Sentence examples for types of sparsity from inspiring English sources

Complex domain denoising is developed and studied as a test-problem for comparison of the designed filters as well as the different types of sparsity modeling.

In the rest of this subsection, we will discuss 3 variants of sparsity-inducing regularizations (norm, norm and norm) that can impose different types of sparsity patterns on the solution of Equation (2) and lead to simultaneous classification and feature selection (Ye and Liu, 2012).

Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through ℓ1-norm regularization, and the other invokes sparsity as an extra measurement.

The nonlocal block-matching technique is used for sparsity implementation and filter design for each type of sparsity.

The mask is an appropriate filter with coefficients of 1's and 0's depending on the type of sparsity in the original signal.

The last topic, depending on the type of sparsity, is either similar to the second topic or related to CS of sparse signal fields in a network.

The reconstruction algorithms have to recover the original sparse signal from the information domain and the type of sparsity in the transform domain.

However, depending on the type of sparsity (lowpass, bandpass, or random) and the type of sampling (uniform, periodic nonuniform, or random), the reconstruction may be unstable and the corresponding reconstruction matrix may be ill-conditioned [51, 52].

The type of sparsity is shown in column 3; for sampling schemes, it is usually low-pass, band-pass, or multiband [24], while for compressed sensing, and most other applications, it is random.

Therefore, the non-uniform distribution of nodes can lead to some type of sparsity in spatial domain that can be exploited to reduce the amount of sensing, processing, and/or communication.

Figure 2 a shows a standard type of sparsity-aware processing.