Introduction

This project explores the development of a snake robot prototype aimed at potential applications in disaster response. The focus lies on its motion control and image classification capabilities, setting a foundation for future advancements in autonomous navigation and casualty detection.

Project Philosophy

By fusing robotics with computer vision, this project seeks to create efficient and adaptable solutions for emergency situations. Snake robots, with their inherent flexibility and maneuverability, offer significant potential for navigating disaster-stricken environments, making them invaluable assets in search and rescue efforts.

Tech Stack

Summary

• Manual Motion Control: The current prototype features user-controlled joint angles for movement, simulating the agility of natural snakes.
• Image Classification Integration: The robot analyzes captured images and video in real-time to identify potential casualties, utilizing the Viola-Jones algorithm to enhance its situational awareness.
• Solid Foundation for Future Advancements: This project serves as a stepping stone toward developing autonomous control algorithms and more robust image classification systems.

Documentation

The project report includes comprehensive details on:

• Hardware Design: Considerations and processes using SolidWorks.
• Software Development: Implemented in MATLAB & Simulink.
• Image Classification Algorithms: Techniques employed for casualty detection.
• Simulation and Results:Analysis and outcomes of the project.

Introduction

This project introduces a Smart Fan that intelligently adapts its rotation and speed based on real-time environmental data and user presence. Leveraging computer vision for distance measurement and rotation angle optimization, LabVIEW automation, the system enhances user comfort while conserving energy.

Project Philosophy

The aim of this project is to integrate modern sensing technology into smart systems that dynamically respond to environmental changes. By utilizing computer vision, data acquisition technologies, and sensors (including one custom-built), this Smart Fan offers an innovative approach to personalized comfort and energy efficiency.

Tech Stack

Features

• Distance-Based Speed Control: Generates PWM signals based on the user's distance, enabling precise fan speed adjustments.
• Adaptive Rotation: Adjusts the fan's rotation angle according to the user's location within a 4-meter range.
• Environmental Sensing: Monitors and adjusts fan operation based on temperature and humidity.
• User-Friendly Interface: Provides manual control through LabVIEW for customizable settings.
• Energy Efficiency: Reduces energy consumption by dynamically adjusting fan operations.

Operation

The project report includes comprehensive details on:

• LabVIEW Integration: Processes inputs from three sensors using NI DAQ mx to adjust the fan speed, maintains connection with the programmable DC power supply, and provides a dashboard for real-time data and system status monitoring.
• Distance Measurement: The area of the bounding box around detected faces is used to estimate the distance to the user and determine the appropriate angles for fan rotation.
• Face Detection: Utilizes a Mediapipe model for face detection, processed in real-time with OpenCV using a webcam connected to the Raspberry Pi.

Introduction

This project involves the frontend development of the Inventory Management Subsystem for Company A, an enterprise focused on selling electronic items. The subsystem plays a crucial role in overseeing and controlling inventory-related activities, interfacing with other key components of the company's operations.

Project Philosophy

The aim of this project is to integrate modern sensing technology into smart systems that dynamically respond to environmental changes. By utilizing computer vision, data acquisition technologies, and sensors (including one custom-built), this Smart Fan offers an innovative approach to personalized comfort and energy efficiency.

Tech Stack

Features

• Distance-Based Speed Control: Generates PWM signals based on the user's distance, enabling precise fan speed adjustments.
• Adaptive Rotation: Adjusts the fan's rotation angle according to the user's location within a 4-meter range.
• Environmental Sensing: Monitors and adjusts fan operation based on temperature and humidity.
• User-Friendly Interface: Provides manual control through LabVIEW for customizable settings.
• Energy Efficiency: Reduces energy consumption by dynamically adjusting fan operations.

Operation

The project report includes comprehensive details on:

• LabVIEW Integration: Processes inputs from three sensors using NI DAQ mx to adjust the fan speed, maintains connection with the programmable DC power supply, and provides a dashboard for real-time data and system status monitoring.
• Distance Measurement: The area of the bounding box around detected faces is used to estimate the distance to the user and determine the appropriate angles for fan rotation.
• Face Detection: Utilizes a Mediapipe model for face detection, processed in real-time with OpenCV using a webcam connected to the Raspberry Pi.

Introduction

The ESP32 Smart Medibox is an innovative IoT-based system designed to improve adherence to prescribed medication schedules. This project leverages the ESP32 microcontroller along with various sensors, actuators, and communication modules to ensure timely reminders, environmental monitoring, and user interaction. The Medibox monitors environmental conditions such as temperature, humidity, and light intensity, while providing scheduled alerts for medication intake. With WiFi connectivity and customizable settings, this system enhances medication management through automation and remote access.

Project Philosophy

The ESP32 Smart Medibox integrates IoT technology with healthcare to enhance medication adherence and daily living. By automating medication reminders and environmental monitoring, the project promotes user health with minimal manual intervention. It aims to create a responsive, user-centric system that adapts to individual needs, offering a flexible and convenient solution for modern healthcare management.

Tech Stack

Features

• WiFi Connectivity: Enables remote monitoring and control via MQTT.
• Temperature & Humidity Monitoring: Utilizes the DHT22 sensor to measure environmental conditions.
• Light Intensity Measurement: Uses LDRs to adjust a servo motor based on ambient light.
• Servo Motor Control: Automatically adjusts a shaded window according to light conditions.
• Buzzer Notifications: Alerts users when it’s time to take medication.
• User Interaction: Includes buttons for confirming actions and canceling alarms.
• Customizable Settings:Allows parameters like the servo motor's angle to be adjusted via MQTT.

Operation

• Environmental Monitoring: The ESP32 continuously monitors temperature, humidity, and light intensity. If the temperature or humidity exceeds predefined thresholds, a buzzer alerts the user. Light intensity data is used to adjust the servo motor, which controls a shaded window to maintain optimal light conditions.
• Medication Alerts: Scheduled alerts for medication intake are managed by the ESP32, which triggers a buzzer at the specified times. Users can interact with the Medibox using buttons to confirm actions or cancel alarms.
• Remote Access & Control: With WiFi connectivity, users can remotely manage the Medibox, monitor its status, and adjust settings via MQTT. The Node-RED dashboard provides an intuitive interface for interacting with the Medibox from anywhere.

Introduction

This management system is designed for a private educational institution to streamline administrative tasks. Developed using C# Windows Forms in Visual Studio and backed by an SQL database, the system allows the institution to efficiently manage student, teacher, and class records. The primary goal is to automate and enhance administrative operations, ensuring accurate and secure record-keeping while improving overall efficiency.

Project Philosophy

The Student, Teacher, and Class Management System is designed to enhance efficiency and accuracy in educational administration. By automating routine tasks and centralizing record management, the system reduces manual errors and frees up staff to focus on educational quality. This project reflects the belief that streamlined data management leads to better decision-making and a more organized educational environment for all.

Tech Stack

Features

• Student Management:Add, update, and delete student records.
• Teacher Management: Add, update, and delete teacher records.
• Class Management:Add, update, and delete class details.
• Attendance Tracking: Maintain and monitor student attendance records.