Raspberry Pi Board (VPL-ET-PI)

Raspberry Pi Board (VPL-ET-PI) • Processor: Quad-core ARM Cortex-A72 64-bit SoC @ 1.5 GHz or better • Memory: 4GB LPDDR4-3200 SDRAM or better • Wireless LAN: Dual-band 802.11 b/g/n/ac (2.4GHz and 5GHz) • Bluetooth: Bluetooth 5.0, Bluetooth Low Energy (BLE) • Ethernet: Gigabit Ethernet • USB Ports: 2 × USB 3.0, 2 × USB 2.0 • Video Output: 2 × micro-HDMI ports supporting up to 4Kp60 resolution • Audio & Video: 4-pole stereo audio and composite video port • Storage: Micro-SD card slot • Expansion: 40-pin GPIO header (Raspberry Pi HAT compatible) • Interfaces: Camera (CSI), Display (DSI) • Power: 5V DC via USB-C connector or GPIO header (flexible power options)

Hardware Overview

• Processor: Quad-core ARM Cortex-A72 64-bit SoC @ 1.5 GHz or better
• Memory: 4GB LPDDR4-3200 SDRAM or better
• Wireless LAN: Dual-band 802.11 b/g/n/ac (2.4GHz and 5GHz)
• Bluetooth: Bluetooth 5.0, Bluetooth Low Energy (BLE)
• Ethernet: Gigabit Ethernet
• USB Ports: 2 × USB 3.0, 2 × USB 2.0
• Video Output: 2 × micro-HDMI ports supporting up to 4Kp60 resolution
• Audio & Video: 4-pole stereo audio and composite video port
• Storage: Micro-SD card slot
• Expansion: 40-pin GPIO header (Raspberry Pi HAT compatible)
• Interfaces: Camera (CSI), Display (DSI)
• Power: 5V DC via USB-C connector or GPIO header (flexible power options)

Key Concepts – Raspberry Pi Board:

1. Embedded Computing: High-performance computing capabilities ideal for home automation, industrial control, and media servers using the ARM Cortex-A72 SoC.
2. Wireless Connectivity: Dual-band Wi-Fi and Bluetooth 5.0 enable seamless communication with cloud services, peripherals, and IoT devices.
3. Multimedia & Graphics: Supports dual 4K video output, suitable for multimedia applications, digital signage, and advanced graphical interfaces.
4. Edge Computing: Local data processing capabilities reduce latency and enhance privacy, essential for real-time analytics, edge AI, and smart camera systems.
5. Flexible Expansion: 40-pin GPIO header allows extensive sensor, actuator, and expansion board interfacing for versatile IoT projects.
6. Cloud Integration: Compatible with AWS IoT, Azure IoT, and Google Cloud IoT for remote monitoring, data storage, and analytics.
7. Power Efficiency: Offers multiple power supply options, ideal for portable and stationary IoT solutions.

Experiment List

1.  Getting Started with GPIO and Python

• • Objective: Learn GPIO pin configuration and basic I/O operations using Python.
  • Key Concepts: GPIO manipulation, digital I/O, event handling.

2. UART Communication Setup

• • Objective: Configure UART for data exchange between Raspberry Pi and external devices.
  • Key Concepts: UART communication, serial debugging, data logging.

3. Sensor Integration using I2C & SPI

• • Objective: Interface and read data from various sensors.
  • Key Concepts: Sensor communication (I2C, SPI), data acquisition, sensor interfacing.
  • Sensors:
    • HTU21 (Temperature & Humidity)
    • BH1750 (Light)
    • MPU6050 (Accelerometer & Gyroscope)

4. MQTT Communication with Cloud

• • Objective: Publish sensor data to cloud using MQTT.
  • Key Concepts: MQTT protocol, cloud integration, IoT data transmission.

5. Real-Time Dashboard with Node-RED

• • Objective: Create a local dashboard for visualizing sensor data.
  • Key Concepts: Node-RED, real-time visualization, IoT monitoring.

6. Camera-Based Data Logging

• • Objective: Capture images/video for analysis.
  • Key Concepts: Image processing, data logging, storage management.

7. Edge AI Model Deployment with TensorFlow Lite

• • Objective: Train/deploy an AI model for object detection.
  • Key Concepts: ML model training, edge inference, TensorFlow Lite deployment.

8. IoT-Based Environmental Monitoring

• • Objective: Collect environmental data, cloud storage, remote visualization.
  • Key Concepts: Remote monitoring, cloud storage, real-time analytics.

9. Designing a Home Automation System

• • Objective: Automate home appliances with mobile app control.
  • Key Concepts: IoT automation, mobile integration, system control.

10. Anomaly Detection using Machine Learning

• • Objective: Implement anomaly detection in sensor data.
  • Key Concepts: Anomaly detection, data analysis, ML application.

11. Building a Web Server

• • Objective: Set up a web server for local/remote IoT data access.
  • Key Concepts: Web server configuration, HTTP protocols, data serving.

Platform and Workstation Details

• Training Environment Setup:
  • Display: 4 Inch capacitive touch LCD for interaction with the Raspberry Pi system.
  • Connectivity: USB OTG, I2C, SPI, UART for various external device interfaces.
  • Power Supply: Powered via USB-C or GPIO header, providing flexible power options.
  • Enclosure: Plastic or durable case for protecting the board and peripherals in an industrial environment.
• Expansion Options: 40-pin GPIO header allows easy integration with additional sensors, actuators, and peripheral devices.