In this paper, we investigate the detection of masked weak moving targets in the adjacent fast strong target by using FAPC algorithm. The matched filter of conventional pulse compression radars induces range sidelobes in surrounding a target with high SNR that could mask the smaller targets. The mismatch created in received signal by Doppler phase shift, degrades APC filter performance in side lobe suppression. In this paper, the FFL-FAPC algorithm is proposed to reduce the range side lobes using the RMMSE estimator in its post-processing method. In various scenarios, we will investigate the detection of masked targets in comparsion with previous methods. Simulation results show that the FFL-FAPC algorithm reduce significantly of computational cost, in addition to provides improved Doppler robustness.

One of the important subjects in the wavelet-based image denoising based on the Bayes theorem is choosing the appropriate density function for modeling the wavelet coefficients.  The interscale dependency between parent and child coefficients is one of the statistical properties of wavelets. So, in the recent years instead of univariate distribution, bivariate density functions have been suggested by the researchers and in this paper we use a mixture of bivariate Laplacian densities for this reason. Using this distribution we are able to model both heavy-tailed property and interscale dependency of wavelets. Using the mentioned density function for a Minimum Mean Squared Error estimator, we obtain a new shrinkage function for denoising. Applying this function to each subband of discrete complex wavelet transform of abdominal computerized tomography images, we will be able to improve the quality of these images better than some reported methods.

The targets that either have low radar cross-section typically, or their return signal has been deliberately reduced are referred to as weak targets in radar terminology. There are several algorithms for detection of a weak moving target. When such a target is in the vicinity of a large target, the side lobes of the matched filter output due to the large target mask or hide the weak target. The adaptive pulse compression filter that uses the RMMSE estimator has the ability to detect the masked weak target. However, there are at least three factors (computational load, Doppler robustness and pulse eclipsing) which limit the practical application of RMMSE. In this paper, an optimized and integrated algorithm based on adaptive post-processing is proposed to detect targets and to overcome the challenges of RMMSE in electronic defense systems. The FFL-APCR proposed algorithm when compared qualitatively to other algorithms indicates better performance for different SNRs and various target velocities, showing that it is more suitable for implementation in real-time systems. The FFL-APCR algorithm can detect high speed and pulse eclipsed weak targets with lower computational load.

In this paper‎, ‎Bayesian and expected Bayesian (E-Bayesian) estimation of premium with exponential claim size is considered‎. ‎After calculation of premium using Heilmann principal‎, ‎its Bayesian and E-Bayesian estimation under three priors of hyper-parameters are obtained‎. ‎Then‎, ‎formulas for E-MSE are provided‎. ‎A Monte Carlo simulation is conducted for comparison of E-Bayesian estimators of premium‎. ‎A real example is presented for illustration of the results‎.

According to the classic sampling theory, Errors that are mainly considered in the estimations are sampling Errors. However, most non-sampling Errors are more effective than sampling Errors in properties of estimators. This has been confirmed by researchers over the past two decades, especially in relation to non-response Errors that are one of the most fundamental non-immolation Errors. More done researches, had studied non-sampling Errors individually, however, in practice, two or more non-sampling Errors occur together. In this paper, are considered two types of non-sampling Errors in the stage of estimation of population Mean: measurement Error and non-response Error. ...

This paper proposes two sampling strategies based on the modified ratio estimator using the population Mean of auxiliary variable and population correlation coefficient between the study variable and the auxiliary variable by Singh and Tailor (2003) for estimating the population Mean (total) of the study variable in a finite population. A comparative study is made with usual sampling strategies and some concluding remarks are given. Finally, an empirical study is included as an illustration which shows that the proposed sampling strategies are better than Singh and Tailor estimator both in terms of unbiasedness and lesser Mean Square Error.

Occurrence of Errors is measured parameters of an industrial unit including steam cracking furnaces due to operator and/or equipment fault is inevitable. In this research a method for data reconciliation of measured parameters in a steam cracking process is presented. Scientific-statistical data reconciliation methods are used to detect Errors in measured process variables as well as to eliminate or to reduce the Errors. The Errors in measured data of an industrial steam cracking furnace are estimated by solving three different data reconciliation problems, and the results compared together. The problem contains an objective function (Error function) with equality equation constraints, mass, heat, and momentum balances, and inequality variable constraints. Least Square, Cauchy and M-estimator objective functions are used to in the three data reconciliation problems respectively. The used radical mechanism in the balance equations contains 400 radical reactions with 56 and 19 molecular and radical species respectively. The accuracy of the mass, energy, and momentum balance an equation was checked by comparing the model simulated results with the industrial reactor design. There is good conformity between the results. The search simplex method and the Rung-Kutta method with the LU decomposition were used to solve the data reconciliation problem, and the differential balance equations used as the constraints. The results show that the used data reconciliation problems are good methods for detecting, reducing, and elimination of the Errors. The results obtained by the different methods show that the M-estimator method as the best method will properly detect the Errors.

In this paper, we have proposed an estimator of finite population Mean in stratified random sampling. The expressions for the bias and Mean Square Error of the proposed estimator are obtained up to the first order of approximation. It is found that the proposed estimator is more efficient than the traditional Mean, ratio, exponential, regression, Shabbir and Gupta (in Commun Stat Theory Method 40: 199– 212, 2011) and Khan et al. (in Pak J Stat 31: 353– 362, 2015) estimators. We have utilized four natural and four artificial data sets under stratified random sampling scheme for assessing the performance of all the estimators considered here.