Keyurkumar Patel – författare

As industries move toward intelligent, adaptive, and efficient manufacturing processes, the integration of (artificial intelligence) AI, (Internet of Things) IoT, signal processing, and computer vision is crucial. This book serves as a comprehensive guide for professionals, researchers, and academics seeking to leverage the power of cutting‑edge technologies in the field of smart manufacturing.Smart Manufacturing with AIoT: Signal Processing, Computer Vision, and Data Analytics is a comprehensive guide that focuses on practical implementation, bridging the gap between theoretical concepts and real‑world applications. Each chapter of this book explores a different aspect of the synergy between AI, IoT, signal processing, and computer vision, starting with foundational concepts and progressing toward advanced applications. From enhancing quality control processes to implementing proactive maintenance strategies and enabling dynamic threat assessment, this book covers a wide range of topics that define the contemporary landscape of smart manufacturing.This book explores the intricate details of AIoT, signal processing, and computer vision, providing valuable insights for both beginners and experienced professionals in the field. Readers will gain actionable knowledge that empowers them to implement transformative solutions in their smart manufacturing environments.

As industries move toward intelligent, adaptive, and efficient manufacturing processes, the integration of (artificial intelligence) AI, (Internet of Things) IoT, signal processing, and computer vision is crucial. This book serves as a comprehensive guide for professionals, researchers, and academics seeking to leverage the power of cutting edge technologies in the field of smart manufacturing.Smart Manufacturing with AIoT: Signal Processing, Computer Vision, and Data Analytics is a comprehensive guide that focuses on practical implementation, bridging the gap between theoretical concepts and real world applications. Each chapter of this book explores a different aspect of the synergy between AI, IoT, signal processing, and computer vision, starting with foundational concepts and progressing toward advanced applications. From enhancing quality control processes to implementing proactive maintenance strategies and enabling dynamic threat assessment, this book covers a wide range of topics that define the contemporary landscape of smart manufacturing.This book explores the intricate details of AIoT, signal processing, and computer vision, providing valuable insights for both beginners and experienced professionals in the field. Readers will gain actionable knowledge that empowers them to implement transformative solutions in their smart manufacturing environments.

As industries move toward intelligent, adaptive, and efficient manufacturing processes, the integration of (artificial intelligence) AI, (Internet of Things) IoT, signal processing, and computer vision is crucial. This book serves as a comprehensive guide for professionals, researchers, and academics seeking to leverage the power of cutting edge technologies in the field of smart manufacturing.Smart Manufacturing with AIoT: Signal Processing, Computer Vision, and Data Analytics is a comprehensive guide that focuses on practical implementation, bridging the gap between theoretical concepts and real world applications. Each chapter of this book explores a different aspect of the synergy between AI, IoT, signal processing, and computer vision, starting with foundational concepts and progressing toward advanced applications. From enhancing quality control processes to implementing proactive maintenance strategies and enabling dynamic threat assessment, this book covers a wide range of topics that define the contemporary landscape of smart manufacturing.This book explores the intricate details of AIoT, signal processing, and computer vision, providing valuable insights for both beginners and experienced professionals in the field. Readers will gain actionable knowledge that empowers them to implement transformative solutions in their smart manufacturing environments.

This book presents few novel Discrete-time Sliding Mode (DSM) protocols for leader-following consensus of Discrete Multi-Agent Systems (DMASs). The protocols intend to achieve the consensus in finite time steps and also tackle the corresponding uncertainties. Based on the communication graph topology of multi-agent systems, the protocols are divided into two groups, namely (i) Fixed graph topology and (ii) Switching graph topology. The coverage begins with the design of Discrete-time Sliding Mode (DSM) protocols using Gao’s reaching law and power rate reaching law for the synchronization of linear DMASs by using the exchange of information between the agents and the leader to achieve a common goal. Then, in a subsequent chapter, analysis for no. of fixed-time steps required for the leader-following consensus is presented. The book also includes chapters on the design of Discrete-time Higher-order Sliding Mode (DHSM) protocols, Event-triggered DSM protocols for the leader-following consensus of DMASs. A chapter is also included on the design of DHSM protocols for leader-following consensus of heterogeneous DMASs.Special emphasis is given to the practical implementation of each proposed DSM protocol for achieving leader-following consensus of helicopter systems, flexible joint robotic arms, and rigid joint robotic arms. This book offers a ready reference guide for graduate students and researchers working in the areas of control, automation, and communication engineering, and in particular the cooperative control of multi-agent systems. It will also benefit professional engineers working to design and implement robust controllers for power systems, autonomous vehicles, military surveillance, smartgrids/microgrids, vehicle traffic management, robotic teams, and aerial robots.

This book presents few novel Discrete-time Sliding Mode (DSM) protocols for leader-following consensus of Discrete Multi-Agent Systems (DMASs). The protocols intend to achieve the consensus in finite time steps and also tackle the corresponding uncertainties. Based on the communication graph topology of multi-agent systems, the protocols are divided into two groups, namely (i) Fixed graph topology and (ii) Switching graph topology. The coverage begins with the design of Discrete-time Sliding Mode (DSM) protocols using Gao’s reaching law and power rate reaching law for the synchronization of linear DMASs by using the exchange of information between the agents and the leader to achieve a common goal. Then, in a subsequent chapter, analysis for no. of fixed-time steps required for the leader-following consensus is presented. The book also includes chapters on the design of Discrete-time Higher-order Sliding Mode (DHSM) protocols, Event-triggered DSM protocols for the leader-following consensus of DMASs. A chapter is also included on the design of DHSM protocols for leader-following consensus of heterogeneous DMASs.Special emphasis is given to the practical implementation of each proposed DSM protocol for achieving leader-following consensus of helicopter systems, flexible joint robotic arms, and rigid joint robotic arms. This book offers a ready reference guide for graduate students and researchers working in the areas of control, automation, and communication engineering, and in particular the cooperative control of multi-agent systems. It will also benefit professional engineers working to design and implement robust controllers for power systems, autonomous vehicles, military surveillance, smartgrids/microgrids, vehicle traffic management, robotic teams, and aerial robots.