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Item Neuronet-convergence of bci and networking technologies(NHCE, 2024) Nandini R- 1NH21EC103 Monisha M- 1NH21EC098 Abdul Khader - 1NH21EE003 Yashas R Yadav-1NH21EE126The NeuroNet project explores the seamless integration of Brain-Computer Interfaces (BCI) with networking systems to perform real-world software operations. By simulating EEG signals and classifying them based on frequency ranges, the system leverages a Python-based client-server model to execute tasks. These tasks demonstrate the potential of BCIs in enabling non-invasive and efficient control of digital environments. The project emphasizes the practical application of networking in processing brain signals, paving the way for enhanced automation, accessibility, and control across domains such as assistive technology and smart systems. Through the NeuroNet framework, we aim to highlight the transformative potential of brainwave-driven digital solutions. Network protocols: Exploring suitable protocols for reliable and efficient transmission of brain signal data and control commands over a network, considering factors like latency, bandwidth, and security. Client-server communication: Designing robust and scalable communication mechanisms between clients and the server, ensuring seamless data exchange and synchronization. Application development: Developing a diverse range of applications demonstrating the potential of the system, such as remote control of assistive devices, collaborative virtual environments, and neurogaming. Usability and evaluation: Conducting user studies to assess the system's usability, performance, and overall user experience, identifying areas for improvement and optimization.Item Real time bus seat matrix(NHCE, 2024) Sagar -1NH22EE410 Dhananjay R –1NH21EE029 Swarup M -1NH21EC085 Sumanth S - 1NH22EC413In the modern era, transportation is indispensable, yet challenges such as irregular bus schedules and overcrowding persist due to ineffective management systems. To tackle these issues, an advanced transportation solution is proposed, leveraging GPS-based tracking and sophisticated IR and pressure sensors for passenger counting. This innovative system offers commuters real-time visibility into bus locations and passenger occupancy levels via their mobile phones. By accessing this information, passengers can make informed decisions about their travel plans, choosing whether to wait for an upcoming bus or opt for the next available one. This not only reduces uncertainty and waiting times but also enhances overall commuting efficiency. Furthermore, the integration of GPS technology ensures accurate tracking of buses along their routes, promoting smoother coordination and timeliness. Meanwhile, the use of IR and pressure sensors provides approximate passenger counts, aiding in managing capacity and optimizing service delivery. The benefits extend beyond individual convenience to encompass broader urban mobility goals. By streamlining transportation operations and improving passenger experience, the system contributes to sustainable urban development. It fosters a more connected city environment where transportation networks are more responsive to commuter needs, ultimately enhancing the quality of urban life.Item Frugal fire detection and firefighting system(NHCE, 2024) Khadar Basha D -1NH22EE404 Kiran Kumar R -1NH22EE405 Pathakala R Saithilak -1NH22IS408 Pradeep S -1NH22IS409Firefighting is the process of putting out fires, such as when our robot mists the fire with water. A water tanker and a pump for water throwing are attached to the robotic vehicle. This project's goal is to present a firefighting system design based on Arduino. The open-source electronics platform Arduino is built on user-friendly hardware and software. By transmitting a set of instructions to the microcontroller, the Arduino board can be controlled. The goal is to create a remote-operated firefighting robot with Arduino technology. A micro controller called an Arduino is used to achieve the intended function. To stop fatalities, property damage, and environmental degradation, a firefighter robot suppresses and puts out fires. In emergency scenarios, firefighters might deploy this firefighting robot as a backup. A temperature, humidity, infrared, and flame sensor have all been employed to operate this robot. The fireplace is simultaneously detected by the flame sensor. The temperature humidity sensor transmits data on the local temperature and humidity, the gas sensor alerts the user to the presence of combustible gases, and the passive infrared sensor verifies the presence of a human. Both human and automatic control systems are capable of operating the robot.Item Frugal fire detection and firefighting system(NHCE, 2024) Khadar Basha D - 1NH22EE404 Kiran Kumar R -1NH22EE405 Pathakala R Saithilak -1NH22IS408 Pradeep S -1NH22IS409The Solar Power Grid EV Charging Station is an interdisciplinary project designed to harness renewable solar energy for charging electric vehicles (EVs) and powering other electrical devices. This innovative system integrates solar power generation, energy storage, and power management to deliver both direct current (DC) and alternating current (AC) outputs, catering to diverse energy needs. At its core, the system consists of a 12V DC solar panel connected to a charge controller, which stabilizes the voltage and prevents harmful fluctuations that could damage the battery. The battery serves as a reliable energy storage unit, supplying power through a toggle switch to either a DC or AC pathway, depending on the selected configuration. The toggle switch enhances the system’s flexibility by allowing seamless transitions between DC and AC outputs. For DC applications, the system incorporates a buck transformer to step down the voltage from 12V to 5V DC. This configuration powers a DC motor and a yellow indicator bulb, demonstrating the system’s capability to support low-voltage DC devices. The efficient energy conversion ensures that small-scale applications such as DC motors or LEDs operate without interruption or inefficiency. For AC applications, the stored 12V DC energy is routed through an inverter to convert it into 220V AC. This high-voltage output is suitable for powering standard household or industrial devices. The AC pathway includes an operational bulb to validate the system’s ability to deliver AC power and a red indicator light to signify active AC output. This dual- output design makes the Solar Power Grid EV Charging Station a versatile energy solution for diverse user requirements. A significant feature of the system is its charge controller, which plays a pivotal role in ensuring the safety and longevity of the battery. By maintaining a stable voltage, the controller not only prevents potential damage to the storage unit but also optimizes energy utilization. This ensures that the energy harvested from the solar panel is used effectively, minimizing wastage and enhancing overall system efficiency. This project exemplifies the potential of solar energy in creating sustainable power systems. By utilizing a modular design and renewable resources, the Solar Power Grid EV Charging Station aligns with global efforts to reduce dependence on non-renewable energy sources. Its adaptability and efficient energy management make it a viable solution for applications ranging from EV charging to powering household devices.Item Solar-Powered EV Charging Station with energy storage(NHCE, 2024) Karthik Satish Patil- 1NH22EE403 Santosh Kumar - 1NH22EE412 Thejas C- 1NH22EC415 Vishal Udgire - 1NH22EC416The growing need for more sustainable transportation options has placed hybrid solar electric vehicles, or HSEVs, at the top of the energy-efficient mobility innovation scenario. This introduces an advanced model of an HSEV with a high-efficiency electric motor, photovoltaic power generation systems, and regenerative braking systems to address major technological challenges in energy management and emission reduction. The model features a dual-battery configuration intended to enhance energy efficiency, with one battery supplying power to the motor and the other recharging via solar panels and regenerative braking mechanisms. The proposed system controls energy flow dynamically by utilizing real-time control algorithms that target constant performance under any conditions of driving. With solar as a green source of energy, the dependency on grid-based charging has been very much reduced, and chances of successful long-term operation of HSEVs in an urban and rural environment have increased manifold. The regenerative braking system further increases efficiency by converting kinetic energy during deceleration into electrical energy stored in the secondary battery for use afterwards. The simulation results also indicated a 15 percent improvement in energy efficiency and a 25 percent reduction in greenhouse gas emissions than conventional configurations of electric vehicles. The dual-battery setup and the adaptive energy control significantly enhance battery lifespan and operational reliability. This research describes the potential of HSEVs toward environmental sustainability and energy efficiency.Item Advanced driver: vehicle navigation system(NHCE, 2024) Buggesh -1NH22EE401 Sanjeevakumar N Pujari -1NH22EE411 Shashikiran - 1NH22IS411 Vinay Kumar K N -1NH22IS415The "Autonomous vehicle navigation, Bluetooth, and Voice control" project focuses on developing an intelligent vehicle system that combines advanced navigation, wireless connectivity, and voice command capabilities. The systems enables the vehicle to operate Access to an affordable and working healthcare system is one of the aspects by which a autonomously by utilizing sensors for path planning, obstacle detection, and real-time country can be classified upon. Today, healthcare in India has become of the fastest decision-making. Bluetooth integration provides a seamless wireless interface for remote growing sectors with respect to both employment and revenue control and monitoring, while the incorporation of voice recognition technology allows an abstract(synopses) not exceeding 1000 words, indicating hands free interaction , enhancing user convenience and accessibility. this hybrid control approach ensures flexibility, enabling the vehicle to adapt to various applications such as personal transport, industrial automation, and delivery systems.The autonomous vehicle navigation, Bluetooth, and voice control project combines cutting-edge technology technology to enhance the functionality and user interaction of autonomous navigation, Bluetooth communication, voice-based contralto create an intelligent and user- friendly transportation systems.Item Hybrid solar-powered electric vehicle(NHCE, 2024) Jayasourya U -1NH22EE402, S Suhel Ahmed - 1NH22EE409, Sanjay Sree Varshan S -1NH21ME065 Raghunanda K S - 1NH22ME410The "Autonomous Vehicle Navigation, Bluetooth, and Voice Control" project focuses on developing an intelligent vehicle system that combines advanced navigation, wireless connectivity, and voice command capabilities. The system enables the vehicle to operate Access to an affordable and working healthcare system is one of the aspects by which a autonomously by utilizing sensors for path planning, obstacle detection, and real-time country can be classified upon. Today, healthcare in India has become of the fastest decision-making. Bluetooth integration provides a seamless wireless interface for remote growing sectors with respect to both employment and revenue. control and monitoring, while the incorporation of voice recognition technology allows An abstract (synopsis) not exceeding 1000 words, indicating s hands-free interaction, enhancing user convenience and accessibility. This hybrid control approach ensures flexibility, enabling the vehicle to adapt to various applications such as personal transport, industrial automation, and delivery systems. The autonomous vehicle navigation, Bluetooth, and voice control project combines cutting-edge technologies to enhance the functionality and user interaction of autonomous vehicles. This project integrates three core components: autonomous navigation, Bluetooth communication, and voice-based control, to create an intelligent and user-friendly transportation system. The autonomous navigation system employs advanced sensors, machine learning algorithms, and real-time path-planning to ensure safe and efficient vehicle movement in diverse environments. Bluetooth technology is utilized for seamless connectivity, enabling remote control, data exchange, and integration with mobile devices. Additionally, voice control provides an intuitive interface for users, allowing commands such as start/stop, direction changes, and other vehicle operations without the need for physical input devices. This convergence of technologies aims to improve the accessibility, safety, and convenience of autonomous vehicles. The project demonstrates the potential for a more connected and intelligent transportation ecosystem, paving the way for smarter mobility solutions..Item Healers blend: integrating technology with allopathy, ayurveda, and homeopathy for a unified(NHCE, 2024) Khushi Patil-1NH21CS135 Kowta Srikari-1NH21CS138 Vaishnavi D –1NH21EE123 Vaishnavi Jb – 1NH21EE124The healthcare system often struggles with fragmentation, as traditional approaches like allopathy, Ayurveda, and homeopathy operate in isolation. The Healers Blend project seeks to bridge these gaps by creating a unified, technology-driven platform that integrates these practices, offering holistic and personalized care. In allopathy, the platform addresses mental health by detecting issues early through symptom analysis. It provides personalized lifestyle and dietary recommendations while ensuring medication adherence with automated SMS reminders. Ayurveda diagnostics, like Nadi Pariksha (pulse diagnosis), are modernized through sensor integration. The system analyses Ayurvedic parameters like Vata, Pitta, and Kapha, improving diagnostic accuracy and accessibility through data analytics. In homeopathy, an interactive chatbot collects detailed patient health histories, securely storing and sharing this data with practitioners. This ensures accurate diagnoses and tailored treatment plans, overcoming the challenge of incomplete records. by combining traditional methods with modern technologies like artificial intelligence, sensors, and data analytics, the Healers Blend platform delivers comprehensive, efficient, and patient-centric healthcare. It fosters collaboration between disciplines, addressing gaps in diagnosis and treatment while improving patient outcomes. This project represents a transformative step toward sustainable and integrative healthcare solutions.Item Multi-house water pump control system(NHCE, 2024) Nandini -1NH21EE071 Surya Kiran Kanagala -1NH21EE116 Naren Parasuraman -1NH21EC105 Naveen Kumar S - 1NH21EC107The escalating demand for efficient water management in residential areas, particularly in shared living scenarios, has given rise to the development of innovative and automated solutions. The Multi-house Water Pump Control System (MHWPCS) addresses a persistent challenge in shared rental accommodations: the equitable distribution of electricity costs associated with shared water pump usage. Designed to optimize water distribution while ensuring fairness and cost efficiency, this system integrates modern sensor technology, microcontroller-based control mechanisms, and IoT-enabled monitoring. The MHWPCS consists of a triad of core components: sensors, a central control unit, and actuators. Sensors are strategically deployed across the water distribution network to monitor key parameters such as water pressure, flow rate, and tank levels in real time. The data collected by these sensors is relayed to a central control unit powered by ad-vanced algorithms. These algorithms leverage machine learning and optimization tech-niques to dynamically adjust pump operations, ensuring the system adapts to fluctuating water demand patterns. This intelligent operation reduces energy consumption and op-erational costs, while meeting the water needs of individual households.Item Non-invasive breast cancer detection(NHCE, 2024)Breast cancer is one of the most common and life-threatening diseases affecting women globally, making early and accurate diagnosis vital for improving survival rates. Traditional diagnostic methods, such as mammography, biopsy, and physical examination, while essential, often face challenges like subjectivity, time consumption, and limited accuracy in certain cases, such as dense breast tissue. To address these issues, this project employs machine learning techniques to develop a reliable and efficient tumor detection system. Using Logistic Regression as the core algorithm, the project focuses on classifying breast tumors as benign or malignant. The Breast Cancer Wisconsin Dataset is utilized for training and testing the model, with preprocessing steps including handling missing values, normalizing features, and applying feature selection techniques such as Principal Component Analysis (PCA). These steps optimize model performance and ensure accurate predictions. The model is evaluated using metrics like accuracy, precision, recall, and F1-score, demonstrating its effectiveness in distinguishing between tumour types.A unique aspect of the project is the integration of real-time prediction capabilities, enabling healthcare providers to input patient data and receive immediate results. This approach minimizes delays in diagnosis and enhances decision-making, supporting personalized treatment strategies. Future enhancements, such as mobile applications and integration with IoT devices for real-time data collection, are proposed to extend the system’s utility and accessibility. By leveraging machine learning, this project aims to reduce diagnostic errors, improve early detection, and provide clinicians with reliable tools to enhance patient care. The findings lay a strong foundation for integrating technology into healthcare and advancing breast cancer diagnostics.Item Design of a power efficient 16-bit posit multiplier and its application in image contrasting(NHCE, 2024) Srinivas Abhinay Gandla – 1NH21EE112 Vishwass R - 1NH21EC188 Yashasvi Linga Reddy - 1NH21EC190The ever-increasing demand for energy-efficient computing has driven the need for innovative approaches to arithmetic logic design. Traditional floating-point arithmetic units, including multipliers and Multiply-Accumulate (MAC) units, have long been essential for applications ranging from artificial intelligence and machine learning to image processing and scientific computation. However, these units are inherently power-hungry and require significant silicon area, which limits their suitability for modern systems that prioritize energy efficiency. Addressing these challenges, this project focuses on the design and implementation of a 16-bit posit multiplier. The posit number system, a promising alternative to IEEE-754 floating-point representation, offers a highly flexible structure that dynamically adjusts precision based on input data. This characteristic allows the posit architecture to achieve a superior trade-off between computational accuracy, power efficiency, and hardware area, making it an ideal candidate for next-generation arithmetic designs.Item Autonomous vehicles – path planning and trajectory optimization(NHCE, 2024) Sreejesh S –1NH21EE111 Manasa Vt –1NH21EE054 Jugal Jishnu A S - 1NH21ME029 Dhanush G Muthu-1NH21ME018Autonomous vehicles (AVs) represent a transformative technology in intelligent transportation systems, relying heavily on the interplay of path planning and trajectory optimization to navigate complex and dynamic environments safely, smoothly, and efficiently. Path planning determines a feasible route from a starting point to a destination while accounting for obstacles, traffic regulations, road constraints, and environmental dynamics. Trajectory optimization refines this planned route by defining precise vehicle motions, such as speed, steering angle, and acceleration, to ensure safe, smooth, and energy-efficient execution that respects the vehicle's kinematic and dynamic constraints. These processes are interdependent, with path planning providing a high-level route and trajectory optimization ensuring that the route can be executed in real time. Autonomous vehicles rely on a range of sensors, including LiDAR, cameras, radar, GPS, and inertial measurement units (IMUs), to perceive and understand their surroundings. The data gathered is continuously processed to generate an accurate environment map, which informs both path and trajectory planning. Algorithms such as A* and Dijkstra’s are commonly employed for global path planning to compute optimal routes on high-level maps. Locally, machine learning techniques, such as behavioral cloning, reinforcement learning, and imitation learning, are used to adaptively refine trajectories and improve decision-making. Behavioral cloning allows AVs to emulate human-like driving by learning from human driving data, enabling natural and intuitive navigation even in challenging scenarios such as urban intersections or minimal-map environments.Item Eco-power monitor(NHCE, 2024) Shravani S - 1NH21EE108 Spoorthi R -1NH21EE11 Nikitha R - 1NH21IS102 Neelam Tusshar -1NH21IS098The Eco Power Monitor is an advanced solar energy monitoring and management system that integrates IoT and machine learning to optimize energy use. Built around ESP32 microcontrollers, the system interfaces with sensors to monitor solar energy generation, household consumption, and environmental factors like temperature and humidity. Realtime data on voltage, current, and energy usage is stored in an SQLite database, ensuring efficient handling of both real-time and historical information.The hardware includes relay modules for seamless switching between solar power, batteries, and grid energy, optimizing resource utilization based on demand. The software component features a Python Flask application and a machine learning model, leveraging regression algorithms to forecast daily energy demand. This enables efficient energy distribution by prioritizing solar power, conserving battery reserves, and minimizing reliance on the grid. A user-friendly web interface built with HTML, CSS, and JavaScript displays real-time energy data, consumption trends, and actionable insights. Users can monitor energy metrics, make informed decisions, and optimize usage with the help of dynamic visualizations. The system’s predictive capabilities, driven by machine learning, automate energy sourcing decisions, reducing energy wastage and costs while promoting sustainability. The SQLite database ensures reliable data storage and fast retrieval, supporting the system’s scalability for larger deployments. By combining IoT-driven data acquisition, machine learning analytics, and real-time energy management, the Eco Power Monitor aligns with global sustainability goals. It addresses challenges like inefficient energy use and dependency on non-renewable sources, providing a scalable, cost-effective solution for residential and industrial applications.Item Smart glasses for visually impaired(NHCE, 2024) Shankaranand A Mahale-1NH21EE105 Harsh Magotra -1NH21EC065 Prateek Vashisht-1NH21EC120 Shashank -1NH21EE106One cutting-edge assistive device that has been developed to improve the mobility and freedom of people with visual impairments is smart glasses. This project makes use of state-of-the-art developments in sensor, embedded, and machine learning technologies to produce a small, wearable device that offers real-time support. The YOLOv8 (You Only Look Once, version 8) machine learning algorithm, a cutting-edge object detection model renowned for its accuracy and speed, is at the core of the system. Utilizing the Raspberry Pi OS to facilitate smooth hardware component integration, YOLOv8 is deployed on a Raspberry Pi platform. Long-term user comfort is guaranteed by the smart glasses' lightweight and portable design. The system also allows for modification to meet user demands, such as changing language preferences and audio feedback loudness. Because of its effective architecture, the YOLOv8 algorithm can operate on the Raspberry Pi's constrained CPU resources without sacrificing performance, making it an affordable option for widespread use. During testing, the smart glasses showed excellent object detection and obstacle avoidance accuracy in a variety of settings, such as open spaces, inside spaces, and city streets. In order to address the mobility issues that visually impaired people confront, YOLOv8 and ultrasonic sensors were integrated in a synergistic way. Additionally, the system's use of open-source software and hardware guarantees scalability and cost, promoting broad accessibility.This study demonstrates how integrating embedded technologies and artificial intelligence may solve problems in the real world. These smart glasses open the door to a more inclusive society by giving visually impaired people better situational awareness. In order to further increase the usefulness and user base of this ground-breaking invention, future iterations of the project plan to include sophisticated capabilities like voice commands, GPS navigation, and support for other languages.Item Agrismart rob spider for spraying and weed cutting(NHCE, 2024) Samuel W S -1NH21EE101 Sandeep R Naikar -1NH21EE103 Sanketh Sheshannanavar-1NH21EC138 Soham Bag – 1NH21EC153This project aims to design and develop a prototype of an eight-legged walking robot inspired by the Theo-Jansen mechanism, which mimics nature’s ability to navigate rough terrains efficiently. The robot uses an eight-bar linkage system to replicate the motion of legged creatures, providing superior mobility in environments where wheeled or tracked vehicles are impractical. The Theo-Jansen mechanism, typically consisting of a 13-bar linkage, is adapted to create a functional walking robot with minimal actuators for low power consumption and reduced weight. The primary objective is to demonstrate the feasibility of creating a manually controlled, off-road walking robot with enhanced agility and durability, suitable for challenging terrains such as extraterrestrial landscapes or hazardous environments like nuclear reactors. This project contributes to advancing legged robotic systems for various practical applications.Item Prototyping a smart medication management system with machine learning-based dosage recommendations(NHCE, 2024) S.Kavya -1NH21EE098 Syeda Mehak Fathima- 1NH21EE118 G. Ravi Kishore -1NH21CE020 M.Gautham Reddy-1NH21CE031Medication management is a critical component of healthcare that directly influences the effectiveness of treatment and patient outcomes. Despite significant advancements in medical technology, many patients still face challenges in adhering to prescribed medication regimens. This project addresses the pressing need for an innovative solution to optimize medication management by leveraging the power of machine learning (ML). The proposed solution is the development of a Smart Medication Management System (SMMS) that uses ML algorithms to recommend personalized medication dosages based on individual patient data, such as age, weight, medical history, and other relevant health factors.The primary goal of this project is to design and prototype an intelligent system capable of providing accurate, real-time dosage recommendations tailored to each patient. The system aims to minimize the risks associated with under or overdosing, reduce medication errors, and improve patient compliance with prescribed treatment plans. By utilizing a data-driven approach, the system can adapt to changes in a patient’s health status over time, ensuring that medication regimens remain optimal and effective. Furthermore,the systemintegrates medication reminders, alerts for potential drug interactions, and feedback on adverse effects, offering a comprehensive solution for both patients and healthcare providers.Machine learning models, particularly supervised learning techniques, form the backbone of the system’s decision-making process. These models are trained on extensive datasets, including medical records and drug information, allowing the system to analyze various factors affecting medication dosages. By using historical health data and real-time inputs, the system can predict the most suitable medication dosages and automatically adjust them as needed. The continuous learning aspect of machine learning ensures that the system’s recommendations improve over time with more data, leading to better decision-making and more personalized treatment.Item Intelligent road safety system(NHCE, 2024) Sajda Zulfaquar-1NH21IS132 Vinutha BA- 1Nh21IS180 Rishab R- 1NH21EE097 Shashank B-1NH21EE107The healthcare system often struggles with fragmentation, as traditional approaches like allopathy, Ayurveda, and homeopathy operate in isolation. The Healers Blend project seeks to bridge these gaps by creating a unified, technology-driven platform that integrates these practices, offering holistic and personalized care. In allopathy, the platform addresses mental health by detecting issues early through symptom analysis. It provides personalized lifestyle and dietary recommendations while ensuring medication adherence with automated SMS reminders. Ayurveda diagnostics, like Nadi Pariksha (pulse diagnosis), are modernized through sensor integration. The system analyses Ayurvedic parameters like Vata, Pitta, and Kapha, improving diagnostic accuracy and accessibility through data analytics. In homeopathy, an interactive chatbot collects detailed patient health histories, securely storing and sharing this data with practitioners. This ensures accurate diagnoses and tailored treatment plans, overcoming the challenge of incomplete records. By combining traditional methods with modern technologies like artificial intelligence, sensors, and data analytics, the Healers Blend platform delivers comprehensive, efficient, and patient-centric healthcare. It fosters collaboration between disciplines, addressing gaps in diagnosis and treatment while improving patient outcomes. This project represenItem Real-time pothole detection and automated refilling with auto-swift using machine learning and computer vision(NHCE, 2024) Gowthami A -1NH21EE033 Raksha Shankar Kajagar- 1NH21EE092 Madhura N B -1NH21IS086 Mahima Ramdas Bhat- 1NH21IS088The increasing demand for efficient road maintenance has highlighted the issue of potholes, which affect safety, vehicle performance, and transportation efficiency. Traditional pothole detection and repair methods are slow, labor-intensive, and costly. To address these challenges, this paper presents an Autonomous Pothole Detection and Repair Bot, an automated system that integrates robotics, Al, computer vision, and automated repair technology to provide real-time, cost-effective road maintenance with minimal human intervention. The system consists of three core subsystems: pothole detection, autonomous navigation, and automated repair. The detection subsystem uses advanced computer vision and deep learning models, particularly Convolutional Neural Networks (CNNs), to identify and analyze potholes in real-time. High-resolution cameras capture road surface data, which is processed by machine learning algorithms to detect and measure defects. The navigation subsystem employs SLAM algorithms and GPS for real-time mapping and path optimization. The bot's mobility platform ensures stability on uneven surfaces, while energy-efficient batteries power its operations. Once a pothole is detected, the repair subsystem activates, deploying a material dispensing and leveling mechanism. Using materials like cold mix asphalt or polymer compounds, the bot fills the pothole with precision and compacts the material to ensure durability. Surface profiling sensors validate the repair quality. An IoT architecture enables real-time monitoring and data sharing, allowing for predictive maintenance and efficient resource allocation. The system offers several advantages over traditional methods, including eliminating human labor in hazardous environments, faster repair times, and minimized waste. The modular design makes it adaptable to various road conditions, and its use of eco-friendly materials supports sustainability goals. Field trials demonstrated high detection accuracy (over 95%) and improved speed and cost-effectiveness, making the bot a promising solution for smart cities and global road infrastructure management.Item Smart lake monitoring system(NHCE, 2024) Raghu Nandan K S - 1NH21EE091 Salanke Anni Rao-1NH21EE100 Chandan Gowda H S - 1NH21IS040 Chinmay K R -1NH21IS044The Smart Lake Monitoring System is an IoT and cloud-based application, which deals with the solution of fire safety and environmental issues in the lake areas. Strategically placed smoke and temperature sensors detect anomalies that indicate the possibility of fire hazards. Data is transmitted in real-time to a central unit through Wi-Fi and then processed using Amazon Web Services for efficient analysis and generation of alerts. This may make early warnings through GSM modules sending in very short messages to the worried group of people concerned like in the locality and other bodies of environment for prompt interventions and responses. Relatively very easy to establish at considerably smaller cost and scale unlike traditional systems. Key Features: 1. Real-Time Monitoring: Temperature and smoke are always monitored for early alerts in case of fire. 2. AWS Cloud Integration: Inclusive data processing and safe storing of historical data through the provision of AWS services. 3. Rapid Alert Mechanism: Immediate SMS alert to stakeholders regarding urgent situations for prompt response to emergencies. 4. Cost-Effectiveness: The cheaper sensors and a simple design will reduce the possible installatory or even maintenance costs. 5. Scalability: The modular design can easily scale and adapt to any environment. 6. Community Awareness: Enhances the community to possess a safe and green environment.Item Design and implementation of battery management system with charge monitor and fire protection for EV(NHCE, 2024) Uday A Kammar-1NH21EE122 R Puneeth Kumar-1NH21EE090 S Naveen -1NH21ME062 Harsha B-1NH21ME022For electric vehicle (EV) battery management systems (BMS) to ensure battery performance, longevity, and safety, temperature regulation is essential. Poor temperature regulation can lead to problems such overheating, delayed cold charging, energy drain, efficiency loss, and cell imbalance. Monitoring the voltage, current, and temperature of individual cells as well as using microcontrollers or System-on-Chips (SoCs) for real-time data processing can help to lessen these difficulties. Determining the battery's State of Health (SoH) and State of Charge (SoC) aids in maximizing performance and averting harm. In order to address temperature regulation and battery safety, this article suggests a comprehensive BMS that includes advanced thermal management and fire protection capabilities. The technology uses thermal insulation and cooling techniques to make sure the battery runs in ideal thermal conditions. Furthermore, fault detection techniques improve battery protection by identifying hazards like short circuits or overheating. The suggested system's testing demonstrates increased charging efficiency, EV safety,and battery longevity. This design makes EV batteries safer, more dependable, and more efficient by addressing important temperature and fire safety concerns. It provides a framework for sustainable and sophisticated battery management systems, which are essential for the development of electric vehicles in the future.
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