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Full Description
As industries move towards intelligent, adaptive, and efficient manufacturing processes, the integration of AI, IoT, signal processing, and computer vision is crucial. This book serves as a comprehensive guide for professionals, researchers, and academics looking to harness the power of cutting-edge technologies in the field of smart manufacturing.
Smart Manufacturing with AIoT: Signal Processing, Computer Vision, and Data Analytics is a comprehensive guide that focuses on practical implementation, bridging the gap between theoretical concepts and real-world applications. Each chapter of the book explores a different aspect of the synergy between AI, IoT, signal processing, and computer vision, starting with foundational concepts and progressing towards advanced applications. From enhancing quality control processes to proactive maintenance strategies and dynamic threat assessment, this book covers a wide range of topics that define the contemporary landscape of smart manufacturing.
This book explores the intricate details of AIoT, signal processing, and computer vision, providing valuable insights for both beginners and experienced professionals in the field. Readers will gain actionable knowledge that empowers them to implement transformative solutions within their smart manufacturing environments.
Contents
Chapter 1: Deep Learning-Based Improved Gait Recognition Using 3D Skeletal Data for Smart Surveillance Systems, Chapter 2: IoT-Based Automated Fog Detection System Using kNN and Real-Time Meteorological Data Analytics, Chapter 3: Intelligent Waste Classification Framework Based on Machine Learning and Deep Learning for Smart City Application, Chapter 4: AI-Powered Data Analytics for Optimizing Tea Tourism: An Investigation on Determinants and Destination Information, Chapter 5: Substation Level Short-Term Load Forecasting Using Graph-Based Signal Processing and Machine Learning Algorithms, Chapter 6: AI-Based Histopathological Image Analysis for Breast Cancer Diagnosis Using Deep and Machine Learning Algorithms, Chapter 7: AI-Optimized Active Cell Balancing Technique Using Single Inductor for Intelligent Battery Management in IoT-Enabled Energy Systems, Chapter 8: Integrated Simulation and Hardware-in-the-Loop Analysis of ISO15118-Based Smart EV Charging Communication Systems, Chapter 9: AI-Driven Analysis of Air Gap Eccentricity Effects in PMSM for Condition Monitoring in Smart Industrial Systems, Chapter 10: Deep Convolutional Neural Network-Based Smart Diagnostic System for Enhanced Breast Cancer Detection
