In this paper, we study the estimation of the spatially Sparse radio emitter locations via the proposed Quad-tree variational Bayesian expectation maximization (QVBEM) algorithm. Firstly, we assume that the emitters are approximately lie on a uniform grid points in the region under surveillance. The VBEM algorithm is applied and the points exceeding the threshold level are considered as potential targets. Then, the grids are refined around the potential targets via the Quad-tree algorithm and the process is iterated. It allows us to find the location of Sparse emitters with much less computational complexity due to the use of fewer grid points. Simulation results show the superiority of the QVBEM to existing methods. The impact of threshold value on the performance of QVBEM is also studied.

Background and Objectives: High resolution multi-spectral (HRMS) images are essential for most of the practical remote sensing applications. Pan-sharpening is an effective mechanism to produce HRMS image by integrating the significant structural details of panchromatic (PAN) image and rich spectral features of multi-spectral (MS) images. Methods: The traditional pan-sharpening methods incur disadvantages like spectral distortion, spatial artifacts and lack of details preservation in the fused image. The pan-sharpening approach proposed in this paper is based on integrating wavelet decomposition and convolutional Sparse representation (CSR). The wavelet decomposition is performed on PAN and MS images to obtain low-frequency and high-frequency bands. The low-frequency bands are fused by exploring the CSR based activity level measurement. Results: The HRMS image is restored by implementing the inverse transform on fused bands. The fusion rules are constructed, thus to transfer the crucial details from source images to the fused image effectively. Conclusion: The proposed method produces HRMS images with rational spatial and spectral qualities. The visual outcomes and quantitative measures approve the eminence of the proposed fusion framework.

One of the most serious causes of disease in the world's population, which kills many people worldwide every year, is heart attack. Various factors are involved in this matter, such as high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc. Various methods have been proposed in this field, but in this article, by using Sparse codes in the classification process, higher accuracy has been achieved in predicting heart attacks. The proposed method consists of two parts: preprocessing and Sparse code processing. The proposed method is resistant to noise and data scattering because it uses a Sparse representation for this purpose. The spars allow the signal to be displayed at its lowest value, which leads to improve computing speed and reduce storage requirements. To evaluate the proposed method, the Cleveland database has been used, which includes 303 samples and each sample has 76 features. Only 13 features are used in the proposed method. FISTA, AMP, DALM and PALM classifiers have been used for the classification process. The accuracy of the proposed method, especially with the PALM classifier, is the highest among other classifiers with 96.23%, and the other classifiers are 95.08%, 94.11% and 94.52% for DALM, AMP, FISTA, respectively.

Background and Objectives: Compressive sensing (CS) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-Sparse systems. We can follow this issue, by using CS theory and block-Sparse signal recovery algorithms. Methods: This paper presents a new block-Sparse signal recovery algorithm for the adaptive block-Sparse system identification scenario, named stochastic block normalized iterative hard thresholding (SBNIHT) algorithm. The proposed algorithm is a new block version of the SSR normalized iterative hard thresholding (NIHT) algorithm with an adaptive filter framework. It uses a search method to identify the blocks of the impulse response of the unknown block-Sparse system that we wish to estimate. In addition, the necessary condition to guarantee the convergence for this algorithm is derived in this paper. Results: Simulation results show that the proposed SBNIHT algorithm has a better performance than other algorithms in the literature with respect to the convergence and tracking capability. Conclusion: In this study, one new greedy algorithm is suggested for the block-Sparse system identification scenario. Although the proposed SBNIHT algorithm is more complex than other competing algorithms but has better convergence and tracking capability performance.

This paper proposes two algorithms for Voice Activity Detection (VAD) based on Sparse representation in spectro-temporal domain. Spectral-temporal components which, in addition to the frequency and time dimensions, have two other dimensions of the scale and rate. Scale means spectral modulation and the rate means temporal modulation. On the other hand, using Sparse representation in learning dictionaries of speech and noise, separate the speech and noise segment to be better separated. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on Sparse representation. Dictionaries with different atomic sizes and two dictionary learning methods: NMF (non-negative matrix factorization) and the K-SVD (k-means clustering method), were investigated in this approach. This algorithm revealed good results at high SNRs (signal-to-noise ratio). The second algorithm, whose approach is more complicated, suggests a speech detector using the Sparse representation in four-dimensional STRF space. Due to the large volume of STRF's four-dimensional space, this space was divided into cubes, with dictionaries made for each cube separately by NMF (non-negative matrix factorization) learning algorithm. Simulation results were presented to illustrate the effectiveness of our new VAD algorithms. The results revealed that the achieved performance was 90. 11% and 91. 75% under-5 dB SNR in white and car noise respectively, outperforming most of the state-of-the-art VAD algorithms.