Daniel Madroñal
Orcid: 0000-0001-5994-7440
According to our database1,
Daniel Madroñal
authored at least 28 papers
between 2016 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons.
CoRR, 2024
CoRR, 2024
Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.
CoRR, 2024
2022
Energy Consumption and Runtime Performance Optimizations Applied to Hyperspectral Imaging Cancer Detection.
Proceedings of the 23rd International Symposium on Quality Electronic Design, 2022
Proceedings of the CPS Summer School PhD Workshop 2022 co-located with 4th Edition of the CPS Summer School (CPS 2022), 2022
2021
CoRR, 2021
Proceedings of the DroneSE and RAPIDO '21: Methods and Tools, 2021
2020
Runtime multi-versioning and specialization inside a memoized speculative loop optimizer.
Proceedings of the CC '20: 29th International Conference on Compiler Construction, 2020
2019
Adaptation of an Iterative PCA to a Manycore Architecture for Hyperspectral Image Processing.
J. Signal Process. Syst., 2019
PAPIFY: Automatic Instrumentation and Monitoring of Dynamic Dataflow Applications Based on PAPI.
IEEE Access, 2019
Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications.
IEEE Access, 2019
IEEE Access, 2019
Proceedings of the Embedded Computer Systems: Architectures, Modeling, and Simulation, 2019
Characterizing Hyperspectral Data Layouts: Performance and Energy Efficiency in Embedded GPUs for PCA-based Dimensionality Reduction.
Proceedings of the XXXIV Conference on Design of Circuits and Integrated Systems, 2019
Proceedings of the 16th ACM International Conference on Computing Frontiers, 2019
2018
Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images.
Sensors, 2018
An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.
Sensors, 2018
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons.
Remote. Sens., 2018
A Unified Hardware/Software Monitoring Method for Reconfigurable Computing Architectures Using PAPI.
Proceedings of the 13th International Symposium on Reconfigurable Communication-centric Systems-on-Chip, 2018
Proceedings of the 15th ACM International Conference on Computing Frontiers, 2018
2017
J. Syst. Archit., 2017
Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a manycore architecture.
J. Syst. Archit., 2017
High-level design using Intel FPGA OpenCL: A hyperspectral imaging spatial-spectral classifier.
Proceedings of the 12th International Symposium on Reconfigurable Communication-centric Systems-on-Chip, 2017
Energy consumption characterization of a Massively Parallel Processor Array (MPPA) platform running a hyperspectral SVM classifier.
Proceedings of the 2017 Conference on Design and Architectures for Signal and Image Processing, 2017
Parallel implementation of an iterative PCA algorithm for hyperspectral images on a manycore platform.
Proceedings of the 2017 Conference on Design and Architectures for Signal and Image Processing, 2017
HELICoiD: interdisciplinary and collaborative project for real-time brain cancer detection: Invited Paper.
Proceedings of the Computing Frontiers Conference, 2017
2016
Proceedings of the 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP), 2016
Hyperspectral image classification using a parallel implementation of the linear SVM on a Massively Parallel Processor Array (MPPA) platform.
Proceedings of the 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP), 2016