In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem...

In existing sparsity-driven inverse synthetic aperture radar (ISAR) imaging framework a sparse recovery (SR) algorithm is usually applied to azimuth compression to achieve high resolution in the cross-range direction. For range compression, however, direct application of an SR algorithm is not very effective because the scattering centers resolved in the high resolution range profiles at differ...

BACKGROUND Compressed sensing(CS) has been well applied to speed up imaging by exploring image sparsity over predefined basis functions or learnt dictionary. Firstly, the sparse representation is generally obtained in a single transform domain by using wavelet-like methods, which cannot produce optimal sparsity considering sparsity, data adaptivity and computational complexity. Secondly, most s...

Predictive modeling of functional neuroimaging data has become an important tool for analyzing cognitive structures in the brain. Brain images are high-dimensional and exhibit large correlations, and imaging experiments provide a limited number of samples. Therefore, capturing the inherent statistical properties of the imaging data is critical for robust inference. Previous methods tackle this ...

Collaborative filtering (CF), as a personalized recommending technology, has been widely used in e-commerce and other many personalized recommender areas. However, it suffers from some problems, such as cold start problem, data sparsity and scalability, which reduce the recommendation accuracy and user experience. This paper aims to solve the data sparsity in CF. In the paper, cliquesbased data...

CONTEXT: Systematic literature reviews largely rely upon using the titles and abstracts of primary studies as the basis for determining their relevance. However, our experience indicates that the abstracts for software engineering papers are frequently of such poor quality they cannot be used to determine the relevance of papers. Both medicine and psychology recommend the use of structured abst...

Statistical model is essential for constraint-free visual image reconstruction, as it may overfit training data and have poor generalization. In this study, we investigate the sparsity of the distributed patterns of visual representation and introduce a suitable sparse model for the visual image reconstruction experiment. We use elastic net regularization to model the sparsity of the distribute...