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.

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

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

Fast object detection using YOLOX optimized for embedded edge devices.

AI-powered image analysis tool for defect detection in industrial environments, with a guided journey for customizing new defect types.

Real-time activity recognition using motion sensor data from wearable fitness devices.

A time-series forecasting model designed to predict sensor data patterns in smart buildings, enabling proactive energy management and maintenance.

High-precision OCR application that detects and transcribes text from real-world images with superior accuracy, ideal for industrial and logistics use cases.

Lightweight OCR application that detects and reads text in images using a faster, lower-resource model designed for embedded and edge devices.

Specialized AI-based vision system for detecting surface and structural defects on printed circuit boards (PCBs) during manufacturing inspection.

Advanced transformer-based detector optimized for accurate, real-time edge performance on SECO platforms.

On-device fine-tuning/validation of ResNet classifiers—customize without cloud dependency.

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.