Preface

The major objective of Smart Electronic Systems is to provide a platform where researchers from the fields of hardware and software may work together under one roof to speed up the development of smart electronic systems. Effective and secure data sensing, storage, and processing are essential in today's information age. Modern smart electronic systems meet the criteria of effective sensing, storage, and processing. Effective techniques and software that allow for a quicker analysis and retrieval of necessary data are simultaneously becoming more important. The internet world now includes big data, which comprises large, complex data collections. It is becoming harder to store and analyse the vast amount of structured and unstructured data that has to be collected. With concurrent hardware and software development, the Internet of Things (IoT) and cyber-physical systems (CPS) have been growing to include everyday consumer electronics. The effectiveness and performance of current and next generations of computing and information processing systems depend on advancements in both hardware and software. Some of the focused areas in this field include memristor and memristive systems, advanced 3D IC technologies, design methodologies, and 3D pacing. Others include molecular electronics, biosensors, bio-molecular and biologically inspired computing, nanoelectronics for energy harvesting, spintronics, domain-wall and phase-change memories, and nanoelectronics for energy harvesting. Quantum computing, communication, information processing, circuit, system, and sensor design based on nanoelectronics for critical applications, chip-to-system design, and techniques for electronic design automation (EDA) or computer-aided design (CAD) are the topics addressed in this book. At the same time, programming and efficient calculations for quicker examination and retrieval of crucial data are slowly getting out of style.

Massive amounts of both organised and unstructured data are challenging to store and manage. Post-CMOS technologies, the Internet of Things (IoT), and the cyber-physical system (CPS) have all been advancing simultaneously with synchronous hardware and software developments and have surpassed standard client devices. Future ages of figuring and data processing frameworks, as well as the present generation, will be largely influenced by advancements in both design and programming. In order to exchange information and research discoveries on all facets of electronic systems and digital electronics, it aims to bring together eminent academic scientists, researchers, and research scholars. It also acts as a premier interdisciplinary platform where researchers, practitioners, and educators may discuss and present the most recent findings, challenges, and trends in the fields of analogue and digital electronic systems, as well as their applications and solutions. The most recent developments in the study of solidification as well as the processing and analytical issues the society is facing in the twenty-first century are discussed. We would like to thank everyone who participated on behalf of the editors. First and foremost, we would like to congratulate wish success to the writers, whose wonderful work forms the basis of the book. We would like to use this opportunity to express our gratitude to our family and friends for their support and inspiration while we wrote this book. First and foremost, we give all praise and adoration to our omnipotent Lord for his abundant grace, which made it possible for me to successfully complete this book. For their contributions to this edited book, the authors deserve our sincere thanks. We also want to thank Bentham Science Publishers and its whole staff for making the work possible and giving us the chance to participate in it.

The content of this book is summarized as follows:

1. Chapter 1 presents that motorcycle accidents are a major societal concern in many countries. Due to improper riding behaviours including not wearing a helmet, driving recklessly, driving while intoxicated, riding while fatigued, etc., the issue persists despite public awareness initiatives. The risk of fatalities and impairments is relatively high as a result of delayed assistance to accident victims. Significant economic and societal repercussions are seen for those involved. As a consequence, several research institutions and significant motorcycle manufacturers have created safety devices to protect riders. A good motorbike safety system is also difficult to implement and expensive. Modern communication technologies are being incorporated into the automobile sector to improve the aid provided to those injured in traffic accidents, speed up the response time of emergency services, and provide them with more information about the incident. If the resources required for each catastrophe could be estimated with more accuracy, the number of deaths may be significantly decreased. According to the proposed plan, every vehicle must have an onboard device that can identify accident situations and report them to an outside control unit, which determines the severity of the issue and gives the necessary resources to address it. The development of a prototype using commercially available equipment shows that this technology may significantly save the amount of time it takes to deploy emergency services after an accident.

2. In Chapter 2, it is discussed that Malaria is a sickness that is brought on by the Plasmodium parasite and spread by the bite of female Anopheles mosquitoes. There are four different types of plasmodium that cause malaria. 1. Plasmodium Falciparum, 2. Vivax Plasmodium, 3. Plasmodium Ovale and 4. Plasmodium Malariae. Although there are a number of clinical and laboratory procedures for detecting the presence of malaria, the speed and precision needed to do so are insufficient. In order to assess if malaria is present in human RBCs, we have developed a method in this study that makes use of image processing techniques. The technique also establishes the malarial parasite's stage and intensity.

3. Defocus blur is a very frequent occurrence in photographs taken by optical imaging devices, as discussed in Chapter 3. It could be unwanted, but it might also be a deliberate aesthetic impact, which means it might help or hurt how we see the scenario in the photograph. A partly blurred picture could be segmented into blurred and non-blurred parts for tasks like object detection and image restoration. In this research, we present a robust segmentation technique to distinguish in- and out-of-focus picture areas and a sharpness measure based on the local Gabor maximum edge position octal pattern (LGMEPOP). The suggested sharpness measure makes use of the finding that the majority of local picture patches in fuzzy parts contain a disproportionately lower number of specific local binary patterns than those in crisp regions. In conjunction with picture matting and multiscale fuzzy inference, this measure was used to produce high-quality sharpness maps in this study. Our blur segmentation algorithm and six competing techniques were put to the test using tests on hundreds of partly blurred photos. The findings demonstrate that our method produces segmentation results comparable to those of the state-of-the-art and has a significant speed advantage over the competition.

4. In Chapter 4, analytics is discussed as one of the leading technologies today since data is amassing in all shapes, sizes, and volumes, as well as in a dynamic way. In the age of social media and social networks, predictive analytics is particularly popular as data sources expand from data banks to data rivers. This chapter provides an overview of the fundamentals of analytics as well as some of the current predictive analytical models used in the analytical community, such as multiple regression, logistic regression, and the k closest neighbor model. Having a predictive analytical tool in our toolbox is even more important now that we live in the age of machine learning and artificial intelligence.

5. In Chapter 5, it is discussed how simulation, a particularly versatile and adaptable area of computer science, is used to model and analyse systems for which an analytical solution is either unavailable or challenging to achieve. Because it is simpler than conventional approaches, which are often challenging, simulation is also chosen as a method of system analysis. Because of this, simulation is an area with extensive application and demand, making it fascinating and helpful to include a chapter for studying simulation with a case study of modelling a Queuing system.

6. Chapter 6 claims that virtualization is a cloud computing solution that only requires one CPU to operate. Through virtualization, many computers seem to be operating together. Because it saves time, virtualization focuses mostly on efficiency and performance-related activities. Virtualization of operating systems is the main topic of this essay. It is a customised version of a typical operating system that enables users to run numerous programmes that create a virtual environment to carry out different jobs on the same computer by running other platforms. Based on the amount of work they accomplish and the amount of memory they use, this virtual machine aids in comparing the performance of Type 1 and Type 2 hypervisors.

7. In Chapter 7 it is discussed that cloud computing offers a dynamic paradigm that enables consumers and organisations to acquire a variety of additional services in accordance with their needs. The cloud provides services including data storage, a platform for developing and testing applications, a way to access online services, and more. Maintaining application performance in a cloud environment is a common challenge due to Quality of Service (QoS) and Service Level Agreements (SLA) supplied by service providers to the organisation. Distributing the workload across many servers is the main duty carried out by service providers. By effectively allocating resources inside Virtual Machines, a load-balancing strategy should meet user needs. This study discusses the review of several LB strategies that affect overall performance and the research gap.

8. The existing electronic voting system may be readily hacked, according to Chapter 8. There are several strategies used to prevent malpractice. This study, allows for safe voting and forgoes human interference, resulting in a seamless and secure election process. The voter's face and biometric fingerprints are used in this study's authentication process. With the voter fingerprint information already in this database, the first step in the verification process for an electronic voting system may be simply accomplished. Voter facial recognition using data previously stored in the database is the second phase of verification. The voter may cast his or her ballot and deliver it if two-phase verification is completed. The ballot will then be encrypted. This stops false votes and guarantees accurate voting free of any corruption. We have developed a fingerprint-based voting system that eliminates the need for the voter to provide an ID with all of the required information. A person is permitted to vote if the facts match the registered fingerprints' previously recorded information. If not, a warning notice is sent and the individual is disqualified from casting a vote. The administrator will decode and count the votes during the counting phase of an election.

9. Chapter 9 presents a research that outlines a method for resolving the problem of real-time decision-making in farming that arises from rapid changes in circumstances, such as atmospheric changes, monsoons, insect assaults, etc. The future of agricultural technology is big data collection and analysis in agriculture to maximise operational performance and reduce labour expenses. The Internet of Things will, however, have an impact on a far wider range of industries than just agriculture since there are more IoT-related concepts to understand. The adaptation of IoT's capacity for data collecting on crop attributes and for automated decision-making using data analytics algorithms is the main goal of this project.

10. In Chapter 10, it is discussed that biometrics innovation is still one of the major predictions that combined biosciences and innovation, serving as a tool for criminology and security analysts to develop more accurate, robust, and certain frameworks. Biometrics, when combined with various combination techniques like feature-level, score-level, and choice-level combination procedures, remained one of the most researched technologies. Uni-modular biometrics, such as unique marks, faces, and iris, are followed by multimodal bio-metrics dependent on. By presenting a similar investigation of frequently used and referred to uni- and multimodal biometrics, such as face, iris, finger vein, face and iris multimodal, face, unique mark, and finger vein multimodal, this paper will attempt to lay the groundwork for analysts interested in biometric frameworks moving forward. This comparative research includes the development of a comparison model based on DWT and IDWT. The method towards combining the modalities also entails applying a single-level, two-dimensional wavelet (DWT) that has been solidified using a Haar wavelet to accomplish the good pre-taking care of to eliminate disruption. Each pixel in the picture is subjected to a different filtering operation in order to determine the Peak-Signal-to-Noise Ratio (PSNR). This PSNR analyzes the mean square error (MSE) to quantify the disruption to hail before the division of the largest data set to the chosen MSE. In the most recent advancement, each pixel's concept is fixed up using the opposing two-dimensional Haar wavelet (IDWT), creating a longer image that is better able to recognise approbation, affirmation, and confirmation of parts. The MATLAB GUI is used to implement the diversions for this enhanced blend investigation, and the obtained outcomes are satisfactory.

11. Chapter 11 presents that performance prediction is the estimate of future performance circumstances based on information from the past and the present. Companies, divisions, systems, procedures, and personnel may all get forecasts. This research focuses on evaluating employee performance in terms of behavior, output, and potential for development. When workers perform effectively for their employers, everyone wins. As a result, forecasting staff performance is crucial for a developing company. To this purpose, we suggest the support vector machine, the decision tree (j48), and the naive Bayes classifier as three machine learning methods. These help forecast an employee's behaviour at work. Based on parameters like accuracy, error loss, and timeliness, the Naive Bayes algorithm outperforms the other two algorithms in terms of their findings.

12. Chapter 12 discusses the idea that the discipline of artificial intelligence (AI), which trains computers to comprehend and analyse pictures using computer vision, remains in its infancy. This is particularly true in the medical industry. Coronary computed tomography angiography, or CCTA, is a well-known non-invasive technique for diagnosing cardiovascular diseases (CD). Pre-processing CT Angiography images is a crucial step in a computer vision-based medical diagnosis. Implementing image enhancement preprocess to reduce noise or blur pixels and weak edges in a picture marks the beginning of the research stages. Using Python and PyCharm(IDE) editor, we can build Edge detection routines, smoothing/filtering functions, and edge sharpening functions as the first step in the pre-processing of CCTA pictures.

13. In Chapter 13, a patient-monitoring smart wheelchair system is developed as an ambulatory assistance for persons with disabilities and for continuous monitoring of the user's vital bodily parameters. Four interfaces—eyeball control, gesture control, joystick control, and voice control—have been created for wheelchair control in order to cater to various limitations. The picture of the eyeball is captured using a camera. In order to make the appropriate decisions based on the location of the eyeball, LabVIEW is employed. The wheelchair movement may also be controlled by the other three modes. Anti-collision mechanisms are implemented using ultrasonic sensors. There is a feature in the wheelchair for measuring body temperature and heart rate. If any parameter is outside of a safe range, this system will notify the appropriate medical authorities and the wheelchair user's chosen persons. The finished product is an innovative kind of assistive technology that would simplify and reduce stress in the life of its user.

14. Chapter 14 presents that the power grid assaults serve as a reminder that although the smart Internet of Things (IoT) might help us regulate our lightbulbs, it also poses the risk of putting us in the dark if it comes under attack. Many works of literature have recently attempted to address the issues surrounding IoT security, but few of them tackle the serious dangers that the development of quantum computing poses to IoT. Lattice-based encryption, a likely contender for the next post-quantum cryptography standard, benefits from strong security and good efficiency, making it well-suited for IoT applications. In this article, we list the benefits of lattice-based cryptography and the most recent developments in IoT device implementations.

15. Due to newly developed technologies in cars, traffic signal prediction devices are made and discussed in Chapter 15. It teaches users how to maneuver a car safely and effectively. Because of the many ways that drivers are distracted nowadays, the number of accidents is rising alarmingly. This technology lowers the danger of distracted driving, which causes accidents, by helping to recognise and deliver information based on data. The concepts of supervised learning, unsupervised learning, and reinforcement learning are addressed under the classification heading and serve as a major directive as the topic of machine learning is introduced. Machine learning may produce many different types of models, including neural networks, naive Bayes, random forests, support vector machines, clustering, etc. The primary concept of machine learning model training is to divide the data into training, testing, and validation sets. In order to deliver the best machine learning project, this chapter's conclusion accesses machine learning methodologies. The suggested method describes how to recognise traffic signs using a model that combines a classic support vector machine (SVM) with a newer convolutional neural network (CNN). In essence, a CNN model was trained to create this model. Several CNN model topologies, including LeNet, AlexNet, and ResNet-50, may be used in this situation. Later CNN layers' output may be utilised to generate features. The SVM was then used to classify using these characteristics.

16. In Chapter 16, machine learning is used to alter the systems that carry out artificial intelligence-related tasks. (AI). It displays the many ML kinds and applications. It also explains the fundamental ideas behind feature selection techniques, which can be applied to a variety of machine learning (ML) methods, including artificial neural networks (ANN), Naive Bayes classifiers (probabilistic classifiers), support vector machines (SVM), K Nearest Neighbor (KNN), and the greedy algorithm-related decision trees algorithm.