Sharif Digital Repository

In this paper, we analyze the effect of applying leaky bucket as a well known traffic shaper on data users in a variable spreading gain CDMA (VSG-CDMA) uplink wireless cellular network. To this end, we model the effect of leaky bucket mechanism on a typical data user, with a closed product form queueing network. Each node in this queueing network corresponds to a specific processing gain or equivalently transmission rate and power. By solving such a queueing network we obtain the steady state probability with which a typical data user will exist at each node. By aggregating the effects of all data users we compute the mean and variance of interference and obtain the throughput and average... 

In this paper, we propose data traffic shaping via leaky bucket in order to reduce outage probability and enhance capacity of voice users for uplink variable spreading gain codedivision multiple-access (VSG-CDMA) cellular networks. In order to analyze the effect of leaky bucket mechanism, we employ a closed Jackson queueing network with arbitrary service time distributions to model different states of a typical traffic-shaped data user. By solving the concerned queueing network, we obtain the steady state probability with which a typical data user exist at each state. By aggregating the effects of all data and voice users we obtain mean and variance of interference and compute the outage... 

Modified perovskite-type oxides were synthesized through co-precipitation and conventional citrate methods. The synthesized perovskite materials had the nominal compositions of LaCoO3, LaCo0.8Cu0.2O3, La0.8Sr0.2Co0.8Cu0.2O 3, and La0.8M0.2FeO3 (where M = Ce and Sr). The catalytic activity of the perovskite samples (for low-temperature CO oxidation) was measured using a quartz reactor with an inlet gas mixture containing 97% N2, 1% O2, and 2% CO. The prepared perovskite samples were characterized by SEM, nitrogen adsorption (BET), XRF, and XRD analyses. The perovskite catalysts showed good structural and chemical stability up to 600 °C and high activity for the catalytic CO oxidation... 

Monte Carlo Approaches for calculating Value-at-Risk (VaR) are powerful tools widely used by financial risk managers across the globe. However, they are time consuming and sometimes inaccurate. In this paper, a fast and accurate Monte Carlo algorithm for calculating VaR and ES based on Gaussian Mixture Models is introduced. Gaussian Mixture Models are able to cluster input data with respect to market's conditions and therefore no correlation matrices are needed for risk computation. Sampling from each cluster with respect to their weights and then calculating the volatility-adjusted stock returns leads to possible scenarios for prices of assets. Our results on a sample of US stocks show that... 

Vapor pressure and liquid density of 20 pure alcohols were correlated using an artificial neural network (ANN) system and statistical associating fluid theory (SAFT) equation of state. The SAFT equation has five adjustable parameters as temperature-independent segment diameter, square-well energy, number of segment per chain, association energy and association volume. These parameters can be obtained by a non-linear regression method using the experimental vapor pressure and liquid density data. In continue, the vapor pressure and liquid densities of pure alcohols were estimated by using an artificial neural network (ANN) system. In the neural network system, it is assumed that thermodynamic...