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.

In this paper, we discuss the two-stage and the modified two-stage procedures for the estimation of the threshold AUTOREGRESSIVE parameter in a first-order threshold AUTOREGRESSIVE model (${\rm TAR(1)}$). This is motivated by the problem of finding a final sample size when the sample size is unknown in advance. For this purpose, a two-stage stopping variable and a class of modified two-stage stopping variables are proposed. Afterward, we {prove} the significant properties of the procedures, including asymptotic efficiency and asymptotic risk efficiency for the point estimation based on least-squares estimators. To illustrate this theory, comprehensive Monte Carlo simulation studies is conducted to observe the significant properties of the procedures. Furthermore, the performance of procedures based on Yule-Walker estimators is investigated and the results are compared in practice that confirm our theoretical results. Finally, real-time-series data is studied to demonstrate the application of the procedures.

How to profoundly change the beliefs of the polytheists during the age of revelation from the denial of the Prophet (pbuh) to the certainty of his Message and the acceptance of his full guardianship is a question worthy of attention. The order of verses about prophecy based on the order of revelation is the reason to lead a transformative PROCESS and a special Quranic method in institutionalizing the belief of prophecy in the lives of Arabs. In the first stage, the Holy Quran in the first Makkī (Arabic: المکّیّ, suras revealed in Mecca) chapters has prepared the community from the motivational side to accept the truth of the Prophet’s (pbuh) prophecy and briefly stating the key issues surrounding it. The Holy Book of Quran at the time of the emergence of oppositions and doubts (the second stage) destroyed the arguments of the polytheists and explained the prophecy and proved its authenticity from a cognitive perspective in the second stage by presenting clear arguments. and the Holy Book of Quran in the third stage after Madanī (Arabic: المدنیّ, suras revealed in Medina) period has undertaken measures in order to fulfill the goals of the Massage by explaining the true position of the Prophet (pbuh) to deepen and operationalize the Prophetic belief through explaining practical duties for the Prophet (pbuh) and behavior based on the acceptance of Walaya (Arabic: ولایة, meaning “guardianship” or “governance”).

One of the traditional methods for forecasting is time series analyzing, which based on tow assumption: stationary and linearity. There is Suspicion to working by the traditional models. One of the substitute methods is Artificial Neural Network (ANN) which sometimes shows good potential ability for forecasting time series. In this paper, after reviewing the recent studies on ability of AUTOREGRESSIVE Integrated Moving Average PROCESS (ARIMA) and ANN, tow models for forecasting the exchange rate between march 2006 and February 2009 in Iran are compared. The results show that ANN has a better estimate than ARIMA. In this research, the MATLAB and Central bank data are utilized.

Introduction: Excessive use of groundwater is one of the most important challenges in the utilization of water resources. Dependence of agricultural and horticultural production to this natural resource is to the extent that excessive use and sometimes imbalanced has caused to decrease of groundwater levels in many parts of the country. …

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.