@article{ 
author = {Kabiri, Peyman and Mohebol-Hojeh, Alirez},  
title = {}, 
abstract ={},  
Keywords = {},
volume = {10},
Number = {2}, 
pages = {3-20}, 
publisher = {Research Center on Developing Advanced Technologies},
url = {http://jsdp.rcisp.ac.ir/article-1-80-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-80-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2014}  
}

@article{ 
author = {yazdchi, mohamadreza and mahnam, ami},  
title = {Designing an Experiment to Improve Automatic Emotion Detection Using EEG}, 
abstract ={Emotions play an important role in daily life of human, so the need and importance of automatic emotion recognition have grown with increasing role of Human Computer Interaction (HCI) applications. Since emotion recognition using EEG can show inner emotions, this method is more attention from other ways. In consideration to lack of emotion induction collection for doing such researches at Iranian culture, in this research some emotion induction experiments are designed to create four emotion states in subjects. Once subjects are experimented by International Affective Picture System (IAPS) that collected at Florida University and then they are experimented by corresponding movies with Iranian culture. Results show that corresponding movies get higher accuracy in comparison with IAPS. Fast computing, using only two electrodes and obtaining high accuracy from EEG signals are other advantages of this research},  
Keywords = {Emotion recognition, EEG signal, International Affective Picture System, Fractal dimension },
volume = {10},
Number = {2}, 
pages = {21-34}, 
publisher = {Research Center on Developing Advanced Technologies},
url = {http://jsdp.rcisp.ac.ir/article-1-83-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-83-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2014}  
}

@article{ 
author = {},  
title = {Development and Enhancement of an interactive Computer-Assisted Translation System for English to Persian}, 
abstract ={In recent years, significant improvements have been achieved in statistical machine translation (SMT), but still even the best machine translation technology is far from replacing or even competing with human translators. Another way to increase the productivity of the translation process is computer-assisted translation (CAT) system. In a CAT system, the human translator begins to type the translation of a given source text by typing each character the MT system interactively offers the choices to enhance and complete the translation. Human translator may continue typing or accept the whole completion or part of it. In this paper, we propose new approaches for increasing the performance of the interactive CAT. Our approaches are included a new search way and a hybrid back-off model. We could achieve 1.3% improvement by using our offered search approach},  
Keywords = {Statistical Machine Translation (SMT), Computer-Assisted Translation (CAT), Interactive CAT, Prefix Search.},
volume = {10},
Number = {2}, 
pages = {35-46}, 
publisher = {Research Center on Developing Advanced Technologies},
url = {http://jsdp.rcisp.ac.ir/article-1-55-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-55-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2014}  
}

@article{ 
author = {},  
title = {An Improvement on Robust Pattern Recognition Using Chaotic Dynamics in Attractor Recurrent Neural Network}, 
abstract ={In this paper, two kinds of chaotic neural networks are proposed to evaluate the efficiency of chaotic dynamics in robust pattern recognition. The First model is designed based on natural selection theory. In this model, attractor recurrent neural network, intelligently, guides the evaluation of chaotic nodes in order to obtain the best solution. In the second model, a different structure of chaotic neural network is presented which includes chaotic neurons in the hidden layer. The behavior of these neurons can be controlled by changing the parameters of chaotic neurons. Furthermore, both models are supposed to recognize the noisy patterns even those with high levels of additional noise (up to 60%). Using the first proposed model, the accuracy of recognition was improved by 37.16%, 29.15% and 8.5% comparing to feedforward neural network, chaotic neural network based on chaotic nodes - NDRAM, and ARNN respectively. The second model increased the accuracy of recognition by an average of 13.91%, and 5.41% in comparison to ARNN and first model. In addition, it has been observed that the second model had a better performance, even in point attractor mode, than ARNN which acts in non chaotic mode.},  
Keywords = {Attractor Recurrent Neural Network, Chaotic Dynamics, Chaotic Neuron, Point Attractor, Robust Pattern Recognition},
volume = {10},
Number = {2}, 
pages = {47-67}, 
publisher = {Research Center on Developing Advanced Technologies},
url = {http://jsdp.rcisp.ac.ir/article-1-145-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-145-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2014}  
}

@article{ 
author = {salimibadr, armin and Homayounpour, Mohammad Mehdi},  
title = {Phrase chunking in Persian texts}, 
abstract ={Text tokenization is the process of tokenizing text to meaningful tokens such as words, phrases, sentences, etc. Tokenization of syntactical phrases named as chunking is an important preprocessing needed in many applications such as machine translation information retrieval, text to speech, etc. In this paper chunking of Farsi texts is done using statistical and learning methods and the grammatical characteristics of Farsi texts. Many features and labeling methods are examined one by one and the best features and labeling techniques are used for the detection of syntactic phrases and their boundaries. Several machine learning techniques including Support Vector Machine and Conditional Random Fields are used as classifier in our experiments. The impact of the size of training texts on chunking performance was studied as well. Using the proposed methods in this paper, a performance of 84.02% was obtained for detection of phrase boundaries and 78.04% for detection of both phrase boundaries and phrase type},  
Keywords = {Natural language processing, Phrase chunking, POS tagging, Support vector machine, Conditional random fields, Text to speech, Machine translation},
volume = {10},
Number = {2}, 
pages = {69-86}, 
publisher = {Research Center on Developing Advanced Technologies},
url = {http://jsdp.rcisp.ac.ir/article-1-73-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-73-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2014}  
}

@article{ 
author = {},  
title = {FARSIARABIC TEXT DETECTION AND LOCALIZATION IN VIDEO FRAMES}, 
abstract ={Video text detection plays an important role in applications such as semantic-based video analysis, text information retrieval, archiving and so on. In this paper, we propose a Farsi/Arabic text detection approach. First, with an appropriate edge detector, edges are extracted and then by using edges cross ponts, artificial corners are extracted. Artificial corner histogram analysis is done for rejecting some non text corners. The discrete cosine transform (DCT) coefficients of input picture are extracted and texture intensity picture is created by combining appropriate coefficients. With combining artificial corners image and texture intensity image, a features vector is extracted and fed into support vector machine (SVM) classifier for detecting text regions. Finally with drawing normalized texture intensity profiles, final verification is done and text lines are sepersted from each others},  
Keywords = {Farsi/Arabic, video sequence, text detection, artificial corner, DCT },
volume = {10},
Number = {2}, 
pages = {87-104}, 
publisher = {Research Center on Developing Advanced Technologies},
url = {http://jsdp.rcisp.ac.ir/article-1-132-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-132-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2014}  
}