Artificial Intelligence - 意法半导体STMicroelectronics

ST offers a complete portfolio of products and solutions for the IoT; making everything smarter, from homes, cities, and industry to all the “Smart Things” that populate our world and make positive contributions to our lives. Highlighted solutions include AR/VR/MR, voice control, imaging, AI, and wireless charging.

	Contextual Activity Recognition Distributed intelligence between ST Smart Sensor Machine Learning Core and a neural network running on STM32 microcontroller. Ultra-low-power always-on Human Activity Recognition on LSM6DSOX Machine Learning Core Low-power sound analysis neural network running on STM32L4 triggered by the activity recognition Audio and motion are combined to recognize 5 activities (stationary, walking, running, biking, driving) in 3 different contexts (outdoor, indoor or in vehicle) ST Parts: SensorTile.box
	Face Expression Recognition Live face expression recognition from a camera video stream. Detection of multiple faces with labeled bounding box displayed in real-time Recognizes 7 expressions: angry, disgusted, scared, happy, neutral, sad, surprised Preprocessing, face detection, image cropping and scaling are performed by the STM32H747 ST Parts: STM32H747 Discovery Kit
	Face Identification Detection and identification of multiple faces with bounding box displayed in real-time from a live video feed. Enrollment via PC of up to 8 new faces Preprocessing, face detection, image cropping and scaling are performed by the STM32H747 ST Parts: STM32H747 Discovery Kit
	Image Classification on Microprocessor Advanced classification of images among 1000 different categories using Tensor Flow Lite on the STM32MP1 dual-core MPU. Images are classified with MobileNet v1: 1000 classes at 8 frames per second Tensor Flow Lite integrated via C++ runtime implementation on dual-core A7 Avenger96 board with parallel interface Camera daughter board. Allows optimal image capture without USB overhead ST Parts: STM32MP1
	Condition Monitoring of a Multi-Fan System NanoEdge AI Studio by Cartesiam easily creates custom machine learning library able to learn and infer in STM32 microcontrollers. No data set, no data scientist, no pre-trained Artificial Neural Network required. Vibration analysis in a multi-source and noisy environment On-chip learning of the normal condition On-chip anomaly detection such as shock, clogging, etc. is displayed real-time ST Parts: STM32G471

Contextual Activity Recognition

Distributed intelligence between ST Smart Sensor Machine Learning Core and a neural network running on STM32 microcontroller.

Face Expression Recognition

Live face expression recognition from a camera video stream.

Face Identification

Detection and identification of multiple faces with bounding box displayed in real-time from a live video feed.

Image Classification on Microprocessor

Advanced classification of images among 1000 different categories using Tensor Flow Lite on the STM32MP1 dual-core MPU.

Condition Monitoring of a Multi-Fan System

NanoEdge AI Studio by Cartesiam easily creates custom machine learning library able to learn and infer in STM32 microcontrollers. No data set, no data scientist, no pre-trained Artificial Neural Network required.