1395 13 4 30 145

مدل‌سازی و پیاده‌سازی نرم‌افزاری عملکرد سامانه‌های تصویربرداری SAR در حالت نورافکن Modelling and Software Implementation of SAR Imaging System Performance in Spotlight Mode 1 1 سامانه‌های تصویربرداری SAR به عنوان مکملی برای سنجنده‌های سنجش از دور غیر فعال مطرح می‌باشند اما از پیچیدگی بالایی در مرحله تشکیل تصویر برخوردارند. به‌طوریکه تصویر نهایی در این سامانه‌ها پس از طی سه گام اساسی جمع‌آوری داده‌ی خام، تشکیل فضای سیگنالی و تشکیل فضای تصویری بوجود می‌آید. به‌علاوه در اطلاعات ثبت شده توسط سامانه‌های تصویربرداری SAR عوامل مختلف درون سیستمی و برون سیستمی چون رادار، سکو حامل رادار، الگوریتم‌های پردازشی، ناحیه تصویربرداری و کانال نقش دارند که هرکدام از زیرپارامترهای فراوانی تشکیل شده‌اند و این موضوع نیز بر پیچیدگی نحوه رفتار SAR می‌افزاید. لذا با توجه به این پیچیدگی‌ها ارائه مدل-هایی که نحوه عملکرد سامانه‌های تصویربرداری SAR را تشریح نماید، بسیار مطلوب است. در این مقاله ابتدا نحوه عملکرد SAR در مرحله تشکیل تصویر و در مد عملکردی Spotlight به صورت تحلیلی مدل‌سازی می‌شود. سپس مدل ارائه شده به صورت نرم افزاری پیاده‌سازی می‌گردد که این پیاده‌سازی در برگیرنده هر سه گام اساسی بخش تشکیل تصویر می‌باشد. به‌گونه‌ای که جمع آوری داده‌ی خام در محیط نرم-افزاری CST و تشکیل فضای سیگنالی و تصویری در محیط نرم‌افزاری MATLAB انجام می‌شود. اهمیت این پیاده‌سازی از آن جهت است که قابلیت‌های زیادی چون امکان تحلیل اثر پارامترهای مختلف سامانه‌های تصویربرداریSAR ، تفسیر بهتر این نوع از تصاویر، امکان بررسی صحت راهکارهای مطرح شده در جنگ الکترونیک یا پدافند غیرعامل برای مقابله با سامانه‌های SAR و ... را بوجود خواهد آورد. 2 SAR imaging systems are as a complement to passive remote sensing but the process of image formation in this systems is so complex So that the final image in the system is formed after the three basic steps: raw data acquisition, forming the signal space and image space. In addition, various factors within the system and outside the system are involved in the information that recorded by SAR, such as radar, platform, processing algorithm, imaging region and channel that each of them have many sub-parameters and this adds the complexity of the behavior of SAR. So due to the complexity, providing the model that describes how the SAR imaging system is highly important. In this paper, at first, the performance of the SAR image formation in spotlight mode placed on analytical modeling and after that the model comes in a soft implement. The implement includes three basic steps of image formation. So that raw data acquisition is done in CST and the signal and image formation are done in MATLAB software. This implementation provides a lot of abilities. So you can simulate the effect of the affect parameters in SAR images and better interpretation of themes. Also, the validity of the proposed solutions in electronic warfare or passive defense for SAR imaging systems can be studied by it. 3 18 2015/06/6 1394/3/16 2016/10/7 1395/7/16 مجتبی بهزاد فلاح پور Mojtaba Behzad Fallahpour دانشگاه صنعتی مالک اشتر ایران m_behzad_fp@yahoo.com حمید دهقانی Hamid Dehghani دانشگاه صنعتی مالک اشتر ایران hamid_deh@yahoo.com علی جبار رشیدی Ali Jabbar Rashidi دانشگاه صنعتی مالک اشتر ایران aiorashid@yahoo.com عباس شیخی Abbas Sheikhi دانشگاه شیراز ایران Sheikhi@shirazu.ac.ir SAR Signal Space Image space Scattering field Functional model Software Implementation SAR فضای سیگنالی فضای تصویری میدان پراکندگی مدل عملکردی پیاده‌سازی نرم‌افزاری [1] I. G. Cumming and F.H. Wong, Digital Processing of Synthetic Aperture Radar Data, 1^st edition, Artech House, London, 2006.##[2] W. L. Melvin, J. A. Scheer, Principles of Modern Radar, SciTech, 2013.##[3] J. A. Richards, Remote Sensing with Imaging Radar, 1^st edition, Springer, New York, 2009.##[4] X. Luo, Y. Deng, R. Wan, W. Luo, Y. Xu and L. Guo, "Full-Aperture SAR Data Focusing in the Spaceborne Squinted Sliding-Spotlight Mode," IEEE Geoscience and Remote Sensing Letters, vol. 11, pp. 1692-1696, October 2014.##[5] C. V. Jakowatz, D. E. Whal, P. H. Eichel, D. C. Ghiglia, P. A. Thompson, Spotlight-Mode Synthetic Aperture Radar a signal Processing Approach, Kluwer Academic Publishers, 1996.## [6] دهقانی، حمید، ریوندی، علی، بهزاد فلاح پور، مجتبی، موسی زاده، کیومرث، "مدل سازی عملکرد سامانه های تصویربرداری"SAR، فصلنامه علمی پژوهشی علوم و فناوری فضایی، جلد 6، شماره 1، ص. ص47-56، بهار92.##[6]H. Dehghani, A. Reyvandi, M. Behzad Fallahpour, k. Mousazade, “SAR Imaging Systems Performance Modeling”, Journal of Space Science and Technology, Vol. 6, No. 1, pp.47-56, 2013.##[7] S. Roedelsperger, C. Trampuz, A. Coccia, "Latest Meta Sensing ground, airborne and space borne SAR developments," IEEE 13th International Symposium Radar(IRS), 2012.##[8] Y. Deng, W. Yu, R. Wang, "On Space Borne Synthetic Aperture Radar (SAR) Systems in China," General Assembly and Scientific Symposium (URSI GASS), 2014.##[9] W. G. Carrara, R. S. Goodman, R. M. Majewski, Spotlight Synthetic Aperture Radar Signal Processing Algorithms, Artech House , Boston, 1995.##[10] F. Banda, L. Ferro-Famil, S.Tebaldini , "Polarimetric Time-Frequency Analysis of Vessels in Spotlight SAR Images," IEEE International Symposium of Geoscience and Remote Sensing (IGARSS), 2014. ##[11] S. Guangcai, J. Guobin, Y. Jun, C. Gushun, X. Mengdao, "Beam Steering SAR Data Processing By a Generalized PFA," IEEE 10th European Conference on Proceedings of Synthetic Aperture Radar (EUSAR), 2014.##[12] Bo. Fan, Q. Yuilang, P. You, H. Qiangwang, "An Improved PFA with Aperture Accommodation for Widefield Spotlight SAR Imaging," Geoscience and Remote Sensing Letter, vol. 12, pp. 3-7, 2015. ##[13] R. Bhalla, H. Ling,"Three-Dimensional Scattering Center Extraction Using the Shooting and Bouncing FLay Technique," IEEE Transactions on Antennas and Propagation, vol. 44, pp.1445-1453, November 1996.##[14] A. Kaya, M. Kartal, "Point Scatterer Model for RCS Prediction Using ISAR Measurements," IEEE 4th International Conference on Recent Advances Technologies in Space (RAST '09), 2009, pp. 422-425.##[15] G. Cakir, L. Sevgi, "Radar Cross-Section (RCS) Analysis of High Frequency Surface Wave Radar Targets," Turkish Journal of Electronic Engineering and Computer Science(TUBITAK), vol.18, pp. 457-467, 2010.##[16] J. Zhang, J. Hu, Y. Gao, R. Zhan, and Q. Zhai, "Three-Dimensional Scattering Centers Extraction of Radar Targets Using High Resolution Techniques," Progress In Electromagnetics Research Journal, vol. 37, pp. 127-137, 2014.##[1] I. G. Cumming and F.H. Wong, Digital Processing of Synthetic Aperture Radar Data, 1^st edition, Artech House, London, 2006.##[2] W. L. Melvin, J. A. Scheer, Principles of Modern Radar, SciTech, 2013.##[3] J. A. Richards, Remote Sensing with Imaging Radar, 1^st edition, Springer, New York, 2009.##[4] X. Luo, Y. Deng, R. Wan, W. Luo, Y. Xu and L. Guo, "Full-Aperture SAR Data Focusing in the Spaceborne Squinted Sliding-Spotlight Mode," IEEE Geoscience and Remote Sensing Letters, vol. 11, pp. 1692-1696, October 2014.##[5] C. V. Jakowatz, D. E. Whal, P. H. Eichel, D. C. Ghiglia, P. A. Thompson, Spotlight-Mode Synthetic Aperture Radar a signal Processing Approach, Kluwer Academic Publishers, 1996.##[6] دهقانی، حمید، ریوندی، علی، بهزاد فلاح پور، مجتبی، موسی زاده، کیومرث، "مدل سازی عملکرد سامانه های تصویربرداری"SAR، فصلنامه علمی پژوهشی علوم و فناوری فضایی، جلد 6، شماره 1، ص. ص47-56، بهار92.##[6]H. Dehghani, A. Reyvandi, M. Behzad Fallahpour, k. Mousazade, “SAR Imaging Systems Performance Modeling”, Journal of Space Science and Technology, Vol. 6, No. 1, pp.47-56, 2013.##[7] S. Roedelsperger, C. Trampuz, A. Coccia, "Latest Meta Sensing ground, airborne and space borne SAR developments," IEEE 13th International Symposium Radar(IRS), 2012.##[8] Y. Deng, W. Yu, R. Wang, "On Space Borne Synthetic Aperture Radar (SAR) Systems in China," General Assembly and Scientific Symposium (URSI GASS), 2014.##[9] W. G. Carrara, R. S. Goodman, R. M. Majewski, Spotlight Synthetic Aperture Radar Signal Processing Algorithms, Artech House , Boston, 1995.##[10] F. Banda, L. Ferro-Famil, S.Tebaldini , "Polarimetric Time-Frequency Analysis of Vessels in Spotlight SAR Images," IEEE International Symposium of Geoscience and Remote Sensing (IGARSS), 2014. ##[11] S. Guangcai, J. Guobin, Y. Jun, C. Gushun, X. Mengdao, "Beam Steering SAR Data Processing By a Generalized PFA," IEEE 10th European Conference on Proceedings of Synthetic Aperture Radar (EUSAR), 2014.##[12] Bo. Fan, Q. Yuilang, P. You, H. Qiangwang, "An Improved PFA with Aperture Accommodation for Widefield Spotlight SAR Imaging," Geoscience and Remote Sensing Letter, vol. 12, pp. 3-7, 2015. ##[13] R. Bhalla, H. Ling,"Three-Dimensional Scattering Center Extraction Using the Shooting and Bouncing FLay Technique," IEEE Transactions on Antennas and Propagation, vol. 44, pp.1445-1453, November 1996.##[14] A. Kaya, M. Kartal, "Point Scatterer Model for RCS Prediction Using ISAR Measurements," IEEE 4th International Conference on Recent Advances Technologies in Space (RAST '09), 2009, pp. 422-425.##[15] G. Cakir, L. Sevgi, "Radar Cross-Section (RCS) Analysis of High Frequency Surface Wave Radar Targets," Turkish Journal of Electronic Engineering and Computer Science(TUBITAK), vol.18, pp. 457-467, 2010.##[16] J. Zhang, J. Hu, Y. Gao, R. Zhan, and Q. Zhai, "Three-Dimensional Scattering Centers Extraction of Radar Targets Using High Resolution Techniques," Progress In Electromagnetics Research Journal, vol. 37, pp. 127-137, 2014.## ##

یک روش جدید افزایش دقت مکانی تصاویر سنجش از دور با استفاده از جدول جستجو A novel method for increasing the spatial resolution of remote sensing images using lookup table 1 1 تحقیقات مختلفی برای افزایش دقت مکانی تصاویر با هدف تجزیه پیکسل­های مخلوط در سنجش از دور انجام شده است. این روش­ها و مشکلات پیش­رو در این تحقیق بررسی خواهند شد. در ادامه مبحث جدیدی برای افزایش دقت مکانی تصاویر پیشنهاد خواهد شد. در این روش یک تصویر با دقت مکانی کمتر، از تصویر ورودی استخراج می­شود که همانند یک جدول جستجو عمل می­کند. با تعریف یک معیار برای شباهت بهینه پیکسل­ها در دو تصویر، برای هر پیکسل از تصویر ورودی یک پیکسل مشابه در جدول جستجو می­یابیم. نشان خواهیم داد که پیکسل­های مشابه در دو تصویر، دارای یک ساختار مشابه از زیرپیکسل­های تشکیل­دهنده آن پیکسل می­باشند. با استفاده از این روش هر پیکسل مخلوط به تعدادی زیرپیکسل که اغلب خالص هستند، تجزیه خواهد شد. شبیه­سازی­های انجام شده بر روی اطلاعات ساختگی و واقعی نشان­دهنده بهبود شاخصهای طبقه­بندی توسط روش پیشنهادی و تجزیه بهتر پیکسل­های مخلوط، نسبت به روش­های قابل مقایسه است. 2 Different methods have been proposed to increase the image spatial resolution by mixed pixels decomposition. These methods can be divided into two groups. Some research have been attempted to obtain percentages of sub pixels and the other try to obtain their locations. These methods and their problems will be examined in this study. Common methods are reviewed with more emphasis. Finally, a new method for increasing the spatial resolution will be proposed to resolve some deficiencies of existing methods. Especially this method, instantly takes percentages and locations of mixed pixels end members without no use of additional information. This method applies a proper lookup table, which is derived from an input image. By defining a similarity metric function, we obtain a similar pixel for every input pixel. These similar pixels have equal sub pixel structures; hence, an input pixel will be decomposed to a proper set of sub pixels. In the high quality images, these sub pixels usually, belong to pure classes. This proposed method is examined on four sets of artificial and real data. First we degrade these data sets by averaging filtering, and then we restore degraded data, using this method and two other methods. One of these methods is a hard classification and the other is a combination of fuzzy c-means and direct method to obtain percentages and locations of sub pixels respectively. We obtain percent of correction classification and KAPPA criterions for these methods. Simulation results on artificial, real data show a good sub pixels decomposition performance of proposed method relative to those of other comparable methods. By particular, this method shows at least 7% of improvement in artificial and 2% in real data relative to other methods.   19 28 2015/06/62013/12/8 1392/9/17 2016/10/72016/12/4 1395/9/14 منصور زینلی mansoor zeinali دانشگاه اراد اسلامی واحد نجف اباد ایران mansoor.zeinali@gmail.com محمد حسن قاسمیان hassan ghasemian دانشگاه تربیت مدرس ایران ghasemi@modares.ac.ir spatial resolution change the image scale lookup table subpixel decomposition افزایش دقت مکانی زیرپیکسل جدول جستجو تجزیه پیکسلهای مخلوط [1] J.J. Settle, N.A. Drake, “Linear Mixing and the Estimation of Ground Cover Proportions”, International Journal of Remote Sensing, Vol. 14, No. 6, pp.1159-1177, 1993.##[2] J. Li, Q. Yuan, H. Shen, X. Meng and L. Zhang, “Hyperspectral Image Super-Resolution by Spectral Mixture Analysis and Spatial–Spectral Group Sparsity”, IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 9, pp. 1250 – 1254, 2016.##[3] C. Wu, A.T. Murray, “Estimating Impervious Surface Distribution by Spectral Mixture Analysis”, Remote Sensing Of Environment, Vol. 84, pp. 493-505, 2003.##[4] Zhang Hongen, Lin Qizhong, Liu Suhong, Shi Jiancheng, “Sub-Pixel Lake Mapping In Tibetan Plateau”, Geoscience And Remote Sensing Symposium, Proceedings IEEE, Vol. 5, pp .3073 – 3076, 2004.##[5] L. Weiguo Elaine, Y. Wu, “Comparison Of Non-Linear Mixture Models: Sub-Pixel Classification”, Remote Sensing Of Environment, Vol. 94, pp.145–154, 2005.##[6] G.M. Foody, R.M. Lucas, P.J. Curran and M. Honzak, “Non-Linear Mixture Modeling Without Endmembers Using An Artificial Neural Network”, International Journal Of Remote Sensing, Vol. 18, pp. 937-953, 1997.##[7] Z. Mitrak, F. Del Frate, “Non-linear spectral mixture analysis of Landsat imagery by means of neural networks”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1765 – 1768, 2015.##[8] G.M. Foody, D.P. Cox, “Sub-Pixel Land Cover Composition Estimation Using A Linear Mixture Model And Fuzzy Membership Functions”, International Journal Of Remote Sensing, Vol. 15, pp. 619-631, 1994.##[9] J. Zhang, G.M. Foody, “Fully-Fuzzy Supervised Classification Of Sub-Urban Land Cover From Remotely Sensed Imagery: Statistical And Artificial Neural Network Approaches”, International Journal Of Remote Sensing, Vol. 22, No.4, pp. 615–628, 2001.##[10] G.M. Foody, “Estimation Of Sub-Pixel Land Cover Composition In The Presence Of Untrained Classes”, Computers & Geosciences, Vol. 26, pp.469-478, 2000.##[11] Q. Fang, “Neuro-Fuzzy Based Analysis Of Hyper Spectral Imagery”, Photogrammetric Engineering & Remote Sensing, Vol. 74, No. 10, pp. 1235–1247, 2008.##[12] P.M. Atkinson, A.R.L. Tatnall, “Introduction: Neural Networks In Remote Sensing”, International Journal Of Remote Sensing, Vol. 18, pp. 699-709, 1997.##[13] G.M. Foody, M.K. Arora, “An Evaluation Of Some Factors Affecting The Accuracy Of Classification By An Artificial Neural Network”, International Journal Of Remote Sensing, Vol. 18, pp. 799-810, 1997.##[14] M.Q. Nguyen, P.M. Atkinson and G.L. Hugh, “Superresolution Mapping Using A Hopfield Neural Network With Fused Images”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 44, No. 3, pp. 736-750, 2006.##[15] C.E. Woodcock, S. Gopal, and W. Albert, “Evaluation Of The Potential For Providing Secondary Labels In Vegetation Maps”, Photogrammetric Engineering & Remote Sensing, Vol. 62, pp. 393-399, 1996.##[16] M.W. Thornton, P.M. Atkinson and D. A. Holland, “Sub-Pixel Mapping Of Rural Land Cover Objects From Fine Spatial Resolution Satellite Sensor Imagery Using Super-Resolution Pixel-Swapping”, International Journal Of Remote Sensing, Vol. 27, No. 3, pp.473–491, 2006.##[17] C.M. Koen, B.D. Baets, L.P.C. Verbeke and R.R. De Wulf, “Direct Sub-Pixel Mapping Exploiting Spatial Dependence”, Geoscience And Remote Sensing Symposium, IEEE, pp. 3046-3050, 2004.##[18] Qunming Wang, Wenzhong Shi and Liguo Wang, “Allocating Classes For Soft-Then-Hard Subpixel Mapping Algorithms In Units Of Class”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 52, No. 5, pp. 2940 - 2959, 2014.##[19] T. Kasetkasem, M.K. Arora, P.K. Varshneyp, “Super-Resolution Land Cover Mapping Using A Markov Random Field Based Approach Remote Sensing Of Environment”, Remote Sensing Of Environment, Vol. 96, pp. 302-314, 2005.##[20] C.M Koen, L.P.C. Verbeke, , T. Westra and R.R. De Wulf., “Sub-Pixel Mapping And Sub-Pixel Sharpening Using Neural Network Predicted Wavelet Coefficients”, Remote Sensing Of Environment, Vol. 91, pp.225–236, 2004.##[21] Y. Zhong, L. Zhang, P. Li, and H. Shen, “A Sub-Pixel Mapping Algorithm Based On Artificial Immune Systems For Remote Sensing Imagery”, IEEE International Geoscience And Remote Sensing Symposium, Vol. 3, pp.1007–1010, 2009.##[22] X. Tong, X. Xu, A. Plaza, H. Xie, H. Pan, W. Cao and D. LvA, “New Genetic Method for Subpixel Mapping Using Hyperspectral Images”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 99, pp. 1-12, 2016.##[23] A. Boucher, “Sub-Pixel Mapping Of Coarse Satellite Remote Sensing Images With Stochastic Simulations From Training Images”, Mathematical Geoscience, Vol. 41, pp.265–290, 2009.##[24] M. Zeinali, H. Ghassemian and M.N. Moghaddasi, “A New Magnification Method For Rgb Color Images Based On Subpixels Decomposition”, IEEE Signal Processing Letters, Vol. 21, No. 5, pp. 577–560, 2014.## ##

تشخیص صرع در سیگنال EEG با استفاده از الگوریتم ابتکاری صفحات شیبدار(IPO) Epileptic seizure detection using Inclined Planes system Optimization algorithm(IPO) 1 1 طبق مطالعات انجام شده، در حدود یک درصد از مردم دنیا از صرع رنج میبرند. اولین مرحله از درمان صرع، تشخیص صحیح آن است. یکی از راه های تشخیص صرع، آنالیز دقیق الکتروانسفالوگرافی(EEG) است. بدین منظور، روش های مختلفی جهت تشخیص خودکار صرع بوسیله تحلیل سیگنال EEG ارائه شده است. در این مقاله با استفاده از یک الگوریتم هوشمند و ابتکاری جدید به نام الگوریتم بهینه سازی صفحات شیبدار(IPO)، به تشخیص و جداسازی سیگنال EEG آغشته به صرع از سیگنال های افراد سالم پرداخته ایم. به دلیل خاصیت غیرخطی و ناایستای سیگنال EEG، از تبدیل ویولت جهت استخراج ویژگی های سیگنال بهره گرفته شده است سپس با استفاده از ویژگی های استخراج شده توسط تبدیل ویولت و اعمال آن به سیستم مبتنی بر الگوریتم IPO به تشخیص صرع پرداخته شده است. با استناد به پژوهش انجام شده، مشخص شد که الگوریتم ابتکاری IPO توانایی بالایی در تشخیص صحیح صرع در سیگنال EEG دارد. 2 Epilepsy is a neurological disorder after stroke. About 1 percent of people in the world are involved with this second most common neurological disorder. Epilepsy can affect people of different ages with an altered behavior or lack of patient awareness and affect one's social life. In 75% of cases, if epilepsy is diagnosed early and properly, it can be treated. Among all existing methods of analysis for the detection of epileptic brain activity, EEG is more applicable, due to its special features (including its low-cost and innocuous). Despite all the advantages of this method, the visual scoring of the EEG records by a human scorer is clearly a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the amount of data needs to be scored. Thus, a tremendous effort has been devoted by researchers towards automatic epileptic seizures detection in EEG. This paper offers a novel method based on heuristic and intelligent algorithms, inclined planes system optimization (IPO), to detect epileptic samples from healthy subjects. Like other heuristic algorithms, IPO is inspired by nature and its laws. How to move sphere objects on the slope without friction and their desire to reach the lowest point, shapes the main idea of the IPO. In the IPO, small balls like particles in the PSO are placed randomly on the search space. The balls search the search space to find the optimal point which is the lowest point (relative to a reference point) on the surface. In the current work, the data described by Andrzejak et al. was used; which contains 5 sets (Z, O, N, F and S). In this work, three different classification problems are created from the above dataset in order to compare the performance of our method with other approaches: In the first, two sets were examined, normal (set Z) and seizure (set S). In the second, four sets of the dataset were used and they were classified into two different classes: non-seizure (sets Z, N, F) and seizure (set S). In the third, all the EEGs from the dataset were used and they were classified into two different classes: sets Z, O, N and F are included in the non-seizure class and set S in the seizure class. The EEG signal under study is firstly decomposed into five sub-bands through DWT (D1–D4 and A4), and each sub-band represents different frequency bands information. Afterwards, four statistical parameters of maximum, minimum, average and standard deviation were calculated for each sub-band. And then, using the optimization algorithm IPO, the best weights are calculated to apply to the OVA classifier in order to find the best hyper plane separating the two classes. The fitness function defined in the IPO algorithm, is the number of signals that have been classified incorrectly. To classify EEG signals in three problems, the 10-fold Cross-Validation method is used. In this method, the data is divided into 10 subsections. And then, one subset is used for test and nine others for training. This procedure is repeated 10 times, until all the data is used for testing. The proposed algorithm have been implemented 10 times for the two wavelet functions Db1 and db2. Using the proposed method, the accuracy obtained for the three problems is 100%, 98/1%, 97/34%, respectively. Also by the proposed method diagnosis of epilepsy can be achieved very quickly. The results show that the algorithm is capable of detecting signals of epileptic and non-epileptic in less than 5 milliseconds. This makes it possible to use this method in real-time systems. 29 42 2015/06/62013/12/82014/05/14 1393/2/24 2016/10/72016/12/42016/10/5 1395/7/14 محمدرضا اسماعیلی Mohammad Reza Esmaeili دانشگاه بیرجند ایران mr.esmaeili@birjand.ac.ir سید حمید ظهیری Seyed Hamid Zahiri دانشگاه بیرجند ایران shzahiri@yahoo.com Electroencephalogram(EEG) Epileptic seizure detection Discrete wavelet transform(DWT) Heuristic algorithm Inclined planes system optimization algorithm(IPO) الکتروانسفالوگرافی تشخیص صرع تبدیل ویولت گسسته الگوریتم های ابتکاری الگوریتم بهینه سازی صفحات شیبدار [1] E. R. Kandel, J. H. Schwartz, T. M. Jessell, S. A. Siegelbaum, and A. J. Hudspeth, Principles of neural science. vol. 4, New York: McGraw-hill, 2000.##[2] S. Sanei and J. A. Chambers, EEG signal processing. England: John Wiley & Sons, 2007.##[3] E. Niedermeyer and F. L. da Silva, Electroencephalography: basic principles, clinical applications, and related fields. 5th ed. Lippincott Williams & Wilkins, 2005.##[4] L. Guo, D. Rivero, J. Dorado, J. R. Rabunal, and A. Pazos, "Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks," Journal of neuroscience methods, vol. 191, pp. 101-109, 2010.##[5] N. Sadati, H. R. Mohseni, and A. Maghsoudi, "Epileptic seizure detection using neural fuzzy networks," in Fuzzy Systems, 2006 IEEE International Conference on, 2006, pp. 596-600.##[6] P. Jahankhani, V. Kodogiannis, and K. Revett, "EEG signal classification using wavelet feature extraction and neural networks," in Modern Computing, 2006. JVA'06. IEEE John Vincent Atanasoff 2006 International Symposium on, 2006, pp. 120-124.##[7] A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, "Automatic seizure detection based on time-frequency analysis and artificial neural networks," Computational Intelligence and Neuroscience, vol. 2007, 2007.##[8] H. Ocak, "Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm," Signal processing, vol. 88, pp. 1858-1867, 2008.##[9] L. Guo, D. Rivero, and A. Pazos, "Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks," Journal of neuroscience methods, vol. 193, pp. 156-163, 2010.##[10] A. R. Naghsh-Nilchi and M. Aghashahi, "Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network," Biomedical Signal Processing and Control, vol. 5, pp. 147-157, 2010.##[11] U. Orhan, M. Hekim, and M. Ozer, "EEG signals classification using the K-means clustering and a multilayer perceptron neural network model," Expert Systems with Applications, vol. 38, pp. 13475-13481, 2011.##[12] J. Wang, X. Gao, J. M. Tanskanen, and P. Guo, "Epileptic EEG Signal Classification with ANFIS based on Harmony Search Method," in Computational Intelligence and Security (CIS), 2012 Eighth International Conference on, Guangzhou, 2012, pp. 690-694.##[13] M. Niknazar, S. Mousavi, B. V. Vahdat, and M. Sayyah, "A new framework based on recurrence quantification analysis for epileptic seizure detection," IEEE journal of biomedical and health informatics, vol. 17, pp. 572-578, 2013.##[14] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state," Physical Review E, vol. 64, p. 061907, 2001.##[15] H. Adeli, Z. Zhou, and N. Dadmehr, "Analysis of EEG records in an epileptic patient using wavelet transform," Journal of neuroscience methods, vol. 123, pp. 69-87, 2003.##[16] C. K. Chui, An introduction to wavelets. Boston: Academic press, 1992.##[17] S. G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation," IEEE transactions on pattern analysis and machine intelligence, vol. 11, pp. 674-693, 1989.##[18] O. Faust, U. R. Acharya, H. Adeli, and A. Adeli, "Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis," Seizure, vol. 26, pp. 56-64, 2015.##[19] I. Güler and E. D. Übeyli, "Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients," Journal of neuroscience methods, vol. 148, pp. 113-121, 2005.##[20] M. H. Mozaffari and S. H. Zahiri, "Unsupervised Data and Histogram Clustering Using Inclined Planes System Optimization Algorithm," Image Analysis & Stereology, vol. 33, pp. 65-74, 2014.##[21] M. Dorigo, "Optimization, learning and natural algorithms," Ph. D. Thesis, Politecnico di Milano, Milan, Italy, 1992.##[22] J. Kennedy and R. Eberhart, "Particle swarm optimization," In Proceedings of IEEE international conference on neural networks, Perth, 1995, pp. 1942-1948.##[23] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information sciences, vol. 179, pp. 2232-2248, 2009.##[24] P. Guo, J. Wang, X. Z. Gao, and J. M. Tanskanen, "Epileptic EEG signal classification with marching pursuit based on harmony search method," in Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on, 2012, pp. 283-288.##[25] S. Xie and S. Krishnan, "Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis," Medical & biological engineering & computing, vol. 51, pp. 49-60, 2013.##[26] G. Chen, "Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features," Expert Systems with Applications, vol. 41, pp. 2391-2394, 2014.##[27] N. F. Güler, E. D. Übeyli, and I. Güler, "Recurrent neural networks employing Lyapunov exponents for EEG signals classification," Expert systems with applications, vol. 29, pp. 506-514, 2005.##[28] H. Ocak, "Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy," Expert Systems with Applications, vol. 36, pp. 2027-2036, 2009.##[29] V. Srinivasan, C. Eswaran, and Sriraam, "Artificial neural network based epileptic detection using time-domain and frequency-domain features," Journal of Medical Systems, vol. 29, pp. 647-660, 2005.##[30] A. Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model," Expert Systems with Applications, vol. 32, pp. 1084-1093, 2007.##[31] K. Polat and S. Güneş, "Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform," Applied Mathematics and Computation, vol. 187, pp. 1017-1026, 2007.##[32] L. Guo, D. Rivero, J. A. Seoane, and A. Pazos, "Classification of EEG signals using relative wavelet energy and artificial neural networks," in Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, 2009, pp. 177-184.##[33] R. Dhiman and J. Saini, "Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures," Applied Soft Computing, vol. 19, pp. 8-17, 2014.##[34] Z. Zainuddin, L. K. Huong, and O. Pauline, "On the use of wavelet neural networks in the task of epileptic seizure detection from electroencephalography signals," Procedia Computer Science, vol. 11, pp. 149-159, 2012.##ندارد ##

استخراج ویژگی‌های ساختاری فایل‌های کامپیوتری مبتنی بر تحلیل و ارزیابی آماری Feature Extraction of Computer Files Structure by Statistical Analysis 1 1 فایل‌ها مهم‌ترین منبع ارائه اطلاعات به صورت‌های مختلف از قبیل متن، صوت، تصویر، صفحات وب و غیره هستند؛ تحلیل و آنالیز فایل‌ها به منظور شناخت و بررسی ویژگی‌ها و خصوصیات منحصربه‌فرد آن‌ها، یکی از مسائل بسیار مهم در زمینه حریم خصوصی، امنیت اطلاعات، شناسایی نوع فایل‌ها، تحلیل ساختاری کدها و غیره می‌باشد. در این مقاله با تحلیل و آنالیز آماری بر روی محتوای باینری فایل‌ها مبتنی بر مدل n-gram، ویژگی‌ها و خصوصیات مختلف یک فایل مورد بررسی قرار گرفته است. علاوه بر این به منظور کاهش حجم محاسبات و حافظه مورد نیاز مدل n-gram، از خوشه‌بندی لغات استفاده شده و محتوای هر فایل در دو حالت کامل و بلوک‌بندی شده مورد تجزیه و تحلیل قرار گرفته است. در حالت کامل ویژگی‌هایی همچون آنتروپی، فراوانی، TF-IDF، خود همبستگی و در حالت بلوکی، ویژگی‌هایی همچون نرخ آنتروپی، بعد فرکتال، فاصله و غیره بررسی شده است. نتایج بررسی‌ها نشان داده ویژگی‌های استخراج شده در روش اول به خوبی می‌توانند خصوصیات منحصر به فرد فایل‌های jpg، mp3، swf و html را منعکس نمایند. ویژگی‌های استخراج شده در روش دوم نیز به خوبی می‌توانند خصوصیات فایل‌های doc، html و pdf را منعکس نمایند. 2 Files are the most important sources of information presenting in various formats such as texts, audio, video, images, web pages, etc. …; (in-depth) analysis of files for the purpose of recognition and investigating their unique properties (or characteristics) is one of the most significant issues in the field of personal security safety, information security, file-type identification, codes structuration analysis etc…. Statistical analytic methodology of working on the binary files contents based on the n-gram model has been opted for in the present paper in order to full investigate all different aspects of a file’s range of characteristics. Moreover, to reduce down the calculations volume and the n-gram model peculiar to the needed amount of memory, use has been made of word clustering. Later on analysis has been conducted on both files’ contents in two states of “blocking” and “full”: it is to be noted that in the “full” case such characteristics as Chi-square, Auto-correlation, Weighted term frequency-Inverse document frequency (TF-IDF), Fractal dimension etc … have been brought under comprehensive study; while in the “blocking” case, other properties like the entropy rate, the distance, etc … have been delved into. The gained results indicate that the extracted characteristics in the first method could well easily reflect the unique properties belonging to jpg, mp3, swf and html files; and in the second method, are able to clearly well reflect doc, html and pdf files properties. 43 62 2015/06/62013/12/82014/05/142013/07/3 1392/4/12 2016/10/72016/12/42016/10/52016/10/5 1395/7/14 مجید وفایی جهان Majid Vafaei Jahan دانشگاه آزاد اسلامی مشهد ایران vafaeija@yahoo.com Files n-gram model word clustering Canberra distance entropy rate Fractal dimension فایل‌های کامپیوتری مدل n-gram خوشه‌بندی لغات ضریب خود همبستگی TF-IDF نرخ آنتروپی فاصله کانبرا [1] "Tube with a Memory Keeps Answer on File," Popular Science Magazine, p. 95, February 1950. ##[2] J. H. Saltzer, "CTSS Technical Notes," 1995.##[3] S. M. Tabish, M. Z. Shafiq and M. Farooq, "Malware detection using statistical analysis of byte-level file content," in ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics, New York, NY, USA, 2009. ##[4] B. Jochheim, "On the Automatic Detection of Embedded Malicious Binary Code using Signal Processing Techniques Project Report," Hamburg, October 17, 2012.##[5] K. Kaushal, P. Swadas and N. Prajapati, "Metamorphic Malware Detection Using Statistical Analysis," International Journal of Soft Computing and Engineering (IJSCE), vol. 2, no. 3, pp. 49-53, July 2012. ##[6] I. Yoo and U. Ultes-Nitsche, "Adaptive detection of worms/viruses in firewalls," in International Conference on Communication, Network, and Information Security (ICCNIS), 2003. ##[7] M. Eskandari and S. Hashemi, " A graph mining approach for detecting unknown malwares," Journal of Visual Languages & Computing, vol. 23, no. 3, p. 154–162, June 2012. ##[8] P. Phunchongharn, S. Pornnapa and T. Achalakul, "File Type Classification for Adaptive Object File System," in TENCON 2006. 2006 IEEE Region 10 Conference, King Mongkut's Univ. of Technol., Bangkok , 14-17 Nov. 2006. ##[9] M. N. A. Khan, "Performance analysis of Bayesian networks and neural networks in classification of file system activities," computers & s e c u rity, vol. 31, p. 3 9 1 e4 0 1, 2012. ##[10] W. C. Calhoun and D. Coles, "Predicting the types of file fragments," digital investigation, vol. 5, pp. 14-20, 2008. ##[11] M. McDaniel and M. Heydari, "Content based file type detection algorithms," in Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS 2003), IEEE Computer Society, Los Alamitos (2003), Washington, DC, USA, 2003. ##[12] W.-J. Li, K. Wang, S. J. Stolfo and B. Herzog, "Fileprints: Identifying File Types by n-gram Analysis," in Proceedings of the 2005 IEEE, Workshop on Information Assurance, United States Military Academy, West Point, NY, 2005. ##[13] M. Karresand and N. Shahmehri, "Oscar – file type identification of binary data in disk clusters and ram pages," in Security and Privacy in Dynamic Environments, Boston, Springer, 2006, p. 413–424.##[14] M. Karresand and N. Shahmehri, "File Type Identification of Data Fragments by Their Binary Structure," in IEEE, Workshop on Information Assurance , United States Military Academy, West Point, NY, 2006. ##[15] G. Hall and W. Davis, "Sliding window measurement for file type identification," Computer Forensics and Intrusion Analysis Group, ManTech. Security and Mission Assurance, Rexas, 2006.##[16] R. Erbacher and J. Mulholland, "Identification and Localization of Data Types within Large-Scale File Systems," in In: SADFE 2007: Proceedings of the Second International Workshop on Systematic Approaches to Digital Forensic Engineering, IEEE Computer Society, Los Alamitos, Washington, DC, USA, 2007. ##[17] M. Amirani, M. Toorani and A. Beheshti, "A new approach to content-based file type detection," in IEEE Symposium on Computers and Communications, 2008. ISCC 2008, Dept. of Electr. Eng., Iran Univ. of Sci. & Technol. (IUST), Tehran, 6-9 July 2008. ##[18] S. J. Moody and R. F. Erbacher, "SÁDI – Statistical Analysis for Data type Identification," in Third International Workshop on Systematic Approaches to Digital Forensic Engineering, 2008. SADFE '08. , Dept. of Comput. Sci., Utah State Univ., Logan, UT , 22 May 2008. ##[19] A. Kattan, E. Galv´an-L´opez, R. Poli and M. O’Neill, "GP-Fileprints: File Types Detection Using Genetic Programming," in EuroGP'10 Proceedings of the 13th European conference on Genetic Programming, Springer-Verlag Berlin, Heidelberg, 2010. ##[20] I. Ahmed, K.-s. Lhee, H. Shin and M. Hong, "Content-based File-type Identification Using Cosine Similarity and a Divide-and-Conquer Approach," IETE Tech Rev , vol. 27, no. 6, pp. 465-77, 2010. ##[21] I. Ahmed, K.-s. Lhee, H. Shin and M. Hong, "Fast File-type Identification," in SAC '10 Proceedings of the 2010 ACM Symposium on Applied Computing, ACM New York, NY, USA, 2010. ##[22] S. Gopal, Y. Yang, K. Salomatin and J. Carbonell, "Statistical Learning for File-Type Identification," in IEEE, 10th International Conference on Machine Learning and Applications, 18-21 Dec. 2011. ##[23] G. A. Fink, Markov Models for Pattern, German language, B.G. Teubner, 2003. ##[24] M. Vafaei Jahan, Computer Modeling and Simulation, Mashhad: Islamic Azad University – Mashhad Branch Press, 2011. ##[25] T. M. Cover and J. A. Thomas, Elements of Information Theory, D. L. Schilling, Ed., Paris, France: John Wiley & Sons, Inc., 1991. ##[26] M. Zubair Shafiq, S. A. Khayam and M. Farooq, "Embedded Malware Detection Using Markov n-Grams," DIMVA Springer-Verlag Berlin Heidelberg , pp. 88-107, 2008. ##[27] P. Shields, The Ergodic theory of discrete sample paths, American Mathematical Society,Graduate studies in mathematics, Vol. 13, 1996. ##[28] J. Dagpunar, Simulation and Monte Carlo: With applications in finance and MCMC, John Wiley & Sons Ltd, 2007. ##[29] M. Cencini, F. Cecconi and A. Vulpiani, Chaos: From Simple Models to Complex Systems, World Scientific Publishing, 2010. ##[30] C. Grinstead and J. Snell, Introduction to probability, 2nd Edition ed., American Mathematical Society, 1997. ##[31] J. Hamilton, Time Series Analysis, New Jersey: Princeton University Press, Princeton, 1994. ##[32] J. Bassingthwaighte, L. Liebovitch and B. West, Fractal Physiology, New York: The American Physiological Society by Oxford University Press, 1994. ##[33] K. Falconer, Fractal geometry: Mathematical Foundations and Applications, 2nd Edition ed., Wiley, 2003. ##[34] D. Lai and M. Danca, "Fractal and statistical analysis on digits of irrational numbers," Chaos, Solitons and Fractals, vol. 36, p. 246–252, 2008. ##[35] H. Tang, J. Wang, J. Zhu, Q. Ao, J. Wang, B. Yang and Y. Li, "Fractal dimension of pore-structure of porous metal materials made by stainless steel powder," Powder Technology 217, p. 383–387, 2012. ##[36] S. Bandyopadhyay and S. Saha, Unsupervised Classification: Similarity Measures, Classical and Metaheuristic Approaches, and Applications, New York Dordrecht London: Springer-Verlag Berlin Heidelberg, 2013. ##[37] E. Deza and M.-M. Deza, Dictionary of Distances, Amsterdam, The Netherlands: Elsevier, First edition 2006. ##[38] K. S. Jones, "A statistical interpretation of term specificity and its application in retrieval," Journal of Documentation, vol. 28, no. 1, pp. 11-21, 1972,2004. ##[39] tf–idf, 2012. [Online]. Available: http://en.wikipedia.org/wiki/Tf%E2%80%93idf.##[40] N. Ali, M. Price, and R. Yampolskiy, "BLN-Gram-TF-ITF as a new Feature for Authorship Identification," 2014##[41] J. D. Uszkoreit, A. Venugopal, and D. M. Bikel, "Parsing rule generalization by n-gram span clustering," ed: Google Patents, 2015##[42] H. Karimi, S. M. Hosseini, M. Vafaei Jahan, “On the Combination of Self-Organized Systems to Generate Pseudo-Random Numbers,” Information Sciences, Volume 221, pp:371–388, 2013.#### ##

تخمین مکان نواحی کدکننده پروتئین در توالی عددی DNA با استفاده پنجره با طول متغیر بر مبنای منحنی سه بعدی Z Estimation of protein-coding regions in numerical DNA sequences using Variable Length Window method based on 3-D Z-curve 1 1 تخمین دقیق نواحی کدکننده پروتئین در ژن ها با استفاده از ابزارهای پردازش سیگنال، در سال های اخیر به چالشی در بیوانفورماتیک تبدیل شده است. بسیاری از روش های پردازش سیگنال های ژنومیک بر مبنای خاصیت تناوب 3 بازهای موجود در رشته های DNA متمرکز بوده و سپس تحلیل های طیفی بمنظور یافتن موقعیت مولفه های متناوب بر روی توالی های عددی DNA اعمال می شود. در این مقاله با استفاده از پنجره با طول متغیر و بر مبنای منحنی Z، الگوریتمی بمنظور تعیین نواحی کدکننده پروتئین ارائه می کنیم. منحنی Z، یک منحنی سه بعدی منحصربفرد برای نمایش توالی DNA می باشد که توصیف کاملی از رفتار بیولوژیکی توالی DNA را بدست می دهد. الگوریتم پیشنهادی بدلیل استفاده از پنجره گوسی با طول قابل تنظیم، از رزولوشن و دقت بسیار بالایی در تخمین نواحی ژنی برخوردار بوده و نواحی غیرپروتئینی در آن کاملا حذف می شود. همچنین بمنظور استخراج مولفه تناوب 3 از یک فیلتر میانگذر باند محدود با فرکانس مرکزی 2π/3 استفاده می نماییم. الگوریتم پیشنهادی ابتدا بر روی توالی F56F11.4 در C.elegans اعمال و نتایج آن با سایر روش های موجود مقایسه شده و سپس آنرا بترتیب برروی ژن های موجود در دو پایگاه داده HMR195 و BG570 اعمال می نماییم. 2 In recent years, estimation of protein-coding regions in numerical deoxyribonucleic acid (DNA) sequences using signal processing tools has been a challenging issue in bioinformatics, owing to their 3-base periodicity. Several digital signal processing (DSP) tools have been applied in order to Identify the task and concentrated on assigning numerical values to the symbolic DNA sequence, then applying spectral analysis tools such as the discrete Fourier transform (DFT) to locate the periodicity components. Despite of many advantages of Fourier transform in detection of exotic regions, this approach has some restrictions, such as high computational complexity and disability in locating the small length coding regions. In this paper, we improve the performance of the conventional DFT in estimating the protein coding regions utilizing a Gaussian window with variable length. First, the DNA strands are converted to numerical signals via the 3-D Z-curve method. Z curve is a robust, independent, less redundant approach, and has clear biological interpretation which can be regarded as a useful visualization technique for DNA analysis of any length. In the second stage, non-coding regions besides the background noise components are completely suppressed using the Gaussian variable length window. Also, we use a narrow-band band-pass filter in order to extract the period-3 components with  central frequency. Performance of the proposed algorithm is tested on F56F11.4 from C.elegans chromosome III, also two eukaryotic datasets, HMR195 and BG570,  is compared with other state-of-the-art methods based on the nucleotide evaluation metrics such as sensitivity, specificity, approximation correlation, and precision. Results revealed that, the area under the receiver operating characteristic (ROC) curve is improved from 4% to 40%, in HMR195 and BG570 datasets compared to other methods. Furthermore, the proposed algorithm reduces the number of incorrect nucleotides which are estimated as coding regions.    63 78 2015/06/62013/12/82014/05/142013/07/32013/06/6 1392/3/16 2016/10/72016/12/42016/10/52016/10/52016/06/15 1395/3/26 حمیدرضا صابرکاری دانشکده مهندسی برق، دانشگاه صنعتی سهند، تبریز، ایران ایران h_saberkari@sut.ac.ir موسی شمسی دانشگاه صنعتی سهند،تبریز،ایران ایران shamsi@sut.ac.ir محمدحسین صداقی Hossein دانشگاه صنعتی سهند،تبریز،ایران ایران sedaaghi@sut.ac.ir Protein Coding Regions Period-3 Digital Signal Processing DNA Variable Length Window Band-Limited Filter. نواحی کدکننده پروتئین تناوب-3 DNA پنجره با طول متغیر فیلتر باند محدود. [1] D. P. Snustad, and M. J. Simmons, Principles of Genetics, John Wiley & Sons Inc, 2000.##[2] E. R. Dougherty, et al., Genomic signal processing and statistics, EURASIP Book Series on Signal Processing and Communications, 2005.##[3] D. L. Brutlag, Understanding the human genome, Eds. New York: Scientific American, 1994.##[4] P. P. Vaidyanathan, and B. J. Yoon, "The role of signal processing concepts in genomics and proteomics," Journal of Franklin Institute, special issue on Genomics, 2004.##[5] E. N. Trifonov, and J. L. Sussman, "The pitch of chromatin DNA is reflected in its nucleotide sequence," Proc. of the Nat. Acad. Sci., vol. 77, pp. 3816–3820, 1980.##[6] X. F. Wan, D. Xu, A. Kleinhofs and J. Zhou, "Quantitative relationship between synonymous codon usage bias and GC composition across unicellular genomes," BMC Evolutionary Biology, vol. 4, no. 19, 2004.##[7] H. E. Herzel, N. Trifonov, O. Weiss, and I. Groβe, "Interpreting correlations in bio-sequences," Physica A, vol. 249, pp. 449–459, 1998.##[8] R. F. Voss, "Evolution of long-range fractal correlations and 1/f noise in DNA base sequences," Phy. Re. Lett, vol. 85, pp. 1342-1345, 1992.##[9] C. A. Chatzidimitriou, and D. Larhammar, "Long-range correlations in DNA," Nature, vol. 361, pp. 212-213, 1993.##[10] J. Henderson, J, et al., "Finding genes in DNA with a Hidden Markov Model," J. Comput. Biol, vol. 4, pp. 127-141, 1997.##[11] C. H. Ding, and I. Dubchak, "Multi-class protein fold recognition using support vector machines and neural networks," Bioinformatics, vol. 17, pp. 349-358, 2001.##[12] D. Anastassiou, "Genomic signal processing," IEEE Sign. Proc. Mag, vol. 18, pp. 8-20, 2001.##[13] T. W. Fox, and A. Carreira, "A digital signal processing method for gene prediction with improved noise suppression," EURASIP J. Appl. Aign. Proc, pp. 108-114, 2004.##[14] S. Tiwari S, S. Ramachandran, A. Bhattacharya, S. Bhattacharya, R. Ramaswamy, "Prediction of probable genes by Fourier analysis of genomic sequences,” Comput Appl Biosci, vol. 13, pp. 263-270, 1997.##[15] H. Saberkari, M. Shamsi, M. H. Sedaaghi, and F. Golabi, "Prediction of protein coding regions in DNA sequences using signal processing methods," 2012 IEEE Symposium on Industrial Electronics and Applications (ISIEA), Bandung, Indonesia, pp. 354-359, September 2012.##[16] S. Datta, A. Asif, "A Fast DFT-Based Gene Prediction Algorithm for Identification of Protein Coding Regions," Proceedings of the 30th International Conference on Acoustics, Speech, and Signal Processing 2005.##[17] M. Akhtar, J. Epps, E. Ambikairajah, "Signal Processing in sequence Analysis: advanced in Eukaryotic gene Prediction," IEEE journal of selected topics in signal processing, vol. 2, pp. 310-321, 2008.##[18] H. Saberkari, M. Shamsi, H. Heravi, and M. H. Sedaaghi, "A Fast Algorithm for Exonic Regions Prediction in DNA," Journal of Medical Signals and Sensors, vol. 3, no. 3, pp. 139-149, 2013.##[19] J. M. Claverie, "Computational methods for the identification of genes in vertebrate genomic sequences," Hum. Mol. Genet, vol. 6, no. 10, PP. 1735-1744, 1997.##[20] W. F. Doolittle, "Phylogenetic classification and the universal tree,” Science, vol. 284, no. 5423, pp.2124–2128, 1999.##[21] F. Gao, and C. T. Zhang, "GC-Profile: a web-based tool for visualizing and analyzing the variation of GC content in genomic sequences," Nucleic Acids Research, vol. 34, pp. 686-691, 2006.##[22] C. T. Zhang, and R. Zhang, "analysis of distribution of bases in the coding sequences by a diagrammatic technique," Nucleic Acids Research, vol. 19, pp. 6313-6317, 1991.##[23] A. Rushdy, and J. Tuqan, “Gene Identification using the Z-Curve Representation,” International Conference on Acoustics, Speech, and Signal Processing, pp. 1024-1027, 2006.##[24] R. G. Stockwell, Why Use the S-Transform? Boulder, CO: Fields Institute Communications, 2007.##[25] S. S. Sahu, G. Panda, "Identification of protein-coding regions in DNA sequences using a time-frequency approach," Genomics Proteomics Bioinformatics, vol. 9, pp. 45-55, 2011.##[26] M. K. Hota, and V. K. Srivastava. "DSP Technique for Gene and Exon Prediction Taking EIIP Indicator Sequence," In Proceedings of the Second International Conference on Information Processing, 117-123. Piscataway, NJ: IEEE Press, 2008.##[27] R. G. Stockwell, L. Mansinha, and R. P. Lowe. "Localization of the Complex Spectrum: The S-Transform, " IEEE Trans. on Sig. Process. vol. 44, no. 4, 998-1001, 1996.##[28] Yu. H. Wang, "The Tutorial: S-Transform," Accessed June 30, 2011.##[29] M. Burset, R. Guigo, "Evaluation of gene structure prediction programs," Genomics, pp. 353-367, 1996.##[30] M. Akhtar, E. Ambikairajah, J. Epps, "Detection of period-3 behavior in genomic sequences using singular value decomposition," Proceedings of the International Conference on Emerging technologies, 2005.##[31] National Center for Biotechnology Information, National Institutes of Health,National Library of Medicine, http://www.ncbi.nlm.nih.gov/GenebANk/index.html.##[32] S. Rogic S, A. K. Mackworth, and B. F. Ouellette, "Evaluation of gene-finding programs on mammalian sequences," Genome, vol. 11, pp. 817-832, 2001.## ##

بهبود سرعت "انطباق مبتنی بر روش برش گراف" جهت انطباق غیر صلب تصاویر تشدید مغناطیسی مغز Speed improvement in graph-cuts-based registration for non-rigid image registration of brain magnetic resonance images 1 1 فرآیند نگاشت نقاط از یک تصویر به نقاط متناظر آن در تصویر دیگر انطباق تصویر نامیده می‌شود که تبدیلی مکانی است. روش‌های انطباق تصویر را می‌توان به لحاظ ماهیت تبدیل انجام گرفته بر روی تصویر به انطباق صلب و ناصلب طبقه‌بندی کرد. یکی از راهکارهای انطباق تصویر ناصلب، حل مسئله انطباق بعنوان یک مسئله بهینه‌سازی است. نمونه‌ای از این روش‌ها انطباق بوسیله برش گراف می‌باشد. اساس این روش بر مبنای ایجاد گرافی اختصاصی شده برای تابع انرژی مورد نظر (که باید بهینه شود) است، به نحوی که برش کمینه روی این گراف منجر به کمینه‌سازی انرژی نیز بشود. با توجه به اهمیت مسئله زمان در کاربردهای کلینیکی و پزشکی، در این تحقیق با ایجاد تغییراتی در روشی که از برش گراف برای انطباق تصویر استفاده می‌کند روشی پیشنهاد دادیم که در آن زمان انجام فرآیند انطباق به شدت کاهش می‌یابد. نتایج حاصل از پیاده‌سازی روش پیشنهادی ما بر روی تصاویر تشدید مغناطیسی مغز نشان می‌دهد که سرعت اجرای الگوریتم پیشنهادی نسبت به الگوریتم اصلی در قبال اندکی افزایش خطا (افزایش مقدار متوسط معیار SAD از 7/0 به 1) تقریباً سه برابر می‌شود. 2 Image processing methods, which can visualize objects inside the human body, are of special interests. In clinical diagnosis using medical images, integration of useful data from separate images is often desired. The images have to be geometrically aligned for better observation. The procedure of mapping points from the reference image to corresponding points in the floating image is called Image Registration. It is a spatial transform. These images might be different because they were taken at different times or applied by using different devices. By the nature of this image transformation, image registration can be classified into rigid registration and non-rigid registration. The freedom’s degree in a rigid transformation is relatively low and the methods of rigid image registration are becoming mature. In contrast, non-rigid image registration is still a challenging problem because of its high degree of freedom. One of the non-rigid image registration methods is turning the registration problem into an optimization problem and obtaining the optimal value as the result of registration. An example of these methods is the graph-cuts based registration. The basic technique is to construct a specialized graph for the energy function to be minimized in a way that the minimum cut on this graph also minimizes the energy. Given that our focus in this research, is on the medical image registration, and time is one of the critical factors in medical applications. It seems that improvement of this method in terms of run time will be helpful for its clinical and medical applications. In order to achieve this goal, in this research, with modifying the energy function, we proposed a method that significantly reduces the run time of registration process. The implementation results of our proposed method on the images with artificial deformations which are similar to the most pessimistic possible deformation modes in real image data, show that the proposed algorithm is about three times faster than the existing algorithm, while the average amount of SAD criterion will be increased from 0.7 to 1. 79 92 2015/06/62013/12/82014/05/142013/07/32013/06/62014/06/21 1393/3/31 2016/10/72016/12/42016/10/52016/10/52016/06/152016/12/27 1395/10/7 فرناز ظهورپرواز Farnaz ZohourParvaz دانشگاه علوم و تحقیقات تهران ایران fzohourparvaz@gmail.com عمادالدین فاطمی زاده Emad Fatemizadeh دانشگاه صنعتی شریف ایران fatemizadeh@sharif.edu حمید بهنام Hamid Behnam دانشگاه علم و صنعت ایران ایران behnam@iust.ac.ir Non-rigid image registration Graph-cuts Magnetic resonance images انطباق تصویر ناصلب برش گراف تصویر تشدید مغناطیسی [1] D. Ruckert, L.I. Sonoda, C. Hayes, D.L.G. Hill , M.O. Leach and D.J. Hawkes, “Non rigid registration using free form deformations: Application to breast MR images,” IEEE transactions on Medical imaging, vol. 18 (8), pp. 712–721, 1999.##[2] J. B. Antoine Maintz and Max A. Viergever, “A Survey of Medical Image Registration,” Medical Image Analysis, vol. 2 (1), pp. 1–36, 1998.##[3] A. Sotiras and N. Paragios, “Deformable Image Registration: A Survey,” in INRIA Research Report n° 7919, Ecole Centrale de Paris, 2012.##[4] J. Kim, J. Fisher, A. Tsai, C. Wible, A. Willsky, and W. Wells, “Incorporating Spatial Priors into an Information Theoretic Approach for FMRI Data Analysis,” in Proc. Medical Image Computing and Computer-Assisted Intervention, 2000, pp. 62–71.##[5] K. Nakagomi, A. Shimizu, H. Kobatake, M. Yakami, K. Fujimoto and K. Togashi, “Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume,” Medical Image Analysis, vol. 17 (1), pp. 62–77, 2013.##[6] Ch. Ballangan, X. Wang, M. Fulham, S. Eberl, and D. Feng, “Lung tumor segmentation in PET images using graph cuts,” Computer Methods and Programs in Biomedicine, vol. 109 (3), pp. 260–268, 2013.##[7] A.A. Meneses, A. Giusti, A.P. de Almeida, L. Nogueira, D. Braz, C.E. de Almeida and R.C. Barroso, “Segmentation of Synchrotron Radiation micro-Computed Tomography Images using Energy Minimization via Graph Cuts,” Applied Radiation and Isotopes, vol. 70 (7), pp. 1284–1287, 2012.##[8] R.W.K. So and A.C.S. Chung, “Multi-level non-rigid image registration using graph-cuts,” in IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, pp. 397–400.##[9] R.W.K. So and A.C.S. Chung, “Non-rigid image registration by using graph-cuts with mutual information,” in 17th IEEE International Conference on Image Processing (ICIP), 2010, pp. 4429–4432.##[10] R.W.K. So, T.W.H. Tang and A.C.S. Chung “Non-rigid image registration of brain magnetic resonance images using graph-cuts,” Pattern Recognition, vol. 44 (10–11), pp. 2450–2467, 2011.##[11] S. Liao and A.C.S. Chung, “Nonrigid Brain MR Image Registration using Uniform Spherical Region Descriptor,” IEEE Transactions on Image Processing, vol. 21 (1), pp. 157–169, 2012.##[12] D. Mahapatra and Y. Sun, “Integrating Segmentation Information for Improved MRF-Based Elastic Image Registration,” IEEE Transactions on Image Processing, vol. 21 (1), pp. 170–183, 2012.##[13] H. Lombaert and F. Cheriet, “Simultaneous image de-noising and registration using graph cuts: Application to corrupted medical images,” in 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012, pp. 264–268.##[14] J. Michalek and M. Capek, “A Piecewise Monotone Subgradient Algorithm for Accurate L1-TV Based Registration of Physical Slices With Discontinuities in Microscopy,” IEEE Transactions on Medical Imaging, vol. 32 (5), pp. 901–918, 2013.##[15] V. Kolmogorov and R. Zabih, “What energy functions can be minimized via graph cuts?,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26 (2), pp. 147–159, 2004.##[16] Y. Boykov and V. Kolmogorov, “An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26 (9), pp. 1124 –1137, 2004.##[17] A. Horé and D. Ziou, “Image Quality Metrics: PSNR vs. SSIM,” in 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 2366–2369.## ##

ارائه یک روش جدید بازیابی اطلاعات مناسب برای متون حاصل از بازشناسی گفتار Introducing a new information retrieval method applicable for speech recognized texts 1 1 در این مقاله، یک پیش پردازش روی روش‎های بازیابی اطلاعات، ارائه می شود که برای بازیابی اطلاعات حاصل از متون بازشناسی شده ی گفتاری، مناسب است. این پیش پردازش، به شکل ترکیبی از اصلاح و گسترش پرس‏ و جو می ‏باشد. ورودی‏ های مسئله، اسناد متنی بدست آمده از بازشناسی گفتار و پرس‏ و جو می باشد و هدف، یافتن اسناد مرتبط با کلمه پرس ‏و جو است. مشکل آن است که متن حاصل از بازشناسی گفتار، همواره دارای درصد خطایی در بازشناسی است که ممکن است منجر به این شود که کلماتی که در واقع مرتبط هستند و به‏ علت وقوع خطای بازشناسی دگرگون شده‏ اند مرتبط تشخیص داده نشوند. ایده ی روش ارائه شده، تشخیص خطای بازشناسی در کلمات و در نظر گرفتن کلمات مشابه برای آن دسته از کلماتی است که به عنوان خطا تشخیص داده‌شده اند. برای تشخیص کلمه ی خطا، پارامتری به عنوان احتمال خطا در کلمه تعریف می‏ شود که بزرگ بودن آن بیانگر امکان بیشتر وقوع خطا در کلمه است. همچنین برای تشخیص کلمات مشابه، ابتدا با استفاده از معیار فاصله لونشتاین، کلمات مشابه اولیه را پیدا می کنیم. سپس احتمال تبدیل این کلمات مشابه به کلمه پرس ‏و جوی اصلی، محاسبه می شود. کلمات مشابه معنایی، از بین کلماتی که احتمال تبدیل بیشتری دارند، بر اساس یک سطح آستانه انتخاب می‌شوند. اکنون در الگوریتم بازیابی، علاوه‏ بر کلمه اصلی، کلمات مشابه آن نیز در جستجو، مرتبط در نظر گرفته می‏ شوند. نتایج پیاده‏سازی‏ها نشان می‏دهد که الگوریتم ارائه‌شده، معیار F را به میزان حداکثر 30‎% بهبود می‌بخشد. 2 In this article a pre-processing method is introduced which is applicable in speech recognized texts retrieval task. We have a text corpus, t generated from a speech recognition system and a query as inputs,  to search queries in these documents and find relevant documents. A basic problem in a typical speech recognized text is some error percentage in recognition. This, results erroneously assigning to irrelevant documents.The idea of this proposed method, is to detect error-prone terms and to find similar words for each term. A parameter is defined which calculates the probability for occurring errors in the error-prone words. To recognize similar words for each specific term, based on a criterion called average detection rate (ADR) and levenshtein distance criterion, some candidates are chosen as the initial similar words set. And then, a conversion probability is defined based on the conversion rate (CR) and the noisy channel model (NCM) and the words with higher probability based on a threshold level are selected as the final similar words. In the retrieval process, these words are considered in the search step in addition to the base word.  Implementation result shows a significant improvement up to 30% of F-measure in information retrieval method with consideration of this pre-processing. 93 108 2015/06/62013/12/82014/05/142013/07/32013/06/62014/06/212015/04/17 1394/1/28 2016/10/72016/12/42016/10/52016/10/52016/06/152016/12/272016/02/26 1394/12/7 روح الله دیانت rouhollah dianat دانشگاه قم ایران rdianat@qom.ac.ir مرتضی علی احمدی morteza ali ahmadi دانشگاه قم ایران morteza.ali.ahmadi@gmail.com یحیی اخلاقی yahya akhlaghi دانشگاه خاتم النبیین ایران yahya.akhlaghi@gmail.com باقر باباعلی bagher babaali دانشگاه تهران ایران babaali@ut.ac.ir Information retrieval Speech recognition Document Query Levenshtein Distance بازیابی اطلاعات- بازشناسی گفتار- سند- پرس و جو- فاصله لونشتاین 1[ شیخ زادگان، جواد، بیجن خان، محمود، "داده‌های گفتاری زبان فارسی"، در دومین کارگاه پژوهشی زبان فارسی و رایانه، دانشگاه تهران، 1385، صفحات 247-261.##[1] J. Sheykhzadegan, M. Bijankhan, (2007). "Persian language speech data", in The Second Persian Langugae and Computer Research Workshop, University of Tehran, 2007, pp. 247-261.##]2[ صرفجو، سعید، "چارچوبی جدید برای بازیابی اطلاعات به منظور استفاده در بازیابی صدای گفتاری فارسی"، پایان‌نامه منتشرشده کارشناسی ارشد، دانشکده فنی و مهندسی دانشگاه قم، قم، ایران، 1390.##[2] Sarfjoo, S., "A New Framework for Information Retrieval to use in Persian Spoken Document Retrieval", Published master's dissertation, Faculty of Technical and Engineering of University of Qom, Qom, Iran, 2012.##]3[ بحرانی، محمد، "به‌کارگیری ساختارهای وابسته به بافت در بازشناسی گفتار پیوسته مبتنی‌بر مدل مخفی مارکوف"، پایان‌نامه منتشرشده کارشناسی ارشد، دانشکده فنی و مهندسی دانشگاه صنعتی شریف، تهران، ایران، 1382.##[3] Bahrani, M., "Using Context Dependent Structures in Continuous Speech Recognition based on Hidden Markov Model", Published master's dissertation, Faculty of Technical and Engineering of Sharif University of Technology, Tehran, Iran, 2004.##[4] Abberley, D., and et. al., "THE THISL BROADCAST NEWS RETRIEVAL SYSTEM", in ESCA Tutorial and Research Workshop (ETRW) on Accessing Information in Spoken Audio, 1999, pp. 14-19.##[5] Bijankhan, M., and et. al., "Lessons from Creation of a Persian Written Corpus: Peykare", Language Resources and Evaluation, vol. 45(2), pp. 143-164, 2011.##[6] Black, Paul E., ed.,. "Levenshtein distance Dictionary of Algorithms and Data Structures", in National Institute of Standards and Technology, U. S., 14 August 2008. [online]. Available: www.nist.gov##[7] Box, G., and Tiao, G., Bayesian Inference in Statistical Analysis. Massachusetts, Addison-Wesley, 1973.##[8] Brill, E., and Moore, R., "An Improved Error Model for Noisy Channel Spelling Correction", in 38th Annual meeting of Association for Computational Linguistics, Hong Kong, 2000. ##[9] Ghias, A., and et. al., "Query by Humming: Musical Information Retrieval in an Audio Database", in ACM Multimedia Conference, San Francisco, CA, USA, 1995, pp. 231-236.##[10] Harper, M. P., and et. al., "Integrating Language Models with Speech Recognition", in AAAI94 Workshop on the Integration of Natural Language and Speech Processing, Seattle, Washington, USA, 1994, pp. 139-146.##[11] Jurafsky, D., and Martin, J., Speech and Language Processing: An Introduction to Natural Language Processing, second ed. Prentice Hall, Pearson Education International, 2000.##[12] Katz, S., "Estimation of Probabilities from Sparse Data for the Language Model Component of a Speech Recognizer", IEEE Transactions on Acoustics, Speech and Signal Processing, vol 35(3), pp. 400-401, 1987##[13] Kukich, K., "Techniques for Automatically Correcting Words in Text", ACM Computing Survey, vol 24, pp. 377-439, 1992.##[14] Logan, B., and Van Thong, J., "Confusion-based query expansion for OOV words in spoken document retrieval", in 7th International Conference on Spoken Language Processing, Denver, Colorado, USA, 2002.##[15] Manning, D. C., and et. al., An Introduction to Information Retrieval. Cambridge University Press, 2009.##[16] Navarro, G., "A guided tour to approximate string matching", ACM Computing Surveys, vol 33(1), pp. 31-88, 2001.##[17] Toutanova, K., Moore, R. C., "Pronunciation Modeling for Improved Spelling Correction", in 40th Annual meeting of Association for Computational Linguistics, Philadelphia, Pennsylvania, 2002, pp. 144-151.##[18] Turunen, V., "Spoken Document Retrieval", in Department of Computer Science and Engineering Helsinki University of Technology, 2006.##[19] Zhang, T., and Jay Kuo, C. C., "Content-based Classification and Retrieval of Audio", in 43th Annual Meeting-Conference on Advances Signal Processing, Algorithms, Architectures and Implementations, San Diego, 1998.##]1[ شیخ زادگان، جواد، بیجن خان، محمود، "داده‌های گفتاری زبان فارسی"، در دومین کارگاه پژوهشی زبان فارسی و رایانه، دانشگاه تهران، 1385، صفحات 247-261.##]2[ صرفجو، سعید، "چارچوبی جدید برای بازیابی اطلاعات به منظور استفاده در بازیابی صدای گفتاری فارسی"، پایان‌نامه منتشرشده کارشناسی ارشد، دانشکده فنی و مهندسی دانشگاه قم، قم، ایران، 1390.##]3[ بحرانی، محمد، "به‌کارگیری ساختارهای وابسته به بافت در بازشناسی گفتار پیوسته مبتنی‌بر مدل مخفی مارکوف"، پایان‌نامه منتشرشده کارشناسی ارشد، دانشکده فنی و مهندسی دانشگاه صنعتی شریف، تهران، ایران، 1382.## ##

بهبود پروتکل AODV جهت مقابله با حملات کرم‌چاله در شبکه‌های اقتضایی Modified AODV Routing Protocol in Order to Defend Wormhole Attacks 1 1 چکیده – حمله کرم چاله یک نوع حمله فعال می‌باشد که در لایه سوم شبکه از شبکه‌های اقتضایی رخ می‌دهد. در این حمله مهاجمین با متقاعد کردن گره فرستنده برای ارسال اطلاعات از یک مسیر جعلی که کوتاه‌تر و سریع‌تر از مسیر عادی به نظر می‌رسد، سعی دارند ارسال بسته‌ها از تونل ایجاد شده انجام شود تا بتوانند، حملات آنالیز ترافیک، انکار سرویس، رها کردن بسته‌ها و یا جلورانی انتخابی را انجام دهند. هر پروتکلی که از مقیاس کم‌ترین تاخیر و کم-ترین تعداد گام برای مسیریابی استفاده کند، در برابر این حمله آسیب پذیر است.در این مقاله یک راه‌کار جدید برای مقابله با حملات کرم چاله ارائه می-دهیم. در راه‌حل پیشنهادی هر گره دارای یک وزن است و مجموع وزن‌ها در شبکه برابر صد خواهد بود. هرگاه گره‌ای قصد ارسال ترافیک به گره دیگر را داشته باشد، در بسته RREQ حداقل وزن درخواستی برای ایجاد ارتباط را بیان می‌کند. گره فرستنده با توجه به اهمیت داده‌هایی که ارسال خواهد کرد مشخص می‌کند که مجموع وزن گره‌های شرکت کننده، در فرایند کشف مسیر باید چقدر باشد. روش پیشنهادی را MAODV نامگذاری می‌کنیم. روش فوق به صورت نرم‌افزاری بوده و با توجه به این‌که از تکنیک رمزنگاری استفاده نخواهیم کرد،پیش‌بینی می‌کنیم سربار کمتری نسبت به سایر تکنیک‌ها داشته باشیم، و همچنین به علت عدم استفاده از الگوریتم‌های سخت، توان گره‌ها که اتفاقا محدود است، کمتر صرف محاسبات خواهد شد.کارایی الگوریتم پیشنهادی را در محیط ns-2 نشان خواهیم داد. 2 Mobile Ad hoc Networks (MANET) are vulnerable to both active and passive attacks. The wormhole attack is one of the most severe security attacks in wireless ad hoc networks, an attack that can be mounted on a wide range of wireless network protocols without compromising any cryptographic quantity or network node.  In Wormhole attacks, one malicious node tunnels packets from its location to the other malicious node. Such wormhole attacks result in a false route with fewer. If the source chooses this fake route, malicious nodes have the option of sniff, modify, selectively forward packets or them. Existing solution defends wormhole attacks, such as SECTOR, Packet Leashes, DelPHI, directional antenna. These solutions require special hardware or strict synchronized clocks or cause message overhead, or generate false-positive alarms. A novel approach MAODV: Modified AODV is proposed to defend wormhole attacks, launched in AODV. The proposed approach is based on weight per hop. Each node in network has its own weight, given by administration due to trusty power capability. Sum of weight will not be exceeded from 100. Whenever a source node wants to send a traffic to destination, puts its minimum weight in RREQ packet to constitute the route. The destination node is selected in the route that its weight is close to destination announcement weight. Since no special hardware and no encryption techniques are used, it is likely to have less overhead and delay, compared to other techniques. The proposed wormhole defend mechanism is discussed in detail. Our proposed system does not require any synchronized clocks or special hardware to defend wormhole attacks. In our proposed system some parameters will be added to AODV routing protocol and make it more secure against wormhole attacks. We will name this new protocol as MAODV. In the first place, there is a master node in network, which  weighs 100 (weighs of whole network). Whenever a node attends to enter the network, sends a join message to nearest neighbor. After receiving the message, master node will share its weights with the node requester, and sends the weight to this node requester. This process and weight sharing will be repeated after any requests to join a network, and total weight of network is not exceeded from 100. In our proposed method, each path which is created between source and destination, has a particular weight and this weight equals to intermediate node weights being added to each other. In MAODV whenever a source node wants to send RREQ packet, it adds the minimum weight to constitute route. After receiving RREQ packets, each intermediate node increases its weight beside increasing hop count. Each intermediate node does the same action, as far as destination node receives, RREQ packet among the received RREQ, one of them will be selected which its weight is the same as minimum requested weight by source, or slightly more than that. For instance, consider fig 1 which has 14 nodes. Assuming the node weights are equal for each node and its 7. As mentioned, the weight of whole network is tantamount to 100. Example 1: consider fig. 1 in which node A sends RREQ to node B. At first, node A checks its cache table to see whether there is a route between A and B, or not. If the answer is positive, it starts to send data. If the answer is negative, it sets up RREQ as follow: <A,B,1,7.25,[]> which means: A: source, B: destination, 1: hop count, 7: constitute path weight, 25: request weight, []: intermediate nodes. Each node which receives RREQ will check if it is the destination or not. If it wasn’t: 1. Increase hop count, 2. puts its weight to constitute path weight, 3. Adds its address as an intermediate node. And then broadcasts RREQ packet to the neighbors. In this example node A sends RREQ to X and C, which are legitimate neighbor of A. When X receives the packet, modifies it as: <A,B, 2,(4,25,[X]> and forwards it to its neighbors on the other hand node. C modifies packet as: <A,B,2,(4,25,[C]> and forwards it to its neighbor D. This action will be repeated until B gets two RREQ - <A,B,4,28,25,[C,D,E]> and <A,B,7,25,48,[X,U,V,W, Z,Y> - among the received RREQ, B will be selected which its weight is the same as minimum requested weight by A, or slightly more than that, so the first route will be chosen by B. node B setup RREP packet as <A,B,1,4,25,7, [E,D,C]> which means: A: source, B: destination, 1: back path weight, 4: hop count, 25: request weight, 7: constitute path weight, [E,D,C]: intermediate nodes. The effectiveness of the propose mechanism is evaluated using ns2 network simulator. The simulator's outcome demonstrates that PDR in MAODV rose by 5% up to 8% in presence of two malicious nodes, compared to PDR in AODV routing protocol. The average delay point to point in MAODV is more than AODV, but on the other hand, it is less than SAODV due to not using encryption. 109 120 2015/06/62013/12/82014/05/142013/07/32013/06/62014/06/212015/04/172014/02/4 1392/11/15 2016/10/72016/12/42016/10/52016/10/52016/06/152016/12/272016/02/262016/10/29 1395/8/8 حسین قرائی hossein gharaee مرکز تحقیقات مخابرات ایران gharaee@itrc.ac.ir فرید محمدی farid mohammadi دانشگاه تهران ایران farid.mohammadi@ut.ac.ir MANET Wormhole attacks AODV NS2 شبکه MANET حمله کرم چاله پروتکل مسیریابی AODV ns-2 [1] A. F. S. D. Vandana, "Evaluation of Impact of wormhole Attack on AODV," International journal of advanced Networking and Applications, vol. 4, no. 4, pp. 1652-1656, 2013.##[2] A. F. S. D. Vandana, "Wormhole attack Detection Using Hop Latency and Adjoining Node Analysis in MANET," International Journal of Engineering Research & Technology (IJERT), vol. 2, no. 3, 2013.##[3] B. H. Capkun, "SECTOR: Secure Tracking of Node encounters in Multihop Wireless Networks," First ACM Workshop on Security of Ad-hoc and Sensor Networks, pp. 21-32, 2003.##[4] B. Patel, "A survey on detecting wormhole attack in MANET", Int. Journal of Engineering Research and Applications, pp.653-656, 2014.##[5] J. Perrig, "Wormhole Attacks in Wireless Networks," IEEE Journal on Selected Areas in Communications, vol. 24, pp. 370-380, 2006.##[6] K. H. N.S Raote, "Approaches toward Mitigating Wormhole Attack in Wireless Ad-hoc Network," International Journal of Advanced Engineering Sciences and Technologies ( IJAEST ), vol. 2, no. 2, pp. 172-175, 2011.##[7] I. Khalil, and K. S. Bagchi, "A Lightweight Countermeasure for the wormhole Attack in Multihop Wireless Networks," In Proceedings of DSN, 2005.##[8] L. Wong, "DELPHI: Wormhole Detection Mechanism for adhoc Wireless Networks," in Proceeding of International Symposium on Wireless Pervasive Computing, 2006, pp. 6-11. ##[9] L. X. H. Phuong Van Tran, "An efficient Mechanism to Detect Wormhole Attacks in Wireless ad-hoc Networks," IEEE Consumer Communications and Networking Conference, 2007.##[10] V. Mahajan, M, Natu, and A, Sethi "Analysis of wormhole Intrusion Attacks in MANETS," in IEEE Military Communications Conference, 2008. ##[11] V. G. Supriya Tayal, "A Survey of Attacks on MANET Routing Protocols," International Journal of Innovative Research in Science, Engineering and Technology, vol. 2, no. 6, pp. 2280-2286, 2013. ##[12] W. Wang, "EDWA: End-to-End detection of wormhole attack in wireless adhoc networks," in Computer Software and Applications Conference, 2007. 31st Annual International, IEEE, 2007.##[13] Y. Zhiwei, and J. Zhongyuan, "A Survey on the Evolution of Risk Evaluation for Information Syst-ems Security," in International Conference on Future Electrical Power and Energy System, 2012, vol. 17, pp. 1288–1294.####منبع فارسی ندارد ##

بررسی مقایسه‌ای تأثیر برچسب‌زنی مقولات دستوری بر تجزیه در پردازش خودکار زبان فارسی A Comparative Study on the Impact of Part-of-Speech Tagging on Parsing for the Persian Language Processing 1 1 در این مقاله، به بررسی جایگاه برچسبزنی مقولات دستوری در تجزیه نحوی خودکار جملات فارسی پرداخته خواهد شد. به همین منظور، تأثیر کیفیت برچسبزنی مقولات دستوری و همچنین تأثیرگذاری میزان اطلاعات موجود در مقولات دستوری بر کارایی تجزیه خودکار جملات مورد مطالعه قرار خواهد گرفت. بهمنظور انجام این دو بررسی، سه سناریو برای تجزیه جملات ارائه شده و مقایسه میشود. در سناریو ۱، تجزیهگر ابتدا داده ورودی را برچسبزنی کرده و سپس جمله را تجزیه میکند. در سناریو ۲، از یک برچسبزن خارج از تجزیهگر و در سناریو ۳ از برچسب معیار واژهها برای تجزیه جملات استفاده میشود. در این بررسی، معیارهای ارزیابی متفاوت مورد استفاده قرار میگیرد تا میزان این تأثیرگذاری از ابعاد مختلف نشان داده شود. نتایج حاصل از آزمایشات نشان میدهد که کیفیت و میزان اطلاعات در مقولات دستوری واژه بر کارایی تجزیهگر تأثیر مستقیم دارد. کیفیت بالای برچسب مقولات دستوری سبب کاهش خطای تجزیهگر و افزایش کارایی آن میگردد. همچنین عدم وجود اطلاعات صرفیـنحوی تأثیر منفی بسزایی بر کارایی تجزیهگر دارد که این تأثیرگذاری در مقایسه با کیفیت برچسب مقولات دستوری بسیار بیشتر است. 2 In this paper, the role of Part-of-Speech (POS) tagging for parsing in automatic processing of the Persian language is studied. To this end, the impact of the quality of POS tagging as well as the impact of the quantity of information available in the POS tags on parsing are studied. To reach the goals, three parsing scenarios are proposed and compared. In the first scenario, the parser assigns the POS tags firstly and then it parses the input sentence. In the second scenario, an external POS tagger is usedto assign the tags, then the sentence is parsed. In the third scenario, the parser uses the gold standard POS tags to parse the input sentence. In this study, various evaluation metrics are used to show the impacts from different points of views. The experimental results show that the quality of the POS tagger and the quantity of the information available in the POS tags have a direct effect on the parsing performance. The high quality of the POS tags causes error reduction in parsing and also it increases parsing performance. Moreover, lack ofmorphological -syntactic information in the POS tags has a high negative impact on parsing performance. This impact is more pronounced than the impact of POS tagger performance.  121 132 2015/06/62013/12/82014/05/142013/07/32013/06/62014/06/212015/04/172014/02/42014/12/13 1393/9/22 2016/10/72016/12/42016/10/52016/10/52016/06/152016/12/272016/02/262016/10/292016/04/11 1395/1/23 مسعود قیومی Masood Ghayoomi پژوهشکده زبانشناسی، پژوهشگاه علوم انسانی و مطالعات فرهنگی، تهران، ایران آلمان masood.ghayoomi@gmail.com processing of the Persian language part-of-speech tagging parsing پردازش زبان فارسی برچسب مقوله دستوری تجزیه خودکار [1] م. بی‌جن‌خان،”نقش پیکره زبانی در نوشتن دستور زبان: معرفی یک نرم‌افزار رایانه‌ای،“ مجله زبانشناسی، ج. 38، صص. ۴۸ـ۶۷، ۱۳۸۳.##M. Bijankhan, “naqše peykarehāye zabāni dar neveštane yek narmafzāre rāyāneyi [The role of corpora in writing a grammar: Introducing a software],” Iranian Journal of Linguistics, vol. 38, pp.46-67, 2004.##[2] م. شریفی آتشگاه، ”تولید نیمه‌خودکار درخت‌بانک گروه‌های نحوی در زبان فارسی معاصر،“ پایان‌‌نامه دکترا، دانشکده ادبیات و علوم انسانی، دانشگاه تهران، ۱۳۸۸.##M. SharifiAtashgah,“toulide nimexodkāre deraxtbānke goruhhāye nahvi dar zabāne fārsi [Semi-automatic Generation of Treebanks in Persian Texts]”, PhD dissertation, University of Tehran, 2009.##[3] م.ب. صادق‌زاده، و همکاران، ”بررسی روش‌های مؤثر بر عملکرد تجزیه‌گر آماری زبان فارسی، “سومین همایش ملی زبان‌شناسی رایانشی، تهران، دانشگاه شریف، ۱۳۹۳.##M.B. Sadeghzade et al., “barresiye ravešhāye mo’asser bar ’amalkerde taǰziyegare ’āmāriye zabāne fārsi [Study the impact of effecting methods on the performance of a statistical parser for the Persian language],” In Proceedings of the 3rd National Conference on Computational Linguistics, Sharif University of Technology, Tehran, 2014.##[4] م. عاصی، ”پایگاه داده زبان فارسی،“ پژوهشگران. پژوهشگاه علوم انسانی و مطالعات فرهنگی، تهران، ۱۳۸۴.##M. Assi, “PLDB: The Persian Linguistics DataBase,” In Researchers, Institute for Humanities and Cultural Studies, Tehran, Iran, 2005.##[5] ه. فیلی، ” استخراج استقرایی گرامر احتمالاتی یک زبان طبیعی به روش بی‌مربی،“ پایان‌‌نامه دکترا، دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شریف، ۱۳۸۵.##H. Faili, “estexrāǰe esteqrāyiye gerāmere ehtemālātiye yek zabāne tabi’i be raveše bimorabbi [Unsupervised Grammar Induction for a Natural Language]”, PhD dissertation, Sharif University of Technology, Tehran, Iran, 2006.##[6] م. قیومی، ”معرفی دادگان درختی و تجزیه درختی فارسی،“ در مجموعه مقالات هشتمین کنفرانس زبانشناسی ایران‌، جلد 2، صص.۶۶۶ـ۶۷۹، ۱۳۹۲.##M. Ghayoomi, “mo’arrefiye dādegāne deraxti va taǰziyegare xodkāre fārsi [Introducing a treebank and a statistical parser for Persian],” In Proceedings of the 8th Conference of Iranian Linguistics, Allāme Tabātabāyi University, vol. 2, pp. 666-679, 2013.##[7] A. AleAhmad et al., “Hamshahri: A standard Persian text collection,” Knowledge-Based Systems, vol. 22, no. 5, pp. 382–387, 2009.##[8] H. Amiri et al., “Investigation on a feasible corpus for Persian POS tagging,” In Proceedings of the 12th International CSI Computer Conference, Iran, 2007.##[9] M. Assi and M. HajiAbdolhosseini, “Grammatical tagging of a Persian corpus,” International Journal of Corpus Linguistics, vol. 5, no. 1, pp. 69–82, 2000.##[10] M. Bahrani et al., “A computational grammar for Persian based on GPSG,” Language Resources and Evaluation, vol. 45, no. 4, pp. 387–408, 2011.##[11] M. Bijankhan et al., “Lessons from building a Persian written corpus: Peykare,” Language Resources and Evaluation, vol. 45, no. 2, pp. 143–164, 2011. ##[12] T. Brants, “TnT - A statistical part-of-speech tagger,” In Proceedings of the Association for Neuro-Linguistic Programming and NAACL, pp. 224–231, 2000.##[13] J. Dehdari and D. Lonsdale, “A link grammar parser for Persian,” In Aspects of Iranian Linguistics, S. Karimi, V. Samiian, and D. Stilo, Eds. Cambridge Scholars Press, vol. 1, 2008.##[14] P. Denis and B. Sagot, “Coupling an annotated corpus and a morpho-syntactic lexicon for state-of-the-art POS tagging with less human effort,” In Pacific Asia Conference on Language, Information and Computation, Hong Kong, Chine, 2009.##[15] M. Ghayoomi, “Bootstrapping the development of an HPSG-based treebank for Persian,” Linguistic Issues in Language Technology, vol. 7, no. 1, 2012.##[16] M. Ghayoomi, “Word clustering for Persian statistical parsing,” Advances in Natural Language Processing, volume 7614 of Lecture Notes in Computer Science: JapTAL ’12: Proceedings of the 8th International Conference on Advances in Natural Language Processing, Springer Berlin Heidelberg, pp. 126–137, 2012.##[17] M. Ghayoomi and B. Guillaume, “Interaction grammar for the Persian language: Noun and adjectival phrases,” In Proceedings of the ACL- IJCNLP 7th Workshop on Asian Language Resources. Suntec, Singapore: ACL, pp.107–114, 2009.##[18] M. Ghayoomi and J. Kuhn, “Converting an HPSG-based treebank into its parallel dependency-based Treebank,” In Proceedings of the 9th International Conference on Language Resources and Evaluation, Reykjavik, Iceland, pp. 802–809, 2014.##[19] M. Ghayoomi et al., “Constituency parsing of Bulgarian: Word- vs class-based parsing,” In Proceedings of the 9th International Conference on Language Resources and Evaluation, Reykjavik, Iceland, pp. 4056–4060, 2014.##[20] M. Ghayoomi and O. Moradiannasab, “The effect of treebank annotation granularity on parsing: A comparative study,” In Proceedings of the 11th International Workshop on Treebanks and Linguistic Theories, Lisbon, Portugal, pp. 109–114, 2012. ##[21] P. Z. Ingerman, A Syntax-oriented Translator, Academic Press, New York and Lonodn, 1966.##[22] M. Khallash et al., “An empirical study on the effect of morphological and lexical features in Persian dependency parsing,” In Proceedings of the 4th Workshop on Statistical Parsing of Morphologically Rich Languages, Seattle, Washington, USA, pp: 97–107, 2013.##[23] D. Klein and C.D. Manning, “Accurate unlexicalized parsing,” In Proceedings of the 41st Meeting of ACL. pp. 423–430, 2003.##[24] G. Leech and A. Wilson, “Standards for Tag Sets,” Text, speech, and language technology, Kluwer Academic Publishers, Dordrecht, The Netherlands, 9 edition, pp: 55–80, 1999.##[25] V. I. Levenshtein, “Binary codes capable of correcting deletions, insertions, and reversals,” Soviet Physics Doklady, vol. 10, no 8, pp. 707–710, 1966.##[26] D. Lin, “A dependency-based method for evaluating broad-coverage parsers,” Natural Language Engineering, vol. 4, no. 2, pp. 97–114, 1998.##[27] M. Mohseni and B. Minaei-bidgoli, “A Persian part-of-speech tagger based on morphological analysis,” In Proceedings of the 7th Conference on International Language Resources and Evaluation, Valletta, Malta, pp. 1253–1257, 2010.##[28] S. Müller and M. Ghayoomi, “PerGram: A TRALE implementation of an HPSG fragment of Persian,” In International Multi-conference on Computer Science and IT, pp. 461-467, 2010.##[29] F. Oroumchian et al., “Creating a feasible corpus for Persian POS tagging,” Technical Report TR3/06, University of Wollongong in Dubai, 2006.##[30] S. Petrov et al., “Learning accurate, compact, and interpretable tree annotation,” In Proceedings of the 21st Inter-national Conference on CoLing and ACL, pp. 433–440, 2006.##[31] M.S. Rasooli and H. Faili, “Fast unsupervised dependency parsing with arc-standard transitions,” In EACL workshop on ROBUS-UNSUP 2012: Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, Avignon, France, 2012.##[32] M.S. Rasooli et al., “Development of a Persian syntactic dependency Treebank,” In Proceedings of the HLT Conference of the NAACL, Atlanta, Georgia, pp. 306–314, 2013.##[33] G. Sampson, “A proposal for improving the measurement of parse accuracy,” International Journal of Corpus Linguistics, vol. 5, pp. 53–68, 2000.##[34] M.A. Sanamrad and H. Matsumoto, “A natural-language analyzer for Persian,” Journal of Information Processing, Vol. 8, No. 4, 1985.##[35] Z. Sarabi and M. Analouie, “A new DOP model for phrase-structure parsing of Persian sentences,” In Proceedings of the 10th Workshop on Asian Language Resources, pp. 45–54, 2012.##[36] H. Schmit, “Efficient parsing of highly ambiguous context-free grammars with bit vectors,” In Proceedings of the 20th International conference on Computational Linguistics, Geneva, Switzerland, 2004.##[37] H. Schütze, “Distributional part-of-speech tagging,” In Proceedings of the 7th Conference on EACL, Morgan Kaufmann Publishers Inc, pp. 141–148, 1995.##[38] M. Seraji, “A statistical part-of-speech tagger for Persian”, In Proceedings of the 18th Nordic Conference of Computational Linguistics, Riga, Latvia, pp. 340–343, 2011.##[39] M. Seraji et al., “Bootstrapping a Persian dependency Treebank,” Linguistic Issues in Language Technology, vol. 7, 2012.##[40] M. Shamsfard and H. Fadaee, “A hybrid morphology-based POS tagger for Persian,” In Proceedings of the 6th International Conference on Language Resources and Evaluation, Marrakech, Morocco,European Language Resources Association, 2008.##[41] S. Tasharofi et al., “Evaluation of statistical part of speech tagging of Persian text,” In International Symposium on Signal Processing and its Applications, Sharjah, (U.A.E.), 2007.##[42] K. Toutanova and C.D. Manning, “Enriching the knowledge sources used in a maximum entropy part-of-speech tagger,” In Proceedings of the Joint SIGDAT Conference on EMNLP and Very Large Corpora, Hong Kong, pp. 63–70, 2000.##[1] م. بی‌جن‌خان،”نقش پیکره زبانی در نوشتن دستور زبان: معرفی یک نرم‌افزار رایانه‌ای،“ مجله زبانشناسی، ج. 38، صص. ۴۸ـ۶۷، ۱۳۸۳.##[2] م. شریفی آتشگاه، ”تولید نیمه‌خودکار درخت‌بانک گروه‌های نحوی در زبان فارسی معاصر،“ پایان‌‌نامه دکترا، دانشکده ادبیات و علوم انسانی، دانشگاه تهران، ۱۳۸۸.##[3] م.ب. صادق‌زاده، و همکاران، ”بررسی روش‌های مؤثر بر عملکرد تجزیه‌گر آماری زبان فارسی، “سومین همایش ملی زبان‌شناسی رایانشی، تهران، دانشگاه شریف، ۱۳۹۳.##[4] م. عاصی، ”پایگاه داده زبان فارسی،“ پژوهشگران. پژوهشگاه علوم انسانی و مطالعات فرهنگی، تهران، ۱۳۸۴.##[5] ه. فیلی، ” استخراج استقرایی گرامر احتمالاتی یک زبان طبیعی به روش بی‌مربی،“ پایان‌‌نامه دکترا، دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شریف، ۱۳۸۵.##[6] م. قیومی، ”معرفی دادگان درختی و تجزیه درختی فارسی،“ در مجموعه مقالات هشتمین کنفرانس زبانشناسی ایران‌، جلد 2، صص.۶۶۶ـ۶۷۹، ۱۳۹۲. ##

روش نوین خوشه‌بندی ترکیبی با استفاده از سیستم ایمنی مصنوعی و سلسله مراتبی New Clustering Technique using Artificial Immune System and Hierarchical technique 1 1 سیستم ایمنی مصنوعی (AIS) یکی از مهمترین الگوریتم‌های متاهیوریستیک به منظور حل مسائل بسیار پیچیده می‌باشد. از این الگوریتم می‌توان در تحلیل خوشه‌بندی داده‌ها استفاده نمود. علی‌رغم اینکه AIS قادر است پیکربندی فضای جستجو را به خوبی نمایش دهد اما تعیین خوشه‌های داده‌ها به طور مستقیم با استفاده از خروجی آن بسیار مشکل است. بر این اساس در این مقاله الگوریتم دو مرحله‌ای پیشنهاد شده است. در مرحله اول با استفاده از الگوریتم AIS پیشنهادی، فضای جستجو مورد بررسی قرار گرفته و پیکربندی فضا تعیین می‌شود و در مرحله دوم با استفاده از روش خوشه‌بندی سلسله ‌مراتبی، خوشه‌ها و تعداد آنها مشخص می‌شود. در انتها الگوریتم پیشنهادی بر روی نمونه واقعی متشکل از داده‌های زلزله در ایران پیاده‌سازی و با نتایج الگوریتم مشابه مقایسه شده است. نتایج نشان داد که الگوریتم پیشنهادی توانسته است نقایص موجود در AIS و روش خوشه‌بندی سلسله مراتبی را پوشش دهد و از طرفی از دقت و سرعت قابل قبولی برخوردار است. 2 Artificial immune system (AIS) is one of the most meta-heuristic algorithms to solve complex problems. With a large number of data, creating a rapid decision and stable results are the most challenging tasks due to the rapid variation in real world. Clustering technique is a possible solution for overcoming these problems. The goal of clustering analysis is to group similar objects. AIS algorithm can be used in data clustering analysis. Although AIS is able to good display configure of the search space, but determination of clusters of data set directly using the AIS output will be very difficult and costly. Accordingly, in this paper a two-step algorithm is proposed based on AIS algorithm and hierarchical clustering technique. High execution speed and no need to specify the number of clusters are the benefits of the hierarchical clustering technique. But this technique is sensitive to outlier data. So, in the first stage of introduced algorithm the search space and the configuration space are identified using the proposed AIS algorithm, and therefore outlier data are determined. Then in second phase, using hierarchical clustering technique, clusters and their number are determined. Consequently, the first stage of proposed algorithm eliminates the disadvantages of the hierarchical clustering technique, and AIS problems will be resolved in the second stage of the proposed algorithm. In this paper, the proposed algorithm is evaluated and assessed through two metrics that were identified as (i) execution time (ii) Sum of Squared Error (SSE): the average total distance between the center of a cluster with cluster members used to measure the goodness of a clustering structure. Finally, the proposed algorithm has been implemented on a real sample data composed of the earthquake in Iran and has been compared with the similar algorithm titled Improved Ant System-based Clustering algorithm (IASC). IASC is based on Ant Colony System (ACS) as the meta-heuristics clustering algorithm. It is a fast algorithm and is suitable for dynamic environments. Table 1 shows the results of evaluation.   Table 4: Compare the two algorithms Proposed algorithm IASC Alg. 12 18 Execution time (s) 5/3 9/4 SSE   The results showed that the proposed algorithm is able to cover the drawbacks in AIS and hierarchical clustering techniques and on the other hand has high precision and acceptable run speed. 133 145 2015/06/62013/12/82014/05/142013/07/32013/06/62014/06/212015/04/172014/02/42014/12/132013/06/6 1392/3/16 2016/10/72016/12/42016/10/52016/10/52016/06/152016/12/272016/02/262016/10/292016/04/112016/10/5 1395/7/14 احمد رضا جعفریان مقدم Ahmad Reza Jafarian-Moghaddam دانشگاه اصفهان،اصفهان،ایران ایران ahmadreza.jafarian@gmail.com فرناز برزین‌پور Farnaz Barzinpour دانشگاه علم و صنعت ایران،تهران،ایران ایران barzinpour@iust.ac.ir محمد فتحیان Mohammad Fathian دانشگاه علم و صنعت ایران،تهران،ایران ایران fathian@iust.ac.ir Clustering Analysis Artificial immune system (AIS) Hierarchical Clustering. تحلیل خوشه‌بندی؛ سیستم ایمنی مصنوعی (AIS)؛ خوشه‌بندی سلسله مراتبی. [1] مینایی، بهروز، فتحیان، محمد، جعفریان مقدم، احمد رضا، و نصیری، مهدی، "استفاده از تکنیک خوشه‌بندی سیستم کلونی مورچگان بهبود یافته به منظور خوشه‌بندی داده‌های زلزله ایران"، نشریه تخصصی مهندسی صنایع، صفحات 221-227، 1391.##]1 [Minaei, B., Fathian, M., Jafarian-Moghaddam, A. R., & Nasiri, M.. Clustering Iran Earthquake Data using Improved Ant System-Based Clustering Algorithm. Journal of Industrial Engineering, 221-227, 2011.##[2] D. Dasgupta, Artificial immune systems and their applications. Springer, 1999.##[3] J. Timmis, M. Neal, and J. Hunt, "An artificial immune system for data analysis," Biosystems, vol. 55, pp. 143–150, 2000##[4] L. N. De Castro and J. I. Timmis, Artificial immune systems: a new computational intelligence approach. Springer, 2002##[5] U. Aicklein, P. Bentley, S. Cayser, K. Jungwon, and J. McLeod, "Danger theory: The link between AIS and IDS," In ICARIS International Conference on Artificial Immune Systems, LNCS Springer, 2003, pp. 147–155.##[6] X. Cao, H. Qiao, and T. Xu, "Negative selection based immune optimization," Advances in Engineering Software, vol. 38, pp. 649–656, 2003.##[7] H. Maulik and S. Bandyopadhyay, "Genetic algorithm-based clustering technique," Pattern Recognition, vol. 33, pp. 1455–1465, 2000.##[8] A. J. Graaff and A. P. Engelbrecht, "Clustering data in an uncertain environment using an artificial immune system," Pattern Recognition Letters, pp. 342-351, 2011.##[9] J. C. L. Pinto and F. J. Von Zuben, "Fault detection algorithm for telephone systems based on the danger theory," In ICARIS International Conference on Artificial Immune Systems, LNCS Springer, 2003, pp. 418–431.##[10] P. Matzinger, "Tolerance, danger and the extended family," Annual Reviews of Immunology, vol. 12, pp. 991–1045, 1994.##[11] S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri, "Self-nonself discrimination in a computer," In IEEE Symposium on Research in Security and Privacy, Los Alamos, CA. IEEE Pres, 1994.##[12] H. Kamber, Data Mining: concepts and technique. Second Edition, Elsevier, 2006.##[13] M. S. Aldenderfer and R. K. Blashfield, Cluster analysis. Newbury Park: Sage Publications, 1986.##[14] L. Kaufman and P. J. Rousseeuw, Finding groups in data: an introduction to cluster analysis. Wiley, New York, 1990.##[15] R. O. Duda and P. E. Hart, Pattern classification and scene analysis. Wiley, New York, 1973.##[16] A. K. Jain, R. P. W. Duin, and J. Mao, "Statistical pattern recognition: A review," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 4–37, 2000.##[17] J. A. Hartigan, Clustering Algorithms. Wiley, New York, 1975.##[18] G. J. McLachlan and K. E. Basford, Mixture models: inference and applications to clustering. Marcel Dekker, New York, 1988.##[19] G. J. McLachlan and T. Krishnan, The EM algorithm and extensions. Wiley, New York, 1997.##[20] S. Grossberg, "Adaptive pattern classification and universal recoding I: Parallel development and coding of neural feature detectors," Biological Cybernetics, vol. 23, pp. 121–134, 1976.##[21] R. P. Lippmann, "An introduction to computing with neural nets," IEEE Transactions on Acoustics, Speech, Signal Processing, pp. 4–22, 1987.##[22] C. F. Tsai, H. C. Wu, and C. W. Tsai, "A new clustering approach for data mining in large databases," In Proceedings of the international symposium on parallel architectures, algorithms and networks (ISPAN’02), IEEE Computer Society, 2002, pp. 1087–4089.##[23] T. Kohonen, Self-Organizing maps. third ed. Springer-Verlag, Berlin, 2001.##[24] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithm. Plenum Press, New York, 1981.##[25] M. S. Yang, "A survey of fuzzy clustering," Mathematical and Computer Modelling, vol. 18, pp. 1–16, 1993.##[26] P. N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining. Addison-Wesley, 2005.##[27] U. Fayyad, "Data mining and knowledge discovery in databases: implications for scientific databases," In Scientific and statistical database management, 1997 proceedings, ninth international conference, 1997, pp. 2–11.##[28] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From data mining to knowledge discovery in database," American Association for Artificial Intelligence, pp. 37–54, 1996.##[29] G. A. Carpenter and S. Grossberg, "ART2: self-organization of stable category recognition codes for analog input pattern," Applied Optics, vol. 26, pp. 4919–4930, 1987.##[30] K. Krishna and M. Murty, "Genetic K-means algorithm," IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 29, pp. 433–439, 1993.##[31] X. B. Yang, J. G. Sun, and D. Huang, "A new clustering method based on ant colony algorithm," In Proceedings of the 4th world congress on intelligent control and automation, 2002, pp. 2222–2226.##[32] R. J. Kuo, H. S. Wang, T. L. Hu, and S. H. Chou, "Application of Ant K-Means on clustering analysis," Computers and Mathematics with Applications, vol. 50, pp. 1709-1724, 2005.##[33] R. J. Kuo, S. Y. Lin, and C. W. Shih, "Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan," Expert Systems with Applications, vol. 33, pp. 794–808, 2007.##[34] R. J. Kuo and C. W. Shih, Association rule mining through the ant colony system for National Health Insurance Research Database in Taiwan," Computers and Mathematics with Applications, vol. 54, pp. 1303–1318, 2007.##[35] X. Z. Gao, S. J. Ovaska, and X. Wang, "A GA-based negative selection algorithm," International Journal on Innovative Computing Information and Control, vol. 4, pp. 971–979, 2008.##[36] N. K. Jerne, "Toward a network theory of the immune system," In Annales d’Immunologic (Institute Pasteur), Paris, France, 1976, 373–389.##[37] L. N. De Castro and F. J. Von Zuben, "aiNET: an artificial immune network for data analysis," In Data mining: A heuristic approach, H. Abbas, R. Sarker, C. Newton, Eds. Idea Group Publishing, 2001, pp. 231– 259.##[38] E. L. Talbi, Metaheuristics: from design to implementation. John Wiley and Sons, Inc., Hoboken, New Jersey, 2009.##[39] F. M. Burnet, The clonal selection theory of acquired immunity. University Press, Cambridge, 1959.##[40] L. N. De Castro and F. J. Von Zuben, "The clonal selection algorithm with engineering applications," In Workshop on Artificial Immune Systems and Their Applications, (GECCO’00), Las Vegas, NV, 2000. Pp. 36–37.##[1] مینایی، بهروز، فتحیان، محمد، جعفریان مقدم، احمد رضا، و نصیری، مهدی، "استفاده از تکنیک خوشه‌بندی سیستم کلونی مورچگان بهبود یافته به منظور خوشه‌بندی داده‌های زلزله ایران"، نشریه تخصصی مهندسی صنایع، صفحات 221-227، 1391. ##