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Total Size:
6.7 MB
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06843CF976CB5281F4A60EAD40C0A4841221410E
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April 20, 2026, 2:40 a.m.
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(Last updated: April 20, 2026, 2:46 a.m.)
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| Choudhury T. Distributed Time-Sensitive Systems 2025.pdf | 6.7 MB |
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15.1 MB
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NOTE
SOURCE: Choudhury T. Distributed Time-Sensitive Systems 2025
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COVER
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MEDIAINFO
Textbook in PDF format The book provides invaluable insights into cutting-edge advancements across multiple sectors of Society 5.0, where contemporary concepts and interdisciplinary applications empower you to understand and engage with the transformative technologies shaping our future. Distributed Time-Sensitive Systems offers a comprehensive array of pioneering advancements across various sectors within Society 5.0, underpinned by cutting-edge technological innovations. This volume delivers an exhaustive selection of contemporary concepts, practical applications, and groundbreaking implementations that stand to enhance diverse facets of societal life. The chapters encompass detailed insights into fields such as image processing, natural language processing, computer vision, sentiment analysis, and voice and gesture recognition and feature interdisciplinary approaches spanning legal frameworks, medical systems, intelligent urban development, integrated cyber-physical systems infrastructure, and advanced agricultural practices. The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems in the factory systems of the future. This revolution strongly affects industrial smart and distributed measurement systems, pointing to more integrated and intelligent equipment devoted to deriving accurate measurements. This volume explores critical cybersecurity analysis and future research directions for the Internet of Things, addressing security goals and solutions for IoT use cases. The interdisciplinary nature and focus on pioneering advancements in distributed time-sensitive systems across various sectors within Society 5.0 make this thematic volume a unique and valuable contribution to the current research landscape. An intelligent AI-Agent that can carry out a variety of tasks in a virtual environment is created using reinforcement learning. This chapter will demonstrate how machine learning and artificial intelligence techniques can be used to deploy a single AI-Agent to carry out a variety of tasks in various settings. As a result, it is possible to use a single AI-Agent to address a variety of problems, doing away with the need to develop separate, highly specialized AI-Agents for each distinct problem encountered in a different Environment. AI-Agent has been transformed into a reusable entity that can be used to train in various scenarios and effectively learn how to solve the specific problem presented by each one. AI-Agents are more resilient and easily adaptable in changing environments when they have the ability to solve multiple problems with a single AI-Agent. In the end, this would decrease and optimize the cost of resources (such as time, money, and energy), as well as increase and optimize human innovation. The Unity 3D Engine, Python, PyTorch, and ML-Agents are the main resource requirements to be used in this chapter. In chapter 1, the author discusses AI data analysis techniques. Artificial Intelligence algorithms are used to automate jobs that were previously completed by humans. The main reason Artificial Intelligence (AI) is used in every aspect and industry is the intelligence it has provided through continuous learning from regularly trained models. The different hardware and software resources required for processing and analyzing large volumes of data is discussed in this chapter. In chapter 2, the author discusses heart rate prediction analysis using ML & DL techniques. In the process of the study, the author investigated several powerful data-driven ML and DL models. In Chapter 3, the author discusses the implementation of high-speed adders for image blending applications. The adder is a core component of the central processing unit (CPU), Digital Signal Processors, image de-noising filters. The performance of the system as a whole depends on tradeoff between power dissipation, area and speed of the adder used. Chapter 4 discussed smart factories and energy efficiency in industry 4.0. Industry 4.0 has completely changed the way that production is done, resulting in the creation of smart factories that combine cutting-edge technology with data-driven systems. By utilizing the power of automation, connection, and intelligent decision-making, these smart factories seek to improve production, efficiency, and sustainability. In Chapter 5, the author discusses computer vision with emerging techniques and their scope in AI. Through the multidisciplinary field of computer vision, computers are able to see, understand, and interpret their visual surroundings. It’s commonly referred to as an artificial intelligence (AI) subfield. This field comprehensively processes, analyzes, and comprehends many picture types as well as high-dimensional data from the real world to offer numerical and figurative information. Power of intelligent robotic process automation is discussed in Chapter 6. This chapter examines the Industrial Research and Patent Administration (IRPA) and its potential to revolutionize the car manufacturing industry. In chapter 7, the author discusses unleashing the potential of Artificial Intelligence in industry 5.0. The author also discussed the challenges and opportunities associated with AI adoption in Industry 5.0, including issues related to data privacy, cybersecurity, and job displacement. A VLSI based multi-level ECG compression scheme with run and variable length encoding for wearable sensor node techniques are discussed in chapter 8. Chapter 9 demonstrates how Machine Learning and Artificial Intelligence techniques can be used to deploy a single AI-agent to carry out a variety of tasks in various settings. An intelligent AI-agent that can carry out a variety of tasks in a virtual environment is created using reinforcement learning. In Chapter 10, the author discusses digital transformation and sustainable international agricultural businesses. In order to address the problems in a developing environment, Chapter 11 analyzes the healthcare issues and evaluates the potential for introducing disruptive emerging technology into the industry. In chapter 12, the author discusses IT innovation: Driving digital transformation and defined IT innovation as the inception and application of novel technologies, strategies, and processes within the realm of information technology. Strategic convergence of advanced technologies in modern warfare is discussed in chapter 13
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