This study presents a novel method for wireless charging of mobile devices, leveraging solar energy as a power source. The system integrates inductive power transfer technology to enhance efficiency by converting radio frequency signals into direct current effectively. This approach enables concurrent charging and energy harvesting, thereby maximizing power utilization. By incorporating solar energy, the system reduces dependence on conventional power outlets, promoting sustainability in device charging practices. This innovative solution represents a significant advancement in wireless charging technology, promising increased convenience and environmental friendliness for mobile device users. In summary, the proposed system offers a dual functionality of wireless charging and solar energy harvesting for mobile devices. By utilizing inductive power transfer technology, it optimizes the conversion of radio frequency signals to direct current, ensuring efficient energy utilization. This integration of solar energy not only facilitates sustainable charging but also reduces reliance on grid-based electricity, contributing to environmental preservation. Consequently, the proposed solution presents a compelling advancement in wireless charging technology, promoting convenience and eco-friendliness in powering mobile devices.

Advantages like high reliability, long cycle life, high energy storage capacity and deep discharge of an Flywheel Energy storage system (FESS) can potentially enhance the performance of the hybrid vehicles. This paper the study related to integration of Flywheel Energy storage system (FESS) to an already available model of parallel hybrid vehicle with retransmission torque coupling, i.e., replacing the conventional chemical battery with an equivalent mechanical battery. FESS employed for the analysis comprises an integrated flywheel homopolar inductor machine with High frequency drive. The simulation results of an Electrically Peaking Hybrid (ELPH) are used as a base work in the present analysis. The ELPH model uses a control strategy to optimize the vehicle performance with a major concern for battery performance. The paper analyzes the performance of considered FESS model under the same control strategy and driving conditions. A MATLAB/SIMULINK model is used for the analysis of the vehicle for both urban and highway drives. Finally a comparison is drawn between the performances of the chemical battery, working in its best efficiency range, as a result of the applied control strategy, to that of the considered FESS. It is inferred from the simulated results that the performance of employed FESS is satisfactory in comparison to chemical batteries. It is therefore expected that FESS can be effectively employed in hybrid vehicles.

The increasing occurrences of fire-related disasters necessitate advanced technologies for efficient firefighting operations. In this context, the development of fire-fighting robots emerges as a promising solution. This abstract outline the design, functionality, and potential applications of a cutting-edge fire-fighting robot. The fire-fighting robot is equipped with a robust mechanical structure designed to withstand high temperatures and navigate through challenging terrains. It integrates state-of-the-art sensors, including thermal imaging cameras and gas detectors, enabling real-time monitoring of the fire environment. These sensors provide crucial data for decision-making algorithms implemented in the robot's control system. The control system incorporates artificial intelligence algorithms for autonomous navigation, fire detection, and suppression. Machine learning techniques enable the robot to analyze environmental data and identify the presence and spread of fire with high accuracy. Upon detection, the robot autonomously devises an optimal path towards the fire source while avoiding obstacles.

Power transformers play a critical role in electrical networks, and their maintenance and longevity are paramount due to the challenges and costs associated with replacements. This paper delves into the realm of condition monitoring for power transformers, focusing on diagnostic tests and techniques used to estimate their remaining lifetime. By examining methods such as dissolved gas analysis, dielectric response, partial discharge testing, and more, this review aims to provide a comprehensive overview of the tools and strategies employed in assessing transformer health and predicting potential failures. Through a synthesis of industry and academic insights, the strengths, limitations, and correlations between various testing approaches are explored, offering a holistic perspective on transformer condition assessment and management.

This project proposes an innovative approach utilizing underwater data transfer to enhance the safety and well-being of fishermen navigating through challenging marine environments. By testing various types of agents to precisely alter experimental water coefficients, the project aims to transmit real-time information about fishermen's organic conditions and interactions to a tracking station. This is achieved through separate transmitter and receiver modules deployed in the water. The fluctuating atmospheric conditions of the ocean pose significant challenges for marine navigators, often leading to health complications such as uncontrollable heart rate fluctuations, particularly during deep dives. To address these challenges, the proposed system integrates GPS coordinates, air pressure sensors, and air humidity sensors to monitor fishermen's health status while navigating. The system facilitates two-way communication between the ground station and sea navigators, enabling prompt response and assistance in case of emergencies. By leveraging this technology, the project aims to mitigate risks and potentially save the lives of numerous fishermen navigating hazardous waters.

In agriculture, the delicate balance between safeguarding crops and protecting wildlife presents a significant challenge. Traditional methods often involve reactive measures, leading to crop damage and wildlife disturbance. However, emerging technologies offer innovative solutions through intelligent surveillance systems. These systems leverage advanced technologies like predictive analytics and AI algorithms to monitor fields and wells proactively. By deploying such intelligent surveillance systems, it becomes possible to protect crops from wildlife damage while ensuring the safety of animals. Intelligent surveillance systems are revolutionizing agricultural practices by providing real-time monitoring and analysis capabilities. By integrating predictive analytics, these systems can anticipate potential threats to crops and wildlife based on historical data, weather patterns, and environmental factors. AI algorithms enhance the accuracy and efficiency of monitoring by identifying and differentiating between benign and harmful activities. With this proactive approach, farmers can implement timely interventions to mitigate risks, such as deploying deterrents or adjusting irrigation schedules, thereby minimizing crop damage while minimizing harm to wildlife populations. Furthermore, the integration of intelligent surveillance systems with IoT (Internet of Things) devices offers seamless connectivity and control. Sensors embedded in fields and wells can relay data to centralized platforms, enabling remote monitoring and management. This not only enhances efficiency but also reduces the need for physical presence, thereby minimizing disruptions to wildlife habitats. Additionally, the data collected from these systems can be analyzed to gain insights into wildlife behavior and habitat usage, facilitating informed decision-making for conservation efforts. The implementation of intelligent surveillance systems represents a proactive and sustainable approach to safeguarding both crops and wildlife in agricultural landscapes.

This research presented a unique architecture based on the Internet of Things (IoT) and ARM for managing soil moisture and irrigation. The framework's goal is to reduce manual fieldwork and move data to a cloud server that can be accessed via an online application. The suggested plan offers benefits in terms of lower expenses, limiting water waste, and lessening physical interference. It also promotes irrigation that requires little upkeep and is ecologically friendly. Temperature, humidity, and sound sensors are paired with an ARM single-board computer, also known as the Raspberry relay module is positioned to control the water flow. The values collected by the sensor are saved on the cloud server, and the web application provides the required values and recommendations. When soil moisture levels are low and ground temperatures are high, the irrigation system automatically activates, allowing for email notification to the user. The model displayed anticipated effects at different levels of humidity.

The integration of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT) has revolutionized data utilization in various sectors. Predictive maintenance (PDM) emerges as a pivotal strategy, leveraging data from diverse manufacturing and sensing sources to anticipate equipment failures. This paper presents a comprehensive overview of the application of machine learning (ML) techniques in PDM, categorizing advancements based on ML algorithms and data acquisition equipment. Through highlighting significant contributions and ongoing pilot projects, this review provides valuable insights for optimizing maintenance strategies and enhancing equipment performance and longevity. 

Load sharing provides su?cient protection to distribution transformer under overloaded conditions. Due to overload on transformer, the e?ciency drops and windings get overheated and may burn. By sharing a load current on transformer for each phase the transformer was protected. Therefore, the objective of this study was to protect transformers from overloaded conditions by sharing the load.

Because of the differences in our college level studies and industry level requirements, we are allotted a project to get knowledge about the on goings at industries. I did the mini project that covered up a practical knowledge of what I have studied so far in books. I did experience an exposure to various electronics devices and equipment’s which I would not have able to get easily anywhere else.

In electric power distribution, an automatic overload protection System Is a circuit breaker equipped with a mechanism that can automatically close the breaker after it has been opened due to a fault. Automatic overload protection systems are used in coordinated protection schemes for overhead line power distribution circuits.

Advanced light scape controlling system is a simple yet powerful concept, which uses transistor as a switch. By using this system manual works are 100% removed. It automatically switches ON light when the sunlight goes below the visible region of our eyes. This is done by a sensor called Light Dependent Resistor (LDR) which senses the light like our eyes. It automatically switches OFF lights whenever the sunlight comes, visible to our eyes. By using this system energy consumption is also reduced because nowadays the manually operated streetlights are not switched off even when the sunlight comes and switched on earlier before sunset. In this project, there is no need for manual operation like ON time and OFF time setting. This project clearly demonstrates the working of transistors in saturation region and cut-off region. The working of relay is also known.

Face spoofing, the act of presenting a fake face or biometric feature to deceive authentication systems, poses a significant threat to the security of facial recognition systems. With the proliferation of biometric authentication in various applications, including mobile devices, banking, and surveillance systems, the vulnerability to face spoofing attacks has become a pressing concern. This paper provides a comprehensive review and analysis of face spoofing detection techniques, focusing on both traditional methods and recent advancements. 

The review begins by outlining the various types of face spoofing attacks, including printed photos, replay attacks, 3D masks, and makeup disguises. Subsequently, it discusses the challenges faced by face spoofing detection systems, such as the high variability in spoofing materials, illumination conditions, and presentation attacks. Traditional techniques, including texture analysis, motion analysis, and color-based methods, are examined, highlighting their strengths and limitations. Furthermore, the paper explores recent advancements in face anti-spoofing, including deep learning-based approaches, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). It discusses the effectiveness of these techniques in mitigating the vulnerabilities of conventional methods and their ability to handle complex spoofing attacks with higher accuracy and robustness.

Additionally, the review investigates datasets commonly used for training and evaluating face spoofing detection algorithms, emphasizing the importance of diverse and representative datasets for reliable performance assessment. Furthermore, it discusses evaluation metrics, benchmarking protocols, and open challenges in the field to provide insights into future research directions.

Electrical equipment failures can cause significant downtime, safety hazards, and economic losses. Traditional fault detection methods in any power system network often rely on human expertise and rule-based systems, which can be limited in their accuracy and adaptability expand more .This paper explores the potential of machine learning (ML) for fault detection and classification in electrical equipment expand more .It discusses the different types of ML algorithms suitable for this task, analyzes the data requirements and challenges, and reviews the current state-of-the-art research in this field. Finally, the paper highlights the benefits and limitations of using ML for fault detection and classification, paving the way for future research directions.

With the advancement of technology, demand for 2D plotter machines in educational institutions and laboratories is rapidly rising. This research paper will present an affordable model of a 2D plotter machine capable of accurately and efficiently drawing 2D diagrams and images on small-sized paper using simple algorithms and readily available components. The '2D PLOTTER' is designed to be a low-cost three-axis 2D plotter utilizing stepper motors, V3 controller, and motor control software. Initially, users need to input an image into Inkscape software and then connect the machine to a computer to feed the image. The machine then plots the required image onto the paper using processing software and mechanical arrangements. Therefore, in our project, we aim to create an affordable 2D plotter of small or medium size with an open structure.

The research examines the dynamics of cybercrimes and awareness in Bihar, focusing on the changing environment of cyber threats in the region. It also investigates approaches to improve cyber security awareness and implement steps to mitigate these threats. This study does a thorough analysis to investigate the prevalence, various types, and consequences of cybercrimes on both persons and organizations in the state of Bihar. This study examines the primary determinants that contribute to the escalation of cybercrimes, encompassing developments in technology, heightened internet connectivity, and insufficient cyber security protocols. This study highlights the pressing necessity for increased consciousness and proactive strategies to successfully mitigate cyber dangers. An alarming rise of cybercrimes targeting citizens and businesses in Bihar, including financial scams, identity theft, cyber bullying, and data breaches, is one of the main conclusions of the study. These criminal activities not only lead to substantial monetary damages but also erode confidence, jeopardize confidentiality, and jeopardize the digital welfare of persons and communities. The research emphasizes the need of advocating for cyber security awareness campaigns and equipping individuals with information regarding cyber dangers and optimal strategies for ensuring online safety. The research underscores the importance of fostering collaborative endeavors among governmental entities, law enforcement agencies, educational establishments, and industry participants in order to effectively address cyber threats in a holistic manner. Stakeholders can bolster cyber resilience at both the human and organizational levels by cultivating collaborations, exchanging resources, and synchronizing response endeavors. The research closes by providing suggestions for improving cyber security awareness and adopting effective mitigation techniques to protect against cyber threats in Bihar and other regions.

Unlike the Industrial Internet of Things (IoT) which links all the equipment in an industry that processes data, this study article takes a more comprehensive approach. to incorporate IoT with mechanical systems. The automotive sector is witnessing a significant transition towards more intelligent, efficient, and connected cars with the introduction of the Internet of Things (IoT). This study examines how IoT is revolutionizing the automotive industry, highlighting its uses, advantages, difficulties, and potential. The automobile industry is utilizing the Internet of Things (IoT) to transform not just the performance and user experience of vehicles, but also to make transportation networks safer, more environmentally friendly, and more efficient. To completely achieve this technological integration, however, a number of issues must be resolved, such as data security, privacy concerns, and infrastructure requirements.

Dynamic Voltage Restorers are becoming essential tools for improving power quality in distribution networks by reducing voltage swells and sags. The dynamic and nonlinear nature of grid disturbances may not be sufficiently addressed by traditional DVR control strategies, which are based on established algorithms. In this paper, we suggest a unique method for controlling DVRs that makes use of ANNs and provides improved responsiveness and adaptability to changing grid conditions. The architecture of the ANN-based DVR is intended to learn from past data and modify its control strategy in real-time, allowing for accurate and effective voltage restoration. The ANN-based DVR control strategy's theoretical foundation is presented in this research, along with its capacity to understand intricate linkages between grid disruptions and the best course of action for remediation. Additionally, we go over how simulation data representative of various grid conditions is used to train the ANN model. Through thorough simulation experiments, the effectiveness of the proposed ANN-based DVR system is assessed in relation to traditional control strategies under a range of fault circumstances and load profiles. The outcomes show that the ANN-based method performs better in terms of response time, accuracy of voltage restoration, and flexibility to changing grid dynamics. Furthermore, the suggested method's resilience and scalability are examined, indicating encouraging possibilities for its application in real-world distribution systems. Overall, by presenting a novel and astute method of DVR control based on Artificial Neural Networks, this research advances the state-of-the-art in power quality enhancement technologies.

The Real-Time Language Translation App is a solution aimed at addressing to the frequently found problem of language barriers in global communication. This app, which bridges linguistic gaps in communication between individuals and organizations, is based on state-of-the-art language processing technologies. The app provides instantaneous and accurate translations for conversations, documents, and various forms of by communication by utilizing artificial intelligence and natural language processing to translate spoken and written text in real-time. The Real-Time Language Translation App fundamentally represents the idea of an inclusive and networked world in which communication barriers between languages are no longer a barrier to comprehension and cooperation. Globalization and multiculturalism have made today's society more interconnected, and being able to communicate effectively across linguistic boundaries is crucial for promoting understanding amongst people. The app's innovative approach to language translation is based on modern innovations that make it possible to instantly and smoothly translate text and speech between several languages. By utilizing advanced algorithms, the application effectively interprets both written and spoken language, maintaining context and meaning while offering translations instantaneously. This feature is especially helpful in situations like business negotiations, international conferences, or emergency situations where quick communication is crucial.

In the current era of digitally enabled world, the expansion of data has become universal, giving rise to the concepts of big data and Data Analytics. This review paper provides a comprehensive view of these interconnected concepts, significance, objectives, implications, and methodologies. Big data refers to the massive volume of data generated from various sources whereas Data Analytics encircle the processes and techniques which are used to extract valuable insights from the massive data. The significance lies in their evolutionary potential for businesses and organizations. By leveraging this power businesses can grow and gain actionable insights. The objective of framing this review paper is to explore the implications for different industries and sectors. We'll look at what's being done now, what problems exist, and where we can go from here.

In this paper modern systematic literature analysis of AC and DC microgrids (MG) in combination with renewable energy sources (RESs) as distributed generations units (DG), loads and battery bank to store electricity. A review on the different DG units arrangements consisting distribution networks of the low voltage AC and DC using numerous applications of microgrid systems in the perspective of current and the future customer apparatus energy market is broadly deliberated. Based on the techno-economic and ecological advantages of the renewable energy-based DG units, a detailed comparison of AC and DC microgrid systems is done. This paper also explores the possibility, controlling and energy management schemes of the both microgrid systems depending upon the most recent research findings. Eventually, the protection and the power management techniques in microgrid systems are discussed and delivered in detail. Out of this literature analysis, it could be evidenced that both type of microgrid systems consisting multiconverter gadgets are inherently conceivable for the upcoming energy systems to attain the consistency, better power supply and efficiency.

This project aims to develop a comprehensive web-based road accident data management system tailored for Udaipur City, Rajasthan, India, utilizing GIS technology. With increasing urbanization and traffic congestion, effective management of road accident data becomes paramount for improving road safety measures. The system will offer tools for efficient collection, storage, analysis, and visualization of accident data, empowering authorities to make evidence-based decisions. Following a structured methodology encompassing data collection, system design, development, testing, and implementation, the project holds great promise for reducing accident rates and enhancing public safety in Udaipur City.

Similarly, a user-friendly accident data management system has been developed for selected locations of Surat City, Gujarat, India, utilizing GIS technology. Designed to be accessible to those unfamiliar with computers, the system facilitates accident data reporting and statistical analysis, aligning with Indian Roads Congress guidelines. By highlighting the necessity of GIS database and user-friendly systems for effective transportation planning, design, and administration, this initiative underscores the importance of data-driven decision-making in enhancing road safety measures.

The Smart traffic light control system (STLCS) is a pioneering initiative aimed at addressing the complex challenges inherent in contemporary urban traffic management. As cities worldwide grapple with escalating issues such as traffic congestion, safety hazards, and environmental degradation, the urgency for innovative solutions has never been more pronounced. The STLCS offers a multifaceted approach, harnessing the power of state-of-the-art technologies such as sensor-based cameras, intelligent traffic lights, and sophisticated real-time data analytics. Central to the functionality of the STLCS are the foundational technologies it employs. Arduino boards serve as the backbone for data acquisition, seamlessly interfacing with a myriad of sensors and data sources dispersed throughout the traffic network. The Raspberry Pi emerges as the cerebral cortex of the system, tasked with processing vast volumes of data, executing complex algorithms, and making informed decisions in real time. Meanwhile, smart LED traffic lights, dynamically responsive to the ebb and flow of traffic conditions, act as the visible manifestation of the system's intelligence, orchestrating traffic flow with precision and finesse. The methodology underpinning the STLCS entails a meticulous process encompassing the installation, calibration, and optimization of sensor arrays and camera networks at strategic vantage points across the urban landscape. Concurrently, a robust suite of algorithms is developed and implemented to translate raw sensor data into actionable insights, informing adaptive traffic signal control strategies. Continuous monitoring, iterative refinement, and rigorous validation protocols underpin the evolution of the STLCS, ensuring its efficacy and reliability in real-world deployment scenarios. Preliminary testing of the STLCS has yielded encouraging results, with demonstrable improvements in traffic flow dynamics observed in controlled simulation environments. However, the journey towards widespread adoption and seamless integration into existing urban infrastructures necessitates further validation through extensive real-world testing and iterative refinement cycles.

Canny edge detection is a widely employed technique in image processing known for its effectiveness in identifying and highlighting edges within digital images. Because of its excellent performance, the Canny Edge Detector is one of the most used edge detection algorithms. For several image processing techniques, including image enhancement, image segmentation, tracking, and image/video coding, edge detection serves as a preliminary step. Compared to the Sobel algorithm, Canny's edge detection approach yields much lower memory requirements, reduced latency, and enhanced throughput without sacrificing edge detection performance. This paper provides a comprehensive review of canny edge algorithm, elucidating each step. In this paper, the canny edge algorithm is implemented on an image as well as in real time using MATLAB and its Simulink model. We have also performed high level synthesis of the proposed algorithm using HDL coder.

Cloud providers are forced to run numerous datacenters throughout the world to host their cloud services due to end-user latency and regulatory requirements. Request allocation, the process of allocating each user request to the best data center to benefit cloud providers, is an emerging issue under such geo-distributed architecture. Nevertheless, previous request allocation solutions have serious drawbacks: They either only optimize benefits for one party (providers or users, for example) or neglect some important but realistic factors (various per-unit bandwidth costs among datacenters and heterogeneous latency requirements of different users) when optimizing benefits for both parties. The challenge of ensuring end user’s latency requirements while minimizing the overall bandwidth cost is discussed in this paper. In order to meet latency constraints and reduce operating costs, it is critical to optimize user request allocation as cloud services continue to expand across diverse geographical regions. The problem of effectively allocating incoming requests in a geo-distributed cloud environment is discussed in this paper. The principal aim is to achieve equilibrium between cost minimization and adherence to predetermined latency constraints, thereby guaranteeing responsive services for users, regardless of their geographical location. The suggested method makes use of intelligent load balancing, dynamic resource scaling, and predictive models to optimize inter-region data transfer while adaptively allocating requests. To assess resource provisioning and data transfer costs, cost models are integrated, which enhances overall cost-effectiveness. Metrics like reliability, scalability, cost efficiency, and latency adherence are used to assess how effective the system is. The findings are intended to contribute to a more responsive and economical cloud infrastructure by offering insights into improving the performance of geo-distributed cloud services.

The BMGs are the materials of interest of present and future scientific interest as these materials show good structural and functional applications and hence viewed as important metallic materials due to their exceptional properties of great strength at room temperature and high flexibility at high temperature. As a result, there is a race to study the thermodynamical properties of Bulk Metallic Glasses (BMGs). The Taylor series expansion is a mathematical tool used to represent a function as an infinite sum of terms calculated from the values of its derivatives at a specific point. However, the success of a Taylor series expansion depends on the behavior of the function and the range of temperatures one is interested in. In the context of thermodynamics and material science, Taylor series expansions can be used to approximate thermodynamic properties or expressions. In the present study, Taylor’s series expansion is used to compute the thermodynamical parameters of some selected undercooled melts characterized under the class of BMGs. 

Multilevel inverters (MLI) with switching capacitors are becoming more common in high-voltage AC applications related to renewable energy. Using the switched capacitor (SC) method, the desired voltage amplitude is created by applying a small DC voltage across the capacitor. Creating an effective MLI takes more work and less space on the keyboard. This work presents a nine-phase, four-phase quad boost inverter (NQBI) topology driven by a single solar photovoltaic source that uses fewer capacitors, switches, and diodes than previous SC-MLI architectures. The voltage across the two capacitors can be measured to provide nine distinct outputs. To illustrate the benefits and drawbacks of the suggested nine-phase quadrupole boost inverter (NQBI) architecture, a comparison of several SC-MLIs has been conducted. Successful trials have been conducted in a variety of experimental settings to show the efficacy of solar photovoltaic-based NQBI without grid connection.

This abstract provides a thorough examination of Mobile Ad Hoc Networking (MANET), highlighting its decentralized structure, dynamic topology, and routing complexities. It emphasizes MANET's crucial role in modern communication systems and explores challenges such as limited bandwidth, energy constraints, node mobility, and security risks. Various routing protocols and innovative solutions tailored to address MANET's unique challenges are discussed, with a focus on its integration into military operations, disaster management, vehicular networks, IoT, and smart cities. By exploring MANET fundamentals, challenges, solutions, and applications, this abstract offers a comprehensive understanding of its evolving landscape. It underscores MANET's adaptability and resilience in environments lacking traditional infrastructure, demonstrating its importance in scenarios requiring rapid deployment and reliable communication. This concise exploration aims to contribute to the ongoing dialogue on advancing MANET technology for diverse operational contexts and real-world applications.

The paradigm of cloud computing is one in which users receive resources like computation power warehousing and applications as a service over the internet. It provides common assets, information, software and other measures based on the requirement of the client at a particular time. Service needs to be improved as cloud computing expands rapidly and required computing attracts more users. Good load balancing techniques are required for efficient resource management. Load adjusting in distributed computing is a basic module for conveying approaching organization traffic or computational responsibilities across different servers or assets to guarantee ideal asset usage, expand throughput, limit reaction time and keep up with framework soundness. It is essential for enhancing cloud-based applications and services performance, scalability and dependability.

Bidirectional DC-DC power converters are increasingly being used in a wide range of applications that require power flow in both forward and reverse directions. These applications encompass various fields such as energy storage systems, uninterruptible power supplies, electric vehicles, and renewable energy systems, among others. The objective of this paper is to meticulously evaluate these converters from both a topological and control scheme perspective. In terms of topology, these converters can be classified into two main categories: non-isolated and isolated configurations. The non-isolated category is further divided into eight distinct groups, each accompanied by their respective schematics and a summary table. Similarly, the isolated configuration category is also divided into eight groups, each with their own schematics and summary table. This comprehensive categorization provides a comprehensive overview of the various converter topologies. In summary, this paper provides a comprehensive review of bidirectional DC-DC power converters, focusing on their topologies. By categorizing the converters into non-isolated and isolated configurations, and thoroughly analyzing their respective schematics and summary tables, the paper offers a holistic understanding of the subject matter.

Bidirectional DC-DC power converters are increasingly finding their way into a wide array of applications where the need for power to flow in both forward and reverse directions is crucial. These applications span across various fields such as energy storage systems, uninterruptible power supplies, electric vehicles, and renewable energy systems, among others, showcasing the versatility and importance of these converters. The primary objective of this article is to delve into the control strategies employed in these converters. When looking at the overall design, these converters can be categorized into two main groups: non-isolated and isolated configurations, each with its specific characteristics and applications. Furthermore, the prevalent control methods and switching techniques used in these converters are thoroughly examined to provide a comprehensive understanding of their operation. While some control strategies like PID, sliding mode, fuzzy logic, and model predictive control are commonly utilized across all DC-DC converters for enhancing their efficiency and performance. The paper delves into the distinct features of converter topology and control scheme, offering insights into their typical uses and advantages, thereby serving as a valuable resource for comparing existing configurations or selecting the most suitable converter for a specific application. By shedding light on these aspects, it opens up avenues for exploring new configurations and optimizing converter selection based on specific needs and requirements in various scenarios.

Relay Coordination is very crucial task as it required coordination of primary and backup relays. in past various authors have proposed modification in standard overcurrent relays characteristic, modification in objective function to achieve optimum results of operating time of primary as well as backup relays. In this paper, considering normal inverse, very inverse and extremely inverse standard relays characteristic, optimized results are obtained using PSO, GA, HAS. Further these results have been validated on radial distribution system.

Nowadays, most existing vehicles use the two-wheel steering system to control the movement of the vehicle whether it is a front-wheel drive, rear-wheel drive, or all-wheel drive. But due to the awareness of safety, four-wheel steering vehicles are being used increasingly, since they are also known for their high performance and stability. In standard two-wheel steering vehicles, the rear wheels do not play any role in association with the steering and follow the path of the front wheels. In four-wheeled steering, the wheels can be rotated either left or right as per the requirements. The rear wheels can be rotated in the same direction as the front or in the opposite direction. The four-wheel system is designed to function in 3 modes namely, in-phase rotation, counter-phase rotation, and zero rotation. The steering systems are designed to give the best control designed for the vehicle. The vehicles are designed with steering control to the front wheels or in certain cases steering control is given to the rear wheels. The dual axle steering system shows the working of the different motions of wheels concerning various turning arrangements. The machine consists of 3 different steering arrangements i.e., neutral phase, negative phase & positive phase. In the neutral phase only the front wheels either run in the right or left direction and the rear wheel is the follower of the front wheels. In the negative phase, both front and rear axles move in the opposite direction relative to each other. In the positive phase, both the axle front and rear move in the same direction relative to each other. Here we need to lift the shifter and place it into the respective slot for the required motion.

Augmented Reality (AR) and Virtual Reality (VR) technologies have undergone remarkable transformations, paving the way for innovative AR/VR glasses. These devices bridge the digital with the physical world, enhancing user engagement across various sectors including education, healthcare, entertainment, and industry. This paper delves into the technological underpinnings, prevalent applications, challenges, and prospective directions of AR/VR glasses, offering insights into their transformative potential and the evolutionary trajectory of immersive technologies.

Crop disease and soil health are critical factors influencing agricultural productivity and food security worldwide. Traditional methods for disease detection and soil analysis often involve time-consuming and labour-intensive processes, leading to delays in response and management. Given the current trajectory of population growth, it is anticipated that by 2050, global crop productivity will need to double from its current levels. Pests and diseases are a major obstacle to achieving this productivity outcome. Hence, it's imperative to devise efficient methodologies for automatically detecting, identifying, and predicting pests and diseases in agricultural crops. Employing Machine Learning (ML) techniques can facilitate extracting insights and correlations from the data under analysis. This paper conducts an extensive review of the literature on machine learning techniques utilized in agricultural contexts, particularly emphasizing their application in tasks related to the classification, detection, and prediction of diseases and pests. The survey endeavours to advance smart farming and precision agriculture by advocating for the creation of methodologies that enable farmers to reduce reliance on pesticides and chemicals while simultaneously enhancing crop quality and productivity. AI models can accurately detect symptoms of diseases such as leaf discoloration, damaged area, and other visual different. the integration of IoT (Internet of Things) devices and wireless sensor networks facilitates real-time monitoring of environmental variables such as temperature, humidity, and soil moisture, providing valuable data for disease prediction and prevention.

Nowadays, speech recognition systems make use of a wide range of multidisciplinary technologies, including unifying statistical frameworks, signal processing, natural language processing, and pattern recognition. Applications for these systems are numerous and include signal processing issues among many other things. The aim of this study is to introduce the fundamentals of speech recognition systems, including their development and recent improvements that have been applied to improve their accuracy and robustness. This paper provides a thorough analysis of the mechanism, obstacles, and strategies for overcoming them. It ends with a prediction that, as technology advances, the world will undoubtedly see dramatic changes in the not-too-distant future.

Smart Cities will endeavour to use technology, information and data to improve infrastructure and services. Smart city is a wave of transformation where people of particular city get all kinds of essential services like drinking water, sanitation, transport, roads, streetlights, facility of education, information technology, hospital, garden, parking and hotels, railways, airport connectivity, fire mitigation, including disaster management and better solid waste management plan, so that immaculate cleanliness can be maintained by civic bodies in their respective cities. Citizen centricity is at the heart of making smart cities a reality. Urban local bodies should develop their smart city plan through rigorous analysis of citizen concern and priorities. It is difficult to define smart city since it concept varies from country to country and in India its connotation changes from city to city. It aims at developing the entire urban eco-system, which is represented by the four pillars of comprehensive development institutional, physical, social and economic infrastructure. In the approach of the smart cities Mission, the objective is to promote cities that provide core infrastructure and give a decent quality of life to its citizens, a clean and sustainable environment, and application of smart solutions. The focus is on sustainable and inclusive development and the idea is to look at compact areas, create a replicable model which will act like a light house to other aspiring cities. This paper focuses on the concept of smart city and also analyses the key performance indicators areas for the development of Udaipur city. It also throws light on the challenges faced by the Rajasthan Government in making Udaipur as a smart city. The methodology adopted in this paper is the collection of data through secondary sources which includes newspaper, internet and books. This paper also highlights the suggestions to overcome the challenges in making Udaipur as a Smart City.

As the semiconductor industry approaches the physical limits of silicon-based technologies, the quest for alternative materials and innovative technologies has become paramount to continuing Moore's Law and addressing the growing demands for computing power and energy efficiency. This paper explores the frontier of semiconductor innovation, highlighting the emergence of novel materials and technologies poised to transcend the limitations of traditional silicon. The paper examines the properties and potential of materials such as graphene, transition metal di-chalcogenides (TMDCs), and topological insulators, alongside advancements in device architectures like quantum dots and spintronic. The methodology combines a systematic review of recent literature with comparative analysis of material properties, device performance, and scalability challenges. The integration of these emerging materials with current semiconductor manufacturing ecosystems is also discussed. Findings indicate that these materials offer significant advantages over silicon, including higher electron mobility, flexibility, transparency, and superior thermal properties, which could revolutionize the semiconductor industry by enabling faster, smaller, and more energy-efficient devices. These materials are showcasing their potential to enhance computational capabilities, sensor sensitivity, and connectivity for a wide range of applications, from wearable electronics to quantum computing. However, challenges related to synthesis, large-scale production, and integration into current systems persist. Addressing these hurdles requires interdisciplinary research and collaboration between academia, industry, and regulatory bodies to develop standardized methods for material synthesis, characterization, and device fabrication. The implications of findings extend beyond the semiconductor industry, offering insights into the future of electronics, telecommunications, and computing. As we stand on the brink of a new era in semiconductor technology, this paper calls for increased investment in research and development, policy support, and international cooperation to harness the full potential of these emerging materials and technologies, ensuring a sustainable and innovative future for the global semiconductor market.

In medical diagnostics, bone fracture detection is critical because quick and precise identification is necessary for efficient treatment and patient care. Conventional approaches frequently have poor accuracy, complicated interfaces, and little user participation. This paper presents a bone fracture detection scheme with GUI-based application that uses Convolutional Neural Networks (CNNs) to identify bone fractures, focusing the ResNet-50 architecture. This proposed technique provides real-time prediction feedback, an easy-to-use image upload interface, and automated result storage, addressing the drawbacks of current systems such as restricted user input and complicated interfaces. The proposed research intends to improve detection efficiency, accuracy, and user experience by comparing ResNet-50 with alternative methods such as AlexNet, VGGNet, and k-means clustering. The proposed method closes the gap between conventional detection techniques and recent CNN technology by achieving fracture recognition with high reliability by utilizing CNN models, specifically ResNet-50. Doctors now have a useful and precise technique for detecting bone fractures.

Liver cancer is a serious health problem with high mortality, which requires early and accurate detection for better patient outcomes. This research emphasizes the effectiveness of combining multiple imaging modalities like MRI and CT scans, which provide a more comprehensive view of the liver and enhance diagnostic accuracy. It Highlights specific feature extraction methods used in the preprocessing phase, such as wavelet transformations or deep learning-based feature extraction, which enable the identification of subtle patterns indicative of liver cancer. It specifies the machine learning algorithms utilized for classification, such as support vector machines (SVM), convolutional neural networks (CNN), or ensemble methods.

This research also discusses their suitability for handling complex medical data. It also mentions the evaluation of the proposed framework's performance, including metrics like sensitivity, specificity, and accuracy, and any comparative studies with existing approaches to demonstrate its superiority. This discusses the potential clinical impact of the proposed methodology, such as early detection leading to timely intervention, improved patient outcomes, and reduced healthcare costs associated with late-stage diagnoses. It addresses the scalability and generalizability of the framework, considering its applicability across diverse 

As technology advances, printed circuit boards (PCBs) add more components and change their layout. One of the most important quality control procedures is PCB surface inspection, as small defects in signal traces can cause major damage to the system. Due to the disadvantages of manual scanning, great efforts have been made to automate scanning using high-resolution CCD or CMOS sensors. In traditional machine vision approaches, it is always difficult to determine pass/fail criteria based on small failure examples. the development of improved sensor technology. To solve this problem, we propose an advanced PCB inspection solution based on a jump-related convolutional autoencoder. A deep autoencoder model is trained to separate imperfect original images from defective ones. The location of defects is determined by comparing the decoded image with the input image. In the first production, we scaled the correct representation to improve the performance of training samples through a small and unbalanced database. The printed circuit board (PCB), which is the basic structure for electronic devices, is very important to the electronics industry. PCB quality and reliability must be ensured, but manual inspection methods are often labor-intensive and error-prone. This study presents a new machine learning (ML) method for PCB fault detection. To automatically detect defects, we use advanced ML models such as Convolutional Neural Networks (CNNs) on a large database of PCB images marked for defects. To provide reliable and accurate detection results, our research focuses on data preparation, feature extraction, model selection, and robust validation. The results show that ML-based PCB defect detection is effective, which enables better quality control. electronics manufacturing industry. The performance of our system has been carefully evaluated, showing good accuracy and efficiency in detecting defects using precise validation methods. This research is an important step towards automating PCB inspection, improving the reliability of electronic products, and reducing production costs. It also laid the groundwork for advances in quality control and defect prevention in electronics manufacturing. Added ML-based PCB defects These findings are expected to revolutionize quality assurance procedures as the electronics industry evolves and open the door to more efficient, error-free, and cost-effective electronics manufacturing.

This study revolves around designing, simulating, and creating an inventive cricket bowling machine to enrich training sessions by mimicking various bowling styles. The machine introduces a unique ball-propelling mechanism capable of handling six balls simultaneously, delivering them accurately with a time delay of 10 seconds. A sophisticated mechanical gear system is devised and integrated into the machine, ensuring smooth operation. The base of the machine is supported by a portable fan stand, facilitating the prototype's development. The innovative ball-propelling mechanism enhances stability in pitch positioning, ball rotation axis, and speed control, providing batsmen with a controlled practice environment. Extensive test trials have been conducted to validate the machine's ability to replicate different ball deliveries, including spin and fast deliveries, showcasing its practical application for training purposes.

The objective of this review is to present a thorough evaluation of the existing knowledge concerning the processing of AlSi10Mg Aluminum alloy by using Selective Laser Melting (SLM). The manufacturing of complex and intricate components has been significantly influenced by additive manufacturing in a wide range of industries, with SLM becoming increasingly popular owing to its numerous advantages. This review explores key characteristics of AlSi10Mg alloy components made by SLM. In this review, the impacts of various process parameters, post-processing treatments, and heat treatments on the mechanical characteristics of AlSi10Mg alloy components are comprehensively elaborated.

The multiplication of cryptographic forms of money has started revenue in elective techniques for obtaining them, especially through mining. Among the different mining systems, cloud-based mining has arisen as a well-known choice because of its openness and possible productivity. This exploration paper means to give an exhaustive assessment of cloud-based cryptographic money mining, including its functional components, monetary practicality, security suggestions, and ecological effect. Through a broad survey of existing writing, this paper blends momentum information on cloud mining, distinguishes key difficulties and amazing open doors, and offers experiences into future examination bearings.

In this paper, A simple power-control scheme for high frequency an LLC resonant inverter with asymmetrical control technique is proposed. The aim is to control the output power for high-temperature applications like steel melting, brazing, and hardening, where the load parameters and resonant frequency vary substantially throughout the system operation. The operating frequency is controlled using phase locked loop to track for the resonant frequency. The output power is controlled by adjusting the switch duty cycle. The LLC resonant tank is designed with a matching transformer in between the series inductor and paralleled LC resonant tank. The important advantage of the proposed topology is the short-circuit protection of the transformer and the induction coil. The validity of the proposed method is verified through computer simulation using MATLAB.

Wireless communication has become essential to daily living in the connected world of today. There is increasing worry about the unlawful use of radio signals and frequencies for activities like unauthorized broadcasting, and interference as a result of the proliferation of devices and networks. Here, the signal seeker, the heart of our project enters the picture to transform signal security.

It uses LoRa (Long Range) technology to identify and block unauthorized communications. As a powerful tool for security application, it makes dependable communication over long distances possible, regardless of the location-urban or rural- which makes it perfect option for monitoring and safeguarding large assets, vital infrastructure and public places. We provide urgently needed signal security services to the telecom, military, law enforcement, utilities, and other sectors.

This study was conducted with few objectives of design a microcontroller based solar powered automatic irrigation system (AIS) model. To quantify the paddy field water content of and as well to provide adequate water supply in the right paddy field areas. In agricultural areas this may help for the production of crops as well can prevent the wastage of energy. To provide an efficient design to the farmers is the main objective of this paper. 

Collaborative robots, or cobots, are transforming the manufacturing industry in unprecedented ways. With their ability to work seamlessly alongside human workers, cobots are augmenting human labor and increasing productivity while reducing operational costs. One of the key advantages of cobots is their versatility. They can handle a wide range of tasks, from repetitive assembly to intricate precision work. This versatility is made possible by their seamless reprogramming capabilities, which enable them to adapt to dynamic production needs quickly and efficiently. In addition to their flexibility, cobots are equipped with advanced sensing and collaborative features that enable them to work safely alongside humans. This is a critical advantage, as it enables manufacturers to leverage the unique strengths of both robots and humans, resulting in a more efficient and effective workforce. One of the primary benefits of cobots is their ability to boost productivity. By automating repetitive tasks, cobots free up human workers to focus on more complex tasks that require human skills and expertise. This, in turn, increases efficiency and output, enabling manufacturers to produce more goods in less time. Cobots also play a critical role in enhancing quality control. With their advanced sensing capabilities, cobots can detect defects and other quality issues that might otherwise go unnoticed. This enables manufacturers to catch and correct quality issues earlier in the production process, resulting in higher-quality products and fewer product recalls.

This study introduces a home automation system based on Bluetooth and Arduino technology. Its purpose is to enable users to remotely monitor and control various household appliances using their Bluetooth-enabled mobile devices. The system comprises a Bluetooth connectivity model, an Arduino board, and powerful sensors. The Arduino board acts as the central management component, receiving commands from the user's mobile devices and transmitting signals to the corresponding appliances. Through Bluetooth connectivity, the system facilitates wireless communication between the Arduino boards and the user's mobile device, enabling the detection and control of multiple home applications such as air conditioning and lighting This study introduces a home automation system based on Bluetooth and Arduino technology. Its purpose is to enable users to remotely monitor and control various household appliances using their Bluetooth-enabled mobile devices. The system comprises a Bluetooth connectivity model, an Arduino board, and powerful sensors. The Arduino board acts as the central management component, receiving commands from the user's mobile devices and transmitting signals to the corresponding appliances. Through Bluetooth connectivity, the system facilitates wireless communication between the Arduino boards and the user's mobile device, enabling the detection and control of multiple home applications such as air conditioning and lighting.

The major problem in renewable energy system is that the variation in power generation from time to time because of the intermittent nature of the renewable sources. In this paper presents a fault analysis, classification, and mitigation technique for a hybrid grid-connected photovoltaic (PV) wind energy conversion system (WECS) using Artificial Neural Network (ANN) based Dynamic Voltage Restorer (DVR). The proposed system aims to improve the performance and reliability of the hybrid energy conversion system during fault conditions. The study uses simulation to evaluate the effectiveness of the proposed fault analysis, classification, and mitigation technique in detecting and mitigating various types of faults in the hybrid system, such as voltage sag, voltage swell, symmetrical and asymmetrical faults. The results show that the proposed system is effective in detecting and classifying the faults accurately and mitigating them using DVR. The ANN-based fault classification algorithm provides high accuracy in identifying different types of faults, and the DVR is capable of quickly restoring the system's voltage to its nominal level. The study concludes that the proposed fault analysis, classification, and mitigation technique can significantly improve the reliability and performance of the hybrid PV-WECS system during fault conditions.

This paper introduces the development and performance evaluation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller designed for a DC-to-DC converter. The proposed system comprises a 2.0 kW PV array, a DC-to-DC boost converter, and a load. Leveraging the advantages of both neural and fuzzy networks, the proposed algorithm aims to optimize solar photovoltaic system parameters through the integration of an adaptive neuro-fuzzy inference system (ANFIS) controller, employing Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The comparison between PSO and GA-based optimization techniques is conducted using MATLAB/SIMULINK software, with a focus on enhancing the converter's performance. The primary objective is to determine whether the GA-tuned ANFIS MPPT algorithm outperforms in terms of tracking Maximum Power Point (MPP), tracking accuracy, and steady-state oscillation—critical parameters for solar energy conversion systems. Simulation results indicate that, across a broad range of input irradiance levels, the GA-tuned ANFIS MPPT algorithm demonstrates superior performance in tracking MPP, accuracy, and steady-state oscillation compared to the PSO-tuned ANFIS MPPT algorithm under various operating conditions.

Cloud computing has revolutionized the landscape of cybersecurity, offering a myriad of advantages over traditional on-premises systems. This abstract explores the security benefits of cloud computing, highlighting its role in enhancing data protection, scalability, disaster recovery, proactive threat detection, centralized management, cost-effectiveness, and continuous updates. By leveraging the cloud, organizations can bolster their security posture, mitigate risks, and ensure business continuity in the face of evolving cyber threats.

This project also aims at integrating the concept of high directivity in Dual switching. Eight directive antennas are arranged in a fan shaped structure. Using appropriate switching arrangement, single or multiple Duals can be switched in the azimuthal plane. The structure is then modified into an umbrella shape by arranging eight directive antennas at the base of a cylindrical support. This arrangement helps in tilting the Dual band in the elevation plane, which is suitable for base station antenna applications. Stacking of more number of elements can further improve directivity and gain. Thus optimal coverage can be obtained with overlapping radiation patterns of high directivity. This paper designs two new dual- frequency microstrip antennas with the use of electromagnetic simulation software- High Frequency Structure Simulation (MATLAB). The two antennas adopt ordinary FR4material as a dielectric substrate, with the advantages of low cost and small size. The first antenna adopts microstrip line feeding, and the antenna radiation patch is composed of a folded T-shaped radiating dipole which reduces the antenna size, and two symmetrical rectangular patches located on both sides of the T-shaped radiating patch. The second antenna is a microstrip patch antenna fed by coaxial line, and the size of the antenna is diminished by opening a stepped groove on the two edges of the patch and a folded slot inside the patch.

Smart grids offer many advantages, but they also come with some risks, especially regarding network security and privacy. This study aims to investigate the privacy and security of existing smart grid systems and to give a cross-section of the latest findings in the field of secure data aggregation and authentication for smart grids. The study focuses on addressing the challenges posed by malicious tampering and unauthorized access to metering data by exploring innovative approaches to ensure the integrity and confidentiality of data through a process termed "secure aggregate" and that the result of secure aggregations is authenticated in a manner that is both scalable and effective using Quantum Key Distribution. A software prototype has been developed to facilitate machine-to-machine authentication because this cryptographic key can be used and machine connections can be verified. This can contribute to the protection of essential infrastructure, the creation of a continuous threat detection feedback system, and the foundations of this technology are essential conditions for increasing the ability of systems to authenticate users.