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By analyzing the problem structure, the user can select from a spectrum of algorithms, the one that best meets a given time-space specification.
Scientific workflows allow the combination of a wide spectrum of algorithms and resources in a single workspace [1 3].
All the experiments show that our design principle can be applied to a wide spectrum of algorithms to achieve comparable performance with much better robustness.
Thanks to its modular architecture, OTB allows fast prototyping and covers the full spectrum of algorithms for remote sensing image processing from pre-processing to advanced feature extractions methods allowing one to go from raw data to value added products.
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We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and (2) recurrent learning.
It contains functions for structural similarity searching, clustering of compound libraries with a wide spectrum of classification algorithms and various utilities for managing complex compound data.
Fig. 4 Spatial spectrum: a Spatial spectrum of TF MUSIC algorithm.
(a) The 3-D spatial-frequency spectrum of MUSIC algorithm.
Fig. 4 The spectra of two algorithms: a the auto-focusing-based MUSIC b the FrDCA-based MUSIC algorithms by using a six-element NLA from an environment containing 14 wideband signals.
Fig. 3 The spectra of two algorithms: a the auto-focusing-based MUSIC and b the FrDCA-based MUSIC by using a six-element NLA from an environment containing nine wideband signals.
Fig. 5 The normalized spectra of two algorithms: a the FrDCA-based CS and b the FrDCA-based joint CS by using a six-element NLA from an environment containing nine wideband signals.
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CEO of Professional Science Editing for Scientists @ prosciediting.com