Advanced Wireless Communication Systems: A Comprehensive Guide 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.

<div>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.</div><div><p>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.</p><p>Key Features:</p><ul><li><p>Comprehensive coverage of distributed energy systems and smart grid integration<br><br></p></li><li><p>Clear explanations supported by illustrations, case studies, and practical solutions<br><br></p></li><li><p>Insights into renewable adoption, grid modernisation, and emerging technologies</p></li></ul></div>

&#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.