Distributed Intelligent Circuits and Systems

The following sections are included:

• Preface
• About the Editors
• Acknowledgments
• Contents

The working approach is changing with the digitalization and advancement of technical tools, along with increased security risks. One of the most common and biggest threats that have been observed is attacks on communicational applications and networks. This threat is caused by denial of service (DoS) or distributed denial of service (DDoS). The behavior of the attacks performed through DoS and DDoS could be understood with an analysis of each step and attack process. A piece of brief information related to the attack through DoS or DDoS helps in understanding its effects, such as the development of countermeasures for the migration. Different types of networks, such as software-defined networks (SDN), virtual networks, and traditional networks, are classified in the model of DoS or DDoS. This chapter describes an analytical view of the different types of DoS or DDoS for network architecture. Mathematical modeling of the attacks through DoS or DDoS on different models, such as epidemic, hierarchical, analytical, and traffic, is described in the chapter.

This chapter provides an overview of the message-oriented architectural pattern. The core components of this type of system are explained, and an overview of common messaging protocols is provided. Two binary messaging protocols (AMQP and MQTT) and a text-based (STOMP) one are analyzed. The exchange of information in these protocols, the message format they expect, and the message exchange order are described. The core components of the message-oriented architectural pattern are the message and the message broker. Message brokers provide implementations of messaging protocols. Five commonly used message brokers (Google Cloud Pub/Sub, Azure Service Bus, RabbitMQ, NATS, and Apache Kafka) and their components are introduced and explained, and common use cases for each broker are listed. A message broker contains a topic and/or queue as a means of exchanging messages between message producers (publishers) and message consumers. The way messages are exchanged between these components is described. An example of a message-oriented architecture is implemented. The central component of the architecture is a Spring Boot application which communicates with the Apache Kafka broker and additional external components. Finally, two use cases of such a component are explained: a chat application in the React framework, and a high-throughput message producer in the Golang programming language.

Computer-aided diagnostic (CAD) systems are gaining popularity because they enhance the analysis and comprehension of clinical images, as opposed to the preliminary identification of different illnesses by physicians and medical experts. Likewise, bone fractures are a frequent occurrence owing to strain, accidents, and osteoporosis. In addition, bone is a solid structure that supports the whole body. Therefore, bone fractures are regarded as one of the most pressing issues in the later stages of life. The detection of bone fractures using computer vision is gaining importance in CAD systems since it has the potential to minimize the doctor’s burden by eliminating simple cases. In this chapter, the identification of tibia fracture types is analyzed utilizing several techniques of image processing. The main purposes of this investigation are to determine whether or not there is a fracture in the lower leg bone from the X-ray scans and to characterize the kind of fracture that is present (tibia). There are three primary stages involved in the process of developing a system to detect tibia fractures. In order to classify the different kinds of fractures and determine the locations of fractures, preprocessing, feature extraction, and classification are used. During preprocessing, the sharpening technique known as unsharp masking is used on the image in order to enhance it and draw attention to the image’s edges. After the image has been sharpened, the Harris corner detection approach is used on it to generate corner feature points for further feature extraction. Then, in order to detect fractures or non-fractures and, if detected, classify the fracture, two different classification techniques are used. A simple decision tree is employed for the classification of fractures, while the K-nearest neighbor algorithm is used for the classification of the fracture type. In this work, many forms of fractures, including normal, transverse, oblique, and comminute, are described. In addition, fracture locations may be determined by examining the Harris corner points that have been formed. Last but not least, the outputs of the system are evaluated utilizing two different performance assessment approaches. The first component is a performance evaluation that may be conducted in fracture or non-fracture (normal) conditions, and it can provide one of four possible results: TP, TN, FP, or FN. The second goal is to use the Kappa approach in order to conduct an analysis of the accuracy of each fracture type under various error settings. The MATLAB programming environment was used throughout the building of the system, which offers a vast array of image processing capabilities. The technique has an 82% classification accuracy for fracture types. This image processing approach is extraordinarily useful for several applications, including biomedical, security, satellite imaging, personal imaging, and medicine. Fracture detection employs image processing techniques such as picture augmentation, feature segmentation, and feature excitation. This study employs an astute edge detection technique for segmentation. Using a shrewd technique, accurate information can be extracted from the bone image. This research’s primary objective is to identify bone fractures using image processing in MATLAB. Preprocessing, segmentation, and fracture identification are the phases that comprise the proposed system. In the feature excitation stage, the Hough transform method is used for the line detection of inline images. The system’s primary function is the extraction of features. Multiple experiments demonstrate that the suggested approach is very precise and effective.

In light of the rapid urbanization throughout the world, it is crucial to combine intelligent and environmentally friendly city design in terms of the carriage of passengers and commodities. This will play an important role in shaping how future civilizations handle transportation for decades to come. Intelligent transportation is a concept which is developed over time, contemporaneous with the increase in interest in the so-called “Intelligent Cities,” in line with the United Nations’ emphasis on the sustainability elements of development over the long term. However, other factors, such as promoting active and inclusive transportation, supporting the use of alternative ecologically friendly fuels, and engaging with individuals, must also be taken into account. Some of the crucial components of smart mobility include a decline in traffic congestion and new route optimizations with a reduced ecological footprint. Internet-of-Everything, artificial intelligence, blockchain, and Big Data technologies can become key points of initiation as well as core supports that encourage the growth of innovation, which will revolutionize the present conceptual framework for cities and their inhabitants. These technologies will also play a key role in the advancement of revolutionary new ideas that will alter every aspect of our lives. The development of current urban areas into intelligent cities will bring about a slew of modifications over the next several decades at least. Some of the impacts on services and applications include optimization of logistics, heavy traffic improvement, and driverless vehicles, and these changes will incorporate a variety of shifts. This study offers a comprehensive analysis of the most emerging advancements and solutions that have been offered within the realm of intelligent connectivity and the supporting advancements in science that are necessary for its success. The purpose of this chapter is to convey a breakdown of how intelligent mobility contributes to the emergence of smart cities by defining specific aspects and primary merits of using intelligent transportation within the context of a smart city ecosystem. This chapter discusses a variety of other potential advantages and constraints associated with smart mobility. In addition, the primary services and applications that are anticipated to emerge in the next few years in the realm of smart connectivity are probed, along with future potential trends and the scope of the industry.

The evolution of communication technologies with high-frequency radio-frequency (RF) devices increased the demand for compact and efficient designs. Micro-electromechanical systems (MEMS) technology revolutionized microwave and RF applications because of its ability to be engineered into miniaturized devices that are highly linear and power efficient. It is more challenging to perform numerical analysis and optimization of such complex MEMS devices. Electromagnetic (EM) simulation-based optimization software using different methodologies employing coupled domains and time domain analysis of MEMS devices requires repeated simulation, which makes it computationally expensive. The artificial neural network (ANN) model is an alternative to these conventional simulation-based design methodologies to expedite the design process. ANN models for RF and microwave modeling are known to be effective, precise, and flexible. ANN is capable of producing accurate results with less computational time than sophisticated EM models. An overview of various RF MEMS components and an introduction to ANN are provided in this chapter. In addition, this chapter presents the concept of modeling an RF MEMS shunt switch using ANN as a case study.

Coronavirus disease 2019 (COVID-19), which causes severe acute respiratory syndrome (SARS-CoV-2), is a persistent global epidemic. The coronavirus was discovered for the first time on March 11, 2020, following an outbreak in the Chinese city of Wuhan in December 2019. This virus causes respiratory issues, fever, coughing, and shortness of breath. In the worst circumstances, infection might result in mortality due to pneumonia, severe acute respiratory syndrome, and other conditions. The second COVID-19 wave in India showed how ill-prepared the country’s healthcare systems were to deal with the sudden rise in the number of daily positive cases. Infected patients were left gasping for air during the second wave of COVID-19 as a result of the oxygen and bed shortage issues, particularly in India. This chapter proposes a wireless, Internet of Things (IoT)-based healthcare system featuring an automatically operated Ambu bag that might be utilized to save patients’ lives. The system can use NodeMCU with IoT technology to continually monitor a patient’s oxygen saturation, pulse rate, and body and room temperatures using a DS18B20 temperature sensor from any location in the world.

The management of increased demand for the utilization of available road infrastructure thanks to urbanization that has attracted the interest of researchers in the domain of Intelligent Transportation System (ITS). This helps the commuter to travel in congestion-free, accident-free, and efficient road scenarios. Vehicular ad hoc networks (VANETs) form a vital element of the ITS. VANET is defined as a subcategory of mobile ad hoc networks (MANETs) with some predefined distinct properties. Unlike MANET, VANET has no constraint on the node’s battery life, but there is a bandwidth constraint in this scenario. VANET supports two types of communication: either among vehicles or among vehicles and infrastructure. The key objectives of VANETs are to modify public safety and transportation efficiency in the highly dynamic and mobile scenario. This chapter provides detailed information regarding the VANETs and their characteristics, network layer-wise communication protocols, shortcomings, features, and applications, and finally the challenges and future scope.

Generally, the current application areas in communication are completely dependent on the establishment and operation of (camera-based/non-camera-based) wireless sensor networks for cooperative communication (CC). CC, being an ideal sub-part of the upcoming 6G telecom infrastructure, ensures that optimum operations can be analyzed and evaluated based on the lifeline of the model of a network. It should not be very power-consuming (i.e., it must be energy efficient with better throughput), and its reliability for the transmission of data packets should be optimal. The majority of researchers working in sensor-based fields have shifted their attention to the application domains of 6G (preferably in the CC domain), with the aim of optimizing the energy, power, and life span of sensor-based networks while also taking reliability into consideration (camera-based or non-camera-based). In light of this, this chapter gives a broader analytical review of CC based on recent research, followed by a discussion on research gaps and performance issues in current wireless networks leading up to CC. Finally, the implementation of several clustering schemas for data transmission in MATLAB is described, and their performances are compared in terms of energy efficiency.

Although research in autonomous vehicles had started in the early 20th century, due to a lack of efficient and highly powerful computational devices, they were only controlled by radio wave technologies, widely known as remotes, despite rapid developments in the related technologies. Then, in the 1950s, research and development activities picked up pace with the development of computers. With the introduction and advancement of artificial intelligence (AI) and computing hardware technologies, autonomous vehicles have become a primary field of research and development for researchers seeking to address various day-to-day problems faced by drivers, traffic controllers, and human transportation as a whole, such as traffic jams, road accidents, expensive infrastructure maintenance, and the engagement of human resources (drivers) in inefficient and repetitive work. Considering these factors, automobile and tech giants have identified a new market opportunity with the potential for huge profits. This has resulted in rapid research and development as companies strive to be the first to capture the market. As we progressed with new developments, various technical, social, and administrative problems and challenges started arising which need to be resolved. This chapter contains a review and analysis of various studies carried out in this field and proposes alternative approaches wherever needed. The main technical challenge is to design and develop a superfast and energy-efficient AI model which must be capable of performing millions of calculations in microseconds, which would require a very powerful GPU and a highly efficient system architecture design. As infrastructure is designed and developed solely for human drivers everywhere, it becomes a challenge for the physical testing and deployment of autonomous vehicles. As such, there are either no clear administrative rules and regulations or no rules at all for such vehicles. Even if all these technical and administrative challenges of autonomous vehicles are overcome, social challenges may remain since the extent to which customers and passengers accept and adopt them will usher in completely new changes to conventional transportation.

In the current scenario, technology scaling is being implemented in digital integrated circuits to improve their power, speed, size, and cost-effectiveness. On the other hand, scaling induces reductions in supply voltage and intrinsic transistor gain, because of which analog circuit designers face various design challenges. This paved the way for using a time domain approach to analog designs, which is based on the representation of signals in time domain parameters, such as time period, transition time, and time delays. These signals are processed using time domain signal processing implemented by time-mode circuits, such as voltage-to-time converters, time amplifiers, time registers, and time-to-digital converters (TDCs). This chapter incorporates various reported time-mode circuits but emphasis is placed on TDCs. It covers the details of a wide range of TDC applications and performance metrics. Further, a classified review of various time interval measurement techniques used to design TDCs is given. The key design concepts of each technique are comprehensively discussed, along with their merits, demerits, salient features, and applications. It concludes with a timeline of the evolution of various TDC techniques, right from the 1960s until today, using a flow diagram, and provides a comprehensive bibliography.

The cornerstone of the Indian economy is the agricultural sector. A large percentage of individuals around the world — more than 40% — have agriculture as their primary line of employment. The advancement of autonomous vehicles in agriculture has gained considerable spotlight in recent years. Soil quality is unaltered by robotization, which also significantly reduces manpower expenses and boosts overall productivity. The machinery described in this project is proficient at performing several farming operations at once. An environmentally safe source of energy is utilized by this equipment: Energy from the sun’s radiation is accumulated and transformed into electrical energy, and a direct current motor then transforms this electrical power into mechanical power to rotate an anchor during a digging operation. For irrigation operations and seed sowing, respectively, we deploy seed hoppers and water reservoirs. The machine can change its course and maneuver over varied ground contours, excavate, sow seed, and water the ground. It also decreases the expense of planting seeds and the manpower required.

The following section is included:

• Index