José Carlos Cabaleiro

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Universidade de Santiago de Compostela

Spain

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About

José Carlos Cabaleiro is a Professor at Universidade de Santiago de Compostela in Spain. His research interests encompass deep learning, LiDAR data analysis, digital forensics, and parallel computing. His recent publications include studies on point cloud classification, thread-level speculative parallelization, and security in cloud computing frameworks.

Recent Grants

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SDNHPC: Solutions for new challenges in high performance computing

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Articles (13)

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2024

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2024

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2024

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2021

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2020

Collaborators (7)

Lukas Winiwarter

Professor of Geodesy

University of Innsbruck

AUSTRIA

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Juan Angel Lorenzo del Castillo

Associate Professor / Head of the High Performance Data Analytics final year's engineering option

CY Cergy Paris Université

FRANCE

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Tomás F. Pena

Universidade de Santiago de Compostela

SPAIN

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Francisco F. Rivera

Professor

Universidade de Santiago de Compostela

SPAIN

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Oscar García Lorenzo

Profesor Axudante Doutor

Universidade de Santiago de Compostela

SPAIN

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Basilio B. Fraguela

Professor

Universidade da Coruña

SPAIN

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Ruben Laso

TU Wien

AUSTRIA

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José Carlos Cabaleiro

Research Interests

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Contact this professor

About

Recent Grants

Articles (13)

Deep learning with simulated laser scanning data for 3D point cloud classification

A new thread-level speculative automatic parallelization model and library based on duplicate code execution

Powerline Detection and Characterization in General-Purpose Airborne LiDAR Surveys

Digital forensic analysis of the private mode of browsers on Android

Characterizing zebra crossing zones using LiDAR data

CIMAR, NIMAR, and LMMA: Novel algorithms for thread and memory migrations in user space on NUMA systems using hardware counters

Security by Design for Big Data Frameworks Over Cloud Computing

A highly optimized skeleton for unbalanced and deep divide-and-conquer algorithms on multi-core clusters

LBMA and IMAR2: Weighted lottery based migration strategies for NUMA multiprocessing servers

Load balanced heterogeneous parallelism for finite difference problems on image denoising

IHP: a dynamic heterogeneous parallel scheme for iterative or time-step methods—image denoising as case study

A Parallel Skeleton for Divide-and-conquer Unbalanced and Deep Problems

Next-generation big data federation access control: A reference model

Collaborators (7)

Lukas Winiwarter

Juan Angel Lorenzo del Castillo

Tomás F. Pena

Francisco F. Rivera

Oscar García Lorenzo

Basilio B. Fraguela

Ruben Laso

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