Tag: Academia – Python Prog

Raspberry Pi Programming 2024/11 – SHAH Pekan

*UMPSA STEM Lab Raspberry Pi Programming Synopsis can be found here.

In the Raspberry Pi IoT session, 30 students from SHAH Pekan were introduced to the concept of the Internet of Things (IoT) using Raspberry Pi on the UMP STEM Cube, a pico-satellite learning kit specifically designed to facilitate engineering learning.

The content covered basic digital input/output operations on onboard LEDs, as well as topics such as dashboard design using gyro meter and BMU280 sensor data, including collecting and storing data in a cloud database. Participants learned to interface sensors with Raspberry Pi boards and develop IoT applications for real-world scenarios. The session provided students with valuable insights into IoT technology and its applications in various domains.

A special appreciation is extended to Cikgu Syakir and Cikgu Rita from SHAH Pekan for coordination in facilitating communication between the participants and the UMPSA STEM Lab :).

Dec 19th

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IoT Dashboard 2024/2– PPD Pekan

In the IoT Dashboard Design session, 100 students from Pekan District explored the fundamentals of creating real-time dashboards for IoT applications using the UMP STEM Cube, a pico-satellite learning kit designed to enhance engineering education.

The session covered essential topics such as sensor integration, data visualization, and cloud-based data management. Students utilized gyrometer and BMU280 sensor data to design interactive dashboards using Streamlit, a powerful Python framework for building real-time web applications. They also learned how to collect, process, and display data effectively, gaining hands-on experience in dashboard development for IoT systems.

The importance of dashboards in satellite systems was also emphasized, as they play a crucial role in monitoring and visualizing critical data such as altitude, orientation, and environmental conditions. Dashboards enable real-time decision-making, ensuring optimal performance and reliability of satellite operations.

Special appreciation goes to Tn Khairul for coordinating and facilitating smooth communication between the participants and the UMPSA STEM Lab team.

Dec 17th.



Raspberry Pi Programming 2024/10 – PPD Pekan

*UMPSA STEM Lab Raspberry Pi Programming Synopsis can be found here.

In the Raspberry Pi IoT session, 100 students from Pekan District Schools were introduced to the concept of the Internet of Things (IoT) using Raspberry Pi on the UMP STEM Cube, a pico-satellite learning kit specifically designed to facilitate engineering learning.

The content covered basic digital input/output operations on onboard LEDs, as well as topics such as dashboard design using gyro meter and BMU280 sensor data, including collecting and storing data in a cloud database. Participants learned to interface sensors with Raspberry Pi boards and develop IoT applications for real-world scenarios. The session provided students with valuable insights into IoT technology and its applications in various domains.

A special appreciation is extended to En Khairul from PPD Pekan for coordination in facilitating communication between the participants and the UMPSA STEM Lab :).

Dec 17th

Raspberry Pi Programming 2024/9 – SBP Integrasi Kuantan

*UMPSA STEM Lab Raspberry Pi Programming Synopsis can be found here.

In the Raspberry Pi IoT session, 80 students from SBPI Integrasi Kuantan were introduced to the concept of the Internet of Things (IoT) using Raspberry Pi on the UMP STEM Cube, a pico-satellite learning kit specifically designed to facilitate engineering learning.

The content covered basic digital input/output operations on onboard LEDs, as well as topics such as dashboard design using gyro meter and BMU280 sensor data, including collecting and storing data in a cloud database. Participants learned to interface sensors with Raspberry Pi boards and develop IoT applications for real-world scenarios. The session provided students with valuable insights into IoT technology and its applications in various domains.

A special appreciation is extended to Cikgu Syahril for coordination in facilitating communication between the participants and the UMPSA STEM Lab :).

Dec 16th

 

BTE1522 DRE2213 – Week 9 Dashboard Design

This week we ventured beyond traditional activities to integrate dashboard development. Building on earlier exercises involving Raspberry Pi for digital making and sensor control (Week 5 – 8), this module introduces students to creating interactive and visually appealing dashboards using Python and Streamlit.

By learning how to design and deploy dashboards, students are able to transform data into actionable insights, fostering creativity and problem-solving skills. This module highlights the potential of dashboards as versatile tools for displaying and interacting with data, especially in STEM-focused projects.

Bridging Digital Making and Dashboards

Earlier modules focused on digital making with Raspberry Pi, where students controlled sensors and engaged in hands-on programming. This practical experience focuses on understanding data collection and manipulation. The next logical step was to showcase this data meaningfully—enter dashboard development.

While third-party platforms like Blynk or Adafruit IO offer straightforward solutions for creating dashboards, their limitations in customization and scalability inspired us to introduce Streamlit. This open-source Python framework allows students to design dashboards with unmatched flexibility and creativity.

Activities in the Dashboard Development Module

  1. Adding Text
    Students learned how to add headings, subheadings, and descriptive text to their dashboards. This simple yet essential step helped them understand how to create user-friendly interfaces.
  2. Data Hunt
    Data is the backbone of any dashboard. Students explored various data sources, including:

    • Local files (e.g., CSVs)
    • URLs and APIs for real-time data
    • Google Sheets for collaborative data storage
      These activities taught students how to retrieve, clean, and prepare data for visualization.
  3. Layout Design
    Through hands-on exercises, students experimented with Streamlit’s layout tools, such as columns, containers, and sidebars. This activity emphasized the importance of designing intuitive dashboards.
  4. Exploring Input Widgets
    Students implemented interactive elements like sliders, checkboxes, and buttons to allow users to customize their experience. They discovered how these widgets make dashboards more dynamic and engaging.
  5. Data Visualization
    Using libraries like Plotly, students transformed raw data into compelling visualizations. From bar charts to interactive maps, they learned to communicate information effectively.
  6. Deployment
    In the final activity, students deployed their dashboards to Streamlit Cloud. This step not only showcased their projects but also reinforced their understanding of deployment pipelines and collaboration tools like GitHub.

Creative Potential with Streamlit

Streamlit’s ability to integrate seamlessly with Python makes it a good platform for fostering creativity. Students are no longer confined to pre-built templates, as they can create dashboards tailored to specific needs, whether it’s monitoring environmental sensors, visualizing IoT data, or developing educational tools.

For instance, a student could design a dashboard that tracks temperature and humidity data collected from Raspberry Pi sensors. The dashboard could display real-time graphs, provide historical comparisons, and even allow users to set alerts—all features that Blynk or Adafruit might limit due to platform constraints.

Expanding Horizons in STEM Education

This dashboard module aligns with the broader goal of empowering students to think critically and creatively in the digital age. By mastering tools like Streamlit, students can transition from passive consumers of technology to active creators, capable of solving real-world problems.

Dashboard development is more than just a technical skill – it’s a gateway to exploring how data shapes decisions in diverse fields, from agriculture to healthcare. Combined with their experience in digital making, students are well-equipped to lead the way in the era of IoT and data-driven solutions.

The integration of Streamlit in our educational modules bridges the gap between data collection and presentation, offering students the tools to develop bespoke dashboards. Unlike third-party platforms, Streamlit encourages limitless creativity, making it the ideal choice for empowering the next generation of innovators.

As you continue to explore these capabilities, take note on the impact of these creations can have—not just in the classroom but also in the communities and beyond. Through dashboard development, you’re not just learning coding; but also learning to turn ideas into impactful realities.

BTE1522 DRE2213 – Week 10 – Project

The Raspberry Pi is a versatile platform for learning, prototyping, and building solutions to real-world problems. These are  20 engaging project titles that you can choose for this semester. 3 students per group per title. Leverage the Raspberry Pi 4 or Raspberry Pi Pico in combination with sensors, LEDs, and camera modules. These projects not only enhance understanding of hardware-software integration but also inspire creativity in IoT, automation, and AI applications.

A. Sensor-Based Projects

  1. Smart Distance Meter
    Use an ultrasonic sensor to measure distances and display the readings on an OLED screen. This project introduces students to proximity sensing and visual data representation.

    • Expected Outcome: Real-time distance measurements displayed on the OLED.
  2. Environmental Monitoring Station
    Monitor temperature, humidity, and pressure using the BME280 sensor, with LED alerts for extreme conditions.

    • Expected Outcome: Environmental data displayed on an OLED with LED alerts for threshold breaches.
  3. Motion Tracker
    Detect motion and orientation using the MPU6050 accelerometer/gyroscope sensor, with LEDs providing visual feedback.

    • Expected Outcome: LEDs blink in response to detected motion or tilts.
  4. GPS Navigation Logger
    Log and display real-time GPS location data on an OLED screen for easy navigation.

    • Expected Outcome: Real-time location displayed on the OLED and saved for later use.
  5. Home Automation Starter
    Detect objects using an ultrasonic sensor and trigger LED alerts, ideal for a basic home automation system.

    • Expected Outcome: LEDs light up when an object is detected within a specific range.

B. Camera-Enhanced Projects

  1. Smart Surveillance System
    Integrate a camera and ultrasonic sensor for motion detection, triggering video recording and LED feedback.

    • Expected Outcome: Video recordings and LED alerts activated by motion or proximity.
  2. Time-Lapse Photography System
    Capture periodic images with a camera module, embed location metadata from a GPS module, and create a time-lapse video.

    • Expected Outcome: A location-aware time-lapse video.
  3. Facial Recognition Attendance System
    Combine facial recognition with an ultrasonic sensor to detect proximity, improving efficiency in logging attendance.

    • Expected Outcome: Faces logged into an attendance system with proximity-triggered scans.
  4. Real-Time Video Streaming System
    Stream live video to a web interface, augmented by GPS integration for location-aware feeds.

    • Expected Outcome: Live video streaming with embedded real-time location updates.
  5. AI Object Detection System
    Leverage AI to identify objects via a camera module, with ultrasonic sensors enhancing accuracy by triggering object detection mode.

    • Expected Outcome: Proximity-triggered AI object detection with visual alerts or logs.

C. IoT and Data Processing Projects

  1. Real-Time Data Logger
    Use multiple sensors to collect data (e.g., GPS, BME280, MPU6050) and log it into a database for analysis.

    • Expected Outcome: Comprehensive environmental and positional data logged into a file.
  2. Interactive Weather Station
    Combine the BME280 sensor with LEDs and an OLED for dynamic weather monitoring.

    • Expected Outcome: Visualized weather data with LED alerts for extreme conditions.
  3. Portable Weather Companion
    Pair a GPS and BME280 with an OLED to create a portable device that displays weather and location data.

    • Expected Outcome: Weather and location data accessible on the go.
  4. Path Mapping Device
    Integrate GPS and ultrasonic sensors to navigate terrain, with LEDs indicating obstacle proximity.

    • Expected Outcome: Real-time GPS display and LED alerts for obstacles.
  5. Fitness Tracker Prototype
    Use the MPU6050 to track motion and display activity data on an OLED screen.

    • Expected Outcome: Activity levels or step counts visualized on the OLED.

Hands-On Learning Outcomes

These projects are crafted with the aim to:

    1. Develop a solid understanding of hardware-software integration.
    2. Enhance programming skills in Python for IoT applications.
    3. Build expertise in data collection, processing, and visualization.
    4. Foster creativity by applying technology to real-world scenarios.

submit you project in KALAM 🙂

 Submission Requirement

  1. Python Code
    1. Submit the Python code with clear comments explaining the project you have made.
  2.  Report
      1. Include a report that consists of
        1. A README file with instructions on how the system works.
        2. An overview of the circuit construction (diagram/block diagram)
        3. Flowcharts or pseudocode to explain your work.
  3. 3-Minute Video
          1. Record a 3-minute video demonstrating your project.
          2. Explain the circuit, functions and how the system work.
          3. Upload the video to YouTube and provide the link.

BTE1522 DRE2213 – Week 9 Rasp Pi – Camera Module and Dashboard Design

Today’s class focused on integrating a camera module with the Raspberry Pi, providing students hands-on experience in capturing images, recording videos, and setting up real-time video streaming. This session was a step forward in understanding how Raspberry Pi can be used for imaging and streaming applications, which is essential for more advanced projects like surveillance systems and AI-based object detection.

BTE1522 DRE2213 – Week 8 Rasp Pi – GPIOs, Sensors, and Data Management

Today’s class introduced students to the versatile capabilities of the Raspberry Pi 4 as a single-board computer (SBC). The activities were structured in two sessions to progressively explore its features, bridging concepts from physical computing to data management.

Session 1: Getting Started with GPIOs and Programming Tools

The session began with an overview of the Raspberry Pi 4, highlighting the differences between an SBC and a microcontroller. While microcontrollers like the Raspberry Pi Pico are designed for running simple, specific tasks, SBCs like the Raspberry Pi 4 function as compact computers capable of multitasking, running a full Linux operating system, and supporting a wide range of applications.

Students set up their Raspberry Pi 4 devices, delving into the Linux-based desktop environment. They explored built-in programming tools such as Thonny IDE and Geany, experimenting with writing and running Python scripts.

Key activities included:

    1. Printing “Hello, World!” to the terminal as a warm-up to Python programming.
    2. Controlling LEDs: Students learned to light up an LED and control its state (on/off) using the GPIO pins.
    3. Keyboard-Controlled LEDs: Building on the basics, students wrote scripts to control an LED’s behavior using their computer keyboards, bridging hardware interaction and software logic.

Session 2: Playing with Sensors and Managing Data

The second session focused on the BME280 sensor, an I2C component capable of measuring temperature, pressure, and humidity. Students connected the sensor to the Raspberry Pi, read data from it, and explored ways to handle this information.

The main objectives included:

    1. Understanding how to communicate with I2C devices using Python libraries.
    2. Writing sensor data to a simple text file (.txt) as a database. This exercise demonstrated basic data logging, an essential concept in IoT and data-driven projects.

Crafting the Learning Journey

This week’s exercises were designed to expose students to the diverse features of the Raspberry Pi 4, an exposure in both hardware and software aspects. By the end of the session, students gained hands-on experience in programming, sensor integration, and data management.

Next week, students will build on these concepts by designing a real-time dashboard using Python, incorporating the skills acquired in today’s class to visualize and interact with data. Stay tuned as we continue our journey into the world of Raspberry Pi and digital making!