) for smarter cities, this book equips academic scholars with the tools they need to navigate the complex terrain of technology and governance. Academic scholars and researchers in technology, governance, and sustainability will find this book to be an indispensable resource. It caters to those seeking a comprehensive understanding of current and future trends in the integration of intelligent systems with cybersecurity applications.

The world is confronting the reality of climate change, which transcends geographical boundaries, necessitating a multidisciplinary approach. Effective mitigation strategies for climate change demand diversity, inclusion, equity, and collaboration as paramount considerations in the formulation and execution of mitigation strategies. Global Perspectives on Climate Change, Social Resilience, and Social Inclusion illuminates the interplay between climate shifts and society's complex tapestry. Authored by leading sociologists and interdisciplinary scholars, this researched volume offers an exploration of climate change through the lens of sociology, unveiling its implications for our global community. Moreover, the book spotlights the proactive voices of voluntary associations and activists dedicated to championing climate change victims' rights. Their collective message resonates throughout: mitigating climate change risks must be integrated into a broader framework of resilient development, elevating the economic standing and quality of life for disadvantaged groups while fostering equity across all strata of society. This book is ideal for scholars, policymakers, and activists seeking a nuanced understanding of the intricate interplay between climate change and the social fabric of our world.

In the ever-evolving landscape of engineering, a pressing challenge looms large—the need to navigate the complexities of modern problems with precision and efficiency. As industries grapple with an array of intricate issues, from sustainable materials to resilient infrastructure, the demand for optimal solutions has never been more pronounced. Traditional approaches are often inadequate, prompting the search for advanced optimization techniques capable of unraveling the intricacies inherent in engineering systems. The problem at hand is clear: how can engineers, researchers, and practitioners harness cutting-edge methodologies to address the multifaceted challenges shaping our technological future? Advanced Optimization Applications in Engineering, is a definitive guide poised to revolutionize problem-solving in civil engineering. This book offers a comprehensive exploration of state-of-the-art optimization algorithms and their transformative applications. By delving into genetic algorithms, particle swarm optimization, neural networks, and other metaheuristic strategies, this collection provides a roadmap for automating design processes, reducing costs, and unlocking innovative solutions. The chapters not only introduce these advanced techniques but also showcase their practical implementation across diverse engineering domains, making this book an indispensable resource for those seeking to stay at the forefront of technological advancements.

Quantum computing is radically different from the conventional approach of transforming bit-strings from one set of zeros and ones to another. With quantum computing, everything changes. The physics used to understand bits of information and the devices that manipulate them are vastly different. Quantum engineering is a revolutionary approach to quantum technology. Technology Road Mapping for Quantum Computing and Engineering explores all the aspects of quantum computing concepts, engineering, technologies, operations, and applications from the basics to future advancements. Covering topics such as machine learning, quantum software technology, and technology road mapping, this book is an excellent resource for data scientists, engineers, students and professors of higher education, computer scientists, researchers, and academicians.