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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)

SKU: VPL-ET-PI Category:

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.
  1. UART Communication Setup
    • Objective: Configure UART for data exchange between Raspberry Pi and external devices.
    • Key Concepts: UART communication, serial debugging, data logging.
  1. 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)
  1. MQTT Communication with Cloud
    • Objective: Publish sensor data to cloud using MQTT.
    • Key Concepts: MQTT protocol, cloud integration, IoT data transmission.
  1. Real-Time Dashboard with Node-RED
    • Objective: Create a local dashboard for visualizing sensor data.
    • Key Concepts: Node-RED, real-time visualization, IoT monitoring.
  1. Camera-Based Data Logging
    • Objective: Capture images/video for analysis.
    • Key Concepts: Image processing, data logging, storage management.
  1. 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.
  1. IoT-Based Environmental Monitoring
    • Objective: Collect environmental data, cloud storage, remote visualization.
    • Key Concepts: Remote monitoring, cloud storage, real-time analytics.
  1. Designing a Home Automation System
    • Objective: Automate home appliances with mobile app control.
    • Key Concepts: IoT automation, mobile integration, system control.
  1. Anomaly Detection using Machine Learning
    • Objective: Implement anomaly detection in sensor data.
    • Key Concepts: Anomaly detection, data analysis, ML application.
  1. 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.