Volume 12 | Issue 5

 Abstract

Wire Electrical Discharge Machining (EDM) has emerged as a prominent machining process due to its capability to fabricate intricate shapes on conductive materials with high precision. This research focuses on investigating the machinability of Aluminum Alloy 6061 using Wire EDM and optimizing the process parameters to enhance machining efficiency and surface quality. The study begins with a comprehensive analysis of the machinability characteristics of Aluminum Alloy 6061, including its mechanical properties, thermal conductivity, and electrical conductivity. Experimental trials are conducted to evaluate the influence of key process parameters such as pulse-on time, pulse-off time, wire tension, and flushing conditions on material removal rate (MRR), tool wear rate (TWR), and surface roughness. Through systematic experimentation and statistical analysis, optimal process parameters are determined to maximize MRR while minimizing TWR and surface roughness. Advanced techniques such as response surface methodology (RSM) and Taguchi method are employed to establish mathematical models correlating process parameters with machining performance indicators. Furthermore, the study explores the effects of different dielectric fluids, electrode materials, and wire diameters on machining characteristics to identify the most suitable combinations for achieving superior machining outcomes. Additionally, the environmental impact and economic feasibility of the optimized Wire EDM process are assessed. The findings of this research contribute to enhancing the understanding of Wire EDM of Aluminum Alloy 6061 and provide practical insights for optimizing the machining process in industrial applications. By improving machining efficiency and surface quality, the proposed methodology can potentially lead to significant advancements in the manufacturing of precision components using Aluminum Alloy 6061.

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Wire Electrical Discharge Machining (EDM) ,Aluminum Alloy 6061 ,Optimization , Machining Parameters , Surface Quality

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Wire electric discharge machining (WEDM) is a crucial non-traditional method of machining. It is primarily utilized for the machining of hard metals and materials that prove challenging to machine using traditional processes. This makes WEDM a valuable tool in aerospace, automotive and tooling applications. The goal of this research paper is to identify the most suitable set of process parameters, including pulse on time (Ton), voltage, and current in the electric discharge machining (WEDM) process to achieve higher material removal rate, improved surface finish. Taguchi method is used to get the optimized result. The Taguchi Method offers a systematic approach to identify and optimize these parameters to enhance machining efficiency and quality. EN31 alloy steel material is used as a workpiece and the cutting wire is made up of Brass with 0.25 mm diameter.

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Wire Electric discharge machining, EN31 alloy steel, pulse on time (Ton), voltage, current, material removal rate, surface finish, Taguchi method

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 Abstract

This work proposes Automated Thread Error Detection, a system employing machine learning to examine program execution traces to discover threading issues such as data races and deadlocks. distinguishes between harmless and serious faults and has scalability and adaptability. Automated Thread Error Detection appears to be a promising solution to improve software quality and productivity through experiments and case studies, as it demonstrates superior performance in identifying threading errors the accuracy statistic shows the proportion of properly predicted labels. In this instance, the model's accuracy was 92.45%, meaning that 92.45% of the test dataset's samples were properly identified. High accuracy shows that the model can generalize well to new data and has picked up useful patterns from the training set.

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AUTOMATED THREAD ERROR DETECTION

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The growing need for food is causing a rise and expansion in the process of changing farming and agricultural production.Its dependability for farmers will be increased by using IOT in agricultural and crop production, which is an urgent need. Increasing crop productivity is necessary due to the rapid rise of the population. For crops to be more productive, the amount of N (nitrogen), P (phosphorus), and K (potassium) in the soil is essential. The minerals in the soil allow us to determine how quickly the crops absorb nutrients. The minerals in the soil allow us to determine how quickly the crops absorb nutrients. The lack of nutrients causes a decline in agricultural yield.The phrase "macro nutrients" describes the primary nutritional needs for necessary plant growth. But a proper fertilizer balance is required for higher development. The industry's current problems can be solved by smart farming, which places a focus on encouraging creativity and innovation in agricultural and agronomic production. IOT devices and sensors can also be used for that. Agricultural laborers could easily keep an eye on their harvests, crops, or fields and gather the relevant data and information.

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Automated Fertigation System

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In the pursuit of sustainable and intelligent building solutions, this abstract introduces an Automatic Light System (ALS) empowered by a microcontroller to revolutionize lighting control. The proposed system employs advanced sensors and microcontroller logic to create a responsive and energy-efficient lighting environment. The Automatic Light System utilizes ambient light sensors to constantly monitor the natural light levels in a given space. The smicrocontroller processes this real-time data and dynamically adjusts artificial lighting accordingly, ensuring optimal illumination while minimizing energy consumption. The system is designed to seamlessly transition between different lighting scenarios, such as daylight harvesting and task- specific lighting, providing a comfortable and visually optimized atmosphere for occupants. This research presents a holistic approach to intelligent lighting control, addressing both energy efficiency and user satisfaction. The microcontroller-based ALS offers a versatile solution applicable to various settings, including residential, commercial, and industrial spaces.

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Intelligent Lighting Control, Ambient Light Sensors, Energy Efficiency, Responsive Lighting, User Preferences, Building Automation, Energy Conservation

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In order to facilitate power transfer between the electric vehicle (EV) and the grid while the vehicle is operating, this paper suggests a dynamic wireless charging system for EVs. The system makes use of an RFID-based method as well as inductive power transfer (IPT) based on the resonance principle. The design and analysis of the wireless charging system, including the hardware prototype's implementation, the computation of the self and mutual inductances, and the modeling and simulation of the power transfer efficiency, are presented in this paper. The advantages, disadvantages, and potential applications of wireless charging technology for EVs are also covered in the paper. The suggested system is a workable, secure, and clever way to charge EVs.

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Design and analysis, RFID technology, Power transfer, A dynamic wireless charging system, EVs.

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Driver weakness may be a noteworthy calculate contributing to street mischances around the world. To address this issue, we show the plan and execution of an Hostile to- Rest Alert Framework (ASAS) pointed at anticipating mishaps caused by tired driving. This extend presents a novel approach that combines real-time checking of driver's physiological signals with progressed flag preparing strategies to distinguish signs of weariness and trigger opportune cautions. The ASAS utilizes non-intrusive sensors, such as electroencephalography (EEG) and electrooculography (EOG), to persistently screen the driver's brainwave designs and eye developments. These physiological signals are prepared utilizing machine learning calculations to recognize designs related with laziness and microsleep scenes. The framework utilizes a wearable gadget coordinates into a driver's headset, making it comfortable and helpful for amplified utilize. Upon identifying signs of weariness, the ASAS enacts an caution component that incorporates sound-related cautions, situate vibrations, and visual notices on the vehicle's dashboard. This multi-modal alarm framework guarantees that the driver is instantly informed, permitting them to require fitting activities to remain alert and caution. Besides, the framework can be customized to adjust its affectability based on person driver characteristics and inclinations. The equipment engineering consolidates low-power microcontrollers and wireless communication modules, making the framework energy-efficient and competent of genuine- time information transmission to a companion versatile application. The versatile app gives extra highlights such as real-time checking of the driver's physiological state, authentic information examination, and personalized weakness chance appraisal.

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vehicles, from individual cars to commercial transport, contributing to more secure streets and more watchful drivers.

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The Data Sync Hub project explores the intricate landscape of data management, storage, and security. It begins with an overview of the historical significance of data centers, emphasizing their central role in modern technology infrastructure. The project underscores the importance of secure and efficient data management, highlighting technical components such as a comprehensive block diagram illustrating the network architecture. A central focus is on unified storage, which provides file-based and block-based storage services across various protocols and devices. Security and real-time data synchronization are key considerations, with robust measures including access controls, authentication, encryption, and user isolation ensuring data integrity and confidentiality. The integration of LDAP and authentication tokens underscores the commitment to safeguarding sensitive information. Additionally, the project emphasizes real-time synchronization for seamless data propagation and high availability, enhancing user experience and minimizing disruptions. Versioning and conflict resolution mechanisms further reinforce data consistency and integrity, promoting a reliable and resilient data ecosystem. In conclusion, the Data Sync Hub project offers a comprehensive approach to data management, integrating technical excellence with security measures to meet the evolving needs of modern enterprises. With a focus on scalability, user education, and technical support, it presents a comprehensive solution to optimize data operations and drive business success.

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NAS Storage, Storage Clusters, Replication, Cloud Storage, Redhat Linux

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Safety is a paramount concern across all sectors, with fire safety measures becoming essential in every environment. Particularly in industrial zones, fire incidents are prevalent and often result in significant harm to both human lives and property. This review examines the primary causes of these fire incidents and evaluates the technological solutions currently in place to mitigate them. Presently, many safety protocols, enhanced by the Internet of Things (IOT), are implemented post-incident initiation. However, the system proposed in this paper focuses on preemptive fire safety protocols specifically designed for the fireworks industry. By identifying and addressing the foundational causes of fires, the system aims to prevent fire incidents before they occur. Such proactive measures could significantly reduce hazardous incidents and save numerous lives.

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Internet of Things (IoT), fire incidents, preemptive safety.

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Diabetic retinopathy (DR) is a significant consequence of diabetic condition (Hyperglycemia) and a primary factor in visual deficiency globally. Prompt identification and timely intervention play a pivotal role in averting vision impairment in individuals with diabetes. Lately, progress in deep learning techniques, notably Convolutional Neural Networks (CNNs), has showcased significant successes in various medical image analysis tasks, including the detection of diabetic retinopathy. This paper introduces an innovative method for automatically detecting diabetic retinopathy utilizing CNNs. Our proposed method involves preprocessing retinal fundus images to enhancing image contrast and reducing noise, followed by feature selection using a pretrained CNN architecture. The extracted features are then fed into a classification model for the recognition of Retinal disease in diabetes. We utilize a voluminous dataset of annotated retinal visuals to train & validate our CNN-based detection system, ensuring robust performance across diverse clinical scenarios. Experimenting the outcomes that showcase the efficiency of our approach in precisely achieving the desired results in detecting diabetic retinopathy, achieving cutting edge performance in sensitivity, specificity, and overall accuracy. Moreover, the proposed method exhibits robustness to variations in image quality and pathological characteristics, making it suitable for real-world clinical applications. In summary, our research underscores the promise of leveraging deep learning, particularly CNNs, as an invaluable resource in the prompt identification and treatment of diabetic retinopathy. Our proposed model offers potential for seamless integration into current healthcare infrastructure, enabling timely detection and intervention strategies to mitigate vision loss in individuals with diabetes.

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Diabetic Retinopathy, Convolutional Neural Network (CNN), Detection, Machine Learning

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 Abstract

Helmetron is a revolutionary system that aims to improve rider safety and enhance its features. This system is designed to prevent the vehicle from starting if the passenger is not wearing a helmet, thereby promoting helmet use and reducing the risk of accidents. In addition, the system has an accident detection feature that uses the GPS-GSM tracking system to notify people via SMS of the bike's location in the event of an accident. This enables emergency services to reach the scene of the accident quickly and provide timely assistance. The system integrates various sensors into the helmet and wirelessly sends data to a module connected to the bike's engine. The two modules (one on the helmet and the other on the bike) communicate via RF technology using a microcontroller. The smart helmet also can show directions and search for locations, and it uses a microphone and speakers for voice navigation via Google Maps. Users can use voice commands to control calls and messages, which encourages better interaction while riding.

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Helmetron: A Helmet-based Electronic and Monitoring Technology

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The growing utilization of liquefied petroleum gas (LPG) in both residential and commercial environments highlights the vital requirement for dependable gas detection systems to guarantee security. In this project, an Arduino-powered LPG gas detection system is designed and put into operation. This project's main goal is to create a reliable, affordable system that can identify LPG gas in the environment and send out early alerts to stop possible dangers. The device uses an Arduino microcontroller interfaced with a gas sensor to continuously monitor the quality of the air. When more than a certain amount of LPG gas is detected, the system sounds an alarm and initiates a notification system. Two crucial components of the project are the MQ series gas sensor and the Arduino Uno. The MQ series gas sensor, an Arduino Uno microprocessor, and an audiovisual alert system are important parts of the project. The Arduino processes the analog output from the gas sensor and subsequently regulates the alarm system. Furthermore, the system's user-friendly design makes it simple to calibrate and customize alarm thresholds. The effectiveness of the LPG Gas Detection System in quickly identifying LPG gas leaks has been shown by thorough testing in simulated real-world circumstances. The system is a workable way to improve safety in both home and commercial settings because of its low cost, ease of use, and dependability. By providing a workable answer to the urgent need for reliable LPG gas monitoring systems, this initiative advances the field of gas detection technology. The Arduino platform is a significant instrument for boosting safety and minimizing potential disasters linked with LPG gas leakage because of its versatility, which ensures adaptability to varied circumstances.

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Deep Learning, Computer Vision, Real-Time Object Detection, Video Analysis, Model Optimization

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 Abstract

In an period checked by advancing security dangers, the request for progressed reconnaissance frameworks has surged. To address this require, we propose the advancement of a Scaled down Observation Quadcopter (MSQ) planned for spry and tactful checking in different situations. The MSQ coordinating cutting-edge innovations to supply real-time airborne reconnaissance capabilities in both indoor and open air settings. Leveraging lightweight materials and compact plan, it guarantees maneuverability and unnoticeable operation, making it perfect for incognito observation missions. In conclusion, the Smaller than expected Reconnaissance Quadcopter speaks to a noteworthy progression in observation innovation, advertising flexible, productive, and inconspicuous observing capabilities to meet the requests of cutting edge security challenges.

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Micro Surveillance Quadcopter, Surveillance Technology, Aerial Surveillance, Real-time Data Transmission, Compact Design

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 Abstract

Parking management in big cities confronts significant challenges with the rise in vehicle populations amidst constrained parking capacities. This paper outlines the development and deployment of a Parking Management System leveraging image processing techniques. The Parking Management System aims to revolutionize parking space management by automating the detection of vacant and occupied parking spaces, thereby providing real-time parking data to both management units and end-users. It introduces an innovative integrated Parking Management System that employs real-time image processing. In concert with this, a user-friendly application crafted using MIT App Inventor allows users to view and reserve available parking spots. The hardware setup involves the installation of IP cameras above the parking lot, connected to a local network, along with an ATmega328 microcontroller facilitating communication with the processing unit. Software development utilizes Python, utilizing OpenCV for image processing and PySerial for serial communication. Additionally, the inclusion of the GSM module facilitates instant SMS notifications to users upon successful parking reservations, enhancing the user experience. This technology demonstrates a notable enhancement in parking efficiency, a decrease in the time spent searching for parking, and potential improvements in traffic flow. Preliminary outcomes reveal a high degree of accuracy in parking spot detection and user endorsement of the application's interface and reservation functionalities, offering a promising resolution to current parking management issues.

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PARKING MANAGEMENT SYSTEM

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In the modern battlefield environment, the need for advanced surveillance and reconnaissance methods has become paramount to ensure the safety and effectiveness of military operations. This project introduces a cutting-edge Military Spying Robot designed to meet these needs through a sophisticated integration of technology and robotics. The robot is equipped with an array of sensors, including a metal detector for uncovering buried explosives, a gas sensor to detect hazardous chemicals, a flame sensor to identify fire sources, and a night vision camera for enhanced visibility in low-light conditions. These capabilities allow the robot to perform critical tasks such as scouting and monitoring restricted areas, detecting and identifying explosive devices, and providing real-time feedback to military personnel, thereby reducing the risk to human lives. The design and development phase of the project focused on creating a robust, all-terrain robot that can navigate challenging environments while carrying its advanced sensor payload. Special attention was given to the robot's autonomy, endurance, and the reliability of its sensor data under various environmental conditions. Integration of a user-friendly control interface allows operators to remotely guide the robot, analyze collected data, and make informed decisions with greater speed and accuracy. Field tests conducted in simulated environments have demonstrated the robot's capability to detect metallic and non-metallic explosive materials, identify hazardous gases, and provide visual reconnaissance during day and night. These tests have also validated the robot's potential to significantly enhance military surveillance operations, offering a strategic advantage in detecting threats and safeguarding military personnel. This project not only contributes to the field of military robotics by introducing a versatile spying robot but also sets the stage for future advancements in autonomous surveillance technologies. The implications of this research extend beyond military applications, offering potential adaptations for disaster response, border security, and anti-terrorism efforts worldwide.

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MILITARY SPYING ROBOT

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An innovative technique to improve safety and efficiency in passenger transportation is the Automatic Railway Station Bridge Control System, which automates the operation of railway station bridges. Infrared (IR) sensors, microcontrollers, and geared motors are just a few of the contemporary technologies this system incorporates to enable smooth bridge opening and shutting in response to incoming trains. Key components of the system include a DC geared motor, IR sensor, ESP8266 controller, and L293D motor driver. The DC geared motor serves as the driving force behind bridge movement, while the IR sensor detects train activity. The ESP8266 controller acts as the central processing unit, executing commands based on sensor input, and the L293D motor driver facilitates motor control. The ESP8266 controller receives a signal from the IR sensor when it detects a train, and this signal causes the DC geared motor to turn on, opening the bridge so that traffic can pass safely. Upon the train's passage, the bridge automatically shuts. By reducing the chance of accidents and optimising pedestrian traffic flow, the technology puts safety first. The Automatic Railway Station Bridge Control System has several advantages, such as increased efficiency, increased safety, and the ability to monitor remotely. The solution guarantees a smoother travel experience for both passengers and train staff by automating bridge operation, hence reducing delays. All things considered, this system is a major improvement over existing train station infrastructure, providing a more user-friendly, effective, and safe approach to control pedestrian traffic.

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RAILWAY TRACK PEDESTRIAN CROSSING SYSTEM WITHOUT USING STAIRCASE

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In the ever-evolving landscape of education technology, the Real-Time Classroom Monitoring and Attendance System serves as an innovative solution to streamline attendance management and enhance classroom monitoring. This multifaceted system integrates QR code technology, Bluetooth communication, and Python-based processing to provide an efficient and automated approach to attendance marking, record-keeping, and communication. The system leverages a smartphone application equipped with a QR code reader, empowering teachers to effortlessly capture student attendance by scanning unique QR codes. The QR code data is transmitted via Bluetooth to an ATmega328 microcontroller embedded in the classroom. The microcontroller processes the information and communicates student names to a central processing unit, a PC, running a Python script. On the PC, the Python script manages attendance records, capturing periodic images of the classroom for additional monitoring. The attendance data is compiled into a secure and privacy-compliant CSV file, ensuring adherence to data protection regulations. The system incorporates robust error handling, user-friendly interfaces for both the smartphone app and PC, and scalable architecture to accommodate varying class sizes. At the end of each day, the system consolidates attendance data and classroom images. Leveraging a Python library, the system sends these updates to the Head of Department via WhatsApp, providing administrators with real-time insights into classroom activities. The integration of secure communication protocols and encryption safeguards student information, while thorough testing ensures reliability under different scenarios. Secure communication protocols and encryption protect student information, while thorough testing ensures reliability in various situations.

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REAL-TIME CLASS MONITORING SYSTEM AND ATTENDANCE SYSTEM

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This study explores Testto.in, a pioneering healthcare platform, which addresses the challenges of accessibility, affordability, and convenience in the medical testing landscape. Leveraging a blend of cutting edge technologies including Django, django allauth, Django, crispy forms, django easy maps, geopy, requests, and razorpay, Testto.in offers users seamless registration, authentication, and account management. The platform's integration of maps, accurate geocoding, and flexible appointment scheduling enhances user experience, particularly for doorstep testing services. By eliminating cut practices and ensuring transparent pricing, Testto.in sets new standards in fair competition and user centric healthcare delivery. This abstract highlights Testto.in's transformative impact on the healthcare sector, emphasizing its commitment to revolutionizing accessibility, affordability, and convenience for all.

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REVOLUTIONIZING HEALTHCARE: TESTTO.IN'S INNOVATIVE APPROACH TO ACCESSIBILITY, AFFORDABILITY, AND CONVENIENCE

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This paper addresses the critical issue of landmine clearance by proposing a robotic solution for safe and efficient detection and marking. Traditional methods, often involving manual detection or mine-sniffing dogs, endanger human lives. This research proposes a landmine detection and marking robot, remotely controlled from a safe distance via wireless technology. The robot employs a suite of sensors to identify and pinpoint the precise location of buried landmines. Metal detectors are a primary tool, but the design can incorporate additional sensors like ground penetrating radar or thermal imaging for improved accuracy across various mine types. The robot's navigation system is crucial. A four-wheeled, high-mobility suspension system allows the robot to traverse uneven terrain often found in minefields. This ensures thorough exploration and minimizes the risk of human deminers needing to access hazardous areas. Beyond detection, the robot will be equipped with a unique landmine marking system. This system could involve paint markers, flags, or even data transmission to a central control station for digital mapping of mine locations. This real-time data would be invaluable for both demining efforts and future land use planning. Creating a fully functional prototype that has these features is the ultimate objective. The prototype will utilize GPS for autonomous movement, allowing the operator to define search patterns and minimize manual control. This level of autonomy further reduces human exposure to danger zones.

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GPS, Landmine Detection, Landmine Marking system, Suspension System, Data Transmission System

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 Abstract

In this paper, we present an innovative Internet of Things (IoT) system designed specifically for retail applications, with a primary focus on enhancing operational efficiency and improving customer experience. Our system leverages NodeMCU as the central controller and integrates ESP32 for advanced human-following capabilities, offering a comprehensive solution to meet the diverse needs of modern retailers. Key features of our system include automatic billing and a human-following robot, both of which contribute to creating a transformative retail environment. The automatic billing component utilizes RFID technology and sensors to facilitate quick and precise transactions, streamlining the checkout process for both customers and retailers. NodeMCU, chosen for its enhanced connectivity and versatility, efficiently coordinates these functions, providing a robust platform for seamless billing processes. In addition to automated billing, our system incorporates a Bluetooth-controlled human-following robot powered by the ESP32 controller. This robot adds an interactive and futuristic element to the retail landscape, engaging customers in a novel and captivating manner. By intelligently tracking and following human movements, the robot enhances customer interaction and contributes to a more immersive retail experience. The integration of NodeMCU and ESP32 controllers addresses common challenges faced in the retail sector, offering improved connectivity, scalability, and advanced human-following capabilities. Real-world testing confirms the efficacy of our system, demonstrating its potential to redefine retail experiences through a combination of automated billing and cutting-edge human-following technology. By seamlessly coordinating these functionalities, our dual-controller system showcases the versatility and transformative impact of advanced IoT applications in the dynamic realm of retail. The result is an innovative and adaptable system poised to elevate efficiency, customer engagement, and overall retail experiences to new heights.

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RoboShop-Cart - An Intelligent Trolley

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