Convergence of Blockchain, AI, and IoT: Concepts and Challenges discusses the convergence of three powerful technologies that play into the digital revolution and blur the lines between biological, digital, and physical objects. This book covers novel algorithms, solutions for addressing issues in applications, security, authentication, and privacy. The book provides an overview of the clinical scientific research enabling smart diagnosis equipment through AI. It presents the role these technologies play in augmented reality and blockchain, covers digital currency managed with bitcoin, and discusses deep learning and how it can enhance human thoughts and behaviors. Targeted audiences range from those interested in the technical revolution of blockchain, big data and the Internet of Things, to research scholars and the professional market.

This book presents a guideline for EWMA filter design for industrial wireless networked control system, both theoretically and practically. The filter's key advantages are simple, effective, low computational overhead. This book also provides a guideline for practical implementation of EWMA filter for improving networked control performance of various process plants. It further discusses not only the advantages of the filter, but also the limitations and how to avoid them when implementing the filter from practical point of view.

This book presents a guideline for EWMA filter design for industrial wireless networked control system, both theoretically and practically. The filter's key advantages are simple, effective, low computational overhead. This book also provides a guideline for practical implementation of EWMA filter for improving networked control performance of various process plants. It further discusses not only the advantages of the filter, but also the limitations and how to avoid them when implementing the filter from practical point of view.

Recent advances in wireless technology have led to the emergence of industry standards such as WirelessHART. These strategies minimise the need for cumbersome cabling, thereby reducing costs. However, applying them involves the challenge of handling stochastic network delays, which can degrade control performance. To address this problem, commonly used simple PID could be employed. However, PID suffers from gain range limitations when used in a delayed environment. Furthermore, model-based controllers are complex and require exact models of the process and systematic system identification for implementation. Therefore, to address these issues, the book proposes control strategies that retain the simplicity of PID in terms of ease of tuning and structure, while improving on the performance of the closed-loop system with regard to stochastic network delays and mismatches. Concretely, it proposes and discusses three strategies, namely: Setpoint Weighting (SW), Filtered Predictive PI (FPPI) and Optimal Fuzzy PID. In order to optimise some of these controllers, two novel hybrid optimisation algorithms combining the dynamism of the Bacterial Foraging Algorithm (BFA) and advantages of both the Spiral Dynamic Algorithm (SDA) and the Accelerated Particle Swarm Optimisation (APSO) have been used. The strategies proposed here can also be applied in stochastic control scenarios (not necessarily wireless) characterised by uncertainties. This book will be useful to engineers and researchers in both industry and academia. In industry, it will be particularly useful to research and development efforts where PID controllers and wireless sensor networks (WSNs) involving both short and long term stochastic network delay are employed. Thus, it can be used for real-time control design in these areas. In the academic setting, the book will be useful for researchers, undergraduate and graduate students of instrumentation and control. It can also be used as reference material for teaching courses on predictive and adaptive controls and their application.

Recent advances in wireless technology have led to the emergence of industry standards such as WirelessHART. These strategies minimise the need for cumbersome cabling, thereby reducing costs. However, applying them involves the challenge of handling stochastic network delays, which can degrade control performance. To address this problem, commonly used simple PID could be employed. However, PID suffers from gain range limitations when used in a delayed environment. Furthermore, model-based controllers are complex and require exact models of the process and systematic system identification for implementation. Therefore, to address these issues, the book proposes control strategies that retain the simplicity of PID in terms of ease of tuning and structure, while improving on the performance of the closed-loop system with regard to stochastic network delays and mismatches. Concretely, it proposes and discusses three strategies, namely: Setpoint Weighting (SW), Filtered Predictive PI (FPPI) and Optimal Fuzzy PID. In order to optimise some of these controllers, two novel hybrid optimisation algorithms combining the dynamism of the Bacterial Foraging Algorithm (BFA) and advantages of both the Spiral Dynamic Algorithm (SDA) and the Accelerated Particle Swarm Optimisation (APSO) have been used. The strategies proposed here can also be applied in stochastic control scenarios (not necessarily wireless) characterised by uncertainties. This book will be useful to engineers and researchers in both industry and academia. In industry, it will be particularly useful to research and development efforts where PID controllers and wireless sensor networks (WSNs) involving both short and long term stochastic network delay are employed. Thus, it can be used for real-time control design in these areas. In the academic setting, the book will be useful for researchers, undergraduate and graduate students of instrumentation and control. It can also be used as reference material for teaching courses on predictive and adaptive controls and their application.

The world is keen to leverage multi-faceted AI techniques and tools to deploy and deliver the next generation of business and IT applications. Resource-intensive gadgets, machines, instruments, appliances, and equipment spread across a variety of environments are empowered with AI competencies. Connected products are collectively or individually enabled to be intelligent in their operations, offering and output. AI is being touted as the next-generation technology to visualize and realize a bevy of intelligent systems, networks and environments. However, there are challenges associated with the huge adoption of AI methods. As we give full control to AI systems, we need to know how these AI models reach their decisions. Trust and transparency of AI systems are being seen as a critical challenge. Building knowledge graphs and linking them with AI systems are being recommended as a viable solution for overcoming this trust issue and the way forward to fulfil the ideals of explainable AI. The authors focus on explainable AI concepts, tools, frameworks and techniques. To make the working of AI more transparent, they introduce knowledge graphs (KG) to support the need for trust and transparency into the functioning of AI systems. They show how these technologies can be used towards explaining data fabric solutions and how intelligent applications can be used to greater effect in finance and healthcare. Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications is aimed primarily at industry and academic researchers, scientists, engineers, lecturers and advanced students in the fields of IT and computer science, soft computing, AI/ML/DL, data science, semantic web, knowledge engineering and IoT. It will also prove a useful resource for software, product and project managers and developers in these fields.

As the MOSFET dimensions scale down to nanoscale level, the reliability of circuits based on these devices decreases. Therefore, a mechanism has to be devised that can make the nanoscale systems perform reliably using unreliable circuit components. The solution is fault-tolerant circuit design. Markov Random Field (MRF) is an effective approach that achieves fault-tolerance in integrated circuit design. The previous research on this technique suffers from limitations at the design, simulation and implementation levels. As improvements, the MRF fault-tolerance rules have been validated for a practical circuit example. The simulation framework is extended from thermal to a combination of thermal and random telegraph signal noise sources to provide a more rigorous noise environment for the simulation of nanoscale circuits. Moreover, an architecture-level improvement has been proposed in the design of previous MRF gates. The re-designed MRF is termed as Improved-MRF. By simulating various test circuits in Cadence, it is found that Improved-MRF circuits are 400 whereas MRF circuits are only 10 times more noise-tolerant than the CMOS alternatives.