Search results for ""author g. r. kanagachidambaresan""

Efficient Single Board Computers (SBCs) and advanced VLSI systems have resulted in edge analytics and faster decision making. The QoS parameters like energy, delay, reliability, security, and throughput should be improved on seeking better intelligent expert systems. The resource constraints in the Edge devices, challenges the researchers to meet the required QoS. Since these devices and components work in a remote unattended environment, an optimum methodology to improve its lifetime has become mandatory. Continuous monitoring of events is mandatory to avoid tragic situations; it can only be enabled by providing high QoS. The applications of IoT in digital twin development, health care, traffic analysis, home surveillance, intelligent agriculture monitoring, defense and all common day to day activities have resulted in pioneering embedded devices, which can offer high computational facility without much latency and delay. The book address industrial problems in designing expert system and IoT applications. It provides novel survey and case study report on recent industrial approach towards Smart City development.

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Rapidly prototype and program new IoT and Edge solutions using low-cost Maker tech, such as those from Arduino, Raspberry Pi and Nvidia. With a focus on the electronics, this book allows experienced computer science students as well as researchers, practitioners, and even hobbyists to dive right into actual engineering of prototypes and not just theoretical programming and algorithms. You'll learn to interface sensors, work with various communication mediums, incorporate wired and wireless communication protocols, and more with these single board computers. All while working in the popular Python programming language. Additionally, you’ll discover both scripting-based and drag and drop solutions for different problems. As well as a variety of useful, data gathering approaches. Then you can apply what you’ve learned to IoT projects and troubleshooting Industry 4.0 problems. The rapid growth of technology and new development initiatives has made the Internet of Things and Edge analytics an inevitable platform in all engineering domains. The need for sophisticated and ambient environments controlled by tech has resulted in an exponential growth of automation and artificial intelligence. Internet of Things Using Single Board Computers reflects these recent developments while providing a low-cost, easy ramp into the fields of IoT using single board computers and Python programming. What You'll Learn Program Arduino, Raspberry Pi, and Nvidia boards for rapid prototyping in IoT and Edge environments Interface sensors, communication mediums, and wired and wireless communications into your programs and projects. Study practical applications of these low-cost solutions in fields such as home automation, smart cities, electrical vehicle charging, and more. Who This Book is ForEngineers 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.

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MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS 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.

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INTEGRATION OF RENEWABLE ENERGY SOURCES WITH SMART GRID 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.

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This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

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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.

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