In this paper, an adaptive detection scheme for fluctuating targets with a swerling I model in AR interference is presented. Since the proposed detector uses more information from the target signal in its structure, it has better performance compared with those detectors which do not use the target amplitude model. Performance improvement of this detector compared with the previously AR model based adaptive detector (ARGLR) is shown by simulation results. Besides, another detector is proposed for the known amplitude situation whose performance can be used as an upper bound for all similar detectors.

The main propose of this research is the survey of affecting globalization on tax revenue in Iran by AUTOREGRESSIVE Distributed Lag (ARDL) approach. So other variables such as real per capita income (as an indicator of economic development), degree of urbanization, the share of agriculture in gross domestic product, dependency ratio (as an index of social and economic structure) and exchange rate, effects on tax revenue have been investigated. Stationary test of variables in the model is shown based on adjusted Dickey – Fuller. For the results of this test, the time series used in this model are characteristics I(0) and I(1). In this study, the pattern AUTOREGRESSIVE Distributed Lag (ARDL) and the method of least squares for first or more order differencing is used. Dynamic model estimation results indicate a tendency towards long run equilibrium model. Additionally, globalization has positive effect on the proportion of total tax and the tax on trade in GDP.

The paper addresses a problem of tracking multiple number of frequencies using Regularized AUTOREGRESSIVE (RAR) APPROXIMATION. The RAR procedure allows to decrease APPROXIMATION bias, comparing to other AR-based frequency detection methods, while still providing competitive variance of sample estimates. We show that the RAR estimates of multiple periodicities are consistent in probability and illustrate dynamics of RAR in respect to sample size and signal-to-noise ration by simulations.

‎‎‎Given a fuzzy normed space, we will introduce the notion of fuzzy near best APPROXIMATION as a generalization of the notion of fuzzy best APPROXIMATION. Some basic properties are characterized and also many examples for illustration are presented. Also, the hereditary properties of the fuzzy near best APPROXIMATION on direct sum and tensor product of linear spaces are discussed.