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.
About the Editors xv List of Contributors xix Preface xxiii 1 Advances in Multi-modal Intelligent Sensing 1 Masood Ur Rehman, Muhammad Ali Jamshed, and Tahera Kalsoom 1.1 Multi-modal Intelligent Sensing 1 1.2 Sensors for Multi-modal Intelligent Sensing 3 1.3 Applications of Multi-modal Intelligent Sensing 14 1.4 Challenges and Opportunities in Multi-modal Sensing 18 2 Antennas for Wireless Sensors 29 Abdul Jabbar, Muhammad Ali Jamshed, and Masood Ur Rehman 2.1 Wireless Sensors: Definition and Architecture 29 2.2 Multi-modal Wireless Sensing 34 2.3 Antennas: The Sensory Gateway for Wireless Sensors 35 2.4 Fundamental Antenna Parameters 36 2.5 Key Operating Frequency Bands for Sensing Antennas 39 2.6 Fabrication Methods for Sensing Antennas 40 2.7 Antenna Types for Wireless Sensing Networks 42 2.8 Advantages of Electronic Beamsteering Antennas in Sensing Systems 46 2.9 Summary 49 3 Sensor Design for Multimodal Environmental Monitoring 55 Muhammad Ali Jamshed, Bushra Haq, Syed Ahmed Shah, Kamran Ali, Qammer H. Abbasi, Mumraiz Khan Kasi, and Masood Ur Rehman 3.1 Environment and Forests 56 3.2 Methods to Combat Deforestation 56 3.3 Design of a WSN to Combat Deforestation 59 3.4 Summary 76 4 Wireless Sensors for Multi-modal Health Monitoring 81 Nadeem Ajum, Shagufta Iftikhar, Tahera Kalsoom, and Masood Ur Rehman 4.1 Wearable Sensors 82 4.2 Flexible Sensors 89 4.3 Multi-modal Healthcare Sensing Devices 90 4.4 AI Methods for Multi-modal Healthcare Systems 100 4.5 Summary 102 5 Sensor Design for Industrial Automation 109 Abdul Jabbar, Tahera Kalsoom, and Masood Ur Rehman 5.1 Multimodal Sensing in Industrial Automation 109 5.2 Sensors for Realizing Industrial Automation 116 5.3 Design Considerations for Effective Multimodal Industrial Automation 121 5.4 Challenges and Opportunities of Multimodal Sensing in Industrial Automation 124 5.5 Summary 126 6 Hybrid Neuromorphic-Federated Learning for Activity Recognition Using Multi-modal Wearable Sensors 133 Ahsan Raza Khan, Habib Ullah Manzoor, Fahad Ayaz, Muhammad Ali Imran, and Ahmed Zoha 6.1 Multi-modal Human Activity Recognition 134 6.2 Machine Learning Methods in Multi-modal Human Activity Recognition 137 6.3 System Model 139 6.4 Simulation Setup 146 6.5 Results and Discussion 150 6.6 Summary 159 7 Multi-modal Beam Prediction for Enhanced Beam Management in Drone Communication Networks 165 Iftikhar Ahmad, Ahsan Raza Khan, Rao Naveed Bin Rais, Muhammad Ali Imran, Sajjad Hussain, and Ahmed Zoha 7.1 Drone Communication 166 7.2 Beam Management 167 7.3 System Model 168 7.4 Simulation and Analysis 171 7.5 Summary 178 8 Multi-modal-Sensing System for Detection and Tracking of Mind Wandering 181 Sara Khosravi, Haobo Li, Ahsan Raza Khan, Ahmed Zoha, and Rami Ghannam 8.1 Mind Wandering 182 8.2 Multi-modal Wearable Systems for Mind-Wandering Detection and Monitoring 184 8.3 Design of Multi-modal Wearable System 187 8.4 Results and Discussion 194 8.5 Summary 197 9 Adaptive Secure Multi-modal Telehealth Patient-Monitoring System 201 Muhammad Hanif , Ehsan Ullah Munir, Muhammad Maaz Rehan, Saima Gulzar Ahmad, Tassawar Iqbal, Nasira Kirn, Kashif Ayyub, and Naeem Ramzan 9.1 Healthcare Systems 202 9.2 Security in Healthcare Systems 205 9.3 Blockchain-Powered ZTS for Enhanced Security of Telehealth Systems 213 9.4 Cyber-resilient Telehealth-Enabled Patient Management System 217 9.5 Summary 222 10 Advances in Multi-modal Remote Infant Monitoring Systems 227 Najia Saher, Omer Riaz, Muhammad Suleman, Dost Muhammad Khan, Nasira Kirn, Sana Ullah Jan, Rizwan Shahid, Hassan Rabah, and Naeem Ramzan 10.1 Remote Patient Monitoring 228 10.2 Remote Infant Monitoring (RIM) System 229 10.3 Disease-Specific Remote Infant Monitoring Systems 232 10.4 Challenges in Remote Infant Monitoring Systems 241 10.5 Summary 245 11 Balancing Innovation with Ethics: Responsible Development of Multi-modal Intelligent Tutoring Systems 253 Romina Soledad Albornoz-De Luise, Pablo Arnau-González, Ana Serrano-Mamolar, Sergi Solera-Monforte, and Yuyan Wu 11.1 Intelligent Tutoring Systems and Ethical Considerations 253 11.2 The Promise and Perils of ITS 255 11.3 Ethical Frameworks for ITS 258 11.4 Bias and Fairness in ITS 261 11.5 Privacy and Security Concerns 263 11.6 Socioeconomic Disparities in Access 265 11.7 Dependency on Technology 267 11.8 Summary 268 12 Road Ahead for Multi-modal Intelligent Sensing in the Deep Learning Era 275 Ahmed Zoha, Naeem Ramzan, Muhammad Ali Jamshed, and Masood Ur Rehman 12.1 Future Challenges and Perspectives for Intelligent Multi-modal Sensing 276 12.2 Summary 282 References 282 Index 285
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.
