sparse network coding was introduced to reduce the computational complexity of the random linear network coding. In this method, most of the decoding matrix coefficients are zero. Partial decoding means the possibility of decoding a part of the raw packets is one of the capabilities of the sparse network coding method. We introduce three different models of sparse coding method as an approach to reduce decoding latency in real-time communication. More precisely, we first evaluate a sparse network coding for a no feedback configuration in terms of the performance of the total number of transmissions required, and the average packet decoding delay for a generation of raw packets, by introducing a Markov chain-based model. Then we evaluate the accuracy of the proposed model using extensive simulation and show that the proposed model can accurately estimate the number of required transmissions and decoding delay for a generation of packets. The results also evaluate the accuracy of the model in the erasure channel. In the following, we introduce the feedback-based model and we show that this model can create a better balance between the functions of the number of transmissions and the average decoding delay per packet. Finally, by focusing on the problem of finding the random spanning tree, we present a graph-based model for analyzing sparse network coding and show that although the proposed model is valid only for grade 2 sparsity, it also has the capacity to develop for lower sparsity.

Classification of brain tumors using MRI images along with medical knowledge can lead to proper decision-making on the patient's condition. Also, classification of benign or malignant tumors is one of the challenging issues due to the need for detailed analysis of tumor tissue. Therefore, addressing this field using image processing techniques can be very important. In this paper, various types of texture-based and statistical-based features are used to determine the type of brain tumor and different types of features are applied in this classification procedure. sparse non-negative matrix factorization algorithm is used to learn the over-complete models based on the characteristics of each data category. Also, sparse structured principal component analysis algorithm is applied to reduce the dimension of training data. The classification process is carried out based on the calculated energy of the sparse coefficients. Also, the results of this categorization are compared with the results of the classification based on the neural network and support vector machine. The simulation results show that the proposed method based on the selected combinational features and learning the over-complete dictionaries can be able to classify the types of brain tumors precisely.

The difficulties in the measurement of rainfall interception in forests confirm the necessity of presenting models. The widely used models for estimating rainfall interception are physical-based models, among which the sparse Gash is the most commonly used. We evaluated the sparse Gash model for estimating the rainfall interception of five forest stands (two chestnut-leaved oak stands, two oriental beech stands, and one velvet maple stand) in the Hyrcanian region. In each stand, the gross rainfall and throughfall were measured using 5 and 20 rainfall collectors, respectively, and rainfall interception was calculated by subtracting the throughfall from gross rainfall. To evaluate the performance of the model, we used statistical metrics: Error percentage (Error), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Model Efficiency coefficient (CE). Based on the Pearson correlation coefficient, the correlation between the values estimated by the model and the observed values was statistically significant at a 95% confidence interval. In all forests, the values of the CE were higher than 0. 5, indicating the appropriate efficiency of the model. Based on the Error, the model showed good capability in estimating the rainfall interception of four forest stands (i. e., oriental beech in Lajim, chestnut-leaved oak in Kohmiyan and Sari, and velvet maple in Sari Error metric were found to be-10. 3%, +12. 7%, +10. 8%, and +15. 4%, respectively). Studying the performance of physically-based models in forests with different species and different allometric, climatic and rainfall characteristics completes the information gap about the efficiency of models to estimate rainfall interception.

This paper studies the problem of Simultaneous sparse Approximation (SSA). This problem arises in many applications that work with multiple signals maintaining some degree of dependency, e. g., radar and sensor networks. We introduce a new method towards joint recovery of several independent sparse signals with the same support. We provide an analytical discussion of the convergence of our method, called Simultaneous Iterative Method (SIM). In this study, we compared our method with other group-sparse reconstruction techniques, namely Simultaneous Orthogonal Matching Pursuit (SOMP) and Block Iterative Method with Adaptive Thresholding (BIMAT), through numerical experiments. The simulation results demonstrated that SIM outperformed these algorithms in terms of the metrics Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIM is considerably less complicated than BIMAT, which makes it feasible for practical applications such as implementation in MIMO radar systems.

One of the important topics of passive defense is threats detection and waning alarm. One of the most widely used methods in detection field is video data investigation in order to identify unknown targets and warning alarm. In order to evaluate a fast and high-precision technique, video summarization is presented. Also, during the past years, creation of digital videos has caused exponential growth of video content. To increase the high volume of video usability, a lot of researches have been done and video summarization has been proposed to quick view of large video collection and quick understanding of the content of video data. In the video summarization, pictures are selected as a representative of each scene to obtain a visual overview of whole video. Recently, new methods using sparse formulation are suggested for video summarization being more effective in video data summarization than other methods. In this paper, video summarization is presented as a sparse dictionary selection problem. For this purpose, using a new method based on sparse coding, have been able to improve video data summarization compared to other video summarization methods based on sparse or other coding. Finally, the results for the ground truth data collection and State of the art methods, shows improvement our claim in the video summary on proposed method.