@article{ author = {sharifi, massoud and sadeghi, vahi}, title = {A Phoneme Recognition Algorithm Design Using the Acoustic Correlates of the Phonological Features}, abstract ={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.}, Keywords = {nonlinear phonology, articulator, phonological feature, acoustic correlate, reference phonological pattern}, volume = {8}, Number = {2}, pages = {13-28}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-699-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-699-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2012} } @article{ author = {Montazer, Gholam Ali and alahKabir, Ehs}, title = {sad}, abstract ={aasdadad}, Keywords = {asd}, volume = {8}, Number = {2}, pages = {29-56}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-705-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-705-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2012} } @article{ author = {shojaeddini, vahab and kabiri, rahm}, title = {A New Method for Jointly Estimation of Delay and Doppler from Ambiguity Function : Combination of Stochastic Processes and Spatial processing for Noise and Clutter Suppression}, abstract ={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.}, Keywords = {Radar, Ambiguity function, Delay, Doppler, Clutter and noise suppression, Stochastic process, Target detection.}, volume = {8}, Number = {2}, pages = {35-12}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-702-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-702-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2012} } @article{ author = {Keyvanpour, MohammadReza and alamdar, fatemeh}, title = {Effective browsing of image search results via diversified visual summarization by Clustering}, abstract ={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.}, Keywords = {image clustering, Folding algorithm, Feature extraction, Visual diversity, Effective browsing, image search engine}, volume = {8}, Number = {2}, pages = {57-74}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-701-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-701-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2012} } @article{ author = {kargarnejad, ali and Masoudnia, Saeed and Kashefi, AmirHosei}, title = {Using Negative Correlated Networks to Improve Neural Ensemble Performance}, abstract ={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.  }, Keywords = {Classifiers Ensemble, Negative Correlation Learning, Decision Templates, Stacked Generalization, Diversity}, volume = {8}, Number = {2}, pages = {75-84}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-700-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-700-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2012} } @article{ author = {shafiyi, behrouz and Yazdchi, Mohammadreza and EmadiAndani, Mehr}, title = {Automatic Affective State Recognition Using Physiological Changes}, abstract ={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.}, Keywords = {Affective state recognition, Autonomic Nervous System, Biosignal processing, Feature extraction, Dimension reduction, Classification}, volume = {8}, Number = {2}, pages = {85-100}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-703-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-703-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2012} } @article{ author = {Mohammadzadeh, Javad and Masoudnia, Saeed and Araani, Ali}, title = {Improving classification performance using combination features of different neural network ensemble methods}, abstract ={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.}, Keywords = {Classification-Neural network ensemble-Negative Correlation Learning-Mixture of Experts}, volume = {8}, Number = {2}, pages = {101-114}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-704-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-704-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2012} } @article{ author = {Mavaji, Vahid and Vazirnezhad, Bahram}, title = {Pars Morph: A Persian Morphological Analyzer}, abstract ={In this paper, the theoretical foundation, the way of implementation and the uses of Pars Morph, a Persian morphological analyzer is introduced. Pars Morph is a rule-based Persian morphological analysis system, which analyzes the internal structure of word in Persian and determines the grammatical category and function of the word parts. Pars Morph being in link with a lexicon covering about 45000 lexemes and based on morphological rules in Persian can analyze the structure of complex lexemes and their possible inflected forms and even out-of-lexicon words. Accuracy rate of the first version of Pars Morph is about 95% which could be enhanced to 100% by applying the regulations on Persian language homographs, some syntactic features and typical features of Persian script. Pars Morph can be used in pure linguistics studies as well as in automatic analysis of Persian language.}, Keywords = {Pars Morph, morphological analyzer, Persian, word-formation, inflection, derivation, compounding}, volume = {8}, Number = {1}, pages = {3-8}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-714-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-714-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {}, title = {The role of Time Conjunctions in the Identification of Temporal Relations between Tensed-Verb Events in the Contemporary Persian Corpus}, abstract ={This paper involves in prediction of temporal relation between tensed-verb events on the basis of conjunctions in texts. For this purpose, tensed verb event data were extracted from Contemporary Persian Corpus and were examined carefully. The temporal relations between events were identified. After analyzing data on the basis of temporal relation according to Bird’s and Allen’s categorization, it was revealed that temporal conjunctions can be categorized in 4 categories: determined, biased, pseudo random and random. Determined signals can predicate the temporal relation between the events accurately. Biased signals predicate one set of relations with a good estimation, Pseudo random signals doesn’t show any orientation toward any kinds of temporal relation and random signals make almost all kinds of relations possible. The results show that Bird’s prediction on the basis of signals is more desirable than Allen’s.  }, Keywords = {Event Temporal Relations, Conjunctions, Verb Event}, volume = {8}, Number = {1}, pages = {9-16}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-708-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-708-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {Faili, Heshaam}, title = {Using Persian Stemmer in Information Retrieval System}, abstract ={Using the language-specific behavior in information retrieval systems can improve the quality of the retrieved results significantly. Part of the word that remains after removing its affixes is called stem. Stemming process can be used for improving the relevancy of the results in information retrieval system. Different morphological variants of words (plural, past tense…) will be mapped into their stem which can be used in the searching process of information retrieval tasks. Using the stem instead of the surface of the word reduces the size of the index file significantly. In this paper, an algorithm for stemming Persian words is described and its effect on information retrieval system is evaluated with different ranking methods. By using Persian Porter stemmer with just 43 rules, the size of index file reduced about 5% while the mean average precision of the retrieval information system improved about 5%.}, Keywords = {Stemmer, Natural language processing, Information retrieval}, volume = {8}, Number = {1}, pages = {17-24}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-713-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-713-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {}, title = {Image Enhancement Using Gamma Correction}, abstract ={In this paper a new automatic method is presented to enhance the image brightness through gamma correction process. Most of current gamma correction methods apply a uniform gamma correction across the image. Considering the fact that gamma variation for a single image is actually nonlinear, the proposed method does the gamma correction in a local approach. Thus the method is able to estimate appropriate gamma values for different regions of the image using a neural network. After windowing several training images with known gamma values, the mean and texture of each window (responsible for brightness and contrast of the window) are computed to train the neural network. The same features will be extracted from the unknown image to estimate the correct gamma values of the different parts of the image. Unlike other gamma correction methods, the proposed method does not change the gamma values of an image which does not need any brightness enhancement. The experimental results prove its better performance over other gamma correction methods.}, Keywords = {Feature selection, gamma correction, image enhancement, neural network}, volume = {8}, Number = {1}, pages = {25-32}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-711-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-711-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {abdolali, fatemeh and dadashi, neda and SeyyedSalehi, SeyyedAli}, title = {Improving face recognition from a single image per person via virtual images produced by imagination using neural networks}, abstract ={This paper deals with the problem of face recognition from a single image per person by producing virtual images using neural networks. To this aim, the person and variation information are separated and the associated manifolds are estimated using a nonlinear neural information processing model. For increasing the number of training samples in neural classifier, virtual images are produced for the neutral pose samples in a gallery dataset. By designing various structures of neural networks, the quality of virtually produced images, and consequently the recognition accuracy rate are improved. To obtain person information manifold codes giving better performance in describing the other persons and in generalizing, a learning method based on unsupervised clustering is presented. Applying this learning method and training classifier with virtual images, gives an accuracy rate of 83.63% on test dataset, which shows 12.73% improvement in comparison with training classifier using neutral pose samples.}, Keywords = {Face recognition, single image per person, virtual images, manifold learning, neural network, expression variant faces}, volume = {8}, Number = {1}, pages = {33-44}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-709-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-709-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {Aghabozorgisahaf, Masoud Reza and pishravian, arash and abutalebi, hamid rez}, title = {Application of Blind Source Separation for Speech-Music Separation}, abstract ={In this paper, the application of the Independent Component Analysis In this paper, the application of the Independent Component Analysis technique in speech-music separation is discussed. The separation algorithm is in the time domain. It needs the score function estimation to minimize the mutual information. For estimating score function, sufficient samples of the mixed (speech-music) signals are needed. In other words, these samples must be included both original sources. Since the speech and music signals could contain the silent gaps, the frame selection is important in our problem. Our proposed method for selecting the optimum frame is based on the score function difference. The experimental results show good performance of the proposed method in elimination of the silent gaps. Also they express the separation algorithm based on Gaussian Mixture estimator achieves a better separation performance and less processing time compared to the separation algorithm based on Minimum Mean Square Error estimator.}, Keywords = {Independent component analysis, speech and music signals, estimation of score function, score function difference, mutual information.}, volume = {8}, Number = {1}, pages = {45-54}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-707-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-707-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {}, title = {Region-based Quality Improvement of Facial Images with Strong Shadows to Enhance Recognition}, abstract ={Varying illuminations, especially the side lighting effects in face images, is one of the major obstacles in face recognition systems. Various methods have been presented for face recognition under different lighting conditions witch require previous knowledge about Light source and shadow area. In this paper, a novel approach based on H-minima transform to image segmentation and illumination normalization is proposed. Firstly, shadow area is extracted and modified by multi-stage method. Then, the gradient based criteria used to determine the best pattern of shadow areas. Subsequently, the obtained pattern of shadow areas is used to improve Retinex method in obscure area, obvious area, and all areas of the face image. Experimental results on the Extended Yale B database show that the proposed method significantly improves the performance of Retinex method. Furthermore, it provides the effective results in illumination normalization even in extreme lighting conditions.}, Keywords = {Face recognition, H-minima transform, Illumination compensation, Image quality improvement.}, volume = {8}, Number = {1}, pages = {55-66}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-712-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-712-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} }