Multimodal Intelligent Sensing in Modern Applications - Tapa dura

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Masood Ur Rehman, PhD, MSc, is a Senior Lecturer at the James Watt School of Engineering, University of Glasgow, UK and leads the Antennas & Radio-wave Propagation group. He received his MSc and PhD in Electronic Engineering from Queen Mary University of London, UK, in 2006 and 2010, respectively.

Ahmed Zoha, PhD, MSc, is a Senior Lecturer at the James Watt School of Engineering, University of Glasgow, UK and leads the Distributed Learning and Intelligence group. He received his PhD degree in Electrical and Electronic Engineering from the 5G Innovation Centre at the University of Surrey, UK, and his MSc in Communication Engineering from the Chalmers University of Technology, Sweden.

Muhammad Ali Jamshed, PhD, MSc, is with University of Glasgow, UK, since 2021. He is a visiting Research Fellow at the University of Sussex. He is endorsed by the Royal Academy of Engineering under exceptional talent category and was nominated for the Departmental Prize for Excellence in Research in 2019 at the University of Surrey.

Naeem Ramzan, PhD, is a Full Professor in Computing Engineering and Chair of Affective and Human Computing for Smart Environment Research Centre and Co-lead of Visual Communication Cluster in AVCN at the University of the West of Scotland, Paisley, UK. He received his PhD in Electronic Engineering from Queen Mary University of London, UK in 2008.

De la contraportada

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.