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

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

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

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

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

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.

Deep learning solution that classifies human actions in video streams for workflow analysis and operational safety.

Guided training environment for creating and preparing custom Face Recognition models, with optimized deployment and inference on SECO hardware.