Naeem Ramzan – författare

Discover the design, implementation, and analytical techniques for multi-modal intelligent sensing in this cutting-edge text The Internet of Things (IoT) is becoming ever more comprehensively integrated into everyday life. The intelligent systems that power smart technologies rely on increasingly sophisticated sensors in order to monitor inputs and respond dynamically. Multi-modal sensing offers enormous benefits for these technologies, but also comes with greater challenges; it has never been more essential to offer energy-efficient, reliable, interference-free sensing systems for use with the modern Internet of Things. Multimodal Intelligent Sensing in Modern Applications provides an introduction to systems which incorporate multiple sensors to produce situational awareness and process inputs. It is divided into three parts—physical design aspects, data acquisition and analysis techniques, and security and energy challenges—which together cover all the major topics in multi-modal sensing. The result is an indispensable volume for engineers and other professionals looking to design the smart devices of the future. Multimodal Intelligent Sensing in Modern Applications readers will also find: Contributions from multidisciplinary contributors in wireless communications, signal processing, and sensor designCoverage of both software and hardware solutions to sensing challengesDetailed treatment of advanced topics such as efficient deployment, data fusion, machine learning, and moreMultimodal Intelligent Sensing in Modern Applications is ideal for experienced engineers and designers who need to apply their skills to Internet of Things and 5G/6G networks. It can also act as an introductory text for graduate researchers into understanding the background, design, and implementation of various sensor types and data analytics tools.

Discover the design, implementation, and analytical techniques for multi-modal intelligent sensing in this cutting-edge text

The Internet of Things (IoT) is becoming ever more comprehensively integrated into everyday life. The intelligent systems that power smart technologies rely on increasingly sophisticated sensors in order to monitor inputs and respond dynamically. Multi-modal sensing offers enormous benefits for these technologies, but also comes with greater challenges; it has never been more essential to offer energy-efficient, reliable, interference-free sensing systems for use with the modern Internet of Things.

Multimodal Intelligent Sensing in Modern Applications provides an introduction to systems which incorporate multiple sensors to produce situational awareness and process inputs. It is divided into three parts—physical design aspects, data acquisition and analysis techniques, and security and energy challenges—which together cover all the major topics in multi-modal sensing. The result is an indispensable volume for engineers and other professionals looking to design the smart devices of the future.

Multimodal Intelligent Sensing in Modern Applications readers will also find:

Contributions from multidisciplinary contributors in wireless communications, signal processing, and sensor design Coverage of both software and hardware solutions to sensing challenges Detailed treatment of advanced topics such as efficient deployment, data fusion, machine learning, and more

Multimodal Intelligent Sensing in Modern Applications is ideal for experienced engineers and designers who need to apply their skills to Internet of Things and 5G/6G networks. It can also act as an introductory text for graduate researchers into understanding the background, design, and implementation of various sensor types and data analytics tools.

Discover the design, implementation, and analytical techniques for multi-modal intelligent sensing in this cutting-edge text

The Internet of Things (IoT) is becoming ever more comprehensively integrated into everyday life. The intelligent systems that power smart technologies rely on increasingly sophisticated sensors in order to monitor inputs and respond dynamically. Multi-modal sensing offers enormous benefits for these technologies, but also comes with greater challenges; it has never been more essential to offer energy-efficient, reliable, interference-free sensing systems for use with the modern Internet of Things.

Multimodal Intelligent Sensing in Modern Applications provides an introduction to systems which incorporate multiple sensors to produce situational awareness and process inputs. It is divided into three parts—physical design aspects, data acquisition and analysis techniques, and security and energy challenges—which together cover all the major topics in multi-modal sensing. The result is an indispensable volume for engineers and other professionals looking to design the smart devices of the future.

Multimodal Intelligent Sensing in Modern Applications readers will also find:

Contributions from multidisciplinary contributors in wireless communications, signal processing, and sensor design Coverage of both software and hardware solutions to sensing challenges Detailed treatment of advanced topics such as efficient deployment, data fusion, machine learning, and more

Multimodal Intelligent Sensing in Modern Applications is ideal for experienced engineers and designers who need to apply their skills to Internet of Things and 5G/6G networks. It can also act as an introductory text for graduate researchers into understanding the background, design, and implementation of various sensor types and data analytics tools.

This comprehensive text/reference examines in depth the synergy between multimedia content analysis, personalization, and next-generation networking. The book demonstrates how this integration can result in robust, personalized services that provide users with an improved multimedia-centric quality of experience. Each chapter offers a practical step-by-step walkthrough for a variety of concepts, components and technologies relating to the development of applications and services. Topics and features: introduces the fundamentals of social media retrieval, presenting the most important areas of research in this domain; examines the important topic of multimedia tagging in social environments, including geo-tagging; discusses issues of personalization and privacy in social media; reviews advances in encoding, compression and network architectures for the exchange of social media information; describes a range of applications related to social media.

This comprehensive text/reference examines in depth the synergy between multimedia content analysis, personalization, and next-generation networking. The book demonstrates how this integration can result in robust, personalized services that provide users with an improved multimedia-centric quality of experience. Each chapter offers a practical step-by-step walkthrough for a variety of concepts, components and technologies relating to the development of applications and services. Topics and features: introduces the fundamentals of social media retrieval, presenting the most important areas of research in this domain; examines the important topic of multimedia tagging in social environments, including geo-tagging; discusses issues of personalization and privacy in social media; reviews advances in encoding, compression and network architectures for the exchange of social media information; describes a range of applications related to social media.

This comprehensive text/reference examines in depth the synergy between multimedia content analysis, personalization, and next-generation networking. The book demonstrates how this integration can result in robust, personalized services that provide users with an improved multimedia-centric quality of experience. Each chapter offers a practical step-by-step walkthrough for a variety of concepts, components and technologies relating to the development of applications and services. Topics and features: introduces the fundamentals of social media retrieval, presenting the most important areas of research in this domain; examines the important topic of multimedia tagging in social environments, including geo-tagging; discusses issues of personalization and privacy in social media; reviews advances in encoding, compression and network architectures for the exchange of social media information; describes a range of applications related to social media.

By specializing in a vertical market, companies can better understand their customers and bring more insight to clients in order to become an integral part of their businesses. This approach requires dedicated tools, which is where artificial intelligence (AI) and machine learning (ML) will play a major role. By adopting AI software and services, businesses can create predictive strategies, enhance their capabilities, better interact with customers, and streamline their business processes.This edited book explores novel concepts and cutting-edge research and developments towards designing these fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking. The chapters focus on models and theoretical approaches to guarantee automation in large multi-scale implementations of AI and ML systems; protocol designs to ensure AI systems meet key requirements for future services such as latency; and optimisation algorithms to leverage the trusted distributed and efficient complex architectures.The book is of interest to researchers, scientists, and engineers working in the fields of ICTs, networking, AI, ML, signal processing, HCI, robotics and sensing. It could also be used as supplementary material for courses on AI, machine and deep learning, ICTs, networking signal processing, robotics and sensing.

This important text/reference presents a comprehensive review of techniques for taxonomy matching, discussing matching algorithms, analyzing matching systems, and comparing matching evaluation approaches. Different methods are investigated in accordance with the criteria of the Ontology Alignment Evaluation Initiative (OAEI). The text also highlights promising developments and innovative guidelines, to further motivate researchers and practitioners in the field.Topics and features: discusses the fundamentals and the latest developments in taxonomy matching, including the related fields of ontology matching and schema matching; reviews next-generation matching strategies, matching algorithms, matching systems, and OAEI campaigns, as well as alternative evaluations; examines how the latest techniques make use of different sources of background knowledge to enable precise matching between repositories; describes the theoretical background, state-of-the-art research, and practical real-world applications; covers the fields of dynamic taxonomies, personalized directories, catalog segmentation, and recommender systems.This stimulating book is an essential reference for practitioners engaged in data science and business intelligence, and for researchers specializing in taxonomy matching and semantic similarity assessment. The work is also suitable as a supplementary text for advanced undergraduate and postgraduate courses on information and metadata management.

This important text/reference presents a comprehensive review of techniques for taxonomy matching, discussing matching algorithms, analyzing matching systems, and comparing matching evaluation approaches. Different methods are investigated in accordance with the criteria of the Ontology Alignment Evaluation Initiative (OAEI). The text also highlights promising developments and innovative guidelines, to further motivate researchers and practitioners in the field.

Topics and features: discusses the fundamentals and the latest developments in taxonomy matching, including the related fields of ontology matching and schema matching; reviews next-generation matching strategies, matching algorithms, matching systems, and OAEI campaigns, as well as alternative evaluations; examines how the latest techniques make use of different sources of background knowledge to enable precise matching between repositories; describes the theoretical background, state-of-the-art research, and practical real-world applications; covers the fields of dynamic taxonomies, personalized directories, catalog segmentation, and recommender systems.

This stimulating book is an essential reference for practitioners engaged in data science and business intelligence, and for researchers specializing in taxonomy matching and semantic similarity assessment. The work is also suitable as a supplementary text for advanced undergraduate and postgraduate courses on information and metadata management.