Telmo Fernández de Barrena

According to our database, Telmo Fernández de Barrena authored at least 8 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2025

Adaptive high frequency data streaming for Soft Real-Time Industrial AI: A scalable microservices based architecture with dynamic downsampling.

Telmo Fernández de Barrena, Alcides Fernandes, Juan Luis Ferrando, Ander García, Hugo Landaluce, Ignacio Angulo

Array, 2025

2024

IIoT Protocols for Edge/Fog and Cloud Computing in Industrial AI: A High Frequency Perspective.

Telmo Fernández de Barrena Sarasola, Ander García, Juan Luis Ferrando

Int. J. Cloud Appl. Comput., 2024

Impact of Real-Time Linux for Industrial Edge AI.

Telmo Fernández de Barrena, Ander García, Javier Franco, Juan Luis Ferrando

Proceedings of the Innovative Intelligent Industrial Production and Logistics, 2024

2023

Containerized Edge Architecture for Centralized Industry 4.0 Fleet Management.

Ander García, Javier Tardos, Juan Luis Ferrando, Telmo Fernández de Barrena

Proceedings of the 9th IEEE World Forum on Internet of Things, 2023

Edge Architecture for the Integration of Soft Models Based Industrial AI Control into Industry 4.0 Cyber-Physical Systems.

Ander García, Telmo Fernández de Barrena, Juan Luis Ferrando Chacón, Xabier Oregui, Zelmar Etxegoien

Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023), 2023

5G and MEC Based Data Streaming Architecture for Industrial AI.

Telmo Fernández de Barrena Sarasola, Juan Luis Ferrando Chacón, Ander García, Michail Dalgitsis

Proceedings of the Innovative Intelligent Industrial Production and Logistics, 2023

2021

A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals.

Juan Luis Ferrando Chacón, Telmo Fernández de Barrena, Ander García, Mikel Sáez de Sáez de Buruaga, Xabier Badiola, Javier Vicente

Sensors, 2021

A Novel Methodology for the Characterization of Cutting Conditions in Turning Processes Using Machine Learning Models and Acoustic Emission Signals.

Telmo Fernández de Barrena, Juan Luis Ferrando, Ander García, Pedro José Arrazola, José Manuel Abete, Diego Herrero

Proceedings of the 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, 2021