This book provides information on advanced communication technology used in Industry 4.0 and 5.0. The book covers a variety of technologies such as signal processing, system designing, computer vision, and artificial intelligence and explains their benefits, usage, and market values in Industry 4.0 and 5.0. The authors present technological tools for industrial applications and give examples of their usage of system design, modeling, artificial intelligence, internet of things and robotics. This book covers the impact of these technologies in various industrial applications and provides future technological tools that will be helpful in future planning and development. The book is pertinent to researchers, academics, professionals, planners, and student’s interest in Industry 5.0.
Author(s): Rohit Thanki, Purva Joshi
Publisher: Springer
Year: 2023
Language: English
Pages: 104
City: Cham
Preface
Contents
1 Introduction
2 System Identification and Its Applications
2.1 What Is System Identification?
2.2 Parametric and Nonparametric System Identification
2.2.1 Parametric Model Estimation Method
2.3 Optimization for Time-Varying System
2.3.1 Adaptive κ-Nearest Neighbor Method
2.3.2 Robust Control Method
2.4 Industrial Applications of Time-Varying System
2.4.1 Robotic-Based Automotive Industries
2.4.2 Chemical Industries
2.4.3 Communication and Networking
2.4.4 Agriculture and Smart Farming
2.4.5 Logistics and Storage Industries
References
3 Signal Processing and Its Applications
3.1 Basic of Signal Processing
3.1.1 Types of Signal Processing
3.1.2 Types of Different Systems
3.2 Transforms Used for Analysis of Signals and Systems
3.2.1 Laplace Transform
3.2.2 Z-Transform
3.2.3 Fourier Transform
3.2.3.1 Discrete Fourier Transform (DFT)
3.2.3.2 Fast Fourier Transform (FFT)
3.2.4 Wavelet Transform
3.3 Designing of Discrete-Time Systems
3.3.1 Finite Impulse Response (FIR) Filter
3.3.2 Infinite Impulse Response (IIR) Filter
3.4 Industrial Applications of Signal Processing (SP)
3.4.1 SP for Digital Front End and Radio Frequency
3.4.2 Development of Chip for All DSP
3.4.3 Usage of SP in Nanotechnology
3.4.4 Development of Reconfigurable and Cognitive Radar
3.4.5 SP in Smart Internet of Things
3.4.6 SP for Cloud and Service Computing
3.4.7 SP for Digital TV Technology
3.4.8 SP for Autonomous System Perception
References
4 Image Processing and Its Applications
4.1 Most Commonly Used Images
4.1.1 Binary Image
4.1.2 Grayscale Image
4.1.3 Color Image
4.2 Fundamental Steps of Image Processing
4.2.1 Image Acquisition
4.2.2 Image Enhancement
4.2.3 Image Restoration
4.2.4 Color Image Processing
4.2.5 Wavelets and Multi-Resolution Processing
4.2.6 Image Compression
4.2.7 Morphological Processing
4.2.8 Image Segmentation
4.2.9 Representation and Description
4.2.10 Object Detection and Recognition
4.2.11 Knowledge Base
4.3 Image Processing Methods
4.3.1 Image Enhancement
4.3.1.1 Image Enhancement in Spatial Domain
4.3.1.2 Image Enhancement in Transform Domain
4.3.2 Image Restoration
4.3.3 Image Morphology
4.3.4 Image Segmentation
4.3.5 Image Compression
4.3.6 Image Registration
4.3.7 Object Detection
4.3.8 Image Manipulation
4.4 Industrial Applications of Image Processing
4.4.1 Agriculture
4.4.2 Manufacturing
4.4.3 Automotive
4.4.4 Healthcare
4.4.5 Robotics Guidance and Control
4.4.6 Defense and Security
References
5 Artificial Intelligence and Its Applications
5.1 Types of Learning Methods
5.1.1 Supervised Learning
5.1.2 Unsupervised Learning
5.1.3 Reinforcement Learning
5.1.4 Deep Learning
5.2 Types of Machine Learning Algorithms
5.2.1 Supervised Learning-Based Algorithms
5.2.1.1 Statistical Learning-Based Algorithms
5.2.1.2 Nearest Neighbor (NN) Algorithm
5.2.1.3 Naive Bayes Algorithm
5.2.1.4 Support Vector Machine (SVM) Algorithm
5.2.1.5 Decision Tree Algorithm
5.2.1.6 Random Forest Algorithm
5.2.1.7 Linear Regression Algorithm
5.2.1.8 Logistic Regression Algorithm
5.2.2 Unsupervised Learning-Based Algorithms
5.2.2.1 K-means Clustering Algorithm
5.2.2.2 Principal Component Analysis
5.2.2.3 Independent Component Analysis
5.2.2.4 Singular Value Decomposition
5.2.2.5 Gaussian Mixture Models
5.2.2.6 Self-Organizing Maps
5.2.3 Reinforcement Learning-Based Algorithms
5.2.3.1 Basic of RL Algorithm and Q-Learning Algorithm
5.2.3.2 State-Action-Reward-State-Action (SARSA) Algorithm
5.2.3.3 Deep Q Network (DQN) Algorithm
5.3 Types of Deep Learning Algorithms
5.3.1 Convolutional Neural Networks (CNNs)
5.3.1.1 Feature Extraction Operation
5.3.1.2 Classification Operation
5.3.2 Other Deep Learning Algorithms
5.4 AI-Based Research in Various Domains
5.4.1 Development of New Algorithms and Models
5.4.2 AI in Computer Vision
5.4.3 AI in Natural Language Processing
5.4.4 AI in Recommender Systems
5.4.5 AI in Robotics
5.4.6 AI in the Internet of Things
5.4.7 AI in Advanced Game Theory
5.4.8 AI in Collaborative Systems
5.5 Industrial Applications of AI
5.5.1 Financial Applications
5.5.2 Manufacturing Applications
5.5.3 Healthcare and Life Sciences Applications
5.5.4 Telecommunication Applications
5.5.5 Oil, Gas, and Energy Applications
5.5.6 Aviation Applications
5.6 Working Flow for AI-Powered Industry
References
6 Advanced Technologies for Industrial Applications
6.1 Industrial IoT (IIoT)
6.1.1 Internet of Health Things
6.1.1.1 Recent Case Study and Enabling Technologies Overview
6.2 Autonomous Robots
6.2.1 Collaborative Robots (Cobots)
6.2.2 Soft Robotics
6.3 Smart and Automotive Industries
6.4 Human and Machine Interfacing (HMI)
6.5 AI Software
6.6 Augmented and Virtual Reality (AR/VR)
6.7 Blockchain and Cybersecurity
6.8 Challenges and Open Research Problems in Various Domains
6.8.1 Machine Learning
6.8.2 Biomedical Imaging
6.8.3 Natural Language Processing
6.8.4 Robotics
6.8.5 Wireless Communications
References
Index
