Sentence examples for sparse spatial from inspiring English sources

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However, the sparse spatial sampling of this data set has posed a significant challenge for efficiently reducing uncertainty of reservoir parameters.

The filters are tested extensively in various turbulent regimes with distinct statistics and with changing observation time frequency and both dense and sparse spatial observations.

To be more specific, in [3], the task of channel reduction is performed by using sparse spatial filter optimization method, where manual intervention is required for obtaining some parameters.

We first provide the sparse spatial spectrums obtained from the two Bayesian compressive sensing algorithms when there is no narrowband interference in Fig. 2. The true DOAs are marked with red vertical lines.

To mitigate this problem, Oliveira et al. introduce sparse spatial coding (SSC) for image classification which combines a sparse coding dictionary learning, a spatial constraint coding, and an online classification stage [63].

In the medical image community, the idea to indicate IHD or infarction accurately via quantification of myocardial kinematic function has stimulated a huge number of computing algorithms to overcome practical difficulties, for example, relatively sparse spatial resolution and low temporal sampling rate of current imaging modalities.

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Arvaneh proposed a sparse common spatial pattern algorithm and a robust sparse common spatial pattern algorithm for channel selection.

Traditional vibration measurement sensors such as accelerometers have high-frequency sampling capacity; however, they are discrete point-wise sensors only providing sparse, low spatial sensing resolution measurements, while dense deployment to achieve high spatial resolution is expensive and results in the mass-loading effect and modification of structure's surface.

Yang's algorithm suffers from the limit of recovering the image that is sparse in spatial domain; when it comes to recover the images that are non-sparse in spatial domain, it fails to reconstruct.

Afterwards, by combining these features with SPM, the authors propose Kernel Sparse Representation Spatial Pyramid Matching (KSRSPM).

Hyperspectral imagery is intrinsically sparse in spatial and spectral domains, and image super-resolution quality largely depends on whether the prior knowledge is utilized properly.

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