Maximal sparsity with deep networks?
The iterations of many sparse estimation algorithms are comprised of a fixed linear filter
cascaded with a thresholding nonlinearity, which collectively resemble a typical neural …
cascaded with a thresholding nonlinearity, which collectively resemble a typical neural …
Exploiting prior knowledge in compressed sensing wireless ECG systems
LF Polania, RE Carrillo… - IEEE journal of …, 2014 - ieeexplore.ieee.org
Recent results in telecardiology show that compressed sensing (CS) is a promising tool to
lower energy consumption in wireless body area networks for electrocardiogram (ECG) …
lower energy consumption in wireless body area networks for electrocardiogram (ECG) …
A survey on compressive sensing: Classical results and recent advancements
Recovering sparse signals from linear measurements has demonstrated outstanding utility
in a vast variety of real-world applications. Compressive sensing is the topic that studies the …
in a vast variety of real-world applications. Compressive sensing is the topic that studies the …
Prior information aided deep learning method for grant-free NOMA in mMTC
In massive machine-type communications (mMTC), the conflict between millions of potential
access devices and limited channel freedom leads to a sharp decrease in spectrum …
access devices and limited channel freedom leads to a sharp decrease in spectrum …
Learning non-locally regularized compressed sensing network with half-quadratic splitting
Deep learning-based Compressed Sensing (CS) reconstruction attracts much attention in
recent years, due to its significant superiority of reconstruction quality. Its success is mainly …
recent years, due to its significant superiority of reconstruction quality. Its success is mainly …
A comprehensive review on compressive sensing
C Shaik, R RajaA, SS Kalapala… - … on Applied Artificial …, 2022 - ieeexplore.ieee.org
Sparse sampling, also known as compressed sampling or compressed sensing (CS), is a
new signal processing technique that samples the signal with considerably fewer samples …
new signal processing technique that samples the signal with considerably fewer samples …
Weighted -minimization for sparse recovery under arbitrary prior information
Weighted-minimization has been studied as a technique for the reconstruction of a sparse
signal from compressively sampled measurements when prior information about the signal …
signal from compressively sampled measurements when prior information about the signal …
ECG compression using wavelet-based compressed sensing with prior support information
Electrocardiogram (ECG) signal compression is a vital signal processing area, especially
with the growing usage of wireless body sensor networks (WBSN). ECG signals need to be …
with the growing usage of wireless body sensor networks (WBSN). ECG signals need to be …
Iterative support detection-based split bregman method for wavelet frame-based image inpainting
The wavelet frame systems have been extensively studied due to their capability of sparsely
approximating piecewise smooth functions, such as images, and the corresponding wavelet …
approximating piecewise smooth functions, such as images, and the corresponding wavelet …
A hybrid with distributed pooling blockchain protocol for image storage
As a distributed storage scheme, the blockchain network lacks storage space has been a
long-term concern in this field. At present, there are relatively few research on algorithms …
long-term concern in this field. At present, there are relatively few research on algorithms …