摘要压缩感知(Compressive sensing, CS) 是一种全新的信息采集与处理的理论框架.
借助信号内在的稀疏性或可压缩性, 可从小规模的线性, 非自适应的测量中通过非线性优化的 …

This work addresses the problem of real-time online reconstruction of dynamic magnetic
resonance imaging sequences. The proposed method reconstructs the difference between …

A fundamental problem in machine learning is to understand how neural networks make
accurate predictions, while seemingly bypassing the curse of dimensionality. A possible …

To accurately extract fault signatures from noisy signals, an improved orthogonal matching
pursuit (OMP) with adaptive Gabor sub-dictionaries is proposed in this paper. Firstly, based …

The presence of periodic sparse impulses in vibration signals often indicates the occurrence
of machine faults. This study focuses on the detection and diagnosis of an exact fault in …

The process of gathering of scientific data plays an important role in telemonitoring and
communications technologies of underwater information. However, to obtain such a huge …

We consider linear regression under a model where the parameter vector is known to be
sparse. Using a Bayesian framework, we derive the minimum mean-square error (MMSE) …

The fact that fewer measurements are needed by log-sum minimization for sparse signal
recovery than the ℓ 1-minimization has been observed by extensive experiments …

This letter investigates the problem of sparse signal recovery in the presence of additive
impulsive noise. The heavytailed impulsive noise is well modeled with stable distributions …

Motivated by the observation that a given signal x admits sparse representations in multiple
dictionaries Ψ d but with varying levels of sparsity across dictionaries, we propose two new …