In the world of robotics, the spotlight often falls on sensors, actuators, and artificial intelligence (AI) algorithms.

The proposed industrial anomaly detection model is computationally efficient, memory-friendly, and also suitable for low-light conditions, common in manufacturing environments, making it well ...

MA-VAE MA-VAE: Multi-head attention-based variational autoencoder approach for anomaly detection in multivariate time-series applied to automotive endurance powertrain testing Paper corresponding to ...

Autoencoder-based SCADA Telemetry Anomaly Detection This project implements an autoencoder-based anomaly detection system for SCADA (Supervisory Control and Data Acquisition) telemetry data. The ...

The study introduces a novel hybrid Variational Autoencoder-SURF (VAE-SURF) model for anomaly detection in crowded environments, addressing critical challenges such as scale variance and temporal ...

With the rise of deep convolutional neural networks (CNNs), considerable attention has been paid to video anomaly detection (VAD). Autoencoders are a popular type of framework for VAD, and many ...

Deep learning models have not been heavily studied in video-based eye movement detection. Methods: We developed, trained, and validated a deep-learning system (aEYE) to classify video recordings as ...

Background: Current EMS stroke screening tools facilitate early detection and triage, but the tools' accuracy and reliability are limited and highly variable. An automated stroke screening tool could ...