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- Hyperspectral compressive sensing: a comparison of embedded GPU and ARM implementationsPublication . Nascimento, Jose; Véstias, MárioHyperspectral imaging involves the sensing of a large amount of spatial information across several adjacent wavelengths. Typically, hyperspectral images can be represented by a three-dimensional data cube. The collected data cube is extremely large to be transmitted from the satellite/airborne platform to the ground station. Compressive sensing (CS) is an emerging technique that acquire directly the compressed signal instead of acquiring the full data set. This reduces the amount of data that needs to be measured, transmitted and stored in first place. In this paper, a comparison of a CS method implementation for an ARM and for a GPU is conducted. This study takes into account the accuracy, the performance, and the power consumption for both implementations. The 256-cores GPU of a Jetson TX2 board, the dual-core ARM Cortex-A9 of a ZYNQ-7000 SoC FPGA and the quad-core ARM Cortex-A53 of a ZYNQ UltraScale SoC FPGA are the target platforms used for experimental validation. The obtained results indicate that the embedded GPU is faster but uses more power. Therefore, the most appropriate platform depends on the performance and power constraints of the project.
- Low power compressive sensing for hyperspectral imageryPublication . Nascimento, Jose; Véstias, MárioHyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands at very narrow channels of a given spatial area. The resulting hyperspectral data cube typically comprises several gigabytes. Such extremely large volumes of data introduces problems in its transmission to Earth due to limited communication bandwidth. As a result, the applicability of data compression techniques to hyperspectral images have received increasing attention. This paper, presents a study of the power and time consumption of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform. The conducted experiments have been performed to demonstrate the applicability of these methods for onboard processing. The results show that by using this low energy consumption GPU and integer data type is it possible to obtain real-time performance with a very limited power requirement while maintaining the methods accuracy.