This paper addresses the issue of cell loss and its consequent effect on video quality in a packet video system, and examines possible compensative measures. In the system’s encoder, adaptive spectral separation is used to develop a two-layer Coding scheme comprising a high priority layer to carry essential video data and a low priority layer with data to enhance the video image. A two-step error detection scheme using cell numbering and variable length decoder is incorporated in the system to detect cell loss in the bit-stream without long mis-interpretation. In the system’s decoder, cell loss is compensated by a two-step recovery procedure based on temporal error concealment to maintain high picture quality. The simulated performance of the proposed two-layer system is studied and compared with that of the single layer system. It is shown that good resilience and PSNR improvement can be achieved even at quite high error rates. Statistics such as the cell-rate distribution and autocorrelation function of both high and low priority streams generated by the adaptive system are analyzed. It is observed that two different classes of gamma function can represent the cell distribution in each layer. A Multiplicative - ARIMA (autoregressive integrated moving average) model is proposed to represent and forecast the number of cells per frame for the encoded video traffic and its goodness-of-fit is compared with AR(1) (autoregressive) model. The cells per frame measured from a sample video sequence compare favorably with those obtained by the ARIMA model. Simulations were performed using three five-second video sequences to demonstrate the efficiency of the two-layer enCoding algorithm for sequences with different characteristics. Statistics gathered from these prioritization schemes are claimed to be useful for network design and resource management.

This paper concerns the transmission of two independent Gaussian sources over a two-user decentralized interference channel, assuming that the transmitters are unaware of the instantaneous CSIs. The availability of the channel state information at receivers (CSIR) is considered in two scenarios of perfect and imperfect CSIR. In the imperfect CSIR case, we consider a more practical assumption of having an MMSE estimation of the channel gain at the receivers. In this case, minimizing the expected achievable distortion associated with each link is considered. Due to the absence of CSI at the transmitters, the Gaussian sources are encoded in a successively refinable manner and the resulting code words are transmitted over the channel using a Multi-layer Coding technique. Accordingly, the optimal power assignment between code layers leading to the least expected achievable distortion, under a mean-square error criterion is derived for both, the perfect and imperfect CSIR scenarios. Finally, some numerical examples are provided and it is demonstrated that the proposed method results in better performance as compared with the conventional single-layer approach, termed as outage approach.

In this paper, we consider cognitive radio network in which two cognitive radio sources communicate with two cognitive destinations via a relay node. The decode and forward (DF) relay node employs physical layer network Coding (PLNC) to improve the data rate. Based on the availability of the spectrum bands at the source, relay and destination, the network employs three different diversity schemes namely source to relay diversity, relay to destination diversity and combination of earlier two diversity schemes with overall source to destination diversity schemes. The optimal allocation of channel and power with per band and sum power constraints of a node in the network is formulated as convex optimization problem to improve the end to end throughput of the cognitive radio network. Simulation results show that the resultant joint channel and power allocation are superior to the equal power allocation in terms of both end to end throughput and outage probability.

Clutter usually has negative influence on the detection performance of radars. So, the recognition of clutters is crucial to detect targets and the role of clutters in detection cannot be ignored. The design of radar detectors and clutter classifiers are really complicated issues. Therefore, in this paper aims to classify radar clutters. The novel proposed MLP-based classifier for separating radar clutters is introduced. This classifier is designed with different hidden layers and five training algorithms. These training algorithms consist of Levenberg-Marquardt, conjugate gradient, resilient back-propagation, BFGS and one step secant algorithms. Statistical distributions are established models which widely used in the performance calculations of radar clutters. Hence In this research, Rayleigh, Log normal, Weibull and K-distribution clutters are utilized as input data. Then Burg’s reflection coefficients, skewness and kurtosis are three features which applied to extract the best characteristics of input data. In the next step, the proposed classifier is tested in different conditions and the results represent that the proposed MLP-based classifier is very successful and can distinguish clutters with high accuracy. Comparing the results of proposed technique and RBF-based classifier show that proposed method is more efficient. The results of simulations prove that the validity of MLP-based method.

Networks are useful tools for presenting the relationships between financial institutions. During the previous years, many scholars have found that using single-layer networks cannot properly characterize and explain complex systems. The purpose of this research is to introduce a Multiplex network in order to analyze, as accurately as possible, all aspects of communication between banks in capital market of Iran. In this article, each bank represents a node and three layers of return, trading volume and market Cap have been presented for analyzing the idea of Multiplex networks. We have used the Granger causality method to determine the direction between nodes. For understanding the topology structure of these layers, different concepts have been used. The research findings show that the value layer topology has a significant similarity with the trading volume layer. Also according to the measure of centrality it can be seen that the centrality varies in different layers.