By George Boole

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**Additional resources for A Treatise on Differential Equations**

**Sample text**

1 Evolution of Cluster Computing in Remote Sensing . . . . . . . . 2 Heterogeneous Computing in Remote Sensing . . . . . . . . . . 3 Specialized Hardware for Onboard Data Processing . . . . . . . . 3 Case Study: Pixel Purity Index (PPI) Algorithm . . . . . . . . . . . . . 1 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . 2 Parallel Implementations . . . . . . . . . . . .

The proposed cluster-based parallel version of the PPI algorithm proposed in this chapter was run from one of such nodes, called thunder1. The operating system used in the experiments was Linux Fedora Core, and MPICH [44] was the message-passing library used. To explore the performance of the heterogeneity-aware implementation of PPI developed in this chapter, we have considered four different NOWs. All of them were custom-designed in order to approximate a recently proposed framework for evaluating heterogeneous parallel algorithms [45], which relies on the assumption that a heterogeneous algorithm cannot be executed on a heterogeneous network faster than its homogeneous version on an equivalent homogeneous network.

Despite the fact that a different skewer j would generate a different extrema set Sextrema (skewer j ), it is very likely that some sample vectors may appear in more than one extrema set. 1) 3. Calculation of PPI scores. 2) j=1 4. Endmember selection. Find the pixel vectors with scores of NPPI (fi ) that are E by calculating the spectral above tv and form a unique set of endmembers {ee }e=1 angle distance (SAD) for all possible vector pairs and discarding those pixels that result in an angle value below ta .