Various nations have distinct visions/missions and energy implementation strategies. Energy sources are essential to a nation's economic development and a necessity for all inhabitants. Several nations face varying degrees of energy catastrophe as a result of insufficient natural resources today mixed with the COVID-19 outbreak. This emergency led to the shutdown of numerous industrialized divisions exacerbated unemployment, constrained energy access, and associated societal shocks. A fundamental reason for these conflicts is the widening chasm between energy delivery and orders, financial issues, logistics, and irrelevant strategic planning considerations. The use of bioresources as a novel source of waste biomass was identified as a crucial criterion for bridging the gap and creating a vast outlook for an environmentally friendly biorefinery and bioremediation process. This presents a potential obstacle, as it suggests a replacement for fossil fuels in the production of specialty compounds and energy carriers. As a carbon-neutral mode/s, this reduces market anxiety and negative environmental repercussions. This ecological bioremediation with the use of biomass (phytoremediation), less expensive sorbents (for bioaccumulation and biosorption), and microorganisms (mainly agricultural byproducts) is more favorable than conventional ones.

100 % Green economy is a dream to be realized by every economy in the world. Every economy is firm in its motives in terms of investing in Green Technology and enhancing its contribution to the Green Economy, strategically any nation that masters the art of maximizing its Green Energy productivity is going to be a crucial player in the world order. In this scenario, Blockchain comes in as a tool that can be positively exploited for mankind’s betterment, especially in the field of Energy Conservation. Microgrid system in this context is a revolutionary mechanism for opening up the market of energy exchange to the public and reducing energy wastage/consumption.

Day by day, because of the increase in the population, energy use also increases, leading this generation towards the end of Non-Renewable sources. A free quotation which is completely unutilized, is available, but very few people are interested in using it, which is Solar Energy. In Solar Energy, this generation can have one-time investments and utilize a free energy source for a long time. The main problem is trust and past investments, which few governments support by giving multiple subsidies and benefits to those who want to use them. In this chapter, the author enlightens the technical know-how of Solar energy systems and their advantages and disadvantages from a technical perspective.

Presently, the role of renewable energy sources (RES) in the energy continuity of a power system is emphasized. Since renewable energy sources are unreliable and intermittent, a smart energy management system is recommended for hybrid energy storage systems (HESS). This study introduces two storage technologies for RES. a battery and a supercapacitor. The generation process in which solar panels, also known as PV panels, are integrated into an array in a grid-connected or off-grid PV system. A dc link is connected to two storage devices and two bidirectional DC/DC converters in the proposed model. The generation from photovoltaic panels is supplied to the load via a converter system. Both supercapacitors and batteries are utilized as storage devices in the proposed system. The voltage of the supercapacitor reached 32V, whereas the value of the capacitor varied slightly between 25V and 25V. The voltage of the supercapacitor in the proposed hybrid paradigm is significantly higher than the voltage of the capacitor.

This research optimises the design of a permanent magnet synchronous generator to meet the output power needs of a small direct-drive wind turbine. Extra care has been taken to reduce the generator's total volume to reduce expenses. The proposed method aims to reduce the cost of PMSG by reducing its volume. In this study, the optimal values of PMSG parameters for minimising the overall volume of the PMSG generator while maintaining its output power at the rated value are determined. To estimate the optimal values of design parameters, three algorithms have been considered. Equilibrium Optimization Algorithm (EOA) as the proposed algorithm, Gravitational Search Algorithm (GSA) as the first existing algorithm, and Particle Swarm Optimization as the second existing algorithm. Comparing the results of the Equilibrium Optimization algorithm (EOA) with those of the Gravitational Search Algorithm (GSA) and the Particle Swarm Optimization algorithm (PSO) (PSO). Simulation results demonstrate that the Equilibrium Optimization algorithm (EOA) outperforms both the Gravitational Search Algorithm (GSA) and the Particle Swarm Optimization algorithm (PSO). When simulated and statistical results of EOA were compared to those of other optimization methods, it was found that EOA is more effective and superior, resulting in the lowest volume value for wind turbine PMSG.

Technological challenges to the widespread adoption of battery-powered devices contain substantial weight with a high cost and low power density. To bring an improvement in over-dependency on batteries, wireless power transfer is a ray of hope in energizing electric-driven devices. Moreover, for high voltage transmission lines, optimization of natural frequency plays an important role in efficient wireless power transfer (WPT) considering dc to load supply. In consideration of different aspects of wireless power transfer technology, a completely optimized method should be adapted for monitoring. In the present work, a model of an electric car vehicle has been developed based on WiTricity. This concept of wireless power transfer has been realized in this work as a small-scale simulated model, which can be used to charge batteries, mobile, door locks, and propeller clocks, Further, the evolving wireless power transfer technologies often face difficulty in asymmetrical variable-frequency pulse-width-modulated (WPT) systems. To deal with these multiple harmonics as inherently generated by variable frequency amplitude pulse width modulation (VFAPWM), a multiple harmonics analysis technique has been adopted in this work. Different parameters like loads and duty cycle have been varied with varying frequencies, to study the charging current harmonic distortion and voltage harmonic distortions. The difference in voltage observed was essentially nonexistent, with a 1.8 to 3 times variation in switching frequency. Moreover, the pattern of deviation has been noticed for output voltage when the load was varied from 20% to 100%. Additionally, a comparative study has also been performed in evaluating the charging current distortion pattern by the implementation of both MHA techniques and conventional first harmonic approximation (FMA). 

Achieving long-term solutions to today's waste challenges necessitates longterm strategy and effort. Population, urbanization, development, and industry all contribute to the increase in trash. Energy use is also strongly related to waste management, which is also a strong component for achieving an effective solution. The waste energy conversion processes have technological limits, called thermodynamic limits. Energy and entropy are variables that may be used to evaluate energy systems and technologies. People's non-segregation tendencies, as well as their consumerism inclinations, make waste management tasks difficult. Landfilling, combustion, pyrolysis, gasification, incineration, etc. are insufficient to deal with such a large volume of waste. Recently developed plasma base waste technology mimics nature's waste management through matter-energy conversion with a scope of waste-to-energy (WtE) conversion. This study shows that plasma-based technology has a high waste volume handling capacity in a short span and also minimises waste exposure to nature and society. Despite its high installation and maintenance costs, the income generated from Syn-Gas and slag makes it financially viable. It is a sustainable way to manage waste because it can handle large amounts of waste, takes the least amount of time to process, and has the least amount of social and environmental impact.

Automated parking systems have been revolutionized exponentially in recent times but configuring an automated parking system capable of incorporating the ability to perform total sanitization is the result of a novel approach, as presented in this chapter. This parking system sanitizes the vehicle as soon as it approaches the parking lot with the help of Infrared sensors coupled with a servo motor. The fundamental objective behind this approach is to suppress the spread of contagious infections like COVID-19, which is more prominent in the present age and has been hugely impacting the world. The proposed design includes Arduino UNO, Infrared sensors, servo motor, Water level sensors, LCD screen, and LEDs as its components. Arduino IDE plays a significant role in controlling the whole setup associated with Proteus-8 Software. This model is divided into three segments, the sanitization chamber, parking entry and parking exit, which also count and display the number of vehicles entering and exiting the parking lot on the LCD screen. In the sanitization chamber, the working of infrared sensors and servo motors are interlinked and configured by the code logic. When the vehicle arrives, the IR sensor senses it and allows the servo motor to eject the liquid from the dispenser vessel to sanitize the vehicle completely. An LCD screen is also employed to display the various percentage stages left in the sanitizing dispenser vessel. In parking entry and exit, the Infrared sensors operate the entry and exit points. LCD screens, along with LEDs of different colours, are also incorporated to indicate the functioning of the whole process.

Simulating a futuristic idea for the implementation of an innovative, efficient and economic sanitization-based parking system is the fulcrum of this paper. This paper depicts the simulation results of the previous chapter, “Sanitized Vehicle Parking System”, designed to solve parking problems and ensure customers' safety. Proteus 8 is the simulating software that has the ability to depict all the components used and their functionality in real-time. Arduino IDE is the coding platform incorporated with the Proteus 8 software. Arduino IDE has a lot of advantages over other platforms; one of them is extremely simple and flexible. This model will visualize the simulation results in detail which have been achieved and can be implemented in all the parking compartments with an upper hand over other parking systems by being completely automatic. Sanitization is the most crucial aspect responsible for the design of this system, as this parking system will sanitize all the automobiles entering the parking lot. This model has been divided into three compartments. The 1st section is responsible for sanitizing the vehicles entering the parking area. The 2nd section operates as the entry point of the parking area, automated through infrared sensors. Simultaneously the count gets incremented by 1, which gets displayed on the LCD Screen. The 3rd section operates as the exit point of the parking area. Each vehicle exiting from the parking area decrements the count by 1. Water- Level sensors are also employed to detect and measure the sanitizing fluid left in the vessel at different stages. The detailed simulation results for the sanitizing dispenser system are displayed on the LCD screen, indicating the next course of action in case of emptying the sanitizing liquid. The infrared sensors entirely handle the entry and exit points.

Direct Torque Control (DTC) is the dominant strategy used in three-phase induction motor control, thanks to its excellent and vibrant characteristics, consistent operation, fewer mathematical calculations, and rigidity in adjustable velocity drives. However, torque ripple is the main drawback of DTC, and it is challenging to reduce it. While DTC based conventional PID controller is utilized, it gets pretentious by lengthy settling time, maximum peak overshoot, and torque and speed curve oscillations. The current research aims to diminish the torque ripple and augment the DTC-enabled induction motor drive performance. Various control methods, such as Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS), were used in the chapter to enhance the DTC-enabled induction motor drive performance. These control methods were carefully verified and simulated under MATLAB/Simulink 2017. The effectiveness of the projected work was confirmed through simulation, which achieved promising results, thus establishing the supremacy of the proposed model.

S.C.A.R.R or ‘Station Controlled Advance Reconnaissance Rover’ presents a modern approach for surveillance at desired areas, remote border locations, buffer zones, or strategic military deployment areas using a multifunctional robot based on the Arduino development board. The purpose of designing and constructing a surveillance robot is simple; it is to adapt to the advancements in technology in the field of modern warfare as well as to have a pragmatic and reliable solution to ever-increasing surveillance demands due to hostile activities. This robotic vehicle can potentially substitute human deployment in concerned areas to provide surveillance and perform desired tasks. The robotic vehicle works as a manually controlled vehicle through Dual Tone Multi-Frequency (DTMF) technology and is equipped with a Radio AV Night Vision camera which can work both day and night to provide real-time video feed. This robot can be used to detect the presence of the enemy in both friendly territory and buffer zones and capture through the camera to give live streaming to authorized personnel. Surveillance is major and active field role while working in the border area and for the same, there is a need for a robot for surveillance purposes. A smart surveillance robot for military and counter-threat applications is presented in this paper. A well-rounded approach to testing, of all the physical and technical factors, was considered and taken into action in the form of improving the rover.

 Farming has been a traditional, manual and labor-intensive industry, and it will continue to be so in the future. Agriculture is one of humanity's oldest businesses and practices. Extrinsic elements, comprising climatic parameters and general environmental variables, have strongly dictated crop yield and productivity. Disruptive technologies such as the Internet of Things (IoT), Big Data Insights, Artificial Intelligence (AI), Machine Learning (ML), etc., have significantly affected most every enumerated industry area. Farming based on the implementation of the above advancements leads to “Smart Farming”, also known as the “Green Revolution 4.0” in agriculture, which combines agricultural methodologies with technologies to accomplish desired processing efficiencies at manageable costs. The entire development is software-based and sensor-monitored from farm to hand-held devices, lowering overall costs, and increasing aggregate yield and ubiquity level, thereby enhancing user engagement. Predictive analytics for crops may certainly contribute to data-driven decision-making with the help of “failure prediction systems.” Climate conditions can be tracked and forecasted to help with forecasting, from seeding to development and delivery of the final crop to the consumer. An increase in global concerns about food safety, as a result of large-scale flood disasters, puts more pressure on smart farming methodologies. The study discusses more of the latest avenues of research and future trends in smart farming with case studies about Indian states. The work also examines major disruptive technologies that govern agricultural phenomena in the twenty-first century. 

The chapter portrays a new development in the field of embedded systems. It showcases the combination of Machine Learning algorithms and low-memory microcontrollers (ESP32-CAM). The uniqueness of this idea lies in the fact that Machine Learning is generally perceived as a processor-intensive task that requires high memory and storage. However, as seen in this chapter, one may soon realize how wrong this notion is with emerging technologies that are taking over the globe. This project portrays the successful implementation of a binary colour classification model on the ESP32-CAM with 68% accuracy post-training result with a mere 15 images of each colour. Machine learning has increased over the years. Some applications include image classification, object detection, and question-answering. This work merely puts out awareness in this domain and is hopeful that dedicated efforts towards it can solve many industrial problems. 

It is anticipated that the capabilities of quantum computers will substantially advance the field of Artificial Intelligence (AI). Quantum computing is similar to conventional computing, which encodes data using bits of 0s and 1s. Quantum computing, however, has its variant of data encoding known as quantum or qubit. AI processes complex datasets and is useful in the development of algorithms to create better learning and understanding environments. Researchers are working on employing quantum computing techniques to achieve the goals of AI. This chapter reviews the correlation between quantum computers and artificial intelligence and its future scope.

At present, ATMs (Automated Teller Machines) are one of the essential services for our daily life. It is also true that the thefts of false transactions and pin thefts are increasing yearly. A significant amount of theft at ATMs is due to pin overlooking and card skimming. Biometrics provide promising security but have high implementation costs. Also, Indian laws discourage using BiometricsBiometrics in all places. So, can AI be the solution to this problem? Instead of using keypad-based inputs for pins, gesture detection with AI can be used for secure inputs. A trained deep neural network can detect count from the hand symbols/gestures. The gesture input is given by inserting the hand inside a safe box with a high-resolution camera attached. The camera takes images and sends them to Raspberry Pi or any other embedded system. The Raspberry Pi executes the lightweight ML model to detect the count. The detected count is then encrypted and passed to the ATM. Using a gesture identification system removes the problem of pin theft and can be developed and implemented with the slightest modification in ATMs. In the current COVID period, execution of ATM works with minimum contact to public surfaces has increased immensely. In this system, a keypad is also removed and can further be incorporated to read a variety of inputs from gestures instead of just hands. This chapter explores how lightweight neural networks can be trained to detect sensors and run on low-processing systems like Raspberry Pi. We achieved an accuracy of 94%-97% in detecting gestures and pins where accuracy varies for each motion.

Big Data is a massive collection of data that continues to grow dramatically over time. It is a data set that is so huge and complicated that no standard data management technologies can effectively store or process it. Big data is similar to regular data, but it is much larger. “There's no doubt that the volumes of data presently available are vast, but that's not the essential element of this new data ecosystem,” says one expert. The term “big data” is now commonly used to refer to the application of predictive analytics. New correlations can be discovered by analyzing data sets to “identify economic trends, prevent diseases, combat crime, and so on.” In sectors such as Internet searches, financial technology, healthcare analytics, geographic information systems, urban informatics, and business informatics, scientists, corporate executives, medical practitioners, advertising, and governments all face challenges with enormous data sets.