Micro-Doppler Parameter Estimation via Parametric Sparse Representation and Pruned Orthogonal Matching Pursuit

2014 ◽  
Vol 7 (12) ◽  
pp. 4937-4948 ◽  
Author(s):  
Gang Li ◽  
Pramod K. Varshney
Keyword(s):  
Parameter Estimation ◽  
Sparse Representation ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Doppler Parameter

scholarly journals Multi-lead ECG Compression Based on Compressive Sensing

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Javad Afshar Jahanshahi
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Matching Pursuit ◽  
Optimization Technique ◽  
Orthogonal Matching Pursuit ◽  
Wavelet Basis ◽  
Basis Matrix ◽  
Ecg Signals ◽  
Gaussian Kernels ◽  
Ecg Compression

Compressed Sensing (CS) has been considered a very effective means of reducing energy consumption at the energy-constrained wireless body sensor networks for monitoring the multi-lead Electrocardiogram (MECG) signals. This paper develops the compressed sensing theory for sparse modeling and effective multi-channel ECG compression. A basis matrix with Gaussian kernels is proposed to obtain the sparse representation of each channel, which showed the closest similarity to the ECG signals. Thereafter, the greedy orthogonal matching pursuit (OMP) method is used to obtain the sparse representation of the signals. After obtaining the sparse representation of each ECG signal, the compressed sensing theory could be used to compress the signals as much as possible. Following the compression, the compressed signal is reconstructed utilizing the greedy orthogonal matching pursuit (OMP) optimization technique to demonstrate the accuracy and reliability of the algorithm. Moreover, as the wavelet basis matrix is another sparsifying basis to sparse representations of ECG signals, the compressed sensing is applied to the ECG signals using the wavelet basis matrix. The simulation results indicated that the proposed algorithm with Gaussian basis matrix reduces the reconstruction error and increases the compression ratio.

scholarly journals A Modified Sparse Representation Method for Facial Expression Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Wang ◽  
LiHong Xu
Keyword(s):  
Facial Expression ◽  
Sparse Representation ◽  
Facial Expression Recognition ◽  
Matching Pursuit ◽  
Iteration Process ◽  
Orthogonal Matching Pursuit ◽  
Expression Recognition ◽  
Training Samples ◽  
Reduction Methods ◽  
Representation Method

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan’s JAFFE and CMU’s CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

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