The automatic recognition of the modulation format of a detected signal is a major task of an intelligent receiver. This task becomes more difficult when the receiver has no information about the transmitted signal or the channel. At first, the maximum likelihood (ML) classifier for classifying phase-amplitude modulated schemes in coherent environment is presented. It is well known that the ML classifier requires a priori knowledge of the incoming signal and channel (including Amplitude, timing information, noise power and the roll-off factor of the pulse shaping filter). To relax this requirement, we introduce a novel estimator to estimate the parameters required by ML classifier which is blind to the modulation scheme of the received signal, and this gives rise to a new completely blind modulation classifier for digital amplitude-phase modulated signals over fading channels. Results are presented from simulations in terms of correct detection probability versus SNR for the class of BPSK, QPSK, 8-PSK, 16-QAM and 64-QAM modulation schemes. The results show that the performance of this classifier is very close to the ideal classifier with perfect estimates.

The paper was to reduce biased estimation using new approach (Penalized maximum likelihood Estimation (PMLE) Method) in Logistic Regression. For this aim, unreal four small data sets were randomly generated. maximum likelihood Estimation (MLE) and PMLE Methods were applied and compared for separation case including biased estimation in Logistic Regression when one of the cells in 2 x 2 tables becomes equal to zero (separation problem). Parameters1 and their standard error obtained by using MLE for four data sets were 12.56±257.8, 13.46±264.3, 13.42±210.3, and 13.41±180.4, respectively, meaning that MLE’s are biased estimates. Corresponding values for PMLE method were found 2.28 ± 1.81, 3.05 ± 1.59, 3.45 ± 1.53, and 3.45 ± 1.53, respectively, meaning that PMLE’s was unbiased estimates. It is clear that standard error value for data set 1 reduced from 257.8 to 1.81 when using PMLE method for separation problem. According to PMLE Method, the odds of being coronary heart disease risk for smokers were increased 21.08 times than that for non-smokers smoking in data set 2, which is significant at 1% level. The odds of being coronary heart disease risk for smokers were increased 31.63 times than that for non-smokers in data set 3 (P < 0.001). The odds of being coronary heart disease risk for smokers were increased 41.93 times than that for non-smokers in data set 4. When one of the cells in 2 x 2 contingency tables becomes equal to zero, PMLE was more superior to MLE Method because PMLE Method may be performed unbiased (reliable) estimation.

The Karkhe River is one of the most important rivers in Iran and is the third largest river in terms of the discharge volume. In its wild state. the river usually left many damage its wake. In order to reduce these detrimental out comes, the Karkhe Dam was built, which is one of the most important and also the largest dams in Iran and the Middle East. This dam has instigated an economic reforminits basin, such as changes in the land-use, amount of water, vegetation and the urban areas. Some of the major changes occurred ofter the dam construction have been evaluated: The Using Landsat satellite images spanning between 1352 and 1392, maximum likelihood classification identifying 7 classes was conducted on the pre-processed images. The results showed the barren soil decrease of 0.2 percent, the residential area, vegetation and water supply have increased by 2.36, 1.4 and 2.5 percent, respectively. In spite of the logical trend of these results, the accuracy assessment was as an added measure to confirmed the previous results. The evaluation showed a high accuracy almost in all of the classification results. The overall accuracy and the Kappa coefficient estimated from the accuracy assessment are higher than 90% and 0.9, respectively, while the user and producer accuracies are more than 80%. This demonstrates the high performance of the maximum likelihood classification.

The present work focuses on the second order Markov chain model which arises in a variety of settings and is well-suited to be modeled in many applications. The efficiency of the maximum quasi-likelihood estimators with the full maximum likelihood estimators for second order Markov chain models are given, besides the limiting normality results on the asymptotic properties of the associated estimates. Some efficiency calculations are also given to discuss the feasibility and computational complexity of the QL approach relative to the full likelihood approach.

One of the most important tasks of remote sensing technology is to producing land use maps. In this study, in order to produce land use map of abolabbas basin, landsat satellite image of TM scanner acquired on 01 June 2009 were employed. the image classified by using three-layer perceptron neural network, support vector machine with the radial basis kernel function and maximum likelihood algorithm. So, The performance of different classification algorithms in producing land use maps were investigated using overall accuracy and kappa coefficient. Results showed that Nonparametric algorithms such as artificial neural network (with 95. 8% overall accuracy and 0. 95 kappa coefficient) and support vector machine with the radial basis kernel function (with 95. 8% overall accuracy and 0. 94 Kappa coefficient) with the same performance were better than the third method which is Parametric maximum likelihood algorithm (with 93. 7% overall accuracy and 0. 91 Kappa coefficient). Overall, this study showed that three classification algorithms, neural network, support vector machine and maximum likelihood are capable to generate land use maps with high accuracy.