<p><b>Advanced Wireless Communication Systems: A Comprehensive Guide</b>&nbsp;bridges foundational principles with emerging paradigms to present a unified and in-depth exploration of the technologies, architectures, and innovations shaping contemporary and next-generation wireless networks.</p><p>Opening with an overview of wireless technology evolution, the book discusses global standards and regulatory frameworks for wireless networks while also systematically exploring wireless channel modelling, simulation methodologies, and a wide range of network architectures, including cellular networks, Wi-Fi, Bluetooth, Zigbee, LoRaWAN, and IoT connectivity frameworks. Advanced physical-layer and system-level technologies such as MIMO, OFDM, millimetre-wave, terahertz communication, and free-space optical systems are examined in detail, along with critical security and privacy challenges in modern wireless networks.</p><p>Later chapters focus on future-facing innovations, including massive MIMO, beamforming, cognitive radio, dynamic spectrum access, and vehicular communication systems. The book culminates with cutting-edge topics such as machine-learning-driven network slicing, latency optimisation for ultra-reliable low-latency communications (URLLC), and noncoherent system design targeting 6G-class applications.</p><p><b>Key Features</b><b></b></p><p>·&nbsp; Comprehensive coverage from wireless communication fundamentals to cutting-edge technologies</p><p>·&nbsp; An integrated perspective on wireless communication methods combining theory, simulation methods, and practical case studies</p><p>·&nbsp; Dedicated chapters on AI-enabled optimisation, cognitive radio, and URLLC</p><p>·&nbsp; Clear illustrations, comparative analyses, and real-world application insights</p>

&lt;blockquote&gt;&lt;div&gt;&lt;div&gt;&lt;blockquote&gt;&lt;div&gt;&lt;blockquote&gt;&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;div&gt;Distributed Energy and Smart Grids: Sustainable Energy for the Future explores the transition from traditional centralised power systems to intelligent, decentralised, and sustainable energy infrastructures. Focusing on the integration of Distributed Energy Resources (DERs) such as solar, wind, and emerging renewables with advanced smart grid technologies, this book highlights how digital communication, automation, and real-time analytics are reshaping modern energy systems.&lt;/div&gt;&lt;div&gt;&lt;p&gt;The book covers critical topics including renewable energy integration, grid stability, energy storage and battery management, IoT-based monitoring, communication technologies, cybersecurity, SCADA systems, demand-response mechanisms, and the roles of AI, machine learning, and electric vehicles in next-generation grids. Case studies, emerging innovations, and future trends provide practical insights and real-world applications for researchers and professionals alike.&lt;/p&gt;&lt;p&gt;Key Features:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;&lt;p&gt;Comprehensive coverage of distributed energy systems and smart grid integration&lt;br&gt;&lt;br&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Clear explanations supported by illustrations, case studies, and practical solutions&lt;br&gt;&lt;br&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Insights into renewable adoption, grid modernisation, and emerging technologies&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/blockquote&gt;&lt;/div&gt;&lt;/blockquote&gt;&lt;/div&gt;&lt;/div&gt;&lt;/blockquote&gt;

&#34;Implementing Hyper Automation: Strategies, Best Practices, and Case Studies&#34; offers insights into the powerful convergence of AI, Machine Learning, Robotic Process Automation, Natural Language Processing, and Advanced analytics. Rooted in modern digital transformation trends, the book demonstrates how intelligent automation reshapes organisational efficiency, enhances decision-making, and drives scalable growth across industries. The book begins with foundational concepts, core principles, and architectural frameworks that guide the development of resilient and intelligent automation ecosystems. It then presents sector-specific case studies from banking, healthcare, manufacturing, and agriculture, showcasing real implementations, measurable impact, and lessons learned. Later chapters address data governance, security, operational risks, enabling technologies, and emerging trends, concluding with a strategic roadmap for sustaining automation-driven transformation. Key Features - Explains core principles and architectural foundations of hyper automation - Demonstrates real-world case studies across major industries - Provides actionable frameworks and implementation strategies - Addresses governance, security, risks, and future trends - Bridges academic insights with practical, enterprise-focused guidance

Research on Engineering Applications in Multidisciplinary Sectors presents a concise yet wide-ranging collection of contemporary research and practical innovations across engineering, technology, and applied sciences. The book highlights how data science, machine learning, smart materials, cryptography, environmental engineering, automation, and digital transformation are being used to solve real-world global challenges. By blending theoretical foundations with case studies and experimental results, it provides a clear view of both emerging trends and practical implementations across various sectors, including smart cities, sustainable manufacturing, healthcare, supply chains, and information security. Key Features: Integrates multidisciplinary engineering and computing approaches Applies data-driven and AI-based methods to real-world problems Analyses sustainable materials, smart systems, and digital transformation Presents case studies, surveys, and comparative research insights Addresses societal, environmental, and technological challenges

Research on Engineering Applications in Multidisciplinary Sectors presents a concise yet wide-ranging collection of contemporary research and practical innovations across engineering, technology, and applied sciences. The book highlights how data science, machine learning, smart materials, cryptography, environmental engineering, automation, and digital transformation are being applied to solve real-world global challenges. By blending theoretical foundations with case studies and experimental results, it offers a clear view of both emerging trends and practical implementations across sectors such as smart cities, sustainable manufacturing, healthcare, supply chains, and information security. Key Features: Integrates multidisciplinary engineering and computing approaches Applies data-driven and AI-based methods to real-world problems Analyses sustainable materials, smart systems, and digital transformation Presents case studies, surveys, and comparative research insights Addresses societal, environmental, and technological challenges

Learn how artificial intelligence is reshaping sustainable development across engineering domains with AI-Driven Technologies for Sustainable Engineering. The book demonstrates how AI-infused solutions can strengthen resilience, enhance efficiency, and support global sustainability goals. Spanning topics ranging from IoT, healthcare, water systems, smart cities, to nanotechnology, education, and renewable energy, each chapter presents in-depth discussions supported by real-world case studies, illustrating how intelligent systems from automation to predictive analytics are reducing environmental impact and enabling responsible innovation. Challenging the perception that technology contributes primarily to degradation, this book highlights how data-driven intelligence can instead reverse harmful trends and serve as a catalyst for sustainable transformation. Key Features Covers diverse AI applications across engineering fields including renewable energy, healthcare, education, transportation, and smart infrastructure. Aligns AI-enabled solutions with the United Nations Sustainable Development Goals (SDGs). Bridges research and real-world practice through detailed case studies and applied examples. Presents insights from leading experts, offering a multidisciplinary understanding of sustainable engineering.

Positioned at the intersection of intelligent transportation systems (ITS), computer vision, and machine learning, this book presents a comprehensive examination of how artificial intelligence and statistical techniques are reshaping traffic monitoring, management, and urban mobility in the era of smart cities. The book begins with the core principles of AI and traffic systems, introducing statistical modeling, data acquisition, and image processing for traffic analysis. Midway, it transitions into deep learning-powered applications such as object detection, vehicle tracking, congestion forecasting, and real-time incident recognition. Later sections address legal, regulatory, and ethical frameworks, while concluding chapters highlight IoT-enabled models and future trajectories in AI-powered traffic management. Key Features: Introduces principles of AI, machine learning, and statistical modeling for traffic systems Demonstrates applications of deep learning in congestion prediction, incident detection, and vehicle tracking Examines AI-driven traffic optimization, urban mobility solutions, and self-driving technologies Evaluates security, data privacy, and legal considerations in AI-based traffic surveillance Integrates AI with IoT frameworks for real-time monitoring in smart city infrastructure Highlights future directions and policy implications for sustainable and ethical traffic management.

Electric Vehicle Technologies: Trends, Control, and Charging Solutions explores the latest innovations transforming electric transportation. This concise and accessible volume presents advances in electric vehicle (EV) systems, focusing on solar energy integration, intelligent motor control, battery charging technologies, and IoT-based applications. The book is divided into three thematic sections. It begins with solar-powered charging systems and hardware design for sustainable mobility. It then covers power electronics and motor control methods, including BLDC control and adaptive charging frameworks. The final section addresses cutting-edge developments like vehicle-to-grid (V2G) systems and IoT-enabled EV automation. Key Features: - Advances in solar EV charging and wireless power transfer. - Control strategies for EV motors and battery charging. - Integration of IoT and autonomous systems in EVs. - Hardware designs for electric bicycles and solar charging stations. - V2G systems and adaptive power control.

This book addresses the pressing need for responsible decision-making in modern engineering contexts, the book explores interdisciplinary approaches that integrate sustainability, corporate responsibility, and innovation within management systems. Divided into three thematic sections, the volume begins with foundational discussions on cultural challenges, ESG reporting frameworks, and CSR integration. It then moves to sector-specific analyses&mdash;including construction, IT, and manufacturing&mdash;highlighting sustainable business practices and organizational effectiveness. The final section presents forward-looking perspectives, such as the role of AI, gamification, HR innovation, and continuous process improvement in shaping the future of engineering management. Key Features: Investigates emerging paradigms in sustainability reporting and ethical leadership Explores real-world case studies across diverse industries Examines the intersection of technology, HR trends, and engineering practices Offers multidisciplinary perspectives from expert contributors Supports practical understanding through contextual analysis

Federated Learning for Internet of Vehicles: IoV Image Processing, Vision, and Intelligent Systems (Volume 3) explores how federated learning is revolutionizing the Internet of Vehicles (IoV) by enabling secure, decentralized, and scalable solutions. Combining theoretical insights with practical applications, this book addresses key challenges such as data privacy, heterogeneous information, and network latency in IoV systems. This volume offers cutting-edge strategies to build intelligent, resilient vehicular systems, from privacy-enhanced data collection to blockchain-based payments, smart transportation systems, and vehicle number plate recognition. It highlights how federated learning drives advancements in secure data sharing, identity-based authentication, and real-time road safety improvements. Key Features: - In-depth exploration of federated learning applications in IoV. - Solutions for privacy, security, and scalability challenges. - Practical examples of blockchain integration and smart systems. - Insights into future research directions for IoV.