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.

Arduino Programming 2024/12 – KV Kajang

UMPSA STEM Lab Arduino Programming can be found here.

Throughout the course, 10 participants from Kolej Vokasional Kajang were introduced to the concepts of programming loops, conditional statements, and sequential execution. Activities include controlling multiple LEDs, understanding the concept of digital output, using a photoresistor to expand their understanding of sensor interfacing, integrating analog sensors with Arduino and controlling digital outputs based on sensor readings. Towards the end, participants visualize data and messages using an OLED display.

Thank you Pn Rahayu, Ts Adam and Mr  for coordinating the communication between UMPSA STEM Lab and the participants.

Day 3 -Dec 8th

Day 2 -Dec 6th

 

 

Day 1 – Dec 2nd

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!