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

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.

A training pipeline for customizing YAMNet to recognize new environmental or industrial sounds.

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

Deep-learning gesture recognition for touchless HMI in dynamic environments.

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

Predictive modeling for TBM settlement to enhance safety and planning in tunneling projects.

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.