@article{ author = {Shayegh, Farzaneh and Ghasemi, Fahimeh and Amirfatahi, Rasoul and Sadri, Saeed and Ansarifard, Karim}, title = {Online Single-Channel Seizure Prediction, Based on Seizure Genesis Model of Depth-EEG Signals Using Extended Kalman Filter}, abstract ={Many efforts have been done to predict epileptic seizures so far. It seems that some kind of abnormal synchronization among brain areas is responsible for the seizure generation. This is because the synchronization-based algorithms have been the most important methods so far. However, the huge number of EEG channels, which is the main requirement of these methods, make them very difficult to use in practice. In this paper, in order to improve the prediction algorithm, the factor underlying the abnormal brain synchronization, i.e., the imbalance of excitation/inhibition neuronal activity, is taken into account. Accordingly, to extract these hidden excitatory/inhibitory parameters from depth-EEG signals, a realistic physiological model is used. The Output of this model (as a function of model parameters) imitate the depth-EEG signals. On the other hand, based on this model, one can estimate the model parameters behind every real depth-EEG signal, using an identification process. In order to be able to track the temporal variation of the parameter sequences, the model parameters, themselvese, are supposed to behave as a stochastic process. This stochastic process, described by a Hidden Markov Model formerly (HMM) and worked by the current researchists, is now modified to a State Space Model (SSM). The advantage of SSM is that it can be described by some differential equations. By adding these SSM equations to the differential equations producing depth-EEG signals, Kalman filter can be used to identify the parameter sequences underlying signals. Then, these extracted inhibition/excitation sequences can be applied in order to predict seizures. By using the four model parametetrs relevant to excitation/inhibition neuronal activity, extracted from just one channel of depth-EEG signals, the proposed method reached the 100% sensitivity, and 0.2 FP/h, which is very similar to the multi-channel algorithms. The algorithm can be done in an online manner.  }, Keywords = {Physiological model of epileptic seizures, Online Single-Channel Seizure Prediction, Kalman Filter, State Space Model (SSM), epileptic, Hidden Markov Model (HMM)}, volume = {15}, Number = {1}, pages = {3-28}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.3}, url = {http://jsdp.rcisp.ac.ir/article-1-509-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-509-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Majidian, Sina and Haddadi, Farz}, title = {Direction of Arrival (DOA) Estimation Using Kronecker Subspace}, abstract ={This paper proceeds directions of arrival (DOA) estimation by a linear array. These years, some algorithms, e.g. Khatri-Rao approach, Nested array, Dynamic array have been proposed for estimating more DOAs than sensors. These algorithms can merely estimate uncorrelated sources. For Khatri-Rao approach, this is due to the fact that Khatri-Rao product discard the non-diagonal entries of the correlation matrix in opposed to Kronecker product. In this article, an algorithm named as Direction of Arrival (DOA) Estimation using Kronecker Subspace is proposed to solve more correlated sources than sensors via some properties of vectorization operator and Kronecker product. The simulations in different scenarios are presented considering various numbers of frames and correlation values, here. These verify our mathematical analysis. Furthermore, Cramer-Rao bound (CRB) which is a crucial criterion to estimate, is under investigating for DOA problem. Although, CRB for DOA estimation has been proposed before, it is applicable only for fewer sources than sensors. In this paper, CRB for more sources than sensor is derived by extending the dimensions with using both real and imaginary parts of the parameters. This bound is compared to the error of the presented algorithm. The simulations show that the error of the presented algorithm is merely 7 dB far from the CRB.}, Keywords = {Array signal processing, Direction of Arrival (DOA), Correlated sources, Kronecker}, volume = {15}, Number = {1}, pages = {29-40}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.29}, url = {http://jsdp.rcisp.ac.ir/article-1-410-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-410-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Biglari, Mohsen and Soleimani, Ali and Hassanpour, Hami}, title = {Using Discriminative Parts for Vehicle Make and Model Recognition}, abstract ={In fine-grained recognition, the main category of object is well known and the goal is to determine the subcategory or fine-grained category. Vehicle make and model recognition (VMMR) is a fine-grained classification problem. It includes several challenges like the large number of classes, substantial inner-class and small inter-class distance. VMMR can be utilized when license plate numbers cannot be identified or fake number plates are used. VMMR can also be used when specific models of vehicles are required to be automatically identified by cameras. Few methods have been proposed to cope with limited lighting conditions. A number of recent studies have shown that latent SVM trained on a large-scale dataset using data mining can achieve impressive results on several object classification tasks. In this paper, a novel method has been proposed for VMMR using a modified version of latent SVM. This method finds discriminative parts of each class of vehicles automatically and then learns a model for each class using features extracted from these parts and spatial relationship between them. The parts weights of each model are tuned using training dataset.  Putting this individual models together, our proposed system can classify vehicles make and model. All training and testing steps of the proposed system are done automatically. For training and testing the performance of the system, a new dataset including more than 5000 vehicles of 28 different make and models has been collected. This dataset poses different kind of challenges, including variations in illumination and resolution. The experimental results performed on this dataset show the high accuracy of our system.  }, Keywords = {Fine-grained recognition, vehicle make and model recognition, VMMR, part-based approach}, volume = {15}, Number = {1}, pages = {41-54}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.41}, url = {http://jsdp.rcisp.ac.ir/article-1-574-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-574-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Rahbaripour, Masoud and MohammadzadehAsl, Babak}, title = {Premature Ventricular Contraction Arrhythmia Detection in ECG Signals via Combined Classifiers}, abstract ={Cardiovascular diseases are the most dangerous diseases and one of the biggest causes of fatality all over the world. One of the most common cardiac arrhythmias which has been considered by physicians is premature ventricular contraction (PVC) arrhythmia. Detecting this type of arrhythmia due to its abundance of all ages, is particularly important. ECG signal recording is a non-invasive, popular method for an assessment of heart's function. Development of quick, accurate automatic ECG classification methods is essential for the clinical diagnosis of heart disease. This research analyzes the ECG signal to detect PVC arrhythmia. Different techniques are provided in order to detect this type of arrhythmia based on ECG signals. As these techniques use different methods for detection, the reaction of each one will be different to detect this type of arrhythmia. There is no classifier to give the best results for all matters at any time and combining classifiers improve the combined system results in comparison with each of the techniques. In this study, the MIT-BIH arrhythmia database is used as a data source. Two datasets are used for training; the first contains 2400 samples, as in other studies, and the second contains 600 samples, including normal and PVC beats. Morphological features and features obtained from wavelet transform used in a combined classifier were used afterwards, which is the combination of the most common classifiers namely artificial neural network, SVM and KNN for PVC beat classification. Statistical significance features were selected using the p-value approach and normalized them. The best results were obtained when combining all three classifiers and using normalized statistical significance features. The designed hybrid system succeeded to detect PVC beats with 98.9±0.2% accuracy, 99.0±0.1% sensitivity, and 98.8±0.2% specificity. Also, the efficiency of the proposed method was shown when using limited training samples. The results showed the success of the proposed approach, specifically in comparison with other related research studies.  }, Keywords = {Premature ventricular contraction, ECG, Morphological features, Combined classifiers}, volume = {15}, Number = {1}, pages = {55-70}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.55}, url = {http://jsdp.rcisp.ac.ir/article-1-584-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-584-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Asgarian, Ehsan and Kahani, Mohsen and Sharifi, Shahl}, title = {HesNegar: Persian Sentiment WordNet}, abstract ={Awareness of others' opinions plays a crucial role in the decision making process performed by simple customers to top-level executives of manufacturing companies and various organizations. Today, with the advent of Web 2.0 and the expansion of social networks, a vast number of texts related to people's opinions have been created. However, exploring the enormous amount of documents, various opinion sources and opposing opinions about an entity have made the process of extracting and analyzing opinions very difficult. Hence, there is a need for methods to explore and summarize the existing opinions. Accordingly, there has recently been a new trend in natural language processing science called "opinion mining". The main purpose of opinion mining is to extract and detect people’s positive or negative sentiments (sense of satisfaction) from text reviews. The absence of a comprehensive Persian sentiment lexicon is one of the main challenges of opinion mining in Persian. In this paper, a new methodology for developing Persian Sentiment WordNet (HesNegar) is presented using various Persian and English resources. A corpus of Persian reviews developed for opinion mining studies are introduced. To develop HesNegar, a comprehensive Persian WordNet (FerdowsNet), with high recall and proper precision (based on Princeton WordNet), was first created. Then, the polarity of each synset in English SentiWordNet is mapped to the corresponding words in HesNegar. In the conducted tests, it was found that HesNegar has a precision score of 0.86 a recall score of 0.75 and it can be used as a comprehensive Persian SentiWordNet. The findings and developments made in this study could prove useful in the advancement of opinion mining research in Persian and other similar languages, such as Urdu and Arabic.  }, Keywords = {Opinion Mining, FerdowsNet (Persian WordNet), Sentiment Lexicon, Persian Text Processing Tools}, volume = {15}, Number = {1}, pages = {71-86}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.71}, url = {http://jsdp.rcisp.ac.ir/article-1-554-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-554-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Boreshban, Yasaman and Yousefinasab, Hamed and Mirroshandel, Seyed Abolghasem}, title = {Providing a Religious Corpus of Question Answering System in Persian}, abstract ={Question answering system is a field in natural language processing and information retrieval noticed by researchers in these decades. Due to a growing interest in this field of research, the need to have appropriate data sources is perceived. Most researches about developing question answering corpus area have been done in English so far, but in other languages as Persian, the lack of these corpora is perceived. In this article, the development of a Persian question answering corpus called Rasayel&massayel will be discussed. This corpus consists of 2,118 non-factoid and 2,051 factoid questions that for each question, question text, question type, question difficulty from questioner and responder’s perspective, expected answer type in coarse-grained and fine-grained level, exact answer, and page and paraghraph number of answer are annotated. The prposed corpus can be applied to learn components of question answering system, including question classification, information retrieval, and answer extraction. This corpus is freely available for the academic purpose as well. In the following, a question answering system is presented on the Rasayel&massayel corpus. Our experimental result represents that the intended proposed system has achieved 82.29 % accuracy and 56.73 % mean reciprocal rank. It could be also claimed that this is the first ever question answering system and corpus with such features in Persian.  }, Keywords = {Question answering system, Natural language processing, Information retrieval, Rasayel&massayel corpus}, volume = {15}, Number = {1}, pages = {87-102}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.87}, url = {http://jsdp.rcisp.ac.ir/article-1-535-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-535-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {shahabi, mina and Nafisi, Vahid rez}, title = {Cuff-less Blood Pressure Estimation Based on Temporal Feature of PPG Signal}, abstract ={Blood pressure is one of the vital signs. Specially, it is crucial for some cases such as hypertension patients and it should be monitored continuously in ICU/CCU. It must be noted that current systems to measure blood pressure, often require trained operators. As an example, in post-hospital cares, blood pressure control is difficult except with the presence of a nurse or use of a device that minimizes the patient's involvement in the measurements. In this way, Photoplotysmography (PPG), which is a noninvasive method for pulse wave recording, seems to be ideal to make simple tools for blood pressure measurement in home care. In other words, it is so helpful or rather necessary to design a non-invasive, cuff-less, subject-independent system for blood pressure measurement. In this study, two optical sensors were located on the finger and the wrist. Twenty healthy volunteers in different situations were examined to record PPG signals. Also, blood pressure values were measured by cuff-based noninvasive blood pressure system on left arm as a reference value. Recorded signals were filtered and processed in MATLAB R2014a software. To promote the estimation accuracy and subject-independency, 16 temporal features in addition to the pulse transit time (PTT) were extracted from the wrist PPG signal. To estimate blood pressure values, three neural networks were used as the estimator: Feedforward Neural Network (FFN), Redial Basis Function Neural Network (RBFN) and General Regression Neural Network (GRNN). After comparison of their results; the General Regression Neural Network was used for blood pressure estimation. The MSE errors estimated by the best estimator, were 0.11±1.18 mmHg and 0.15±2.3 mmHg for systole and diastole pressure respectively.}, Keywords = {Blood Pressure Monitoring, Photoplethysmography (PPG), Pulse Transit Time (PTT), Neural Network}, volume = {15}, Number = {1}, pages = {103-114}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.103}, url = {http://jsdp.rcisp.ac.ir/article-1-534-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-534-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Salami, Shahram and Shamsfard, Mehrnoush}, title = {Phrase-Boundary Translation Model Using Shallow Syntactic Labels}, abstract ={Phrase-boundary model for statistical machine translation labels the rules with classes of boundary words on the target side phrases of training corpus. In this paper, we extend the phrase-boundary model using shallow syntactic labels including POS tags and chunk labels. With the priority of chunk labels, the proposed model names non-terminals with shallow syntactic labels on the boundaries of the target side phrases. In comparison to the base phrase-boundary model, our variant uses phrase labels in addition to word classes. In other words, if there is no chunk label in one boundary, the labeler uses the word POS tag. The boundary labels are concatenated where there is no label for the whole target span. Using chunks as phrase labels, the proposed model generalizes the rules to decrease the model sparseness. The sparseness has more importance in the language pairs with a lot of differences in the word order because they have less number of aligned phrase pairs for extraction of rules. Compared with Syntax Augmented Machine Translation (SAMT) that labels rules with the syntax trees of the target side sentences, the proposed model does not need deep syntactic parsing. Thus, it is applicable even for low-resource languages having no syntactic parser. Some translation experiments are performed from Persian and German to English as the source and target languages with different word orders. In the experiments, our model achieved improvements of about 0.5 point of BLEU over a variant of SAMT.}, Keywords = {Statistical machine translation, Hierarchical models, Word tag, Chunk label}, volume = {15}, Number = {1}, pages = {115-126}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.115}, url = {http://jsdp.rcisp.ac.ir/article-1-540-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-540-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Akbarzadeh, Negar and Timarchi, Somayeh}, title = {Modulo 2n+1 Multiply and MAC Units Specified for DSPs}, abstract ={Nowadays, digital signal processors (DSPs) are appropriate choices for real-time image and video processing in embedded multimedia applications not only due to their superior signal processing performance, but also of the high levels of integration and very low-power consumption. Filtering which consists of multiple addition and multiplication operations, is one of the most fundamental operations of DSPs. Therefore, there is a need for an additional unit just after the multiplication unit in DSPs. By combining multiply and add units, new structure named MAC (Multiply and ACcumulate) unit is provided. Residue Number System (RNS) can improve speed and power consumption of arithmetic circuits as it offers parallel arithmetic operations on each moduli and confines carry propagation to each moduli. In order to improve the efficiency of the MAC unit, RNS could be utilized. RNS divides large numbers to smaller numbers, called residues, according to a moduli set and enables performing arithmetic operations on each moduli independently. The moduli set {2n-1,2n,2n+1} is the most famous among others because of its simple and efficient implementation. Among this moduli set, modulo 2n+1 circuits are the critical path due to (n+1)-bit wide data path despite other two modules which all have n-bit wide operands. In order to overcome the problem of (n+1) bits operands, three representations has been suggested: diminished-1, Signed-LSB and Stored-Unibit. Although different multipliers have been proposed for diminished-1 representation, no multiplication structure has been proposed for the last two ones. Modulo 2n+1 multipliers are divided into 3 categories depending on their inputs and outputs types: both operands use standard (weighted) representation; one input uses standard representation, while the other one utilizes diminished-1 representation; both inputs use diminished-1 representation. Although several multiply and add units have been proposed for the first 2 categories, no MAC unit is proposed for the multipliers of a third category which outperform multipliers of other categories. In this article at first, one modulo 2n+1 MAC unit for the third category is proposed and then for further improvement, pipeline and multi-voltage techniques are utilized. Pipeline structure enables a trade-off between power consumption and delay. Whenever high-performance with least delay is desirable, nominal supply voltage can be chosen (high performance mode) otherwise by reducing supply voltage to the amount at which pipeline circuit and normal circuit without pipeline would have the same performance, power consumption decreases significantly (low power mode). Simulations are performed in two phases. At first phase, proposed MAC unit without pipeline structure is described via VHDL code and synthesized with synopsys design vision tool. Results indicate that the proposed structure outperforms PDP (Power-Delay-Product) up to 39% compared to the state of the art MAC units. At second phase, CMOS transistor level implementation in two modes i.e. low power and high performance modes with Cadence Design Systems tool is provided. Simulation results indicate that at low power condition, proposed pipeline MAC unit yields to 71% power savings compared to existing circuits without declining efficiency. Furthermore, at high performance condition, however power consumption has increased, reducing delay up to 54% yields to 39% PDP savings for proposed pipeline MAC unit.  }, Keywords = {Digital signal processor, MAC, Residue number system, diminished-1 representation, multiplier}, volume = {15}, Number = {1}, pages = {127-138}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.127}, url = {http://jsdp.rcisp.ac.ir/article-1-543-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-543-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {RanjbarHassaniMahmoodAbadi, Mahdi and Faraahi, Ahm}, title = {Choosing the most appropriate query language to use Outer Joins for data extraction in Datalog mode in the Deductive Database System DES}, abstract ={Deductive Database systems are designed based on a logical data model. Data (as opposed to Relational Databases Management System (RDBMS) in which data stored in tables) are saved as facts in a Deductive Database system. Datalog Educational System (DES) is a Deductive Database system that Datalog mode is the default mode in this system. It can extract data to use outer joins with three query languages (Datalog, SQL and RA) in default mode. In 2004, system DES was designed and implemented by Fernando S´aenz-P´erez from Department of Artificial Intelligence and Software Engineering, Complutense University, Madrid, Spain. In a paper, this researcher introduced outer joins of system DES  in 2012. The most important objective of present research is to complement and extend the paper authored by mentioned researcher. Therefore, in prior research, choosing the most appropriate query language has not been investigated to use outer joins for data extraction in Datalog mode in DES system. In this study, by considering two parameters (cost of writing a query and memory usage of a query) choosing the most appropriate query language has been investigated to use outer joins for data extraction in Datalog mode in Deductive system DES. Cost of writing a query parameter is considered in this study to decrease the query typing time, but other parameters are related to the query processing are not considered. If the processing time of the three query languages is assumed identical, after entering the query in the system DES, the idea of the present study (reduction of the typing time) can lead to the reduction of the response time. Also, there are two hypotheses in this study as follows: 1) it is assumed that the user is fluent in all three query languages and wants to access the given data quickly through the most appropriate query language. 2) In the present study, the simplicity or difficulty of a query language is not considered. The results of the research show that one language cannot be appropriate for all queries; therefore, for every different query the most appropriate query language must be chose to use outer joints. In the current research, the most appropriate query language is the one in which, in comparison with other two query languages, the user will need to use less buttons of the keyboard to press in order to fulfill the query. The decrease in the number of buttons pressed by the user will decrease the time consumed to fulfill the query and, therefore, it will lead to a faster access to data.  }, Keywords = {Deductive Database, DES, Datalog mode, Outer Joins, Data extraction}, volume = {15}, Number = {1}, pages = {139-150}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.1.139}, url = {http://jsdp.rcisp.ac.ir/article-1-583-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-583-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Omranpour, Hesam and Azadian, Fahime}, title = {Presenting a Fuzzy Approach to Optimize Predicting High Order Time Series}, abstract ={It is difficult to apply the real world’s conceptions due to their uncertainty. Generally, time series are known to be non-linear or non-stationary. Regarding these two features, a system should be sensitive enough to apply the unity of time series and repeat this sensitiveness in the prediction. A predict system can exactly scrutinize the hidden features of time series and also can have high predicting runs. Lots of statistical tools such as regression analysis, gradient average, exponential gradient average and auto regression gradient average are used in traditional predictions. One of the biggest challenges of these approaches is the necessity of greater observations and the avoidance of linguistic variables or subjective experts’ ideas. Also these methods are limited to linear being assumptions. In order to dominate the limitations of traditional methods, many researchers have utilized soft computations like fuzzy logic, fuzzy neural networks, evolutionary algorithms and etc. In this paper, we proposed a new fuzzy prediction novel based on the high order fuzzy time series. Our proposed model is based on the higher order fuzzy time series prediction computational approach. In this method a group of features are evaluated, by adding the value of the preceding element of predicting element to the result of the series’ differences. At that, particle swarm optimization is used to optimize Calculation algorithm features, which renders a better performance in order to solve the problems of higher order fuzzy time series. Then by choosing the best features, a result can be inferred as the predicting value. The performance of the approach is presented in which after the fuzzification of time series and creating the logical fuzzy relations, by using the lower limit of the predicting element’s range and its consecutive range, and the resulted difference of sequential elements, some specific computations are done and a set of features are gained. Then, using the particle swarm optimization function, the best parameter is selected. The fitness function in the proposed method has two parts: a general section (the average of all orders) and a partial (Every columns orders). In general section, the overall average of error is shown. In Every columns orders section each column individually considered. For the second to tenth order (9 PSO separate) the answer is checked. The method is as follow; we used two parameters b and d for the feature calculation algorithm. The amount of d   was manually and randomly between 3 – 1000, but PSO find the amount of b. Properties obtained by this method, have less outliers data and waste, which it causes predicted closer, with less error. Finally, defuzzification is performed. The yielded score is the predicted integer value of considered element. In order to decide the precision of the prediction’s rate, we compare the proposed model to other methods using the mean square error and the average error. In order to show the efficiency of the proposed approach, we have implemented this method on the Alabama University’s enrollment database. It can be observed that the suggested method provides better results compared to the other methods and also renders a lower error.  }, Keywords = {Predict, Time series, Optimization, Fuzzy logic, High-order fuzzy time series, Fuzzification, Defuzzification}, volume = {15}, Number = {2}, pages = {3-16}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.3}, url = {http://jsdp.rcisp.ac.ir/article-1-603-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-603-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Bagherzadeh, Hojjat and Harati, Ahad and Amiri, Zahra and KamyabiGol, RajabAli}, title = {Video Denoising Using block Shearlet Transform}, abstract ={Parabolic scaling and anisotropic dilation form the core of famous multi-resolution transformations such as curvelet and shearlet, which are widely used in signal processing applications like denoising. These non-adaptive geometrical wavelets are commonly used to extract structures and geometrical features of multi-dimensional signals and preserve them in noise removal treatments. In discrete setups, it is shown that shearlets can outperform other rivals since in addition to scaling, they are formed by shear operator which can fully remain on integer grid. However, the redundancy of multidimensional shearlet transform exponentially grows with respect to the number of dimensions which in turn leads to the exponential computational and space complexity. This, seriously limits the applicability of shearlet transform in higher dimensions. In contrast, separable transforms process each dimension of data independent of other dimensions which result in missing the informative relations among different dimensions of the data. Therefore, in this paper a modified discrete shearlet transform is proposed which can overcome the redundancy and complexity issues of the classical transform. It makes a better tradeoff between completeness of the analysis achieved by processing full relations among dimensions on one hand and the redundancy and computational complexity of the resulting transform on the other hand. In fact, how dilation matrix is decomposed and block diagonalized, gives a tuning parameter for the amount of inter dimension analysis which may be used to control computation complexity and also redundancy of the resultant transform. In the context of video denoising, three different decompositions are proposed for 3x3 dilation matrix. In each block diagonalization of this dilation matrix, one dimension is separated and the other two constitute a 2D shearlet transform. The three block shearlet transforms are computed for the input data up to three levels and the resultant coefficients are treated with automatically adjusted thresholds. The output is obtained via an aggregation mechanism which combine the result of reconstruction of these three transforms. Using experiments on standard set of videos at different levels of noise, we show that the proposed approach can get very near to the quality of full 3D shearlet analysis while it keeps the computational complexity (time and space) comparable to the 2D shearlet transform.  }, Keywords = {anisotropic dilation matrix, curvelet transform, multidimensional shearlet transform, block diagonal dilation matrix, video denoising}, volume = {15}, Number = {2}, pages = {17-30}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.17}, url = {http://jsdp.rcisp.ac.ir/article-1-547-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-547-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Hazrati, Isa and Daneshpour, Negi}, title = {Improving Near Real Time Data Warehouse Refreshment}, abstract ={Near-real time data warehouse gives the end users the essential information to achieve appropriate decisions. Whatever the data are fresher in it, the decision would have a better result either. To achieve a fresh and up-to-date data, the changes happened in the side of source must be added to the data warehouse with little delay. For this reason, they should be transformed in to the data warehouse format. One of the famous algorithms in this area is called X-HYBRIDJOIN. In this algorithm the data characteristics of real word have been used to speed up the join operation. This algorithm keeps some partitions, which have more uses, in the main memory. In the proposed algorithm in this paper, disk-based relation is joined with input data stream. The aim of such join is to enrich stream. The proposed algorithm uses clustered index for disk-based relation and join attribute. Moreover, it is assumed that the join attribute is exclusive throughout the relation. This algorithm has improved the mentioned algorithm in two stages. At the first stage, some records of source table which are frequently accessible are detected. Detection of such records is carried out during the algorithm implementation. The mechanism is in the way that each record access is counted by a counter and if it becomes more than the determined threshold, then it is considered as the frequently used record and placed in the hash table. The hash table is used to keep the frequently used records in the main memory. When the stream is going to enter in to join area, it is searched in this table. At the second stage, the choice method of the partition which is going to load in the main memory has been changed. One dimensional array is used to choose the mentioned partition. This array helps to select a partition of source table with highest number of records for the join among all partitions of source table. Using this array in each iteration, always leads to choose the best partition loading in memory. To compare the usefulness of the suggested algorithm some experiments have been done. Experimental results show that the service rate acquired in suggested algorithm is more than the existing algorithms. Service rate is the number of joined records in a time unit. Increasing service rate causes the effectiveness of the algorithm.}, Keywords = {Near Real Time Data Warehouse, Join, Data Stream, Decision Making}, volume = {15}, Number = {2}, pages = {31-44}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.31}, url = {http://jsdp.rcisp.ac.ir/article-1-636-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-636-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Karimi, Abbas and Hoseini, Leila Sadat}, title = {An Optimal Algorithm for Dividing Microscopic Images of Blood for the Diagnosis of Acute Pulmonary Lymphoblastic Cell Using the FCM Algorithm and Genetic Optimization}, abstract ={Cancer is type of disease caused by irregular, uncontrollable growth of blood cells in bone marrow. The process of generating three main blood cells including pallets, red and white blood cells, is started from a progenitor cell called as blast. Blast generates a considerable number of immature cells which are developed affected by differentiation factors. If any interruption occurs during this process, leukemia may be initiated. Diagnosis of leukemia is performed at hospitals or medical centers by examination of the blood tissue smeared across a slide and under a microscope by a pathologist. Processing the digital images of blood cells, in order to improve the quality of the image or highlighting the malicious segments of the image, is important in early stages of the disease. There are four types of leukemia consisting acute or chronic and myeloid or lymphocytic. Acute lymphocytic (or lymphoblastic) leukemia (ALL) is concentrated in this study. ALL is caused by continuous generation of immature, malignant lymphocytes in bone marrow which are speeded by blood circulation to other organs. In this research, fuzzy C-means (FCM) algorithm is applied to blood digital images for clustering purpose, neural networks for feature selection and Genetic Algorithm (GA) for optimization. This model diagnoses ALL at early stages and categorizes it into three morphological subcategories (i.e., L1, L2, and L3). For performance evaluation of the proposed method, 38 samples of patients with ALL were collected. It was performed on 68 microscopic images in terms of 15 features and yielded to higher percentage of sensitivity, specificity, and accuracy for 10 out of 15 features. The proposed method was compared to three recent methods. The evaluations showed that the sensitivity, specificity and accuracy reached to 85.15%, 98.17% and 96.53%, respectively.}, Keywords = {leukemia, FCM algorithm, neural network, genetic algorithm, clustering}, volume = {15}, Number = {2}, pages = {45-54}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.45}, url = {http://jsdp.rcisp.ac.ir/article-1-567-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-567-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {ghanbarisorkhi, ali and Fateh, Mansour and Hassanpour, Hami}, title = {Intelligent Identifications and Filtering of Unconventional Images Based on Deep Neural Networks}, abstract ={Currently vast improvement of internet access and significant growth of web based broadcasters have resulted in distribution and sharing of informative resources such as images worldwide. Although this kind of sharing may bring many advantages, there are certain risks such as access of kids to porn images which should not be neglected. In fact, access to these images can be a threat to the culture of any society where kids and adults are included. However, many of internet users are members of social websites including Facebook or Instagram and without an appropriate intelligent filtering system, presence of few unconventional images may result in total filtering of these websites causing unpleasant feeling of members. In this paper, an attempt was made to propose an approach for classification and intelligent filtering of unconventional images. One of the major issues on these occasions is the analysis of a large scale of data available in the websites which might be a very time consuming task. A deep neural network might be a good option to resolve this issue and provide a good accuracy in dealing with huge databases. In this research, a new architecture for identifying unconventional images is proposed. In the proposed approach, the new architecture is presented with a combination of AlexNet and LeNet architecture that uses convolutional, polling and fully-connected layers. The activation function used in this architecture, is the Rectified Linear Unit (ReLU) function. The reason of using this activation function is the high speed of convergence in deep convolution networks and simplicity in implementation. The proposed architecture consists of several parts. The first two parts consist of convolutional layers, ReLUs and pooling. In this section, convolution is applied to the input image with different dimensions and filters. In the next section, the convolutional layer with ReLU is used without pooling. The next section, like the first two parts, includes convolutional layers, ReLU and pooling. Finally, the last three parts include the fully-connected layers with ReLU. The output of the last layer is the two classes, which specifies the degree of belonging of each input to the class of unconventional and conventional images. The results are tested on a large-scale dataset. These tests show that the proposed method is more accurate than the other methods recently developed for identifying unconventional images.  }, Keywords = {Intelligent filtering system, unconventional images, deep neural network, conventional neural network}, volume = {15}, Number = {2}, pages = {55-68}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.55}, url = {http://jsdp.rcisp.ac.ir/article-1-590-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-590-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {MohammadiDashti, Mina and Harouni, Maji}, title = {Smile and Laugh Expressions Detection Based on Local Minimum Key Points}, abstract ={In this paper, a smile and laugh facial expression is presented based on dimension reduction and description process of the key points. The paper has two main objectives; the first is to extract the local critical points in terms of their apparent features, and the second is to reduce the system’s dependence on training inputs. To achieve these objectives, three different scenarios on extracting the features are proposed. First of all, the discrete parts of a face are detected by local binary pattern method that is used to extract a set of global feature vectors for texture classification considering various regions of an input-image face. Then, in the first scenario and with respect to the correlation changes of adjacent pixels on the texture of a mouth area, a set of local key points are extracted using the Harris corner detector. In the second scenario, the dimension reduction of the extracted points of first scenario provided by principal component analysis algorithm leading to reduction in computational costs and overall complexity without loss of performance and flexibility; and in the final scenario, a set of critical points is extracted through comparing the extracted points’ coordinates of the first scenario and the BRISK Descriptor, which is utilized a neighborhood sampling strategy of directions for a key-point. In the following, without training the system, facial expressions are detected by comparing the shape and the geometric distance of the extracted local points of the mouth area. The well-known standard Cohn-Kaonde, CAFÉ, JAFFE and Yale benchmark dataset are applied to evaluate the proposed approach. The results shows an overall enhancement of 6.33% and 16.46% for second scenario compared with first scenario and third scenario compared with second scenario. The experimental results indicate the power efficiency of the proposed approach in recognizing images more than 90 % across all the datasets.    }, Keywords = {Local key points extraction, facial expression detection, corner detector, descriptor algorithm, dimension reduction}, volume = {15}, Number = {2}, pages = {69-88}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.69}, url = {http://jsdp.rcisp.ac.ir/article-1-658-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-658-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Najafzadeh, Mohsen and RahatiQuchan, Saeed and Ghaemi, Rez}, title = {A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis}, abstract ={With the appearance of Web 2.0 and 3.0, users’ contribution to WWW has created a huge amount of valuable expressed opinions. Considering the difficulty or impossibility of manually analyzing such big data, sentiment analysis, as a branch of natural language processing, has been highly considered. Despite the other (popular) languages, a limited number of research studies have been conducted in Persian sentiment analysis. In this study, for the first time, a semi-supervised framework is proposed for Persian sentiment analysis. Moreover, considering that one of the most recent studies in Persian, is an algorithm based on extracting adaptive (dataset-sensitive) expert-based emotional patterns. In this research, extraction of the same state-of-the-art emotional patterns is proposed to be performed automatically. Moreover, application of the HMM classifier, by utilizing the mentioned features (as its states) is analyzed; and additionally, HMM-based sentiment analysis is upgraded by being combined with a rule-based classifier for the opinion assignment process. In addition, toward intelligent self-training, a criterion for evaluating, the high reliability of output is presented by which (assuming satisfaction of the criterion) the self-training process is performed in “lexicon-extraction” and “classifier,” as learning systems. The proposed method, by being applied on the basis dataset, provides 90% of accuracy (despite its expert-independent lexicon generation nature), which in comparison with the supervised and semi-supervised methods in the state-of-the-art has a considerable superiority. Moreover, this semi-supervised method is evaluated by a 10/90 ratio of train/ test and its reliability is demonstrated by providing 80% of accuracy.}, Keywords = {Opinion Mining, Self-training, Self-constructed Lexicon, Hidden Markov Model, Adaptive Dictionary}, volume = {15}, Number = {2}, pages = {89-102}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.89}, url = {http://jsdp.rcisp.ac.ir/article-1-644-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-644-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Yaghoubi, Mehdi and Zahedi, Morteza and Ahmadyfard, Alirez}, title = {A Dynamic Programing Algorithm for Tuning Concurrency of Business Processes}, abstract ={Business process management systems (BPMS) are vital complex information systems to compete in the global market and to increase economic productivity. Workload balancing of resources in BPMS is one of the challenges have been long studied by researchers. Workload balancing of resources increases the system stability, improves the efficiency of the resources and enhances the quality of their products. Workload balancing of resources in BPMS is considered as an important factor of the performance and the stability in systems. Setting the workload of each source at a certain level increases the efficiency of the resources. The main objectives of this research are the concept of resource workload balance and uniformity of the workload for each source at a specified level. To optimize the balance workload and uniformity of each source, the ​​setting multi-process concurrency was offered and studied. Also, the regulation of multi-process concurrency was mentioned as an optimization problem. In this paper, tuning concurrency of the business process is introduced as a problem in BPMS, which is an application issue to improve at workload balance of resources and uniformity in the workload of each resource. To solve this problem, a delay vector is defined, each element of delay vector makes the synthetic delay at the first of each business process, then a dynamic optimization algorithm is presented to compute delay vector and the speed of the proposed algorithms is compared with and state-space search algorithm and evolutionary algorithm of PSO. The comparison shows that the speed of the proposed algorithm is 37 hours to 5.8 years compared to the state-space search algorithm, while the POS algorithm solves the same problem in just 3 minutes. The experimental results on a real dataset show 21.64 percent improvement in the performance of the proposed algorithm.  }, Keywords = {Business process management systems, tuning concurrency of business processes, workload balancing, dynamic optimization, time complexity}, volume = {15}, Number = {2}, pages = {103-118}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.103}, url = {http://jsdp.rcisp.ac.ir/article-1-623-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-623-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Hosseini, Monireh and Nasrollahi, Maghsood and Baghaei, Ali}, title = {A hybrid recommender system using trust and bi-clustering in order to increase the efficiency of collaborative filtering}, abstract ={In the present era, the amount of information grows exponentially. So, finding the required information among the mass of information has become a major challenge. The success of e-commerce systems and online business transactions depend greatly on the effective design of products recommender mechanism. Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Recommender systems have been developed in order to respond this problem in order to customize the required information for users. So far, several types of recommender systems have been developed such as collaborative filtering recommender systems, content-based recommender systems and knowledge-based recommender systems. Each of these systems has advantages and disadvantages. Most of the recommender systems are based on collaborative filtering; Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. In fact, the main idea of this technique is to benefit from the past behavior or existing beliefs of the user community to predict products that are likely to be liked by the current user of the system. In collaborative filtering, we use the similarity between users or items to recommend products. However, this technique has several inherent problems such as cold start, sparsity and scalability. Since the collaborative filtering system is considered to be the most widely used recommender system, solving these problems and improving the effectiveness of collaborative filtering is one of the challenges raised in this context. None of the proposed hybrid systems have ever been able to resolve all of the collaborative filtering problems in a single and desirable manner; in this paper, we proposed a new hybrid recommender system that applies trust network as well as bi-clustering to improve the effectiveness of collaborative filtering. Therefore, the objectives of this research can be summarized as follows: sparsity reduction, increasing the speed of producing recommendations and increasing the accuracy of recommendations. In the proposed system, the trust between users is used to fill the user-item matrix which is a sparse matrix to solve the existing problem of sparsity. Then using bi-clustering, the user-item matrix is subdivided into matrices to solve the problem of scalability of the collaborative filtering and then the collaborative filtering is implemented for each sub matrix and the results from the implementation of the collaborative filtering for the sub-matrices are combined and recommendations are made for the users. The experimental results on a subset of the extended Epinions dataset verify the effectiveness and efficiency of our proposed system over user-based collaborative filtering and hybrid collaborative filtering with trust techniques. Improve sparsity problem Experimental results showed that our proposed system solves some of the sparsity problems which is due to the using the trust in the hybrid recommender system. By using trust, we can predict many uncertain ratings. Thus, transforming the user-item sparsity matrix into a half-full matrix. Improve scalability problem The results show that the proposed system has a higher speed compared with the user-based collaborative filtering algorithm and hybrid collaborative filtering with trust, and increasing the volume of data has little effect on increase online computing time. The reason can be summarized as a using of bi-clustering. Bi-directional clusters are made offline and break down the matrix of rankings into smaller subsets. Implementing the collaborative filtering on these smaller sets has led to increased computing speed. Improve the new user problem This system can provide accurate results for the new users due to the use of trust, because product collections viewed by new user can increase with the trust between the users. This system can predict the similarity between the new user and other users. So, the results are more accurate than the results of the user-based collaborative filtering and hybrid collaborative filtering with trust.}, Keywords = {Recommender systems, Collaborative filtering, Trust, Bi-clustering, Hybrid recommender systems}, volume = {15}, Number = {2}, pages = {119-132}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.119}, url = {http://jsdp.rcisp.ac.ir/article-1-613-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-613-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Nezamzadeh, Marzieh}, title = {An Improved Rician Noise Correction Technique from the Magnitude of Diffusion MR Images}, abstract ={The true MR signal intensity extracted from noisy MR magnitude images is biased with the Rician noise caused by noise rectification in the magnitude calculation for low intensity pixels. This noise is more problematic when a quantitative analysis is performed based on the magnitude images with low SNR(<3.0). In such cases, the received signal for both the real and imaginary components will fluctuate around a low level (e.g. zero) often producing negative values. The magnitude calculation on such signals will rectify all negative values to produce only positive magnitudes, thereby artificially raising the average level of these pixels. The signal thus will be biased by the rectified noise. Diffusion MRI using high b-values (using strong magnetic gradients) is one the most important cases of biased Rician noise.  A technique for removing this bias from individual pixels of magnitude MR images is presented in this study. This method provides a bias correction for individual pixels using a linear equation with the correction term separated from the term to be corrected (i.e. the pixel intensity). The correction is exact when the mean and variance of the pixel intensity probability density functions are known. When accurate mean values are not available, a nearest neighbor average is used to approximate the mean in the calculation of the linear correction term. With a nine pixel nearest neighbor average (i.e. one layer of nearest neighbors) the bias correction for individual pixel intensities is accurate to within 10% error for signal to noise ratios SNR=1.0. Several different noise correction schemes from the literature are presented and compared. The new Rician bias correction presented in this work represents a significant improvement over previously published techniques. The proposed approach substantially removes the Rician noise bias from diffusion MR signal decay over an extended range of b-values from zero to very high b-values.}, Keywords = {magnitude signal, Diffusion MRI, probability distribution function, Rician bias}, volume = {15}, Number = {2}, pages = {133-147}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.2.133}, url = {http://jsdp.rcisp.ac.ir/article-1-643-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-643-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Yadolahi, Mohammadmahdi and Zargari, Farzad and Farhoodi, Mojg}, title = {Automatic Evaluation of Video Search engines in Persian Web domain based on Majority Voting}, abstract ={Today, the growth of the internet and its high influence in individuals’ life have caused many users to solve their daily needs by search engines and hence, the search engines need to be modified and continuously improved. Therefore, evaluating search engines to determine their performance is of paramount importance. In Iran, as well as other countries, extensive researches are being performed on search engines. To evaluate the quality of search engines and continually improve their performance, it is necessary to evaluate search engines and compare them to other existing ones. Since the speed plays an important role in the assessment of the performance, automatic search engine evaluation methods attracted grate attention. In this paper, a method based on the majority voting is proposed to assess the video search engines. We introduced a mechanism to assess the automatic evaluation method by comparing its results with the results obtained by human search engine evaluation. The results obtained, shows 94 % correlation of the two methods which indicate the reliability of automated approach. In general, the proposed method can be described in three steps. Step 1: Retrieve first k_retrieve results of n different video search engines and build the return result set for each written query. Step 2: Determine the level of relevance of each retrieved result from the search engines Step 3: Evaluating the search engines by computing different evaluation criteria based on decisions on relevance of the retrieved videos by each search engine  Clearly, the main part of any evaluation system with the goal of evaluating the accuracy of search engines is the second step. In this paper, we have tried to present a new solution based on the aggregation of votes in order to determine whether a result is relevant or not, as well as its level of relevance. For this purpose, for each query the return results from different search engines are compared with each other, and the result returned by more than m of the search engines (m ; and the result of which their URLs (after the normalization) are similar to the normalized URL from the m-1 of the other search engines, are considered as the relevant results. At the second level, the retrieved results will be compared in terms of content. In this way, after calculating the address-like similarity, all the results are transmitted to the motion vector extraction component to extract and store the motion vector. In the content based similarity algorithm, the set of motion vectors is initially considered as a sequence of motion vector. We, then, try to find the greatest similarity of the smaller sequence with the larger sequence. After this step, we will report the maximum similarity of the two videos. The process of finding the maximum similarity is that we consider a window with a smaller video sequence length. In this window we calculate and hold the similarity of two sequences. In the proposed method, after identifying the similarity between the return results of different search engines, their level is ranked at three different levels: "unrelated" (0), "quantitatively related" (1) and "related" (2). Since Google's search engine is currently the world’s largest and best-performing search engine, and most search engines have been compared to it, and are also trying to achieve the same function, the first five Google search engine results are get the minimum relevance, by default, "slightly related". Then the similarity module is used to evaluate the similarity of the retrieved n results of the tested search engines.  }, Keywords = {Automatic assessment, Video search engine, Persian Web}, volume = {15}, Number = {3}, pages = {3-12}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.3}, url = {http://jsdp.rcisp.ac.ir/article-1-606-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-606-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Zoughi, Toktam and Homayounpour, Mohammad Mehdi}, title = {Adaptive Windows Convolutional Neural Network for Speech Recognition}, abstract ={Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov model (HMM) leads to considerable performance achievement in speech recognition problem because deep networks model complex correlations between features. The main aim of this paper is to achieve a better acoustic modeling by changing the structure of deep Convolutional Neural Network (CNN) in order to adapt speaking variations. In this way, existing models and corresponding inference task have been improved and extended. Here, we propose adaptive windows convolutional neural network (AWCNN) to analyze joint temporal-spectral features variation. AWCNN changes the structure of CNN and estimates the probabilities of HMM states. We propose adaptive windows convolutional neural network in order to make the model more robust against the speech signal variations for both single speaker and among various speakers. This model can better model speech signals. The AWCNN method applies to the speech spectrogram and models time-frequency varieties. This network handles speaker feature variations, speech signal varieties, and variations in phone duration. The obtained results and analysis on FARSDAT and TIMIT datasets show that, for phone recognition task, the proposed structure achieves 1.2%, 1.1% absolute error reduction with respect to CNN models respectively, which is a considerable improvement in this problem. Based on the results obtained by the conducted experiments, we conclude that the use of speaker information is very beneficial for recognition accuracy.}, Keywords = {Speech recognition, deep neural network, Convolutional neural network, Adaptive windows convolutional neural network}, volume = {15}, Number = {3}, pages = {13-30}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.13}, url = {http://jsdp.rcisp.ac.ir/article-1-706-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-706-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {EbrahimiAtani, Reza and Sadeghpour, Mehdi}, title = {A New Privacy Preserving Data Publishing Technique Conserving Accuracy of Classification on Anonymized Data}, abstract ={Data collection and storage has been facilitated by the growth in electronic services, and has led to recording vast amounts of personal information in public and private organizations databases. These records often include sensitive personal information (such as income and diseases) and must be covered from others access. But in some cases, mining the data and extraction of knowledge from these valuable sources, creates the need for sharing them with other organizations. This would bring security challenges in user’s privacy. The concept of privacy is described as sharing of information in a controlled way. In other words, it decides what type of personal information should be shared and which group or person can access and use it. “Privacy preserving data publishing” is a solution to ensure secrecy of sensitive information in a data set, after publishing it in a hostile environment. This process aimed to hide sensitive information and keep published data suitable for knowledge discovery techniques. Grouping data set records is a broad approach to data anonymization. This technique prevents access to sensitive attributes of a specific record by eliminating the distinction between a number of data set records. So far a large number of data publishing models and techniques have been proposed but their utility is of concern when a high privacy requirement is needed. The main goal of this paper to present a technique to improve the privacy and performance data publishing techniques. In this work first we review previous techniques of privacy preserving data publishing and then we present an efficient anonymization method which its goal is to conserve accuracy of classification on anonymized data. The attack model of this work is based on an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. Our privacy model and technique uses a decision tree to prevent publishing of information that removing them provides privacy and has little effect on utility of output data. The presented idea of this paper is an extension of the work presented in [20]. Experimental results show that classifiers trained on the transformed data set achieving similar accuracy as the ones trained on the original data set.}, Keywords = {Privacy preservation, Data sharing, Anonymization, Classification, Decision tree, Suppression}, volume = {15}, Number = {3}, pages = {31-46}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.31}, url = {http://jsdp.rcisp.ac.ir/article-1-609-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-609-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Gharavi, Naser Hosein and Mirqadri, Abdorasool and Abdollahiazgomi, Mohammad and Mousavi, Sayyed Ahm}, title = {Expected coverage rate for the Hellman matrices}, abstract ={Hellman’s time-memory trade-off is a probabilistic method for inverting one-way functions, using pre-computed data. Hellman introduced this method in 1980 and obtained a lower bound for the success probability of his algorithm.  After that, all further analyses of researchers are based on this lower bound. In this paper, we first studied the expected coverage rate (ECR) of the Hellman matrices, which are constructed by a single chain. We showed that the ECR of such matrices is maximum and equal to 0.85. In this process, we find out that there exists a gap between the Hellman’s lower bound and experimental coverage rate of a Hellman matrix. Specifically, this gap is larger, when considering the Hellman matrices constructed with one single chain. So, we are investigated to obtain an accurate formula for the ECR of a Hellman matrix. Subsequently, we presented a new formula that estimate the ECR of a Hellman matrix more accurately than the Hellman’s lower bound. We showed that the given formula is closely match experimental data. In the last, we introduced a new method to construct matrices which have much more ECR than Hellman matrices. In fact, each matrix in this new method is constructed with one single chain, which is non-repeating trajectory from a random point. So, this approach result in a number of matrices that each one contains a chain with variable length. The main advantage of this method is that we have more probability of success than Hellman method, however online time and memory requirements are increased. We have also verified theory of this new method with experimental results.  }, Keywords = {Time-Memory Trade-off, one way function, Hellman matrix, Expected coverage rate}, volume = {15}, Number = {3}, pages = {47-58}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.47}, url = {http://jsdp.rcisp.ac.ir/article-1-607-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-607-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Golgouneh, Alireza and Tarvirdizadeh, Bahram}, title = {Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals}, abstract ={Stress has affected human’s lives in many areas, today. Stress can adversely affect human’s health to such a degree as to either cause death or indicate a major contributor to death. Therefore, in recent years, some researchers have focused to developing systems to detect stress and then presenting viable solutions to manage this issue. Generally, stress can be identified through three different methods including (1) Psychological Evaluation, (2) Behavioral Responses and finally (3) Physiological Signals. Physiological signals are internal signs of functioning the body, and therefore nowadays are commonly used in various medical and non-medical applications. Since these signals are correlated with the stress, they have been commonly used in detection of the stress in humans. Photoplethysmography (PPG) and Galvanic Skin Response (GSR) are two of the most common signals which have been widely used in many stress related studies. PPG is a noninvasive method to measure the blood volume changes in blood vessels and GSR refers to changes in sweat gland activity that are reflective of the intensity of human emotional state. Design and fabrication of a real-time handheld system in order to detect and display the stress level is the main aim of this paper. The fabricated stress monitoring device is completely compatible with both wired and wireless sensor devices. The GSR and PPG signals are used in the developed system. The mentioned signals are acquired using appropriate sensors and are displayed to the user after initial signal processing operation. The main processor of the developed system is ARM-cortex A8 and its graphical user interface (GUI) is based on C++ programming language. Artificial Neural Networks such as MLP and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to modeling and estimation of the stress index. The results show that ANFIS model have a good accuracy with a coefficient of determination values of 0.9291 and average relative error of 0.007.  }, Keywords = {Stress, Physiological signals, GSR, PPG, Artificial Neural Networks, ANFIS}, volume = {15}, Number = {3}, pages = {59-74}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.59}, url = {http://jsdp.rcisp.ac.ir/article-1-611-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-611-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Feizi, Asghar}, title = {Application of Sparse Representation and Camera Collaboration in Visual Surveillance Systems}, abstract ={With the growth of demand for security and safety, video-based surveillance systems have been employed in a large number of rural and urban areas. The problem of such systems lies in the detection of patterns of behaviors in a dataset that do not conform to normal behaviors. Recently, for behavior classification and abnormal behavior detection, the sparse representation approach is used. In this paper, feature sparse representation in a multi-view network is used for the purpose of behavior classification and abnormal behavior detection. To serve this purpose, a geometrically independent feature is first extracted for each location in the image. Then, for each camera view, the matrix for the dictionary A is calculated, which is considered as a set of behavior models. In order to share information and make use of the trained models, the learned dictionary matrix from the experienced camera is transferred to inexperienced cameras. The transferred matrix in the new camera is subsequently used to detect abnormal behaviors. A hierarchical method on the basis of spectral clustering is proposed for learning the dictionary matrix. After sparse feature representation, a measurement criterion, which makes use of the representation, is presented for abnormal behavior detection. The merit of the method proposed in this paper is that the method does not require correspondence across cameras. The direct use of the dictionary matrix and transfer of the learned dictionary matrix from the experienced camera to inexperienced ones, are tested on several real-world video datasets. In both cases, desirable improvements in abnormal behavior detection are obtained. The experimental results point to the efficacy of the proposed method for camera cooperation in order to detect abnormal behaviors.  }, Keywords = {Visual Surveillance, Sparse Representation, Behavior Recognition, Abnormal Behavior, Camera Collaboration}, volume = {15}, Number = {3}, pages = {75-88}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.75}, url = {http://jsdp.rcisp.ac.ir/article-1-559-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-559-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Aslani, Akram and Esmaeili, Mahdi}, title = {Finding Frequent Patterns in Holy Quran UsingText Mining}, abstract ={Quran’s Text differs from any other texts in terms of its exceptional concepts, ideas and subjects. To recognize the valuable implicit patterns through a vast amount of data has lately captured the attention of so many researchers. Text Mining provides the grounds to extract information from texts and it can help us reach our objective in this regard. In recent years, Text Mining on Quran and extracting implicit knowledge from Quranic words have been the object of researchers’ focus. It is common that in Quranic experts’ arguments, different sides of the discussion present different intellectual, logical and some non-integrated minor evidence in order to prove their own theories. More often than not, every side of these arguments disapproves of the other’s hypothesis and in the end it is impossible for them to reach a state of consensus on the matter, the reason is that, they do not have a common basis for their arguments and they do not make use of scientific, logical methods to strongly support their theories. Therefore, using modern technological trends regarding Quranic arguments could lead to resolving so many of current discrepancies, caused by human errors, which exist among Quranic researchers. It can help providing a common ground for their arguments in order to reach a comprehensive understanding. The method used in this research implements frequent pattern mining algorithms, singular frequent patterns as well as dual and tripe frequent patterns in order to analyze Quranic text, in addition to this, Association rules have also been evaluated in the research. Out of 54226 extracted association rules for Quranic words which have been evaluated by the use of criteria such as confidence coefficient, support coefficient, lift criteria as well as Co-efficient criteria. Top 10 rules for each criterion have been analyzed and reviewed throughout the project.}, Keywords = {Data Mining, Text Mining, Association rules, Holy Quran, Frequent patterns}, volume = {15}, Number = {3}, pages = {89-100}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.89}, url = {http://jsdp.rcisp.ac.ir/article-1-620-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-620-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Khodagholi, Mona and Dolati, Ardeshir and Hosseinzadeh, Ali and Shamsolketabi, khashayar}, title = {A New Method to Determine Data Membership and Find Noise and Outlier Data Using Fuzzy Support Vector Machine}, abstract ={Support Vector Machine (SVM) is one of the important classification techniques, has been recently attracted by many of the researchers. However, there are some limitations for this approach. Determining the hyperplane that distinguishes classes with the maximum margin and calculating the position of each point (train data) in SVM linear classifier can be interpreted as computing a data membership with certainty. A question may be raised here: how much the level of the certainty of this classification, based on hyperplane, can be trusted. In the standard SVM classification, the significance of error for different train data is considered equal and every datum is assumed to belong to just one class. However, in many cases some of train data, including outlier and vague data with no defined model, cannot be strictly considered as a member of a certain class. That means, a train datum may does not exactly belong to one class and its features may show 90 percent membership of one class and 10 percent of another. In such cases, by using fuzzy SVM based on fuzzy logic, we can determine the significance of data in the train phase and finally determine relative class membership of data. The method proposed by Lin and Wang is a basic method that introduces a membership function for fuzzy support vector machine. Their membership function is based on the distance between a point and the center of its corresponding class. In this paper, we introduce a new method for giving membership to train data based on their distance from distinctive hyperplane. In this method, SVM classification together with primary train data membership are used to introduce a fuzzy membership function for the whole space using symmetrical triangular fuzzy numbers. Based on this method, fuzzy membership function value of new data is selected with minimum difference from primary membership of train data and with the maximum level of fuzzification. In the first step, we define the problem as a nonlinear optimization problem. Then we introduce an efficient algorithm using critical points and obtain final membership function of train data. According to the proposed algorithm, the more distant data from the hyperplane will have a higher membership degree. If a datum exists on the hyperplane, it belongs to both classes with the same membership degree. Moreover, by comparing the primary membership degree of train data and calculated final distribution, we compute the level of noise for train data. Finally, we give a numerical example for illustration the efficiency of the proposed method and comparing its results with the results of the Lin and Wang approach.  }, Keywords = {Fuzzy Logic, Machine learning, Data mining, Fuzzy support vector machine, fuzzy membership function}, volume = {15}, Number = {3}, pages = {101-112}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.101}, url = {http://jsdp.rcisp.ac.ir/article-1-394-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-394-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Ashari, Fatemeh and Riahi, Nooshi}, title = {Audio Steganalysis based on Inter-frame correlation and recursive feature elimination}, abstract ={Dramatic changes in digital communication and exchange of image, audio, video and text files result in a suitable field for interpersonal transfers of hidden information. Therefore, nowadays, preserving channel security and intellectual property and access to hidden information make new fields of researches naming steganography, watermarking and steganalysis. Steganalysis as a binary classification distinguish clean signals from stego signals. Features extracted from time and transform domain are proper for this classifier. Some of steganalysis methods are depended on a specific steganography algorithm and others are independent. The second group of methods are called Universal steganalysis. Universal steganalysis methods are widely used in applications because of their independency to steganography algorithms. These algorithms are based on characteristics such as distortion measurements, higher order statistics and other similar features. In this research we try to achieve more reliable and accurate results using analytical review of features, choose more effective of them and optimize SVM performance. In new researches Mel Frequency Cepstral Coefficient and Markov transition probability matrix coefficients are used to steganalysis design. In this paper we consider two facts. First, MFCC extract signal   features in transform domain similar to human hearing model, which is more sensitive to low frequency signals. As a result, in this method there is more hidden information mostly in higher frequency audio signals. Therefore, it is suggested to use reversed MFCC. Second, there is an interframe correlation in audio signals which is useful as an information hiding effect. For the first time, in this research, this features is used in steganalysis field. To have more accurate and stable results, we use recursive feature elimination with correlation bias reduction for SVM. To implement suggested algorithm, we use two different data sets from TIMIT and GRID. For each data sets,Steghide and LSB-Matching steganography methods implement with 20 and 50 percent capacity. In addition, one of the LIBSVM 3.2 toolboxes is sued for implementation. Finally, the results show accuracy of steganalysis, four to six percent increase in comparison with previous methods. The ROC of methods clearly shows this improvement.  }, Keywords = {steganalysis, steganography, Mel, SVM-RFE+CBR}, volume = {15}, Number = {3}, pages = {113-122}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.113}, url = {http://jsdp.rcisp.ac.ir/article-1-625-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-625-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Deylami, Saniyeh and Farjami, Yaghoub}, title = {Optimizing Web programs Response in Cloud Using Pre-processing, Case study Nginx, Varnish}, abstract ={The response speed of Web pages is one of the necessities of information technology. In recent years, renowned companies such as Google and computer scientists focused on speeding up the web. Achievements such as Google Pagespeed, Nginx and varnish are the result of these researches. In Customer to Customer(C2C) business systems, such as chat systems, and in Business to Customer(B2C) systems, such as online stores and banks, the power and speed of the system’s response to the high volume of visitors are very effective in customer satisfaction and the efficiency of the business system. Increasing the speed of web pages from the origin of the advent of this technology, used from known and proven methods such as preprocessing, cookie, Ajax, cache and so on, to speed up the implementation of Internet applications, but it still needs to increase the speed of running and operating systems under the web. Recently, successful and effective methods and tools devised to increase the loading speed of Web pages, which consist mainly two approaches, increasing the speed on the client-side user and increasing the speed of the server-side. Research and technology on the performance and speed of Web technology on server side are divided into two categories of content enhancements, such as the Google Page Speed tool and Web server performance improvements such as Reverse Proxies. Reverse proxy is the most effective way to increase the speed on the server-side. Web server performance is measured by various metrics such as process load, memory usage and response speed to requests. Reverse proxy technology has been implemented in the Varna and Engineer systems. Implementing the reverse proxy in Varna has focused on caching processing content and on the engineering to cache static content. Our goal is to evaluate the performance of these two systems as reverse proxies to improve the response speed and loading of web pages in two types of dynamic (processing) and static (multimedia) content and provide a framework for the appropriate selection of a reverse proxy on web servers. In this paper, we introduce reverse proxy and analyze the performance of the four web servers, namely apache + varnish, nginx, nginx + varnish and apache, with both static and dynamic content, in terms of response speed of web pages as a measure of performance. First, our results show that, using a reverse proxy response speed is increased. Second, the resulted speed up is related not only to web server type but also to the content type of web pages requested repeatedly. Finally, a ranking is provided which helps to select the appropriate web server and reverse proxy when the web content type is static (multimedia) or dynamic (processed).}, Keywords = {reverse proxy, varnish, nginx, accelerated web, response time}, volume = {15}, Number = {3}, pages = {123-133}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.3.123}, url = {http://jsdp.rcisp.ac.ir/article-1-504-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-504-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2018} } @article{ author = {Eftekhari, Mahdi and Majidimomenabadi, Maryam and Khamar, Mojtab}, title = {Proposing an evolutionary-fuzzy method for software defects detection}, abstract ={Software defects detection is one of the most important challenges of software development and it is the most prohibitive process in software development. The early detection of fault-prone modules helps software project managers to allocate the limited cost, time, and effort of developers for testing the defect-prone modules more intensively.  In this paper, according to the importance of software defects detection, a method based on fuzzy sets and evolutionary algorithms is proposed. Due to the imbalanced nature of software defect detection datasets, benefits of fuzzy clustering algorithms were used to data sampling and more attention to the minority class. This method is a combined algorithm which, firstly has used fuzzy c-mean clustering as weighted bootstrap sampling. Weight of data (their membership’s degrees) increases for minority class. In the next step, the subtractive clustering algorithm is applied to produce the classifier which was trained by produced data in the previous step. The binary genetic algorithm was utilized to select appropriate features. The results and also comparisons with eight popular methods in software defect detection literature, show an acceptable performance of the proposed method. The experiments were performed on ten real-world datasets with a wide range of data sizes and imbalance rates. Also T-test is used as the statistical significance test for pair wise comparison of our proposed method against the others. The final results of T-test are shown in tables for three performance measures (G-mean, AUC and Balanced) over various datasets. (As the obtained results apparently show our proposed method has the ability to improve three aforementioned performance criteria simultaneously). Some methods just have improved the G-mean measure while the AUC and Balance criteria have lower values than the others. Securing a high level of three performance measures simultaneously illustrates the ability of our proposed algorithm for handling the imbalance problem of software defects detection datasets.}, Keywords = {classification, evolutionary algorithm, fuzzy logic, imbalance datasets, software defect detection}, volume = {15}, Number = {4}, pages = {3-16}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.3}, url = {http://jsdp.rcisp.ac.ir/article-1-656-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-656-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {LatifiPakdehi, Alireza and Daneshpour, Negi}, title = {Cluster ensemble selection using voting}, abstract ={Clustering is the process of division of a dataset into subsets that are called clusters, so that objects within a cluster are similar to each other and different from objects of the other clusters. So far, a lot of algorithms in different approaches have been created for the clustering. An effective choice (can combine) two or more of these algorithms for solving the clustering problem. Ensemble clustering combines results of existing clusterings to achieve better performance and higher accuracy. Instead of combining all of existing clusterings, recent decade researchers show, if only a set of clusterings is selected  based on quality and diversity, the result of ensemble clustering would be more accurate. This paper proposes a new method for ensemble clustering based on quality and diversity. For this purpose, firstly first we need a lot of different base clusterings to combine them. Different base clusterings are generated by k-means algorithm with random k in each execution. After the generation of base clusterings, they are put into different groups according to their similarities using a new grouping method. So that clusterings which are similar to each other are put together in one group. In this step, we use normalized mutual information (NMI) or adjusted rand index (ARI) for computing similarities and dissimilarities between the base clustering. Then from each group, a best qualified clustering is selected via a voting based method. In this method, Cluster-validity-indices were used to measure the quality of clustering. So that all members of the group are evaluated by the Cluster-validity-indices. In each group, clustering that optimizes the most number of Cluster-validity-indices is selected.  Finally, consensus functions combine all selected clustering. Consensus function is an algorithm for combining existing clusterings to produce final clusters. In this paper, three consensus functions including CSPA, MCLA, and HGPA have used for combining clustering. To evaluate proposed method, real datasets from UCI repository have used. In experiment section, the proposed method is compared with the well-known and powerful existing methods. Experimental results demonstrate that proposed algorithm has better performance and higher accuracy than previous works.  }, Keywords = {Ensemble clustering, select member, validity index}, volume = {15}, Number = {4}, pages = {17-30}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.17}, url = {http://jsdp.rcisp.ac.ir/article-1-541-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-541-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {azizi, shadi and Ashouri-Talouki, maede and mala, hami}, title = {An Efficient and Secure Frequent Multiparty Summation protocol}, abstract ={In secure multiparty computation (SMC), a group of users jointly and securely computes a mathematical function on their private inputs, such that the privacy of their private inputs will be preserved. One of the widely used applications of SMC is the secure multiparty summation which securely computes the summation value of the users’ private inputs. In this paper, we consider a secure multiparty summation problem where each group member has m private inputs and wants to efficiently and securely computes the summation values of their corresponding inputs; in other words, users compute m summation values where the first value is the summation of users’ first private inputs, the second one is the summation of users’ second private inputs and so on. We propose an efficient and secure protocol in the semi honest model, called frequent-sum, which computes the desired values while preserving the privacy of users’ private inputs as well as the privacy of the summation results. Let  be a set of n users and the private inputs of user  is denoted as . The proposed frequent-sum protocol includes three phases: In the first phase, each user  selects a random number , computes and publishes the vectors  of  components where each component  of  is of  form . After it,  computes the vector , such that each component  is of form. In the second phase, users jointly and securely compute their AV-net (Anonymous Veto network) masks and the Burmester-Desmedt (BD) conference key. To do so, each user  selects two random numbers  and  and publishes  to the group. Then,  computes and sends  to the group. Then, each user is able to compute  and ;  is the AV-net mask of  and  is the conference key. In the third phase, using the AV-net mask and the conference key, group members securely and collaboratively compute the summation of their random numbers , . To achieve this, each user broadcasts  to the group, where  is the AV-net mask of  and  is the ’s portion of the conference key. Multiplying all s results in canceling the AV-net mask and getting the value of . Then each member is able to compute  by the following Eq.: Now each user is able to compute  by subtracting  from each component of : It is shown that the proposed protocol is secure against collusion attack of at most  users. In other words, the frequent-sum protocol is secure against partial collusion attack; only a full collusion (collusion of  users) would break the privacy of the victim user, in this situation there is no reason for the victim user to join to such a group. The performance analysis shows that the proposed protocol is efficient in terms of the computation and communication costs, comparing with previous works. Also, the computation cost of the frequent-sum protocol is in-dependent of the number of inputs of each user  which makes the protocol more efficient than the previous works. Table 1 compares the proposed protocol with previous works.}, Keywords = {secure multiparty sum, without secure channel, partial collusion, semi honest model}, volume = {15}, Number = {4}, pages = {31-40}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.31}, url = {http://jsdp.rcisp.ac.ir/article-1-649-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-649-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {Abdollahi, Mitra and Khosravi, Hossei}, title = {Design and Implementation of Real-Time License Plate Recognition System in Video Sequences}, abstract ={An automatic Number Plate Recognition (ANPR) is a popular topic in the field of image processing and is considered from different aspects, since early 90s. There are many challenges in this field, including; fast moving vehicles, different viewing angles and different distances from camera, complex and unpredictable backgrounds, poor quality images, existence of multiple plates in the scene, variable lighting conditions throughout the day, and so on. ANPR systems have many applications in today’s traffic monitoring and toll-gate systems. In this paper, a real-time algorithm is designed and implemented for simultaneous detection and recognition of multiple number plates in video sequences. Already some papers on plate localization and recognition in still? images have been existed , however, they do not consider real time processing. While for the related applications, real-time detection and recognition of multiple plates on the scene is very important. Unlike methods with high computational complexity, we apply simple and effective techniques for being real-time. At first, background is modeled using Gaussian Mixture Model (GMM) and moving objects are determined. Then, plate candidate regions are found by vertical edge detection and horizontal projection. After that, license plates are localized and extracted by morphological operations and connected components analysis. When plates were are detected, their characters are separated with another algorithm. Finally a neural network is applied for character recognition. This system is implemented in C++ using OpenCV library. The average localization time per frame is 25 ms and total processing time, including localization and recognition, is 40 ms that can be used in real-time applications. The proposed method is evaluated on videos from highway cameras and the detection rate of 98.79% and recognition rate of 97.83% is obtained. Our real-time system can also recognize multiple plates of different types in each frame. Experimental results show that our method have higher speed and better recognition rate than previous works therefore it is suitable for real-time applications.  }, Keywords = {Real-time License Plate Recognition System, Gaussian Mixture Model, Projection, Connected Components Analysis, Neural Network}, volume = {15}, Number = {4}, pages = {41-56}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.41}, url = {http://jsdp.rcisp.ac.ir/article-1-665-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-665-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {Rahimi, Marziea and Zahedi, Morteza and Mashayekhi, Ho}, title = {A Probabilistic Topic Model based on Local Word Relationships in Overlapped Windows}, abstract ={A probabilistic topic model assumes that documents are generated through a process involving topics and then tries to reverse this process, given the documents and extract topics. A topic is usually assumed to be a distribution over words. LDA is one of the first and most popular topic models introduced so far. In the document generation process assumed by LDA, each document is a distribution over topics and each word in the document is sampled from a chosen topic of that distribution. It assumes that a document is a bag of words and ignores the order of the words. Probabilistic topic models such as LDA which extract the topics based on documents-level word co-occurrences are not equipped to benefit from local word relationships. This problem is addressed by combining topics and n-grams, in models like Bigram Topic Model (BTM). BTM modifies the document generation process slightly by assuming that there are several different distributions of words for each topic, each of which correspond to a vocabulary word. Each word in a document is sampled from one of the distributions of its selected topic. The distribution is determined by its previous word. So BTM relies on exact word orders to extract local word relationships and thus is challenged by sparseness. Another way to solve the problem is to break each document into smaller parts for example paragraphs and use LDA on these parts to extract more local word relationships in these small parts. Again, we will be faced with sparseness and it is well-known that LDA does not work well on small documents. In this paper, a new probabilistic topic model is introduced which assumes a document is a set of overlapping windows but does not break the document into those parts and assumes the whole document as a single distribution over topics. Each window corresponds to a fixed number of words in the document. In the assumed generation process, we walk through windows and decide on the topic of their corresponding words. Topics are extracted based on words co-occurrences in the overlapping windows and the overlapping windows affect the process of document generation because; the topic of a word is considered in all the other windows overlapping on the word. On the other words, the proposed model encodes local word relationships without relying on exact word order or breaking the document into smaller parts. The model, however, takes the word order into account implicitly by assuming the windows are overlapped. The topics are still considered as distributions over words. The proposed model is evaluated based on its ability to extract coherent topics and its clustering performance on the 20 newsgroups dataset. The results show that the proposed model extracts more coherent topics and outperforms LDA and BTM in the application of document clustering.  }, Keywords = {probabilistic topic models, Gibbs sampling, co-occurrence, graphical models}, volume = {15}, Number = {4}, pages = {57-70}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.57}, url = {http://jsdp.rcisp.ac.ir/article-1-673-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-673-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {paksima, jav}, title = {A novel model for phrase searching based-on Minimum Weighted Relocation Model}, abstract ={Finding high-quality web pages is one of the most important tasks of search engines. The relevance between the documents found and the query searched depends on the user observation and increases the complexity of ranking algorithms. The other issue is that users often explore just the first 10 to 20 results while millions of pages related to a query may exist. So search engines have to use suitable algorithms with high performance to find the most relevant pages. The ranking section is an important part of search engines. Ranking is a process in which the web page quality is estimated by the search engine. There are two main methods for ranking web pages. In the first method, ranking is done based on the documents’ content (traditional rankings). Models, such as Boolean model, probability model and vector space model are used to rank documents based on their contents. In the second method, based on the graph, web connections and the importance of web pages, ranking process is performed. Based on researches on search engines, the majority of user queries is more than one term. For queries with more than one term, two models can be used. The first model assumes that query terms are independent of each other while the second model considers a location and order dependency between query terms. Experiments show that in the majority of queries there are dependencies between terms. One of the parameters that can specify dependencies between query terms is the distance between query terms in the document. In this paper, a new definition of distance based on Minimum Weighted Displacement Model (MWDM) of document terms to accommodate the query terms is presented. In the Minimum Weighted Displacement Model (MWDM), we call the minimum number of words moving a text to match the query term by space. In addition, because most of the ranking algorithms use the TF (Term Frequency) to score documents and for queries more than one term, there is no clear definition of these parameters; in this paper, according to the new distance concept, Phrase Frequency and Inverted Document Frequency are defined. Also, algorithms to calculate them are presented. The results of the proposed algorithm compared with multiple corresponding algorithms shows a favorable increase in average precision.  }, Keywords = {Search engine, Ranking, Distance, Phrase Frequency}, volume = {15}, Number = {4}, pages = {71-84}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.71}, url = {http://jsdp.rcisp.ac.ir/article-1-670-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-670-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {emami, nasibeh and hassani, zeinab}, title = {Prediction and determining the effective factors on the survival transplanted kidney for five-year in imbalanced data by the meta-heuristic approach and machine learning}, abstract ={Chronic kidney failure is one of the most widespread diseases in Iran and the world. In general, the disease is common in high health indexes societies due to increased longevity. Treatment for chronic kidney failure is dialysis and kidney transplantation. Kidney transplantation is an appropriate and effective strategy for patients with End-Stage Renal Disease (ESRD), and it provides a better life and reduces mortality risk for patients. In contrast to many benefits that kidney transplantation has in terms of improving physical and mental health and the life’s quality in kidney transplantation patients, it may be rejected because of host's immune response to the received kidney, and it consequences the need for another transplantation, or even death will have to. In fact, a patient that can survive for years with dialysis, he may lose his life with an inappropriate transplantation or be forced into high-risk surgical procedures.  According to the above, the study of predicting the survival of kidney transplantation, its effective factors and providing a model for purposing of high prediction accuracy is essential. Studies in the field of survival of kidney transplantation include statistical studies, artificial intelligence and machine learning. In all of the studies in this feild, researchers have sought to identify a more effective set of features in survival of transplantation and the design of predictive models with higher accuracy and lower error rate. This study carried out on 756 kidney transplant patients with 21 features of Imam Reza and Fourth Shahid Merab hospital in Kermanshah from 2001 to 2012. Some features set to binary value and other features have real continuous values. Due to data are unbalance, which led to convergence of classification model to majority class, so over sampling and under sampling techniques has been used for achieving higher accuracy. To identify the more effective features on the survival of the kidney transplantation, the genetic meta-heuristic algorithm is used. For this purpose binary coding for each chromosome has been used; it is combining three single-point, two-point, and uniform operators to make better generations, better convergence and achieve higher accuracy rate. The genetic search algorithm plays a vital role in searching for such a space in a reasonable time because data search space is exponential. In fact, in balanced data, genetic algorithm determines the effective factors and the K-nearest neighbor model with precision of classification as the evaluator function was used to predict the five-year survival of the kidney transplantation. Based on the results of this study, in comparison to similar studies for prediction of survival transplanted kidney, the five-year survival rate of transplanted kidney was appropriate in these models. Also the effective factors in over sampling and under sampling methods with a precision of 96.8% and 89.2% are obtained respectively. in addition weight, donor and recipient age, pre-transplantation urea, pre-transplantation creatinine, hemoglobin before and after transplantation, donor gender, donor and recipient RH, primary illness, donor age up 30 and receipt age up 40 were identified as the effective features on kidney transplantation survival. Comparing the results of this study with previous studies shows the superiority of the proposed model from the point of view of the models' precision. In particular, balancing the data along the selection of optimal features leads to a high precision predictive model.  }, Keywords = {Kidney Transplantation, imbalance data, Genetic Algorithm, K- nearest neighbors}, volume = {15}, Number = {4}, pages = {85-94}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.85}, url = {http://jsdp.rcisp.ac.ir/article-1-684-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-684-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {Sharifi, Atieh and Mahdavi, M.Ami}, title = {Supervised approach for keyword extraction from Persian documents using lexical chains}, abstract ={Keywords are the main focal points of interest within a text, which intends to represent the principal concepts outlined in the document. Determining the keywords using traditional methods is a time consuming process and requires specialized knowledge of the subject. For the purposes of indexing the vast expanse of electronic documents, it is important to automate the keyword extraction task. Since keywords structure is coherent, we focus on the relation between words. Most of previous methods in Persian are based on statistical relation between words and didn’t consider the sense relations. However, by existing ambiguity in the meaning, using these statistic methods couldn’t help in determining relations between words. Our method for extracting keywords is a supervised method which by using lexical chain of words, new features are extracted for each word. Using these features beside of statistic features could be more effective in a supervised system. We have tried to map the relations amongst word senses by using lexical chains. Therefore, in the proposed model, “FarsNet” plays a key role in constructing the lexical chains. Lexical chain is created by using Galley and McKeown's algorithm that of course, some changes have been made to the algorithm. We used java version of hazm library to determine candidate words in the text. These words were identified by using POS tagging and Noun phrase chunking. Ten features are considered for each candidate word. Four features related to frequency and position of word in the text and the rest related to lexical chain of the word. After extracting the keywords by the classifier, post-processing performs for determining Two-word key phrases that were not obtained in the previous step. The dataset used in this research was chosen from among Persian scientific papers. We only used the title and abstract of these papers. The results depicted that using semantic relations, besides statistical features, would improve the overall performance of keyword extraction for papers. Also, the Naive Bayes classifier gives the best result among the investigated classifiers, of course, eliminating some of the features of the lexical chain improved its performance.  }, Keywords = {Keyword Extraction, Persian Document, Supervised Learning, Lexical Chain, FarsNet}, volume = {15}, Number = {4}, pages = {95-110}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.95}, url = {http://jsdp.rcisp.ac.ir/article-1-733-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-733-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {Janbabaei, Shadi and Gharaee, Hossein and Mohammadzadeh, Naser}, title = {The Lightweight Authentication Scheme with Capabilities of Anonymity and Trust in Internet of Things (IoT)}, abstract ={The Internet of Things (IoT), is a new concept that its emergence has caused ubiquity of sensors in the human life. All data are collected, processed, and transmitted by these sensors. As the number of sensors increases,   the first challenge in establishing a secure connection is authentication between sensors. Anonymity, lightweight, and trust between entities are other main issues that should be considered. However, this challenge also requires some features so that the authentication is done properly. Anonymity, light weight and trust between entities are among the issues that need to be considered. In this study, we have evaluated the authentication protocols concerning the Internet of Things and analyzed the security vulnerabilities and limitations found in them. A new authentication protocol is also proposed using the hash function and logical operators, so that the sensors can use them as computationally limited entities. This protocol is performed in two phases and supports two types of intra-cluster and inter-cluster communication. The analysis of proposed protocol shows that security requirements have been met and the protocol is resistant against various attacks. In the end, confidentiality and authentication of the protocol are proved applying AVISPA tool and the veracity of the protocol using the BAN logic. Focusing on this issue, in this paper, we have evaluated the authentication protocols in the Internet of Things and analyzed their limitations and security vulnerabilities. Moreover, a new authentication protocol is presented which the anonymity is its main target. The hash function and logical operators are used not only to make the protocol lightweight but also to provide some computational resources for sensors. In compiling this protocol, we tried to take into account three main approaches to covering the true identifier, generating the session key, and the update process after the authentication process. As with most authentication protocols, this protocol is composed of two phases of registration and authentication that initially register entities in a trusted entity to be evaluated and authenticated at a later stage by the same entity. It is assumed that in the proposed protocol we have two types of entities; a weak entity and a strong entity. The poor availability of SNs has low computing power and strong entities of CH and HIoTS that can withstand high computational overhead and carry out heavy processing. We also consider strong entities in the proposed protocol as reliable entities since the main focus of this research is the relationship between SNs. On the other hand, given the authenticity of the sensors and the transfer of the key between them through these trusted entities, the authenticity of the sensors is confirmed, and the relationship between them is also reliable. This protocol supports two types of intra-cluster and inter-cluster communication. The analysis of the proposed protocol shows that security requirements such as untraceability, scalability, availability, etc. have been met and it is resistant against the various attacks like replay attack, eavesdropping attack.}, Keywords = {Internet of things, Authentication, Anonymity, Lightweight}, volume = {15}, Number = {4}, pages = {111-122}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.111}, url = {http://jsdp.rcisp.ac.ir/article-1-795-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-795-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} } @article{ author = {badpeima, mohammad and hourali, Fatemeh and hourali, Maryam}, title = {Part Of Speech Tagging of Persian Language using Fuzzy Network Model}, abstract ={Part of speech tagging (POS tagging) is an ongoing research in natural language processing (NLP) applications. The process of classifying words into their parts of speech and labeling them accordingly is known as part-of-speech tagging, POS-tagging, or simply tagging. Parts of speech are also known as word classes or lexical categories. The purpose of POS tagging is determining the grammatical category of the words in a sentence. Grammatical and syntactical features of words are determined based on these tags. The function of existing tagging methods depends on the corpus. As if the educational and test data are extracted from a corpus, the methods are well-functioning, or if the number of educational data is low, especially in probabilistic methods, the accuracy level also decreases. The words used in sentences are often vague. For example, the word 'Mahrami' can be a noun or an adjective. Existing ambiguity can be eliminated by using neighbor words and an appropriate tagging method. Methods in this domain are divided into several categories such as:based on memory [2], rule based methods [5], statistical [6], and neural network [7]. The precision of more of these methods is an average of 95% [1]. In the paper [13], using the TnT probabilistic tagging and smoothing and variations on the estimation of the three-words likelihood function, a tagging model has been created that has reached 96.7% in total on the Penn Treebank and NEGRA entities. [14] Using the representation of the dependency network and extensive use of lexical features, such as the conditional continuity of the sequence of words, as well as the effective use of the foreground in the linear models of linear logarithms and fine-grained modeling of the unknown words, on the Penn Treebank WSJ model, 97.24% accuracy is achieved. The first work in Farsi that has used the word neighborhoods and the similarity distribution between them. The accuracy of the system is 57.5%. In [19], a Persian open source tagger called HunPoS was proposed. This tag uses the same TnT method based on the Hidden Markov model and a triple sequence of words, and 96.9% has reached on the ''Bi Jen Khan'' corpus. In this paper a statistical based method is proposed for Persian POS tagging. The limitations of statistical methods are reduced by introducing a fuzzy network model, such that the model is able to estimate more reliable parameters with a small set of training data. In this method, normalization is done as a preprocessing step and then the frequency of each word is estimated as a fuzzy function with respect to the corresponding tag. Then the fuzzy network model is formed and the weight of each edge is determined by means of a neural network and a membership function. Eventually, after the construction of a fuzzy network model for a sentence, the Viterbi algorithm as s subset of Hidden Markov Model (HMM) algorithms is used to specify the most probable path in the network. The goal of this paper is to solve a challenge of probabilistic methods when the data is low and estimation made by these models  is mistaken. The results of testing this method on ``Bi Jen Khan'' corpus verified that the proposed method has better performance than similar methods, like hidden Markov model, when fewer training examples are available. In this experiment, several times the data is divided into two groups of training and test with different sizes ascending. On the other hand, in the initial experiments, we reduced the train data size and, in subsequent experiments, increased its size and compared with the HMM algorithm. As shown in figure 4, the train and test set and are directly related to each other, as the error rate decreases with increasing the training set and vice versa. In tests, three criteria involving precision, recall and F1 have been used. In Table 4, the implementation of HMM models and a fuzzy network is compared with each other and the results are shown.  }, Keywords = {: Natural language processing, Part of speech (POS) tagging, Persian language, Fuzzy, Neural network}, volume = {15}, Number = {4}, pages = {123-130}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.29252/jsdp.15.4.123}, url = {http://jsdp.rcisp.ac.ir/article-1-536-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-536-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2019} }