G. R. Kanagachidambaresan – författare

This reference book explores the integration of cognitive computing technologies in the automotive industry to enhance smart transportation systems. It focuses on how AI, machine learning, and data analytics can improve vehicle automation, safety, and efficiency. Automation can support driverless vehicle transportation and bridge the gap between manual control and fully automated navigation systems. The text introduces a discussion on numerous applications of cognitive computing in smart transportation, motion planning, situation awareness, dynamic driving, adaptive behavior, human intent measurement, and predictive analysis.

Key Features:

• Discusses basic concepts and architecture of cognitive computing for vehicular systems.

• Presents technologies to measure human intent for vehicle safety, including emergency management systems (EMS).

• Covers the perception and localization processes in autonomous driving through LiDAR, GPS, and Stereo vision data with critical decision‑making and simulation results.

• Elucidates the application of motion planning for smart transportation.

• Covers visual perception technologies for advanced driver assistance systems (ADAS) through deep learning.

The text is primarily written for graduate students, academic researchers, and professionals in the fields of computer science, electrical engineering, automotive engineering, and civil engineering.

This reference book explores the integration of cognitive computing technologies in the automotive industry to enhance smart transportation systems. It focuses on how AI, machine learning, and data analytics can improve vehicle automation, safety, and efficiency. Automation can support driverless vehicle transportation and bridge the gap between manual control and fully automated navigation systems. The text introduces a discussion on numerous applications of cognitive computing in smart transportation, motion planning, situation awareness, dynamic driving, adaptive behavior, human intent measurement, and predictive analysis.

Key Features:

• Discusses basic concepts and architecture of cognitive computing for vehicular systems.

• Presents technologies to measure human intent for vehicle safety, including emergency management systems (EMS).

• Covers the perception and localization processes in autonomous driving through LiDAR, GPS, and Stereo vision data with critical decision‑making and simulation results.

• Elucidates the application of motion planning for smart transportation.

• Covers visual perception technologies for advanced driver assistance systems (ADAS) through deep learning.

The text is primarily written for graduate students, academic researchers, and professionals in the fields of computer science, electrical engineering, automotive engineering, and civil engineering.

The cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices.

This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results.

The cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices.

This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results.

Provides comprehensive coverage of renewable energy and its integration with smart grid technologies.

This book starts with an overview of renewable energy technologies, smart grid technologies, and energy storage systems and covers the details of renewable energy integration with smart grid and the corresponding controls. It also provides an enhanced perspective on the power scenario in developing countries. The requirement of the integration of smart grid along with the energy storage systems is deeply discussed to acknowledge the importance of sustainable development of a smart city. The methodologies are made quite possible with highly efficient power convertor topologies and intelligent control schemes. These control schemes are capable of providing better control with the help of machine intelligence techniques and artificial intelligence. The book also addresses modern power convertor topologies and the corresponding control schemes for renewable energy integration with smart grid. The design and analysis of power converters that are used for the grid integration of solar PV along with simulation and experimental results are illustrated. The protection aspects of the microgrid with power electronic configurations for wind energy systems are elucidated. The book also discusses the challenges and mitigation measure in renewable energy integration with smart grid.

Audience

The core audience is hardware and software engineers working on renewable energy integration related projects, microgrids, smart grids and computing algorithms for converter and inverter circuits. Researchers and students in electrical, electronics and computer engineering will also benefit reading the book.

Provides comprehensive coverage of renewable energy and its integration with smart grid technologies.

This book starts with an overview of renewable energy technologies, smart grid technologies, and energy storage systems and covers the details of renewable energy integration with smart grid and the corresponding controls. It also provides an enhanced perspective on the power scenario in developing countries. The requirement of the integration of smart grid along with the energy storage systems is deeply discussed to acknowledge the importance of sustainable development of a smart city. The methodologies are made quite possible with highly efficient power convertor topologies and intelligent control schemes. These control schemes are capable of providing better control with the help of machine intelligence techniques and artificial intelligence. The book also addresses modern power convertor topologies and the corresponding control schemes for renewable energy integration with smart grid. The design and analysis of power converters that are used for the grid integration of solar PV along with simulation and experimental results are illustrated. The protection aspects of the microgrid with power electronic configurations for wind energy systems are elucidated. The book also discusses the challenges and mitigation measure in renewable energy integration with smart grid.

Audience

The core audience is hardware and software engineers working on renewable energy integration related projects, microgrids, smart grids and computing algorithms for converter and inverter circuits. Researchers and students in electrical, electronics and computer engineering will also benefit reading the book.

As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.

Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.

Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

What You''ll Learn

Who This Book is For

Engineers and hobbyists working on Internet of Things prototypes and applications.  Basic skills in electronics and a working knowledge of Python are recommended. Engineers and scientists working on smart environment projects and smart city applications will also benefit.

This book Identifies the right sensors and single board computers for any application to achieve the best performance in Industry 4.0 settings and applications. You’ll see what technologies apply the IIoT with elegant efficiency to drastically improve remote monitoring and controlling, decision making, and preventative maintenance. 

Start by learning exactly what Industry 4.0 is and advance your knowledge from simple Internet of Things projects to full-on Industrial IoT deployment. You''ll automate advanced processes, incorporate professional procedures, and take your IoT skills to a professional level. Then move into the protocols and standards expected for industrial applications of sensors at an industrial level. Match the right SBCs to the right use cases and sensor technologies to optimize efficiency and ensure peak performance. Then move into setting up a smart factory and monitoring your supply chain with tech. Finally, you''ll dive into programming with theNode-Red platform and Python packages for CPS.

By the end of this book, you’ll have jumped from simple, home-based IoT systems up to industrial and professional applications. Conquer your supply chain and both forward and backward processes with accessible Maker tech! 

What You''ll Learn

Who This Book Is For

Students and aspiring professionals working with Internet of things technologies moving towards industrial applications.  Prerequisite knowledge in basic Internet of Things, simple python concepts,  automation, and industry procedures is required for this book.  Engineers and scientists who are pursuing research and contributing to industrial internet of things might also benefit. 

This book provides a novel solution for existing challenges in wireless body sensor networks (WBAN) such as network lifetime, fault tolerant approaches, reliability, security, and privacy. The contributors first discuss emerging trends of WBAN in the present health care system. They then provide possible solutions to challenges inherent in WBANs. Finally, they discuss results in working environments. Topics include communication protocols of implanted, wearable and nano body sensor networks; energy harvesting methodologies and experimentation for WBAN; reliability analysis and fault tolerant architecture for WBAN; and handling network failure during critical duration. The contributors consist of researchers and practitioners in WBAN around the world.

This book covers challenges and solutions in establishing Industry 4.0 standards for Internet of Things. It proposes a clear view about the role of Internet of Things in establishing standards. The sensor design for industrial problem, challenges faced, and solutions are all addressed. The concept of digital twin and complexity in data analytics for predictive maintenance and fault prediction is also covered. The book is aimed at existing problems faced by the industry at present, with the goal of cost-efficiency and unmanned automation. It also concentrates on predictive maintenance and predictive failures. In addition, it includes design challenges and a survey of literature.