Secure face recognition HMI system for personalized user access in industrial environments.

HMI system that uses AI to recognize operators during alerts and show personalized troubleshooting instructions.

Detects critical alarm sounds (e.g., fire alarms) and triggers immediate machine shutdown for safety compliance.

Lightweight real-time people counting model based on quantized YOLOX, optimized for edge devices using LiteRT (TensorFlow Lite) .

Lightweight and quantized PoseNet model for real-time multi-person pose estimation on edge hardware.

Efficient single-camera depth estimation using quantized MiDaS v2 model optimized for edge AI deployment.

High-resolution neural network for accurate keypoint detection in human pose estimation tasks.

Neural network model for forecasting household electricity consumption using historical time series data.

Energy-efficient, real-time object detection optimized for low-power edge devices with strict battery and thermal constraints.

AI vision application to detect human falls in real time for safety, elderly care, and industrial risk monitoring.

Deep learning solution that classifies human actions in video streams for workflow analysis and operational safety.

Energy-efficient object detection optimized for edge platforms with power and thermal constraints.

Bridging the Field and Digital: Universal Connectivity, Zero Latency, and On-Device Machine Learning for Industry 5.0.