MEMS-Studio

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Software solution for MEMS sensors with graphical no-code design of algorithms and development of embedded AI features

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Product overview

Description

MEMS-Studio is a complete desktop software solution designed to develop embedded AI features, evaluate embedded libraries, analyze data, and design no-code algorithms for the entire portfolio of MEMS sensors. This unique software solution offers a versatile development environment, enabling the evaluation and programming of all MEMS sensors, and launches a new generation of solutions to expand the functions of the well-established applications Unico-GUI, Unicleo-GUI, and AlgoBuilder.

MEMS-Studio facilitates the process of implementing proof of concept using a graphical interface without writing code for STM32 microcontrollers. This solution allows configuring sensors and embedded AI (machine learning and neural networks), leveraging on a machine learning core (MLC), neural networks for the ISPU, and finite state machines (FSM). It reuses embedded software libraries, combines multiple functionalities in a single project, and visualizes data in real time using plot and display.

MEMS-Studio offers the following user experience:

  • Sensor evaluation for motion, environmental, and infrared sensors in the MEMS portfolio
  • Configuration and testing of in-sensor features such as the finite state machine (FSM), machine learning core (MLC), intelligent sensor processing unit (ISPU)
  • Runtime and offline data analysis
  • No-code graphical design of algorithms

  • All features

    • Sensor configuration
      • Easy sensor configuration and evaluation
      • Access to the full sensor register map
      • Interrupt status monitoring
    • Sensor data analysis
      • Runtime sensor data visualization charts (line charts, bar graphs, 3D plots, …)
      • Data logging to .csv file
      • Offline data visualization, data labeling, and editing
      • Fast Fourier transform (FFT) analysis of online and offline data
      • Spectrogram analysis of online and offline data
    • Application development
      • Testing of the advanced embedded features (FIFO, pedometer, free fall, …) in the sensor
      • In-sensor AI design and programming for the finite state machine (FSM), machine learning core (MLC), and intelligent sensor processing unit (ISPU)
      • Embedded autoML tool (automatic filter and feature selection) to simplify the MLC configuration process
      • Visualization and data logging of the output of the embedded software libraries
      • Development of no-code algorithms for data processing in STM32 microcontrollers
    • Support for Windows, macOS, and Linux operating systems
    • Network updates with automatic notification of new releases

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