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

Lightweight anomaly detector for time series data using Isolation Forest, designed for edge environments.

Real-time environmental sound classifier optimized for low-power edge devices using MobileNet v1.

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.

Real-time AI-powered vision system for detecting defects in manufacturing environments

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.

Lightweight semantic segmentation model optimized for real-time street scene analysis on edge devices.

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.