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Edge-to-cloud machine learning solution
30 April 2019, Telecoms, Datacoms, Wireless, IoT

NXP Semiconductors has collaborated with Microsoft to bring artificial intelligence (AI) and machine learning (ML) capabilities for anomaly detection to Azure IoT users.

The solution consists of a small form factor, low-power system-on-module (SOM) powered by NXP’s i.MX RT106C crossover processors, a full suite of sensors, and an associated Anomaly Detection Toolbox. The toolbox utilises various ML algorithms such as random forest and simple vector machine (SVM) to model normal behaviour of devices, detecting anomalous behaviour through combined local and cloud mechanisms. This allows much lower cloud bandwidth requirements while maintaining full online logging and processing capabilities at a fraction of the cost. Applications include predictive maintenance for rotating components, presence detection and intrusion detection.

The NXP i.MX RT106C crossover microcontroller (MCU), running at up to 600 MHz, is capable of collecting and analysing sensor data in real time locally at the edge. The solution provides an easy way to seamlessly transfer data to the Azure IoT cloud, where customers can visualise the data and utilise powerful data analytical tools to train behaviour prediction models for deploying on edge devices.

For more information visit https://iotedge.nxp.com/anomaly-detection/


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