Название: Modern Wireless Communication Systems and Networks: Design, Practice, and Implementation
Автор: Budhaditya Bhattacharyya, Anindita Kundu, Anantha Krishna Chintanpalli, Xavier Fernando
Издательство: CRC Press
Год: 2026
Страниц: 390
Язык: английский
Формат: True PDF, True EPUB
Размер: 28.1 MB

This book examines real-world applications, including mobile communication, the Internet of Things (IoT), and wireless sensor networks, emphasizing practical deployment. It further discusses device-to-device communication protocols, wireless personal area networks, and wireless body area networks. The book highlights opportunities and challenges with experimental software-defined radio networks.

3D computer vision-based production and immersive holographic imaging technologies, big data computing and synthetic data-based autonomous production systems, and deep learning-based ambient sound processing and geospatial intelligence tools are instrumental for industrial 6G and wireless visual sensor networks in simulated machines and factories across Web3-powered metaverse worlds. Behavior pattern clustering in Internet of Things (IoT)-based industrial environments develops on visual tracking and multisensory stimulation algorithms, 3D computer vision-based production and digital scent technologies, and context-aware augmented reality and affective computing systems.

Multiple autonomous mobile robots for cyber-physical system-based smart manufacturing in interconnected digital realms develop on semantic-based cognitive and multisensory data fusion technologies, computer vision artificial intelligence (AI) and metaverse decentralized governance systems, and event modeling and forecasting tools. Integrated digital‒physical workflows across the metaverse’s decentralized infrastructure for virtual machines and factories necessitate metaverse and digital twin technologies, bioinspired AI and Internet of Robotic Things systems, and spatiotemporal fusion and semantic 3D mapping algorithms.

Cognitive computing and digital twin technologies, explainable AI-based decision support and networked manufacturing systems, and bioinspired robotic navigation and visual perception algorithms can be harnessed for IoT sensing infrastructures and multiple autonomous mobile robots in the multisensory immersive extended reality industrial metaverse. Swarm intelligence and vision-based robotic sensing techniques, visual localization and mapping devices, and distributed decision and control algorithms shape semantic network representations and visual digital twins for virtual machines and factories in 3D simulation spaces.

Real-Time Workplace Safety Detection Using Raspberry PI 5 and Jetson Nano with Computer Vision: Safety at work is an important requirement of any industrial or construction environment since the equipment is more likely to be risky and dangerous. The company is responsible for safety being paramount, including personal protective equipment (PPE), a helmet, a vest, and a mask to avoid any untoward incident leading to injury or even death. In the past, safety inspection was based upon manual supervision and was largely confined by human error and fatigue and was unable to respond to suspects in real time. Thanks to developments in both embedded systems and computer vision, automatic safety detection is now more viable than ever. Devices such as Raspberry Pi, powered by heavy-hitting object detection models like you only look once (YOLO), are a mobile and effective means to perform real-time monitoring. With Artificial Intelligence (AI) tools like Gemini, safety violations can be easily detected and accurately described to send off alerts in a timely manner.

Features:

Provides an in-depth exploration of the recent technologies in the domain of wireless communication systems and networks.
Examines real-world applications, including mobile communication, the IoT, and wireless sensor networks and emphasizes practical deployment.
Addresses key challenges in 5G wireless networks and beyond, like spectrum management and maximizing utilization while minimizing interference.
Discusses mmWave communication, software-defined radio, cognitive radio networks, and non-orthogonal multiple access (NOMA).
Explains the concepts of Machine Learning and Deep Learning models and how they can be applied in the domain of wireless communication and networks to draw meaningful conclusions.

It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, wireless communications, networking communications, telecommunications, and communications system design.

Contents: