Descripción

He received his B.Sc. degree in Electronic Engineering Communication Engineering from Universidad Politécnica de Madrid (UPM), Spain, in 2020, and his M.Sc. degree in Science in Internet of Things from Universidad Politécnica de Madrid (UPM), Spain, in 2021. He is currently a contracted researcher in Electronic and Microelectronic Design Group (GDEM) working actively in the Nemesis-3D-CM project. His research interests include hyperspectral image processing and machine learning classification and immersive computer vision applications.

Publicaciones

• An Embedded GPU Accelerated Hyperspectral Video Classification System in Real-Time

• IEEE, editor. (2022). An Embedded GPU Accelerated Hyperspectral Video Classification System in Real-Time, 2021 XXXVI Conference on Design of Circuits and Integrated Systems (DCIS).

• Stitching technique based on SURF for Hyperspectral Pushbroom Linescan Cameras

• IEEE, editor. (2022). Stitching technique based on SURF for Hyperspectral Pushbroom Linescan Cameras, 2021 XXXVI Conference on Design of Circuits and Integrated Systems (DCIS).

• GoRG: Towards a GPU‐Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications

• Sancho Aragón, J., Sutradhar, P., Chavarrías Lapastora, M., Pérez-Núñez, Á., Salvador, R., Lagares, A. et al
  2021
  Sensors, 21 (12)(4091), 1‐30

• Hyperspectral Images Acquisition: an Efficient Capture and Processing Stitching Procedure for Medical Environments

• IEEE, editor. (2020). Hyperspectral Images Acquisition: an Efficient Capture and Processing Stitching Procedure for Medical Environments, 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS).

• Multiclass Brain Tumor Classification Using Hyperspectral Imaging and Supervised Machine Learning

• IEEE, editor. (2020). Multiclass Brain Tumor Classification Using Hyperspectral Imaging and Supervised Machine Learning, 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS).

Proyectos

• NEMESIS-3D-CM: clasificacióN intraopEratoria de tuMores cErebraleS mediante modelos InmerSivos 3D