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This paper introduced an experimental evolution of the effectiveness of utilizing various moments as pattern features in human face technology. In this paper, we apply Pseudo Zernike Moments (PZM) for recognition human faces in two-dimensional images, and we compare their performance with other type of moments. The moments that we have used are Zernike Moments (ZM),Pseudo Zernike Moments (PZM) and Legendre Moments (LM). We have used shape information for human face localization, also we have used Radial Basis Function (RBF) neural network as classifier for this application. The performance of classification is dependent on the moment order as well as the type of moment invariant, but the classification error rate was below Yo10 in all cases. Simulation results on face database of Olivetti Research Laboratory (ORL) indicate that high order degree of Pseudo Zernike Moments contain very useh1 information about face recognition process, while low order degree contain information about face expression. The PZM of order of 6 to 8 with %1.3 error rate are very good features for human face recognition that we have proposed.

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This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF) neural network with a hybrid learning algorithm (HLA) has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT) is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI) with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR) of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%

The automatic detection of epileptic seizures in EEG data from extended recordings can make an important contribution to the diagnosis of epilepsy as it can efficiently reduce the workload of medical staff. Methods: This paper describes how features based on cross-bispectrum can help with the detection of epileptic seizure activity in EEG data. Features were extracted from multi-channel intracranial EEG (iEEG) data from the Freiburg iEEG recordings of 21 patients with focal epilepsy. These features were used as a support vector machine classifier input to discriminate ictal from inter-ictal states. A post-processing method was applied to the classifier output in order to improve classification accuracy. Results: A sensitivity of 95.8%, specificity of 96.7%, and accuracy of 96.8% were achieved. The false detection rate (FDR) was zero for 10 patients and very low for the rest. Conclusions: The results show that the proposed method distinguishes better between ictal and inter-ictal iEEG epochs than other seizure detection methods. The proposed method has a higher accuracy index than achievable with a number of previously described approaches. Also, the method is rapid and easy and may be helpful in online epileptic seizure detection and prediction systems.

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The threat evaluation is significant component in target classification process and is significant in military and non military applications. Small errors or mistakes in threat evaluation and target classification especial in military applications can result in huge damage of life and property. Threat evaluation helps in case of weapon assignment, and intelligence sensor support system. It is very important factor to analyze the behavior of enemy tactics as well as our surveillance. This paper represented a precise description of the threat evaluation process using fuzzy sets theory. A review has been carried out regarding which parameters that have been suggested for threat value calculation. For the first time in this paper, eleven parameters are introduced for threat evaluation so that this parameters increase the accuracy in designed system. The implemented threat evaluation system has been applied to a synthetic air defense scenario and four real time dynamic air defense scenarios. The simulation results show the correctness, accuracy, reliability and minimum errors in designing of threat evaluation system