Dr. Eduardo Juárez Martínez Profile

Dr. Eduardo Juárez Martínez

Assistant Professor at Univ Politécnica de Madrid

SPIE Involvement:

Author

Proceedings Article | 5 October 2017 Paper

Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

R. Lazcano, D. Madroñal, H. Fabelo, S. Ortega, R. Salvador, G. Callicó, E. Juárez, C. Sanz

Proc. SPIE. 10430, High-Performance Computing in Geoscience and Remote Sensing VII

KEYWORDS: Hyperspectral imaging, Principal component analysis, Image processing, Parallel processing, Machine learning, Image classification, Algorithm development

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Proceedings Article | 24 October 2016 Presentation + Paper

Parallelism exploitation of a PCA algorithm for hyperspectral images using RVC-CAL

R. Lazcano, I. Sidrach-Cardona , D. Madroñal, K. Desnos, M. Pelcat, E. Juárez, C. Sanz

Proc. SPIE. 10007, High-Performance Computing in Geoscience and Remote Sensing VI

KEYWORDS: Hyperspectral imaging, Principal component analysis, Data modeling, Sensors, Image processing, Remote sensing, Data processing, Spatial resolution, Chemical elements, Electromagnetism

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Hyperspectral imaging (HI) collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. The tremendous development of this technology within the field of remote sensing has led to new research fields, such as cancer automatic detection or precision agriculture, but has also increased the performance requirements of the applications. For instance, strong time constraints need to be respected, since many applications imply real-time responses. Achieving real-time is a challenge, as hyperspectral sensors generate high volumes of data to process. Thus, so as to achieve this requisite, first the initial image data needs to be reduced by discarding redundancies and keeping only useful information. Then, the intrinsic parallelism in a system specification must be explicitly highlighted.

In this paper, the PCA (Principal Component Analysis) algorithm is implemented using the RVC-CAL dataflow language, which specifies a system as a set of blocks or actors and allows its parallelization by scheduling the blocks over different processing units. Two implementations of PCA for hyperspectral images have been compared when aiming at obtaining the first few principal components: first, the algorithm has been implemented using the Jacobi approach for obtaining the eigenvectors; thereafter, the NIPALS-PCA algorithm, which approximates the principal components iteratively, has also been studied. Both implementations have been compared in terms of accuracy and computation time; then, the parallelization of both models has also been analyzed.

These comparisons show promising results in terms of computation time and parallelization: the performance of the NIPALS-PCA algorithm is clearly better when only the first principal component is achieved, while the partitioning of the algorithm execution over several cores shows an important speedup for the PCA-Jacobi. Thus, experimental results show the potential of RVC–CAL to automatically generate implementations which process in real-time the large volumes of information of hyperspectral sensors, as it provides advanced semantics for exploiting system parallelization.

Proceedings Article | 24 October 2016 Presentation + Paper

Parallel implementation of a hyperspectral image linear SVM classifier using RVC-CAL

D. Madroñal , H. Fabelo, R. Lazcano, G. M. Callicó, E. Juárez, C. Sanz

Proc. SPIE. 10007, High-Performance Computing in Geoscience and Remote Sensing VI

KEYWORDS: Hyperspectral imaging, Infrared imaging, Image processing, Ultraviolet radiation, Remote sensing, Spectral resolution, Analytical research, Binary data, Information security, Classification systems

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Proceedings Article | 20 October 2015 Paper

RVC-CAL library for endmember and abundance estimation in hyperspectral image analysis

R. Lazcano López, D. Madroñal Quintín, E. Juárez Martínez, C. Sanz Álvaro

Proc. SPIE. 9646, High-Performance Computing in Remote Sensing V

KEYWORDS: Signal to noise ratio, Hyperspectral imaging, Cancer, Surgery, Matrices, Image processing, Remote sensing, Image analysis, Analytical research, Algorithm development

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Hyperspectral imaging (HI) collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. Although this technology was initially developed for remote sensing and earth observation, its multiple advantages - such as high spectral resolution - led to its application in other fields, as cancer detection. However, this new field has shown specific requirements; for instance, it needs to accomplish strong time specifications, since all the potential applications - like surgical guidance or in vivo tumor detection - imply real-time requisites. Achieving this time requirements is a great challenge, as hyperspectral images generate extremely high volumes of data to process. Thus, some new research lines are studying new processing techniques, and the most relevant ones are related to system parallelization.

In that line, this paper describes the construction of a new hyperspectral processing library for RVC–CAL language, which is specifically designed for multimedia applications and allows multithreading compilation and system parallelization. This paper presents the development of the required library functions to implement two of the four stages of the hyperspectral imaging processing chain--endmember and abundances estimation. The results obtained show that the library achieves speedups of 30%, approximately, comparing to an existing software of hyperspectral images analysis; concretely, the endmember estimation step reaches an average speedup of 27.6%, which saves almost 8 seconds in the execution time. It also shows the existence of some bottlenecks, as the communication interfaces among the different actors due to the volume of data to transfer. Finally, it is shown that the library considerably simplifies the implementation process. Thus, experimental results show the potential of a RVC–CAL library for analyzing hyperspectral images in real-time, as it provides enough resources to study the system performance.

Proceedings Article | 20 October 2015 Paper

Dimensionality reduction and endmember extraction for hyperspectral imaging using an RVC-CAL library

D. Madroñal Quintín, R. Lazcano López, E. Juárez Martínez, C. Sanz Álvaro

Proc. SPIE. 9646, High-Performance Computing in Remote Sensing V

KEYWORDS: Hyperspectral imaging, Infrared imaging, Principal component analysis, Cancer, Surgery, Image processing, Ultraviolet radiation, Infrared radiation, Chemical elements, Algorithm development

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Proceedings Article | 3 May 2011 Paper

Energy consumption estimation of an OMAP-based Android operating system

Gabriel González, Eduardo Juárez, Juan José Castro, César Sanz

Proc. SPIE. 8067, VLSI Circuits and Systems V

KEYWORDS: Digital signal processing, Clocks, Video, Manufacturing, Signal processing, Video processing, Embedded systems, Multimedia, Operating systems, Process modeling

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Proceedings Article | 10 May 2007 Paper

Multiformat decoder for a DSP-based IP set-top box

F. Pescador, M. J. Garrido, C. Sanz, E. Juárez, D. Samper, R. Antoniello

Proc. SPIE. 6590, VLSI Circuits and Systems III

KEYWORDS: Internet, Digital signal processing, Surface plasmons, Clocks, Data storage, Video, Computer programming, Digital video discs, Computer architecture, Video coding

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Showing 5 of 7 publications

Conference Committee Involvement (4)

High-Performance Computing in Geoscience and Remote Sensing

13 September 2018 | Berlin, Germany

High-Performance Computing in Geoscience and Remote Sensing

12 September 2017 | Warsaw, Poland

High-Performance Computing in Geoscience and Remote Sensing

28 September 2016 | Edinburgh, United Kingdom

High-Performance Computing in Remote Sensing

21 September 2015 | Toulouse, France

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