فصلنامه پردازش علائم و داده ها
سال هشتم شماره 2 (پیاپی 16، پاییز و زمستان 1390)

In this paper a new method is introduced for jointly delay and doppler estimation in ambiguity function based radars. In this method firstly each cell of ambiguity function is considered as a random variable, then an stochastic processes is estimated for each cell based on its value during consecutive radar scans. In the second step the ambiguity function is divided to high probability target and high probability clutter zones by using parameters of the estimated stochastic processes. Finally exact values of delay and doppler of radar targets is extracted and localized from the divided ambiguity function by employing spatial processing techniques. Performance of the proposed method is evaluated in two different scenarios, which the first scenario belongs to high speed targets and in the latter, targets are low speed. The obtained results showed the greater ability of the suggested method in detection both of above types of targets comparing with present approaches. Furthermore it can be shown that the proposed method causes the more considerable improvement in detection of low speed targets than high speed targets comparing with available methods.

In the present paper, the phonological feature geometry of the Persian phonemes is analyzed in the form of articulate-free and articulate-bound features based on the articulator model of the nonlinear phonology. Then, the reference phonetic pattern of each feature that consists of one or a set of acoustic correlates, characterized by the quantitative or qualitative values in its phonological representation, is determined by the acoustic and statistical analysis of the collected data. Finally, an algorithm is designed which implements multiple modules based on the identified acoustic correlates of the phonological features and gets as input an acoustic signal of a Persian phoneme in CV or VC context and outputs the recognized phoneme. The findings of the paper can considerably improve the speed and accuracy of the Persian speech recognition systems.

With unprecedented growth in production of digital images and use of multimedia references, requirement of image and subject search has been increased. Systematic processing of this information is a basic prerequisite for effective analysis, organization and management of it. Likewise, large collections of images have been made available on the Web and many search engines have provided the possibility of Web image searching based on keywords. For finding the image according to desire and requirement of user by image search engine, there are some problems as inexpressiveness of queries in description of user requirement, large number of unrelated images to the intended search, lacking of summarization, time consuming review of overall images, lack of diversity. Clustering of image search results can be an efficient solution for solving of these problems. In this research, several algorithms have been proposed for clustering of image search results. The developed summarization allows user to browse images conveniently and to get the overall content of the all returned images in a short time and by a few simple clicks. Through clustering, a diversified set of images presented that reflecting multiple senses of the query and the formed clusters represent visually diverse as well as diverse of ambiguity. According to the experiences, this proposed method improves the acceptable precision of image clustering.

This paper investigates the effect of diversity caused by Negative Correlation Learning(NCL) in the combination of neural classifiers and presents an efficient way to improve combining performance. Decision Templates and Averaging, as two non-trainable combining methods and Stacked Generalization as a trainable combiner are investigated in our experiments. Utilizing NCL for diversifying the base classifiers leads to significantly better results in all employed combining methods. Experimental results on five datasets from UCI and ELENA repositories indicate that by employing NCL, the performance of the ensemble structure can be more favorable compared to that of an ensemble use independent base classifiers.

Recently, automatic affective state recognition has been noteworthy for improving Human Computer Interaction (HCI), clinical researches and other various applications. Little attention has been paid so far to physiological signals for affective state recognition compared to audio-visual methods. Different affective states stimulate the Autonomic Nervous System (ANS) and lead to changes in physiology via the Sympathetic and Parasympathetic system and generation of specific patterns in physiological signals. In this study, we setup a reliable experiment to elicit four specific affective states in 25 healthy cases and record the physiological signals simultaneously. We also proposed a novel method to choose the cases. In addition, after the appropriate preprocessing, different features were extracted from the signals. Furthermore we compared various dimension reduction and classification methods to obtain a higher classification’s accuracy. An average accuracy of 84.3% was achieved by using the different dimension reduction and classification methods. The results show that our proposed method improved the accuracy of recognition and it can result in developing a realistic application.

Both theoretical and experimental studies have shown that combining accurate Neural Networks (NN) in the ensemble with negative error correlation greatly improves their generalization abilities. Negative Correlation Learning (NCL) and Mixture of Experts (ME), two popular combining methods, each employ different special error functions for the simultaneous training of NN experts to produce negatively correlated NN experts. In this paper, we review the properties of the NCL and ME methods, discussing their advantages and disadvantages. Characterization of both methods showed that they have different but complementary features, so if a hybrid system can be designed to include features of both NCL and ME, it may be better than each of its basis approaches. In this study, an approach is proposed to combine the features of both methods, i.e., Mixture of Negatively Correlated Experts (MNCE). In this approach, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables the training algorithm of ME to establish better balance in bias-variance-covariance trade-offs. The proposed hybrid ensemble method, MNCE, are compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed method preserve the advantages and alleviate the disadvantages of their basis approaches, offering significantly improved performance over the original methods.