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Intro: β-lactam/β-lactamase inhibitor (BLBLI) and carbapenem are often used antibiotics in developing countries for sepsis due to the high occurrence of gramnegative infections with ESBL producing organisms. Current western guidelines advice that carbapenem is the drug of choice for gram-negative infections. However, carbapenem resistance is raising in the developing world and BLBLI is equally effective to carbapenem and cost effective. The aim of this study is to compare BLBLI with carbapenem. Methods: This prospective observational study was conducted in tertiary care hospital in India for 4months. Data regarding demographics, antibiotic therapy, side effects and sensitivity of both groups were collected by Pharm-D interns from the medical records and analysed using SPSS version 26.0. Findings: During the study period 321 subjects were receiving BLBLI and CARBAPENEM. The comparative data is shown on the table. Conclusion: The aim of this study is to compare BLBLI with carbapenem. Efficacy of BLBLI (94.77%) is equivalent to carbapenem (94.03%), as far as efficacy is concerned safe. Safety of BLBLI (58.53%) is better than carbapenem (42.03%). Sensitivity of BLBLI (79.76%) is less compared to carbapenem (91.17%). It is a single-centric study; However, the finding should be confirmed in large randomized control trial to evaluate more efficiently.

For the first time, CeO2 nanoparticles (NPs) were synthesized via green solution combustion method using Ocimum sanctum Linn (Tulsi) leaves extract as a reducing agent. The as-formed and calcined (500∘C for 3[Formula: see text]h) CeO2 NPs were characterized with different techniques. The Bragg reflections clearly indicate that the formation of cubic fluorite structure with crystallite size was found to be 10[Formula: see text]nm. The surface morphology of as-formed and calcined sample is made up of large number of irregular shaped NPs. However, agglomeration of the NPs is more in calcined sample. The estimated direct energy band gap from Tauc’s relation was found to be 2.92[Formula: see text]eV and 2.87[Formula: see text]eV for the as-formed and calcined sample, respectively. Photoluminescence spectra, CIE and CCT values clearly indicate that the present nanophosphor might find an application in display technology. Antifungal and anticancer studies show that CeO2 NPs are noncytotoxic against fungus and cancerous cells. Whereas, electrochemical studies show that the synthesized CeO2 NPs show redox peaks and pseudo capacitance and hence it finds importance in supercapacitor applications.

This research compares federated and centralized learning paradigms to discover the best machine learning privacy-model accuracy balance. Federated learning allows model training across devices or clients without data centralization. It's innovative distributed machine learning. Keeping data on individual devices reduces the hazards of centralized data storage, improving user privacy and security. However, centralized learning concentrates data on a server, which raises privacy and security problems. It evaluates two learning approaches using simulated data in a simple regression problem framework. Federated learning seems to be as accurate as centralized learning while protecting privacy. The paper also shows how federated learning works in popular machine learning frameworks like TensorFlow Federated. This research shows that federated learning protects privacy while producing accurate machine learning models. It challenges the idea that machine learning must constantly choose between privacy and accuracy. Empirical facts and theoretical ideas from this study advance machine learning methodology discussions. In the digital era, it promotes privacy-conscious, dispersed learning frameworks. [ABSTRACT FROM AUTHOR]

Efficient techniques for classifying diabetes types are essential for effective treatment of this chronic condition. Artificial intelligence (AI) algorithms, particularly Support Vector Machine (SVM) algorithms, have shown great promise in this area. A recent study utilized SVMs with different functions, including linear, polynomial, and sigmoid, to categorize diabetes into distinct types. The dataset was divided into training and test sets to perform prediction analysis, with the SVM classifier applied to both sets. The proposed system excels in segregating data for processing, training the model to accurately differentiate between Type 1 and Type 2 diabetes. By leveraging AI-based algorithms, the system enhances the precision of diabetes classification, thereby facilitating more tailored and effective treatment plans for patients. The implementation of SVM algorithms in diabetes classification not only demonstrates the capabilities of AI in medical diagnostics but also underscores the potential for integrating advanced computational techniques in healthcare. The study's approach ensures that the classifier can handle diverse patient data, improving its generalizability and robustness. As diabetes cases continue to rise globally, such AI-driven solutions could play a pivotal role in early diagnosis, personalized treatment, and ongoing management, ultimately contributing to better health outcomes and reduced healthcare costs. [ABSTRACT FROM AUTHOR]

This study outlines the creation of an RFID-based attendance tracking system using a NodeMCU ESP8266 microcontroller, MFRC522 RFID card module, and Google Sheets integration. The system streamlines attendance procedures by automating the process, offering a user-friendly solution suitable for educational and organizational environments. Utilizing RFID technology for identification and authentication, combined with the internet connectivity of the NodeMCU ESP8266, attendance data is uploaded to Google Sheets in real-time. Integration with Google Sheets facilitates efficient data management and accessibility for administrators and stakeholders. The system is built using affordable hardware components such as NodeMCU ESP8266, MFRC522 RFID card module, 5V buzzer, breadboard, and jumper wires, resulting in a compact and cost-effective attendance monitoring setup. The paper delves into the system architecture, hardware design, software implementation, and the integration process with Google Sheets. Experimental results validate the system's efficacy and reliability in accurately recording attendance data and simplifying administrative tasks. [ABSTRACT FROM AUTHOR]

Vayu : EMV is a project that is aimed at making a cost-effective, portable simple structured efficient ventilator that would be able to solve multiple problems faced by the world. During the Pandemic, the entire world saw that developing nations and under-developed nations were at the most vulnerable moments as there was a scarcity of ventilators. Due to this, lives that could have been saved with the help of an efficient breathing assistant were lost. Also, in the scenario of natural disasters, it has been seen that irrespective of the nations that are affected by it, countless lives are lost at sight but many more are lost while being transported to the nearest healthcare facility. It has been observed that this loss is because of the unavailability of a breathing assistant. If we see almost from every front there has been a similar pattern that has been observed which would range from the scenario of warfare to natural disasters, and the Pandemic. This pattern is an unavailable breathing system. This is why ventilators are very important devices that can save lives in these cases but it is expensive, and heavy, and more importantly to make them to combat the pandemic would require us to have a huge capital which is inefficient in nations that are developing and under-developed. Our project’s objective is to solve this exact problem and put forward before the world, a project that is capable of doing ventilator work to a larger extent while being able to be cost-effective and portable such that it can be mass-produced and serve the people. [ABSTRACT FROM AUTHOR]

The Mini Thermal Printer project is designed to guide users in integrating a compact thermal printer with a microcontroller like the Arduino. This project is ideal for hobbyists and developers looking to incorporate printing capabilities into their electronic projects. The guide begins by detailing the necessary components, including the printer, microcontroller, and associated wiring. Emphasis is placed on ensuring proper power supply and understanding the communication protocols essential for the printer's operation Following the hardware setup, the project delves into the software requirements. Users are guided through the installation of necessary libraries and drivers, which facilitate communication between the microcontroller and the printer. The tutorial provides sample code that demonstrates how to send data to the printer, including text. This section is crucial for understanding how to manipulate the data format to achieve the desired print output. Additionally, the project showcases various practical applications of the mini thermal -printer. Examples include creating shopping lists, printing out sensor data, bills, tokens, This examples serve to inspire users and demonstrate the versatility of the thermal printer in different projects. By the end of the guide, users are equipped with the knowledge and skills to implement thermal printing in their own projects, leveraging the full potential of this compact and powerful printer. Overall, the Mini Thermal Receipt Printer project is a comprehensive resource that combines clear instructions with practical examples. It caters to a wide range of skill levels, from beginners to advanced users, and provides a solid foundation for incorporating printing functionality into microcontroller-based projects The mini thermal printer works on the principle of thermal ribbon system and uses thermally treated papers which turn black on heating according to the need or data to be printed, The thermal printer is connected to Arduino UNO which is the microcontroller, The data is printed using the Arduino application and the library used is Adafruit thermal library. The following journal states on how the printer is connected and works. It also focuses on application and its advantages, The following journal also includes a literature survey with the name of the author and a small description of the research papers which were used and also the name of the research papers are also present in the references. [ABSTRACT FROM AUTHOR]

Announcing is a primary thing in any institution/organization or public utility places like bus stations, railway stations and parks. The notice board is a common display for effective mode of providing information to the people, but this is not easy for updating the messages instantly. In the last couple of decades, communication technology has developed by leaps and bounds. The proposed method consists of lab scaled wireless electronic display system that is controlled by an android device and displays message on it. Traditionally, there were boards where any information in the form of notices had to be stick daily. This requires regular maintenance as a person has to manually interchange it regularly. The project overcomes this problem by introducing an wireless electronic display system interfaced to an android device through Bluetooth connectivity. The Bluetooth receives the message from the android device that is sent to an Arduino and the information is extracted by the microcontroller (Arduino UNO) and is displayed on the LCD display. This deals with an innovative rather an interesting manner of intimating the message to the people using a wireless electronic display system which is synchronized using the Bluetooth technology. Now-a-days information displaying is going digital with a high speed. This will help us in passing any message almost immediately without any delay just by sending a SMS which is better and more reliable than the old way of passing the message on the display boards board. The project aims to design and implement a versatile and efficient communication system that allows for the wireless transmission and display of information across multiple electronic screens. Utilizing advanced wireless communication protocols and display technologies, this system seeks to replace traditional wired display setups, providing greater flexibility, ease of installation, and scalability. The system comprises a central control unit that wirelessly transmits data to remote display units, ensuring seamless and synchronized content updates. Key features include real-time data transmission, low power consumption, and robust security measures to prevent unauthorized access. The project explores the potential applications of the wireless electronic display system in various sectors, including education, advertising, public information dissemination, and smart home environments. [ABSTRACT FROM AUTHOR]

Library management system from ancient age one of the research area and bar codes are extensively used in most of the modern library management system. This Research project is aimed to develop smart library management system using QR code. QR codes are two-dimensional matrix barcodes. QR codes can be widely used in both commercial and institutions with the increased use of smart phones, as smart mobile phones can scan the sensible data. Use of QR code in Library management system is very easy but still in primary stage. Library should try to keep up with digital world with various technologies in this modern age. The importance of library system is because these systems are used as primary resource of information by many peoples. Most of the libraries currently working with large number of persons for daily activities and supervision. QR code based Library management system is very efficient and reliable for Librarian, staff and students. This system will simplify library processes to keep track of all records, and make library more directly accessible to its users. In this system. RASPBERRY PI PICO frame work application is being used for College Library database which can be accessed through out the institution. This system works in real time and easy access to information to every member associated with the system. Users must be registered with the system administration after which they can access their accounts and database. It reduces the bourdon on the librarian as it will help to track the books available and issued person for any book and total number of books along with ageing analysis. This research paper presents the design and implementation of a Smart Library Management System leveraging QR code technology and Raspberry Pi Pico W. The system aims to enhance the efficiency and accuracy of library operations, such as book lending, returns, and inventory management. By integrating QR codes with a Raspberry Pi Pico W microcontroller, the proposed solution offers a low-cost, scalable, and user-friendly method for automating library processes. The system utilizes QR code scanning to facilitate quick and error-free transactions, while the Raspberry Pi Pico W serves as the central processing unit, managing data and communication with the library database. The paper details the hardware and software architecture, implementation procedures, and performance evaluation of the system. Results demonstrate significant improvements in transaction speed, accuracy, and overall user experience, highlighting the potential of this technology in modernizing library management systems. [ABSTRACT FROM AUTHOR]

In the current hectic schedule, cleaning houses and surrounding environment is more arduous. At present, there are vacuum cleaners which require humans to handle it. Manual work is taken over the robot technology and many of the related robot appliances are being used extensively also. Here represents the technology that proposed the working of robot for Floor cleaning. Thus, there is a dire need to implement vacuum cleaner which works without human intervention. An efficient method to clean the desired area has been implemented through this project. In a present-day situation, people are so occupied with their work that they often lack the capacity to properly clean their homes. The solution to this problem is a home vacuum cleaner robot, such as iRobot Roomba, which can clean the house with the press of a button. However, commercial products often share a common issue, which is cost. Today, a team has decided to create a simple floor cleaner robot. The new Arduino Vacuum Cleaner that we will create is expected to be cost-effective and practical. The advancement of robotics and automation has revolutionized various aspects of everyday life, including household chores. One such innovation is the development of a robotic vacuum cleaner, which autonomously navigates through indoor spaces, detecting and cleaning dirt and debris. This project focuses on designing and implementing a robotic vacuum cleaner using Arduino microcontroller technology. This project aims to demonstrate the feasibility of building an affordable robotic vacuum cleaner using readily available components and Arduino programming. The outcomes include a functional prototype capable of autonomously cleaning floors, contributing to the advancement of home automation technologies. [ABSTRACT FROM AUTHOR]

The project titled "Design & Development of a Robotic Vacuum Cleaner for Domestic Applications" aims to create an efficient, autonomous cleaning solution tailored for home environments. Utilizing an Arduino microcontroller, the system integrates various sensors, including ultrasonic and infrared, to enable obstacle detection and path planning. The robotic vacuum cleaner is designed to navigate autonomously, ensuring thorough floor cleaning while avoiding obstacles and optimizing cleaning paths. The project focuses on developing a cost-effective, user-friendly device that enhances home automation and convenience. The system's design, coding, and assembly processes are meticulously documented, providing a comprehensive guide for future enhancements such as wireless control and advanced navigation algorithms. This innovative approach leverages microcontroller technology to deliver a practical, smart home appliance that significantly reduces manual cleaning efforts, contributing to the advancement of domestic robotics and smart home technologies. This paper presents the design and implementation of development of a vacuum cleaner robot using an Arduino with an Android smartphone. Vacuum cleaner robot is designed to make cleaning process become easier rather than by using a manual vacuum. The main idea is primarily by having the sensor to sense any object like wall and dust. Then send the output data to an Arduino that will control the robot vacuum movement. To facilitate targeted functions interactivity in conjunction with high quality sensors plays essential roles. Meanwhile, for the manually controlled cleaning mode operation, the user can control the movement of the robot vacuum by using an Android smartphone through android application. The major aim of the system is to provide cleaning assistance to individuals who cannot manage to do the same due to various reasons. As it’s already known that a vacuum's suction is caused by a difference in air pressure. A fan/exhaust driven by an electric motor (as desired) reduces the pressure inside the suction creating module. Atmospheric pressure then pushes the air through the surface and into the nozzle, hence collecting the dust in the collector. [ABSTRACT FROM AUTHOR]

Conventional billing systems used in the supermarket nowadays are a big hassle for the customers as it creates a long waiting time for payment of the products purchased. In the evolving landscape of retail, the integration of technology has become pivotal to elevate the shopping experience. This abstract introduces a "Smart Shopping Cart System," a technologically advanced solution designed to redefine traditional retail interactions. The system employs a combination of RFID (Radio Frequency Identification) technology, user authentication mechanisms, and real-time data analytics to create an intelligent and seamless shopping experience. The primary objective of the Smart Shopping Cart System is to streamline the checkout process and customer engagement. Through automated product identification using RFID tags, customers can effortlessly add items to their cart, eliminating the need for manual scanning. Real time data analytics play a crucial role in providing actionable insights for both retailers and customers. The system continuously updates inventory levels, allowing retailers to optimize stock, reduce instances of out-of-stock items, and implement dynamic pricing strategies. For customers, the system offers personalized recommendations based on their purchase history and preferences. The Smart Shopping Cart System, which is designed using Node MCU, and LCD to display the total price of the items in the cart. To make it more feasible the bill is also made available on the web server, which makes it easy for the customer to check the list of items added and so they can manage the purchase. The Smart Shopping Cart System goes beyond transactional efficiency, incorporating features like navigation assistance within the store. Utilizing advanced algorithms, the system guides customers to locate products, creating a more convenient and enjoyable shopping experience. [ABSTRACT FROM AUTHOR]

Object recognition is a crucial task in computer vision and artificial intelligence, as it plays a vital role in many applications such as autonomous vehicles, surveillance systems, and robotics. In this paper, we present an overview of AI Trained object recognition using Google Teachable Machine. Google Teachable Machine is a web-based platform that allows users to train machine learning models without any coding or programming skills. We explore the steps involved in training an object recognition model using Google Teachable Machine and evaluate the performance of the model on a real-world dataset. Our results show that Google Teachable Machine is a powerful and user-friendly tool for training object recognition models with high accuracy. This research focuses on the development of an AI-ML trained object recognition system using Google Teachable Machine, enhanced by data science methodologies. The objective is to create a robust and efficient system capable of accurately identifying and classifying various objects in real-time. Leveraging the user-friendly interface of Google Teachable Machine, the system is trained with diverse datasets, incorporating advanced machine learning algorithms and data preprocessing techniques. This approach ensures high accuracy and reliability in object recognition tasks. The integration of data science principles allows for thorough analysis and optimization of the training data, improving the system's performance and adaptability. The resulting system demonstrates significant potential for applications in areas such as security, automation, and augmented reality, showcasing the synergy between AI, machine learning, and data sciences in solving complex recognition problems. [ABSTRACT FROM AUTHOR]

This report explores a methodology for flower classification using a pre-trained ResNet model. The approach involves fine-tuning the ResNet model on a flower dataset to achieve high classification accuracy. Key processes include data preprocessing, augmentation, model training, and evaluation, resulting in a validation accuracy of 95%. The project "Flower Classification Using Pre-trained ResNet Models in Computer Vision" aims to explore the capabilities of advanced deep learning techniques in accurately classifying flower species. Leveraging pre-trained Residual Networks (ResNet), this study demonstrates the efficacy of transfer learning in the domain of image classification, particularly for flora identification. This abstract outlines the methodology, key findings, and potential applications of the project, emphasizing the significant improvements in classification accuracy and computational efficiency achieved through the use of pre-trained models. [ABSTRACT FROM AUTHOR]

The Enhanced Car Safety System is a novel approach to improving road safety by integrating real-time notifications to emergency services, including hospitals and police departments, in the event of a vehicular accident. This system employs advanced sensors and telematics to detect collisions and other critical incidents automatically. Upon detecting an accident, the system promptly gathers vital data such as the vehicle's location, the severity of the impact, and the number of passengers. This information is then transmitted to nearby emergency services through a secure communication network. The primary goal of this project is to reduce response times of emergency personnel, thereby increasing the chances of survival and reducing the severity of injuries for accident victims. The system leverages GPS, accelerometers, and IoT (Internet of Things) technology to ensure accurate and swift notification. Additionally, it includes features to avoid false alarms, such as verifying collision data against predefined criteria before sending alerts. The Enhanced Car Safety System aims to bridge the gap between accident occurrence and emergency response, providing a critical time advantage that can save lives and enhance overall road safety. By integrating this system into modern vehicles, we anticipate a significant reduction in fatalities and serious injuries on the road, making driving a safer experience for all. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a Phase-Locked Loop (PLL) is a crucial component in many electronic systems, used for frequency synthesis, clock generation, and data recovery. In Cadence, designing a PLL involves a comprehensive process that includes creating, simulating, and verifying various subcomponents such as the phase detector, voltage-controlled oscillator (VCO), loop filter, and frequency divider. Phase Detector (PD) Compares the phase of the input reference signal with the feedback signal from the VCO. It generates an error signal proportional to the phase difference. Charge Pump (CP) Converts the error signal from the phase detector into a current, which is then used to adjust the control voltage of the VCO. Loop Filter (LF) Filters the output of the charge pump to produce a smooth control voltage for the VCO, ensuring stable operation and reducing noise. Voltage-Controlled Oscillator (VCO) Generates an output signal whose frequency is controlled by the input voltage. The VCO output frequency is adjusted to match the phase and frequency of the reference signal. Frequency Divider (FD) Divides the VCO output frequency by a predetermined factor, feeding it back to the phase detector for comparison with the reference signal. In Cadence, the design flow starts with behavioral modeling using Verilog-A or similar languages, followed by schematic capture and layout design. Extensive simulation is performed using Spectre or other simulation tools to analyze the PLL's performance metrics such as lock time, phase noise, jitter, and stability. The design is iteratively refined based on simulation results to meet the required specifications. [ABSTRACT FROM AUTHOR]

Water leakage is a significant factor in overall water losses, and effective metering can help identify and locate leaks, raising consumer awareness about water usage. An automated water billing and leak detection system in smart cities aims to address these issues by employing advanced technologies. Metering not only helps in tracking consumption but also plays a crucial role in detecting leaks, thus allowing utilities to automate their water systems and identify problem areas early. The development of such a system provides numerous benefits, including proactive leak detection, enhanced customer tools for monitoring water use, and more accurate billing rates. By integrating message and Wi-Fi (IoT) technology, the system can automatically collect consumption data, diagnose issues, and gather status information from energy meters. This automation saves utilities the cost and effort of periodic trips to physical locations for meter readings, streamlining the process and making it more efficient. One of the key advantages of this smart water management system is its ability to base billing on near real-time consumption rather than on estimates or historical data. This approach not only makes the billing process faster and more accurate but also helps in reducing water wastage. By providing customers with real-time data, they can be more mindful of their water usage, thereby contributing to overall water conservation efforts in smart cities. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of an automated defence system interface leveraging Arduino technology. In an era of evolving security threats, the development of automatic defense systems has become increasingly crucial. This abstract provides a concise overview of automatic defense systems, focusing on their technologies and applications. Automatic defense systems encompass a wide range of technologies, including sensors, artificial intelligence, robotics, and communication networks. These systems are designed to detect, analyze, and respond to potential threats in real-time, minimizing human intervention and response time. Key components of automatic defense systems include sensor arrays for threat detection, data processing algorithms for threat analysis, and autonomous or semi-autonomous response mechanisms. The core typically involves sensors (radar, lidar), a command-and-control system for threat evaluation, and countermeasures (missiles, lasers, guns). Applications of automatic defense systems span various domains, including military, homeland security, critical infrastructure protection, and public safety. These systems offer enhanced situational awareness, rapid threat response, and scalability to adapt to evolving security challenges. As technology continues to advance, the integration of emerging technologies such as machine learning, drones, and IoT further enhances the capabilities of automatic defense systems. The project "Design & Development of an Automatic Defense System for Warfare Applications" focuses on creating a sophisticated, autonomous defense mechanism aimed at enhancing security and operational efficiency in military environments. The system integrates advanced sensor technologies, real-time data processing, and artificial intelligence to detect, identify, and neutralize potential threats with minimal human intervention. Key features include robust target recognition algorithms, adaptive threat response capabilities, and seamless integration with existing defense infrastructure. The project aims to provide a reliable and efficient defense solution that can operate under diverse and dynamic combat conditions, ultimately improving the safety and effectiveness of military operations. The system's design emphasizes resilience, precision, and adaptability, ensuring it can handle various warfare scenarios while minimizing collateral damage and operational risks. [ABSTRACT FROM AUTHOR]

This paper explores the design and implementation of a dancing robot controlled by an Arduino Nano microcontroller. The objective is to create a versatile and engaging robotic system capable of performing dance movements in response to external stimuli or programmed sequences. The hardware setup includes servo motors for articulation, sensors for interaction, and LEDs for visual effects, all controlled through the Arduino Nano board. Key components of the project include the mechanical design of the robot for fluid motion, the integration of sensors to detect the environment or audience cues, and the programming of choreographed dance routines using Arduino's IDE. The system's flexibility allows for real-time adaptation to surroundings or user inputs, enhancing its interactive capabilities. Through this project, we demonstrate how Arduino Nano can serve as a robust platform for creating dynamic and entertaining robotic performances. Future developments could focus on expanding the robot's repertoire of movements, enhancing sensory capabilities, or integrating wireless communication for synchronized group performances. This paper provides insights into the practical applications of robotics in entertainment and educational contexts, emphasizing creativity and technical skill development. This project, "Fabrication of A Dancing Robot Using Arduino Nano," aimed to design and construct a dancing robot using Arduino Nano microcontroller technology. The project integrated various components such as sensors, motors, and LEDs to create a visually appealing and dynamically moving robot. The primary focus was on the mechanical design, electronic integration, and programming aspects to achieve a functional and entertaining robot. The fabrication process involved selecting and assembling mechanical components to form the robot's structure, ensuring stability and mobility. Electronic components such as servo motors and LED lights were incorporated to enable movement and visual effects, enhancing the robot's aesthetic appeal. Arduino Nano served as the central control unit, managing sensor inputs and executing programmed dance routines to create a lively and engaging performance. Programming the Arduino Nano was a critical aspect of the project, involving the development of algorithms for choreographing dance sequences and controlling the robot's movements. Participants utilized the Arduino Integrated Development Environment (IDE) to write and upload code, learning the fundamentals of programming and algorithm design in the context of robotics. The project provided hands-on experience in integrating hardware and software components to create a functional and interactive robotic system. In conclusion, the project "Fabrication of A Dancing Robot Using Arduino Nano" demonstrated the application of Arduino technology in robotics and provided valuable learning experiences in mechanical design, electronics, and programming. The dancing robot showcased the creative potential of robotics, highlighting the integration of technology and artistry in engineering projects. The project underscored the importance of interdisciplinary skills in robotics, preparing participants for future endeavors in the field of robotics and automation. [ABSTRACT FROM AUTHOR]

This research work explores the innovative use of mechanical energy generated by human walking and its conversion into usable electrical energy. By harnessing this often-overlooked energy source, we address the growing demand for sustainable energy solutions. Utilizing simple mechanisms such as piezoelectric materials and mechanical footfall pressure plates, the project demonstrates how everyday human movement can contribute to renewable energy generation. This dual-purpose approach promotes walking for transportation and exercise while simultaneously serving as a renewable energy resource. By integrating these technologies into common surfaces like floors and pavements, we envision sustainable urban environments where human activity directly powers infrastructure. This project highlights the potential of human-powered energy, emphasizing its role in fostering environmental sustainability and public health, and showcases innovative thinking in the pursuit of a greener future. The Footstep Futura project explores innovative solutions to harness human kinetic energy for sustainable power generation. By integrating advanced piezoelectric materials and efficient mechanical systems, the project converts the mechanical energy generated by walking into usable electrical power. This energy can be stored in batteries and used to power small electronic devices, demonstrating a practical application of human-powered energy. Designed for user-friendliness and portability, Footstep Futura offers a convenient and eco-friendly energy solution for everyday use. The project aims to contribute to greener urban environments and promote the adoption of sustainable technologies in daily life. Index Terms— Footstep Futura, human-powered energy, piezoelectric materials, kinetic energy, electrical power, energy storage, portable energy solution, sustainable technology, green energy, pedestrian power, energy generation, user-friendly design, eco-friendly innovation, renewable energy, everyday convenience. [ABSTRACT FROM AUTHOR]

A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in a way that they eventually take up all the nutrients meant for the healthy cells and tissues, which results in brain failure. Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. We are using a Deep Learning architectures CNN (Convolution Neural Network) generally known as NN (Neural Network) Transfer learning for detecting brain tumors. The performance of the model is predicting image tumor is present or not in image. If the tumor is present, it returns yes otherwise return no. Here the location of the tumor is identified by K-means clustering using Raspberry pi. The Arduino controller is used for the movement of the robotic wheels to move in the exact location and determines the position of the tumor. [ABSTRACT FROM AUTHOR]

Our project addresses the critical need for accurate and efficient fruit identification in key industries, such as agriculture, food processing, and retail, where precision and speed are paramount. By leveraging the cutting- edge capabilities of CNNs, we've elevated the accuracy of fruit classification to unprecedented levels, enabling stakeholders to make informed decisions with confidence. With TensorFlow as our foundation, we've meticulously designed and optimized our CNN architecture to extract nuanced features from fruit images, ensuring robust performance across diverse datasets. Through rigorous experimentation and fine-tuning, we've honed our model to deliver consistently high levels of accuracy, setting a new standard in fruit identification technology. The seamless integration of Flask into our system enhances user accessibility, allowing for effortless interaction with the fruit identification interface. Moreover, our deployment on AWS guarantees scalability and reliability, empowering users with instant access to our advanced classification capabilities, regardless of their location or scale of operations. In summary, our comprehensive solution represents a significant leap forward in fruit identification technology, offering unparalleled accuracy, efficiency, and scalability. By democratizing access to advanced classification capabilities, we aim to revolutionize decision-making processes and drive productivity gains across industries, ultimately paving the way for a more sustainable and efficient future. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a sophisticated smart mirror interface leveraging Raspberry Pi technology. The smart mirror serves as a multifunctional display, offering real-time information essential for daily activities. Integrating various modules including time, calendar, weather updates, to-do list management, and news updates, the smart mirror enhances user interaction and productivity in modern living spaces. Utilizing Raspberry Pi as the core computational unit, the smart mirror seamlessly integrates with diverse data sources and APIs to fetch and display pertinent information. The time module ensures accurate timekeeping synchronized with network time protocols, while the calendar feature provides users with at-a-glance visibility into their upcoming appointments and events. Moreover, the weather module fetches real-time weather data from reliable sources, offering users comprehensive insights into current conditions and forecasts. The to-do list management feature empowers users to organize their tasks efficiently, displaying prioritized lists directly on the mirror interface. Furthermore, the news module aggregates news headlines and updates from reputable sources, keeping users informed about global events and developments. The smart mirror interface employs intuitive user interaction methods, such as touch or voice commands, to navigate through different modules seamlessly. Through the integration of Raspberry Pi technology, the smart mirror not only provides essential information but also offers flexibility for future expansions and customizations. [ABSTRACT FROM AUTHOR]

Water leakage is an important component of water losses. In addition to raising consumer awareness of their water use, metering is also an important way to identify and locate water leakage. A leakage detection program can be highly proactive, helping water utilities automate water system, detecting problem areas earlier, giving customer tools to monitor water use, providing more accurate rates and reducing demand. The objective is to overcome the disadvantages of using meter technology and make the billing troubleshooting process faster along with reducing the wastage of water. Message and Wi-Fi(IOT) technology automatically collect consumption, diagnosis, and collect status data from energy meter and advancement save utility provides the expense of periodic trips to each physical location to read a meter. Another advantage is billing can be based on near real time consumption rather than one estimates based or previous or predicted consumption. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), approximately 537 million individuals are currently living with diabetes, with projections suggesting an annual increase of 3%. Efficient techniques for determining the types of diabetes are crucial for effective treatment of this chronic condition. Artificial intelligence-based algorithms offer promising avenues for such classification tasks. In a recent study, Support Vector Machine (SVM) algorithms were employed to classify diabetes into distinct categories using various functions such as linear, polynomial, and sigmoid functions. The dataset was split into training and test sets for prediction analysis, with the SVM classifier applied to both datasets. The proposed system effectively segregates data for processing, training the model to accurately distinguish between Type 1 and Type 2 diabetes. [ABSTRACT FROM AUTHOR]

River bodies have been polluted indiscriminately by being subjected to waste generated by industries and human beings. Apart from the soluble waste, there is a lot of insoluble waste which creates a layer on the river body that not only affects the quality of water but also prevents the sun rays from moving down the river bed; this seriously affects the aquatic life within the river—considering the above factor we have designed and fabricated a remote controlled machine which can collect the floating waste from the river bodies. The machine is controlled with the help of a Bluetooth module. This Bluetooth module [1] controls the DC motors on the machine and helps the machine to move and make various movements. The machine includes a garbage tray that accumulates the garbage collected. The Indian government has assumed care of river cleaning and has invested a significant amount of money in projects such as "Namami Gange" as well as other cities such as Ahmadabad and Varanasi. Taking this into account, this machine was created to clean the surface of river water. The project's major goal is to reduce the amount of manpower and time required to clean the river. [ABSTRACT FROM AUTHOR]

In recent years, there has been significant progress in the development of waste heat recovery techniques, particularly through thermo-electric generator (TEG) technologies. These advancements have spurred interest in their utilization within the alternative energy industry. Past studies have demonstrated the feasibility of TEGs as a means of harvesting waste heat. The quest for renewable energy sources has become a global priority as a sustainable alternative to fossil fuels. In line with this, the aim is to embark on a journey to explore the potential of heat energy harvesting and its conversion into electrical energy. This endeavour was made feasible through the application of the See-beck effect, wherein thermo-electric generators (TEGs) serve as semiconductor-based devices to transform heat into electricity. Through designing and engineering, the aim is to devise a generator capable of harnessing alternative sources of energy, subsequently converting them into usable electric power. The stored energy holds potential for various applications, including the powering and recharging of mobile devices, offering a sustainable solution for energy needs. Based on the temperature difference between the heat generated from the heat source and the environment, the TEG is able to convert heat energy to thermoelectric energy. Heat energy is a renewable source that is easily accessible. This device is a robust and highly reliable energy converter which aims to generate electricity in applications in which the heat would be otherwise dissipated. The significant request for thermoelectric energy harvesting is justified by developing new thermoelectric materials and the design of new TEG devices. [ABSTRACT FROM AUTHOR]

In the era of digital transformation, personal assistant technologies have evolved to become integral parts of daily life. This project aims to develop a voice-based personal desktop assistant, leveraging machine learning and natural language processing (NLP) techniques. This abstract explores the key features and functionalities of these assistants, including speech recognition for accurate voice-to-text conversion, natural language understanding (NLU) for interpreting user commands, task automation for executing various tasks, information retrieval for providing real-time updates and personalized recommendations, and multi-platform integration for ubiquitous accessibility across devices. The assistant will be designed to perform a variety of tasks, including the voice Command Recognition in the implementing of a robust speech recognition system to accurately understand and interpret user commands. Task Automation for the Integration with various applications and services to automate tasks such as scheduling meetings, setting reminders, sending emails, and managing to-do lists. Information Retrieval: Utilizing web scraping and API integration to retrieve real-time information such as weather updates, news headlines, and stock market data based on user queries. Personalization: Incorporating machine learning algorithms to learn user preferences and tailor responses and recommendations accordingly. Security and Privacy: Implementing encryption and secure authentication mechanisms to protect user data and ensure privacy. The project aims to provide a seamless and intuitive user experience, enhancing productivity and convenience in daily tasks through the power of voice interaction with the desktop assistant. [ABSTRACT FROM AUTHOR]

This paper presents the development of an RFID-based attendance monitoring system utilizing NodeMCU ESP8266 microcontroller, MFRC522 RFID card module, and Google Sheets integration. The proposed system aims to automate the attendance process, offering a convenient and efficient solution for various educational and organizational settings. The system employs RFID technology for identification and authentication, and leverages the internet connectivity of NodeMCU ESP8266 to upload attendance data to Google Sheets in realtime. The integration with Google Sheets provides seamless data management and accessibility for administrators and stakeholders. The hardware components including NodeMCU ESP8266, MFRC522 RFID card module, 5V buzzer, breadboard, and jumper wires are utilized to build a compact and cost-effective attendance monitoring setup. The system architecture, hardware design, software implementation, and integration with Google Sheets are discussed in detail. Experimental results demonstrate the effectiveness and reliability of the proposed system in accurately recording attendance data and streamlining administrative tasks. [ABSTRACT FROM AUTHOR]

This paper presents the development of cutting-edge solar-powered cold storage units designed specifically for street vendors. This innovative technology addresses the critical need for reliable and mobile cold storage solutions in off-grid areas by integrating portability, flexibility, and efficiency through solar energy. Historically, street vendors have heavily relied on non-renewable energy sources and faced limited access to cold storage facilities. Our system aims to provide a user-friendly interface, efficient cooling mechanisms, and real-time monitoring and management through IoT connectivity. By offering dependable cold storage for perishable items in a portable and user-friendly design, the Solar Powered Cold Storage system for street vendors has the potential to revolutionize their business practices. The system's key features include its portability, which allows street vendors to transport and set up the cold storage unit wherever needed, and its reliance on solar energy, making it a sustainable and costeffective solution. Additionally, the integration of IoT technology enables remote monitoring and management of the cold storage unit, ensuring optimal performance and energy efficiency. Overall, this technology offers street vendors a reliable and efficient cold storage solution that can significantly enhance the quality and shelf life of their products, ultimately improving their business operations and profitability. [ABSTRACT FROM AUTHOR]

The project "Connecting Safety on the Railway Tracks" introduces an innovative strategy for preventing train accidents, specifically tailored for implementation within the Indian Railways system. Emphasizing the paramount importance of railway safety, the initiative employs cutting-edge technologies and strategic measures to reduce accident risks. Central to the strategy is the integration of advanced sensors, machine learning algorithms, and communication systems to establish a robust safety network along the railway tracks. Through real-time monitoring and predictive analytics, the system aims to identify potential hazards, detect track irregularities, and predict accidents before they happen. The implementation plan for Indian Railways involves a phased approach, including sensor deployment, data infrastructure development, and collaborative partnerships with stakeholders. The project's objectives extend beyond safety enhancement, aiming to optimize train operations and improve overall efficiency and reliability of the railway network. By addressing the unique challenges of Indian Railways, the project sets a new standard for railway safety, showcasing the transformative potential of technology in accident prevention and serving as a model for railway systems worldwide. The project also introduces an innovative strategy for preventing train accidents, specifically tailored for implementation within the Indian Railways system. Emphasizing the paramount importance of railway safety, the initiative employs cutting-edge technologies and strategic measures to reduce accident risks. Central to the strategy is the integration of advanced sensors, machine learning algorithms, and communication systems to establish a robust safety network along the railway tracks. Through real-time monitoring and predictive analytics, the system aims to identify potential hazards, detect track irregularities, and predict accidents before they happen. The implementation plan for Indian Railways involves a phased approach, including sensor deployment, data infrastructure development, and collaborative partnerships with stakeholders. The project's objectives extend beyond safety enhancement, aiming to optimize train operations and improve overall efficiency and reliability of the railway network. By addressing the unique challenges of Indian Railways, the project sets a new standard for railway safety, showcasing the transformative potential of technology in accident prevention and serving as a model for railway systems worldwide. [ABSTRACT FROM AUTHOR]

The "Automated Robot with Healthcare Applications" project aims to revolutionize healthcare by integrating cutting- edge robotics technology into medical environments. The proposed automated robot is designed to perform various tasks, ranging from patient assistance to logistical support, enhancing overall efficiency and patient care. The robot is equipped with advanced sensors, obstacle detection and path finding algorithms, and a versatile mechanical platform, allowing it to navigate and carry medi-kits for providing healthcare facilities autonomously. Its capabilities include patient monitoring, medication delivery, and assistance in routine tasks, thereby alleviating healthcare professionals' workloads and improving the quality of patient care. In addition to its caregiving functions, the robot contributes to infection control by minimizing human contact and ensuring adherence to hygiene protocols. The project emphasizes the seamless integration of the robot with existing healthcare systems, promoting collaboration between human healthcare providers and robotic assistants. Key features of the automated robot include real-time data collection, analysis, and communication capabilities, providing healthcare professionals with valuable insights for decision-making. The project also addresses ethical considerations, data security, and user acceptance to ensure the successful adoption of the automated robot in diverse healthcare settings. This report discusses the design, development, and testing phases of the automated robot, showcasing its potential impact on healthcare delivery. Through this innovative solution, the project aims to enhance healthcare efficiency, improve patient outcomes, and contribute to the ongoing evolution of technology in the medical field. [ABSTRACT FROM AUTHOR]

The research introduces an advanced approach to robot path planning by incorporating Dijkstra's algorithm along with the inclusion of path weighting. The integration of path weights is designed to improve navigation efficiency from the starting point to the destination across diverse environments. This optimization aims to refine trajectory decisions and enhance overall performance, particularly in intricate and challenging scenarios. [ABSTRACT FROM AUTHOR]

This paper gives the developments of the Indian lunar mission and the research works carried out on the moon. Chandrayaan-3, the latest mission in India's lunar exploration program by ISRO, follows the successes of Chandrayaan-1 and Chandrayaan-2. The mission aims to demonstrate safe landing and roving on the lunar surface, focusing on the south pole region. Utilizing advanced technology, including lunar lander and rovers, Chandrayaan-3 seeks to enhance our understanding of the Moon's geological and mineralogical features. Objectives include analyzing lunar soil, mapping surface composition, and studying the Moon's exosphere. This mission represents a significant step forward in India's lunar exploration, contributing to global lunar exploration efforts and bolstering India's position in space exploration. The entire work that is being presented in this paper is the data that was available on the internet, ISRO website, news channels & papers, etc… & is just a review article about the Chandrayan Mission – 3.0. [ABSTRACT FROM AUTHOR]

Object detection is a fundamental task in computer vision with diverse applications across various domains. Template matching, a classical technique, remains a significant method for detecting objects in images. This paper presents a novel approach to object detection using template matching, leveraging the correlation between a reference template and regions within an image to identify instances of the target object. The proposed method involves the extraction of feature templates representing the object of interest and employing similarity metrics to locate these templates within the image. The process encompasses various steps, including template creation, correlation computation, and localization, enabling accurate object detection while considering scale, rotation, and partial occlusion. Through experiments conducted on benchmark datasets, the effectiveness and robustness of the proposed template matching approach are demonstrated, showcasing its potential for real-world object detection applications with promising results in terms of accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the solution of a 4-axis selective compliance assembly robot arm (SCARA) for picking & placing an IC component onto the PCB is presented along with the simulation results. The SCARA robot arm, known for its horizontal movement and accurate positioning, presents a versatile platform for assembly tasks in various industries, particularly in electronics manufacturing. The main goal is to get the forward kinematics equations required to precisely calculate the end-effector's location and orientation using the 4-axis SCARA robot arm's joint angles. The mathematical model explaining the link between the robot's joint variables and the position/orientation of the end-effector is developed by specifying the DenavitHartenberg (DH) parameters and using transformation matrices. The work intends to simulate and test the computed end-effector locations and orientations against experimental or simulated data using the obtained forward kinematics equations. The forward kinematics approach will be implemented using code developed as part of the project, enabling systematic investigation and testing of the pick-and-place capabilities of the SCARA robot. Additionally, it also provides a brief overview of the possible uses and consequences of using this SCARA-based solution for accurate and efficient assembly jobs in industrial settings. The incorporation of a graphical user interface (GUI) has the potential to enhance user engagement and aid in the real-time management and observation of the pick-and-place processes. By offering a methodical solution to the direct kinematics problem of a 4-axis SCARA robot arm for the purpose of picking and placing IC components onto PCBs, it advances robotic assembly technologies and highlights their potential to increase productivity and accuracy in manufacturing processes. [ABSTRACT FROM AUTHOR]

The paper proposes the development and construction of a bipedal walking mechanism with a focus on planar locomotion, incorporating advanced Artificial Intelligence (AI) concepts. The two-legged walking robot employs Machine Learning (ML) for Gait Optimization to adapt its walking pattern based on factors like speed, terrain and energy efficiency. Integration of multiple sensors with AI for improved balance, utilizing predictive analytics for obstacle avoidance and collision prevention. The study includes a GUI developed using Python to facilitate user interaction. A comprehensive literature survey on obstacle avoidance contributes to the understanding of existing approaches. In this paper, we explore the intersection of robotics and AI, discussing key technologies, challenges, and potential future developments. ML algorithms and computer vision play pivotal roles in enabling robots to acquire and apply knowledge etc.. [ABSTRACT FROM AUTHOR]

The research work presented in this paper gives a comprehensive approach to the design and implementation of an innovative obstacle avoidance algorithm tailored for mobile robotic vehicles. The primary aim is to develop a robust system capable of effectively detecting and navigating around obstacles in real-time scenarios. The algorithm is constructed using MATLAB, leveraging its computational capabilities and extensive toolset for algorithm development and simulation. The methodology encompasses several key stages. Initially, sensor data acquisition is performed through various sensors integrated into the robotic platform, including but not limited to cameras, LiDAR, ultrasonic sensors, or a combination thereof. Subsequently, a systematic and multi-faceted approach is employed for obstacle detection, involving techniques such as image processing, depth perception, and sensor fusion. The detected obstacle information is then processed and integrated to generate a dynamic and adaptable avoidance strategy. This strategy accounts for the vehicle's kinematics, environmental constraints, and real-time feedback, allowing for agile and safe navigation in complex and dynamic environments. The algorithm's responsiveness is optimized through iterative testing and refinement, ensuring its reliability and efficiency in diverse scenarios. The algorithm's performance is evaluated through extensive simulation studies and practical experiments with the mobile robotic platform operating in controlled and real-world environments. Quantitative metrics, including obstacle detection accuracy, avoidance efficiency, and computational performance, are employed to assess and validate the algorithm's effectiveness. The outcomes of this research contribute to the advancement of obstacle avoidance methodologies for mobile robotics, offering insights into the development of adaptive and intelligent systems capable of navigating challenging terrains autonomously. The proposed algorithm demonstrates promising results in enhancing the autonomy and safety of mobile robotic vehicles, paving the way for applications in various fields, including but not limited to autonomous transportation, surveillance, and exploration. [ABSTRACT FROM AUTHOR]

This paper undertakes a comprehensive exploration of the application of Reinforcement Learning (RL) in robotic systems, with a specific focus on the navigation tasks carried out in intricate environments. The primary objective is to elevate the adaptability, efficiency, and generalization capabilities of robots. The study delivers an insightful overview of RL concepts, accentuating their relevance in the context of robot navigation. It systematically evaluates various RL algorithms, with a particular emphasis on Deep Reinforcement Learning (DRL). The analysis incorporates crucial considerations such as state representation, action space, and reward shaping. In addition to scrutinizing RL algorithms, the paper delves into the integration of sensor data, mapping techniques, and Simultaneous Localization and Mapping (SLAM) within RL frameworks, aiming to enhance the robot's perception capabilities. The discussion extends to the adaptation of RL methodologies to address real-world challenges, including dynamic obstacles and unknown terrains. The paper substantiates its findings through the presentation of case studies and experiments that exemplify practical applications of RL in robotic navigation. The conclusion of the paper encapsulates a thoughtful discussion on existing challenges, potential future research directions, and anticipated advancements in the field. The overarching goal is to provide a comprehensive resource for researchers and practitioners interested in harnessing RL to augment autonomous robot navigation within complex and dynamic environments. By offering a thorough examination of RL applications and addressing practical challenges, this paper contributes significantly to the evolving landscape of robotics research and technology. [ABSTRACT FROM AUTHOR]

Efficient path planning is crucial for the successful navigation of robots in complex environments. This research explores the integration of General Voronoi Diagram (GVD) methods and Configuration Space (CS) methods to generate optimal paths for robots moving from a source to a goal. The study leverages advanced software tools for implementation and analysis. The General Voronoi Diagram provides a robust framework for partitioning space based on proximity relationships, enabling the creation of Voronoi cells that define regions of influence around obstacles. Meanwhile, Configuration Space captures the entire spectrum of feasible robot configurations, considering both its kinematic constraints and the presence of obstacles. The research proposes a hybrid approach, utilizing the strengths of both GVD and CS methods to enhance path planning accuracy and efficiency. The Voronoi diagram contributes to the generation of initial paths by defining regions of interest, while the Configuration Space methods refine these paths, accounting for the robot's kinematics and obstacle avoidance constraints. Software tools, including [mention specific software tools], are employed to implement and visualize the proposed path planning techniques. Simulation results demonstrate the effectiveness of the hybrid approach in generating collision-free and optimal paths for robots navigating complex environments. Comparative analyses with traditional methods showcase the advantages of integrating GVD and CS techniques. The study contributes to the field of robotics by presenting a comprehensive approach to robot path planning that leverages the complementary strengths of General Voronoi Diagram methods and Configuration Space methods. The findings have practical implications for various applications, including autonomous vehicles, industrial robotics, and unmanned aerial vehicles, where efficient and safe navigation is of paramount importance. [ABSTRACT FROM AUTHOR]

In this paper, the development of the work-space, work-space envelope, trajectory generation of a 2-axis planar articulated robot arm for various operating conditions is presented. The goal of this paper is to create a detailed trajectory model for a fixed-scale, two-axis planar articulated robot arm, or mini-drafter. The main goal is to design a flexible workspace that can accommodate various operation environments. For the mini-drafter to move around its work area efficiently, trajectory generation will be a crucial component. Strict mathematical modelling will be used in this research to account for the dynamical and mechanical limitations of the 2-axis planar articulated robot arm. The mini-drafter's attainable points and orientations will define the work area, taking into account the set size needed to ensure accuracy while drafting activities are completed. In addition, an analysis of the work-space envelope will be conducted in order to pinpoint any possible constraints and refine the design for realistic use. To increase the minidrafter's adaptability in diverse situations, special consideration will be given to allowing for a range of operating circumstances. The creation of algorithms that can effectively guide the minidrafter along desired routes inside the designated work-space is a necessary step in the trajectory generation process. This entails including energy-efficient, fast, and accurate parameters into the trajectory planning. The technology of planar articulated robot arms will improve with the successful completion of this project, especially in the context of mini-drafters. The created trajectory model will find use in drafting, design, and other related industries where accurate and controlled movements are necessary. The simulation results shows the effectiveness of the methodology that has been developed by the research team and also compared with the work done by others to show its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, the object identification & classification methods in a class of objects using AI based supervised and unsupervised training algorithms is presented. This paper explores object identification and classification methods within a specific class of objects using a combination of AI-based supervised and unsupervised training algorithms. The design involves a path that integrates both approaches to identify distinct patterns within the object class. In supervised learning, a labeled dataset is utilized, employing algorithms such as Support Vector Machines, Random Forests, or Convolutional Neural Networks. The model trained on this dataset learns to recognize patterns associated with different classes, enabling accurate identification of new, unlabeled data. In contrast, unsupervised learning, using algorithms like K-Means or Hierarchical Clustering, clusters objects based on inherent similarities, revealing patterns without predefined labels. The synergistic application of both methods offers a comprehensive understanding of intricate patterns within the object class, facilitating a nuanced analysis. [ABSTRACT FROM AUTHOR]

In this paper, the software implementation of a 4-axis Selective Compliance Assembly Robot Aram (SCARA) for the determination of various possible sets of joint variable vectors to reach the goal is presented. This MATLAB code implements the inverse kinematics of a SCARA robot to generate and simulate a linear trajectory in three-dimensional space. The SCARA robot's parameters, including link lengths and end-effector dimensions, are defined, and a trajectory is generated by specifying desired points in the XYZ space. Inverse kinematics calculations are performed to determine the corresponding joint angles for each point in the trajectory. The code also includes optional forward kinematics to validate the results by computing the end-effector position. The trajectory is visualized through a three-dimensional plot. [ABSTRACT FROM AUTHOR]

This paper explores the application of Dijkstra's algorithm for robot path planning in a 2D environment, with a focus on weighted paths. The goal is to enable efficient and optimized navigation from a source to a goal while accommodating obstacles. The methodology begins by defining objectives, identifying source and goal points, and representing the environment as a weighted graph. Dijkstra's algorithm is then applied iteratively, utilizing priority queues for optimal node exploration. The resulting path is determined through backtracking, considering factors like distance and terrain complexity. The methodology accounts for dynamic changes in the environment through real-time adaptations. Validation and optimization involve testing the generated path in simulated environments and assessing performance metrics. Implemented and integrated into the robot's software framework, the system accommodates sensor data for dynamic updates. This comprehensive approach ensures adaptable and efficient navigation, optimizing paths based on specific weights and environmental constraints. We conclude with a demonstration of the methodology's effectiveness through simulation and performance evaluations in a concise, practical manner. [ABSTRACT FROM AUTHOR]

Fire Alarm Circuit is a simple circuit that detects the fire and activates the Siren Sound or Buzzer. Fire Alarm Circuits are very important devices to detect fire in the right time and prevent any damage to people or property. Fire Alarm Circuits and Smoke Sensors are a part of the security systems which help in detecting or preventing damage. Installing Fire Alarm Systems and Smoke Sensors in commercial buildings like offices, movie theatres, shopping malls and other public places is compulsory. There are many expensive and sophisticated Fire Alarm Circuit in the form of stand-alone devices, but we have designed five very simple Fire Alarm Circuits using common components like Thermistor, LM358, Germanium Diode, LM341 and NE555.This is a very simple alarm circuit using Thermistor, LM358 Operational Amplifier and a Buzzer. The primary purpose of fire alarm system is to provide an early warning of fire so that people can be. evacuated & immediate action can be taken to stop or eliminate of the fire effect as soon as possible Alarm can be triggered by using detectors or by manual call point (Remotely). The research paper explores the integration of Internet of Things (IoT) principles and instrumentation concepts from physics in the creation of an advanced fire alarm circuit. The study focuses on leveraging IoT technology to enhance fire detection capabilities through smart and wireless communication. The circuit's design incorporates physics-based instrumentation concepts to ensure accuracy and reliability in fire detection. The research aims to contribute to the development of innovative solutions in fire safety by combining modern IoT approaches with fundamental principles from the field of physics. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) & Machine Learning (ML) stands as a robust technology empowering cybersecurity team with the automation of repetitive tasks, expediting threat detection and response, and enhancing the precision of actions to fortify security against a myriad of threats and cyberattacks. This article conducts a meticulous systematic literature review, analyzing the diverse use cases of AI in cybersecurity. Out of 2395 studies examined, 236 were identified as primary sources. Employing a thematic analysis approach and aligning with the NIST cybersecurity framework, this article categorizes the identified AI use cases. The resulting classification framework offers readers a comprehensive understanding of AI's potential to enhance cybersecurity across various contexts. The review also unveils future research prospects in burgeoning cybersecurity application domains, advanced AI methodologies, data representation techniques, and the establishment of novel infrastructures, pivotal for the successful integration of AI-based cybersecurity in the contemporary landscape marked by digital transformation and polycrisis. This research delves into the domain of cybersecurity within electronic communication systems, employing advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. The study aims to present comprehensive design concepts for the implementation of robust cybersecurity measures. With the ever-growing complexity and sophistication of cyber threats, the integration of AI and ML becomes imperative to fortify electronic communication systems against potential risks. The research explores innovative approaches, strategies, and technologies, offering a holistic view of how AI and ML can be effectively harnessed to enhance cybersecurity in electronic communication systems. The outcomes of this study provide valuable insights for the development and implementation of advanced cybersecurity measures in the realm of electronic communications. [ABSTRACT FROM AUTHOR]

The article presents the CMOS implementation of multipath differential OTA (operational trans conductance amplifier) with two flipped voltage follower. OTA is a type of analog amplifier used in integrated circuits for signal processing applications and used to convert input voltage signal into an output current signal. The design aims to enhance performance metrics such as gain, bandwidth and addressing the demands for modern analog and mixed signal applications. Utilizing two Flipped’ Voltages Followers [FVF’s] cell as variable current’s source will enables both class A-B operation & adaptive in biased for all other type of driver units. The structures incorporates multiple input-to-output paths serving as dynamic current’s boosters in the time of their slewing rates phases, enhancing their slewing rate performances. The amplifier performance is analyzed in terms of gain and bandwidth. Their proposed method of two stage flipped voltage follower with multipath fully differential OTA was developed at 50nm using 1.4V and 75 nm using 1.6V CMOS technology and finally this work was developed in Tanner EDA tool. This paper explores advancements in assistive technology aimed at enhancing mobility for individuals with visual impairments. The proposed solution involves the CMOS implementing of the multiple path full difference type of Operational Transconductance Amplifier (OTA), incorporating 2 Flip type of Voltage level Followers (FVF) cell. The study investigates the effectiveness of this design in both 75nm and 50nm CMOS technologies. The results showcase improvements in transconductance and operational characteristics, offering a promising avenue for the development of assistive devices. The findings contribute to the ongoing efforts to create inclusive and accessible technological solutions for individuals facing challenges in visual navigation. [ABSTRACT FROM AUTHOR]

This article introduces the Grey Wolf Optimizer (GWO) algorithm, a novel method aimed at tackling the challenges posed by the multi-objective Optimal Power Flow (OPF) problem. Drawing inspiration from the foraging behavior of grey wolves, GWO stands apart from traditional approaches by enhancing initial solutions without relying on gradient data collection from the objective function. In the domain of power system optimization, the OPF problem is widely acknowledged, involving constraints related to generator parameters, valve-point loading, reactive power, and active power. The proposed GWO technique is applied to IEEE 14-bus and 30-bus power systems, targeting four case objectives: minimizing cost with quadratic cost function, minimizing cost with inclusion of valve point, minimizing power loss, and minimizing both cost and losses simultaneously. For the IEEE-14 bus system, which requires meeting a power demand of 259 MW, GWO yields optimal costs of 827.0056 $/hr, 833.4691 $/hr, 1083.2410 $/hr, and 852.2255 $/hr across the four cases. Similarly, for the IEEE-30 bus system aiming to satisfy a demand of 283.4 MW, GWO achieves optimal costs of 801.8623 $/hr, 825.9321 $/hr, 1028.6309 $/hr, and 850.4794 $/hr for the respective cases. These optimal results are then compared with existing research outcomes, highlighting the efficiency and cost-effectiveness of the GWO algorithm when juxtaposed with alternative methods for solving the OPF problem. [ABSTRACT FROM AUTHOR]