Application Hub

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

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

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.

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.