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This book describes the next generation of industry-Industry
4.0-and how it holds the promise of increased flexibility in
manufacturing, along with automation, better quality, and improved
productivity. The authors discuss how it thus enables companies to
cope with the challenges of producing increasingly individualized
products with a short lead-time to market and higher quality. The
authors posit that intelligent cloud services and resource sharing
play an important role in Industry 4.0 anticipated Fourth
Industrial Revolution. This book serves the different issues and
challenges in cloud resource management CRM techniques with proper
propped solution for IT organizations. The book features chapters
based on the characteristics of autonomic computing with its
applicability in CRM. Each chapter features the techniques and
analysis of each mechanism to make better resource management in
cloud.
This book describes the next generation of industry-Industry
4.0-and how it holds the promise of increased flexibility in
manufacturing, along with automation, better quality, and improved
productivity. The authors discuss how it thus enables companies to
cope with the challenges of producing increasingly individualized
products with a short lead-time to market and higher quality. The
authors posit that intelligent cloud services and resource sharing
play an important role in Industry 4.0 anticipated Fourth
Industrial Revolution. This book serves the different issues and
challenges in cloud resource management CRM techniques with proper
propped solution for IT organizations. The book features chapters
based on the characteristics of autonomic computing with its
applicability in CRM. Each chapter features the techniques and
analysis of each mechanism to make better resource management in
cloud.
Applying mechanisms and principles of human intelligence and
converging the brain and artificial intelligence (AI) is currently
a research trend. The applications of AI in brain simulation are
countless. Brain-inspired intelligent systems will improve
next-generation information processing by applying theories,
techniques, and applications inspired by the information processing
principles from the brain. Exploring Future Opportunities of
Brain-Inspired Artificial Intelligence focuses on the convergence
of AI with brain-inspired intelligence. It presents research on
brain-inspired cognitive machines with vision, audition, language
processing, and thinking capabilities. Covering topics such as data
analysis tools, knowledge representation, and super-resolution,
this premier reference source is an essential resource for
engineers, developers, computer scientists, students and educators
of higher education, librarians, researchers, and academicians.
Applying mechanisms and principles of human intelligence and
converging the brain and artificial intelligence (AI) is currently
a research trend. The applications of AI in brain simulation are
countless. Brain-inspired intelligent systems will improve
next-generation information processing by applying theories,
techniques, and applications inspired by the information processing
principles from the brain. Exploring Future Opportunities of
Brain-Inspired Artificial Intelligence focuses on the convergence
of AI with brain-inspired intelligence. It presents research on
brain-inspired cognitive machines with vision, audition, language
processing, and thinking capabilities. Covering topics such as data
analysis tools, knowledge representation, and super-resolution,
this premier reference source is an essential resource for
engineers, developers, computer scientists, students and educators
of higher education, librarians, researchers, and academicians.
Due to the rise of new applications in electronic appliances and
pervasive devices, automated hand gesture recognition (HGR) has
become an area of increasing interest. HGR developments have come a
long way from the traditional sign language recognition (SLR)
systems to depth and wearable sensor-based electronic devices.
Where the former are more laboratory-oriented frameworks, the
latter are comparatively realistic and practical systems. Based on
various gestural traits, such as hand postures, gesture recognition
takes different forms. Consequently, different interpretations can
be associated with gestures in various application contexts. A
considerable amount of research is still needed to introduce more
practical gesture recognition systems and associated algorithms.
Challenges and Applications for Hand Gesture Recognition highlights
the state-of-the-art practices of HGR research and discusses key
areas such as challenges, opportunities, and future directions.
Covering a range of topics such as wearable sensors and hand
kinematics, this critical reference source is ideal for
researchers, academicians, scholars, industry professionals,
engineers, instructors, and students.
Due to applications in recent electronic appliances and pervasive
devices, Automated Hand Gesture Recognition (HGR) is of particular
interest nowadays. HGR developments have come a long way from the
traditional Sign Language Recognition (SLR) systems to innovations
such as wearable sensor based electronic devices. A large amount of
research on HGR is still on the way, both from the industry and
academia, that is working towards introducing more practical
gesture recognition systems and associated algorithms. This book
highlights state-of-the-art practices in the direction of HGR
research. It is organized into five coherent heads: HGR
introduction, modalities, and challenges, practical hand
segmentation schemes capable of working under cluttered
backgrounds, gesture recognition schemes targeting different
acquisition mechanisms, solutions sticking to different, practiced
methodologies, and conclusions from the HGR works witnessed so far
and future options. The book is ideal for undergraduates,
researchers at all levels, and the developer community as it
provides a basis of information about HGR, as well as new and
in-depth research in the field.
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