IOT Based IV Bag Monitoring and Alert System Using Arduino with GSM - SM...

IOT Based IV Bag Monitoring and Alert System Using Arduino with GSM - SMS📱Notification | IOT IV Bag Monitoring and Alert System | IV Drip Monitoring and Control System | IoT Intravenous Bag Monitoring and Alert System | Design Of IOT Based Iv Bag Monitoring System | IV BAGS WEIGHT MONITORING AND ALARM SYSTEM | IoT Based IV Bag Automatic Monitoring and Control System. *********************************************************** If You Want To Purchase the Full Working Project KIT Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link We are Located at Telangana, Hyderabad, Boduppal. Project Changes also Made according to Student Requirements http://svsembedded.com/  https://www.svskits.in/ http://svsembedded.in/  http://www.svskit.com/ M1: +91 9491535690  M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / DHL / Blue Dart / First Flight Courier Service We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos 9. Project Kit Working Video links Latest Projects with Year Wise YouTube video Links 157 Projects  https://svsembedded.com/ieee_2022.php 135 Projects  https://svsembedded.com/ieee_2021.php 151 Projects  https://svsembedded.com/ieee_2020.php 103 Projects  https://svsembedded.com/ieee_2019.php 61 Projects  https://svsembedded.com/ieee_2018.php 171 Projects  https://svsembedded.com/ieee_2017.php 170 Projects  https://svsembedded.com/ieee_2016.php 67 Projects  https://svsembedded.com/ieee_2015.php 55 Projects  https://svsembedded.com/ieee_2014.php 43 Projects  https://svsembedded.com/ieee_2013.php 1100+ Projects https://www.svskit.com/2022/02/900-pr... *********************************************************** Creating an IoT-based IV bag monitoring and alert system using Arduino and GSM for SMS notifications involves integrating Arduino boards, sensors, and GSM modules to achieve real-time monitoring and alerting capabilities. Here's a high-level overview of how you could implement such a system: Components Needed: 1. Arduino Board (e.g., Arduino Uno or Arduino Nano) 2. GSM Module (e.g., SIM800L or SIM900) 3. Flow rate sensor (e.g., YF-S201) 4. Temperature sensor (e.g., DS18B20) 5. Pressure sensor (e.g., BMP180) 6. IV Bag holder with suitable mounts for the sensors 7. Power supply 8. Connecting wires 9. SIM card for the GSM module System Implementation: 1. Assemble Sensors and Hardware: • Mount the flow rate sensor, temperature sensor, and pressure sensor on the IV bag holder to measure relevant parameters. • Connect the sensors to the Arduino board using appropriate pins. • Connect the GSM module to the Arduino board using serial communication pins. 2. Programming the Arduino: • Write the Arduino sketch (code) that reads data from the sensors and communicates with the GSM module. • Use suitable libraries for each sensor to simplify data acquisition. • The Arduino sketch should continuously monitor the sensor data and trigger actions based on predefined thresholds. For example, if the flow rate drops below a certain level or the pressure becomes abnormal, an alert should be triggered. 3. GSM Communication and SMS Alerts: • Use the GSM module's AT commands to establish communication with the cellular network. • Configure the GSM module to send SMS messages. • When an anomaly is detected by the Arduino (e.g., low flow rate), the system should send an SMS notification to predefined phone numbers. 4. Alert Logic: • Implement logic in the Arduino sketch to decide when to send alerts. • You can set threshold values for each parameter (flow rate, temperature, pressure) beyond which an alert is triggered. • Implement debouncing or filtering mechanisms to avoid false alarms due to sensor noise. 5. Power Supply: • Ensure that the system is powered by a reliable and stable power source to avoid interruptions in monitoring and alerting. 6. Testing and Calibration: • Test the system using different IV bags with known parameters to calibrate the sensors and verify the accuracy of measurements. • Conduct rigorous testing to ensure that the system responds appropriately to different scenarios. 7. Deployment: • Install the system in a healthcare setting, such as a hospital or clinic, where IV administration takes place. • Train healthcare staff on using and interpreting the alerts generated by the system. 8. Monitoring Interface: • Optionally, you can create a monitoring interface using a computer or mobile device to visualize sensor data and receive alerts remotely.

AI Wireless Hand🖐️Gesture🤟Controlled Robot🤖 Using Raspberry Pi Pico with...

AI Wireless Hand🖐️Gesture🤟Controlled Robot🤖 Using Raspberry Pi Pico, RP2040, ARM Cortex-M0+ with OpenCV & Python | AI Wireless Hand Gesture Recognition & Home Automation Using Raspberry Pi Pico with OpenCV & Python | Wireless Zigbee Multiple Color(R-G-B-Y-O) Detection Robot🤖Using Raspberry Pi Pico with Python-OpenCV | Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry Pi Pico with OpenCV & Python. *********************************************************** If You Want To Purchase the Full Working Project KIT Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link We are Located at Telangana, Hyderabad, Boduppal. Project Changes also Made according to Student Requirements http://svsembedded.com/  https://www.svskits.in/ http://svsembedded.in/  http://www.svskit.com/ M1: +91 9491535690  M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / DHL / Blue Dart / First Flight Courier Service We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos 9. Project Kit Working Video links Latest Projects with Year Wise YouTube video Links 157 Projects  https://svsembedded.com/ieee_2022.php 135 Projects  https://svsembedded.com/ieee_2021.php 151 Projects  https://svsembedded.com/ieee_2020.php 103 Projects  https://svsembedded.com/ieee_2019.php 61 Projects  https://svsembedded.com/ieee_2018.php 171 Projects  https://svsembedded.com/ieee_2017.php 170 Projects  https://svsembedded.com/ieee_2016.php 67 Projects  https://svsembedded.com/ieee_2015.php 55 Projects  https://svsembedded.com/ieee_2014.php 43 Projects  https://svsembedded.com/ieee_2013.php 1100+ Projects https://www.svskit.com/2022/02/900-pr... *********************************************************** Creating an AI wireless hand gesture-controlled robot using a Raspberry Pi Pico with OpenCV and Python is an exciting project! This guide will provide you with a general overview of the steps involved. Keep in mind that this is a complex project, and you may need to adapt the instructions based on your specific components, requirements, and programming skills. Let's get started: Materials Needed: 1. Raspberry Pi Pico 2. Raspberry Pi Camera Module 3. Motor driver (e.g., L298N or L293D) 4. DC motors and wheels 5. Power source (battery pack) 6. Chassis for the robot 7. Jumper wires 8. Hand-worn sensor (e.g., flex sensors, accelerometer, or IMU) 9. Wi-Fi module (optional, for wireless control) Steps: 1. Set Up Raspberry Pi Pico: • Set up the Raspberry Pi Pico with the required firmware and libraries. • Make sure you have a Python development environment set up on the Pico. 2. Install OpenCV: • Install OpenCV on your Raspberry Pi Pico. You can use the pip package manager to do this. 3. Connect Hardware: • Connect the motor driver to the Raspberry Pi Pico's GPIO pins. • Connect the DC motors to the motor driver's output pins. • Connect the Raspberry Pi Camera Module to the Pico. 4. Capture Hand Gestures: • Use the Raspberry Pi Camera Module to capture real-time images of the hand gestures. • Process the images using OpenCV to detect and extract the hand gesture features. 5. Implement Gesture Recognition: • Train a machine learning model (such as a Convolutional Neural Network or SVM) to recognize hand gestures. • Preprocess the captured images, extract relevant features, and feed them to the model for classification. • Map each recognized gesture to a specific robot command (e.g., move forward, turn left, stop, etc.). 6. Wireless Communication (Optional): • If you want to control the robot wirelessly, you can add a Wi-Fi module to the Raspberry Pi Pico. • Create a simple server on the Pico that listens for commands from a remote device (e.g., smartphone or computer). 7. Robot Movement: • Depending on the recognized gesture, send commands to the motor driver to control the movement of the robot. • Implement the logic to make the robot move forward, backward, turn left, turn right, and stop. 8. Testing and Refinement: • Test the hand gesture-controlled robot in a controlled environment. • Refine the gesture recognition algorithm and the robot's response based on the testing results. 9. Enclosure and Aesthetics: • Design and build a chassis for the robot to protect its components and give it a finished look. 10. User Interface (Optional): • Create a user interface on a remote device (e.g., a smartphone app or a web page) to visualize the robot's camera feed and control its movements.

Reinforcement Learning Projects List _ SVSEMBEDDED _ 9491535690

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Reinforcement Learning Projects List

1. Adaptive Camera Control.

2. Adaptive Music Playlist

3. Anomaly Detection.

4. Augmented Reality Gaming.

5. Automated Photography.

6. Automated Plant Care System.

7. Autonomous Drone Navigation.

8. Autonomous Drone.

9. Autonomous Maze Solver.

10. Autonomous Maze Solving Robot.

11. Autonomous Navigation.

12. Autonomous Plant Care System.

13. Autonomous Quadcopter.

14. Autonomous Quadcopter. Line Following Robot with RL.

15. Autonomous Robot Navigation

16. Autonomous Robot.

17. Autonomous Vacuum Cleaner.

18. Balancing Robot with RL.

19. Elevator Control Optimization.

20. Elevator Control.

21. Energy Management System.

22. Energy-Efficient IoT Device.

23. Energy-Efficient IoT Node.

24. Energy-Efficient Operation.

25. Fighting Robot AI.

26. Flappy Bird RL Agent.

27. Game AI on ESP32.

28. Game AI using RL.

29. Game Playing Agent.

30. Game Playing AI.

31. Gaming AI.

32. Gesture Recognition with RL.

33. Gesture Recognition.

34. Gesture Recognition.

35. Gesture-Controlled Appliances.

36. Gesture-Controlled Device.

37. Gesture-Controlled Robot Arm.

38. Gesture-controlled Robot with RL.

39. Gesture-Controlled Robot.

40. Health Monitoring Wearable.

41. Home Automation Scheduler.

42. Home Automation System.

43. Home Automation with RL.

44. Indoor Environment Monitoring and Control.

45. Indoor Navigation System.

46. Industrial Process Control.

47. Language Learning Assistant.

48. Light-seeking Robot using RL.

49. Line Following Robot using Q-Learning.

50. Line Following Robot with Adaptive Learning.

51. Line Following Robot with RL.

52. Mobile Robot Navigation.

53. Object Tracking and Following.

54. Optimal Path Planning for Maze Solving.

55. Optimal Resource Allocation.

56. Personalized Fitness Trainer.

57. Personalized Recommendation System.

58. Ping Pong Playing Robot.

59. Plant Watering System.

60. Pong Game AI.

61. Pong Game using RL.

62. Power Management in IoT Devices.

63. QR Code Recognition and Interaction.

64. Robot Arm Control with RL.

65. Self-Balancing Robot with RL.

66. Self-Balancing Robot.

67. Smart Agriculture with Reinforcement Learning.

68. Smart Agriculture.

69. Smart Camera Angle Control.

70. Smart Home Automation.

71. Smart Home Energy Management.

72. Smart Irrigation System.

73. Smart Plant Watering System.

74. Smart Security System.

75. Smart Thermostat Control.

76. Stock Trading Agent.

77. Stock Trading Bot.

78. Temperature Control with RL.

79. Traffic Light Control with RL.

80. Traffic Light Control.

81. Traffic Light Optimization.

82. Traffic Management System.

83. Traffic Signal Control.

84. Traffic Signal Optimization.

85. Virtual Pet with RL.

86. Visual Search and Rescue.

87. Voice-controlled Home Automation.

88. WiFi Signal Strength Optimization.

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Machine Learning Based Project list _ SVSEMBEDDED _ 9491535690

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Machine Learning Based Project list

1. AI-Powered Wildlife Monitoring

2. Anomaly Detection for Energy Consumption: 

3. Anomaly Detection for Industrial Equipment: 

4. Anomaly Detection: 

5. Automated Plant Care System: 

6. Autonomous Drone: 

7. Autonomous Line Following Robot: 

8. Autonomous Robot Navigation: 

9. Autonomous Robot or Vehicle: 

10. Autonomous Robot: 

11. Colour Sorting Robot: 

12. Currency Recognition: 

13. Emotion Detection:

14. Emotion Recognition from Facial Expressions: 

15. Environmental Monitoring: 

16. Face Recognition Door Lock: 

17. Face Recognition Doorbell: 

18. Face Recognition: 

19. Food Recognition and Calorie Estimation: 

20. Gesture Control: 

21. Gesture Recognition System: 

22. Gesture Recognition using Accelerometer and Gyroscope: 

23. Gesture Recognition: 

24. Gesture-Controlled Appliances: 

25. Handwriting Recognition: 

26. Health Monitoring System: 

27. Health Monitoring Wearable: 

28. Home Security System: 

29. Human Activity Recognition: 

30. Image Classification: 

31. Indoor Air Quality Monitoring: 

32. Indoor Positioning System: 

33. Intruder Detection: 

34. Language Translation: 

35. License Plate Recognition: 

36. Music Generation System: 

37. Music Generation: 

38. Music or Song Generation: 

39. Object Detection and Recognition: 

40. Object Detection and Tracking: 

41. Object Detection with Raspberry Pi Camera: 

42. Object Detection with Ultrasonic Sensors: 

43. Plant Health Monitoring: 

44. Predicting Room Occupancy: 

45. Predicting Stock Prices: 

46. Predictive Maintenance for Appliances: 

47. Predictive Maintenance for Machinery: 

48. Predictive Maintenance System: 

49. Predictive Maintenance System: Plant Disease Detection: 

50. Predictive Maintenance: 

51. Predictive Temperature Control: 

52. Predictive Text Entry System: 

53. Predictive Weather Station: 

54. Real-time Language Translation: 

55. Real-time Object 

56. Sentiment Analysis of Social Media Data: 

57. Sentiment Analysis: 

58. Smart Agriculture System: 

59. Smart Home Automation: 

60. Smart Home Control: 

61. Smart Home Energy Management: 

62. Smart Home Security System: 

63. Smart Irrigation System: 

64. Smart Surveillance Camera: 

65. Smart Traffic Light Control: 

66. Speech Recognition and Home Automation: 

67. Speech Recognition Assistant: 

68. Text Sentiment Analyzer: 

69. Traffic Analysis and Prediction: 

70. Traffic Flow Prediction: 

71. Trash Classification: 

72. Voice Command Recognition: 

73. Voice Recognition with Arduino: 

74. Voice-controlled Home Automation: 

75. Waste Management and Sorting: 

76. Waste Sorting System: 

77. Weather Prediction System:

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Artificial Intelligence Based Projects List_svsembedded_9491535690

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CONTACT: 9491535690, 7842358459

Artificial Intelligence Based Projects List

 

1. AI-Powered Camera Trap:

2. Anomaly Detection:

3. Artificial Intelligence Projects:

4. Augmented Reality Apps:

5. Automated Garbage Collection:

6. Automated Photography Booth:

7. Automated Plant Watering:

8. Autonomous Car Simulation:

9. Autonomous Drone:

10. Autonomous Robot:

11. Autonomous Vehicles:

12. Barcode/QR Code Scanner:

13. Emotion Detection:

14. Emotion Recognition:

15. Energy Management System:

16. Face Detection:

17. Face Recognition Door Lock:

18. Gesture Recognition System:

19. Gesture Recognition:

20. Gesture-Controlled Robot:

21. Handwriting Recognition:

22. Health Monitoring System:

23. Health Monitoring Wearables:

24. Health Monitoring:

25. Healthcare Diagnostics:

26. Home Security Camera:

27. Image Classification:

28. Image Processing Security System:

29. Language Translation:

30. Livestreaming Camera:

31. Music Generation:

32. Natural Language Processing (NLP) Apps:

33. Natural Language Processing:

34. Object Detection

35. Object Recognition:

36. Object Tracking:

37. Pan-and-Tilt Camera System:

38. Plant Health Monitoring:

39. Predictive Analytics:

40. Predictive Maintenance System:

41. Predictive Maintenance:

42. Remote Monitoring

43. Robot Arm Control:

44. Robot-Assisted Elderly Care:

45. Sentiment Analysis:

46. Smart Agriculture:

47. Smart Health Monitoring:

48. Smart Home Automation:

49. Smart Home Controller:

50. Smart Irrigation System:

51. Smart Mirror:

52. Smart Plant Monitoring:

53. Time-Lapse Photography:

54. Traffic Management System:

55. Virtual Pet:

56. Voice Assistant:

57. Voice-Controlled Appliances:

58. Weather Prediction System:

59. Weather Station with Imagery:

60. Wildlife Monitoring Camera: 

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Wireless Multiple Colour ( R-G-B-Y-O ) Detection Robot🤖 Using Raspberry ...

Wireless Zigbee Multiple Colour ( R-G-B-Y-O ) Detection Robot🤖 Using Raspberry Pi Pico with OpenCV & Python | Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry Pi Pico with OpenCV & Python | Color detection using python and OpenCV | Color Detection with Python | Multiple Color Detection in Real-Time using Python-OpenCV | opencv color detection | Color Detection Using OpenCV | Augmented Reality using OpenCV | Simple Color recognition with Opencv and Python. *********************************************************** If You Want To Purchase the Full Working Project KIT Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link We are Located at Telangana, Hyderabad, Boduppal. Project Changes also Made according to Student Requirements http://svsembedded.com/  https://www.svskits.in/ http://svsembedded.in/  http://www.svskit.com/ M1: +91 9491535690  M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / DHL / Blue Dart / First Flight Courier Service We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos 9. Project Kit Working Video links Latest Projects with Year Wise YouTube video Links 157 Projects  https://svsembedded.com/ieee_2022.php 135 Projects  https://svsembedded.com/ieee_2021.php 151 Projects  https://svsembedded.com/ieee_2020.php 103 Projects  https://svsembedded.com/ieee_2019.php 61 Projects  https://svsembedded.com/ieee_2018.php 171 Projects  https://svsembedded.com/ieee_2017.php 170 Projects  https://svsembedded.com/ieee_2016.php 67 Projects  https://svsembedded.com/ieee_2015.php 55 Projects  https://svsembedded.com/ieee_2014.php 43 Projects  https://svsembedded.com/ieee_2013.php 1100+ Projects https://www.svskit.com/2022/02/900-pr... *********************************************************** Creating a wireless Zigbee-controlled robot that uses a Raspberry Pi Pico for color detection using OpenCV and Python is an exciting project! This project involves both hardware and software components. Here's a high-level overview of the steps you'd need to take: Hardware Components: 1. Raspberry Pi Pico: This microcontroller board will serve as the brain of the robot. 2. Zigbee Module: You'll need a Zigbee module (like XBee) for wireless communication. 3. Motors and Wheels: These components will enable the robot to move. 4. Color Sensor: A color sensor or camera will be used to detect colors. 5. Power Supply: Provide power to both the Raspberry Pi Pico and the motors. Software Steps: 1. Setting up Raspberry Pi Pico: • Install the required MicroPython firmware on the Raspberry Pi Pico. • Set up the Pico to communicate with the Zigbee module using UART. 2. Zigbee Communication: • Implement a communication protocol using UART to send and receive commands between the remote control (Zigbee-connected device) and the robot. 3. Motor Control: • Interface with the motor driver to control the robot's movement (forward, backward, left, right) based on commands received through Zigbee. 4. Color Detection using OpenCV: • Connect a color sensor or camera module to the Raspberry Pi Pico. • Write a Python script that captures images using OpenCV. • Process the images to detect colors using OpenCV's color detection methods. 5. Sending Color Information: • Once a color is detected, send this information back to the remote control using Zigbee communication. 6. Remote Control Interface: • Set up the remote control interface, which can be a separate Zigbee module connected to a computer or smartphone. • Develop a simple user interface to send movement commands and receive color information. 7. Integration: • Combine the motor control, color detection, and Zigbee communication code into a single program running on the Raspberry Pi Pico. 8. Testing and Refinement: • Test your robot's color detection and movement in different environments. • Refine your code to ensure accurate color detection and smooth movement. Remember that this is a complex project that involves both hardware and software aspects. It's important to break down each step into smaller tasks and tackle them one by one. You'll also need to refer to the datasheets of the components you're using, the MicroPython documentation for the Raspberry Pi Pico, and the OpenCV documentation for image processing. Additionally, be prepared to troubleshoot issues that may arise during the development process. Good luck, and have fun creating your Zigbee-controlled color-detecting robot!