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 YOLOv8n, optimized for edge devices using 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.

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

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

Efficient CNN model for image classification on mobile and embedded edge devices.

Fast and accurate object detection using quantized YOLOv8n optimized for embedded edge devices.