
Advanced Signal Processing for Industry 4.0, Volume 2
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Industry 4.0 related digital revolution is happening around us in the form of better production, enhanced security techniques and improved and faster communication technologies development. Industry 4.0 is required for enhancing production, flexibility and scalability in industries. Industry 4.0 is a journey towards an integrated environment with human-machine interaction being its important aspect. This field of research is a rapidly changing domain. It is also a multifaceted area of research including signal processing, computer vision, artificial intelligence, manufacturing, production engineering, etc. This book brings together professionals from academia and industry to present a review of state of knowledge in the fields of Industry 4.0 related signals. Volume two particulary looks at issues relating to sensor mechanism for evaluating operations, supply chain, management of machines, analysing the generated data, low-cost solutions for Industry 4.0, and vision processing for various applications, etc. This book is an ideal text for Industrial and academic researchers across electrical engineering, signal processing, computer vision, sensors, AI, and Internet of Things.
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Persons
Irshad Ahmad Ansari is a faculty member of the Electrical and Electronics Engineering at the ABV-Indian Institute of Information Technology and Management (IIITM) Gwalior. Prior to current assignment he was a faculty at IIITDM Jabalpur. He completed his PhD from IIT Roorkee and subsequently joined Gwangju Institute of Science and Technology, South Korea as a Postdoctoral fellow. His major research interest includes Image Processing, Signal Processing, Machine Learning and Electronics Device Development. He has published over 70 articles in various journals, conferences and books of repute.
Varun Bajaj (PhD, SMIEEE) is a faculty member at the Electronics and Communication Engineering department at the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur. Prior to this he worked as a visiting faculty in IIITDM Jabalpur and Assistant Professor at Department of Electronics and Instrumentation, Shri Vaishnav Institute of Technology and Science, Indore, India. He received B.E. degree in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2006, M.Tech. Degree with Honors in Microelectronics and VLSI design from Shri Govindram Seksaria Institute of Technology & Science, Indore, India in 2009. He received his Ph.D. degree in the Discipline of Electrical Engineering, at Indian Institute of Technology Indore, India in 2014. He is an Associate Editor of IEEE Sensor Journal and Subject Editor-in-Chief of IET Electronics Letters. He also served as a Subject Editor of IET Electronics Letters. He is Senior Member of the IEEE and also contributes as active technical reviewer of leading International journals of IEEE, IET, and Elsevier, etc. He has authored numerous research papers and edited several book projects. His research interests include biomedical signal processing, image processing, time frequency analysis, and computer-aided medical diagnosis.
Content
Volume 2
Preface
Acknowledgments
Editor biographies
List of contributors
Contributor biographies
1 IoT for Industry 4.0: performance monitoring in manual production
2 Vision assistance to work in a foggy industrial area
3 Smart factories of Industry 4.0: determination of the effective smartphone position for human activity recognition using deep learning
4 Intelligent technologies in manufacturing cyber-physical systems: evolution and future development
5 Supply chain flexibility issues for Industry 4.0
6 Surveillance of industrial area using drone: design, modeling and uses
7 Solution of the problem of terminal nonlinear filtering of stochastic processes
8 Predictive maintenance of pump: a case study in exploratory data analysis
9 Applications of statistical signal processing for infrared non-destructive testing and evaluation
10 A hybrid system for anomaly detection in industrial big data
11 Multi-faults diagnosis of gearbox by deep convolution neural network
12 Solar energy driven Industry 4.0: MPPT learning approaches for photovoltaic panel
13 Real-time application for Industry 4.0: road crack assessment using image processing method
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