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کشف تقلب در بازار بورس اوراق بهادار با استفاده از کاربرد نامساوی چبیشف Stock Market Fraud Detection, A Probabilistic Approach 1 1 یکی از راه­‌های مشارکت افراد در توسعه اقتصادی کشور، سرمایه‌­گذاری در بازار سرمایه و به‌خصوص، بورس اوراق بهادار است. به این منظور، بازارهای اوراق بهادار باید مورد اعتماد مردم و فعالان اقتصادی باشند. شفافیت و کارایی بازار می‌­تواند حقوق و منافع سرمایه‌­گذاران را حمایت کند و باعث رونق بازار شود. در این میان، بعضی از افراد با توجه به موقعیت خود از اطلاعات نهانی مربوط به بازار بورس اوراق بهادار سوء استفاده می‌­کنند و باعث بی‌­اعتمادی افراد به بازار سرمایه می‌­شود؛ از‌این‌رو در این مقاله، با استفاده از کاربرد نامساوی چبیشف، روشی برای شناسایی افرادی که از اطلاعات نهانی، استفاده شخصی کرده­ و در مدت کوتاهی سود کلانی به‌­دست آورد‌ه‌­اند، ارائه شده است. به‌منظور استفاده از این روش دو فیلتر در نظر گرفته شده­ است، به‌طوری‌که فیلتر نخست تراکنش‌­های بزرگ را شناسایی می‌کند و فیلتر دوم، افرادی که بیشترین سود حاصل از خرید و فروش سهام در مدت زمان اندک (سه روز)، به‌دست آورده‌­ا‌ند؛ در‌حالی‌که دست‌کم یک تراکنش بزرگ در این حد فاصل زمانی رخ داده باشد، شناسایی می­‌کند؛ سپس روش پیشنهادی، بر روی دو دسته از داده­‌های واقعی بازار بورس اعمال شده است. با تغییر ضرایب فیلترها، می‌­توان معیارهای مورد نظر را تغییر داد. 2 In order to have a fair market condition, it is crucial that regulators continuously monitor the stock market for possible fraud and market manipulation. There are many types of fraudulent activities defined in this context. In our paper we will be focusing on "front running". According to Association of Certified Fraud Examiners, front running is a form of insider information and thus is very difficult to detect. Front running is committed by brokerage firm employees when they are informed of a customer's large transaction request that could potentially change the price by a substantial amount. The fraudster then places his own order before that of the customer to enjoy the low price. Once the customer's order is placed and the prices are increased he will sell his shares and makes profit. Detecting front running requires not only statistical analysis, but also domain knowledge and filtering. For example, the authors learned from Tehran's Over The Counter (OTC) stock exchange officials that fraudsters may use cover-up accounts to hide their identity. Or they could delay selling their shares to avoid suspicion.  Before being able to present the case to a prosecutor, the analyst needs to determine whether predication exists. Only then, can he start testing and interpreting the collected data. Due to large volume of daily trades, the analyst needs to rely on computer algorithms to reduce the suspicious list. One way to do this is by assigning a risk score to each transaction. In our work we build two filters that determine the risk of each transaction based on the amount of statistical abnormality. We use the Chebyshev inequality to determine anomalous transactions. In the first phase we focus on detecting a large transaction that changes market price significantly. We then look at transactions around it to find people who made profit as a consequence of that large transaction. We tested our method on two different stocks the data for which was kindly provided to us by Tehran Exchange Market. The officials confirmed we were able to detect the fraudster.  3 14 2018/06/2 1397/3/12 2019/09/2 1398/6/11 سید جواد کاظمی تبار Seyed Javad Kazemitabar دانشگاه صنعتی نوشیروانی بابل ایران j.kazemitabar@nit.ac.ir مجید شهباززاده Majid Shahbazzadeh دانشگاه صنعتی نوشیروانی بابل ایران m.shahbazzadeh@stu.nit.ac.ir Stock Exchange Market manipulation Front running Fraud detection Chebyshev inequality بازار بورس اوراق بهادار دست‌کاری بازار پیش‌روی کشف تقلب نامساوی چبیشف [1] L. Joneidi, M. Norouzi, "Recognizing hidden information in stock market," The Law Quarterly, vol. 39, no. 3, 2009.##[2] N. Katouzian, "General Theory of Commitment", Mizan Publications, Tehran 2012.##[3] M. E. Roszkowski, "Business law: Principles, cases and policy", Newyork. Addison Wesley. 1997.##[4] S. M. Bainbridge, "Insider treading", Newyork. Foundation. 1999.##[5] S. E. Sharifi, "Investigating legal aspects of stock market transactions via private information", The Journal of Legal Research, no. 6, 2004.##[6] H. E. Leland, "Insider treading: should it be prohibited?" The journal of political economy, 1992.##[7] I. Dadashi, S. Kord-Monjiri, M. Baradaran, "The impact of internal auditing on Financial Statement Fraud in companies registered in Tehran stock market", The Auditing Science, vol. 18, no. 70, 2018.##[8] A. Ansari, M. H. Sourashjani, "Legal analysis of stock market transactions", Stock Market Quarterly, no. 27, 2014.##[9] Y. Kim, and S. Y. Sohn, "Stock fraud detection using peer group analysis," Expert Systems with Applications. vol. 39, no. 10, pp. 8986-92, 2012.##[10] D. Diaz, B. Theodoulidis, and P. Sampaio, "Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices," Expert Systems with Applications, vol. 38, no. 10, pp. 12757-71, 2011.##[11] H. Öğüt, M. M. Doğanay, and R. Aktaş, "Detecting stock-price manipulation in an emerging market: The case of Turkey," Expert Systems with Applications. Vol. 36, no. 9, pp. 11944-49, 2009.##[12] K. Golmohammadi, and O. R. Zaiane, "Data mining applications for fraud detection in securities market", In Intelligence and Security Informatics Conference (EISIC), pp. 107-114, 2012.##[13] M. L. Huang, J. Liang, and Q. V. Nguyen, "A visualization approach for frauds detection in financial market. In Information Visualisation", 2009 13th International Conference. IEEE, pp. 197-202,##[14] K. Golmohammadi, O. R. Zaiane, D. Díaz, "Detecting stock market manipulation using supervised learning algorithms," In2014 Inter-national Conference on Data Science and Advanced Analytics (DSAA), pp. 435-441, 2014.##[15] P. Ravisankar, V. Ravi, G. R. Rao, and I. Bose, "Detection of financial statement fraud and feature selection using data mining techni-ques," Decision Support Systems, 50(2), pp. 491-500, 2011.##[16] F. H. Glancy, and S. B. Yadav, "A computational model for financial reporting fraud detec-tion," Decision Support Systems, no.50(3), pp. 595-601, 2011.##[17] R. Kanapickienė, and Ž. Grundienė, "The model of fraud detection in financial statements by means of financial ratios", Procedia: Social and Behavioral Sciences, 213, pp. 321-327, 2015.##[18] C. C. Lin, A. A. Chiu, S. Y. Huang, and D. C. Yen, "Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts' judgments".- Knowledge-Based Systems, no. 89, pp.459-470, 2015.##[19] H. Etemadi, H. Zolfi, "Application of logistic regression in financial statement fraud detection", The Auditing Science, vol. 13, no. 51, 2013.##[20] K. F. Haghighi, A. Hashemi, A. F. Dehkordi, "A study of correlation between profit management and financial statement fraud in the companies registered in Tehran stock market", The Auditing Science, vol. 4, no. 21, pp. 47-68, 2014.##[21] C. D. Katsis, Y. Goletsis, P. V. Boufounou, G. Stylios, and E. Koumanakos, "Using ants to detect fraudulent financial statements", Journal of Applied Finance and Banking, no.2(6), pp. 73, 2012.##[22] D. I. Topor, "The Auditor's Responsibility for Finding Errors and Fraud from Financial Situations: Case Study", International Journal of Academic Research in Accounting, Finance and Management Sciences, no.7(1), pp. 342-352, 2017.##[23] B. W. Silverman, "Density estimation for statistics and data analysis," Routledge, 2018.##[1] جنیدی لعیا ، نوروزی محمد ، "شناخت ماهیت اطلاعات نهانی در بورس اوراق بهادار"، فصلنامه حقوق، دوره 39، شماره 3 ، 1388.##[1] L. Joneidi, M. Norouzi, "Recognizing hidden information in stock market," The Law Quarterly, vol. 39, no. 3, 2009.##[2] کاتوزیان ناصر. "نظریه عمومی تعهدات"، تهران، میزان. 1391.##[2] N. Katouzian, "General Theory of Commitment", Mizan Publications, Tehran 2012.##[3] M. E. Roszkowski, "Business law: Principles, cases and policy", Newyork. Addison Wesley. 1997.##[4] S. M. Bainbridge, "Insider treading", Newyork. Foundation. 1999.##[5] شریفی سید الهام الدین. "بررسی تطبیقی جنبه‌های حقوقی معاملات در بازار بورس با استفاده از اطلاعات محرمانه". پژوهش‌های حقوقی، شماره 6. 1383.##[5] S. E. Sharifi, "Investigating legal aspects of stock market transactions via private information", The Journal of Legal Research, no. 6, 2004.##[6] H. E. Leland, "Insider treading: should it be prohibited?" The journal of political economy, 1992.##[7] داداشی ایمان، کردمنجیری سجاد، مریم برادران، "تأثیر ساختار حسابرسی داخلی بر احتمال تقلب در صورت‌های مالی شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران". دانش حسابرسی. دوره 18. شماره 70. 1397.##[7] I. Dadashi, S. Kord-Monjiri, M. Baradaran, "The impact of internal auditing on Financial Statement Fraud in companies registered in Tehran stock market", The Auditing Science, vol. 18, no. 70, 2018.##[8] انصاری علی، حیدری سورشجانی مریم، "تحلیل حقوقی تأیید بورس در معاملات اوراق بهادار". فصلنامه بورس اوراق بهادار. شماره 27 سال هفتم. 1393.##[8] A. Ansari, M. H. Sourashjani, "Legal analysis of stock market transactions", Stock Market Quarterly, no. 27, 2014.##[9] Y. Kim, and S. Y. Sohn, "Stock fraud detection using peer group analysis," Expert Systems with Applications. vol. 39, no. 10, pp. 8986-92, 2012.##[10] D. Diaz, B. Theodoulidis, and P. Sampaio, "Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices," Expert Systems with Applications, vol. 38, no. 10, pp. 12757-71, 2011.##[11] H. Öğüt, M. M. Doğanay, and R. Aktaş, "Detecting stock-price manipulation in an emerging market: The case of Turkey," Expert Systems with Applications. Vol. 36, no. 9, pp. 11944-49, 2009.##[12] K. Golmohammadi, and O. R. Zaiane, "Data mining applications for fraud detection in securities market", In Intelligence and Security Informatics Conference (EISIC), pp. 107-114, 2012.##[13] M. L. Huang, J. Liang, and Q. V. Nguyen, "A visualization approach for frauds detection in financial market. In Information Visualisation", 2009 13th International Conference. IEEE, pp. 197-202,##[14] K. Golmohammadi, O. R. Zaiane, D. Díaz, "Detecting stock market manipulation using supervised learning algorithms," In2014 Inter-national Conference on Data Science and Advanced Analytics (DSAA), pp. 435-441, 2014.##[15] P. Ravisankar, V. Ravi, G. R. Rao, and I. Bose, "Detection of financial statement fraud and feature selection using data mining techni-ques," Decision Support Systems, 50(2), pp. 491-500, 2011.##[16] F. H. Glancy, and S. B. Yadav, "A computational model for financial reporting fraud detec-tion," Decision Support Systems, no.50(3), pp. 595-601, 2011.##[17] R. Kanapickienė, and Ž. Grundienė, "The model of fraud detection in financial statements by means of financial ratios", Procedia: Social and Behavioral Sciences, 213, pp. 321-327, 2015.##[18] C. C. Lin, A. A. Chiu, S. Y. Huang, and D. C. Yen, "Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts' judgments".- Knowledge-Based Systems, no. 89, pp.459-470, 2015.##[19] اعتمادی حسین، زلقی حسن، "کاربرد رگرسیون لجستیک درشناسایی گزارشگری مالی متقلبانه". دانش حسابرسی. دوره 13. شماره 51. 1392.##[19] H. Etemadi, H. Zolfi, "Application of logistic regression in financial statement fraud detection", The Auditing Science, vol. 13, no. 51, 2013.##[20] فرقاندوست حقیقی، کامبیز، هاشمی، عباس و فروغی دهکردی، امین، "مطالعه رابطه مدیریت سود و امکان تقلب در صورت‌های مالی شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران"، مجله دانش حسابرسی، دوره 4، شماره 21، ص 68-47، 1393.##[20] K. F. Haghighi, A. Hashemi, A. F. Dehkordi, "A study of correlation between profit management and financial statement fraud in the companies registered in Tehran stock market", The Auditing Science, vol. 4, no. 21, pp. 47-68, 2014.##[21] C. D. Katsis, Y. Goletsis, P. V. Boufounou, G. Stylios, and E. Koumanakos, "Using ants to detect fraudulent financial statements", Journal of Applied Finance and Banking, no.2(6), pp. 73, 2012.##[22] D. I. Topor, "The Auditor's Responsibility for Finding Errors and Fraud from Financial Situations: Case Study", International Journal of Academic Research in Accounting, Finance and Management Sciences, no.7(1), pp. 342-352, 2017.##[23] B. W. Silverman, "Density estimation for statistics and data analysis," Routledge, 2018. ##

طراحی فیلترهای دیجیتال IIR با تأخیر کم با استفاده از الگوریتم‌های بهینه‌سازی فرا ابتکاری Low latency IIR digital filter design by using metaheuristic optimization algorithms 1 1 طراحی فیلترهای دیجیتال IIR مبتنی بر کمینه‌کردن اختلاف پاسخ فرکانسی فیلتر طراحی‌شده و دلخواه به همراه قیودی همچون پایداری، فاز خطی و کمینه فازی توسط الگوریتم­‌های بهینه‌سازی فرا‌ابتکاری توجه زیادی را به خود جلب کرده است. یکی از مشخصه­‌های مهم زمانی فیلترها تأخیر کم آنها است که در کاربردهای هم‌زمان ضروری است. در این مقاله جهت طراحی یک فیلتر با تأخیر کم، مفهوم انرژی جزئی وزن‌دار پاسخ ضربه فیلتر پیشنهاد شده است. با کمینه‌‌کردن این معیار، انرژی پاسخ ضربه فیلتر در ابتدای آن متمرکز شده و باعث سریع‌تر از بین رفتن پاسخ گذرا و همچنین کاهش تأخیر فیلتر در پاسخ به ورودی می­‌شود. این خاصیت در کنار کمینه فازی منجر به مشخصات زمانی خوب علاوه‌بر مشخصات خوب فرکانسی برای فیلتر می‌شود، به این معنی که پاسخ پله فیلتر به پله نزدیک می‌شود که در بسیاری از کاربردها ضروری است. در تابع هزینه پیشنهادی برای افزایش حاشیه پایداری از کمینه‌کردن معیار بزرگ‌ترین اندازه قطب‌­ها، جهت کمینه فازی از کمینه‌کردن معیار تعداد صفرهای بیرون دایره واحد و جهت دست­‌یابی به فاز خطی از معیار تأخیر گروهی ثابت استفاده شده است. کمینه‌کردن تابع هزینه پیشنهادی به‌دلیل نا محدب‌بودن و تعداد زیادی بهینه­‌های محلی توسط الگوریتم‌­های فرا‌ابتکاری PSO، GA و GSA انجام شده است. نتایج گزارش‌شده، قابلیت و انعطاف‌­پذیری روش پیشنهادی را در طراحی انواع  فیلترهای دیجیتال فرکانس گزین، مشتق گیر و انتگرال گیر، همسان‌ساز و هیلبرت با مشخصات فرکانسی و زمانی خوب در مقایسه با روش­‌های رایج را نشان می­‌دهد. فیلتر طراحی‌شده با استفاده از روش پیشنهادی تنها 79/1 نمونه تأخیر دارد که برای بیش‌تر کاربردها ایده‌ال است. 2 Filters are particularly important class of LTI systems. Digital filters have great impact on modern signal processing due to their programmability, reusability, and capacity to reduce noise to a satisfactory level. From the past few decades, IIR digital filter design is an important research field. Design of an IIR digital filter with desired specifications leads to a no convex optimization problem. IIR digital filter which design by minimizing the error between frequency response of desired and designed filters with some constraints such as stability, linear phase, and minimum phase by meta heuristic algorithms has gained increasing attention. The aim of this paper is to develop an IIR digital filter designing method that can provide relatively good time response characterizations beside good frequency response ones. One of the most important required time characterizations of digital filters for real time applications is low latency. To design a low latency digital filter, minimization of weighted partial energy of impulse response of the filter is used, in this paper. By minimizing weighted partial energy of impulse response, energy of impulse response concentrates on its beginning, consequently low latency for responding to inputs. This property beside minimum phase property of designed filter leads to good time specifications. In the proposed cost function in order to ensure the stability margin the term maximum pole radius is used, to ensure the minimum phase state the number of zeros outside the unit circle is considered, to achieve linear phase the constant group delay is considered. Due to no convexity of proposed cost function, three meta-heuristc algorithms GA, PSO, and GSA are used for optimization processes. Reported results confirmed the efficiency and the flexibility of the proposed method for designing various types of digital filters (frequency selective, differentiator, integrator, Hilbert, equalizers, and …) with low latency in comparison with the traditional methods. Designed low pass filter by proposed method has only 1/79 sample delay, that is ideal for most of the applications. 15 28 2018/06/22018/07/8 1397/4/17 2019/09/22018/09/15 1397/6/24 مهدی کماندار Mehdi Kamandar دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته ایران mehdi_kamandar@yahoo.com یاسر مقصودی yaser Maghsoudi دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته ایران yaser.maghsoudi69@gmail.com Digital signal processing IIR digital filter design Low latency Weighted partial energy Meta-heuristic optimization algorithms پردازش سیگنال دیجیتال طراحی فیلتر دیجیتال IIR تأخیر کم انرژی جزئی وزن‌دار الگوریتم‌های بهینه‌سازی فرا ابتکاری [1] G. Bernard, T. G. Stockham, A. V. Oppenheim and C. M. Rader. Digital processing of signals. 1969.##[2] D. S. Coelho, L. and V. Cocco Mariani. "Combining of differential evolution and implicit filtering algorithm applied to electromagnetic design optimization", In Soft Computing in Industrial Applications, pp. 233-240. Springer, Berlin, Heidelberg, 2007.##[3] S. Ahmad, Design of digital filters using genetic algorithms. PhD diss., 2008.##[4] J. H. Holland, "Genetic algorithms and the optimal allocation of trials," SIAM Journal on Computing, Vol. 2, no. 2, pp. 88-105, 1973.##[5] S. Bernardino, Heder, and H. Barbosa, "Artificial immune systems for optimization", Nature-Inspired Algorithms for Optimisation, pp. 389-411, 2009.##[6] M. Dorigo, and C. Blum. "Ant colony optimization theory: A survey", Theoretical computer science, Vol. 344, no. 2-3, pp. 243-278, 2005.##[7] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi. "GSA: A gravitational search algorithm", Information sciences, Vol. 179, No. 13, pp. 2232-2248, 2009.##[8] J. Kennedy, R. Eberhart, "Particle swarm optimization," Neural Networks, 1995.##[9] S. Kockanat, and N. Karaboga, "The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature ", Artificial Intelligence Review, Vol. 44, No. 2, pp. 265-287, 2015.##[10] A. Aggarwal, T. K. Rawat, and D. K. Upadhyay, "Design of optimal digital FIR filters using evolutionary and swarm optimization techniques", AEU-International Journal of Electronics and Communi-cations, Vol. 70, No. 4, pp. 373-385, 2016.##[11] A. Gotmare, S. S. Bhattacharjee, R.Patidar, and N. V. George, "Swarm and evolutionary computing algorithms for system identification and filter design: a comprehensive review", Swarm and Evolutionary Compu-tation, Vol. 32, pp. 68-84, 2017.##[12] M. Kumar and T. K. Rawat, "Optimal fractional delay-IIR filter design using cuckoo search algorithm", ISA transactions, Vol. 59, pp. 39-54, 2015.##[13] J. Dash, B. Dam, and R. Swain, "Optimal design of linear phase multi-band stop filters using improved cuckoo search particle swarm optimization", Applied Soft Computing, Vol. 52, pp. 435-445, 2017.##[14] D. Bose, S. Biswas, A. V. Vasilakos, , and S.Laha, "Optimal filter design using an improved artificial bee colony algo-rithm", Information Sciences, Vol. 281, pp. 443-461, 2014.##[15] A. Chottera and G. Jullien, "A linear programming approach to recursive digital filter design with linear phase", IEEE transactions on circuits and systems, Vol. 29, no. 3, pp. 139-149, 1982.##[16] A. Jiang. IIR digital filter design using convex optimization, PhD diss., 2010.##[1] G. Bernard, T. G. Stockham, A. V. Oppenheim and C. M. Rader. Digital processing of signals. 1969.##[2] D. S. Coelho, L. and V. Cocco Mariani. "Combining of differential evolution and implicit filtering algorithm applied to electromagnetic design optimization", In Soft Computing in Industrial Applications, pp. 233-240. Springer, Berlin, Heidelberg, 2007.##[3] S. Ahmad, Design of digital filters using genetic algorithms. PhD diss., 2008.##[4] J. H. Holland, "Genetic algorithms and the optimal allocation of trials," SIAM Journal on Computing, Vol. 2, no. 2, pp. 88-105, 1973.##[5] S. Bernardino, Heder, and H. Barbosa, "Artificial immune systems for optimization", Nature-Inspired Algorithms for Optimisation, pp. 389-411, 2009.##[6] M. Dorigo, and C. Blum. "Ant colony optimization theory: A survey", Theoretical computer science, Vol. 344, no. 2-3, pp. 243-278, 2005.##[7] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi. "GSA: A gravitational search algorithm", Information sciences, Vol. 179, No. 13, pp. 2232-2248, 2009.##[8] J. Kennedy, R. Eberhart, "Particle swarm optimization," Neural Networks, 1995.##[9] S. Kockanat, and N. Karaboga, "The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature ", Artificial Intelligence Review, Vol. 44, No. 2, pp. 265-287, 2015.##[10] A. Aggarwal, T. K. Rawat, and D. K. Upadhyay, "Design of optimal digital FIR filters using evolutionary and swarm optimization techniques", AEU-International Journal of Electronics and Communi-cations, Vol. 70, No. 4, pp. 373-385, 2016.##[11] A. Gotmare, S. S. Bhattacharjee, R.Patidar, and N. V. George, "Swarm and evolutionary computing algorithms for system identification and filter design: a comprehensive review", Swarm and Evolutionary Compu-tation, Vol. 32, pp. 68-84, 2017.##[12] M. Kumar and T. K. Rawat, "Optimal fractional delay-IIR filter design using cuckoo search algorithm", ISA transactions, Vol. 59, pp. 39-54, 2015.##[13] J. Dash, B. Dam, and R. Swain, "Optimal design of linear phase multi-band stop filters using improved cuckoo search particle swarm optimization", Applied Soft Computing, Vol. 52, pp. 435-445, 2017.##[14] D. Bose, S. Biswas, A. V. Vasilakos, , and S.Laha, "Optimal filter design using an improved artificial bee colony algo-rithm", Information Sciences, Vol. 281, pp. 443-461, 2014.##[15] A. Chottera and G. Jullien, "A linear programming approach to recursive digital filter design with linear phase", IEEE transactions on circuits and systems, Vol. 29, no. 3, pp. 139-149, 1982.##[16] A. Jiang. IIR digital filter design using convex optimization, PhD diss., 2010.## ##

خوشه‌بندی سلسله‌مراتبی فازی برای کشف روابط معنایی پنهان در اسناد وب‌معنایی Hierarchical Fuzzy Clustering Semantics (HFCS) in Web Document for Discovering Latent Semantics 1 1 رشد انبوه اطلاعات در وب مشکلاتی را به‌دنبال داشته است که از مهم‌ترین آن‌ها می‌توان به چالش­‌های ایجاد‌شده برای جستجو در وب اشاره کرد. با توجه به این که بیشتر محتویات وب امروزی برای استفاده توسط انسان طراحی ‌شده است، ماشین‌ها تنها قادر به دست‌کاری و فهم داده‌ها در سطح لغت هستند؛ این مسأله مهم‌ترین مانع در سرویس‌دهی بهتر به کاربران وب است. هدف این مقاله ارائه نتایج بهتر در پاسخ به جستجوی کاربران وب معنایی است. به این منظور در روش پیشنهادی ابتدا عبارت مورد نظر کاربر با توجه به میزان موضوعات مرتبط با آن، مورد بررسی قرار می‌گیرد. پاسخ به‌دست‌آمده از این بررسی، وارد یک سامانه رتبه‌دهی متشکل از سامانه تصمیم‌گیری فازی و خوشه‌بندی سلسله‌مراتبی می‌شود تا نتایج مطلوب‌تری را به کاربر بازگرداند. گفتنی است که روش پیشنهادی نیاز به هیچ‌گونه دانش قبلی برای خوشه‌بندی داده‌ها ندارد؛ علاوه‌بر این دقت و جامعیت این پاسخ نیز اندازه‌گیری می‌شود؛ درنهایت، بر روی نتایج به‌دست‌آمده آزمون F اعمال می‌شود که اغلب به‌عنوان یک معیار از عملکرد سامانه، برای ارزیابی الگوریتم و سامانه‌های مورد استفاده در نظر گرفته می‌شود. نتایج حاصل از این آزمون نشان می‌دهد که روش ارائه‌شده در این مقاله می‌تواند پاسخ دقیق‌تر و جامع‌تری نسبت به روش‌های مشابه خود ارائه دهد و به‌طور میانگین دقت را تا 22/1 درصد افزایش دهد. 2 This paper discusses about the future of the World Wide Web development, called Semantic Web. Undoubtedly, Web service is one of the most important services on the Internet, which has had the greatest impact on the generalization of the Internet in human societies. Internet penetration has been an effective factor in growth of the volume of information on the Web. The massive growth of information on the Web has led to some problems, the most important one is search query. Nowadays, search engines use different techniques to deliver high quality results, but we still see that search results are not ideal. It should also be noted that information retrieval techniques to a certain extent can increase the search accuracy. Most of the web content is designed for human usage and machines are only able to understand and manipulate data at word level. This is the major limitation for providing better services to web users. The solution provided for this topic is to display the content of the web in such a way that it can be readily understood and comprehensible to the machine. This solution, which will lead to a huge transformation on the Web is called the Semantic Web and will begin. Better results for responding to the search for semantic web users, is the purpose of this research. In the proposed method, the expression, searched by the user, will be examined according to the related topics. The response obtained from this section enters to a rating system, which is consisted of a fuzzy decision-making system and a hierarchical clustering system, to return better results to the user. It should be noted that the proposed method does not require any prior knowledge for clustering the data. In addition, accuracy and comprehensiveness of the response are measured. Finally, the F test is applied to obtain a criterion for evaluating the performance of the algorithm and systems. The results of the test show that the method presented in this paper can provide a more precise and comprehensive response than its similar methods and it increases the accuracy up to 1.22%, on average. 29 46 2018/06/22018/07/82018/07/14 1397/4/23 2019/09/22018/09/152019/07/10 1398/4/19 بهنام طاهری خامنه behnam taheri khameneh گروه مهندسی کامپیوتر، واحد پردیس، دانشگاه آزاد اسلامی ایران behnam.taheri@pardisiau.ac.ir حمید شکرزاده hamid shokrzadeh گروه مهندسی کامپیوتر، واحد پردیس، دانشگاه آزاد اسلامی ایران shokrzadeh@gmail.com Semantic Web Fuzzy Logic Hierarchical Clustering Latent Semantic HFCS وب‌ معنایی منطق فازی خوشه‌بندی سلسله‌مراتبی روابط معنایی پنهان الگوریتم HFCS [1] I. J. Chiang, C. C. H. Liu, Y. H. Tsai, and A. Kumar, "Discovering Latent Semantics in Web Documents Using Fuzzy Clustering," IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2122-2134, 2015.##[2] C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2009, pp. 496.##[3] K. R. Pole and V. R. Mote, "Name Entity Recognition and Natural Language Processing for Improvised Fuzzy clustering in Web Documents," Intenational Journal of Advance Research in Science and Engineering, vol. 6, no. 09, 2017.##[4] D. Bollegala, Y. Matsuo, and M. Ishizuka, "A Web Search Engine-Based Approach to Measure Semantic Similarity between Words," IEEE Transactions on Knowledge and Data Engi-neering, vol. 23, no. 7, pp. 977-990, 2011.##[5] B. Jiang, Z. Li, H. Chen, and A. G. Cohn, "Latent Topic Text Representation Learning on Statistical Manifolds," IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12, 2018.##[6] C. S. S. Kumar, M. Mohanapriya, and C. Kalaiarasan, "A new approach for information retrieval in semantic web mining involving weighted relationship," in 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017, pp. 1-4.##[7] R. Zhao and K. Mao, "Fuzzy Bag-of-Words Model for Document Representation," IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 794-804, 2018.##[8] M. K. Rafsanjani, Z. A. Varzaneh, and N. E. Chukanlo, "A Survey Of Hierarchical Clustering Algorithms," Journal of Mathematics and Computer Science(JMCS), vol. 5, no. 3, pp. 229-240, 2012.##[9] H. Park, K. Kwon, A. i. Z. Khiati, J. Lee, and I. J. Chung, "Agglomerative Hierarchical Clustering for Information Retrieval Using Latent Semantic Index," in 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), 2015, pp. 426-431.##[10] D. Rahmawati, G. A. P. Saptawati, and Y. Widyani, "Document clustering using sequential pattern (SP): Maximal frequent sequences (MFS) as SP representation," in 2015 International Conference on Data and Software Engineering (ICoDSE), 2015, pp. 98-102.##[11] G. Bordogna and G. Pasi, "Hierarchical-Hyperspherical Divisive Fuzzy C-Means (H2D-FCM) Clustering for Information Retrieval," in 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 2009, vol. 1, pp. 614-621.##[12] Nisha and P. J. Kaur, "Cluster quality based performance evaluation of hierarchical clustering method," in 2015 1st International Conference on Next Generation Computing Technologies (NGCT), 2015, pp. 649-653.##[13] C. Subbalakshmi, G. R. Krishna, S. K. M. Rao, and P. V. Rao, "A Method to Find Optimum Number of Clusters Based on Fuzzy Silhouette on Dynamic Data Set," Procedia Computer Science, vol. 46, pp. 346-353, 2015.##[14] M. Kaur and U. Kaur, "Comparison between k-means and hierarchical algorithm using query redirection," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, 2013.##[15] J. T. Chien, "Hierarchical Theme and Topic Modeling," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 3, pp. 565-578, 2016.##[16] Q. Mao, W. Zheng, L. Wang, Y. Cai, V. Mai, and Y. Sun, "Parallel Hierarchical Clustering in Linearithmic Time for Large-Scale Sequence Analysis," in 2015 IEEE International Conference on Data Mining, 2015, pp. 310-319.##[17] A. J. C. Trappey, C. V. Trappey, F. C. Hsu, and D. W. Hsiao, "A Fuzzy Ontological Knowledge Document Clustering Methodology," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 3, pp. 806-814, 2009.##[18] T. X. Society, S. Wang, Q. Jiang, and J. Z. Huang, "A Novel Variable-order Markov Model for Clustering Categorical Sequences," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 10, pp. 2339-2353, 2014.##[19] A. Ensan and Y. Biletskiy, "Matching semi-structured documents using similarity of regions through fuzzy rule-based system," in Industrial Conference on Data Mining, 2013, pp. 205-217: Springer.##[20] D. A. Grossman and O. Frieder, Information retrieval: Algorithms and heuristics. Springer Science & Business Media, 2012.##[21] A. N. Langville and C. D. Meyer, Google's PageRank and beyond: The science of search engine rankings, Princeton University Press, 2011.##[22] D. M. W. Powers, "Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation," Journal of Machine Learning Technologies, pp. 37-63, 2011.##[23] A. Dalli, "Adaptation of the f-measure to cluster based lexicon quality," EACL 2003 Workshop on Evaluation, pp. 51-56, 2003.##[24] C. J. v. RIJSBERGEN, INFORMATION RETRIEVAL. Newton, MA: Butterworth, 1979.##[1] I. J. Chiang, C. C. H. Liu, Y. H. Tsai, and A. Kumar, "Discovering Latent Semantics in Web Documents Using Fuzzy Clustering," IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2122-2134, 2015.##[2] C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2009, pp. 496.##[3] K. R. Pole and V. R. Mote, "Name Entity Recognition and Natural Language Processing for Improvised Fuzzy clustering in Web Documents," Intenational Journal of Advance Research in Science and Engineering, vol. 6, no. 09, 2017.##[4] D. Bollegala, Y. Matsuo, and M. Ishizuka, "A Web Search Engine-Based Approach to Measure Semantic Similarity between Words," IEEE Transactions on Knowledge and Data Engi-neering, vol. 23, no. 7, pp. 977-990, 2011.##[5] B. Jiang, Z. Li, H. Chen, and A. G. Cohn, "Latent Topic Text Representation Learning on Statistical Manifolds," IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12, 2018.##[6] C. S. S. Kumar, M. Mohanapriya, and C. Kalaiarasan, "A new approach for information retrieval in semantic web mining involving weighted relationship," in 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017, pp. 1-4.##[7] R. Zhao and K. Mao, "Fuzzy Bag-of-Words Model for Document Representation," IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 794-804, 2018.##[8] M. K. Rafsanjani, Z. A. Varzaneh, and N. E. Chukanlo, "A Survey Of Hierarchical Clustering Algorithms," Journal of Mathematics and Computer Science(JMCS), vol. 5, no. 3, pp. 229-240, 2012.##[9] H. Park, K. Kwon, A. i. Z. Khiati, J. Lee, and I. J. Chung, "Agglomerative Hierarchical Clustering for Information Retrieval Using Latent Semantic Index," in 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), 2015, pp. 426-431.##[10] D. Rahmawati, G. A. P. Saptawati, and Y. Widyani, "Document clustering using sequential pattern (SP): Maximal frequent sequences (MFS) as SP representation," in 2015 International Conference on Data and Software Engineering (ICoDSE), 2015, pp. 98-102.##[11] G. Bordogna and G. Pasi, "Hierarchical-Hyperspherical Divisive Fuzzy C-Means (H2D-FCM) Clustering for Information Retrieval," in 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 2009, vol. 1, pp. 614-621.##[12] Nisha and P. J. Kaur, "Cluster quality based performance evaluation of hierarchical clustering method," in 2015 1st International Conference on Next Generation Computing Technologies (NGCT), 2015, pp. 649-653.##[13] C. Subbalakshmi, G. R. Krishna, S. K. M. Rao, and P. V. Rao, "A Method to Find Optimum Number of Clusters Based on Fuzzy Silhouette on Dynamic Data Set," Procedia Computer Science, vol. 46, pp. 346-353, 2015.##[14] M. Kaur and U. Kaur, "Comparison between k-means and hierarchical algorithm using query redirection," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, 2013.##[15] J. T. Chien, "Hierarchical Theme and Topic Modeling," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 3, pp. 565-578, 2016.##[16] Q. Mao, W. Zheng, L. Wang, Y. Cai, V. Mai, and Y. Sun, "Parallel Hierarchical Clustering in Linearithmic Time for Large-Scale Sequence Analysis," in 2015 IEEE International Conference on Data Mining, 2015, pp. 310-319.##[17] A. J. C. Trappey, C. V. Trappey, F. C. Hsu, and D. W. Hsiao, "A Fuzzy Ontological Knowledge Document Clustering Methodology," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 3, pp. 806-814, 2009.##[18] T. X. Society, S. Wang, Q. Jiang, and J. Z. Huang, "A Novel Variable-order Markov Model for Clustering Categorical Sequences," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 10, pp. 2339-2353, 2014.##[19] A. Ensan and Y. Biletskiy, "Matching semi-structured documents using similarity of regions through fuzzy rule-based system," in Industrial Conference on Data Mining, 2013, pp. 205-217: Springer.##[20] D. A. Grossman and O. Frieder, Information retrieval: Algorithms and heuristics. Springer Science & Business Media, 2012.##[21] A. N. Langville and C. D. Meyer, Google's PageRank and beyond: The science of search engine rankings, Princeton University Press, 2011.##[22] D. M. W. Powers, "Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation," Journal of Machine Learning Technologies, pp. 37-63, 2011.##[23] A. Dalli, "Adaptation of the f-measure to cluster based lexicon quality," EACL 2003 Workshop on Evaluation, pp. 51-56, 2003.##[24] C. J. v. RIJSBERGEN, INFORMATION RETRIEVAL. Newton, MA: Butterworth, 1979.## ##

سامانه تشخیص سقوط افراد مبتنی بر منطق فازی نوع دو و الگوریتم بهینه‌سازی اجتماع ذرات چندهدفه A Fall Detection System based on the Type II Fuzzy Logic and Multi-Objective PSO Algorithm 1 1 توجه به سلامت سالمندان به‌عنوان سرمایه‌های ارزشمند کشور، امری ضروری و شایان توجه است. آسیب‌های جدی یا حتی مرگ ناشی از زمین‌خوردن برای افراد سالمند بسیار محتمل است؛ بنابراین تشخیص سریع وقوع این رخداد در بسیاری موارد می‌تواند منجر به نجات جان شخص شود. در این مقاله روشی پیشنهاد شده است که بر اساس آن تصاویر ویدئویی نظارتی از محل حضور شخص همواره مورد پردازش قرار می‌گیرد. در ادامه، با استفاده از الگوریتم استخراج پس‌زمینه بصری (ViBe)، شخص متحرک از پس‌زمینه جدا شده و شش ویژگی مؤثر از تصویر استخراج می‌شود. در انتها سامانه منطق فازی نوع دو برای تشخیص سقوط فرد به کار گرفته می شود؛ همچنین به‌منظور کاهش پیچیدگی محاسباتی سامانه فازی، از الگوریتم بهینه سازی اجتماع ذرات چندهدفه برای انتخاب توابع تعلق مؤثر استفاده شده است. نتایج اعمال روش پیشنهادی تصدیق می‌کند که این سامانه قادر به تشخیص سقوط شخص با سرعت قابل قبول و دقت تصمیم‌گیری مناسب است. 2 The Elderly health is an important and noticeable issue; since these people are priceless resources of experience in the society. Elderly adults are more likely to be severely injured or to die following falls. Hence, fast detection of such incidents may even lead to saving the life of the injured person. Several techniques have been proposed lately for the fall detection of people, mostly categorized into three classes. The first class is based on the wearable or portable sensors [1-6]; while the second class works according to the sound or vibration sensors [7-8]. The third one is based on the machine vision. Although the latter methods require cameras and image processing systems, access to surveillance cameras -which are economical- has made them be extensively used for the elderly.  By this motivation, this paper proposes a real-time technique in which, the surveillance video frames of the person’s room are being processed. This proposed method works based on the feature extraction and applying type-II fuzzy algorithm for the fall detection. First, using the improved visual background extraction (ViBe) algorithm, pixels of the moving person are separated from those of the background. Then, using the obtained image for the moving person, six features including ‘aspect ratio’, ‘motion vector’, ‘center-of-gravity’, ‘motion history image’, ‘the angle between the major axis of the bounding ellipse and the horizontal axis’ and the ‘ratio of major axis to minor axis of the bounding ellipse’ are extracted. These features should be given to an appropriate classifier. In this paper, an interval type-II fuzzy logic system (IT2FLS) is utilized as the classifier. To do this, three membership functions are considered for each feature. Accordingly, the number of the fuzzy laws for six features is too large, leading to high computational complexity. Since most of these laws in the fall detection are irrelevant or redundant, an appropriate algorithm is used to select the most effective fuzzy membership functions. The multi-objective particle swarm optimization algorithm (MOPSO) is an operative tool for solving large-scale problems. In this paper, this evolutionary algorithm tries to select the most effective membership functions to maximize the ‘classification accuracy’ while the ‘number of the selected membership functions’ are simultaneously minimized. This results in a considerably smaller number of rules. In this paper to investigate the performance of the proposed algorithm, 136 videos from the movements of people were produced; among which 97 people fell down and 39 ones were related to the normal activities (non-fall). To this end, three criteria including accuracy (ACC), sensitivity (Se.), and specificity (Sp.) are used. By changing the initial values of the parameters of the ViBe algorithm and frequent re-tuning after multiple frames, detecting the moving objects is done faster and with higher robustness against noise and illumination variations in the environment. This can be done via the proposed system even in microprocessors with low computational power. The obtained results of applying the proposed approach confirmed that this system is able to detect the human fall quickly and precisely. 47 60 2018/06/22018/07/82018/07/142018/08/14 1397/5/23 2019/09/22018/09/152019/07/102019/09/2 1398/6/11 آذر محمودزاده Azar Mahmoodzadeh دانشگاه آزاد اسلامی، واحد شیراز ایران mahmoodzadeh@iaushiraz.ac.ir حامد آگاهی Hamed Agahi دانشگاه آزاد اسلامی،واحد شیراز ایران agahi@iaushiraz.ac.ir مهسا واقفی Mahsa Vaghefi دانشگاه آزاد اسلامی،واحد شیراز ایران vaghefi@iaushiraz.ac.ir Fall Detection ViBe Algorithm Type II Fuzzy Logic MOPSO تشخیص سقوط الگوریتم ViBe منطق فازی نوع دو الگوریتم بهینه سازی اجتماع ذرات چندهدفه [1] Y. S. Delahoz and M. A. Labrador, "Survey on fall detection and fall prevention using wearable and external sensors," Sensors, vol. 14, pp. 19806-19842, 2014.##[2] S. Kulkarni and M. Basu, "A review on wearable tri-axial accelerometer based fall detectors," J. Biomed. Eng. Technol, vol. 1, pp. 36-39, 2013.##[3] K. Yang, C. R. Ahn, M. C. Vuran, and S. S. Aria, "Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit," Automation in Construction, vol. 68, pp. 194, 2016.##[4] P. Pierleoni, A. Belli, L. Palma, M. Pellegrini, L. Pernini, and S. Valenti, "A high reliability wearable device for elderly fall detection," IEEE Sensors Journal, vol. 15, pp. 4544-4553, 2015.##[5] H. Al-Nashash, S. Khan, S. Naqvi, R. Zaheen, A. Al-Ali, and A. Al Nabulsi, "IoT based multi-sensor patient fall detection system," Healthcare Technology Letters, 2019.##[6] N. Lapierre, N. Neubauer, A. Miguel-Cruz, A.R. Rincon, L. Liu, and J. Rousseau, "The state of knowledge on technologies and their use for fall detection: A scoping review," International journal of medical informatics, vol.111, pp. 58-71, 2018.##[7] H. Rimminen, J. Lindström, M. Linnavuo, and R. Sepponen, "Detection of falls among the elderly by a floor sensor using the electric near field," IEEE Transactions on Information Technology in Biomedicine, vol. 14, pp. 1475-1476, 2010.##[8] Y. Zigel, D. Litvak, and I. Gannot, "A method for automatic fall detection of elderly people using floor vibrations and sound-Proof of concept on human mimicking doll falls," IEEE Transactions on Biomedical Engineering, vol. 56, pp. 2858-2867, 2009.##[9] M. Mubashir, L. Shao, and L. Seed, "A survey on fall detection: Principles and approaches," Neurocomputing, vol. 100, pp. 144-152, 2013.##[10] X. Ma, H. Wang, B. Xue, M. Zhou, B. Ji, and Y. Li, "Depth-based human fall detection via shape features and improved extreme learning machine," IEEE J. Biomedical and Health Informatics, vol. 18, pp. 1915-1922, 2014.##[11] D. H. Hung and H. Saito, "Fall detection with two cameras based on occupied area," in Proc. of 18th Japan-Korea Joint Workshop on Frontier in Computer Vision, 2012, pp. 33-39.##[12] Y. Yun and I. Y. H. Gu, "Human fall detection in videos via boosting and fusing statistical features of appearance, shape and motion dynamics on Riemannian manifolds with applications to assisted living," Computer Vision and Image Understanding, vol. 148, pp. 111-122, 2016.##[13] N. Lu, Y. Wu, L. Feng, and J. Song, "Deep learning for fall detection: Three-dimensional CNN combined with LSTM on video kinematic data," IEEE journal of biomedical and health informatics, vol. 23, no. 1, pp. 314-323, 2018.##[14] A. Shojaei-Hashemi, P. Nasiopoulos, J.J. Little, and M.T. Pourazad, "Video-based human fall detection in smart homes using deep learning," IEEE International Symposium on Circuits and Systems, pp. 1-5, 2018.##[15] R. T. Collins, A. J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. 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Chambers, "Fall detection in a smart room by using a fuzzy one class support vector machine and imperfect training data," in Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, 2011, pp. 1833-1836.##[20] M. Yu, S. M. Naqvi, A. Rhuma, and J. Chambers, "One class boundary method classifiers for application in a video-based fall detection system," IET computer vision, vol. 6, pp. 90-100, 2012.##[21] J.-L. Chua, Y. C. Chang, and W. K. Lim, "A simple vision-based fall detection technique for indoor video surveillance," Signal, Image and Video Processing, vol. 9, pp. 623-633, 2015.##[22] K. Rezaee and J. Haddadnia, "Design of fall detection system: a dynamic pattern approach with fuzzy logic and motion estimation," Information Systems & Telecommunication, pp. 181, 2014.##[23] J. M. Mendel and R. B. 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آنالیز آنتروپومتری چهره به‌منظور کاربرد در جراحی چهره با استفاده از توزیع گوسین مکانی Anthropometric Analysis of Face using Local Gaussian Distribution Fitting Applicable for Facial Surgery 1 1 آنالیز تصاویر چهره انسان به­دلیل کاربردهای فراوان آن در جراحی­های چهره دارای اهمیت زیادی است. وجود ابزارهای سخت­افزاری و نرم‌افزاری در زمینه آنالیز جراحی­‌های چهره کمک شایانی را می‌­تواند به متخصصان جراحی­‌های چهره در قبل و بعد عمل جراحی داشته باشد. در این راستا، نیاز به دانستن آنتروپومتری­‌های موردنظر در آنالیز جراحی­‌های چهره و استخراج ویژگی­ هستیم. جهت استخراج کانتور نمای جانبی چهره و ناحیه گوش برای آنالیز در جراحی­های رینوپلاستی  و اتوپلاستی از مدل کانتور فعال مبتنی بر توزیع گوسین مکانی (مدل LGDF) استفاده شده است. در جراحی‌­های اشاره‌شده، ابتدا کانتور ناحیه موردنظر را با استفاده از مدل LGDF استخراج کرده و در مرحله بعد با اعمال گوشه‌­یاب هریس نقاط شاخص موردنظر جهت آنالیز آنتروپومتری موردنظر آشکارسازی شده‌­اند. دقت الگوریتم پیشنهادی در جراحی رینوپلاستی برای پایگاه داده دانشگاه سهند بالای %90 بوده و  در جراحی اتوپلاستی دقت الگوریتم پیشنهادی برای پایگاه داده AMI جهت اندازه­‌گیری طول، عرض و زاویه خارجی گوش به­‌ترتیب 432/96%، 423/97% و 546/85% هستند. 2 Human facial plays a very important role in the human’s appearance. Many defects in the face affect the facial appearance, significantly. Facial plastic surgeries can correct the defects on the face. Analysis of facial color images is very important due to its numerous applications in facial surgeries. Different types of facial surgeries, such as Rhinoplasty, Otoplasty, Belpharoplasty and chin augmentation are performed on the face to make beautiful structure. Rhinoplasty and Otoplasty are widely used in the facial plastic surgeries. the former is performed to correct air passage, correct structural defects, and make a beautiful structure on bone, cartilage, and soft nasal tissue. Also, the latter is performed to correct defects in the ear area. Development of different tools in the field of facial surgery analysis can help surgeons before and after surgery. The main purpose of this study is the anthropometry analysis of facial soft tissue based on image processing methods applicable to Rhinoplasty and Otoplasty surgeries. The proposed method includes three parts.; (1) contour detection, (2) feature extraction, and (3) feature selection. An Active Contour Model (ACM) based on Local Gaussian Distribution Fitting (LGDF) has been used to extract contours from facial lateral view and ear area. The LGDF model is a region-based model which unlike other models such as the Chan-Vese (CV) model is not sensitive to the inhomogeneity of image spatial intensity. Harris Corner Detector (HCD) has been applied to extracted contour for feature extraction. HCD is a method based on calculating of auto-correlation matrix and changing the gray value. In this study, dataset of orthogonal stereo imaging system of Sahand University of Technology (SUT), Tabriz, Iran has been used. After detecting facial key points, metrics of facial profile view and ear area have been measured. In analysis of profile view, 7 angles used in the Rhinoplasty have been measured. Analysis of ear anthropometry includes measuring the length, width and external angle. In the Rhinoplasty analysis, accuracy of the proposed method was about %90 in the all measurement parameters, as well as, it was %96.432, %97.423 and %85.546 in the Otoplasty analysis for measuring in the length, width and external angle of the ear on AMI database, respectively. Using the proposed system in planning of facial plastic surgeries can help surgeons in the Rhinoplasty and Otoplasty analysis. This research can be very effective in developing simulation and evaluation systems for the mentioned surgeries. 61 78 2018/06/22018/07/82018/07/142018/08/142018/05/24 1397/3/3 2019/09/22018/09/152019/07/102019/09/22019/07/10 1398/4/19 علی فهمی جعفرقلخانلو Ali Fahmi دانشگاه صنعتی سهند ایران a_fahmi@sut.ac.ir موسی شمسی Mousa Shamsi دانشگاه صنعتی سهند ایران shamsi@sut.ac.ir هوشنگ روحی Houshang Rouni دانشگاه آزاد اسلامی واحد اردبیل ایران h.rohi@iauardabil.ac.ir Facial Soft Tissue Analysis Surgery Analysis Orthogonal Stereo Imaging Facial Key Points LGDF Model آنالیز آنتروپومتری بافت نرم چهره آنالیز جراحی چهره سامانه تصویربرداری متعامد چهره نقاط کلیدی چهره مدل LGDF [1] M.Shamsi, R. A. Zoroofi, C. Lucas, M. S. Hasanabadi, and M. R. Alsharif, " Automatic Facial Skin Segmentation based on EM Algorithm under Varying Illumination", IEICE Transaction on Information and Systems, vol. 91(5), pp. 1543-1551,‌ 2008.##[2] S. C. Matz, and R. 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هم‌مرجع‌یابی مبتنی بر پیکره در متون فارسی Corpus based coreference resolution for Farsi text 1 1 مرجع‌یابی یا مرجع‌گزینی یا پیدا‌کردن واژگان هم‌مرجع در متن، یکی از وظایف مهم در پردازش زبان طبیعی است که یک بخش عملیاتی مهم در مسائلی مانند خلاصه‌سازی خودکار، پرسش و پاسخ خودکار و استخراج اطلاعات به‌شمار می‌رود. طبق تعاریف زمانی، دو واژه زمانی هم‌مرجع هستند که هر دو به موجودیت واحدی در متن یا جهان حقیقی ارجاع بدهند. تاکنون برای حل این مسأله تلاش‌های متعددی صورت گرفته است که بنابر نتایج این مطالعات، عملیات مرجع‌گزینی را می‌توان با روش‌های متفاوتی مانند روش‌های قاعده‌مند، مبتنی بر قوانین مکاشفه‌ای و روش‌های یادگیری ماشین (بانظارت یا بی‌ناظر) انجام داد. نکته قابل توجه این است که در سال‌های اخیر استفاده از پیکره‌های برچسب‌گذاری‌شده در این زمینه رواج زیادی داشته و منجر به تولید نتایج مناسبی هم شده است. با تکیه بر این موضوع، در پژوهش حاضر، یک پیکره از واژگان هم‌مرجع تولید شده که حدود یک‌میلیون واژه به‌همراه برچسب موجودیت نامدار دارد. در بخش مرجع‌گزینی تمام گروه‌های اسمی، ضمایر و موجودیت‌های نامدار برچسب‌گذاری شده‌اند و برچسب‌های موجودیت نامدار پیکره شامل هفت برچسب است. در پژوهش حاضر با استفاده از این پیکره، یک ابزار مرجع‌گزینی خودکار با استفاده از ماشین بردار پشتیبان تولید شده که دقت آن بر روی داده‌های آزمایش طلایی در حدود شصت درصد است. 2 "Coreference resolution" or "finding all expressions that refer to the same entity" in a text, is one of the important requirements in natural language processing. Two words are coreference when both refer to a single entity in the text or the real world. So the main task of coreference resolution systems is to identify terms that refer to a unique entity. A coreference resolution tool could be used in many natural language processing tasks such as machine translation, automatic text summarization, question answering, and information extraction systems. Adding coreference information can increase the power of natural language processing systems. The coreference resolution can be done through different ways. These methods include heuristic rule-based methods and supervised/unsupervised machine learning methods. Corpus based and machine learning based methods are widely used in coreference resolution task in recent years and has led to a good performance. For using such these methods, there is a need for manually labeled corpus with sufficient size. For Persian language, before this research, there exists no such corpus. One of the important targets here, was producing a through corpus that can be used in coreference resolution task and other associated fields in linguistics and computational linguistics. In this coreference resolution research, a corpus of coreference tagged phrases has been generated (manually annotated) that has about one million words. It also has named entity recognition (NER) tags. Named entity labels in this corpus include 7 labels and in coreference task, all noun phrases, pronouns and named entities have been tagged. Using this corpus, a coreference tool was created using a vector space machine, with precision of about 60% on golden test data. As mentioned before, this article presents the procedure for producing a coreference resolution tool. This tool is produced by machine learning method and is based on the tagged corpus of 900 thousand tokens. In the production of the system, several different features and tools have been used, each of which has an effect on the accuracy of the whole tool. Increasing the number of features, especially semantic features, can be effective in improving results. Currently, according to the sources available in the Persian language, there are no suitable syntactic and semantic tools, and this research suffers from this perspective. The coreference tagged corpus produced in this study is more than 500 times bigger than the previous Persian language corpora and at the same time it is quite comparable to the prominent ACE and Ontonotes corpora. The system produced has an f-measure of nearly 60 according to the CoNLL standard criterion. However, other limited studies conducted in Farsi have provided different accuracy from 40 to 90%, which is not comparable to the present study, because the accuracy of these studies has not been measured with standard criterion in the coreference resolution field. 79 98 2018/06/22018/07/82018/07/142018/08/142018/05/242018/06/9 1397/3/19 2019/09/22018/09/152019/07/102019/09/22019/07/102019/06/1 1398/3/11 زینب رحیمی Zeinab Rahimi پژوهشگاه توسعه فناوری‌های پیشرفته خواجه نصیرالدین طوسی ایران rahimi.zeinab@gmail.com شادی حسین نژاد Shadi HosseinNejad پژوهشگاه توسعه فناوری‌های پیشرفته خواجه نصیرالدین طوسی ایران shadi.hn@gmail.com Automatic coreference resolution Anaphora resolution mention هم‌مرجع یابی خودکار مرجع‌گزینی تحلیل مرجع ضمیر عبارات ارجاعی [1] Sh. Tabatabaee and Y. Shekofteh, "The basic coreference resolution system for noun phrases in Persian language using simple rules", The first conference on national search engine, Tehran 2015.##[2] B. Amit and B. Baldwin, "Algorithms for scoring coreference chains", The first international conference on language resources and evaluation workshop on linguistics coreference. Vol. 1. 1998.##[3] B. Eric and D. Roth, "Understanding the value of features for coreference resolution," Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2008.##[4] B. Baldwin , M. Collins , J. Eisner , A. Ratnaparkhi , J. Rosenzweig and A. Sarkar, University of Pennsylvania: description of the University of Pennsylvania system used for MUC-6, Proceedings of the 6th conference on Message understanding, November 06-08, 1995, Columbia, Maryland.##[5] Ch. Chen and Ng.Vincent, "Combining the best of two worlds: A hybrid approach to multilingual coreference resolution," Joint Conference on EMNLP and CoNLL-Shared Task, Association for Computational Linguistics, 2012.##[6] N. Chinchor and S. Beth, "Message understanding conference (MUC) 6," LDC2003T13 (2003(, 2013.##[7] N. Chinchor, "Message Understanding Conference (MUC) 7", LDC2001T02. Web Download. Philadelphia: Linguistic Data Conso-rtium, 2001.##[8] C. Jacob , "A coefficient of agreement for nominal scales", Educational and Psychological Measurement, vol. 20, 1960, pp.37-46.##[9] G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel , "The automatic content extraction (ace) program-tasks, data, and evaluatio"n"., In LREC, vol. 2, pp. 1, 2004.##[10] D. Greg, D. Leo Wright Hall and D. Klein, "Decentralized Entity-Level Modeling for Coreference Resolution," ACL (1), 2013.##[11] F. Fallahi and M. Shamsfard, "Recognizing anaphora reference in Persian sentences," Int. J. Comput. Sci, vol. 8, pp. 324-329, pp. 2011.##[12] A.M. Green,"Kappa statistics for multiple raters using categorical classifications", In Proceedings of the Twenty, 1997.##[13] A. Haghighi and D. Klein, "Simple coreference resolution with rich syntactic and semantic features", In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing : Association for Computational Linguistics, Vol. 3, pp. 1152-1161, 2009.##[14] A. Haghighi and K. Dan, "Unsupervised coreference resolution in a nonparametric bayesian model," Annual meeting-Association for Computational Linguistics. vol. 45. No. 1. 2007.##[15] Sh. Hosseinnejad, Y. Shekofteh, & T. Emami Azadi, "A'laam Corpus: A Standard Corpus of Named Entity for Persian Language", Signal and Data Processing, vol.14, pp.127-142, 2017.##[16] H. Lee, et al, "Joint entity and event coreference resolution across documents," Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computa-tional Linguistics, 2012.##[17] X. Luo, "On coreference resolution performance metrics," Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2005.##[18] N. S. Moosavi and Gh. Ghassem-Sani, "A Ranking Approach to Persian Pronoun Resolution," Advances in Computational Linguistics. Research in Computing Science 41, pp. 169-180, 2009.##[19] V. Ng, and C. Cardie, "Improving machine learning approaches to coreference resolution," In Proceedings of the 40th annual meeting on association for computational linguistics, pp. 104-111, 2002.##[20] M. Nazaridoust, B. Minaei Bidgoli, S. Nazaridoust, "Co-reference Resolution in Farsi Corpora", Advance Trends in Soft Computing Studies in Fuzziness and Soft Computing, vol. 312, pp.155-162, 2014.##[21] S. Pradhan et al, "CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes," Joint Conference on EMNLP and CoNLL-Shared Task, Association for Computational Linguistics, 2012.##[22] M.S. Rasooli, M. Kouhestani, and A. Moloodi, "Development of a Persian Syntactic Dependency Treebank", In The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), Atlanta, USA.##[23] M. Seraji, B. Megyesi, J. Nivre , "Bootstrapping a Persian Dependency Treebank", Published as a Journal in Special Issue of the Linguistic Issues in Language Technology (LiLT), Heidelberg, Germany, 2012.##[24] M. Shamsfard, H. Fadaee, "A Hybrid Morphology-Based POS Tagger for Persian", In Proceedings of 6th Language Resources and Evaluation Conference (LREC 2008), Morocco, 2008.##[25] M. Stamborg, et a, "Using syntactic dependencies to solve coreferences," Joint Conference on EMNLP and CoNLL-Shared Task. Association for Computational Linguistics, 2012.##[26] V. Stoyanov, et al. "Reconciling ontonotes: Unrestricted coreference resolution in ontonotes with reconcile," Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, Association for Computational Linguistics, 2011.##[27] O. Uryupina, M. Alessandro, and Massimo Poesio. "BART goes multilingual: The UniTN/Essex submission to the CoNLL-2012 shared task," Joint Conference on EMNLP and CoNLL-Shared Task, Association for Computational Linguistics, 2012.##[28] Y. Versley, et al, "BART: A modular toolkit for coreference resolution," Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session. Association for Computational Linguistics, 2008.##[29] M. Vilain, et al, "A model-theoretic coreference scoring scheme," Proceedings of the 6th conference on Message understanding, Association for Computational Linguistics, 1995.##[30] S. Wiseman, A. M. Rush and S. M. Shieber, "Learning Global Features for Coreference Resolution," arXiv preprint arXiv:1604.03035, 2016.##[31] A. salimibadr and M.Homayounpour, Phrase chunking in Persian texts . JSDP, vol. 10 (2), pp. 69-86,2014.##[1] طباطبایی، ش. شکفته، ی. «سامانه پایه مرجع‌یابی گروه‌های اسمی در زبان فارسی با استفاده از قوانین ساده». اولین همایش جویشگر بومی، تهران، 1394.##[1] Sh. Tabatabaee and Y. Shekofteh, "The basic coreference resolution system for noun phrases in Persian language using simple rules", The first conference on national search engine, Tehran 2015.##[2] B. Amit and B. Baldwin, "Algorithms for scoring coreference chains", The first international conference on language resources and evaluation workshop on linguistics coreference. Vol. 1. 1998.##[3] B. Eric and D. Roth, "Understanding the value of features for coreference resolution," Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2008.##[4] B. Baldwin , M. Collins , J. Eisner , A. Ratnaparkhi , J. Rosenzweig and A. Sarkar, University of Pennsylvania: description of the University of Pennsylvania system used for MUC-6, Proceedings of the 6th conference on Message understanding, November 06-08, 1995, Columbia, Maryland.##[5] Ch. Chen and Ng.Vincent, "Combining the best of two worlds: A hybrid approach to multilingual coreference resolution," Joint Conference on EMNLP and CoNLL-Shared Task, Association for Computational Linguistics, 2012.##[6] N. Chinchor and S. Beth, "Message understanding conference (MUC) 6," LDC2003T13 (2003(, 2013.##[7] N. Chinchor, "Message Understanding Conference (MUC) 7", LDC2001T02. Web Download. Philadelphia: Linguistic Data Conso-rtium, 2001.##[8] C. Jacob , "A coefficient of agreement for nominal scales", Educational and Psychological Measurement, vol. 20, 1960, pp.37-46.##[9] G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel , "The automatic content extraction (ace) program-tasks, data, and evaluatio"n"., In LREC, vol. 2, pp. 1, 2004.##[10] D. Greg, D. Leo Wright Hall and D. Klein, "Decentralized Entity-Level Modeling for Coreference Resolution," ACL (1), 2013.##[11] F. Fallahi and M. Shamsfard, "Recognizing anaphora reference in Persian sentences," Int. J. Comput. Sci, vol. 8, pp. 324-329, pp. 2011.##[12] A.M. Green,"Kappa statistics for multiple raters using categorical classifications", In Proceedings of the Twenty, 1997.##[13] A. Haghighi and D. Klein, "Simple coreference resolution with rich syntactic and semantic features", In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing : Association for Computational Linguistics, Vol. 3, pp. 1152-1161, 2009.##[14] A. Haghighi and K. Dan, "Unsupervised coreference resolution in a nonparametric bayesian model," Annual meeting-Association for Computational Linguistics. vol. 45. No. 1. 2007.##[15] Sh. Hosseinnejad, Y. Shekofteh, & T. Emami Azadi, "A'laam Corpus: A Standard Corpus of Named Entity for Persian Language", Signal and Data Processing, vol.14, pp.127-142, 2017.##[16] H. Lee, et al, "Joint entity and event coreference resolution across documents," Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computa-tional Linguistics, 2012.##[17] X. Luo, "On coreference resolution performance metrics," Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2005.##[18] N. S. Moosavi and Gh. Ghassem-Sani, "A Ranking Approach to Persian Pronoun Resolution," Advances in Computational Linguistics. Research in Computing Science 41, pp. 169-180, 2009.##[19] V. Ng, and C. Cardie, "Improving machine learning approaches to coreference resolution," In Proceedings of the 40th annual meeting on association for computational linguistics, pp. 104-111, 2002.##[20] M. Nazaridoust, B. Minaei Bidgoli, S. Nazaridoust, "Co-reference Resolution in Farsi Corpora", Advance Trends in Soft Computing Studies in Fuzziness and Soft Computing, vol. 312, pp.155-162, 2014.##[21] S. Pradhan et al, "CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes," Joint Conference on EMNLP and CoNLL-Shared Task, Association for Computational Linguistics, 2012.##[22] M.S. Rasooli, M. Kouhestani, and A. Moloodi, "Development of a Persian Syntactic Dependency Treebank", In The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), Atlanta, USA.##[23] M. Seraji, B. Megyesi, J. Nivre , "Bootstrapping a Persian Dependency Treebank", Published as a Journal in Special Issue of the Linguistic Issues in Language Technology (LiLT), Heidelberg, Germany, 2012.##[24] M. Shamsfard, H. Fadaee, "A Hybrid Morphology-Based POS Tagger for Persian", In Proceedings of 6th Language Resources and Evaluation Conference (LREC 2008), Morocco, 2008.##[25] M. Stamborg, et a, "Using syntactic dependencies to solve coreferences," Joint Conference on EMNLP and CoNLL-Shared Task. Association for Computational Linguistics, 2012.##[26] V. Stoyanov, et al. "Reconciling ontonotes: Unrestricted coreference resolution in ontonotes with reconcile," Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, Association for Computational Linguistics, 2011.##[27] O. Uryupina, M. Alessandro, and Massimo Poesio. "BART goes multilingual: The UniTN/Essex submission to the CoNLL-2012 shared task," Joint Conference on EMNLP and CoNLL-Shared Task, Association for Computational Linguistics, 2012.##[28] Y. Versley, et al, "BART: A modular toolkit for coreference resolution," Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session. Association for Computational Linguistics, 2008.##[29] M. Vilain, et al, "A model-theoretic coreference scoring scheme," Proceedings of the 6th conference on Message understanding, Association for Computational Linguistics, 1995.##[30] S. Wiseman, A. M. Rush and S. M. Shieber, "Learning Global Features for Coreference Resolution," arXiv preprint arXiv:1604.03035, 2016.##[31] A. salimibadr and M.Homayounpour, Phrase chunking in Persian texts . JSDP, vol. 10 (2), pp. 69-86,2014.## ##

بهسازی گفتار به‌کمک یادگیری واژه‌نامه مبتنی‌بر داده Speech Enhancement using Adaptive Data-Based Dictionary Learning 1 1 بهسازی گفتار یکی از پرکاربردترین حوزه‌ها در زمینه پردازش گفتار است. در این مقاله، یکی از روش‌های بهسازی گفتار مبتنی‌بر اصول بازنمایی تُنُک بررسی می‌شود. بازنمایی تُنُک این امکان را فراهم می‌سازد که عمده اطلاعات لازم برای بازنمایی سیگنال‌، براساس بُعد بسیار کمتری از پایه‌های فضایی اصلی قابل مدل‌سازی باشد. روش‌ یادگیری در این مقاله براساس تصحیح الگوریتم تطبیقی حریصانه مبتنی‌بر داده خواهد بود که واژه‌نامه در آن، به‌طور مستقیم از روی سیگنال داده و براساس شاخص تُنُکی مبتنی‌بر نُرم به منظور تطابق بیشتر میان اتم‌ها و ساختار داده آموزش می‌بیند. در این مقاله شاخص تُنُکی جدیدی براساس معیار جینی پیشنهاد می‌شود. همچنین محدوده پارامتر تُنُکی بخش‌های نوفه‌ای با توجه به فریم‌های ابتدایی گفتار تعیین و طی یک روال پیشنهادی در تشکیل واژه‌نامه مورد استفاده قرار می‌گیرد. نتایج بهسازی نشان می‌دهد که عملکرد روش پیشنهادی در انتخاب قاب‌‌های داده براساس معیار معرفی‌شده در شرایط نوفه‌ای مختلف بهتر از شاخص تُنُکی مبتنی‌بر نُرم و سایر الگوریتم‌های پایه در این راستا است. 2 In this paper, a speech enhancement method based on sparse representation of data frames has been presented. Speech enhancement is one of the most applicable areas in different signal processing fields. The objective of a speech enhancement system is improvement of either intelligibility or quality of the speech signals. This process is carried out using the speech signal processing techniques to attenuate the background noise without causing any distortion in the speech signal. In this paper, we focus on the single channel speech enhancement corrupted by the additive Gaussian noise. In recent years, there has been an increasing interest in employing sparse representation techniques for speech enhancement. Sparse representation technique makes it possible to show the major information about the speech signal based on a smaller dimension of the original spatial bases. The capability of a sparse decomposition method depends on the learned dictionary and matching between the dictionary atoms and the signal features. An over complete dictionary is yielded based on two main steps: dictionary learning process and sparse coding technique. In dictionary selection step, a pre-defined dictionary such as the Fourier basis, wavelet basis or discrete cosine basis is employed. Also, a redundant dictionary can be constructed after a learning process that is often based on the alternating optimization strategies. In sparse coding step, the dictionary is fixed and a sparse coefficient matrix with the low approximation error has been earned. The goal of this paper is to investigate the role of data-based dictionary learning technique in the speech enhancement process in the presence of white Gaussian noise. The dictionary learning method in this paper is based on the greedy adaptive algorithm as a data-based technique for dictionary learning. The dictionary atoms are learned using the proposed algorithm according to the data frames taken from the speech signals, so the atoms contain the structure of the input frames. The atoms in this approach are learned directly from the training data using the norm-based sparsity measure to earn more matching between the data frames and the dictionary atoms. The proposed sparsity measure in this paper is based on Gini parameter. We present a new sparsity index using Gini coefficients in the greedy adaptive dictionary learning algorithm. These coefficients are set to find the atoms with more sparsity in the comparison with the other sparsity indices defined based on the norm of speech frames. The proposed learning method iteratively extracts the speech frames with minimum sparsity index according to the mentioned measures and adds the extracted atoms to the dictionary matrix. Also, the range of the sparsity parameter is selected based on the initial silent frames of speech signal in order to make a desired dictionary. It means that a speech frame of input data matrix can add to the first columns of the over complete dictionary when it has not a similar structure with the noise frames. The data-based dictionary learning process makes the algorithm faster than the other dictionary learning methods for example K-singular value decomposition (K-SVD), method of optimal directions (MOD) and other optimization-based strategies. The sparsity of an input frame is measured using Gini-based index that includes smaller measured values for speech frames because of their sparse content. On the other hand, high values of this parameter can be yielded for a frame involved the Gaussian noise structure. The performance of the proposed method is evaluated using different measures such as improvement in signal-to-noise ratio (ISNR), the time-frequency representation of atoms and PESQ scores. The proposed approach results in a significant reduction of the background noise in comparison with other dictionary learning methods such as principal component analysis (PCA) and the norm-based learning method that are traditional procedures in this context. We have found good results about the reconstruction error in the signal approximations for the proposed speech enhancement method. Also, the proposed approach leads to the proper computation time that is a prominent factor in dictionary learning methods.  99 116 2018/06/22018/07/82018/07/142018/08/142018/05/242018/06/92017/11/6 1396/8/15 2019/09/22018/09/152019/07/102019/09/22019/07/102019/06/12020/01/22 1398/11/2 سمیرا مودتی Samira Mavaddati دانشگاه مازندران ایران s.mavaddati@umz.ac.ir محمد احدی Mohammad Ahadi دانشگاه صنعتی امیرکبیر ایران sma@aut.ac.ir Speech enhancement Sparse representation Dictionary learning Data-Based learning Greedy adaptive بهسازی گفتار بازنمایی تُنُک یادگیری واژه‌نامه مبتنی‌بر داده تطبیقی حریصانه شاخص تُنُکی جینی [1] H. Guo, B. Zhao, G. Zhou, "Image compression based on compressed sensing theory and wavelet packet analysis", Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, vol. 2, pp. 1426-1429, 2011.##[2] J. L. Starck, M. Elad, D. L. Donoho, "Image decomposition via the combination of sparse representation and a variational approach", IEEE Trans. on Image Proces, vol. 14, No. 10, pp. 1570-1582 , 2005.##[3] F. Rodriguez, G. Sapiro, "Sparse representation for image classification: Learning discriminative and reconstructive non-parametric dictionaries", IMA Preprint, 2007.##[4] S. Zhang, J. Huang, D. Metaxas, W. Wang, X. Huang, "Discriminative sparse representations for cervigram image segmentation", IEEE Inter-national Symposium on Biomedical Imaging: From Nano to Macro, pp.133-136, 2010.##[5] M. S. Lewiki, T. J. Sejnowski, "Learning overcomplete representations", Neural Computing, vol. 12, No. 2, pp. 337-365, 2000.##[6] D. Giacobello, M. G. Christensen, M. N. Murthi, S. H. Jensen, M. Moonen, "Retrieving sparse patterns using a compressed sensing framework: applications to speech coding based on sparse linear prediction", IEEE Signal Processing Letters, vol. 17, No. 1, pp.103-106, 2010.##[7] D. Wu, Z. Ping, M. N. S. Swamy, "A compressive sensing method for noise reduction of speech and audio signals", IEEE 54th International Midwest Symposium on Circuits and Systems, pp. 1-4, 2011.##[8] D. Wu, Z.W. Ping, M. N. S. Swamy, "On sparsity issues in compressive sensing based speech enhancement", IEEE International Symposium on Circuits and Systems, pp. 285-288, 2012.##[9] E. Candès, J. Romberg, T. Tao, "Robust uncertainty principles: exact signal recons-truction from highly incomplete frequency information", IEEE Trans. Inform. Theory, vol. 52, No. 2, pp. 489-509, 2006.##[10] E. J. Candes, T. Tao, "Near-optimal signal recovery from random projections and universal encoding strategies", IEEE Trans. Inform. Theory, vol. 52, No. 12, pp. 5406-5425, 2006.##[11] H. Xu, "Speech enhancement based on compressed sensing technology", Sensors & Transducers Journal, vol. 181, pp. 141-145, 2014.##[12] G. S. Sivaram, S. K. Nemala, M. Elhilali, H. Hermansky, "Sparse coding for speech recognition", IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 4346-4349, 2010.##[13] D. D. Lee, H. S. Seung, "Learning the parts of objects by non-negative matrix factorization", Nature 401, 788- 791, 1999.##[14] N. Mohammadiha, P. Smaragdis, A. Leijon, "Supervised and unsupervised speech enhan-cement using nonnegative matrix factor-ization", IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, No. 10, pp.2140-2151, 2013.##[15] J. T. Geiger, J. F. Gemmeke, B. Schuller, and G. Rigoll, "Investigating NMF speech enhancement for neural network based acoustic models", Proceedings of the Annual Con-ference of International Speech Communi-cation Association (INTERSPEECH), 2014.##[16] P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints", J. Mach. Learn. Res., vol. 5, pp. 1457-1469, 2004.##[17] C. D. Sigg, T. Dikk, J. M. Buhmann, "Speech enhancement with sparse coding in learned dictionaries", In Acoustics Speech and Signal Processing (ICASSP), IEEE International Conference on, pp. 4758-4761, 2010.##[18] C. D. Sigg, T. Dikk, J. M. Buhmann, "Speech enhancement using generative dictionary learning", IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, No. 6, pp. 1698-1712, 2012.##[19] M. Aharon, M. Elad, A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation", IEEE Trans. Signal Process, vol. 54, No. 11, pp. 4311-4322, 2006.##[20] H. Yongjun, J. Han, S. Deng, T. Zheng, G. Zheng, "A solution to residual noise in speech denoising with sparse representation", In Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on, pp. 4653-4656, 2012.##[21] S. Mavaddaty, S. M. Ahadi, S. Seyedin, "A novel speech enhancement method by learnable sparse and low-rank decomposition and domain adaptation", Speech Communi-cation, vol. 76, pp. 42-60, 2016.##[22] M. G. Jafari, M. D. Plumbley, "Speech denoising based on a greedy adaptive dic-tionary algorithm", 17th European Signal Processing Conference (EUSIPCO), pp. 1423-1426, 2009.##[23] M. G. Jafari, M. D. Plumbley, "Fast dictionary learning for sparse representations of speech signals", IEEE Journal of selected topics in signal processing, vol. 5, pp. 1025-1031, 2011.##[24] R. Rubinstein, M. Zibulevsky, M. Elad, "Double sparsity: Learning sparse dictionaries for sparse signal approximation", IEEE Trans. on Signal Processing, Vol. 58, pp. 1553-1564, 2010.##[25] M. G. Jafari, E. Vincent, S. A. Abdallah, M. D. Plumbley, M. E. Davies, "An adaptive stereo basis method for convolutive blind audio source separation", Neuro computing, vol. 71, pp. 2087-2097, 2008.##[26] http://www.dcs.shef.ac.uk/spandh/gridcorpus.##[27] N. Hurley, S. Rickard, "Comparing measures of sparsity", IEEE Trans. on Information Theory, vol. 55, pp. 4723-4741, 2009.##[28] S. Rickard, M. Fallon, "The gini index of speech", In Proc. Of Information Sciences and Systems conference, Princeton, NJ, 2004.##[29] P. J. Wolfe, "Sparse time-frequency repre-sentations in audio processing, as studied through a symmetrized lognormal model," In Proc. of the European Signal Processing Con-ference (EUSIPCO), pp. 355-359, 2007.##[30] H. Dalton, "The measurement of the inequity of incomes", Economic Journal, Vol. 30, pp. 348-361, 1920.##[31] I. Cohen, B. Berdugo, "Speech enhancement for non-stationary noise environments", Signal processing, vol. 81, No. 11, pp. 2403-2418, 2001.##[32] A. Rix, J. Beerends, M. Hollier, A. Hekstra, "Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs", In Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process., pp. 749-752, 2001.##[1] H. Guo, B. Zhao, G. Zhou, "Image compression based on compressed sensing theory and wavelet packet analysis", Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, vol. 2, pp. 1426-1429, 2011.##[2] J. L. Starck, M. Elad, D. L. Donoho, "Image decomposition via the combination of sparse representation and a variational approach", IEEE Trans. on Image Proces, vol. 14, No. 10, pp. 1570-1582 , 2005.##[3] F. Rodriguez, G. Sapiro, "Sparse representation for image classification: Learning discriminative and reconstructive non-parametric dictionaries", IMA Preprint, 2007.##[4] S. Zhang, J. Huang, D. Metaxas, W. Wang, X. Huang, "Discriminative sparse representations for cervigram image segmentation", IEEE Inter-national Symposium on Biomedical Imaging: From Nano to Macro, pp.133-136, 2010.##[5] M. S. Lewiki, T. J. Sejnowski, "Learning overcomplete representations", Neural Computing, vol. 12, No. 2, pp. 337-365, 2000.##[6] D. Giacobello, M. G. Christensen, M. N. Murthi, S. H. Jensen, M. Moonen, "Retrieving sparse patterns using a compressed sensing framework: applications to speech coding based on sparse linear prediction", IEEE Signal Processing Letters, vol. 17, No. 1, pp.103-106, 2010.##[7] D. Wu, Z. Ping, M. N. S. Swamy, "A compressive sensing method for noise reduction of speech and audio signals", IEEE 54th International Midwest Symposium on Circuits and Systems, pp. 1-4, 2011.##[8] D. Wu, Z.W. Ping, M. N. S. Swamy, "On sparsity issues in compressive sensing based speech enhancement", IEEE International Symposium on Circuits and Systems, pp. 285-288, 2012.##[9] E. Candès, J. Romberg, T. Tao, "Robust uncertainty principles: exact signal recons-truction from highly incomplete frequency information", IEEE Trans. Inform. Theory, vol. 52, No. 2, pp. 489-509, 2006.##[10] E. J. Candes, T. Tao, "Near-optimal signal recovery from random projections and universal encoding strategies", IEEE Trans. Inform. Theory, vol. 52, No. 12, pp. 5406-5425, 2006.##[11] H. Xu, "Speech enhancement based on compressed sensing technology", Sensors & Transducers Journal, vol. 181, pp. 141-145, 2014.##[12] G. S. Sivaram, S. K. Nemala, M. Elhilali, H. Hermansky, "Sparse coding for speech recognition", IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 4346-4349, 2010.##[13] D. D. Lee, H. S. Seung, "Learning the parts of objects by non-negative matrix factorization", Nature 401, 788- 791, 1999.##[14] N. Mohammadiha, P. Smaragdis, A. Leijon, "Supervised and unsupervised speech enhan-cement using nonnegative matrix factor-ization", IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, No. 10, pp.2140-2151, 2013.##[15] J. T. Geiger, J. F. Gemmeke, B. Schuller, and G. Rigoll, "Investigating NMF speech enhancement for neural network based acoustic models", Proceedings of the Annual Con-ference of International Speech Communi-cation Association (INTERSPEECH), 2014.##[16] P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints", J. Mach. Learn. Res., vol. 5, pp. 1457-1469, 2004.##[17] C. D. Sigg, T. Dikk, J. M. Buhmann, "Speech enhancement with sparse coding in learned dictionaries", In Acoustics Speech and Signal Processing (ICASSP), IEEE International Conference on, pp. 4758-4761, 2010.##[18] C. D. Sigg, T. Dikk, J. M. Buhmann, "Speech enhancement using generative dictionary learning", IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, No. 6, pp. 1698-1712, 2012.##[19] M. Aharon, M. Elad, A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation", IEEE Trans. Signal Process, vol. 54, No. 11, pp. 4311-4322, 2006.##[20] H. Yongjun, J. Han, S. Deng, T. Zheng, G. Zheng, "A solution to residual noise in speech denoising with sparse representation", In Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on, pp. 4653-4656, 2012.##[21] S. Mavaddaty, S. M. Ahadi, S. Seyedin, "A novel speech enhancement method by learnable sparse and low-rank decomposition and domain adaptation", Speech Communi-cation, vol. 76, pp. 42-60, 2016.##[22] M. G. Jafari, M. D. Plumbley, "Speech denoising based on a greedy adaptive dic-tionary algorithm", 17th European Signal Processing Conference (EUSIPCO), pp. 1423-1426, 2009.##[23] M. G. Jafari, M. D. Plumbley, "Fast dictionary learning for sparse representations of speech signals", IEEE Journal of selected topics in signal processing, vol. 5, pp. 1025-1031, 2011.##[24] R. Rubinstein, M. Zibulevsky, M. Elad, "Double sparsity: Learning sparse dictionaries for sparse signal approximation", IEEE Trans. on Signal Processing, Vol. 58, pp. 1553-1564, 2010.##[25] M. G. Jafari, E. Vincent, S. A. Abdallah, M. D. Plumbley, M. E. Davies, "An adaptive stereo basis method for convolutive blind audio source separation", Neuro computing, vol. 71, pp. 2087-2097, 2008.##[26] http://www.dcs.shef.ac.uk/spandh/gridcorpus.##[27] N. Hurley, S. Rickard, "Comparing measures of sparsity", IEEE Trans. on Information Theory, vol. 55, pp. 4723-4741, 2009.##[28] S. Rickard, M. Fallon, "The gini index of speech", In Proc. Of Information Sciences and Systems conference, Princeton, NJ, 2004.##[29] P. J. Wolfe, "Sparse time-frequency repre-sentations in audio processing, as studied through a symmetrized lognormal model," In Proc. of the European Signal Processing Con-ference (EUSIPCO), pp. 355-359, 2007.##[30] H. Dalton, "The measurement of the inequity of incomes", Economic Journal, Vol. 30, pp. 348-361, 1920.##[31] I. Cohen, B. Berdugo, "Speech enhancement for non-stationary noise environments", Signal processing, vol. 81, No. 11, pp. 2403-2418, 2001.##[32] A. Rix, J. Beerends, M. Hollier, A. Hekstra, "Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs", In Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process., pp. 749-752, 2001.## ##

بهبود الگوریتم ماشین بردار پشتیبان با الگوریتم رقابت استعماری برای دسته‌بندی اسناد متنی An Improvement in Support Vector Machines Algorithm with Imperialism Competitive Algorithm for Text Documents Classification 1 1 با توجه به رشد نمایی متون الکترونیکی، سازماندهی و مدیریت متون، مستلزم ابزاری است که اطلاعات و داده‏‌های مورد جستجوی کاربران را در کمترین زمان ارائه دهد؛ از‌این‌رو در سال‌های اخیر روش‌های دسته‌بندی اهمیت ویژه‎ای پیدا کرده است. هدف دسته‌بندی متون دست‌یابی به اطلاعات و داده‌ها در کسری از ثانیه است. یکی از مشکلات اصلی در دسته‏‌بندی متون، ابعاد بالای ویژگی‎هاست. برای کاهش ویژگی‎های متون، انتخاب ویژگی‎ها یکی از مؤثرترین راه‎حل‎هاست. چراکه هزینه محاسباتی که تابعی از طول بردار ویژگی‎هاست، بدون انتخاب ویژگی‌ها افزایش می‏‌یابد. در این مقاله روشی براساس بهبود الگوریتم ماشین بردار پشتیبان با الگوریتم رقابت استعماری برای دسته‌بندی اسناد متنی ارائه شده است. در روش پیشنهادی، از الگوریتم رقابت استعماری برای انتخاب ویژگی‎های و از الگوریتم ماشین بردار پشتیبان برای دسته‎بندی متون استفاده شده است. آزمایش و ارزیابی روش پیشنهادی بر روی مجموعه داده‌های Reuters21578, WebKB و Cade 12 انجام شده است. نتایج شبیه‎سازی حاکی از آن است که روش پیشنهادی در معیارهای دقت، بازخوانی و F Measure از روش‌ ماشین بردار پشتیبان بدون انتخاب ویژگی عملکرد بهینه‎تری دارد. 2 Due to the exponential growth of electronic texts, their organization and management requires a tool to provide information and data in search of users in the shortest possible time. Thus, classification methods have become very important in recent years. In natural language processing and especially text processing, one of the most basic tasks is automatic text classification. Moreover, text classification is one of the most important parts in data mining and machine learning. Classification can be considered as the most important supervised technique which classifies the input space to k groups based on similarity and difference such that targets in the same group are similar and targets in different groups are different. Text classification system has been widely used in many fields, like spam filtering, news classification, web page detection, Bioinformatics, machine translation, automatic response systems, and applications regarding of automatic organization of documents. The important point in obtaining an efficient text classification method is extraction and selection of key features of texts. It is proved that only 33% of words and features of the texts are useful and they can be used to extract information and most words existing in texts are used to represent purpose of a text and they are sometimes repeated. Feature selection is known as a good solution to high dimensionality of the feature space. Excessive number of Features not only increase computation time but also degrade classification accuracy. In general, purpose of extracting and selecting features of texts is to reduce data volume, time required for training, computational time and increase performance speed of the methods proposed for text classification. Feature extraction refers to the process of generating a small set of new features by combining or transforming the original ones, while in feature selection dimension of the space is reduced by selecting the most prominent features. In this paper, a solution to improve support vector machine algorithm using Imperialism Competitive Algorithm, are provided. In this proposed method, the Imperialism Competitive Algorithm for selecting features and the support vector machine algorithm for Classification of texts are used. At the stage of extracting the features of the texts, using weighting schemes such as NORMTF, LOGTF, ITF, SPARCK, and TF, each extracted word is allocated a weight in order to determine the role of the words in terms of their effects as the keywords of the texts. The weight of each word indicates the extent of its effect on the main topic of the text compared to other words used in the same text. In the proposed method, the TF weighting scheme is used for attributing weights to the words. In this scheme, the features are a function of the distribution of different features in each of the documents . Moreover, at this stage, using the process of pruning, low-frequency features and words that are used fewer than two times in the text are pruned. Pruning basically filters low-frequency features in a text [18]. In order to reduce the number of dimensions of the features and decrease computational complexity, the imperialist competitive algorithm (ICA) is utilized in the proposed method. The main goal of employing the imperialist competitive algorithm (ICA) in the proposed method is minimizing the loss of data in the texts, while also maximizing the reduction of the dimensions of the features. In the proposed method, since the imperialist competitive algorithm (ICA) has been used for selecting the features, there must be a mapping created between the parameters of the imperialist competitive algorithm (ICA) and the proposed method. Accordingly, when using the imperialist competitive algorithm (ICA) for selecting the key features, the search space includes the dimensions of the features, and among all the extracted features, , , or  of all the features are attributed to each of the countries. Since the mapping is carried out randomly, there may be repetitive features in any of the countries as well. Next, based on the general trend of the imperialist competitive algorithm (ICA),some countries which are more powerful are considered as imperialists, while the other countries are considered as colonies. Once the countries are identified, the optimization process can begin. Each country is defined in the form of an  array with different values for the variables as in Equations 2 and 3. (2) Country = [ , , …,  , ] (3) Cost = f (Country) The variables attributed to each country can be structural features, lexical features, semantic features, or the weight of each word, and so on. Accordingly, the power of each country for identifying the class of each text is increased or decreased based on its variables. One of the most important phases of the imperialist competitive algorithm (ICA) is the colonial competition phase. In this phase, all the imperialists try to increase the number of colonies they own. Each of the more powerful empires tries to seize the colonies of the weakest empires to increase their own power. In the proposed method, colonies with the highest number of errors in classification and the highest number of features are considered as the weakest empires. Based on trial and error, and considering the target function in the proposed method, the number of key features relevant to the main topic of the texts is set to  of the total extracted features, and only through using   of the key features of each text along with a classifier algorithm such as , support vector machine (SVM),  nearest neighbors, and so on, the class of that text can be determined in the proposed method. Since the classification of texts is a nonlinear problem, in order to classify texts, the problem must first be mapped into a linear problem. In this paper, the RBF kernel function along with  is used for mapping the problem. The hybrid algorithm is implemented on the Reuters21578, WebKB, and Cade 12 data sets to evaluate the accuracy of the proposed method. The simulation results indicate that the proposed hybrid algorithm in precision, recall and F Measure criteria is more efficient than primary support machine carriers.   117 130 2018/06/22018/07/82018/07/142018/08/142018/05/242018/06/92017/11/62018/06/3 1397/3/13 2019/09/22018/09/152019/07/102019/09/22019/07/102019/06/12020/01/222019/07/10 1398/4/19 زهرا عاشقی دیزجی Zahra Asheghi Dizaji گروه مهندسی کامپیوتر، واحد ارومیه، دانشگاه آزاد اسلامی، ارومیه، ایران ایران zahra_ashegi@yahoo.com سکینه اصغری آقجه‎دیزج Sakineh Asghari Aghjehdizaj گروه مهندسی کامپیوتر، واحد بناب، دانشگاه آزاد اسلامی، بناب، ایران ایران Sakineh154asghari@gmail.com فرهاد سلیمانیان قره چپق Farhad Soleimanian Gharehchopogh گروه مهندسی کامپیوتر، واحد ارومیه، دانشگاه آزاد اسلامی، ارومیه، ایران ایران farhad@iaurmia.ac.ir Feature Selection Text Classification Imperialism Competitive Algorithm Support Vector Machines Algorithm Optimization انتخاب ویژگی دسته‌بندی متون الگوریتم رقابت استعماری الگوریتم ماشین بردار پشتیبان بهینه‌سازی [1] R. Feldman, and J. Sanger, The Text Mining Handbook, "Advanced Approach in Analyzing Unstructured Data", Cambridge University Press, 2007.##[2] D. Chiang, H. Keh, H. Huang, and D. Chyr, "The Chinese text categorization system with association rule and category priority", Expert System with Applications, vol. 35, no. 1-2, pp. 102-110, 2008.##[3] L. Khreisat , "A machine learning approach for Arabic text classification using N-gram frequency statistics", Proceeding of the 3nd Journal of Informetrics, vol.3(1), pp 72-77, 2009.##[4] A. An, B. Dauletbakov and E. Levner, "Multi-attribute Classification of Text Documents as a Tool for Ranking and Categorization of Educational Innovation Projects", Lecture Notes in Computer Science, vol. 8404, pp 404-416, 2014.##[5] A. K. Uysal, "An improved global feature selection scheme for text classification", Expert systems with Applications, vol. 43, pp.82-92, 2016.##[6] C. H. Wan, L. H. Lee, R. Rajkumar and D. Isa, "A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine", Expert Systems with Applications, vol. 39(15), pp.11880-11888, 2013.##[7] B. Ramesh and J. G. R. Sathiaseelan, "An advanced Multi Class instance selection based Support Vector Machine for Text Classification", Procedia Computer Science, vol. 57, pp. 1124-1130, 2015.##[8] Y. Ko and J. Seo, "Text classification from unlabeled documents with bootstrapping and feature projection techniques", Information Processing & Management, vol. 45(1), pp. 70-83, 2009.##[9] N. Shafiabady, L. H. Lee, R. Rajkumar,, V. P. Kallimani, , N. A. Akram and D. Isa, "Using unsupervised clustering approach to train the Support Vector Machine for text classification", Neurocomputing, vol. 211, pp. 4-10, 2016.##[10] L. H. Lee, D. Isa, W. O. Choo and W. Y. Chue, "High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic", Expert Systems with Applications, vol. 39(1), pp. 1147-115, 2013.##[11] J. He, A. H. Tan and C. L. Tan, "On Machine Learning methods for Chinese document categorization", Applied Intelligence, vol. 18(3), pp. 311-322, 2003.##[12] D. Isa, L. H. Lee, V. P. Kallimani and R. Rajkumar, "Text document pre-processing with the bayes Formula for classification using the support vector machine", IEEE Transaction on Knowledge and Data Engineering, vol. 20(9), pp. 1264-1272, 2008.##[13] D. S. Guru and M. Suhil, "A Novel Term_Class Relevance Measure for Text Categorization", Procedia Computer Science, Vol. 45, pp. 13-22, 2015.##[14] A. Onan, S. Korukoğlu and H. Bulut, "Ensemble of keyword extraction methods and classifiers in text classification", Expert Systems with Applications, vol. 57, pp. 232-247, 2016.##[15] Y. Ko, J. Park and J. Seo, "Automatic Text Categorization using the Importance of Sentences", 19th international linguistics- Association for Computational Linguistics, vol. 1, PP.1-7, 2002.##[16] M. Sivakumar, C. Karthika and P. Renuga, "A Hybrid Text Classification Approach Using KNN And SVM ", Intenational Journal of Innovative Research In Science Engineering And Technology,Special Issue 3, vol. 3, pp.1987-1991, 2014.##[17] G. Feng, J. Guo, B. Y. Jing and T. Sun, "Feature Subset Selection Using Naive Bayes for Text Classification", Pattern Recognition Letters, vol. 65, pp. 109-115, 2015.##[18] H. Uguz, "A two-stage feature selection method for text categorization by using information gain", principal component analysis and genetic algorithm. Knowledge-Based Systems, vol. 24(7), pp. 1024-1032, 2011.‌##[19] E. H. S. Han, G. Karypis and V. Kumar, "Text Categor ization Using Weight Adjusted k-Nearest Neighbor Classification", In Pacific-asia conference on knowledge discovery and data mining, PP: 53-65. Springer Berlin Heidelberg, 2001.##[20] K. Nigam, A. K. McCallum, S. Thrun and T. Mitchell, "Text Classification from Labeled and Unlabeled Documents using EM", Kluwer Academic Publishers, Printed in The Netherlands. Machine Learning, vol.39 (2), pp. 103-134, 2000.##[21] R. Habibpour and K. Khalilpour, "A New Hybrid K-means and K-Nearest-Neighbor Algorithms for Text Document Clustering", International Journal of Academic Research, vol.6(3), pp. 7984, 2004.##[22] S. Kashef and H. Nezamabadi-pour, "An advanced ACO algorithm for feature subset selection", Neurocomputing, vol.147, pp. 271-279, 2015.##[23] A. S. Ghareb, A. A. Bakar and A. R. Hamdan, "Hybrid feature selection based on enhanced genetic algorithm for text categorization", Expert SystemsWith Applications, vol.49, pp.31-47, 2016.##[24] Y. Lu, M. Liang, Z. Ye and L. Cao, "Improved particle swarm optimization algorithm and its applicationin text feature selection", Applied Soft Computing, vol. 35, pp. 629-636, 2015.##[25] H. Wang and B. Niu, "A novel bacterial algorithm with randomness control for feature selection in classification", Neurocomputing, vol. 228, pp. 176-186, 2017.##[26] V. Rezaie, M. Mohammadpour, H. parvin, S. Nejatian, "An Approach for Extraction of Keywords and Weighting Words for Improvement Farsi Documents Classification", JSDP, vol. 14 (4), pp.55-78. 2018.##[27] F. Rad, H. Parvin, A. Dehbashi, B. Minaee, "Improved Clustering Persian Text Based on Keyword Using Linguistic and Thesaurus Knowledge", JSDP, vol. 13 (1), pp.87-100, 2016.##[28] F. Hoseinkhani, B. Nasersharif, "Two Featuer Transformation Methods Based on Genetic Algorithm for Reducing Support Vector Machine Classification Error", JSDP. Vol. 12 (2), pp. 23-39, 2015##[29] E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, pp. 4661-4667, 2007.##[30] C. Lucas, Z. Nasiri-Gheidari and F. Tootoonchian, "Application of an imperialist competitive algorithm to the design of a linear induction motor", Energy Conversion and Management, Elsevier, vol. 51(7).‌ pp. 1407-1411, 2010.##[31] E. Atashpaz-Gargari, F. Hashemzadeh, R. Rajabioun, and C. Lucas, "Colonial Competitive Algorithm, a novel approach for PID controller design in MIMO distillation column process", International Journal of Intelligent Computing and Cybernetics, vol. 1(3). pp. 337-355, 2008.##[32] T.Mitchell, K.Nigam, D.Freitag, M,Craven, "Learning to extract symbolic knowledge from the world wide web", In: DTIC Document, 1998.##[33] A. Asuncion and D.J. Newmen, UCI Machine Learning Repository, Irvine, CA: Uni-versity of California, Department of information and Computer Science, 2007.##[34]http://archive.ics.uci.edu/ml/datasets/Reuters21778+Text+Categorization+Collection [Last Access: 12-19-2112.##[35] http://ana.cachopo.org/datasets-for-single-label-text-categorization [Lase Access: 12-19-2112].##[36] J. J. Rocchio, "Document Retrieval Systems - Optimization and Evaluation", PhD thesis, Harvard, 1966.##[37] K. M. Elhadad, Kh. M. Badran, and G. I. Salama, "A Novel Approach for Ontology-based Dimensionality Reduction for Web Text Document Classification", Computer society, pp.373-378, 2017.##[1] R. Feldman, and J. Sanger, The Text Mining Handbook, "Advanced Approach in Analyzing Unstructured Data", Cambridge University Press, 2007.##[2] D. Chiang, H. Keh, H. Huang, and D. Chyr, "The Chinese text categorization system with association rule and category priority", Expert System with Applications, vol. 35, no. 1-2, pp. 102-110, 2008.##[3] L. Khreisat , "A machine learning approach for Arabic text classification using N-gram frequency statistics", Proceeding of the 3nd Journal of Informetrics, vol.3(1), pp 72-77, 2009.##[4] A. An, B. Dauletbakov and E. Levner, "Multi-attribute Classification of Text Documents as a Tool for Ranking and Categorization of Educational Innovation Projects", Lecture Notes in Computer Science, vol. 8404, pp 404-416, 2014.##[5] A. K. Uysal, "An improved global feature selection scheme for text classification", Expert systems with Applications, vol. 43, pp.82-92, 2016.##[6] C. H. Wan, L. H. Lee, R. Rajkumar and D. Isa, "A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine", Expert Systems with Applications, vol. 39(15), pp.11880-11888, 2013.##[7] B. Ramesh and J. G. R. Sathiaseelan, "An advanced Multi Class instance selection based Support Vector Machine for Text Classification", Procedia Computer Science, vol. 57, pp. 1124-1130, 2015.##[8] Y. Ko and J. Seo, "Text classification from unlabeled documents with bootstrapping and feature projection techniques", Information Processing & Management, vol. 45(1), pp. 70-83, 2009.##[9] N. Shafiabady, L. H. Lee, R. Rajkumar,, V. P. Kallimani, , N. A. Akram and D. Isa, "Using unsupervised clustering approach to train the Support Vector Machine for text classification", Neurocomputing, vol. 211, pp. 4-10, 2016.##[10] L. H. Lee, D. Isa, W. O. Choo and W. Y. Chue, "High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic", Expert Systems with Applications, vol. 39(1), pp. 1147-115, 2013.##[11] J. He, A. H. Tan and C. L. Tan, "On Machine Learning methods for Chinese document categorization", Applied Intelligence, vol. 18(3), pp. 311-322, 2003.##[12] D. Isa, L. H. Lee, V. P. Kallimani and R. Rajkumar, "Text document pre-processing with the bayes Formula for classification using the support vector machine", IEEE Transaction on Knowledge and Data Engineering, vol. 20(9), pp. 1264-1272, 2008.##[13] D. S. Guru and M. Suhil, "A Novel Term_Class Relevance Measure for Text Categorization", Procedia Computer Science, Vol. 45, pp. 13-22, 2015.##[14] A. Onan, S. Korukoğlu and H. Bulut, "Ensemble of keyword extraction methods and classifiers in text classification", Expert Systems with Applications, vol. 57, pp. 232-247, 2016.##[15] Y. Ko, J. Park and J. Seo, "Automatic Text Categorization using the Importance of Sentences", 19th international linguistics- Association for Computational Linguistics, vol. 1, PP.1-7, 2002.##[16] M. Sivakumar, C. Karthika and P. Renuga, "A Hybrid Text Classification Approach Using KNN And SVM ", Intenational Journal of Innovative Research In Science Engineering And Technology,Special Issue 3, vol. 3, pp.1987-1991, 2014.##[17] G. Feng, J. Guo, B. Y. Jing and T. Sun, "Feature Subset Selection Using Naive Bayes for Text Classification", Pattern Recognition Letters, vol. 65, pp. 109-115, 2015.##[18] H. Uguz, "A two-stage feature selection method for text categorization by using information gain", principal component analysis and genetic algorithm. Knowledge-Based Systems, vol. 24(7), pp. 1024-1032, 2011.‌##[19] E. H. S. Han, G. Karypis and V. Kumar, "Text Categor ization Using Weight Adjusted k-Nearest Neighbor Classification", In Pacific-asia conference on knowledge discovery and data mining, PP: 53-65. Springer Berlin Heidelberg, 2001.##[20] K. Nigam, A. K. McCallum, S. Thrun and T. Mitchell, "Text Classification from Labeled and Unlabeled Documents using EM", Kluwer Academic Publishers, Printed in The Netherlands. Machine Learning, vol.39 (2), pp. 103-134, 2000.##[21] R. Habibpour and K. Khalilpour, "A New Hybrid K-means and K-Nearest-Neighbor Algorithms for Text Document Clustering", International Journal of Academic Research, vol.6(3), pp. 7984, 2004.##[22] S. Kashef and H. Nezamabadi-pour, "An advanced ACO algorithm for feature subset selection", Neurocomputing, vol.147, pp. 271-279, 2015.##[23] A. S. Ghareb, A. A. Bakar and A. R. Hamdan, "Hybrid feature selection based on enhanced genetic algorithm for text categorization", Expert SystemsWith Applications, vol.49, pp.31-47, 2016.##[24] Y. Lu, M. Liang, Z. Ye and L. Cao, "Improved particle swarm optimization algorithm and its applicationin text feature selection", Applied Soft Computing, vol. 35, pp. 629-636, 2015.##[25] H. Wang and B. Niu, "A novel bacterial algorithm with randomness control for feature selection in classification", Neurocomputing, vol. 228, pp. 176-186, 2017.##[26] رضایی، وحیده.، محمدپور، مجید.، پروین، حمید.، نجاتیان، صمد.، 1396. ارائه روشی برای استخراج کلمات کلیدی و وزن‎دهی کلمات برای بهبود طبقه‌بندی متون فارسی. فصل‎نامه‎ی پردازش علائم و داده‎ها، شماره 4 پیاپی 34.##[26] V. Rezaie, M. Mohammadpour, H. parvin, S. Nejatian, "An Approach for Extraction of Keywords and Weighting Words for Improvement Farsi Documents Classification", JSDP, vol. 14 (4), pp.55-78. 2018.##[27] راد، فرهاد.، پروین، حمید.، دهباشی، آتوسا.، مینایی، بهروز.، 1395. ارائه روشی جدید برای شاخص‎گذاری خودکار و استخراج کلمات کلیدی برای بازیابی اطلاعات و خوشه‎بندی متون. فصل‎نامه‎ی پردازش و علائم داده‎ها، شماره 1 پیاپی 27.##[27] F. Rad, H. Parvin, A. Dehbashi, B. Minaee, "Improved Clustering Persian Text Based on Keyword Using Linguistic and Thesaurus Knowledge", JSDP, vol. 13 (1), pp.87-100, 2016.##[28] حسین‎خانی، فاطمه.، ناصرشریف، بابک.، دو روش تبدیل ویژگی مبتنی بر الگوریتم‎های ژنتیک برای کاهش خطای دسته‎بندی ماشین بردار پشتیبان. فصل‎نامه‎ی پردازش علائم و داده‎ها، شماره 2 پیاپی 24.##[28] F. Hoseinkhani, B. Nasersharif, "Two Featuer Transformation Methods Based on Genetic Algorithm for Reducing Support Vector Machine Classification Error", JSDP. Vol. 12 (2), pp. 23-39, 2015##[29] E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, pp. 4661-4667, 2007.##[30] C. Lucas, Z. Nasiri-Gheidari and F. Tootoonchian, "Application of an imperialist competitive algorithm to the design of a linear induction motor", Energy Conversion and Management, Elsevier, vol. 51(7).‌ pp. 1407-1411, 2010.##[31] E. Atashpaz-Gargari, F. Hashemzadeh, R. Rajabioun, and C. Lucas, "Colonial Competitive Algorithm, a novel approach for PID controller design in MIMO distillation column process", International Journal of Intelligent Computing and Cybernetics, vol. 1(3). pp. 337-355, 2008.##[32] T.Mitchell, K.Nigam, D.Freitag, M,Craven, "Learning to extract symbolic knowledge from the world wide web", In: DTIC Document, 1998.##[33] A. Asuncion and D.J. Newmen, UCI Machine Learning Repository, Irvine, CA: Uni-versity of California, Department of information and Computer Science, 2007.##[34]http://archive.ics.uci.edu/ml/datasets/Reuters21778+Text+Categorization+Collection [Last Access: 12-19-2112.##[35] http://ana.cachopo.org/datasets-for-single-label-text-categorization [Lase Access: 12-19-2112].##[36] J. J. Rocchio, "Document Retrieval Systems - Optimization and Evaluation", PhD thesis, Harvard, 1966.##[37] K. M. Elhadad, Kh. M. Badran, and G. I. Salama, "A Novel Approach for Ontology-based Dimensionality Reduction for Web Text Document Classification", Computer society, pp.373-378, 2017.## ##

حس‌گری فشرده بلوکی با استفاده از آستانه‌گیری نرم ضرایب تبدیل تطبیقی Block-Based Compressive Sensing Using Soft Thresholding of Adaptive Transform Coefficients 1 1 در این مقاله، روشی نوین جهت بازسازی تصاویر بر اساس نمونه‌های به‌دست‌آمده از اعمال «حس‌گری فشرده بلوکی» ارائه می‌شود. جهت حصول به کیفیت بازسازی بالا در روش پیشنهادی، ابتدا یک تبدیل تطبیقی بلوکی توسعه داده می‌شود که از همبستگی و شباهت بلوک‌های همسایه یک بلوک موردنظر در تصویر برای حصول به تُنُک‌سازی بالاتر آن بلوک، استفاده می‌کند؛ سپس، برای کاهش نوفه و اعوجاجات احتمالی به‌وجود‌آمده در فرآیند بازسازی و در عین‌حال حفظ میزان تُنُکی ضرایب، از یک تابع آستانه‌گیری نرم استفاده می‌شود که قادر است به‌صورت تطبیقی، ضرایب تبدیل را برای افزایش کیفیت بازسازی تصویر، پالایش کند. نتایج تجربی به‌دست‌آمده نشان می‌دهند که روش پیشنهادی از دقت و کیفیت بازسازی بالاتری در مقایسه با چندین روش مطرح موجود برخوردار است. 2 Compressive sampling (CS) is a new technique for simultaneous sampling and compression of signals in which the sampling rate can be very small under certain conditions. Due to the limited number of samples, image reconstruction based on CS samples is a challenging task. Most of the existing CS image reconstruction methods have a high computational complexity as they are applied on the entire image. To reduce this complexity, block-based CS (BCS) image reconstruction algorithms have been developed in which the image sampling and reconstruction processes are applied on a block by block basis. In almost all the existing BCS methods, a fixed transform is used to achieve a sparse representation of the image. however such fixed transforms usually do not achieve very sparse representations, thereby degrading the reconstruction quality. To remedy this problem, we propose an adaptive block-based transform, which exploits the correlation and similarity of neighboring blocks to achieve sparser transform coefficients. We also propose an adaptive soft-thresholding operator to process the transform coefficients to reduce any potential noise and perturbations that may be produced during the reconstruction process, and also impose sparsity. Experimental results indicate that the proposed method outperforms several prominent existing methods using four different popular image quality assessment metrics. 131 146 2018/06/22018/07/82018/07/142018/08/142018/05/242018/06/92017/11/62018/06/32018/08/9 1397/5/18 2019/09/22018/09/152019/07/102019/09/22019/07/102019/06/12020/01/222019/07/102019/07/23 1398/5/1 هادی هادی زاده Hadi Hadizadeh دانشگاه صنعتی قوچان ایران h.hadizadeh@qiet.ac.ir Compressive Sampling Soft Thresholding Adaptive Transform Sparsity حس‌گری فشرده آستانه‌گیری نرم تبدیل تطبیقی تُنُکی [1] E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal recons-truction from highly incomplete frequency infor-mation," IEEE Transactions on. Information Theory, vol. 52, pp. 489-509, Feb. 2006.##[2] D. L. Donoho, "De-noising by soft-thresholding," IEEE Transactions on. Info-rmation Theory, vol. 41, no. 3, pp. 613-627, May 1995.##[3] Y. Tsaig and D. L. Donoho, "Extensions of compressed sensing," Signal Procesing, vol. 86, pp. 533-548, July 2006.##[4] D. L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit," Mar. 2006, pre-print.##[5] M. A. T. Figueiredo, R. D. Nowak and S. J. Wright, "Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems," IEEE Journal of Selected Topics in Signal Procesing, vol. 1, no. 4, Dec. 2007.##[6] T. T. Do, L. Gan, N. Nguyen, and T. D. Tran, "Sparsity adaptive matching pursuit algorithm for practical compressed sensing," in 42nd Asilomar Conference on Signals, Systems and Computers, Oct. 2008.##[7] L. Gan, "Block compressed sensing of natural images," in Proceedings of the International Conference on Digital Signal Processing, Cardiff, UK, July 2007, pp. 403-406.##[8] M. Bertero and P. Boccacci, Introduction to Inverse Problems in Imaging. Bristol, UK: Institute of Physics Publishing, 1998.##[9] J. E. Fowler, S. Mun, and E. W. Tramel, "Block-Based Compressed Sensing of Images and Video," Foundations and Trends in Signal Processing, vol. 4, no. 4, pp 297-416, 2012.##[10] S. Mun and J. E. Fowler, "Block compressed sensing of images using directional transforms," in 16th IEEE International Conference on Image Processing, Nov. 2009.##[11] N. Eslahi, A. Aghagolzadeh, and S. M. H. Andargoli, "Block Compressed Sensing Images using Curvelet Transform," in 22nd Iranian Conference on Electrical Engineering, May. 2014.##[12] D. L. Donoho, "De-noising by soft-thresholding," IEEE Transactions on. Infor-mation Theory, vol. 41, no. 3, pp. 613- 627, May 1995.##[13] E. Candes, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Communications on Pure and Applied Mathematics, vol. 59, no. 8, pp. 1207-1223, August 2006.##[14] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, Apr. 2004.##[15] L. Zhang, L. Zhang, X. Mou, and D. Zhang, " FSIM: A Feature Similarity Index for Image Quality Assessment," IEEE Transactions on Image Processing, vol. 20, no. 8, Aug. 2011.##[16] T. T. Cai and L. Wang, "Orthogonal Matching Pursuit for Sparse Signal Recovery with Noise," IEEE Transactions on. Information Theory, vol. 57, no. 7, pp. 4680-4688, Jul., 2011.##[17] G. H. Golub and C. F. Van Loan, Matrix Computations, The Johns Hopkins University Press, 2013.##[18] H.R. Sheikh.and A.C. Bovik, "Image information and visual quality," IEEE Transactions on Image Processing, vol.15, no.2, pp. 430- 444, Feb. 2006.##[19] R. Li, H. Liu, Y. Zeng, and Y. Li, "Block Compressed Sensing of Images Using Adaptive Granular Reconstruction," Advances in Multi-media, pp. 1-9, Nov. 2016.##[20] A. S. Unde, and P. P. Deepthi, "Block Compressive Sensing: Individual and Joint Reconstruction of Correlated Images," Journal of Vision Commun. Image R. vol. 44, pp. 187-197, 2017.##[21] T. V. Chien, K. Q. Dinh, B. Jeon, and M. Burger, "Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation," Image Communications, vol. 54, no. C, pp. 93-106, May 2017.##[22] Z. Xue, W. Anhong, Z. Bing, L. Lei, and L. Zhuo, "Adaptive Block-Wise Compressive Image Sensing Based on Visual Perception," IEICE, vol. E96-D, no. 2, pp. 383-386, 2013.##[23] X. Zhang, Y. Wang, D. Wang, and Y. Li, "Adaptive image compression based on compressive sensing for video sensor nodes," Multimedia Tools and Applications, vol. 77, no. 11, pp. 13679-13699, 2018.##[24] M. Rani, S. B. Dhok, and R. B. Deshmukh, "A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications," IEEE Access, vol. 6, pp. 4875-4894, 2018.##[25] A. S. Unde and P. P. Deepthi, "Fast BCS-FOCUSS and DBCS-FOCUSS with augmented Lagrangian and minimum residual methods," Journal of Visual Communication and Image Representation, vol. 52, pp. 92-100, 2018.##[1] E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal recons-truction from highly incomplete frequency infor-mation," IEEE Transactions on. Information Theory, vol. 52, pp. 489-509, Feb. 2006.##[2] D. L. Donoho, "De-noising by soft-thresholding," IEEE Transactions on. Info-rmation Theory, vol. 41, no. 3, pp. 613-627, May 1995.##[3] Y. Tsaig and D. L. Donoho, "Extensions of compressed sensing," Signal Procesing, vol. 86, pp. 533-548, July 2006.##[4] D. L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit," Mar. 2006, pre-print.##[5] M. A. T. Figueiredo, R. D. Nowak and S. J. Wright, "Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems," IEEE Journal of Selected Topics in Signal Procesing, vol. 1, no. 4, Dec. 2007.##[6] T. T. Do, L. Gan, N. Nguyen, and T. D. Tran, "Sparsity adaptive matching pursuit algorithm for practical compressed sensing," in 42nd Asilomar Conference on Signals, Systems and Computers, Oct. 2008.##[7] L. Gan, "Block compressed sensing of natural images," in Proceedings of the International Conference on Digital Signal Processing, Cardiff, UK, July 2007, pp. 403-406.##[8] M. Bertero and P. Boccacci, Introduction to Inverse Problems in Imaging. Bristol, UK: Institute of Physics Publishing, 1998.##[9] J. E. Fowler, S. Mun, and E. W. 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موقعیت‌یابی سه‌بُعدی یک هدف ناشناخته با استفاده از دو حس‌گر نامتجانس Three Dimensional Localization of an Unknown Target Using Two Heterogeneous Sensors 1 1 در شبکه­های حس‌گرغیر‌همگن، برای موقعیت­یابی هدف از چندین نوع گیرنده استفاده می­شود و هر گیرنده می­تواند کمیت متفاوتی از هدف را اندازه­گیری کند. استفاده از چندین کمیت اندازه­گیری‌شده از یک هدف باعث می­‌شود که تخمین موقعیت آن با سادگی و دقت بیشتری انجام شود. در اینجا، موقعیت­یابی هدف در یک فضای سه‌بُعدی با استفاده از یک شبکه غیر‌همگن شامل یک حس‌گر همه‌جهته و یک حس‌گر برداری مورد نظر است. الگوریتم موجود برای چنین شبکه­ای، به موقعیت نسبی هدف و حس‌گرها وابسته بوده و در پنجاه درصد موارد نمی‌تواند جوابی برای فاصله هدف ارایه کند. در الگوریتم پیشنهادی در این مقاله برای به‌دست‌آوردن یک تخمین بدون ابهام از فاصله هدف در هندسه مورد نظر، تنها از توان­های اندازه­گیری‌شده در دو حس‌گر استفاده می­‌شود. در این راستا، ابتدا یک تحلیل تئوری از مسأله انجام گرفته و سپس به‌منظور یافتن فاصله هدف یک الگوریتم جست­وجوی ساده و مؤثر مبتنی بر روش ریشه­یابی تصنیف پیشنهاد می‌شود. در الگوریتم ارایه‌شده، دست‌یابی به یک تخمین یکتا از فاصله هدف مستقل از موقعیت مکانی آن تضمین می­‌شود. شبیه­سازی­های انجام‌شده، سرعت و دقت الگوریتم ارایه‌شده و همچنین مقاوم‌بودن آن در برابر تغییرات فاصله هدف و موقعیت­های مختلف دو حس‌گر را اثبات می‌کند. 2 Heterogeneous wireless sensor networks consist of some different types of sensor nodes deployed in a particular area. Different sensor types can measure different quantity of a source and using the combination of different measurement techniques, the minimum number of necessary sensors is reduced in localization problems. In this paper, we focus on the single source localization in a heterogeneous sensor network containing two types of passive anchor-nodes: Omni-directional and vector sensors. An omni-directional sensor can simply measure the received signal strength (RSS) without any additional hardware. In other side, an acoustic vector sensor (AVS) consists of a velocity-sensor triad and an optional acoustic pressure-sensor, all spatially collocated in a point-like geometry. The velocity-sensor triad has an intrinsic ability in direction finding process. Moreover, despite its directivity, a velocity-sensor triad can isotropically measure the received signal strength and has a potential to be used in RSS-based ranging methods. Employing a heterogeneous sensor-pair consisting of one vector and one omni-directional sensor, this study tries to obtain unambiguity estimation for the location of an unknown source in a three-dimensional (3D) space. Using a velocity-sensor triad as an AVS, it is possible to determine the direction of arrival (DOA) of the source without any restriction on the spectrum of the emitted signal. However, the range estimation is a challenging problem when the target is closer to the omnidirectional sensor than the vector sensor. The existence method proposed for such configuration suffers from a fundamental limitation, namely the localization coverage. Indeed, this algorithm cannot provide an estimate for the target range in 50 percent of target locations due to its dependency to the relative sensor-target geometry. In general, our proposed method for the considered problem can be summarized as follows: Initially, we assume that the target's DOA is estimated using the velocity-sensor triad’s data. Then, considering the estimated DOA and employing the RSS measured by two sensors, we propose a computationally efficient algorithm for uniquely estimation of the target range. To this end, the ratio of RSS measured by two sensors is defined and, then, shown that this power ratio can be expressed as a monotonic function of the target range. Finally, the bisection search method is proposed to find an estimate for the target range. Since the proposed algorithm is based on bisection search method, a solution for the range of the target independent of its location is guaranteed. Moreover, a set of future aspects and trends is identified that might be interesting for future research in this area. Having a low computational complexity, the proposed method can enhance the coverage area mostly two times of that explored by the existence method. The simulated data confirms the speed and accuracy of developed algorithm and shows its robustness against various target ranges and different sensor spacing. 147 158 2018/06/22018/07/82018/07/142018/08/142018/05/242018/06/92017/11/62018/06/32018/08/92018/04/26 1397/2/6 2019/09/22018/09/152019/07/102019/09/22019/07/102019/06/12020/01/222019/07/102019/07/232019/06/19 1398/3/29 ولی کاوسی Vali kavoosi دانشگاه آزاد اسلامی، واحد مرودشت ایران kavoosi@miau.ac.ir محمد جواد دهقانی mohammad javad Dehghani دانشگاه صنعتی شیراز و مرکزمنطقه‌ای اطلاع‌رسانی علوم وفنآوری، شیراز ایران dehghani@sutech.ac.ir رضا جاویدان Reza Javidan دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی شیراز ایران reza.Javidan@gmail.com Heterogeneous network Unambiguous localization Vector sensor RSS Bisection algorithm شبکه غیر همگن موقعیت‌یابی بدون ابهام حس‌گر برداری شدت سیگنال دریافتی الگوریتم تصنیف [1] G. Han, J. Jiang, L. Shu, Y. Xu, and F. Wang, "Localization algorithms of underwater wireless sensor networks: a survey," Sensors, vol. 12, no.2, pp. 2026-2061. 2012.##[2] D. Li, and Y. H. Hu, "Energy-based collaborative source localization using acoustic microsensor array," EURASIP Journal on Applied Signal Processing, Vol. 2003, No. 4, pp. 321-337, 2003.##[3] Y. I. Wu, and K. T. Wong, "Acoustic near-field source-localization by two passive anchor-nodes," IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 1, 2012.##[4] Z. X Yao, and J. Y. Hui, "Four approaches to DOA estimation based on a single vector hydrophone," Ocean Engineering, vol. 24, pp.122-127, 2006.##[5] Y. I. Wu, K. T. Wong and S.-K. Lau, "The acoustic vector-sensor's near-field array-manifold," IEEE Transactions on Signal Processing, vol. 58, no. 7, pp. 3946-3951, July 2010.##[6] X. Zhong and A. B. Premkumar, "Particle filtering approaches for multiple acoustic source detection and 2-D direction of arrival estimation using a single acoustic vector sensor," IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4719-4733, 2012.##[7] V. N. Hari, A. B. Premkumar, and X. Zhong, "A decoupled approach for near-field source localization using a single acoustic vector sensor," Circuits, Systems, and signal Processing, vol. 32, no. 2, pp 843-859, 2013.##[8] M. Laaraiedh, "Contributions on hybrid localization techniques for heterogeneous wireless networks," Ph.D. Thesis, University of Rennes, 2010.##[9] Z. M. Saric, D. D. Kukolj, and N. D. Teslic, "Acoustic source localization in wireless sensor network," Circuits, Systems, and Signal Processing, vol. 29, no. 5, pp.837-856, 2010.##[10] J. Wang, J. Chen, and D. Cabric, "Cramer-Rao bounds for joint RSS/DoA-based primary-user localization in cognitive radio networks," IEEE Transactions on Wireless Communications, vol. 12, no. 3, pp.1363-1375, 2013.##[11] W. Meng, L. Xie, and W. Xiao, "Optimality analysis of sensor-source geometries in heterogeneous sensor networks," IEEE Transactions on Wireless Communication, vol. 12, no. 4, pp. 1958-1967, 2013.##[12] S. Tomic, M. Beko, R. Dinis, and L. Bernardo, "On target localization using combined RSS and AoA measurements," Sensors, vol. 18, no. 4, 2018.##[13] S. Wang, B. R. Jackson, and R. Inkol, "Hybrid RSS/AOA emitter location estimation based on least squares and maximum likelihood criteria," in 26th Biennial Symposium onCommunications (QBSC), 2012, pp. 24-29.##[14] L. Gazzah, L. Najjar, and H. Besbes, "Selective hybrid RSS/AOA weighting algorithm for NLOS intra cell localization," IEEE WCNC, Turkey, Istanbul, 2014, pp. 2546-2551.##[15] L. Gazzah, L. Najjar, and H. Besbes, "Hybrid RSSD/AoA cooperative localization for 4G wireless networks with uncooperative emitters," in International Wireless Communications and Mobile Computing Conference (IWCMC), 2015, pp. 874-879.##[16] V. Kavoosi, M. J. Dehghani, and R. Javidan, "Selective geometry for near-field three-dimensional localization using one-pair sensor," IET Radar Sonar and Navigation, vol. 10, no. 5, pp. 844-849, 2015.##[17] X. Sheng, and Y. Hu, "Energy based acoustic source localization," In IPSN'03: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, Springer Berlin Heidelberg, Germany, 2003, pp. 285-300.##[18] A. Quarteroni, R. Sacco, and F. Saleri, Numerical mathematics, Springer Science & Business Media, vol. 37, 2010.##[19] X. Zhong, and A. B. Premkumar, "Particle filtering approaches for multiple acoustic source detection and 2-D direction of arrival estimation using a single acoustic vector sensor," IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4719-4733, 2012.##[20] Z. Baoping, G. R. Wood, and W. P. Baritompa, "Multidimensional bisection: the performance and the context," Journal of Global Optimization, vol.3, no. 3, pp. 337-358, 1993##[1] G. Han, J. Jiang, L. Shu, Y. Xu, and F. Wang, "Localization algorithms of underwater wireless sensor networks: a survey," Sensors, vol. 12, no.2, pp. 2026-2061. 2012.##[2] D. Li, and Y. H. Hu, "Energy-based collaborative source localization using acoustic microsensor array," EURASIP Journal on Applied Signal Processing, Vol. 2003, No. 4, pp. 321-337, 2003.##[3] Y. I. Wu, and K. T. Wong, "Acoustic near-field source-localization by two passive anchor-nodes," IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 1, 2012.##[4] Z. X Yao, and J. Y. Hui, "Four approaches to DOA estimation based on a single vector hydrophone," Ocean Engineering, vol. 24, pp.122-127, 2006.##[5] Y. I. Wu, K. T. Wong and S.-K. Lau, "The acoustic vector-sensor's near-field array-manifold," IEEE Transactions on Signal Processing, vol. 58, no. 7, pp. 3946-3951, July 2010.##[6] X. Zhong and A. B. Premkumar, "Particle filtering approaches for multiple acoustic source detection and 2-D direction of arrival estimation using a single acoustic vector sensor," IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4719-4733, 2012.##[7] V. N. Hari, A. B. Premkumar, and X. Zhong, "A decoupled approach for near-field source localization using a single acoustic vector sensor," Circuits, Systems, and signal Processing, vol. 32, no. 2, pp 843-859, 2013.##[8] M. Laaraiedh, "Contributions on hybrid localization techniques for heterogeneous wireless networks," Ph.D. Thesis, University of Rennes, 2010.##[9] Z. M. Saric, D. D. Kukolj, and N. D. Teslic, "Acoustic source localization in wireless sensor network," Circuits, Systems, and Signal Processing, vol. 29, no. 5, pp.837-856, 2010.##[10] J. Wang, J. Chen, and D. Cabric, "Cramer-Rao bounds for joint RSS/DoA-based primary-user localization in cognitive radio networks," IEEE Transactions on Wireless Communications, vol. 12, no. 3, pp.1363-1375, 2013.##[11] W. Meng, L. Xie, and W. Xiao, "Optimality analysis of sensor-source geometries in heterogeneous sensor networks," IEEE Transactions on Wireless Communication, vol. 12, no. 4, pp. 1958-1967, 2013.##[12] S. Tomic, M. Beko, R. Dinis, and L. Bernardo, "On target localization using combined RSS and AoA measurements," Sensors, vol. 18, no. 4, 2018.##[13] S. Wang, B. R. Jackson, and R. Inkol, "Hybrid RSS/AOA emitter location estimation based on least squares and maximum likelihood criteria," in 26th Biennial Symposium onCommunications (QBSC), 2012, pp. 24-29.##[14] L. Gazzah, L. Najjar, and H. 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Premkumar, "Particle filtering approaches for multiple acoustic source detection and 2-D direction of arrival estimation using a single acoustic vector sensor," IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4719-4733, 2012.##[20] Z. Baoping, G. R. Wood, and W. P. Baritompa, "Multidimensional bisection: the performance and the context," Journal of Global Optimization, vol.3, no. 3, pp. 337-358, 1993## ##