Smart Surveillance Robot with Raspberry Pi Integration
- "Smart Surveillance Robot with Raspberry Pi Integration 100% completed
This project introduces a cutting-edge Smart Surveillance Robot seamlessly integrated with Raspberry Pi technology. The robot leverages the power of Raspberry Pi for advanced surveillance capabilities, combining mobility with intelligent monitoring. The integration of Raspberry Pi facilitates real-time data processing, enabling the robot to perform autonomous surveillance tasks efficiently. This project aims to enhance the field of robotic surveillance by providing a versatile and adaptable solution for various applications.
Features:
1.Raspberry Pi Integration: The core of the surveillance robot is powered by Raspberry Pi, enabling high-performance computing for real-time data analysis and decision-making.
2. Autonomous Navigation: Equipped with intelligent sensors and algorithms, the robot navigates autonomously, avoiding obstacles and efficiently patrolling predefined areas.
3. Live Video Streaming: The robot features a high-quality camera with live video streaming capabilities, providing a real-time visual feed to the operator or a remote monitoring station.
4. Remote Control and Monitoring: Users can remotely control and monitor the robot through a user-friendly interface, enhancing the flexibility and accessibility of surveillance operations.
5. Object Recognition: Utilizing image processing algorithms, the robot can identify and track objects of interest, providing enhanced situational awareness during surveillance missions.
6. Customizable Alert System: The robot is equipped with a customizable alert system that can trigger notifications based on predefined criteria, ensuring timely responses to potential security threats.
7. Long Battery Life: With an efficient power management system, the robot boasts a long-lasting battery life, enabling extended surveillance missions without frequent recharging.
8. Compact and Robust Design: The robot is designed to be compact and robust, making it suitable for deployment in various environments, including indoor and outdoor settings.
9. Open-Source Software: The project encourages collaboration and customization by using open-source software, allowing developers to modify and enhance the robot’s functionality according to specific needs.
10. Scalability: The architecture of the Smart Surveillance Robot is scalable, allowing for the integration of additional sensors and features to adapt to evolving surveillance requirement
IoT-Powered smart Helmet: Gas, Temperature, and GPS Detection System
- Project "IoT-Powered smart Helmet: Gas, Temperature, and GPS Detection System"
- Team Nipen Majumdar, Shajahan, Khadiza Noor, Partha Das
Features
– Equipped with advanced gas sensors for detecting and monitoring various gases, ensuring user safety in different environments.
Temperature Monitoring:
– Integrated temperature sensors provide real-time temperature data, helping users stay informed about their surroundings and avoid extreme conditions
GPS Tracking:
– Utilizes GPS technology to accurately track and record the wearer’s location, enhancing safety and enabling location-based services.
Real-Time Data Transmission:
– Enables continuous and seamless transmission of gas levels, temperature, and GPS data to a central server or cloud platform in real-time.
Wireless Connectivity:
– Utilizes IoT communication protocols to establish wireless connectivity, allowing for remote monitoring and control of the helmet’s functionalities.
Smart Alerts and Notifications:
– Issues instant alerts and notifications to the wearer or designated stakeholders in the event of abnormal gas levels, extreme temperatures, or specific location-based incidents.
Data Logging and Analysis:
– Logs historical data for gas levels, temperature variations, and location history, facilitating comprehensive analysis and trend identification.
User-Friendly Interface:
– Incorporates an intuitive and user-friendly interface, accessible through a mobile application or web portal, for easy configuration and monitoring.
Customizable Thresholds:
– Allows users to set personalized thresholds for gas levels and temperature, providing a customizable safety experience based on individual preferences and requirements.
By combining gas detection, temperature monitoring, and GPS tracking, the IoT-powered smart helmet provides a comprehensive safety solution for users in various environments.
IntelligentGuard: Advanced Deep Learning Doorbell with Secure User Recognition and Instant Notifications
We rely entirely on the internet for our daily activities in the modern world. The project’s goal is to give the user a straightforward, personalized piece of technology that will help him or her properly man- age visitors who come to the location. The primary goals of this project are to intelligently operate the doorbell and notify the user of visitors by sending an image. The project’s objectives are to discuss an inexpensive IOT-based doorbell with increased security features using the Raspberry Pi platform and various components. This new idea effectively incorpo- rates the security-related problem prevalent in the current system. The Raspberry Pi will check to see if the person’s image matches the stored images in the database; if it doesn’t, a photo will be taken. The inter- net of things is used remotely to see the activities outside the door and get notifications when visitors are near the doorbell. The user’s mobile device’s application receives the photographs through Wi-Fi. The Rasp- berry Pi maintains the information of allowed individuals in the user-fed database. The modern security doorbell has no interior wiring and re- quires a few installation tools. The camera sensor in this smart doorbell will detect visitors and proclaim their names if they are frequent visitors and their information is stored in the database; else, they will be treated as strangers. And the owner’s application will receive all of these details.
Enhancing Road Safety with an IoT-Based Eye Blink Monitoring System
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Implementing this IoT-based eye blink monitoring system to enhance road safety is a commendable initiative. Such a system can help detect driver drowsiness or fatigue, which are common causes of accidents. Here’s an outline of how the system can work:
Hardware Setup:
- Eye Blink Monitoring Device: Utilize an IoT-enabled device, such as a small camera or specialized eye-tracking sensor, to monitor the driver’s eye blinks.
- Data Processing Unit: Connect the eye blink monitoring device to a processing unit, like a microcontroller or a small computer, capable of analyzing the captured eye blink data.
- Communication Module: Incorporate a communication module, such as Wi-Fi or cellular connectivity, to transmit the data to a central monitoring system.
Data Collection and Processing:
- Eye Blink Detection: Use computer vision algorithms or machine learning techniques to analyze the captured images from the eye blink monitoring device and detect abnormal eye blink patterns.
- Data Analysis: Process the detected eye blink data in real-time to determine if the driver’s blink rate or patterns deviate from normal parameters, indicating potential drowsiness or fatigue.
Alert Generation:
- Thresholds and Alerts: Set thresholds for abnormal eye blink patterns based on research or established guidelines. If the monitored blink patterns exceed these thresholds, trigger an alert.
- Alert Mechanisms: Employ multiple alert mechanisms to ensure the driver is promptly notified. This can include audible alarms, visual indicators, haptic feedback, or even sending alerts to a mobile device or smartwatch worn by the driver.
Gradual Vehicle Slowdown:
- System Integration: Integrate the eye blink monitoring system with the vehicle’s control system to enable gradual vehicle slowdown when abnormal eye blink patterns persist.
- Vehicle Speed Control: Activate the braking or speed control mechanisms of the vehicle to reduce its speed safely. Gradual deceleration is essential to prevent abrupt stops that could cause accidents themselves.
- Emergency Stop: If the driver’s abnormal eye blink patterns persist or worsen despite the gradual slowdown, activate an emergency stop mechanism to bring the vehicle to a complete halt safely.
Data Logging and Analysis:
- Data Storage: Store the eye blink data and related events in a secure manner for future analysis and reporting.
- Data Analytics: Perform regular analysis on the collected data to identify patterns, trends, and potential improvements to the system’s accuracy and effectiveness.
User Interface and Monitoring:
- Dashboard: Develop a user-friendly dashboard or mobile application that provides real-time monitoring of the driver’s eye blink patterns, alerts, and vehicle status.
- Monitoring Center: Establish a centralized monitoring center where trained personnel can oversee multiple vehicles and respond to emergency situations if necessary.
Smart Shoes with IoT & ML for Health Monitoring and Customized Food Chart Based on BMI
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An IoT-based smart shoe for health monitoring can be a valuable tool for tracking various metrics related to physical activity and overall health.
Features of an IoT-based smart shoe for health monitoring could include:
Step Counting: The shoe can track the number of steps you take throughout the day, providing insights into your overall activity level.
Calorie Expenditure: Based on the steps taken and the distance covered, the shoe can estimate the number of calories burned, giving you an idea of your energy expenditure.
Foot Pressure Mapping: The shoe can measure pressure points and weight distribution while walking or running, helping to identify potential issues or imbalances in your stride.
Heart Rate Monitoring: Some smart shoes may include sensors that can measure your heart rate, providing insights into your cardiovascular health and exercise intensity.
Real-time Feedback: The shoe can connect to a smartphone app or other devices via Bluetooth or Wi-Fi, allowing you to receive real-time feedback on your activity level, progress, and performance.
- BMI measure: Take weight from every step and calculate BMI.
Based on the measured BMI, here are some general guidelines exam for a healthy diet using ML:
Underweight (BMI less than 18.5):
- Focus on nutrient-dense foods to increase calorie intake.
- Include lean proteins (chicken, fish, legumes), whole grains, healthy fats (avocado, nuts, olive oil), and a variety of fruits and vegetables.
- Consume regular meals and snacks to meet energy needs.
Normal Weight (BMI 18.5 to 24.9):
- Maintain a balanced diet with a variety of foods from different food groups.
- Include lean proteins, whole grains, healthy fats, fruits, and vegetables.
- Pay attention to portion sizes and listen to your body’s hunger and fullness cues.
Overweight (BMI 25 to 29.9):
- Focus on portion control and consuming fewer calories than expended.
- Increase intake of fruits, vegetables, whole grains, and lean proteins.
- Limit processed foods, sugary beverages, and high-fat foods.
Obese (BMI 30 or higher):
- Seek guidance from a healthcare professional or registered dietitian for a personalized plan.
- Emphasize a balanced diet with controlled portions and reduced calorie intake.
- Incorporate regular physical activity to support weight loss goals.
An Innovative ML-Enabled IoT Device for Soil Nutrients Monitoring and Crop Recommendation
Introducing the AgriSense Pro, an ML-enabled IoT device for soil nutrients monitoring, crop recommendation, and food health reporting, complemented by a user-friendly mobile app. This state-of-the-art solution harnesses the power of machine learning and IoT technology to revolutionize farming practices and optimize agricultural outcomes.
Key Features:
Soil Nutrients Monitoring: The AgriSense Pro device is equipped with advanced sensors that measure crucial soil parameters such as pH levels, moisture content, temperature, and nutrient concentrations. These sensors provide real-time data, allowing farmers to monitor soil health and make informed decisions regarding fertilization strategies and irrigation schedules.
Machine Learning Crop Recommendations: Leveraging machine learning algorithms, the AgriSense Pro analyzes data from soil sensors, historical crop information, weather conditions, and other relevant factors. By doing so, it generates personalized crop recommendations, including ideal planting times, suitable crop varieties, and precise fertilization plans. This empowers farmers to optimize their crop selection and improve overall productivity.
Food Health Reporting: The AgriSense Pro device goes beyond soil monitoring by ensuring the health and safety of harvested produce. By analyzing data from embedded sensors, it assesses nutritional content, pesticide residues, and potential contaminants in the food. This valuable information enables farmers to verify the quality of their crops, make informed decisions, and instill consumer confidence.
User-Friendly Mobile App: The AgriSense Pro comes with a user-friendly mobile application, compatible with both iOS and Android platforms. The app provides farmers with seamless access to their device and data, allowing them to remotely monitor and control their agricultural operations. Users can view real-time sensor readings, track historical trends, receive actionable insights, and access crop recommendations and food health reports, all within the app’s intuitive interface.
Real-Time Alerts and Notifications: The AgriSense Pro app delivers real-time alerts and notifications to farmers regarding critical conditions such as water stress, nutrient deficiencies, disease outbreaks, or adverse weather conditions. These timely alerts empower farmers to take proactive measures, mitigate risks, and minimize potential crop losses.
Data Visualization and Analysis: The mobile app offers comprehensive data visualization and analysis tools. Farmers can view sensor data in intuitive graphs and charts, identify patterns, and gain valuable insights into soil health, crop performance, and food quality. These features enable farmers to make data-driven decisions, optimize resource allocation, and improve overall agricultural efficiency.
Seamless Cloud Connectivity: AgriSense Pro securely connects to the cloud, ensuring seamless data synchronization and storage. This cloud connectivity enables farmers to access their data anytime, anywhere, from multiple devices. It also facilitates collaboration among farmers and agricultural experts, allowing for shared insights and improved farming practices.
The AgriSense Pro, coupled with its user-friendly mobile app, empowers farmers with valuable insights, promotes sustainable farming practices, and enhances crop quality. By leveraging the capabilities of ML and IoT, this innovative solution paves the way for increased agricultural efficiency, reduced environmental impact, and improved food security.
Digital Bangladesh Mela 2023
- Purpose Research, social awareness and sustainable development
- Date 26 Jan 2023
- Fund BUBT
- Thanks BUBT,BASIS, JU, Media, DU, BUET, A2i
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I attended the Digital Bangladesh Mela 2023 along with my five research team members. This event is known as the largest exposition in Bangladesh, dedicated to showcasing IT and ITES products and services. Its main objective is to foster collaborative relationships on both national and international levels, inviting trade delegations to witness the vast potential of the Bangladeshi ICT industry.
At the Digital Bangladesh Mela 2023, we presented our research projects to the attendees. Our projects covered a wide range of innovative ideas and technologies. These included an IoT-based health monitoring system, an automatic water pump controller with a reserve checker, a deep learning-based IoT system for remote monitoring and early detection of health issues in real-time, an IoT-based automatic pet feeder and water dispenser, a project focused on sexual harassment detection using a smart dress, and an IoT-based smart AC dimmer switch with an Android app.
During the event, visitors were curious and engaged with our projects. They went around, exploring the displays, and asked various questions about our research. It was a rewarding experience to see the interest and enthusiasm generated by our projects among the attendees.
Overall, our participation at the Digital Bangladesh Mela 2023 was a success, allowing us to showcase our research and contribute to the advancement of the ICT industry in Bangladesh. Finally we have achieved 5 national awards.
Raspberry Pi-based multipurpose calling bell controlled with android App
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This project presents an enhanced Raspberry Pi-based multipurpose calling bell system controlled by an Android application, allowing users to register with full information and providing personalized identification when the bell is triggered. The system aims to improve user identification and make it easier for the assistant to determine the caller’s identity and location.
The hardware component of the system consists of a Raspberry Pi, a Wi-Fi module, and a traditional calling bell mechanism. The Raspberry Pi is connected to the bell and hosts a server that communicates with the Android application over a Wi-Fi network.
The Android application is designed to facilitate user registration by providing a user-friendly interface for entering personal information such as name and room number. Upon registration, this information is stored in a database associated with the Raspberry Pi.
When a user triggers the bell using the Android application, the server on the Raspberry Pi retrieves the corresponding user information from the database. The calling bell rings with the user’s name and room number, providing a personalized identification signal.
The assistant can easily identify the caller by hearing the personalized identification from the calling bell. The user’s information, including name and room number, can be displayed on a connected screen or accessed through the Android application for quick reference.
The system ensures a secure and reliable communication link between the Android application and the Raspberry Pi, guaranteeing that the user’s information is transmitted and stored securely. Additionally, the system allows for easy management of registered users, enabling new registrations, updates, or deletions as necessary.
The enhanced Raspberry Pi-based multipurpose calling bell system provides an efficient solution for user identification and location determination. By incorporating personalized identification through user registration, the system streamlines the assistant’s ability to identify callers and improves overall operational efficiency.