With STM32H5 + ITTIA DB Lite and ITTIA DB Lite AI, developers can continuously capture motor data, maintain local time-series history, calculate meaningful features, and enable AI-based anomaly detection directly on the MCU.
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With STM32 devices and ITTIA DB Lite AI, developers can build intelligent motor health monitoring at the edge by capturing sensor data, preserving time-series history, generating AI-ready features, and logging inference results locally on the microcontroller. For robotics, cloud-only intelligence…
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By combining STM32H7 with ITTIA DB Lite Product Family, developers can build high-performance, data-centric edge devices that reliably collect, manage, process, and transform sensor data into AI-ready insights directly on the microcontroller. From deterministic data management and time-series…
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The ITTIA DB Platform provides the trusted data foundation for next-generation embedded systems, enabling devices to acquire, manage, process, and transform sensor data with accuracy, consistency, and determinism. By ensuring high-quality, AI-ready data pipelines, ITTIA helps vehicles, medical…
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Discover how the ITTIA DB Platform enables next-generation Battery Management Systems (BMS) through deterministic data management, real-time analytics, AI-ready feature engineering, and Edge AI. Learn how battery telemetry can be transformed into actionable intelligence for accurate State of Health…
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STM32H7 and the ITTIA DB Lite Product Family provide a powerful foundation for intelligent embedded systems with deterministic data management, time-series processing, power-fail-safe storage, and AI-ready data pipelines. Build smarter industrial, robotics, medical, and IoT applications faster by…
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By combining STM32U5 with ITTIA DB Lite and ITTIA DB Lite AI, developers can build data-centric edge devices that reliably collect, manage, process, and transform sensor data into AI-ready insights directly on the microcontroller. From deterministic data management and time-series processing to…
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With STM32 Sensor Fusion and ITTIA DB Lite AI, developers can collect, clean, synchronize, and manage real-time sensor data directly on microcontrollers to build reliable Edge AI pipelines for robotics applications. By transforming raw sensor streams into AI-ready insights, ITTIA DB Lite AI enables…
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As vehicles become software-defined, trusted edge data drives real intelligence. With relational models in ECUs, systems become deterministic, explainable, and real-time, enabling instant decisions without connectivity.
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The ITTIA DB Platform now supports the latest STM32N6, enabling developers to build data-centric Edge AI systems with deterministic on-device data management. Now STM32 devices can reliably capture and manage high-frequency sensor signals, structure time-series data, and prepare feature windows for…
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The ITTIA DB Platform turns the STM32H7 from a fast controller into a smart, self-reliant data node by combining three components: ITTIA DB Lite for MCU-grade, power-fail-safe data management with deterministic ingestion, compact time-series/indexed storage (even vector support) and tiny footprint…
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This is why developers are turning to ITTIA DB Lite for STM32 devices. It allows embedded systems to perform real-time data stream processing, ingesting, transforming, and analyzing data the moment it arrives. Unlike traditional approaches that rely on batch uploads and cloud-based workflows, ITTIA…
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The future of embedded systems data management lies in integrating lightweight, real-time databases with AI-enabled edge processing, allowing devices to collect, analyze, and act on data autonomously.
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Data management for microcontrollers such as STM32 devices play a crucial role in modern automation by enabling real-time data collection, processing, and control, which is essential for optimizing processes, improving efficiency, and ensuring safety at the place of origin. For the edge…
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The future of embedded devices will be built on software and data management fundamentals. Embedded edge data management enables device applications to process and manage data locally, near the source, within embedded systems or edge devices. This approach offers benefits like reduced latency,…
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