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لب‌خوانی: روش جدید احراز هویت در برنامه‌های کاربردی گوشی‌های تلفن همراه اندروید Lip Reading: a New Authentication Method in Android Mobile Phone’s Applications 1 1 در این مقاله با استفاده از پردازش اطلاعات تصویری لب‌های کاربر، کلمه عبور  با استفاده از دوربین گوشی دریافت می‌شود تا با استفاده از الگوریتم‌های لب‌خوانی، حرکات لب دنبال شده و تشخیص داده شود. تشخیص تصویری کلمه عبور، مانع از دزدیدن آن توسط نرم‌افزارهای واقعه‌نگار می‌شود. با این حال، سیار‌بودن گوشی همراه منجر به تغییر نور محیط می‌شود. در این پژوهش، روشی برای حل این چالش مطرح شده و در‌نهایت یک نمونه برنامه کاربردی برای اجرا در سیستم عامل اندروید طراحی و پیاده‌سازی شده است. این لب‌خوان به‌صورت غیر‌بر‌خط و بدون نیاز به ارتباطات اینترنتی و وجود یک سرور خارجی، عمل شناسایی کلمه عبور کاربر را انجام می‌دهد. موفقیت روش پیاده‌سازی‌شده در تشخیص،حدود 70 درصد است. این برنامه برای پردازش ویدیوی حرفی که 10 قاب دارد، به 3.8 ثانیه زمان و 628 کیلوبایت حافظه نیاز دارد که به‌راحتی در گوشی‌های تلفن همراه امروزی قابل دسترس است.  2 Today, mobile phones are one of the first instruments every individual person interacts with. There are lots of mobile applications used by people to achieve their goals. One of the most-used applications is mobile banks. Security in m-bank applications is very important, therefore modern methods of authentication is required. Most of m-bank applications use text passwords which can be stolen by key-loggers. Key-loggers are hidden software to record the keys struck by users. To overcome the key-logging issue, One-Time Passwords are used. They are secure but require additional tools to be used, therefore they cannot be user-friend. Moreover, the voice-based passwords are not secure enough, since they can be heard by other people easily. In other hand, Image-based passwords cannot satisfy users, cause of screen limitation in mobile phones.  In this article, a new authentication method is introduced. The password is based on user lip’s motion which is received via a mobile cellphone camera.  The visual information extracted from the user’s lips movement forms the password. Then the lip motion is tracked to recognize the password by incorporating the lip reading algorithms. The algorithm is based on the Viola-Jones method. It combines the method with a pixel-based approach to segment lips and extract features. After segmenting the lips, some special points of Region of Interests are selected. The information extracted from lips are saved in order to act as algorithm’s features. In addition, some normalizing methods are considered to normalize the features and prepare them to enter classification phase. In classification step, some known algorithms like Support Vector Machine and K-Nearest Neighbor are applied on features to recognize password and authenticate people. Visual passwords prevent key-loggers from stealing passwords. However, the mobility of a mobile user causes ambient lights to vary in different environments. In this research, a solution is designed to tackle this challenge. Finally a mobile banking application is designed and developed to run on android mobile phones platform. It incorporates a lip reader which recognizes the passwords in offline mode.  The application is independent from the internet connection or a dedicated server. The implemented recognition method has achieved a 70% success rate. In this application a video capture of a letter with 10 frames could be processed in 3.8 seconds using 628 kilo bytes of memory.  These resources are easily available in today’s mobile phones. Some mobile bank users tested the application to feedback about lip reading password. Most of them were satisfied when using it. They believed the lip reader is more trustable than text passwords and voice-based passwords. In addition, the user-friendliness of it, is a bit more than text password which means that the method can satisfies a mobile bank application user. 3 14 2015/05/30 1394/3/9 2016/01/15 1394/10/25 فاطمه سادات لسانی Fatemeh Sadat Lesani دانشگاه قم ایران aslesani@gmail.com فرانک فتوحی قزوینی Faranak Fotouhi Ghazvini دانشگاه قم ایران faranak_fotouhi@hotmail.com روح الله دیانت Rouhollah Dianat دانشگاه قم ایران rouhollah.dianat@gmail.com Mobile Phone Authentication Automatic Lip Reading Mobile Commerce Lip Tracking Android احراز هویت در تلفن همراه لب‌خوانی خودکار تجارت سیار ردیابی لب اندروید [1]K. Alghatbar, "Real-time algorithmic design for silent pass lip reading authentication system," International Journal of the Physical Sciences, vol. 6, no. 7, pp. 1665-1672, 2011.##[2]S. Chai, "Mobile Challenges for Embedded Computer Vision," in Embedded Computer Vision, B. Kisačanin, S. Bhattacharyya, and S. Chai, Eds. (Advances in Pattern Recognition: Springer London, 2009, pp. 219-235.##[3]K. Young-Un, K. Sun-Kyung, and J. Sung-Tae, "Design and implementation of a lip reading system in smart phone environment," in Information Reuse & Integration,IEEE International Conference on, 2009.##[4]L. Lamport, "Password Authentication with Insecure Communication," Comm. ACM, vol. 24, no. 11, pp. 770-772, 1981.##[5]L. Gong, J. Pan, B. Liu, and S. Zhao, "A novel one-time password mutual authentication scheme on sharing renewed finite random sub-passwords," Journal of Computer and System Sciences, vol. 79, no. 1, pp. 122-130, 2013.##[6]Y. Huang, Z. Huang, H. Zhao, and X. Lai, "A new One-time Password Method," IERI Procedia, vol. 4, pp. 32-37, 2013.##[7]M. Khitrov, "Talking passwords: voice biometrics for data access and security," Biometric Technology Today, vol. 2013, no. 2, pp. 9-11, 2013.##[8]R. Dhamija and A. Perrig, "Déjà vu: A user study using images for authentication," In: 9th USENIX Security Symposium, 2000.##[9]S. Brostoff and M. A. Sasse, "Are passfaces more usable than passwords? a field trial investigation," In: People and Computers XIV—Usability or Else: Proceedings of HCI, pp. 405–424, 2000.##[10]S. Wiedenbeck, J. Waters, J. C. Birget, A. Brodskiy, and N. Memon, "Authentication using graphical passwords: basic results," In: 11th Human–Computer Interaction International (HCII), 2005.##[11]W. A. Jansen, "Authenticating users on handheld devices," In: Canadian Information Technology Security Symposium, 2003.##[12]W. A. Jansen, "Authenticating mobile device users through image selection," In: Data Security, 2004.##[13]T.-Y. Chang, C.-J. Tsai, and J.-H. Lin, "A graphical-based password keystroke dynamic authentication system for touch screen handheld mobile devices," Journal of Systems and Software, vol. 85, no. 5, pp. 1157-1165, 5// 2012.##[14]D. G, X. Chao, and K. Sriadibhatla, "Face Recognition in Mobile Phones," epartment of Electrical Engineering Stanford University, USA, 2010.##[15]D. S. S. A, C. S. Avila, A. MendazaOrmaza, and J. G. Casanova, "Towards Hand Biometrics in Mobile devices," In Proceeding of BIOSIG, Darmstadt, 2011.##[16]G. M, J. J. S. Rani, M. Ramiah, N. T. N. Babu, A. A. Fathima, and V. Vaidehi, "Mobile Authentication Using Iris Biometrics," Published by Springer Berlin Heidelberg, Networked Digital Technologies, vol. 294, pp. 332-341, 2012.##[17]K. O. Bailey, J. S. Okolica, and G. L. Peterson, "User identification and authentication using multi-modal behavioral biometrics," Computers & Security, vol. 43, pp. 77-89, 6// 2014.##[18]O. Alpar, "Intelligent biometric pattern password authentication systems for touchscreens," Expert Systems with Applications, vol. 42, no. 17–18, pp. 6286-6294, 10// 2015.##[19]A. Drosou, D. Ioannidis, D. Tzovaras, K. Moustakas, and M. Petrou, "Activity related authentication using prehension biometrics," Pattern Recognition, vol. 48, no. 5, pp. 1743-1759, 5// 2015.##[20]P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, 2001.##[21]I. Matthews, T.Cootes, J. Bangham, S. Cox, and R. Harvey, "Extraction of visual features for lipreading," IEEE Trans. on Pattern Analysis and Machine Vision, vol. 24, no. 2, pp. 198-213, 2002.##[22]N. Lee and e. al, "Facial Landmark Extraction for Lip Tracking of Patients with Cleft Lip Using Active Appearance Model," in HCI International 2011 – Posters’ Extended Abstracts, C. Stephanidis, Editor. 2011, Springer Berlin Heidelberg, pp. 350-354, 2011.#### ##

ارائه رویکردی نوین یادگیری ماشین برای شناسایی و تجزیه و تحلیل دانش پدیده‌های استثنایی A Novel Approach for Exceptional Phenomena Knowledge Detection and Analysis by Data mining 1 1 کشف پدیده‌­های استثنایی پنهان در حجم انبوهی از رکوردهای موجود در پایگاه داده و استخراج دانش آن­ها در این مطالعه مورد بررسی قرار گرفته است. پدیده‌­های استثنایی به‌ندرت رخ می­‌دهد و در حجم انبوهی از داده‌­های عادی پنهان‌­اند. دست‌یابی به دانش رفتاری این پدیده‌­ها، ارزشمند و جذاب است. روش‌های موجود یادگیری، در هنگام پاک‌سازی پایگاه داده اغلب پدیده­‌های استثنایی را به‌عنوان داده‌های پرت شناسایی کرده و از محاسبات خارج می­‌کند و یا اینکه به‌­دلیل تمایل به کلّیت، قابلیت شناسایی و دسته‌­بندی درست این پدیده­‌ها را ندارند. به همین دلیل، ایجاد چارچوبی کارآمد برای کشف دانش و یادگیری رفتار پدیده‌­های استثنایی معدود که در میان انبوه رکوردهای یک پایگاه داده مخفی هستند، حائز اهمیت است. در این پژوهش، با به‌‏کارگیری تئوری استثنائات و تئور‌‌ی­‌های اطلاعات و دانه‌بندی اطلاعات نسبت به استخراج دانش رفتار پدیده‌­های استثنایی اقدام شده است. کارآیی روش پیشنهادی با در‌نظر‌گرفتن اطلاعات 30 ماهۀ سهام شرکت‌­های فعال در بازار اوراق بهادار ایران به‌منظور شناسایی و یادگیری رفتار سهام استثنایی، سنجیده می‌­شود.  2 Learning logic of exceptions is a substantial challenge in data mining and knowledge discovery. Exceptional phenomena detection takes place among huge records in a database which contains a large number of normal records and a few of exceptional ones. This is important to promote the confidence to a limited number of exceptional records for effective learning. In this study, a new approach based on the abnormality theory, information and information granulation theories are presented to detect exceptions and recognize their behavioral patterns. The efficiency of the proposed method was determined by using it to detect exceptional stocks from Iran stock market in a 30-month- period and learn their exceptional behavior. The proposed Enhanced-RISE algorithm (E-RISE) as a bottom-up learning approach was implemented to extract the knowledge of normal and exceptional behavior. The extracted knowledge was utilized to design an expert system based on the proposed abnormality theory to predict new exceptions from 6022 stocks. The superior findings show the results of this proposed approach in exceptional phenomena detection, is in accordance with experts' opinions.   15 28 2015/05/302015/01/11 1393/10/21 2016/01/152016/10/5 1395/7/14 الهه حاجی گل یزدی elahe hajigol yazdi دانشگاه یزد ایران elahehajigol@gmail.com مسعود عابسی masood abessi دانشگاه یزد ایران mabessi@gmail.com محمدباقر فخرزاد Mohammad bagher Fakhrzad دانشگاه یزد ایران mfakhrzad@yazd.ac.ir حسن حسینی نسب Hasan Hoseini nasab دانشگاه یزد ایران hosseininasab@gmail.com Data mining Exceptional phenomena Abnormality theory Bottom-Up learning approach E-RISE Algorithm Information theory. داده‌کاوی پدیده‌های استثنایی تئوری استثنائات رویکرد یادگیری پایین به بالا تئوری اطلاعات کشف ]1[ G. Albanis and R. Batchelor, "Combining heterogeneous classifiers for stock selection, Intelligent Systems in Accounting", Finance and Management , vol. 15, no. 1-2, pp. 1-27, 2007. ##[2] J. Boshes, Change point detection in cyber-attack data, Ph.D. dissertation, Arizona state university, 2009.##[3] J. Burez and D. Van den Poel, "Handling class imbalance in customer churn prediction", Expert Systems with Applications, vol. 36, pp. 4626–4636, 2009.##[4] M. E. Califf and R. J. Mooney, "Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction", Journal of Machine Learning Research, vol. 4, pp.177-210, 2003.##[5] L .Cao, Y. Zhao and C .Zhang, "Mining Impact-Targeted Activity Patterns in Imbalanced Data", IEEE Transactions on knowledge and data engineering, vol. 20, pp.1053-1066, 2008.##[6] N. V. Chawla, N .Japkowicz and A. K. lcz, "Editorial: Special Issue on Learning from Imbalanced Data Sets", SIGKDD Explorations, vol. 6, pp.1–6, 2004. ##[7] M. C. Chen, L. S. Chen, C. C. Hsu and W. R. Zeng, "An information granulation based data mining approach for classifying imbalanced data", Information Sciences , vol.178, pp. 3214–3227, 2008.##[8] E. Clark, "Exploiting stochastic dominance to generate abnormal stock returns", Journal of Financial Markets, vol. 20, pp.20–38, 2014. ##[9] T. M. Cover and J. A. Thomas, Entropy, Relative "Entropy and Mutual Information; Elements of Information Theory", ISBN 0-471-06259-6, pp. 12-49, 1991.##[10] T. V. Duong, H. H .Bui, D. Q. Phung and S. Venkatesh, "Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005.##[11] V. García, J.S. Sánchez and R.A. Mollineda, "On the effectiveness of preprocessing methods when dealing with different levels of class imbalance", Knowledge-Based Systems, vol. 25, pp. 13–21, 2012.##[12] R.S. Gong, "A Segmentation and Re-balancing Approach for Classification of Imbalanced Data", Ph.D. dissertation, University of Cincinnati, 2010.##[13] D. H.Hu, X. X. Zhang, J. Yin, V. W. Zheng and Q. Yang, "Abnormal Activity Recognition Based on HDP-HMM Models", the Twenty-First International Joint Conference on Artificial Intelligence, 2009.##[14] M. L. Hoffman, "Moral internalization: Current theory and research", In L. Berkowitz (Ed.), Advances in experimental social psychology, vol.10, pp.85-133, 1977.##[15] N. Japkowicz, "The class imbalance problem: Significance and strategies", the international conference on artificial intelligence: Special track on inductive learning, 2000.##[16] M. V. Joshi, "Learning Classifier Models for Predicting Rare Phenomena", Ph.D. dissertation, University of Minnesota, Twin Cites, Minnesota, USA, 2002.##[17] Y. Kim and S. Y. Sohn, "Stock fraud detection using peer group analysis", Expert Systems with Applications, vol. 39, pp. 8986–8992, 2012. ##[18] Y. Kou, "Abnormal Pattern Recognition in Spatial Data", Ph.D. dissertation, Faculty of Virginia Polytechnic Institute and State University, 2006. ##[19] X. Li and F. Rao, "Outlier Detection Using the Information Entropy of Neighborhood Rough Sets",Journal of Information & Computational Science, vol. 9, pp. 3339–3350, 2012.##[20] J. McCarthy, "Applications of circumscription to formalizing common-sense knowledge", Artificial Intelligence, vol. 28, pp. 89-116, 1986.##[21] J. Nagi, "An intelligent system for detection of non-technical losses in Tanaga National Berhad (TNB) Malaysia low voltage distribution network", Ph.D. dissertation, Tenaga national university,2009.##[22] U. Qamar, "Automated Entropy Value Frequency (AEVF) Algorithm for Outlier Detection in Categorical Data", Recent Advances in Knowledge Engineering and Systems Science, pp. 28-35, 2011.##[23] R. Reiter, "A Theory of Diagnosis from First Principles", Artificial Intelligence, vol. 32, pp. 57-95, 1987.##[24] D. B. Setyohadi, A. Abu Bakar and Z. A. Othman, "Rough K-means Outlier Factor Based on Entropy Computation, Research Journal of Applied Sciences", Engineering and Technology, vol. 8, pp. 398-409, 2014.##[25] G. Weiss, "Mining with rarity: A unifying framework", SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets, vol. 6, pp. 7–19, 2004.##[26] T. Xiang and S. Gong, "Video Behavior Profiling for Anomaly Detection", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 30, pp. 893–908,2008. ##[1]Albanis G., Batchelor R., Combining heterogeneous classifiers for stock selection, Intelligent Systems in Accounting, Finance and Management , vol. 15, no. 1-2, pp. 1-27, 2007. ##[2] Boshes J., Change point detection in cyber attack data, PHD theses, Arizona state university, 2009.##[3] Burez J., Van den Poel D., Handling class imbalance in customer churn prediction, Expert Systems with Applications 36, 4626–4636, 2009.##[4] Califf M. E., Mooney R. J., Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction, Journal of Machine Learning Research 4,177-210, 2003.##[5] Cao L., Zhao Y., Zhang C., Mining Impact-Targeted Activity Patternsin Imbalanced Data, IEEE Transactions on knowledge and data engineering, Vol. 20, NO. 8, 2008.##[6] Chawla N. V., Japkowicz N., lcz A. K., Editorial: Special Issue on Learning from Imbalanced Data Sets, Sigkdd Explorations, 6(1):1–6, 2004. ##[7] Chen M. C., Chen L. S., Hsu C. C., Zeng W. R., An information granulation based data mining approach for classifying imbalanced data, Information Sciences 178, 3214–3227, 2008.##[8] Clark E. Exploiting stochastic dominance to generate abnormal stock returns, Journal of Financial Markets 20, 20–38, 2014. ##[9] Cover T. M., Thomas J. A., Entropy, Relative Entropy and Mutual Information; Elements of Information Theory, ISBN 0-471-06259-6-pp: 12-49, 1991.##[10] Duong T. V., Bui H. H., Phung D. Q., Venkatesh S., Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005.##[11] García V., Sánchez J.S., Mollineda R.A., On the effectiveness of preprocessing methods when dealing with different levels of class imbalance, Knowledge-Based Systems 25, 13–21, 2012.##[12] Gong R.S., A Segmentation and Re-balancing Approach for Classification of Imbalanced Data, PHD theses, University of Cincinnati, 2010.##[13] Hu D. H., Zhang X. X., Yin J., Zheng V. W., Yang Q., Abnormal Activity Recognition Based on HDP-HMM Models, the Twenty-First International Joint Conference on Artificial Intelligence, 2009.##[14]Hoffman M. L., Moral internalization: Current theory and research, In L. Berkowitz (Ed.), Advances in experimental social psychology10, 85-133, 1977.##[15] Japkowicz, N. , The class imbalance problem: Significance and strategies, the international conference on artificial intelligence: Special track on inductive learning, 2000.##[16] Joshi M. V, Learning Classifier Models for Predicting Rare Phenomena, PhD thesis, University of Minnesota, Twin Cites, Minnesota, USA, 2002.## [17] Kim Y., Sohn S.Y., Stock fraud detection using peer group analysis, Expert Systems with Applications 39, 8986–8992, 2012. ##[18] Kou Y, Abnormal Pattern Recognition in Spatial Data, PHD theses, Faculty of Virginia Polytechnic Institute and State University, 2006. ##[19] Li X., Rao F., Outlier Detection Using the Information Entropy of Neighborhood Rough Sets, Journal of Information & Computational Science, 3339–3350, 2012.##[20] McCarthy J., Applications of circumscription to formalizing common-sense knowledge, Artificial Intelligence 28, 89-116, 1986.## [21] Nagi J., An intelligent system for detection of non-technical losses in Tanaga National Berhad (TNB) Malaysia low voltage distribution network, PhD Thesis, Tenaga national university,2009.## [22] QamarU., Automated Entropy Value Frequency (AEVF) Algorithm for Outlier Detection in Categorical Data, Recent Advances in Knowledge Engineering and Systems Science,28-35, 2011.##[23] Reiter R., A Theory of Diagnosis from First Principles, Artificial Intelligence 32, 57-95, 1987.##[24] Setyohadi D. B., Abu Bakar A., Othman Z.A., Rough K-means Outlier Factor Based on Entropy Computation, Research Journal of Applied Sciences, Engineering and Technology 8(3): 398-409, 2014.##[25] Weiss G., Mining with rarity: A unifying framework. SIGKDDExplorations Special Issue on Learning from Imbalanced Datasets,6(1):7–19, 2004.##[26] Xiang T., Gong S., Video Behavior Profiling for Anomaly Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(5), 893–908, 2008.## ##

بهبود الگوریتم انتخاب دید در پایگاه داده‌‌ تحلیلی با استفاده از یافتن پرس‌ وجوهای پرتکرار An Improved View Selection Algorithm in Data Warehouses by Finding Frequent Queries 1 1 پایگاه داده تحلیلی منبعی برای ذخیره‌‏سازی داده‌‏های تاریخی جهت تحلیل است. به‌طورمعمول زمان پاسخ به پرس‌‏و‏جوهای تحلیلی، زمانی طولانی است. استفاده از دید به جای دسترسی مستقیم به پایگاه داده‏، سرعت پاسخ‏گویی را بهبود می‌‏دهد. راه‌کارهای مختلفی برای ذخیره‌‏سازی دید وجود دارد؛ که مناسب‏ترین راهکار برای ذخیره‏سازی دید، ذخیره‌‍‏سازی دیدهای پراستفاده و پرکاربرد است. پرس‌‏وجوهایی که درقبل مورد استفاده پایگاه داده‏ تحلیلی بود‏ه‌‏اند، حاوی اطلاعات مهمی هستند که به‌احتمال زیاد در آینده نیز مورد استفاده خواهند بود‏. این مقاله، الگوریتمی برای ذخیره‌‏سازی دیدهای پرکاربرد ارائه می‌‏دهد. این الگوریتم با استفاده از پرس‏‌وجوهای قبلی، دیدهای پرکاربرد را یافته و آن‏ها را ذخیره‏‏ می‏‌کند. این دیدها توانایی پاسخ‏گویی را به بسیاری از پرس‌‏وجوهایی که در آینده اتفاق خواهند ‏افتاد، دارند. روش پیشنهادی این مقاله از الگوریتم Index-BittableFI برای یافتن دیدهای پرتکرار استفاده ‏کرده ‏است که باعث بهبود روش‌‏های قبلی و کاهش زمان پاسخ به پرس‌‏وجوها شده است‏. آزمایش‌های انجام‌شده نشان می‌‏دهند که الگوریتم پیشنهادی از لحاظ زمانی نسبت به الگوریتم‏‌های قبلی 23 درصد و از لحاظ فضای ذخیره‌‏سازی 50 درصد بهبود داشته است. 2 A data warehouse is a source for storing historical data to support decision making. Usually analytic queries take much time. To solve response time problem it should be materialized some views to answer all queries in minimum response time. There are many solutions for view selection problems. The most appropriate solution for view selection is materializing frequent queries. Previously posed queries on the data warehouse have profitable information. These queries probably will be used in the future. So, previous queries are clustered using clustering algorithms. Then frequent queries are found using data mining algorithms. Therefore optimal queries are found in each cluster. In the last stage optimal queries are merged to produce one (query) view for each cluster, and materializes this view. This paper proposes an algorithm for materializing frequent queries. The algorithm finds profitable views using previously posed queries on the data warehouse. These views can answer the most of the queries being posed in the future. This paper uses Index-BittableFI algorithm for finding frequent views. Using this algorithm improves previous view selection algorithms and reduces the response time. The experiments show that the proposed algorithm has %23 improvement in response time and %50 improvement in storage space.   29 40 2015/05/302015/01/112015/08/13 1394/5/22 2016/01/152016/10/52016/10/29 1395/8/8 ریحانه صباغ گل دانشگاه تربیت دبیر شهید رجایی ایران sabbagh.rsg@gmail.com نگین دانشپور Negin Daneshpour دانشگاه تربیت دبیر شهید رجایی ایران ndaneshpour@srttu.edu Data warehouse Frequent queries View materialization Clustering پایگاه داده‌ تحلیلی پرس‌و‌جو‌های پرتکرار خوشه‌بندی ذخیره‌سازی دید ]1] J. Widom, "Research Problems in Data Warehousing," in International Conference on Information and Knowledge, Baltimore, Maryland, 1995. ##]2] T. V. Kumar and K. Devi, "Frequent Queries Identification for Constructing Materialized Views," in Electronics Computer Technology (ICECT), Kanyakumari, 2011. ##]3] T. V. V. Kumar, G. Dubey and A. singh, "Frequent Queries Selection for View Materialization," Advances in Computing and Information Technology, vol. 177, pp. 521-530, 2013. ##]4] W. Song, B. Yang and Z. Xu, "Index-BitTableFI: An improved algorithm for mining frequent itemsets," Knowledge-Based Systems, vol. 21, pp. 507-513, 2008. ##]5] J. Yang, K. Karlapalem and Q. Li, "Algorithms for materialized view design in data warehousing environment," VLDB, vol. 97, 1997. ##]6] I. Mami and Z. Bellahsene, "A survey of view selection methods," ACM SIGMOD, vol. 41, no. 1, pp. 20-29, 2012. ##]7] C. A. Dhote and M. S. Ali, "Materialized view selection in data warehousing: a survey," Journal of Applied sciences, vol. 9, no. 3, pp. 401-414, 2009. ##]8] J.-S. Sohn, J.-H. Yang and I.-J. Chung, "Improved view selection algorithm in data warehouse," IT Convergence and Security, pp. 921-928, 2013. ##]9] A. B. Rashid, M. Islam and A. L. Hoque, "Dynamic Materialized View Selection Approach for Improving Query Performance," Computer Networks and Information Technologies, vol. 142, pp. 202-211, 2011. ##]10] W. Xu, D. Theodoratos, C. Zuzarte, X. Wu and V. Oria, "A dynamic view materialization scheme for sequences of query and update statements," Data Warehousing and Knowledge Discovery, pp. 55-56, 2007. ##]11] N. Daneshpour and A. Abdollahzadeh Barfourosh, "Dynamic view Management System for Query Prediction to view materialization," International Journal of Data Warehousing and Mining, vol. 7, no. 2, pp. 67-96, 2011. ##]12] I. Mami, R. Coletta and Z. Bellahsene, "Modeling view selection as a constraint satisfaction problem," in International Conference on Database and Expert Systems Applications, France, 2011. ##]13] I. Mami, Z. Bellahsene and R. Coletta, "View selection under multiple resource constraints in a distributed context," in International Conference on Database and Expert Systems Applications, Vienne, 2012. ##]14] I. Mami, Z. Bellahsene and R. Coletta, "A Declarative Approach to View Selection Modeling," Transactions on Large-Scale Data-and Knowledge-Centered Systems, pp. 115-145, 2013. ##]15] R. Huang, R. Chirkova and Y. Fathi, "Advances in Databases and Information Systems," in Deterministic view selection for data analysis queries: Properties and algorithms, Berlin, Springer Berlin Heidelberg, 2012, pp. 195-208.##]16] Z. Asgharzadeh, R. Chirkova and Y. Fathi, "Exact and inexact methods for selecting views and indexes for olap performance improvement," in international conference on Extending database technology: Advances in database technology, France, 2008. ##]17] T. V. Kumar and M. Haider, "Query answering-based view selection," International Journal of Business Information Systems, vol. 18, no. 3, pp. 338-353, 2015. ##]18] V. T. Kumar and M. Haider, "Selection of views for materialization using size and query frequency," Information Technology and Mobile Communication, pp. 150-155, 2011. ##]19] V. T. Kumar and M. Haider, "Materialized views selection for answering queries," Data Engineering and Management, pp. 44-51, 2012. ##]20] M. S. Chaudhari and D. Chandrashekhar, "Dynamic materialized view selection algorithm: a clustering approach," Data Engineering and Management, pp. 57-66, 2012. ##]21] V. T. Kumar and B. Arun, "Materialized View Selection Using HBMO," International Journal of System Assurance Engineering and Management, pp. 1-14, 2015. ##]22] P. Vishwanath and R. Sridhar, "An Association Rule Mining for Materialized View Selection and View Maintenance," International Journal of Computer Applications, vol. 105, no. 5, 2015. ##]23] D. Yao, A. abulizi and R. Hou, "An improved algorithm of materialized view selection within the confinement of space," 2015. ##]24] T. V. Kumar and K. Devi, "Materialised view construction in data warehouse for decision making," International Journal of Business Information Systems, vol. 11, no. 4, pp. 379-396, 2012. ##]25] T. V. V. Kumar, A. Singh and G. Dubey, "Mining Queries for Constructing Materialized Views in a Data Warehouse," Advances in Computer Science, Engineering & Applications, pp. 149-159, 2012. ##]26] T. V. V. Kumar, A. Goel and N. Jain, "Mining information for constructing materialised views," Int. J. Information and Communication Technology, vol. 2, no. 4, pp. 386-405, 2010. ##]1] J. Widom, "Research Problems in Data Warehousing," in International Conference on Information and Knowledge, Baltimore, Maryland, 1995. ##]2] T. V. Kumar and K. Devi, "Frequent Queries Identification for Constructing Materialized Views," in Electronics Computer Technology (ICECT), Kanyakumari, 2011. ##]3] T. V. V. Kumar, G. Dubey and A. singh, "Frequent Queries Selection for View Materialization," Advances in Computing and Information Technology, vol. 177, pp. 521-530, 2013. ##]4] W. Song, B. Yang and Z. Xu, "Index-BitTableFI: An improved algorithm for mining frequent itemsets," Knowledge-Based Systems, vol. 21, pp. 507-513, 2008. ##]5] J. Yang, K. Karlapalem and Q. Li, "Algorithms for materialized view design in data warehousing environment," VLDB, vol. 97, 1997. ##]6] I. Mami and Z. Bellahsene, "A survey of view selection methods," ACM SIGMOD, vol. 41, no. 1, pp. 20-29, 2012. ##]7] C. A. Dhote and M. S. Ali, "Materialized view selection in data warehousing: a survey," Journal of Applied sciences, vol. 9, no. 3, pp. 401-414, 2009. ##]8] J.-S. Sohn, J.-H. Yang and I.-J. Chung, "Improved view selection algorithm in data warehouse," IT Convergence and Security, pp. 921-928, 2013. ##]9] A. B. Rashid, M. Islam and A. L. Hoque, "Dynamic Materialized View Selection Approach for Improving Query Performance," Computer Networks and Information Technologies, vol. 142, pp. 202-211, 2011. ##]10] W. Xu, D. Theodoratos, C. Zuzarte, X. Wu and V. Oria, "A dynamic view materialization scheme for sequences of query and update statements," Data Warehousing and Knowledge Discovery, pp. 55-56, 2007. ##]11] N. Daneshpour and A. Abdollahzadeh Barfourosh, "Dynamic view Management System for Query Prediction to view materialization," International Journal of Data Warehousing and Mining, vol. 7, no. 2, pp. 67-96, 2011. ##]12] I. Mami, R. Coletta and Z. Bellahsene, "Modeling view selection as a constraint satisfaction problem," in International Conference on Database and Expert Systems Applications, France, 2011. ##]13] I. Mami, Z. Bellahsene and R. Coletta, "View selection under multiple resource constraints in a distributed context," in International Conference on Database and Expert Systems Applications, Vienne, 2012. ##]14] I. Mami, Z. Bellahsene and R. Coletta, "A Declarative Approach to View Selection Modeling," Transactions on Large-Scale Data-and Knowledge-Centered Systems, pp. 115-145, 2013. ##]15] R. Huang, R. Chirkova and Y. Fathi, "Advances in Databases and Information Systems," in Deterministic view selection for data analysis queries: Properties and algorithms, Berlin, Springer Berlin Heidelberg, 2012, pp. 195-208.##]16] Z. Asgharzadeh, R. Chirkova and Y. Fathi, "Exact and inexact methods for selecting views and indexes for olap performance improvement," in international conference on Extending database technology: Advances in database technology, France, 2008. ##]17] T. V. Kumar and M. Haider, "Query answering-based view selection," International Journal of Business Information Systems, vol. 18, no. 3, pp. 338-353, 2015. ##]18] V. T. Kumar and M. Haider, "Selection of views for materialization using size and query frequency," Information Technology and Mobile Communication, pp. 150-155, 2011. ##]19] V. T. Kumar and M. Haider, "Materialized views selection for answering queries," Data Engineering and Management, pp. 44-51, 2012. ##]20] M. S. Chaudhari and D. Chandrashekhar, "Dynamic materialized view selection algorithm: a clustering approach," Data Engineering and Management, pp. 57-66, 2012. ##]21] V. T. Kumar and B. Arun, "Materialized View Selection Using HBMO," International Journal of System Assurance Engineering and Management, pp. 1-14, 2015. ##]22] P. Vishwanath and R. Sridhar, "An Association Rule Mining for Materialized View Selection and View Maintenance," International Journal of Computer Applications, vol. 105, no. 5, 2015. ##]23] D. Yao, A. abulizi and R. Hou, "An improved algorithm of materialized view selection within the confinement of space," 2015. ##]24] T. V. Kumar and K. Devi, "Materialised view construction in data warehouse for decision making," International Journal of Business Information Systems, vol. 11, no. 4, pp. 379-396, 2012. ##]25] T. V. V. Kumar, A. Singh and G. Dubey, "Mining Queries for Constructing Materialized Views in a Data Warehouse," Advances in Computer Science, Engineering & Applications, pp. 149-159, 2012. ##]26] T. V. V. Kumar, A. Goel and N. Jain, "Mining information for constructing materialised views," Int. J. Information and Communication Technology, vol. 2, no. 4, pp. 386-405, 2010. ## ##

استفاده از ترکیب الگوریتم ژنتیک و شبکه های عصبی مصنوعی برای پیش بینی نیروی گاز گرفتن از روی سیگنال الکترومایوگرام Application of an ANN-GA Method for Predicting the Biting Force Using Electromyogram Signals 1 1 امروزه بررسی ارتباط بین سیگنال­‌های نیرو و فعالیت الکتریکی عضله‌­ها بسیار حائز اهمیت بوده و در مسائل مهمی مانند تحلیل حرکت، علوم ارتوپدی، توانبخشی، طراحی ارگونومیک و  تعامل انسان- ماشین و کاربردهای پزشکی مانند کنترل پروتزهای مصنوعی کاربرد فراوانی دارد. از مزیت­‌های استفاده از الکترودهای سطحی، ارزان­تر و قابل­‌حمل‌بودن آن­ها در مقایسه با حس‌گرهای نیرو است که به‌طورمعمول گران هستند و ساختار حجیمی دارند. از آنجایی که اندازه­‌گیری نیروی گاز‌گرفتن بسیار سخت و پیچیده است، در این مقاله می‌­خواهیم توانایی شبکه‌های عصبی چند لایه پرسپترون (MLPANN) و توابع با پایه شعایی (RBFANN) را در پیش­بینی نیروی گاز‌گرفتن توسط دندان پیشین از روی سیگنال­‌های اکترومایوگرام صورت بررسی کنیم. بدین منظور سیگنال الکترومایوگرام عضلات گیجگاهی و ماضغه و نیروی گاز‌گرفتن به‌ترتیب به‌عنوان ورودی و خروجی شبکه‌­های عصبی در نظر گرفته شده­‌اند. برای پیدا‌کردن بهترین ساختار شبکه و تأخیر زمانی مناسب سیگنال­‌های الکترومایوگرام، از الگوریتم ژنتیک (GA)  استفاده شده است. نتایج نشان می‌­دهند که سیگنال الکترومایوگرام عضلات یادشده شامل اطلاعات مفیدی از نیروی گازگرفتن هستند. روش‌­های MLPANN و RBFANN دینامیک مورد نظر را با دقت مناسبی شناسایی می­‌کنند. درصد میانگین مربع خطا در مرحله آموزش و آزمون به‌ترتیب 3/2%و 4/19% برای MLPANN و 3/8% و 7/22% برای  RBFANN است. همچنین روش تحلیل واریانس نشان می‌­دهد که تفاوت معناداری بین نتایج حاصله از MLPANN و RBFANN  وجود ندارد. 2 Human mastication is a common rhythmic behavior and a complex biomechanical process which is hard to reproduce. Today, investigating the relation between electrical activity of muscles and force signals is of high importance in many applications including gait analysis, orthopedics, rehabilitation, ergonomic design, haptic technology, tele-presence surgery and human-machine interaction. Surface electrodes have many advantages over force sensors which are often expensive and of massive structure, two of which are less expensive and portable. Since the biting force is too difficult to be measured, in this paper, we aim to investigate the ability of a Multi-Layer Perceptron artificial neural network (MLPANN) and Radial Basis Function artificial neural network (RBFANN) to predict the biting force of incisor teeth based on surface electromyography (EMG) signals. RBFANN and MLPANN are two of the most widely used neural network architecture. These two methods are both known as universal approximates for nonlinear input-output mapping. To do this, biting force and EMG signals from the masticatory muscles were recorded and used as output and input of neural networks, respectively. Genetic algorithm was applied to find the best structure for ANNs and the appropriate total time-delay of EMGs. Results show that the EMG signals recorded from aforementioned muscles contain useful information about the biting force. Furthermore, they indicate that MLPANN and RBFANN can detect the dynamics of the system with good precision. The mean percentage error in the training and validation phase is %2.3 and %19.4 for MLPANN and %8.3 and %22.7 for RBFANN, sequentially. Also the variance analysis technique shows that there is no significant difference between results achieved through MLPANN and RBFANN. The provided analysis will aid researchers in characterizing and investigating the mastication process, through the specification of SEMG signal patterns and the observation of the resulting biting force. Such models can provide clinical insight into the development of more effective rehabilitation therapies, and can aid in assessing the effects of an intervention. This methodology can be applied to any tele-operated robot or orthotic device (exoskeleton), either for rehabilitation or extension of human ability. 41 52 2015/05/302015/01/112015/08/132015/04/18 1394/1/29 2016/01/152016/10/52016/10/292016/10/29 1395/8/8 نازنین گوهریان Nazanin Goharian دانشگاه فردوسی مشهد ایران goharian_n@yahoo.com سحر مقیمی Sahar Moghimi دانشگاه فردوسی مشهد ایران s.moghimi@um.ac.ir هادی کلانی Hadi Kalani دانشگاه فردوسی مشهد ایران hadi.kalani@yahoo.com Electromyogram (EMG) signal biting force multi-Layer perceptron artificial neural networks (MLP) Radial basis function (RBF) Genetic algorithm. سیگنال‌های الکترومایوگرام نیروی گاز‌گرفتن شبکه عصبی چند لایه پرسپترون تابع پایه شعاعی الگوریتم ژنتیک. [1] M. A. Al-Saleh, S. Armijo-Olivo, C. Flores-Mir, N. M. 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یادگیری نیمه نظارتی کرنل مرکب با استفاده از تکنیک‌های یادگیری معیار فاصله Semi Supervised Multiple Kernel Learning using Distance Metric Learning Techniques 1 1 معیار فاصله، نقشی کلیدی در بسیاری از الگوریتم‌های آموزش ماشین و شناسایی آماری الگو دارد؛ به‌گونه‌ای که انتخاب تابع فاصله مناسب، تأثیر مستقیمی بر عملکرد این الگوریتم‌ها دارد. در سال­های اخیر، آموزش معیار فاصله با استفاده از نمونه‌های برچسب‌دار و یا دیگر اطلاعات موجود، یکی از حوزه‌های بسیار فعال در حوزه آموزش ماشین شده است. پژوهش‌ها در این راستا، نشان داده است که معیارهای سنجش فاصله مبتنی بر یادگیری، عملکرد بسیار بهتری در مقایسه با معیارهای فاصله مرسوم از قبیل فاصله اقلیدسی دارند. با گسترش این الگوریتم‌ها، نوع مبتنی بر کرنل برخی از این الگوریتم‌ها نیز ارائه شده که در آنها با استفاده از تابع کرنل، نمونه‌ها به‌طور غیر صریح به فضای ویژگی جدیدی با ابعاد بالاتر نگاشت یافته و سپس در این فضای ویژگی جدید، معیار فاصله برای کاربرد مورد نظر آموزش داده می‌شود. برخلاف عملکرد بسیار خوب توابع کرنل در الگوریتم‌های مختلف، یکی از مسائلی که در این الگوریتم‌ها وجود دارد، انتخاب کرنل مناسب و یا پارامترهای مناسب برای یک کرنل مشخص است. استفاده از کرنل مرکب به جای استفاده از یک کرنل به‌تنهایی، بهترین راه حلی است که تاکنون برای این مسئله ارائه شده است. در فرآیند دست‌یابی به کرنل مرکب بهینه نیز، استفاده از الگوریتم‌های یادگیری اهمیت دارد. در این پژوهش، با ادغام این دو فرآیند یادگیری، ساختارهای نیمه نظارتی متفاوتی برای تعیین وزن کرنل‌ها در یک ترکیب کرنلی ارائه می‌شود. کرنل مرکب نهایی برای سنجش فاصله داده‌ها در کاربرد خوشه‌بندی مورد استفاده واقع می‌شود. در ساختارهای نیمه نظارتی بررسی‌شده، سعی بر آن است که در فرآیند بهینه‌سازی با تعیین تابع هدف مناسب، وزن کرنل‌ها به‌گونه‌ای تعیین شود که فاصله زوج‌های مشابه کمینه و فاصله زوج‌های نامشابه بیشینه شود. بررسی عملکرد این ساختارهای پیشنهادی بر روی داده مصنوعی XOR و همچنین مجموعه داده‌های پایگاه داده UCI نشان دهنده مؤثر بودن ساختارهای پیشنهادی است. 2 Distance metric has a key role in many machine learning and computer vision algorithms so that choosing an appropriate distance metric has a direct effect on the performance of such algorithms. Recently, distance metric learning using labeled data or other available supervisory information has become a very active research area in machine learning applications. Studies in this area have shown that distance metric learning-based algorithms considerably outperform the commonly used distance metrics such as Euclidean distance. In the kernelized version of the metric learning algorithms, the data points are implicitly mapped into a new feature space using a non-linear kernel function. The associated distance metric is then learned in this new feature space. Utilizing kernel function improves the performance of pattern recognition algorithms, however choosing a proper kernel and tuning its parameter(s) are the main issues in such methods. Using of an appropriate composite kernel instead of a single kernel is one of the best solutions to this problem. In this research study, a multiple kernel is constructed using the weighted sum of a set of basis kernels. In this framework, we propose different learning approaches to determine the kernels weights. The proposed learning techniques arise from the distance metric learning concepts. These methods are performed within a semi supervised framework where different cost functions are considered and the learning process is performed using a limited amount of supervisory information. The supervisory information is in the form of a small set of similarity and/or dissimilarity pairs. We define four distance metric based cost functions in order to optimize the multiple kernel weight. In the first structure, the average distance between the similarity pairs is considered as the cost function. The cost function is minimized subject to maximizing of the average distance between the dissimilarity pairs.  This is in fact, a commonly used goal in the distance metric learning problem. In the next structure, it is tried to preserve the topological structure of the data by using of the idea of graph Laplacian. For this purpose, we add a penalty term to the cost function which preserves the topological structure of the data. This penalty term is also used in the other two structures. In the third arrangement, the effect of each dissimilarity pair is considered as an independent constraint. Finally, in the last structure, maximization of the distance between the dissimilarity pairs is considered within the cost function not as a constraint.  The proposed methods are examined in the clustering application using the kernel k-means clustering algorithm. Both synthetic (a XOR data set) and real data sets (the UCI data) used in the experiments and the performance of the clustering algorithm using single kernels, are considered as the baseline. Our experimental results confirm that using the multiple kernel not only improves the clustering result but also makes the algorithm independent of choosing the best kernel. The results also show that increasing of the number of constraints, as in the third structures, leads to instability of the algorithm which is expected. 53 70 2015/05/302015/01/112015/08/132015/04/182015/04/23 1394/2/3 2016/01/152016/10/52016/10/292016/10/292016/12/18 1395/9/28 طاهره زارع بیدکی Tahereh Zare Bidoki دانشگاه یزد ایران t.zare@yazd.ac.ir محمد تقی صادقی Mohammad Taghi Sadeghi دانشگاه یزد ایران m.sadeghi@yazd.ac.ir حمیدرضا ابوطالبی Hamid Reza Abutalebi دانشگاه یزد ایران habutalebi@yazd.ac.ir Distance Metric Learning Multiple Kernel Learning Similarity pairs Dissimilarity pairs Semi supervised یادگیری معیار فاصله یادگیری کرنل‌ مرکب زوج‌های مشابه زوج‌های نامشابه یادگیری نیمه‌نظارتی ]1[ O. Sojoodi, “Just-in-time adaptive distance metric learning in nonstationary environments”, Journal of Electronics industries, Vol. 6, No. 2, 2015.##[2] R. O. Duda, P. E. Hart and D. G.Stork,Pattern Classification, 2nd edition, New York: Wiley, 2001.##[3] D. A. Forsyth and J Ponce, Computer Vision: A Modern Approach, Englewood Cliffs, NJ: Prentice Hall, 2003.##[4] L. Yang, R. Jin, “Distance metric learning: a comprehensive survey”, Technical Report, Michigan State University, 2006.##[5] L. Yang, “An Overview of Distance Metric Learning”, Technical Report, Michigan State University, 2007.##[6] C. Domeniconi, J. Peng, D. Gunopulos, “Locally adaptive metric nearest neighbor classification”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 9, pp. 1281-1285, 2002.##[7] C. Domeniconi, D. Gunopulos, “Large margin nearest neighbour classifiers”, IEEE Transactions on Neural Networks, Vol.16, No.4, pp.899-909, 2005.##[8] D.Wang, J. S. Lim, M.-M. Han, B.-W. Lee, “Learning similarity for semantic images classification”, Neurocomputing, Vol. 67, pp. 363-368, 2005.##[9] J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, “Neighbourhood components analysis,” In Advancesin Neural Information Processing Systems (NIPS),2005.##[10] K. Weinberger, J. Blitzer, and L. Saul, “Distance metric learning for large margin nearest neighborclassification,”InAdvancesin Neural Information Processing Systems (NIPS),2006.##[11] E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, “Distance metric learning, with application to clustering with side-information”, In Advancesin Neural Information Processing Systems (NIPS), 2003. ##[12] S.Shalev-Shwartz, Y. Singer, and A. Y.Ng, “Online and Batch Learning of Pseudo-Metrics”, In Proc.Int. Conf. on Machine Learning (ICML), 2004.##[13] N. Kumar, K. Kummamuru, “Semi-supervised clustering with metric learning using relative comparisons”, IEEE Transactions on Knowledge and Data Engineering, Vol.20, No. 4, 496-503, 2007.##[14] H. Chang, D.-Y. Yeung, “Locally linear metric adaptation with application to semi-supervised clustering and image retrieval”, Pattern Recogition,Vol. 39, No. 7, pp.1253-1264, 2006.##[15] M. Schultz, T. Joachims, “Learning a distance metric from relative comparisons”, In Advancesin Neural Information Processing Systems (NIPS),2004.##[16] S. Xiang, F. Nie, C. Zhang, “Learning a Mahalanobis distance metric for data clustering and classification”, Pattern Recognition, Vol. 41, No. 12, pp. 3600-3612, 2008.##[17] A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall, “Learning distance functions using equivalence relations,”InProc.Int. Conf. on Machine Learning (ICML), Washington, DC, 2003.##[18] H. Chang, D.-Y. Yeung,, “Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints”, Pattern Recognition, Vol. 39, No.5, pp. 1007-1010, 2006.##[19] James T. Kwok and IvorW. Tsang, “Learning with idealized kernels”, In Proc.Int. Conf. on Machine Learning (ICML), 2003.##[20] D. Y. Yeung and H.Chang, “A kernel approach for semisupervised metric learning”, IEEE Transactions on Neural Networks,vol. 18, no. 1, pp. 141-149, Jan. 2007.##[21] S. C. H. Hoi, R. Jin, M. R. Lyu, “Learning nonparametric kernel matrices from pairwise constraints”, In Proc.Int. Conf. on Machine Learning (ICML),New York, USA, 2007.##[22] M. S. Baghshah and S. B. Shouraki, “Kernel-based metric learning for semi-supervised clustering”, Neurocomputing, Vol. 73, No. 7-9, pp. 1352-1361, 2010.##[23] M. Gönen, E. Alpaydın, “Multiple kernel learning algorithms”, Journal of Machine Learning Research, Vol. 12, pp. 2211–2268, 2011.##[24] J. Wang, H. Do, A. Woznica, and A. Kalousis. Metric learning with multiple kernels. In Advances in Neural Information Processing Systems(NIPS), MIT Press, 2011.##[25] F. Yan, K. Mikolajczyk and J. Kittler, “Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval”,InProc. Multiple Classifier Systems, pp. 147-156, 2011.##[26] T. Zare, M. T. Sadeghi and H. R. Abutalebi, “Semi-supervised Metric Learning Using Composite Kernels”, In Proc.6th Int. Telecommunication symposium (IST), 2012. ##[27] T.Zare, M. T. Sadeghi, H. R. Abutalebi, “A Novel Multiple Kernel Learning Approach for Semi-Supervised Clustering”, In Proc.the 8th Iranian Conference on Machine Vision and Image Processing (MVIP), 2013.##[28] T. Zare, M. T. Sadeghi and H. R. Abutalebi, “A Comparative Study of Multiple Kernel Learning Approaches for SVM Classification”, In Proc.6th Int. Telecommunication symposium (IST), 2014. ##[29] D. 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کاهش ابعاد داده‌های ابرطیفی به منظور افزایش جدایی‌پذیری کلاس‌ها و حفظ ساختار داده Feature reduction of hyperspectral data for increasing of class separability and preserving of data structure 1 1 امروزه تصویربرداری ابرطیفی به‌منظور طبقه‌بندی داده‌های سطح زمین با دقت و جزئیات بالا بسیار مورد توجه است. به‌دلیل کمبود نمونه آموزشی در دسترس، کاهش ابعاد دادۀ ابرطیفی به‌عنوان یک گام مهم پیش‌پردازش در تحلیل و طبقه‌بندی تصاویر ابرطیفی به شمار می‌رود. در این مقاله یک روش استخراج ویژگی پیشنهاد شده که سعی می‌کند، علاوه‌بر افزایش جدایی‌پذیری طبقه‌ها، ساختار داده را نیز حفظ کند. برای این منظور، دو تابع هدف پیشنهاد شده است. تابع هدف نخست از نمونه‌های آموزشی برچسب‌دار بهره می‌برد و سعی می‌کند نمونه‌های هم‌طبقه را در فضای کاهش‌یافته تا جای ممکن به هم نزدیک کند. تابع هدف دوم از نمونه‌های بدون برچسب خوشه‌بندی‌شده‌ بهره برده و سعی می‌کند نمونه‌های متعلق به یک خوشه را در فضای کاهش‌یافته، تا جای ممکن به هم نزدیک گرداند. روش پیشنهادی بر روی سه دادۀ ابرطیفی واقعی مورد آزمایش قرار گرفته و برتری آن از نظر دقت طبقه‌بندی نسبت به تعدادی از روش‌های پرکاربرد استخراج ویژگی نشان داده شده است.  2 Hyperspectral imaging with gathering hundreds spectral bands from the surface of the Earth allows us to separate materials with similar spectrum. Hyperspectral images can be used in many applications such as land chemical and physical parameter estimation, classification, target detection, unmixing, and so on. Among these applications, classification is especially interested. A hyperspectral image is a cube data containing two spatial dimensions and a spectral one. Generally, the Hughes phenomenon is occurred in the supervised classification of hyperspectral images due to the limited available labeled samples and the curse of dimensionality. So, feature reduction is an important preprocessing step for analysis and classification of hyperspectral data. Feature reduction methods are categorized into feature selection approaches and feature extraction ones. Our main focus in this paper is on feature extraction. The feature extraction methods are also divided into three main groups: supervised (with labeled samples), unsupervised (without labeled samples), and semi-supervised (with both labeled and unlabeled samples). The first group of feature extraction methods usually suffers from problems due to limited available training samples. These methods often consider the separability between classes, and so are efficient for classification applications. The second group has no need for training samples, but they often do not consider the separability between different classes and so, are not appropriate for classification. These methods are usually used for signal representation or preserving the local structure of data. The use of both labeled and unlabeled samples in the third group can increase the abilities of the feature extractor.  A feature extraction method is proposed in this paper which belongs to the third group. The proposed method increases the class separability and tries to preserves the structure of data. The proposed feature extraction method uses the ability of unlabeled samples in addition to available limited training samples to improve the classification performance. The experimental results on three real hyperspectral images show the better performance of proposed method compared to some popular feature extraction methods in terms of classification accuracy. 71 82 2015/05/302015/01/112015/08/132015/04/182015/04/232015/03/7 1393/12/16 2016/01/152016/10/52016/10/292016/10/292016/12/182016/12/6 1395/9/16 مریم ایمانی Maryam Imani دانشگاه تربیت مدرس ایران maryam.imani@modares.ac.ir حسن قاسمیان Hassan Ghassemian دانشگاه تربیت مدرس ایران ghassemi@modares.ac.ir high dimension small training set hyperspectral classification feature reduction. ابعاد بالا نمونه آموزشی کم ابرطیفی طبقه‌بندی کاهش ویژگی [1] G. F. Hughes, “On the mean accuracy of statistical pattern recognition,” IEEE Transactions on Information Theory, vol. IT-14, no. 1, pp. 55–63, 1968.##[2] Q. Zhang, Y. Tian, Y. Yang, and C. Pan, “Automatic Spatial–Spectral Feature Selection for Hyperspectral Image via Discriminative Sparse Multimodal Learning”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 261-279, 2015.##[3] J. Xia, J. Chanussot, P. Du, X. He, “Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests with Local Feature Extraction and Markov Random Fields”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 2532–2546, 2015.##[4] M. Imani and H. Ghassemian, “Feature Extraction Using Weighted Training Samples”, IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 7, pp. 1387-1391, 2015. ##[5] X. Kang, S. Li, L. Fang, J. A. Benediktsson, “Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 4, pp. 2241-2253, 2015.##[6] L. Ladha, and T. Deepa, “Feature Selection Methods and Algorithms”, International Journal on Computer Science and Engineering, vol. 3, no. 5, pp. 1787–1797, 2011.##[7] C. Persello, and L. Bruzzone, “Advanced Techniques for the Classification of Very High Resolution and Hyperspectral Remote Sensing Images”, PhD Dissertation, university of Trento, 2010.## [8] D. Korycinski, M. Crawford, J.W. Barnes, J. Ghosh, “Adaptive feature selection for hyperspectral data analysis using a binary hierarchical classifier and tabu search”, IEEE Symposium on Geoscience and Remote Sensing, vol. 1, pp. 297- 299, 2003.##[9] S.B. Serpico and L. Bruzzone, “A New Search Algorithm for Feature Selection in Hyperspectral Remote Sensing Images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 7, pp. 1360–1367, 2001.##[10] S. Li, H. Wu, D. Wan, J. Zhu, “An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine”, Knowledge-Based Systems, vol. 24, pp. 40-48, 2011.##[11] H. Peng, F. Long, C. and Ding, “Feature Selection Based on Mutual Information: Criteria of Max Dependency, Max-Relevance, and Min-Redundancy”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226-1238, 2005.##[12] J.M. Sotoca, and F. Pla, “Band selection using mutual information matrix for hyperspectral data”, marmota.dlsi.uji.es/WebBIB/papers, 2006. ##[13] J. Yin, C. Gao, and X. Jia, “Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction”, IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 4, pp. 705 – 709, 2012.##[14] L. Bruzzone, and C. Persello, “A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 9, pp. 3180–3191, 2009.##[15] K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press Inc, San Diego, 1990.##[16] L. Zhang, L. Zhang, D. Tao, and X. Huang, “Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 242–256, 2013.##[17] J. Yin, Y. Wang, and J. Hu, “A new dimensionality reduction algorithm for hyperspectral image data using evolutionary strategy”, IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp. 935–943, 2012.##[18] D. Lunga and O. Ersoy, “Spherical Nearest Neighbor Classification: Application to hyperspectral Data”, ECE Technical Reports, Purdue University, 2010.##[19] D. Lunga and O. 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طراحی و ساخت یک سیستم تشخیص خواب آلودگی راننده مبتنی بر پردازش‌گر سیگنال TMS320C5509A Design and Hardware Implementation of a Driver Drowsiness Detection System Based on TMAS320C5505A DSP Processor 1 1 همه‌ ساله، بسیاری از مردم جان خود را در تصادفات جاده‌ای و در حین رانندگی از دست می‌دهند. یکی از عوامل اصلی وقوع این تصادفات، خستگی و خواب‌آلودگی است؛ بنابراین، تشخیص زودهنگام خواب‌آلودگی راننده تأثیر زیادی در کاهش آمار تصادفات جاده‌ای دارد. در این مقاله، ابتدا یک پایگاه داده شامل سیگنال‌های مغزی ده داوطلب مرد در شرایط مشخص، ثبت و گردآوری شده است؛ سپس، روشی برای تشخیص سطح خواب‌آلودگی فرد از روی سیگنال‌های مغزی پیشنهاد می‌شود که مبتنی بر تبدیل موجک و طبقه‌بند ماشین بردار پشتیبان، SVM، بوده و تنها از دو کانال سیگنال‌های مغزی استفاده می‌کند. در ادامه، یک سامانه سخت‌افزاری مبتنی بر پردازش‌گر سیگنال TMS320C5509A برای پیاده‌سازی عملی روش پیشنهادی، طراحی و ساخته شده است. این سامانه قابل حمل بوده و به‌کمک باتری قادر است تا حدود ده ساعت کار کند. نتایج نشان از دقت صد درصد در برخی نمونه‌ها داشته است.  2 Every year, many people lose their lives in road traffic accidents while driving vehicles throughout the world. Providing secure driving conditions highly reduces road traffic accidents and their associated death rates. Fatigue and drowsiness are two major causes of death in these accidents; therefore, early detection of driver drowsiness can greatly reduce such accidents. Results of NTSB investigations into serious and dangerous accidents, where drivers had survived the crash, pinpointed intense driver fatigue and drowsiness as their two major causes [1]. This research study first developed a database including brain signals from ten male volunteers under certain conditions. A combination of Wavelet Transform (WT) and Support Vector Machine (SVM) classifier was then used to propose a drowsiness level detection method which used only two EEG signal channels. A hardware system was then adopted for practical implementation of the proposed method. The building blocks of this hardware system included a two-channel module for receiving and pre-processing EEG signals based on a TMS320C5509A digital signal processor. This processor was adopted in this study for the first time for detecting drowsiness level, and a real-time implementation of the SVM classifier revealed its functionality. This is a portable system backed by a battery for a 10-hour operation. Results from simulation and hardware implementation of the proposed method on ten volunteers indicated an up-to-100 percent accuracy. Works done on determining drowsiness level of drivers are two-fold: The first group uses shape and general conditions of the body with a focus on: Head movements Eye tracking Eye blink percent There are a few hardware systems developed for this group. The second group of research works use biometric signals (e.g. ECG and EEG) to detect drowsiness level in drivers [2-4]. EEG signals are the most applied biometric signals for drowsiness level determination purposed due to their low risk and high reliability [21, 28]. Accordingly, EEG Signals were used in this work for the same purpose. This research study first developed a database including brain signals from ten male volunteers under certain conditions. A combination of Wavelet Transform (WT) and Support Vector Machine (SVM) classifier was then used to propose a drowsiness level detection method which used only two EEG signal channels. A hardware system was then adopted for practical implementation of the proposed method. The building blocks of this hardware system included a two-channel module for receiving and pre-processing EEG signals based on a TMS320C5509A digital signal processor. This processor was adopted in this study for the first time for detecting drowsiness level, and a real-time implementation of the SVM classifier revealed its functionality. This is a portable system backed by a battery for a 10-hour operation. Results from simulation and hardware implementation of the proposed method on ten volunteers indicated an up-to-100 percent accuracy. A proper, valid, and accessible database with sufficient data entries plays an important role in the success rate of proposed approaches. On the other hand, available databases were either inaccessible or their data were in no good condition or were insufficient. Therefore, a new database including EEG signals of ten male volunteers with the mean age of 24 and at least two years road driving experience was first developed for the purpose of this study. EEG signals of volunteers were recorded in two alertness and drowsiness modes during driving simulation using a driving simulator and driving computer game. In most drowsiness level detection methods, more than two brain channels are usually used [20]; however, in this work, only two channels were used while maintaining the efficiency of drowsiness level determination. This made the system less cluttered for the driver, scaled down the processing workload for detecting and displaying the drowsiness level, reduced power consumption, and finally maximized the hardware system's operation time. Recorded signals were pre-processed to prepare them for the next stages including feature extraction and classification. Spectral features related to a number of bands (especially, Alpha and Theta) were the main features ever used for this purpose. So far, wavelet transform (WT) has been an important method for extracting these bands and computing their related features [7-9]. In addition, for this purpose, SVM and neural networks have been widely used as classifiers [15, 16, 18]. In this study, however, WT and the energy of some frequency bands were adopted for feature extraction whereas SVM was used for classification. Hardware-wise, very few studies have implemented their proposed approach. On the other hand, developments in applications of signal processors have raised their significance and also hope of using them in large scale processing algorithms, on a daily basis. Manufactured by Texas Instruments, TMS320C55xx family signal processors are an important and widely-used type [23]. Thanks to its low-consumption members, this family of processors is specialized for processing 1-D signals used in portable applications. Some of the main characteristics of this signal processors include low power consumption, fair prices, diverse functional peripherals (e.g. USB and McBSP), direct memory access (DMA), timer, LCD controller, supporting a number of major widely-used communication protocols, A/D converter, fast internal dual access memories, high operating frequency (typically 200 to 300 MHz), supporting dedicated signal processing instructions (such as the LMS and Viterbi algorithms), parallel execution of two commands. To the best of our knowledge, this signal processor has not been used for any drowsiness level detection applications. A major contribution of this paper was using a TMS320C5505A digital signal processor in a portable hardware system applied for drowsiness level detection of drivers. The frequency band of EEG signals usually ranges from 0.5 to 30 Hz that is partitioned into delta (0.5 to 4 Hz), theta (4 to 8 Hz), alpha (8 to 13 Hz) and beta (13 to 30 Hz) sub-bands. EEG signals' energy is raised in low frequency bands (e.g. delta and theta) during meditation, deep relaxation and the alertness-to-fatigue transition. With regards to these major sub-bands, an FIR band-pass filter with high and low cut-off frequencies set at 30 and 0.3 Hz, respectively, was designed using the windowing method. The developed hardware board had four inputs relating to two EEG signal channels (O1 and O2), a CZ reference channel and a ground signal. It had low power consumption (less than 25 mW) capable of operating for 10 hours with only two 3V CR2032 batteries. Using batteries with high A·h values would lead to longer circuit life. Signals from electrodes were pre-amplified and filtered in this board to remove noises outside the 0.5 to 30 Hz range. The electronic board designed and developed for EEG signal processing and alertness/drowsiness detection incorporated a TMS320C5509A digital signal processor made by Texas Instruments. For converting analog to digital signals, the TLV320AIC23B codec was used, and a TPS767D301 IC supplied power to the digital signal processor, both made by Texas Instruments. In the circuit's power supply section, a fuse and a Zener diode were placed consecutively in the path for supplying a 5V voltage to the power IC. These two items served as a protection circuit together. This protection circuit would automatically cut off the power once the current exceeds the 500 mA threshold, protecting the circuit against any damage. The 6.5V Zener diode prevents excessive supply of input voltage to the power IC. The power IC consisted of two inputs providing two output voltages (1.6V and 3.3V) for the switch, which distributed them throughout the circuit. The codec IC had one microphone input and one stereo input. The two received EEG signal channels entered the stereo input and exited the converter in a series arrangement. This IC included constants that should have been properly programmed before the conversion operation. This could be done by the I2C protocol using SDA and SCL pins connected to the processor. 83 98 2015/05/302015/01/112015/08/132015/04/182015/04/232015/03/72015/03/17 1393/12/26 2016/01/152016/10/52016/10/292016/10/292016/12/182016/12/62016/10/24 1395/8/3 علی رجائیان Ali Rajaeyan دانشگاه صنعتی شاهرود ایران ali.rajaian@gmail.com هادی گرایلو Hadi Grailu دانشگاه صنعتی شاهرود ایران grailu@shahroodut.ac.ir Drowsiness detection EEG DSP Processor TMS3205509A Wavelet Transform. سیگنال‌های مغزی تشخیص خواب‌آلودگی راننده پردازش‌گر سیگنال پیاده‌سازی سخت‌افزاری. [1] A. R. Varma, S. V. Arote, and C. Bharti, “Accident Prevention Using Eye Blinking and Head Movement”, Emerging Trends in Computer Science and Information Technology, ETCSIT2012, pp. 18-22, 2012.##[2] V. Von, “Driver Mental States Monitoring Based on Brain Signals,” Ms.C. Thesis, Technischen Universität Berlin, 2001.##[3] A. Williamson and T. Chamberlain, “Review of On-Road Driver Fatigue Monitoring Devices,” NSW Injury Risk Management Research Centre, University of New South Wales, 2005.##[4] A. Subasi, “Application of Classical and Model-Based Spectral Methods to Describe the State of Alertness in EEG,” Journal of Medicine Systems, Vol. 29, No. 5, pp. 473-486, 2005b.##[5] A. Subasi, “Automatic Recognition of Alertness Level from EEG by Using Neural Network and Wavelet Coefficients,” Expert System Applications, Vol. 28, No. 4, pp.701-11, 2005a.##[6] E. Magosso, M. Ursino, F. Provini, and P. Montagna, "Wavelet Analysis of Electro encephalographic and Electro-oculographic Changes During the Sleep Onset Period," Proc of the 29th Annual International Conferenceof the IEEE EMBS Cité Internationale, Lyon, 2007.##[7] Mardi Z., Miri Ashtiani S. N., and Mikaili M., “EEG-Based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests,” Journal of Medical Signals and Sensors, Vo. 1, No. 2, pp. 130-137, 2011.##[8] Makeig S., Jung T.-P., and Sejnowski T. J., “Using Feedforward Neural Networks to Monitor Alertness from Changes in EEG Correlation and Coherence,” Advances in Neural Information Processing Systems 8 (NIPS96), D. Touretzky, M. Mozer and M. Hasselmo (Eds.), MIT Press, pp. 931-937, 1996.##[9] G. Sattibabu, B. V. Satyanarayana, and K. Satyanarayana, “Design and Implementation of Wireless Brainwave Stimulated Accident Prevention System,” International Journal of Innovative Technology and Research, Vol. 1, No. 1, pp. 86-89, 2013. ##[10] K. J. Umar and C. S. Kumar, “Non-Invasive EEG-Based Wireless Brain Computer Interface for Safety Applications Using Embedded Systems,” International Journal of Innovative research in Computer and Communication Engineering, Vol. 2, Special Issue 1, 2014.##[11] S. R. Raut and S. M. Kulkarni, “A Real-Time Drowsiness Detection System for Safe Driving,” International Journal of Electronics, Electrical and Computational System, Vol. 3, No. 5, pp. 15-19, 2014.##[12] B. Paulchamy and Y. Vennila, “A Proficient System for Preventing and Acknowledging About the drunken Drive by Analysing the Neuronal-Activity of the Brain,” Asian Research Publishing Network (ARPN): Journal of Engineering and Applied Sciences, Vol. 7, No. 8, pp. 1029-1036, 2012.##[13] R. Rossi, M. Gastaldi, and G. Decchele, “Analysis of Driver Task-Related Fatigue Using Driving Simulator Experiments,” Procedia - Social and Behavioral Sciences, Vol. 20, pp. 666- 675, 2011.##[14] M. B. Kurt, N. Sezgin, M. Akin, G. Kirbas, and M. Bayram, “The ANN-Based Computing of Drowsy Level,” Expert System Applications, Vol. 36, No. 2, pp.2534-2542, 2009.##[15] R. S. Huang, L. L. Tsai, C. J. Kuo, “Selection of Valid and Reliable EEG Features for Predicting Auditory and Visual Alertness Levels,” Proceedings of the National Science Council, Republic of China. Part B, Life sciences, Vol. 25, No. 1, pp. 17-25, 2001.##[16] M. V. Yeo, X. Li, K. Shen, S. Wilder and P. V. Einar, “Can SVM be Used for Automatic EEG Detection of Drowsiness During Car Driving?,” Safety Science, Vol. 47, No. 1, pp. 115–24, 2009.##[17] S.F. Liang, C.T. Lin, R.C. Wu, Y.C. Huang, and T.P. Jung, "Monitoring Driver’s Alertness Based on the Driving Performance Estimation and the EEG Power Spectrum Analysis," Proc. IEEE 27th Annual Conf. on Engineering in Medicine and Biology (IEEE-EMBS), Shanghai, China, pp. 5738 ­ 5741, 1-4 September 2005.##[18] T.- P. Jung, S. Makeig, M. Stensmo, and T. J. Sejnowski, “Estimating Alertness from the EEG Power Spectrum,” IEEE Transactions on Biomedical Engineering, Vol. 44, No. 1, pp. 60-69, 1997.##[19] C.- T. Lin, R.-C. Wu, S.- F. Liang, W.- H. Chao, Y.- J. Chen, and T.- P. 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الگوریتم کنترل نرخ بیت متغیر ویدئو در سطح گروه تصاویر برای استاندارد فشرده‎سازی H.265 A GOP-Level Variable Bit Rate Control Algorithm for H.265 Video Coding Standard 1 1 در این مقاله، یک الگوریتم کنترل نرخ بیت در سطح گروه تصاویر ((GOP برای استاندارد ویدئویی جدید H.265 جهت کاربردهایی با نرخ بیت متغیر با قید بافر ارائه ‌شده است. با توجه به تغییرات ساختاری کدگذار استاندارد H.265 نسبت به استانداردهای قبلی، نیاز به طراحی الگوریتم‎های جدید کنترل نرخ بیت احساس می‎شود. در الگوریتم پیشنهادی، تغییرات پارامتر چندی‎سازی (QP) برای هر گروه تصاویر نسبت به گروه تصاویر قبلی با توجه به نرخ بیت هدف و وضعیت بافر محاسبه می‎شود. این روش امکان تغییرات کوتاه‌مدت هدفمند در نرخ بیت ویدئوی فشرده‌شده را به‌نحوی فراهم می‌کند تا ویدئوی بازسازی‌شده کیفیت دیداری یکنواخت‌تر و مطلوب‎تری داشته باشد. برخلاف روش‎های متداول، این الگوریتم به‌جای استفاده از مدل‎های نرخ-اعوجاج (R-D)، از یک جدول مراجعه بهره می‎برد که باعث کاهش چشم‌گیر حجم محاسبات شده است. نتایج پیاده‎سازی نشان می‎دهد، نه‌تنها نرخ بیت خروجی مطابق قید بافر به‌طورکامل کنترل می‌شود، بلکه کیفیت ویدئوی خروجی نیز نسبت به حالت بدون کنترل به‌خوبی حفظ می‌شود. 2 A rate control algorithm at the group of picture (GOP) level is proposed in this paper for variable bit rate applications of the H.265/HEVC video coding standard with buffer constraint. Due to structural changes in the HEVC compared to the previous standards, new rate control algorithms are needed to be designed. In the proposed algorithm, quantization parameter (QP) of each GOP is obtained by modifying QP of previous GOP according to target bit rate and buffer status. Buffer status and target bit rate are input variables selected to expand a two dimensional lookup table. Output of the lookup table is provided in a way to allow short-term variations in bit rate, in order to reach better and more uniform visual quality of reconstructed video. In addition, a QP cascading technique is used for calculating QP of frames in each GOP that operates like a bit allocation scheme and causes suitable trade-off between quality and compression rate. Unlike conventional methods, proposed scheme uses a lookup table instead of using a rate-distortion model that significantly reduces the computational complexity. Several video sequences with completely different contents were used for experiments. Some short video sequences are concatenated to attain long video sequences which are closer to variable bit rate applications.  Lookup table based (LUT) proposed algorithm is implemented on HM reference software and compared with λ-domain rate control algorithm (λ-RC) and constant QP (CQP) case that defined as anchor. In almost the same average bit rate (CQP: 1527.97, LUT: 1520.92, λ-RC: 1529.41), average QP (28.09, 28.18, 29.91) and average peak signal to noise ratio (PSNR) (37.88, 37.87, 37.76) of LUT is closer to CQP than that of λ-RC. Average values of QP standard deviation (1.13, 2.28, 4.27) and PSNR standard deviation (1.37, 2.11, 2.15) of LUT is smaller than λ-RC and closer to CQP. From rate control point of view, minimum buffering delay on average for all video sequences resulted by LUT is the same with that of λ-RC which is one of the best rate controllers proposed for the HEVC (0.94, 0.36, 0.35). Consequently, experimental results show that not only bit rate is perfectly controlled according to buffer constraints, but also the quality of reconstructed video is well maintained. 99 110 2015/05/302015/01/112015/08/132015/04/182015/04/232015/03/72015/03/172015/05/14 1394/2/24 2016/01/152016/10/52016/10/292016/10/292016/12/182016/12/62016/10/242016/06/15 1395/3/26 داود فانی Davoud Fani دانشگاه سیستان و بلوچستان - دانشکده مهندسی برق و کامپیوتر ایران fani.davoud@gmail.com مهدی رضائی Mehdi Rezaei دانشگاه سیستان و بلوچستان - دانشکده مهندسی برق و کامپیوتر ایران mehdi.rezaei@ece.usb.ac.ir مریم سرحدی اول Maryam Sarhaddi Avval دانشگاه سیستان و بلوچستان - دانشکده مهندسی برق و کامپیوتر ایران msarhaddi67@gmail.com H.265/HEVC standard Lookup table Rate control algorithm Variable bit rate Video coding استاندارد H.265/HEVC جدول مراجعه (Lookup Table) فشرده‎سازی ویدئو کنترل نرخ بیت نرخ بیت متغیر [1] V. Sze, M. Budagavi, and G. J. Sullivan, High Efficiency Video Coding (HEVC). Springer Cham Heidelberg, London, 2014.##[2] ITU-T, Series H: Audiovisual and Multimedia Systems, 2015.##[3] K. Ugur, K. Andersson, A. Fuldseth, G. Bjontegaard, L. P. Endresen, J. Lainema, A. Hallapuro, J. Ridge, D. Rusanovskyy, C. Zhang, A. Norkin, C. Priddle, T. Rusert, J. Samuelsson, R. Sjoberg, and Z. Wu, “High Performance, Low Complexity Video Coding and the Emerging HEVC Standard,” IEEE transactions on Circuits and Systems For Video Technology, vol. 20, no. 12, pp.1688-1697, 2010.##[4] M. T. Pourazad, C. Doutre, M. Azimi, and P. Nasiopoulos, “HEVC: The new gold standard for video compression: how does HEVC compare with H.264/AVC?,” IEEE CE Magazine, vol. 1, no. 3, pp. 36-46, 2012.##[5] K. Kim, K. McCam, K. Sugimoto, B. Bross, and W. J. Han, HM-9: High Efficiency Video Coding (HEVC) Test Model 9 Encoder Description, document Rec. JCTVC-K1002-v1, 11th Meeting: Shanghai, 2012.##[6] G. Sullivan, J. R. Ohm, J. H. Woo, and T. Wiegand, “Overview of the High-Efficiency Video Coding (HEVC) Standard, ” IEEE transactions on Circuits and Systems For Video Technology, vol. 22, no. 12, pp. 1649-1668, 2012.##[7] S. Ma, J. Si, and S. Wang, “A study on the rate distortion modeling for high efficiency video coding,” in Proc. IEEE ICIP, Orlando, FL, 2012, pp. 181–184.##[8] H. Choi, J. Yoo, J. Nam, D. Sim, and I. V. Bajic, “Pixel-wise unified rate-quantization model for multi-level rate control,” Selected Topics in Signal Processing, IEEE Journal of, vol. 7, no. 6, pp. 1112-1123, 2013.##[9] B. Lee, M. G. Kim, and T. Q. Nguyen, “A frame-level rate control scheme based on texture and non-texture rate models for High Efficiency Video Coding,” Circuits and Systems for Video Technology, IEEE Transactions on, vol. 24, no. 3, pp. 465-479, 2014.##[10] Ch. W. Seo, J. H. Moon, and J. K. Han, “Rate control for consistent objective quality in high efficiency video coding,” Image Processing, IEEE Transactions on, vol. 22, no. 6, pp. 2442-2454, 2013.##[11] J. Si, S. Ma, S. Wang, and W. Gao, “Laplace distribution based CTU level rate control for HEVC,” in Visual Communications and Image Processing (VCIP), Kuching , 2013, pp. 1-6.##[12] J. Si, S. Ma, and W. Gao, “Efficient bit allocation and CTU level rate control for High Efficiency Video Coding,” in Picture Coding Symposium (PCS), 2013, San Jose, CA, 2013, pp. 89-92.##[13] J. Si, S. Ma, X. Zhang, W. Gao, “Adaptive Rate control for High Efficiency video Coding, ” in Proc. SPIE Conference on Visual Communicat-ions and Image Processing, San Diego, CA, 2012, pp. 1-6.##[14] Sh. Wang, S. Ma, Sh. Wang, D. Zhao, and W. Gao, “Rate-GOP Based Rate Control for High Efficiency Video Coding,” IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 6, pp. 1101 – 1111, 2013.##[15] Sh. Wang, S. Ma, L. Zhang, Sh. Wang, D. Zhao, and W. Gao, “Multilayer based rate control algorithm for HEVC, ” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), Beijing, 2013, pp. 41-44.##[16] Sh. Wang, S. Ma, S. Wang, D. Zhao, and W. Gao, “Quadratic ρ-domain based rate control algorithm for HEVC,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, Vancouver, BC, 2013, pp. 1695-1699.##[17] B. Li, H. Li, L. Li, and Zhang J. Zhang, “λ Domain Rate Control Algorithm for High Efficiency Video Coding,” IEEE transactions on Image Processing, vol. 23, no. 9, pp. 3841-3854,2014.##[18] Zh. Yang, L. Song, Z. Luo, and X. Wang, “Low delay rate control for HEVC,” in Broadband Multimedia Systems and Broadcasting (BMSB), 2014 IEEE International Symposium on, Beijing, 2014, pp. 1-5.##[19] S. Rodriguez S, and Th. Schierl, “A rate control algorithm for HEVC with hierarchical GOP structures,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, 2013, pp. 1719-1723.##[20] M. Rezaei, M. M. Hannuksela, and M. Gabbouj, “Semi-fuzzy rate controller for variable bit rate video,” Circuits and Systems for Video Technology, IEEE Transactions on, vol. 18, no. 5, pp. 633-645, 2008.##[21] JCT-VC of ISO/IEC MPEG and ITU-T VCEG, “HM Reference Software 16.1” [Online], Available: https://hevc.hhi.fraunhofer.de/svn/svn HEVCSoftware/tags/HM-16.1.## ##

مقایسه روش های طیفی برای شناسایی زبان گفتاری A survey on spectral methods in spoken language identification 1 1  شناسایی خودکار زبان گفتاری به تشخیص زبان از روی سیگنال گفتار گفته می­شود. شناسایی زبان به‌طورمعمول به یکی از  دو دسته روش آوایی و طیفی انجام می­شود. در این مقاله، انواع روش­های مختلف طیفی برای بازشناسی زبان گفتاری معرفی شده و نتایج به‌کارگیری آنها بر روی یک مجموعه دادگان گفتاری تلفنی محاوره­ای مقایسه شده است. روش طیفی پایۀ شناسایی زبان، مدل مخلوط گوسی-مدل جهانی (GMM-UBM) است. برای بهبود مدل گوسی هر زبان از روش تمایزی MMI و برای مدل­کردن دینامیک زبان از مدل پنهان مارکوف ارگودیک (EHMM) استفاده می­شود. روش­های GSV-SVM و روش نشانه­گذار مبتنی بر GMM (GMM Tokenizer) نیز دو روش طیفی دیگر است که مورد بررسی قرار گرفته است. در این مقاله همچنین روش­های جدیدِ مدل­سازی تنوعات کانال و گوینده (تحلیل توأم عامل­ها (JFA) و بردار شناسایی (i-Vector)) به‌کار رفته و برای بهبود نتایج آن از چند روش­ جبران­سازی تنوعات استفاده شده است. علاوه‌براین برای سهولت تصمیم­گیری و کاهش خطای سامانۀ شناسایی زبان، از پس­پردازش امتیاز استفاده شده است. این مقاله بخشی از هفت سال پژوهش‌ در زمینه شناسایی زبان گفتاری در پژوهشگاه توسعه فناوری­های پیشرفته خواجه نصیرالدین طوسی است و تنها خلاصه­ای از روش­ها و نتایج به‌دست‌آمده در این مقاله آورده شده است. 2 Identifying spoken language automatically is to identify a language from the speech signal. Language identification systems can be divided into two categories, spectral-based methods and phonetic-based methods. In the former, short-time characteristics of speech spectrum are extracted as a multi-dimensional vector. The statistical model of these features is then obtained for each language. The Gaussian mixture model is the most common statistical model in spectral-based language identification systems. On the other hand, in phonetic-based methods, speech signals are divided into a sequence of tokens using the hidden Markov model (HMM) and a language model is trained using the obtained sequence. Approaches like PRLM, PPRLM, and PR-SVM are some examples of phonetic-based methods. In research papers, usually a combination of phonetic-based and spectral-based systems are used to achieve a high quality language identification system. Spectral-based methods have been the focus of researchers, since they have no need for labeled data and usually achieve better results than phonetic approaches. Therefore, in this paper, these methods used for language identification and different spectral methods, are introduced, implemented, and compared with spoken language recognition. The basic spectral language identification method is Gaussian Mixture Model-Universal Background Model (GMM-UBM). In this paper, the MMI discrimination method is used to improve the Gaussian model of each language. Moreover, in order to model the language dynamically, GMM is replaced with the ergodic hidden Markov model (EHMM). GSV-SVM and GMM tokenizer methods are also implemented as two popular spectral approaches. In this paper, novel speaker and channel variation modeling methods are used as language identification approaches, including joint factor analysis (JFA), identity vector (i-Vector) and several variations compensation methods exploited to improve the results of i-Vector. Furthermore, in order to boost the performance of language recognition systems, different post-processing methods are applied. For post-processing, each element of raw score vector indicates the degree by which the spoken signal belongs to a language. Post-processing methods are applied to this vector as a classifier and allows making better language detection decisions by mapping the raw score vector to a space of desired languages. Different studies have employed different post-processing methods, including GMM, NN, SVM, and LLR. This study exploits several score post-processing methods to improve the quality of language recognition. The goal of the experiments in this article is to detect and distinguish Farsi, English, and Arabic, individually and simultaneously from other languages. The latter is also called open-set language identification. The signals considered in this paper include two-sided conversations, whose quality is usually not desirable due to strong noise signals, background noises of individuals or music, accents, etc. Gaussian mixture-universal model (GMM-UBM) was implemented as the basic method. In this approach, mean EER of the three target languages (Farsi, English, and Arabic) was 13.58. Experimental results indicated that training the GMM language identification system with the MMI discrimination training algorithm is more efficient than systems only trained by the ML algorithm. More specifically, the mean EER of the three target languages was reduced about 8 percent in comparison to GMM-UBM. The GMM tokenizer method was also tested as a novel spectral approach. Using this method, the mean EER of the three target languages was also about 5 percent better than GMM-UBM. In this study, the GSV-SVM discrimination method was also used for language recognition. The results of this method were considerably better than those of common spectral approaches, such that the mean EER of the three target languages was reduced by 11 percent in comparison to GMM-UBM. This study improves the low speed of this method using a model pushing method. This study also implemented two novel methods, JFA and i-Vector. According to the results, both of these methods provide better results than GMM-UBM, such that the mean EER values of the three target languages in JFA and i-Vector are respectively reduced by 1% and 12%. Generally, experimental results showed that i-Vector provides better results than other spectral language identification systems. This study is a result of a seven-year research in spoken language identification in the advanced technology development center of Khajeh Nasiredin Tousi. The ongoing research includes studying and implementing novel spectral language identification algorithms like PLDA and state-of-the-art phonetic language identification methods to combine the two spectral and phonetic systems and eventually, achieving a high quality language identification system. 111 134 2015/05/302015/01/112015/08/132015/04/182015/04/232015/03/72015/03/172015/05/142015/07/5 1394/4/14 2016/01/152016/10/52016/10/292016/10/292016/12/182016/12/62016/10/242016/06/152016/10/29 1395/8/8 شقایق رضا shaghayegh reza پژوهشکده پردازش داده، پژوهشگاه توسعه فناوری‌های پیشرفته خواجه‌نصیرالدین طوسی ایران shaghayegh.reza@gmail.com جهانشاه کبودیان jahanshah kabudian دانشگاه رازی کرمانشاه ایران kabudian@razi.ac.ir Automatic Spoken Language Recognition Acoustic Approaches Discriminative training Channel compensation Identity Vector. شناسایی خودکار زبان گفتاری روش‌های طیفی آموزش تمایزی جبران‌سازی تنوعات کانال بردار شناسایی. ] 1 [Sh. Reza, Z. Zeynalkhani, J. 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مدل میکروسکوپی دوگوشی مبتنی بر فیلتر بانک مدولاسیون برای پیش گویی قابلیت فهم گفتار در افراد دارای شنوایی عادی Binaural Microscopic Model Based on Modulation Filterbank for the Prediction of Speech Intelligibility in Normal-Hearing Listeners 1 1 در این مطالعه، مدل پیش‌گویی قابلیت فهم دوگوشی میکروسکوپی بر مبنای فیلتربانک مدولاسیون ارائه می‌شود. تاکنون در مدل‌های دوگوشی، از معیارهای طیفی مانند STI و SII و یا دیگر روابط تحلیلی برای تعیین میزان قابلیت فهم دوگوشی استفاده شده است. در مدل پیشنهادی، بر خلاف تمام مدل‌های پیش‌گویی قابلیت فهم دوگوشی، از بازشناساگر خودکار گفتار در قسمت پایانی به­عنوان واحد تصمیم‌گیری استفاده می‌شود. یک مزیت استفاده از این روش، امکان تحلیل میزان بازشناسی قسمت‌های کوچک گفتار مانند واج و سیلاب­ است. مزیت دیگر این مدل استفاده از پیش‌پردازش‌هایی است که وجود آنها در دستگاه شنوایی انسان به اثبات رسیده است. با استفاده از ماتریس ویژگی پیشنهادی در بازشناساگر گفتار، این مدل دارای پیش‌گویی­‌های خوبی در حضور یک منبع نوفه ایستان شبه­گفتار است. مقایسه نتایج مدل با نتایج حاصل از آزمایش‌های شنوایی، مقادیر همبستگی بالا و میانگین قدر مطلق خطای پایین را نشان می‌دهد. همچنین، ماتریس‌های ابهام برای همخوان‌ها همبستگی بالایی را بین پیش­گویی­ها و اندازه‌گیری‌ها نشان می­دهد. آستانه ادراک گفتار پیش‌گویی­شده توسط مدل پیشنهادی دارای میانگین قدر مطلق خطای کمتری (6/0 دسیبل) در مقایسه با مدل مبنای BSIM است. 2 In this study, a binaural microscopic model for the prediction of speech intelligibility based on the modulation filter bank is introduced. So far, the spectral criteria such as the STI and SII or other analytical methods have been used in the binaural models to determine the binaural intelligibility. In the proposed model, unlike all models of binaural intelligibility prediction, an automatic speech recognizer (ASR) is used in the back-end as the decision unit. One advantage of using this approach is the possibility of analyzing the recognition rate of small parts of speech such as phonemes and syllables. Another advantage of this model lies in the use of pre-processing that their existence in the human auditory system has been verified. Using the proposed feature matrix in the speech recognizer, this model has good predictions in the presence of one source of stationary speech-shaped noise. Comparing the results of the proposed model with those of listening tests show high correlations and low mean absolute error values. Also, the confusion matrices of the consonants represent high correlation between predictions and measurements. The predicted speech reception threshold by the proposed model has a smaller mean absolute error (0.6 dB) than the baseline model of BSIM.   135 151 2015/05/302015/01/112015/08/132015/04/182015/04/232015/03/72015/03/172015/05/142015/07/52015/08/1 1394/5/10 2016/01/152016/10/52016/10/292016/10/292016/12/182016/12/62016/10/242016/06/152016/10/292016/10/29 1395/8/8 علی فلاح Ali Fallah دانشگاه تبریز ایران ali.fallah@tabrizu.ac.ir مسعود گراوانچی زاده Masoud Geravanchizadeh دانشگاه تبریز ایران geravanchizadeh@tabrizu.ac.ir Prediction of Speech Intelligibility Binaural Models Modulation Filter bank Microscopic Models Macroscopic Models. پیش‌گویی قابلیت فهم گفتار مدل‌های دوگوشی فیلتربانک مدولاسیون مدل‌های میکروسکوپی مدل‌های ماکروسکوپی [1] M. Geravanchizadeh, A. Fallah, and A. 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