The smart medicine box contains multiple compartments for storing different tablets. Users or caregivers can program the medicine name, dosage quantity, and medication timings through a web page or mobile application. The scheduled information is stored and monitored by a Raspberry Pi, which acts as the central controller of the system.

IR sensors are placed inside the medicine compartments to detect whether the medicine has been taken. At the scheduled time, the system provides an audible reminder through the APR33A3 voice playback module, announcing the medicine name and dosage details. If the medicine is not removed from the compartment within the specified time, the system can generate repeated alerts to ensure compliance.

The medication data, reminder schedules, and status of medicine intake are monitored through a mobile application, allowing caregivers and family members to track the patient’s medication adherence remotely. This smart system improves medication management, reduces the chances of missed doses, and enhances patient health and safety.

The major features of this project are:

  To remind patients to take medicines at scheduled times.

  To monitor medicine intake using IR sensors.

  To provide voice-based medicine alerts through the APR33A3 module.

  To enable remote monitoring through a mobile application.

  To reduce missed doses and improve medication adherence.

  To assist elderly people and patients in managing their medications effectively.

  To maintain a record of medicine consumption for better healthcare monitoring.

The major building blocks of this project are:

1. Power supply from adapter
2. Raspberry Pi zero 2W.
3. IR Sensors
4. Wi-Fi
5. APR33A3 Voice Module with Speaker.
6. LCD display.

Software’s used:

1. Raspbian OS.
2. Python programming.
3. Express SCH for Circuit design.

Block Diagram:

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Machine learning models analyze historical and real-time weather data, including rainfall predictions, temperature, and humidity, to make intelligent irrigation decisions. The Raspberry Pi Zero 2W acts as the central controller, processing sensor data and triggering the irrigation system accordingly.

The proposed system significantly enhances irrigation efficiency, reduces water wastage, and improves corn crop yield. By leveraging machine learning, this system contributes to sustainable agriculture and resource conservation, ensuring a cost-effective and eco-friendly irrigation solution.

Features:

• Automatic Watering – The system waters the corn crop only when needed using sensors and a water pump.
• Smart Weather Prediction – Uses machine learning to check rainfall, temperature, and humidity before irrigating.
• Soil Moisture Check – Sensors detect soil dryness and trigger irrigation only if necessary.
• Live Updates on LCD – A display shows real-time moisture, weather, and system status.
• Energy Efficient – Saves electricity by running the pump only when required.
• Designed for Corn Crops – Optimized specifically for corn but can be adjusted for other crops.
• Water Conservation – Uses only the necessary amount of water, preventing wastage.

The main blocks of this project are:

1. Power supply.
2. Raspberry pi zero 2W.
3. AC water Motor with relay driver.
4. Soil moisture sensor.
5. LCD display.
6. SD card.

Software used:

1. Embedded Linux programming.
2. Express SCH for Circuit design.
3. Python Language.
4. Machine learning.

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The Raspberry Pi Zero 2W is programmed using Python, with a smart control algorithm that processes user commands from the web interface and controls the robot accordingly. By utilizing solar energy as its primary power source, the robot ensures sustainable farming while reducing operational costs. This automated system helps improve agricultural productivity by automating repetitive and hazardous tasks, significantly reducing manual labor, and optimizing time management. With its web-based remote control, farmers can manage their fields conveniently without being physically present, making this project a modern, efficient, and eco-friendly solution for smart agriculture.

The main objectives of the project are:

• Automate weed cutting using a grass-cutting mechanism.
• Automate pesticide spraying to reduce human exposure to chemicals.
• Use solar energy for sustainable and eco-friendly operation.
• Enable remote control and monitoring through a web interface.
• Provide real-time video streaming using a Pi Camera.

The major building blocks of this project are:

1. Solar.
2. Charging circuit.
3. Battery Power Supply.
4. LM2596.
5. Raspberry Pi Zero 2W.
6. Pi Camera.
7. DC motors with driver.
8. Grass cutter.
9. Relays.
10. Pesticide motor.
11. Servo motor.
12. LED indicators.

Software’s used:

1. Python language.
2. Express SCH for Circuit design.

video:

Upon detecting an approaching vehicle, the ultrasonic sensors trigger the signal lights, alerting pedestrians to wait for a safe crossing opportunity. The system also includes a DC motor driver and motors for mobility, enabling the robot to navigate and position itself at crossing points. This innovative approach aims to enhance pedestrian safety by providing real-time assistance, thereby reducing accidents and improving traffic management. The project showcases a practical application of robotics and smart technology in urban environments, emphasizing the importance of safety in public spaces.

The objectives of the project include:

1. Design a road crossing robot.
2. Obstacle/object presence detection using Ultrasonic sensor.

The major building blocks of this project are:

1. Battery Power Supply.
2. PIC Micro Controller.
3. DC Motor with l293d motor driver.
4. Two Ultrasonic sensor.
5. Pi camera.
6. Pi zero 2w.
7. Signal lights.
8. Reset Button.
9. Crystal oscillator.
10. LED indicators.

Software’s used:

1. PIC-C compiler for Embedded C programming.
2. PIC kit 2 programmer for dumping code into Micro controller.
3. Express SCH for Circuit design.
4. Raspbian OS.

Block Diagram:

video:

The Raspberry Pi Zero 2W runs a lightweight CNN model (SEmbedNet or LMUEBCNet) trained for ectopic beat classification using preprocessed ECG spectrograms generated via Continuous Wavelet Transform (CWT). The system classifies heartbeats into categories defined by the ANSI/AAMI EC57 standard, including normal and various arrhythmic conditions (N/S/V/F/Q). The classification result is displayed on an LCD, while the raw ECG signal and classification status are simultaneously visualized on a web dashboard via IoT (e.g., Flask, MQTT, or HTTP server).

The proposed system eliminates the need for cloud connectivity, offering real-time, on-device inference with low latency (~239 ms) and ultra-low power consumption (~0.4 W), making it ideal for wearable and portable health monitoring applications. This solution can effectively transform basic ECG monitors into intelligent diagnostic tools with arrhythmia classification capabilities.

The Arduino UNO is used for monitoring critical health parameters, including ECG, body temperature, and heart rate using the MAX30100 sensor, which measures heart rate and temperature. The Arduino converts the analog sensor data into digital signals, which are then transmitted to the Raspberry Pi through serial communication for further processing.

Additional components include a fall detection sensor to monitor and detect any falls, triggering an emergency alert if necessary. The LCD display provides users with real-time feedback on their health metrics, while the Buzzer alerts users to any abnormal health conditions or emergencies, ensuring immediate attention. An SD card is integrated into the system to store sensor data securely, allowing for later retrieval and analysis. This IoT Thingspeak cloud-based health system combines continuous monitoring, predictive analytics, and emergency prediction to offer timely interventions and improve overall patient care.

The main objectives of the project are:

1. Develop a compact and low-power arrhythmia detection system using Raspberry Pi Zero 2W and lightweight CNN models (SEmbedNet or LMUEBCNet) for real-time classification of ECG signals.
2. Acquire and process multi-parameter health data (ECG, body temperature, heart rate, and SpO₂) through Arduino Uno and additional sensors, ensuring accurate digitization and reliable transmission to the Raspberry Pi for analysis.
3. Enable on-device machine learning inference without cloud dependency, ensuring low-latency (~239 ms), ultra-low power consumption (~0.4 W), and suitability for wearable/portable health monitoring devices.
4. Integrate IoT and user interface components such as ThingSpeak/cloud dashboards, LCD display, buzzer alerts, and SD card storage to provide real-time visualization, emergency alerts, and secure data logging.
5. Enhance patient safety and care through continuous health monitoring, arrhythmia classification, fall detection, and predictive analytics for timely intervention and emergency response.

The major building blocks of the project are:

1. Power Supply.
2. Raspberry pi zero 2W.
3. Arduino UNO.
4. DS18B20 Temperature sensor.
5. MAX30100(heartbeat&spo2) sensor.
6. AD8232 ECG sensor.
7. Fall detection sensor.
8. LCD display.
9. Buzzer.
10. SD card.

Software’s used:

1. Python programming.
2. Express SCH for Circuit design.
3. Raspbian OS.

video:

The objectives of the project include:

• To detect human presence or obstacles using an ultrasonic sensor.
• To stop the robot automatically upon detection.
• To enable manual control via a web-based interface.
• To stream live video using the Pi Camera for surveillance.

The major building blocks of this project are:

• Battery
• Raspberry pi zero 2w.

• DC Motors with L298N driver.
• Pi camera.
• Ultrasonic sensor.

Software’s used:

• Python language.
• Linux OS.

video:

The sensor data is acquired and conditioned by an Arduino Nano and transmitted to a Raspberry Pi Zero 2W for advanced analysis. The Raspberry Pi processes the incoming data to calculate a stress score ranging from 0 to 100, representing the user’s current mental stress level. Based on this score, intelligent decision logic determines the system response.

When the stress level exceeds a predefined threshold, the Raspberry Pi automatically activates a relay to drive an integrated massage unit, providing immediate physical relaxation. In addition to automatic control, the system also offers a user-controlled massager through a web-based interface, allowing the user to manually turn the massager ON or OFF at any time without affecting EEG and GSR signal acquisition.

The Raspberry Pi Zero 2W generates alert messages and notifications during high-stress conditions and logs all sensor data and stress scores on an SD card for future analysis. A dedicated web page displays real-time stress levels, historical trends, and personalized AI-based recommendations such as relaxation exercises and breathing techniques.

Powered by a Li-ion battery, the proposed wearable head cap is compact, portable, and energy efficient. The system provides a comprehensive solution for mental fatigue monitoring, stress reduction, and user-centric control, making it suitable for healthcare monitoring, workplace wellness, and safety-critical applications.

The major Objectives of this project are:

• To acquire EEG and GSR signals related to mental stress
• To process sensor data and compute a stress score (0–100)
• To automatically activate a massage unit during high stress
• To provide a web-based interface for real-time monitoring and control
• To log stress data for future analysis and trends
• To generate AI-based stress relief recommendations

The major building blocks of this project are:

1. Li-ion Battery Power Supply.
2. Raspberry pi Zero 2W.
3. SD Card.
4. EEG amplifier bio amp EXG pill.
5. GSR.
6. Relay along with Massage.
7. Arduino nano.
8. CAP.

Software’s used:

• Python programming.
• Raspbian OS.

3. Express SCH for Circuit design.

• WEB technology.

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The Digital image processing plays important role in the sign capturing and detection system. The image processing algorithms to takes the necessary action for resizing the captured signs. The objective of the proposed work is to recognize the speed limits on speed-limit signs automatically using pi camera and raspberry pi zero 2 W.

The proposed system will get the image of the real world from the Pi camera which is interfaced to the raspberry pi and deep learning are used to detect the speed limit of road signs. We can create a data set into the raspberry pi. This data set contains a greater number of speed limit signs. The raspberry pi processor takes the input from pi camera and it will compare with the data set and based on that it can identify the speed limit sign will be display on LCD module and also control the vehicle speed accordingly. If the system detects (20km, 30km, 40km) speed signs it will activate the buzzer for alerts. Here DC motor works as vehicle.

So, this system is able to take action and reduces the chances of human errors like driver mistakes that results road accidents. The coding for this whole system is in python and for image processing we are using deep learning.

The major features of this project are:

• Camera based automatic speed limit detection using deep learning.
• The main objective of proposed system is automatic speed sign detection and vehicle speed control system.
• Alert speed sign detection using BUZZER.
• This system avoids the road accidents.

The major building blocks of this project are:

• Power Supply.
• Raspberry pi zero 2 W.
• Pi camera.
• LCD display.
• Buzzer.
• DC motor with driver.

Software’s used:

• Linux OS
• PYTHON LANGUAGE.
• Deep learning for image processing.

Block Diagram:

video:

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