Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a manycore architecture

This paper presents a study of the parallelism of a Principal Component Analysis (PCA) algorithm and its adaptation to a manycore MPPA (Massively Parallel Processor Array) architecture, which gathers 256 cores distributed among 16 clusters. This study focuses on porting hyperspectral image processin...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Journal of systems architecture 2017-06, Vol.77, p.101-111
Hauptverfasser:	Lazcano, R., Madroñal, D., Salvador, R., Desnos, K., Pelcat, M., Guerra, R., Fabelo, H., Ortega, S., Lopez, S., Callico, G.M., Juarez, E., Sanz, C.
Format:	Artikel
Sprache:	eng
Schlagworte:	Bioengineering Computer Science Dimensionality reduction Hyperspectral imaging Image Processing Imaging Life Sciences Massively parallel processing Medical Imaging Real-time processing
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!