#SeeedWeekly: New Raspberry Pi CM4 based EdgeBox for harsh industrial environment and PIzza for gateways and routers. Heatsink for Jetson Nano/NX module arrived!


If you’re designing any kind of computing application with the Jetson Nano, you seriously can’t do without a heatsink if you want to avoid overheating problems. 
  • Fully compatible with NVIDIA Jetson Nano Module
  • Essential for high-performance computing: Keeps module temperatures low to avoid overheating and throttling
  • Effective thermal management: Full Aluminum Alloy Heatsink
  • Safe for use: Spring mounting interface keeps excessive pressure off the Jetson Nano module
Jetson Mate Cluster Mini $199.00
 
 
Combine Aluminum Heatsink for Jetson Nano Module with the NVIDIA Jetson Nano Module and Jetson Mate Cluster Mini, and you’ll get a thoroughly well-cooled GPU cluster solution!
Run intensive workloads like data analytics and even machine learning on the edge without any worry of overheating and performance throttling issues.
Jetson Xavier NX Module Aluminum Heatsink $19.90
  • Fully compatible with NVIDIA Jetson Xavier NX Module
  • Essential for high-performance computing: Keeps module temperatures low to avoid overheating and throttling
  • Effective thermal management: Full Aluminum heatsink with fan
  • Easy to install: Mounting bracket & screws included
EdgeBox-RPi4 Edge Computing Controller with 4GB RAM, 32GB eMMC and 2.4/5GHz WiFi $209.00
 
EdgeBox-RPi is a rugged, fanless Edge Computing Controller powered by the Raspberry Pi Compute Module 4 (CM4) for harsh industrial environments, built with Industrial-grade isolated DI/DO terminal and RS-485 interface It can be used to connect field networks in the cloud or IoT applications. 
  • Rich interfaces for industrial applications
    • Industrial-grade isolated DI/DO terminal
    • Industrial-grade isolated RS-485 interface
  • Diverse network connectivity options
  • Dual-band 2.4GHz/5GHz Wi-Fi
  • Gigabit Ethernet
  • 4G/LTE communication
  • Designed for reliability with built-in uninterruptible power supply (UPS) with supercap
  • Integrated thermal management with full Aluminum heatsink enclosure
  • Convenient form factor with 35mm DIN and wall mount
EdgeBox-RPi4 Edge Computing Controller with 1GB RAM and 8GB eMMC $159.00

Perfect for:
  • IoT Gateway
  • 4G/LTE Router
  • Edge Computing Controller
  • Software-defined PLC (Programmable Logic controller)
  • Industrial Controller
  • Automation and Control Systems

PIzza is a micro server based on the Raspberry Pi Compute Module 4, designed for makers and commercial applications alike. With diverse connectivity options and expandable storage, it can be used for various networking or multimedia applications.
  • Diverse network connectivity options
    • Dual-band 2.4GHz/5GHz WiFi
    • Dual Gigabit Ethernet ports
    • 4G/LTE communication via mini PCIe (other modules supported)
  • Expandable Storage: M.2 NVME Socket for SSD Expansion
  • Multimedia Ready: HDMI Output at up to 4Kp60
  • Integrated Thermal Management: Aluminum Heatsink with ABS Body
PIzza Raspberry Pi CM4 Micro Server with 1GB RAM, 8GB eMMC $109.00

Perfect for:
  • IoT Gateway
  • Lora/Zigbee Gateway
  • 4G/LTE Router
  • Software Router
  • SD-WAN APPs
  • Network-Attached Storage (NAS)
  • Multimedia Server
  • Virtual Private Networks (VPNs)
  • Bitcoin Nodes
  • Digital Signage
  • Smart Home Applications
Azure Certified Temperature & Humidity (SHT31) detector with Wio Terminal $50.00

This Temperature & Humidity detector must be one of your choices if you are looking for a temperature & humidity sensor that is accurate and also easy to use for your IoT projects! 

This compact device can periodically send data of temperature and humidity of the environment to Azure IoT. 

Azure Certified Wio Terminal CO2 Checker $102.80
  • A cost-effective CO2 environmental measurement device that can be connected to the Internet and cloud.
  • Easy to detect temperature, humidity, and CO2 concentration and display the measurement on the screen. Also display the WBGT (wet bulb globe temperature).
  • Analyze data and enable notifications by sending values to Azure IoT. 

People Counting System built with Wio Terminal and Ultrasonic Sensor $41.20

Normal ultrasonic sensors can easily measure changes in distance to obstacles, but what about complex real-world tasks like people counting? Well, with TinyML running right on Wio Terminal, we can train a machine learning model on distance patterns to recognize when people are moving in or out of a room!

  • Powered by TinyML, train machine learning model on distance patterns
  • Gather the data through Edge Impulse 
  • Use continuous inference example to make sure not missing any important data.
  • Store the room occupancy data in the cloud and visualize it on PC
  • Connect to Azure IoT Central, watch the detailed progress feedback on the Serial Terminal

Smart Garden IoT Monitor built with Wio Terminal $46.70

With the Wio Terminal Smart Garden Kit, you can collect and measure soil moisture, light, temperature, and humidity for plants on the Wio Terminal LCD and visualize data! 

Conveniently use a suite of plug-and-play Grove sensor modules, and display the information neatly on the Wio Terminal’s built-in display. With onboard Bluetooth or WiFi, you can even add cloud capabilities to your smart garden system and view the collected data remotely!


To help beginners get started with Raspberry Pi Pico quickly, we are releasing the Starter Guide of Raspberry Pi Pico Based on MicroPython. Through four chapters and a total of 16 lessons of study, this course can help you get started with Raspberry Pi Pico easily. With its help, you can learn MicroPython from scratch, and build some interesting projects.

It is not required to have knowledge of MicroPython programming or electronics to begin. The course will take you through learning this knowledge step by step, and quickly put it into practice in each project.
 

It’s GIVEAWAY time! In late May, we have released the RP2040 Microcontrollers including a module and a dev board based on Raspberry Pi RP2040 chip. Currently, we’re manufacturing the first batch of the series with the expected shipping date in early July.
 

If you want the chance to win a free Wio RP2040 Module or a Wio RP2040 Dev Board, simply do the following two easy steps (we have 5 of each to give away):

1) Follow us on LinkedIn for future chances to win if you do not win this time.

2) Check out more details about the products, and then fill in an online application form with your project idea to win them away.

Already have Wio Terminal in hands? Don’t forget to join our Free Trial by sending us your project! We will refund back coupons to you for Wio Terminal you purchased! 
Starting Machine Learning with Wio RP2040 board

Train and deploy a simple neural network for classifying paper-rock gestures with just a single light sensor. Let’s see how to get Wio RP2040 mini Dev Board started with Machine learning at the very edge!

Like many of us during Covid-19, Benjamin Cabé was baking sourdough bread but he was at home in France. He wondered how he might tell if his dough was done proofing. He began working on an artificial nose that uses physical sensors to detect gas but also uses machine learning to identify the smell. His project is on the cover of Make: Volume 77. In this conversation, Make team and Benjamin talk about how his nose works, how it knows what it does and the limits of what it knows.
by Antonio Ruiz 
 
Antonio Ruiz developed a solar-powered sensing device, able to determine and track key environmental parameters in real-time.

The LCD screen on the Wio Terminal, not only allowed a display of the results but it could also be used to show in a map the position where the different measurements had been taken. To power this project, Antonio Ruiz employed a solar panel and a rechargeable lithium-ion battery, that could be connected with the Wio Terminal.

Age Gender Recognition model training/inference/evaluation scripts+reference model are published in
Seeed_Python_MachineLearning
 Final accuracy of pre-trained MobileFaceNet model (alpha = 2.0, total params: 798,982, model size: 3.1 Mb) is 92% for gender and 78% for age.
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