Sharif Digital Repository

An ECG signal is composed of many similar beats which makes it to behave semi-periodic. This paper deals with beat variation periods and exploits the correlation between cycles (inter-beat) and correlation within each cycle (intra-beat) for compression. For efficient compression a 2-dimensional array is constructed from the one dimensional ECG signal using decimation and interpolation in the discrete cosine transform (DCT) domain. Since reasonable results in image compression have been achieved by means of set partitioning in hierarchical trees (SPIHT) algorithm, we use SPIHT algorithm to code the 2-D wavelet transform of the ECG signals. Experimental results on selected records of ECG from... 

During the last decade, there has been a growing interest toward the problem of sparse decomposition. A very important task in this field is dictionary learning, which is designing a suitable dictionary that can sparsely represent a group of training signals. In most dictionary learning algorithms, the cost function to determine the the optimum dictionary is the ℓ0 norm of the matrix of decomposition coefficients of the training signals. However, we believe that this cost function fails to fully express the goal of dictionary learning, because it only sparsifies the whole set of coefficients for all training signals, rather than the coefficients for each training signal individually. Thus,... 

Signal compression is an important element encountered in data storage applications. Over the years various techniques for data reductions have been proposed. In this paper we introduce an effective method for compressing semi-periodic signals. Although the approach is applicable to any semi-periodic signal, our attention is focused on the compression of electrocardiogram (ECG) signals. An ECG signal is composed of many similar beats which makes it to behave semi-periodic This paper deals with beat variation periods and exploits the correlation between cycles (inter-beat) and correlation within each cycle (intra-beat) for compression. For efficient compression a 2-dimensional array is... 

ABSTRACT Compressed sensing is a new theory that samples a signal below the Nyquist rate. While Gaussian and Bernoulli random measurements perform quite well on the average, structured matrices such as Toeplitz are mostly used in practice due to their simplicity. However, the signal compression performance may not be acceptable. In this paper, we propose to optimize the Toeplitz matrices to improve its compression performance to recover sparse signals. We establish the optimization on minimizing the coherence of the measurement matrix by an intelligent optimization method called Particle Swarm Optimization. Our simulation results show that the optimized Toeplitz matrix outperforms the... 

Reducing the time required for MRI, has taken a lot of attention since its inventions. Compressed sensing (CS) is a relatively new method used a lot to reduce the required time. Usage of ordinary compressed sensing in MRI imaging needs conversion of 2D MRI signal (image) to 1D signal by some techniques. This conversion of the signal from 2D to 1D results in heavy computational burden. In this paper, based on separable transforms, a method is proposed which enables the usage of CS in MRI directly in 2D case. By means of this method, imaging can be done faster and with less computational burden  

Exploiting Compressive Sensing (CS) in MIMO radars, we can remove the need of the high rate A/D converters and send much less samples to the fusion center. In distributed MIMO radars, the received signal can be modeled as a block sparse signal in a basis. Thus, block CS methods can be used instead of classical CS ones to achieve more accurate target parameter estimation. In this paper a new method of energy allocation to the transmitters is proposed to improve the performance of the block CS-based distributed MIMO radars. This method is based on the minimization of an upper bound of the sensing matrix block-coherence. Simulation results show a significant increase in the accuracy of multiple... 

In this paper, we consider sparse decomposition (SD) of two-dimensional (2D) signals on overcomplete dictionaries with separable atoms. Although, this problem can be solved by converting it to the SD of one-dimensional (1D) signals, this approach requires a tremendous amount of memory and computational cost. Moreover, the uniqueness constraint obtained by this approach is too restricted. Then in the paper, we present an algorithm to be used directly for sparse decomposition of 2D signals on dictionaries with separable atoms. Moreover, we will state another uniqueness constraint for this class of decomposition. Our algorithm is obtained by modifying the Smoothed L0 (SL0) algorithm, and hence...