List of Articles MMA

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  1 - Achieving Better Performance of S-MMA Algorithm in the OFDM Modulation
  Saeed Ghazi-Maghrebi Babak Haji Bagher Naeeni Mojtaba Lotfizad
  10.7508/jist.2013.01.002
  20.1001.1.23221437.2013.1.1.2.7
  Effective algorithms in modern digital communication systems provide a fundamental basis for increasing the efficiency of the application networks which are in many cases neither optimized nor very close to their practical limits. Equalizations are one of the preferred More

  Effective algorithms in modern digital communication systems provide a fundamental basis for increasing the efficiency of the application networks which are in many cases neither optimized nor very close to their practical limits. Equalizations are one of the preferred methods for increasing the efficiency of application systems such as orthogonal frequency division multiplexing (OFDM). In this paper, we study the possibility of improving the OFDM modulation employing sliced multi-modulus algorithm (S-MMA) equalization. We compare applying the least mean square (LMS), multi modulus algorithm (MMA) and S-MMA equalizations to the per tone equalization in the OFDM modulation. The paper contribution lies in using the S-MMA technique, for weight adaptation, to decreasing the BER in the OFDM multicarrier modulation. For more efficiency, it is assumed that the channel impulse response is longer than the cyclic prefix (CP) length and as a result, the system will be more efficient but at the expense of the high intersymbol interference (ISI) impairment existing. Both analysis and simulations demonstrate better performance of the S-MMA compared to LMS and MMA algorithms, in standard channels with additive white Gaussian noise (AWGN) and ISI impairment simultanously. Therefore, the S-MMA equalization is a good choice for high speed and real-time applications such as OFDM based systems. Manuscript profile
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  2 - Performance Analysis of SVM-Type Per Tone Equalizer Using Blind and Radius Directed Algorithms for OFDM Systems
  Babak Haji Bagher Naeeni
  10.7508/jist.2013.02.001
  In this paper, we present Support Vector Machine (SVM)-based blind per tone equalization for OFDM systems. Blind per tone equalization using Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA) are used as the comparison benchmark. The SVM-based cost funct More

  In this paper, we present Support Vector Machine (SVM)-based blind per tone equalization for OFDM systems. Blind per tone equalization using Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA) are used as the comparison benchmark. The SVM-based cost function utilizes a CMA-like error function and the solution is obtained by means of an Iterative Re-Weighted Least Squares Algorithm (IRWLS). Moreover, like CMA, the error function allows to extend the method to multilevel modulations. In this case, a dual mode algorithm is proposed. Dual mode equalization techniques are commonly used in communication systems working with multilevel signals. Practical blind algorithms for multilevel modulation are able to open the eye of the constellation, but they usually exhibit a high residual error. In a dual mode scheme, once the eye is opened by the blind algorithm, the system switches to another algorithm, which is able to obtain a lower residual error under a suitable initial ISI level. Simulation experiments show that the performance of blind per tone equalization using support vector machine has better than blind per tone equalization using CMA and MMA, from viewpoint of average Bit-Error Rate (BER). Manuscript profile
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  3 - Improving Image Dynamic Range For An Adaptive Quality Enhancement Using Gamma Correction
  Hamid Hassanpour
  This paper proposes a new automatic image enhancement method by improving the image dynamic range. The improvement is performed via modifying the Gamma value of pixels in the image. Gamma distortion in an image is due to the technical limitations in the imaging device, More

  This paper proposes a new automatic image enhancement method by improving the image dynamic range. The improvement is performed via modifying the Gamma value of pixels in the image. Gamma distortion in an image is due to the technical limitations in the imaging device, and impose a nonlinear effect. The severity of distortion in an image varies depends on the texture and depth of the objects. The proposed method locally estimates the Gamma values in an image. In this method, the image is initially segmented using a pixon-based approach. Pixels in each segment have similar characteristics in terms of the need for Gamma correction. Then the Gamma value for each segment is estimated by minimizing the homogeneity of co-occurrence matrix. This feature can represent image details. The minimum value of this feature in a segment shows maximum details of the segment. The quality of an image is improved once more details are presented in the image via Gamma correction. In this study, it is shown that the proposed method performs well in improving the quality of images. Subjective and objective image quality assessments performed in this study attest the superiority of the proposed method compared to the existing methods in image quality enhancement. Manuscript profile
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  4 - A Novel User-Centric Method for Graph Summarization Based on Syntactical and Semantical Attributes
  Nosratali  Ashrafi Payaman Mohammadreza Kangavari
  10.7508/jist.2018.02.002
  In this paper, we proposed an interactive knowledge-based method for graph summarization. Due to the interactive nature of this method, the user can decide to stop or continue summarization process at any step based on the summary graph. The proposed method is a general More

  In this paper, we proposed an interactive knowledge-based method for graph summarization. Due to the interactive nature of this method, the user can decide to stop or continue summarization process at any step based on the summary graph. The proposed method is a general one that covers three kinds of graph summarization called structural, attribute-based, and structural/attribute-based summarization. In summarization based on both structure and vertex attributes, the contributions of syntactical and semantical attributes, as well as the importance degrees of attributes are variable and could be specified by the user. We also proposed a new criterion based on density and entropy to assess the quality of a hybrid summary. For the purpose of evaluation, we generated a synthetic graph with 1000 nodes and 2500 edges and extracted the overall features of the graph using the Gephi tool and a developed application in Java. Finally, we generated summaries of different sizes and values for the structure contribution (α parameter). We calculated the values of density and entropy for each summary to assess their qualities based on the proposed criterion. The experimental results show that the proposed criterion causes to generate a summary with better quality. Manuscript profile
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  5 - Farsi Conceptual Text Summarizer: A New Model in Continuous Vector Space
  Mohammad Ebrahim Khademi Mohammad Fakhredanesh Seyed Mojtaba Hoseini
  10.7508/jist.2019.01.003
  Traditional methods of summarization were very costly and time-consuming. This led to the emergence of automatic methods for text summarization. Extractive summarization is an automatic method for generating summary by identifying the most important sentences of a text. More

  Traditional methods of summarization were very costly and time-consuming. This led to the emergence of automatic methods for text summarization. Extractive summarization is an automatic method for generating summary by identifying the most important sentences of a text. In this paper, two innovative approaches are presented for summarizing the Persian texts. In these methods, using a combination of deep learning and statistical methods, we cluster the concepts of the text and, based on the importance of the concepts in each sentence, we derive the sentences that have the most conceptual burden. In the first unsupervised method, without using any hand-crafted features, we achieved state-of-the-art results on the Pasokh single-document corpus as compared to the best supervised Persian methods. In order to have a better understanding of the results, we have evaluated the human summaries generated by the contributing authors of the Pasokh corpus as a measure of the success rate of the proposed methods. In terms of recall, these have achieved favorable results. In the second method, by giving the coefficient of title effect and its increase, the average ROUGE-2 values increased to 0.4% on the Pasokh single-document corpus compared to the first method and the average ROUGE-1 values increased to 3% on the Khabir news corpus. Manuscript profile
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  6 - A Survey on Multi-document Summarization and Domain-Oriented Approaches
  Mahsa Afsharizadeh Hossein Ebrahimpour-Komleh Ayoub Bagheri Grzegorz  Chrupała
  10.52547/jist.16245.10.37.68
  20.1001.1.23221437.2022.10.37.4.4
  Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decisio More

  Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations. Manuscript profile