none5noThe balanced weighted orthogonal matching pursuit (bWOMP) algorithm for recovering signals in compressed sensing (CS) based system is presented as a specialized recovering tool for Electrocardiograph (ECG) signals. Being based on the standard OMP approach, bWOMP is a lightweight reconstruction algorithm both in terms of complexity and memory footprint. Furthermore, the concept of weighting is introduced in the algorithm by exploring a prior knowledge on ECG signals. Experimental results show a performance increase of about 10 dB with respect to the standard OMP approach, and also an increase with respect to the decoding approaches considered as the state-of-the-art. In this case the gain could be as high as 4 dB with respect to the b...
In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumptio...
Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy ...
The recovery of sparse signals from their linear mapping on a lower-dimensional space is traditional...
The balanced weighted orthogonal matching pursuit (bWOMP) algorithm for recovering signals in compre...
This paper presents a new approach for the optimization of a dictionary used in ECG signal compressi...
Abstract: Compressed Sensing (CS) has been used in ECG signal compressing with the rapid development...
none5siWhen transmission or storage costs are an issue, lossy data compression enters the processing...
Compressed Sensing (CS) attempts to acquire and reconstruct a sparse signal from a sampling much bel...
none5noCompressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., ...
Compressed sensing (CS) [1,13,14] is a novel idea wherein a signal can be sampled at sub-Nyquist rat...
none7noIn recent years, compressed sensing (CS) has proved to be effective in lowering the power con...
In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumptio...
AbstractThe main drawback of current ECG systems is the location-specific nature of the systems due ...
Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy ...
Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy ...
In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumptio...
Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy ...
The recovery of sparse signals from their linear mapping on a lower-dimensional space is traditional...
The balanced weighted orthogonal matching pursuit (bWOMP) algorithm for recovering signals in compre...
This paper presents a new approach for the optimization of a dictionary used in ECG signal compressi...
Abstract: Compressed Sensing (CS) has been used in ECG signal compressing with the rapid development...
none5siWhen transmission or storage costs are an issue, lossy data compression enters the processing...
Compressed Sensing (CS) attempts to acquire and reconstruct a sparse signal from a sampling much bel...
none5noCompressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., ...
Compressed sensing (CS) [1,13,14] is a novel idea wherein a signal can be sampled at sub-Nyquist rat...
none7noIn recent years, compressed sensing (CS) has proved to be effective in lowering the power con...
In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumptio...
AbstractThe main drawback of current ECG systems is the location-specific nature of the systems due ...
Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy ...
Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy ...
In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumptio...
Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy ...
The recovery of sparse signals from their linear mapping on a lower-dimensional space is traditional...