Anti Sleeping Alarm for Drivers using GSM Module
Optimization of Waste Classification System: Leveraging Sensor Technologies for Advanced Segregation and Waste Reduction
Designing a Hybrid Technology For Real-Time Driving Monitoring System
Intelligent Waste Management System for Smart Cities
Smart Dual-Compartment Waste Separator Bin For Sustainable Waste Management
Verilog Based UART System Design
Intel ® Processor Architectural and Integrated Development Environment Exploration
IoT based Smart Agriculture Monitoring Framework with Automation
Designing of an Embedded system for Wireless Sensor Network for Hazardous Gas leakage control for industrial Application
An Integrated Model of Digital Fuel Indicator and GPS Tracking System for Vehicles
Hardware Implementation of Artificial Neural Networks
Fault Analysis on Grid Connected MPPT BasedPhotovoltaic System
High Efficiency Hybrid Intelligent Street Lighting Using A Zigbee Network And Sensors
Design of Dual-Band Bandpass Filter Using Interdigital Embedded Open Loop Triangular Resonator Loaded with Stubs
License Plate Localization Using Novel Recursive Algorithm And Pixel Count Method
Driver fatigue is a major contributor to road accidents, frequently leading to serious outcomes. This project suggests an anti-sleep alarm system for drivers, incorporating a GSM module to improve road safety. The system monitors the driver's state of awareness and triggers warnings when signs of fatigue are identified. It employs a combination of sensors, such as an eye-blink sensor or head position detector, to continuously assess the driver’s state. When the system identifies fatigue, it triggers a sound alarm. Additionally, the integrated GSM module sends an alert message to a preconfigured emergency contact, providing real-time updates about the driver's status and location. This dual-layer alert mechanism ensures timely intervention and `reduces the risk of accidents. The system is affordable, simple to install, and appropriate for both commercial and personal vehicles, providing an effective solution to enhance driver safety on the road.
Taking inspiration from the Swachh Bharat Mission, this project focuses on designing a Smart Waste Segregation System to promote cleanliness and environmental sustainability. Smart solutions to waste management can significantly boost productivity as technology continues to advance. This system uses an Arduino Uno microcontroller to detect and classify waste into three categories: wet, dry, and metallic. Other components include IR sensors, proximity sensors, rain drop sensors, and stepper motors. The automated system makes sure that waste is separated and disposed of in the right way, reducing environmental pollution and health risks from improper waste disposal. Additionally, the system regularly logs waste data, making it useful for analysis and monitoring. By making proper waste disposal accessible to individuals from all economic backgrounds, this cost-effective and scalable solution not only enhances waste management processes but also contributes to a cleaner and healthier society.
This research analyzes a driving monitoring system built with hybrid technologies like yolo, cnn, haar cascade by integrating IoT sensors, computer vision, AI, and embedded systems to assess the driver's actions alongside vehicular and environmental conditions in real- time. By leveraging AI-based deep learning techniques such as CNN and Haar Cascade, the system accurately detects driver fatigue, drowsiness, and distraction. IoT sensors enhance accuracy by capturing physiological and vehicular movement patterns, employing both wearable and non-wearable methods to comprehensively analyze driver behaviour and issue proactive accident- prevention measures. The study explores emerging AI-driven driver monitoring technologies, emphasizing the benefits of AI-embedded detection models with Arduino Uno for efficient sensor data processing. Additionally, it addresses challenges related to data quality, computational power, and system integration while discussing potential solutions. The conclusion provides recommendations for further research in developing advanced real-time monitoring systems to enhance road safety.
This manuscript presents the design and development of an Intelligent Waste Management System for Smart Cities, integrating Internet of Things (IoT), artificial intelligence (AI), and sensor-based automation to revolutionize urban waste disposal. The system employs smart bins equipped with real-time sensors to monitor waste levels, segregate waste at the source, and optimize collection routes using AI-driven predictive analytics. Additionally, it facilitates waste-to-resource conversion through automated composting and energy generation techniques, enhancing sustainability. IoT-enabled dashboards provide municipal authorities with real-time insights, ensuring efficient waste collection, reducing operational costs, and minimizing environmental impact. Privacy and ethical concerns regarding data collection are addressed through secure cloud-based encryption and compliance with smart city data regulations. By promoting a cleaner, more efficient urban ecosystem, this system contributes to sustainable waste management, circular economy principles, and smart city advancements.
In cities everywhere, trash is typically collected in a single bin that contains a mix of food scraps, paper, plastic, and everything else, all mixed together. This makes recycling difficult and causes landfills to overflow faster than they should. Valuable materials get wasted, and the environment suffers. Manual sorting isn’t much better. It’s messy, slow, and often inaccurate. Workers are exposed to unhygienic conditions, and mistakes in sorting can contaminate entire batches of recyclable waste. It’s simply not a scalable solution. What’s missing is a more innovative, low- cost way to separate waste, right when it’s thrown away automatically. That's where the Smart Dual-Compartment Waste Separator Bin comes in. This intelligent system utilizes moisture and ultrasonic sensors to detect whether an item is wet (such as food scraps) or dry (like paper or plastic). An Arduino microcontroller processes the data and activates a servo motor to direct the waste into the correct compartment, eliminating the need for user input. The ultrasonic sensor also monitors the level of each bin and sends alerts when it’s nearly complete. Prototype testing showed over 90% accuracy in sorting, and volume estimates were reliably within a 5% margin. The hardware is modular and includes built-in error handling, making it easy to upgrade with features like metal detection, AI-based material recognition, or even integration with municipal waste systems. In short, this bin offers a practical, scalable way to make waste sorting cleaner, easier, and more efficient, right at the source.
