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Bench Talk for Design Engineers

Bench Talk

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Bench Talk for Design Engineers | The Official Blog of Mouser Electronics


Internet-Connected AI Face Tracker Greg Toth

(Source: ZinetroN/Shutterstock.com)

The Seeed Studio Sipeed Maixduino Kit for RISC-V AI + IoT rapid development platform helps designers quickly develop Artificial Intelligence (AI) and IoT applications using a dual-core 64-bit RISC-V CPU coupled with a 400MHz neural network processor. The neural network processor enables AI applications using loadable neural networks that are available either pre-built or can be custom developed using several deep learning toolsets. An on-board ESP32 Wi-Fi + Bluetooth® module provides wireless connectivity, and camera and LCD display connectors allow you to integrate the included digital camera and LCD display for machine-vision applications. The on-board digital microphone supports speech recognition and sound processing applications, and an audio output connector allows a speaker to be connected for sound output. Power is provided through either a USB Type C cable or a DC input connector.

The Maixduino board has an Arduino Uno form factor with Arduino-compatible connectors. This allows various types of Arduino shields to be used for input/output or connectivity expansion using digital, analog, UART and I2C signals.

Software development is supported using MicroPython, Arduino IDE, PlatformIO IDE, OpenMV IDE and with deep learning tools including Tiny-Yolo, Mobilenet and TensorFlow Lite. MaixPy is MicroPython ported to the K210 processor contained in the MAIX module. MaixPycontains pre-built library packages that support board operation including initialization, input/output peripherals, and sensor data processing. Source code for MaixPy is available on GitHub along with example MaixPy programs. The MaixPy libraries include functions for initialization, input/output, Fast Fourier Transform, video and audio processing, networking, and a variety of other functions.

Medium One IoT Platform

The Medium One IoT Platform is a cloud-based platform designed to help early-stage developers prototype their IoT project or connect their existing hardware to the cloud. It offers an IoT Data Intelligence platform enabling customers to quickly build IoT applications with less effort. Programmable workflows allow you to quickly build processing logic without having to create your own complex software stack. A graphical workflow builder and run-time engine lets you process IoT data as it arrives and route or transform it as needed for your application. Workflow library modules are available for data analytics, charting, geolocation, weather data, MQ Telemetry Transport (MQTT), SMS text messaging, and integration with Twitter, Salesforce, and Zendesk. Snippets of Python code create custom workflow modules. The web-based Workflow Studio, which provides a drag-and-drop visual programming environment, designs and builds end-to-end workflows. Workflow versioning and debugging tools support the development, test, and deployment lifecycle. Communications between IoT devices and the Medium One cloud are done through REST APIs or MQTT protocol. Configurable dashboards allow you to visualize application data and view real-time data in a variety of formats. Dashboard widgets are included for tabular data, charts, geopoint maps, gauges, and user inputs. Medium One’s iOS and Android apps allow you to also build simple mobile app dashboards that can communicate with your devices through the platform.

Using Your Own Sipeed Maixduino Kit with the Medium One IoT Platform

To use your own Sipeed Maixduino Kit for RISC-V AI + IoT with the Medium One IoT Platform to perform real-time face detection, check out our step-by-step article that walks you through the entire process of:

  • Setting up the hardware and development tools
  • Installing and running the necessary software components
  • Building the code and downloading it to the board
  • Configuring the board’s cloud connection parameters
  • Running the board to generate real-time sensor measurements that are sent to the cloud.

Here, we also show you how to observe the published data on a real-time dashboard created in the Medium One environment. A set of next steps gives suggestions for how to extend and adapt the application for different IoT prototyping scenarios or to learn more.



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Greg is an architect, engineer and consultant with more than 30 years experience in sensors, embedded systems, IoT, telecommunications, enterprise systems, cloud computing, data analytics, and hardware/software/firmware development. He has a BS in Electrical Engineering from the Univ. of Notre Dame and a MS in Computer Engineering from the Univ. of Southern California.


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