Other software tools

Introduction In a significant advancement to our software ecosystem for embedded sensor applications, ST has established a GitHub repository with platform-independent Rust drivers for MEMS sensors. The repository, accessible at https://github.com/STMicroelectronics/st-mems-rust-drivers, offers a comprehensive collection of Rust libraries designed to facilitate and expedite the integration of ST MEMS sensors across a broad spectrum of embedded platforms. Expanding MEMS sensor support in Rust Rust is increasingly recognized for its powerful memory safety guarantees, modern language features, and rapidly expanding ecosystem. It is gaining widespread adoption among embedded developers and is endorsed by leading technology companies like Google, Microsoft, and Meta as a reliable, industry-standard programming language. We have developed a suite of platform-independent Rust drivers that deliver robust, efficient, and user-friendly interfaces for a wide range of MEMS sensors. The repository

Are you a developer looking to streamline your workflow with MEMS sensors? Look no further! MEMS-Studio is here to enhance your experience with ST’s MEMS sensors. This intuitive software tool integrates various functionalities, from sensor evaluation to code creation, all in one platform. Why MEMS-Studio? MEMS-Studio combines the best of ST's existing tools like Unico-GUI, Unicleo-GUI, and AlgoBuilder into a single, powerful platform. It offers seamless configuration and testing of in-sensor features such as the finite state machine (FSM), machine learning core (MLC), and intelligent sensor processing unit (ISPU). Key features Comprehensive sensor evaluation: Easily evaluate a broad range of sensors and visualize data in real-time. No-Code algorithm design: Create and test algorithms graphically without writing a single line of code. Advanced data analysis: Perform runtime and offline data analysis with ease. Broad hardware support: Compatible with STM32 Nucleo bo

ST AIoT Craft, a new web-based tool, simplifies developing and provisioning node-to-cloud AIoT (Artificial Intelligence of Things) projects that use the machine-learning core (MLC) of ST’s smart MEMS sensors. The MLC is unique to the ST MEMS portfolio and enables decision-tree learning models to run directly in the sensor. Capable of operating autonomously, without host-system involvement, the MLC ensures low latency with low power consumption and efficiently handles tasks that require AI skills such as classification and pattern detection.   ST AIoT Craft also integrates all the steps needed for developing and provisioning IoT projects that leverage the MLC for in-sensor AI and offers a secure and user-friendly approach. There is robust cyber protection for data in the cloud and the web-based tool is conveniently accessed online without downloading to desktop. This saves time for users as it requires no installation, and eases collaboration between different team members such as