Advanced Signal Processing for Industry 4.0, Volume 1: Evolution, communication protocols, and applications in manufacturing systems

PRELIMS.pdf
	Preface
	Acknowledgments
	Editor biographies
		Irshad Ahmad Ansari
		Varun Bajaj
	List of contributors
	Contributors’ biographies
		Outline placeholder
			Dr Vanita Arora
			Matadeen Bansal
			Dr Kausik Basak
			Dr Gundars Berzins
			Dr Geetika Dua
			Dr Meral Erdirençelebi
			Dr V Ganesan
			Dr Güzide Karakuş
			Navpreet Kaur
			Mr Tapomoy Koley
			Amirhossein Koochekian
			Anurodh Kumar
			Associate Professor Ir. Dr Balakrishnan Malarvili
			Miguel Angel Mañanas
			Hamid R Marateb
			Shikha Maurya
			Ashutosh Mishra
			Dr Ravibabu Mulaveesala
			Dr Sc.Ing. Konstantin Nechval
			Nicholas A Nechval
			Dr Neelamshobha Nirala
			Dr Santheraleka Ramanathan
			Kari Rannaste
			Dr R Ravindraiah
			Dr Antti Rissanen
			Dr Marjo Rissanen
			Dr Mónica Rojas-Martínez
			Mr Rohit Roy Chowdhury
			Deepak Sahu
			Dr P D Selvam
			Mehdi Shirzadi
			Resham Raj Shivwanshi, (PhD Pursuing)
			Dr J Sridhar
			Dinesh Kumar V
			Dr Amit Vishwakarma
			Shivam Vyas
			Shadi Zamani
CH001.pdf
	Chapter 1 Robotics vision for industrial automation
		1.1 Introduction
			1.1.1 Code scanning
			1.1.2 Robotic guidance
			1.1.3 Machine control
			1.1.4 Sorting
		1.2 Computer vision system
			1.2.1 Image representation
			1.2.2 RGB colour model
			1.2.3 HSV colour model
			1.2.4 Greyscale image
			1.2.5 Image segmentation
			1.2.6 Thresholding
			1.2.7 Blurring
			1.2.8 Edge detection
			1.2.9 Object detection
			1.2.10 Region of interest (ROI)
		1.3 Applications of vision system
			1.3.1 Vision controlled robotic arm
			1.3.2 In manufacturing and mining
			1.3.3 In industry application
		1.4 Proposed work
			1.4.1 Proposed model
			1.4.2 Model design
			1.4.3 Working (model-I)
			1.4.4 Evolution (model-I)
			1.4.5 Working (model-II)
			1.4.6 Evaluation (model-II)
		1.5 Industrial demo (apple sorting)
			1.5.1 Yellow apple sorting
			1.5.2 Red apple sorting
		1.6 Conclusion
		References
CH002.pdf
	Chapter 2 Capnography signal processing in trend with Industry 4.0 advancement
		2.1 Capnography 4.0
		2.2 Capnography measurement and physiology
		2.3 Capnography signal interpretation
		2.4 Capnography—annotation and classification
		2.5 Capnography in medical interpretation
			2.5.1 Airway integrity
			2.5.2 Asthma
			2.5.3 Procedural sedation
			2.5.4 Apnea monitoring
			2.5.5 Cardiac arrest and resuscitation
			2.5.6 Pulmonary embolism
			2.5.7 Laparoscopy
		2.6 Capnography at intensive care units
		2.7 Capnogram modeling for respiratory monitoring
		2.8 Capnography signal outside healthcare environment
			2.8.1 Ice core analysis
			2.8.2 Mines
			2.8.3 Space station
			2.8.4 Submarines
		2.9 Conclusion
		Acknowledgments
		References
CH003.pdf
	Chapter 3 The future of Industry 4.0: private 5G networks
		3.1 Introduction
			3.1.1 Advantage of 5G: high speed and capacity
			3.1.2 Industry 4.0 and mobile connectivity
			3.1.3 Industry 4.0 opportunities and challenges
		3.2 Private 5G networks’ flexibility in Industry 4.0
		3.3 Use cases of 5G networks in manufacturing
		3.4 Specific technological elements for private networks
		3.5 Other benefits of 5G features for private networks
			3.5.1 Edge computing
			3.5.2 5G network slicing
			3.5.3 Open networking
		3.6 Advantages and applications of private 5G networks
			3.6.1 Advantages of private 5G networks
			3.6.2 Automated and robotic deployments in retail
			3.6.3 Smart cities, smart offices, smart factories, and the gaming industry
			3.6.4 Applications in healthcare
			3.6.5 Fixed wireless access
		3.7 Citizens broadband radio service (CBRS) for private 5G network
			3.7.1 CBRS overview
			3.7.2 Requirements for CBRS
			3.7.3 Components of the CBRS network architecture
			3.7.4 Network structure for CBRS
		3.8 Modeling of 5G private networks’ economic impact
			3.8.1 Overview
			3.8.2 Business perspective
			3.8.3 Perspective of a service provider
			3.8.4 View from an infrastructure vendor
			3.8.5 New player perspective
			3.8.6 Various models of funding for private networks
			3.8.7 Finding synergies in common
		3.9 Conclusion
		References
CH004.pdf
	Chapter 4 Applications of infrared imaging for non-destructive testing and evaluation of industrial components
		4.1 Introduction
		4.2 Theory
			4.2.1 Linear frequency modulated thermal wave imaging
			4.2.2 Pre-processing using polynomial fit
			4.2.3 Signal processing using pulse compression
			4.2.4 Image processing: segmentation, edge detection
		4.3 Modeling and simulation
		4.4 Experimentation
		4.5 Results and discussions
		4.6 Conclusion
		Acknowledgments
		References
CH005.pdf
	Chapter 5 Customer-driven healthcare through mission-focused approach in 4IR
		5.1 Introduction
		5.2 Fourth Industrial Revolution in healthcare
			5.2.1 Possibilities and challenges
			5.2.2 Digital twins in healthcare
			5.2.3 Wearable sensors in continuous health monitoring
		5.3 Customer-centric design in the context of Industry 4.0
			5.3.1 Targeting and evaluating customer-centered service design
			5.3.2 Customer-centric healthcare with a mission-strategic emphasis
			5.3.3 Mission-strategic perspective, ethics, and overall quality in healthcare
			5.3.4 Customer-centric design through metadesign
		5.4 Enhancing customer orientation in organizations
			5.4.1 Customer-centric service policy through comprehensive training
			5.4.2 Shared mission in multidisciplinary design and action
		5.5 Process reengineering with renewed mission
			5.5.1 Case: customer-centric dental care
			5.5.2 Process reengineering with mission-strategic vision
		5.6 Conclusions
		Acknowledgments
		References
CH006.pdf
	Chapter 6 The application of Industry 4.0 technologies for automated health monitoring and surveillance during pandemics and post-pandemic life
		6.1 Introduction
		6.2 Industry 4.0
			6.2.1 IoT
			6.2.2 AI, ML, big data, and cloud computing
		6.3 Comprehensive literature review
		6.4 Automated monitoring and surveillance techniques
		6.5 Conclusion and future scope
		Acknowledgments
		References
CH007.pdf
	Chapter 7 A novel computational intelligence approach to making efficient decisions under parametric uncertainty of practical models and its applications to Industry 4.0
		7.1 Introduction
		7.2 Exponential distribution
			7.2.1 Two-parameter exponential distribution
		7.3 Analytical inferences for constructing new-sample prediction limits
			7.3.1 Example of constructing new-sample (1−α)-prediction limits
		7.4 Analytical inferences for constructing within-sample prediction limits
			7.4.1 Example of constructing within-sample (1−α)-prediction limits
		7.5 Optimization of anticipated inspection process
		7.6 Optimization of single-period decision-making models
		7.7 Advanced techniques of signal processing in terms of hypotheses testing and misclassification probability
			7.7.1 Optimizing the product acceptance process in terms of misclassification probability
			7.7.2 Model of signal detection process in terms of hypotheses testing
			7.7.3 Statistical hypotheses testing whether two samples come from the same distribution under parametric uncertainty
			7.7.4 Parametric estimation via shortest-length confidence intervals
		7.8 Conclusion
		References
CH008.pdf
	Chapter 8 Role of artificial intelligence in industries for advanced applications
		8.1 Introduction
		8.2 Seven top technologies of AI that are responsible for profoundly influencing the fourth industrial revolution
			8.2.1 Artificial intelligence of things (AIOT)
			8.2.2 Additive manufacturing
			8.2.3 Data science
			8.2.4 Cloud computing
			8.2.5 Computer vision
			8.2.6 Natural language processing (NLP)
			8.2.7 Robotics
		8.3 Conclusion
		References
CH009.pdf
	Chapter 9 Artificial intelligence based flexible manufacturing system (FMS)
		9.1 Introduction
		9.2 Background of AI in automation and FMS
		9.3 Application areas of AI
			9.3.1 Supply chain management
			9.3.2 AI in design and manufacturing
			9.3.3 AI in warehouse management
			9.3.4 AI process automation
			9.3.5 AI for predictive maintenance
			9.3.6 AI-based product development
			9.3.7 AI-based visual inspections and quality control
			9.3.8 AI order management
		9.4 Some of the real industrial set up and AI application
			9.4.1 Maintenance applications
			9.4.2 Warehouse logistics, MES, and ERP applications
			9.4.3 Operational simulation and optimization
		9.5 Process planning
			9.5.1 Problem description
			9.5.2 Selection
			9.5.3 Elaboration
			9.5.4 Sequencing
		9.6 Design strategies
		9.7 Rule-based model
		9.8 Structure of FMS
			9.8.1 Process planning in FMS
			9.8.2 Process control and scheduling
			9.8.3 Scheduling
			9.8.4 Intelligent scheduling
			9.8.5 Knowledge base
			9.8.6 Declarative knowledge
			9.8.7 Knowledge-based scheduling system
		9.9 Conclusion
		References
CH010.pdf
	Chapter 10 Applications of deep learning in revolutionizing industrial sectors
		10.1 Introduction
		10.2 Relevance in Industry 4.0
		10.3 How does a network learn?
		10.4 Application in stock market
			10.4.1 A brief about the stock market
			10.4.2 A generalized approach
			10.4.3 The RNN and LSTM models
			10.4.4 Deep learning researches in stock market analysis
		10.5 Application in marketing industry
			10.5.1 A brief overview
			10.5.2 Resolving issues through deep learning
			10.5.3 Recommender systems
		10.6 Application in bioinformatics
			10.6.1 Diverse areas and scope
			10.6.2 Deep learning implementation in genomics
			10.6.3 Challenges and strategies
			10.6.4 Concept of federated learning
		10.7 Application in cybersecurity
			10.7.1 The need
			10.7.2 Landscape and desired solutions
			10.7.3 Deep learning researches
			10.7.4 Challenges of using deep learning models in industry
		10.8 Case study: stock market prediction
			10.8.1 About the dataset
			10.8.2 Statistical analysis and preprocessing
			10.8.3 Computational models and their comparative analysis
		10.9 Conclusion
		References
CH011.pdf
	Chapter 11 Digitalization in family businesses—a case study in a food industry in Turkey
		11.1 Introduction
		11.2 Conceptual framework
			11.2.1 Industry 4.0
			11.2.2 Digitalization and digital transformation
			11.2.3 Family businesses and digital transformation process
		11.3 Research method
		11.4 Research results
			11.4.1 Information about the business and interviewees
			11.4.2 Where does the business see itself in digital transformation?
			11.4.3 What kind of digitalization actions have been taken within the scope of which business functions in the enterprise?
			11.4.4 The impact of being a family business on digitalization
		11.5 Conclusion
		11.6 Future research directions
		References
		Further reading
		Key terms and definitions
CH012.pdf
	Chapter 12 Automatic identification of finger movements for industrial robotic applications using electromyogram signals
		12.1 Introduction
		12.2 Methodology
			12.2.1 Dataset
			12.2.2 TQWT
			12.2.3 Feature extraction
			12.2.4 Classification methods
			12.2.5 Performance metrics
		12.3 Results
		12.4 Conclusion
		References
CH013.pdf
	Chapter 13 Data-driven approach to design energy-efficient precoder for QoS-aware MIMO-MRCN system in context of Industry 4.0
		13.1 Introduction
		13.2 Related works
		13.3 System model
			13.3.1 Relay selection and precoder design schemes
			13.3.2 A DL-based low complexity approach for precoder designing
			13.3.3 DNN architecture
			13.3.4 Dataset generation, training, and deployment phase
		13.4 Numerical results
		13.5 Conclusion
		References