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.

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

Deep learning model to detect presentation attacks in biometric systems, enhancing security against spoofing attempts.

Deep learning system to upscale and enhance low-resolution images while preserving fine details.

A lightweight face recognition solution leveraging Edge Impulse’s FOMO model, designed for fast, accurate deployment on edge devices in industrial environments.

Robust speaker ID/verification using ECAPA-TDNN embeddings for secure, personalized HMI and analytics.

Automated detection and mapping of land-cover changes from satellite or aerial imagery for environmental monitoring and urban planning.

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.