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.

In this paper SYS-PNLMS algorithm is proposed. The analysis reveals that it performs a faster convergence rate compared to that of the recently introduced SPNLMS, PNLMS algorithms. Compared with its proportionate counterparts e.g. PNLMS and SPNLMS, the proposed SYS-PNLMS algorithm not only results in a faster convergence rate for both white and colored noise inputs, but also preserves its initial fast convergence rate until it reaches to its steady state condition. It also presents a higher tracking behavior for quasi-stationary inputs such as speech signal in addition to better performance in terms of computational complexity and resulting ERLE. In addition, the proposed SYS-PNLMS algorithm is also evaluated with previously proposed algorithms in a theoretical framework which validates the computer simulation results in terms of CPU time and number of iterations needed for each algorithm to get converged. Finally, a region of convergence for the proposed algorithm is derived for different input cases including white, colored noise and speech signal. This region is also compared with the practical value usually used in echo cancellation application.

In present paper a new decomposition algorithm is introduced to reduce energy consumption. In this algorithm, some rules and assumptions are considered to minimize the costs of fluid transmission such as piping costs, associated pumping costs and the plant's complexity. The algorithm has a good flexibility to be applied for vast majority of industrial process plants. The algorithm is consisted of two steps. At first, by using the concept of zoning in grassroots design, the existing HENs divided into two or more primary substructures (sub-networks) with considering the limitations of fluid transmission. At primary substructures, each heat exchanger could be belonged to one or more substructures. The second step is to decrease the substructure components by applying plant's constrains such as operability, layout of components and heat exchanger's materials in each substructure. Eventually, the final substructures achieved, are independent and there is no heat transfer across the different substructures due to the limitation of the fluid transmission. The existence of path in each substructure is led to opportunities for heat recovery in retrofit design. Path analysis in each substructure is a beneficial tool to identify the streams which have capability to adapt process to process heat transfer. These streams may cross different substructure simultaneously. Each substructure could be modified by retrofit methods. Also, in this a new algorithm of retrofitting HENs which is useful when there is no process to process heat exchanger is introduced. The case-study which is chosen is Acetic Acid plant of FPC (Fanavaran Petrochemical Company). The HENs of FPC has been divided into substructures and each one has been retrofitted by use of new retrofit method.

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.

In this paper, an algorithm to detect obstacles surrounding an autonomous vehicle and the method usedto navigate this vehicle on the road were studied. For this purpose, road was divided into cells in lateraland longitudinal directions. The assumption was that some special tools specify the cells positions andthen full and empty-cell corresponding matrix was generated. In this matrix, full cells were displayedwith digit 1 and empty cells are displayed with digit 0. In the next step, by analyzing the matrix inMATLAB®, the vehicle was navigated. In this analysis, first, the position of the vehicle and theobstacles were identified. Then, based on the road conditions and the obstacles positions, requiredorders to move the vehicle were determined. If a lane change is needed, according to the road’scurvature and the distance between the vehicle and the obstacle, appropriate path for the vehicle will bechosen. In this paper, for the first time in autonomous vehicle navigations, the road was considered as a1 and 0 matrix. In this method, the road matrix was updated over time and provides the possibility ofanalyzing the vehicle’s movement. In addition, the algorithm used to solve the problem is very simple.