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تصمیم‌گیری گروهی چند‌معیاره ترکیبی برای مسأله انتخاب تأمین‌کننده با داده‌های فازی شهودی بازه‌ای مقدار Hybrid multi-criteria group decision-making for supplier selection problem with interval-valued Intuitionistic fuzzy data 1 1 فرآیند انتخاب تأمین­‌کننده مناسب که قادر به فراهم‌کردن نیاز خریدار از نظر محصولات باکیفیت با قیمت مناسب و در یک زمان و حجم مناسب باشد، یکی از ضروری‌­ترین فعالیت‌­ها برای ایجاد یک زنجیره تأمین کارا است. با توجه به اهمیت موضوع، در این مقاله، برای حل مسأله انتخاب تأمین­‌کننده، رویکردی ترکیبی به همراه تصمیم‌­گیری گروهی در مسائل تصمیم­‌گیری چند­معیاره در بستر فازی شهودی بازه‌ای مقدار ارائه شده است. در این روش مقادیر متناسب با هر تأمین­‌کننده در بستر فازی شهودی بازه­ای مقدار مشخص شده است؛ سپس ­اولویت­‌های جمعی متناسب با هر تأمین­‌کننده به‌دست آورده می‌شوند و از روش تاپسیس، ضریب نزدیکی (شاخص شباهت) محاسبه و سپس تأمین‌­کننده‌­ها بر اساس این مقدار ارزیابی می­‌شوند. در انتها از روش برنامه­‌ریزی هدفمند با تابع رضایت­‌بخش برای رتبه­‌بندی نهایی به تأمین­‌کننده‌­ها استفاده می‌شود. مدل پیشنهادی به‌وسیله نرم‌افزار متلب پیاده‌­سازی شده و با طرح سناریویی روند کاری مدل پیشنهادی برای رتبه‌­بندی تأمین­‌کننده‌­ها تشریح شده است. 2 The main objectives of supply chain management are reducing the risk of supply chain and production cost, increase the income, improve the customer services, optimizing the achievement level, and business processes which would increase ability, competency, customer satisfaction, and profitability. Further, the process of selecting the appropriate supplier capable of providing buyer's requirements in terms of quality products with suitable price and at a suitable time and size is one of the most essential activities to create an efficient supply chain. Consequently, false decisions in the context of supplier selection would lead to negative effects. Usually, suitable supplier selection methods have been multi-criteria or attribute, so finding the optimal solution for supplier selection is demanding. The customary methods in this field have struggled with quantitative criteria however there are a wide range of qualitative criteria in supplier selection. this article has used interval valued intuitionistic fuzzy sets for selecting the appropriate suppliers, which reflect ambiguity and uncertainty far better than other methods. In this article, trapezoidal fuzzy membership function is used for lingual qualitative values. Goal programming satisfaction function (GPSF) is a kind of technique that helps decision makers in solving problems involving conflicting and competing criteria and objectives. Due to the importance of the issue, in this paper, hybrid approach with a group decision-making in Multiple Criteria Decision Making (MCDM) in the context of a range of interval-valued intuitionistic fuzzy sets is implemented to solve the supplier selection problem. In this model in phase 1, decision makers express their opinion about each alternative based on different attribute qualitatively, and after creating interval valued intuitionistic fuzzy membership, a new variable is defined that via its help, interval-valued intuitionistic fuzzy amounts are calculated for each alternative. because of Having capabilities and comprehensiveness in their inside, not only they are better than other fuzzy sets but also they are the best for tracing the real condition and environment in order to select suppliers. Thereafter, for each alternative upper and lower bonds are calculated based on interval-valued intuitionistic fuzzy amounts. In phase 2, Operator Weighted Average (OWA) algorithm is used to reach a collective consensus. After computing the degree of consensus, closeness coefficients is evaluated within the help of TOPSIS method, which is in fact one of the most practicable methods between multi-criteria decision-making methods, such as SAW, AHP, CP, VIKOR. With regard to closeness coefficient, the amount of closeness between individual and collective’s agreement is accounted. The main aim of this article is optimizing the closeness coefficient. The alternative with maximum closeness coefficient is closer to the ideal solution. The final goal of proposed model is ranking the suppliers, meaning that satisfy the main factors of decision making, which is why GPSF model is used. After giving goal and restrict functions, GPSF model will be solved and rank alternatives.  3 16 2018/12/23 1397/10/2 2019/11/13 1398/8/22 مرجان کوچکی رفسنجانی Marjan Kuchaki Rafsanjani دانشگاه شهید باهنر کرمان ‌ ایران kuchaki@uk.ac.ir ارشام برومند سعید Arsham Borumand Saeid دانشگاه شهید باهنر کرمان ‌ ایران arsham@uk.ac.ir فرزانه میرزاپور Farzane Mirzapour دانشگاه شهید باهنر کرمان ‌ ایران fmirzapour@math.uk.ac.ir Interval-valued intuitionistic fuzzy set Collective preference Fuzzy TOPSIS Multi-criteria Supplier selection Goal programming satisfaction function مجموعه فازی شهودی بازه‌ای مقدار اولویت‌های جمعی تاپسیس فازی معیارهای چند‌گانه انتخاب تأمین‌کننده برنامه‌ریزی خطی هدف‌دار [1] S. H. Ghodsypour and C. O'Brien, "A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming", International Journal of Pro-duction Economics, vol. 56, pp. 199-212, 1998.##[2] V. Thiagarasu and R. Dharmarajan, "An intuitionistic fuzzy topsis DSS model with weight determining methods", International Journal of Engineering and Computer Science, vol. 6, pp. 20354-20361, 2017.##[3] C. T. Lin, C. B. Chen, and Y. C. Ting, "An ERP model for supplier selection in electronics industry", Expert Systems with Applications, vol. 38, no. 3, pp. 1760-1765, 2011.##[4] A. K. Kar, "Revisiting the supplier selection problem: An integrated approach for group decision support", Expert systems with appli-cations, vol. 41, no. 6, pp. 2762-2771, 2014.##[5] A. Aamodt and E. Plaza, "Case-based reasoning: Foundational issues, methodological variations, and system approaches", Artificial Intelligence Communications, vol. 7, no. 1, pp. 39-59, 1994.##[6] A. Amid, S. Ghodsypour, and C. O'Brien, "A weighted max-min model for fuzzy multi-objective supplier selection in a supply chain", International Journal of Production Economics, vol. 131, no. 1, pp. 139-145, 2011.##[7] R.-H. Lin, "An integrated model for supplier selection under a fuzzy situation", International Journal of Production Economics, vol. 138, no. 1, pp. 55-61, 2012.##[8] F. Arikan, "A fuzzy solution approach for multi objective supplier selection", Expert Systems with Applications, vol. 40, no. 3, pp. 947-952, 2013.##[9] J. Rezaei, P. B. M. Fahim and L. Tavasszy, "Supplier Selection in the airline retail industry using a funnel methodology: Conjuncative screening method and fuzzy AHP", Expert Systems with Applications, vol. 41, no. 18, pp. 679-693, 2014.##[10] J. Chai, J. N. Liu, and Z. Xu, "A new rule-based SIR approach to supplier selection under intuitionistic fuzzy environments", International Journal of Uncertainty, Fuzziness and Know-ledge-Based Systems, vol. 20, no. 03, pp. 451-471, 2012.##[11] A. Makui, M. R. Gholamian, and E. Mohammadi, "A hybrid intuitionistic fuzzy multi-criteria group decision making approach for supplier selection", Journal of Optimization in Industrial Engineering, vol. 9, no. 20, pp. 61-73, 2016.##[12] M. Izadikhah, "Group decision making process for supplier selection with TOPSIS method under interval-valued intuitionistic fuzzy numbers", Advances in Fuzzy Systems, vol. 12, p. 2, 2012.##[13] Z. Y. Bai, "An interval-valued intuitionistic fuzzy TOPSIS method based on an improveed score function", The Scientific World Journal, vol. 13, no.1, 2013.##[14] C. Yu, Y. Shao, K. Wang, L. Zhang, "A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environ-ment", Expert Systems with Applications, vol. 121, pp. 1-17, 2019.##[15] S. E. Omosigho and D. E. Omorogbe, "Supplier selection using different metric functions", Yugoslav Journal of Operations Research, vol. 25, no.3, pp. 413-423, 2015.##[16] J. Chai, J. N. Liu, and A. Li, "A new intuitionistic fuzzy rough set approach for decision support", Proceedings of the International Conference on Rough Sets and Knowledge Technology, Chengdu, China, August 17-20, 2012, pp. 71-80.##[17] D. Liang and Z. Xu, "The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets," Applied Soft Computing, vol. 60 , pp. 167-179, 2017.##[18] A. C. Pan, "Allocation of order quality among suppliers", Journal of Purchasing and Materials Management, vol. 25, no.3, pp. 36-39, 1989.##[19] A. A. Gaballa, "Minimum cost allocation of tenders", Journal of the Operational Research Society, vol. 25, no. 3, pp. 389-398, 1974.##[20] C. L. Hwang and K. Yoon, "Multiple Attribute Decision Making: methods and applications:A State-of-the-Art Survey", Springer Science & Business Media, vol. 186, 2012.##[21] I. Igoulalene, L. Benyoucef and M. K. Tiwari, "Novel fuzzy hybrid multi-criteria group decision making approaches for the strategic supplier selection problem", Expert Systems with Applications, vol. 42, no. 7, pp. 3342-3356, 2015.##[22] J. A. Goguen, "L-fuzzy sets", Journal of Mathematical Analysis and Applications, vol. 18, no. 1, pp. 145-174, 1967.##[23] A. Chaudhuri, D. Kajal, "Fuzzy multi-objective linear programming for traveling salesman problem", African Journal of Mathematics and Computer Science Research, vol. 4, no. 2, pp. 64-70, 2011.##[24] A. Keufman and M. Gupta, "Introduction to fuzzy arithmetic: Theory and application", NY: Van Nostrand Reinhold, 1991.##[25] N. E. Alam, A. A. Hasin, "Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)", Knowledge-Based Systems, vol. 24, no. 3, pp. 367-377, 2011.##[1] S. H. Ghodsypour and C. O'Brien, "A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming", International Journal of Pro-duction Economics, vol. 56, pp. 199-212, 1998.##[2] V. Thiagarasu and R. Dharmarajan, "An intuitionistic fuzzy topsis DSS model with weight determining methods", International Journal of Engineering and Computer Science, vol. 6, pp. 20354-20361, 2017.##[3] C. T. Lin, C. B. Chen, and Y. C. Ting, "An ERP model for supplier selection in electronics industry", Expert Systems with Applications, vol. 38, no. 3, pp. 1760-1765, 2011.##[4] A. K. Kar, "Revisiting the supplier selection problem: An integrated approach for group decision support", Expert systems with appli-cations, vol. 41, no. 6, pp. 2762-2771, 2014.##[5] A. Aamodt and E. Plaza, "Case-based reasoning: Foundational issues, methodological variations, and system approaches", Artificial Intelligence Communications, vol. 7, no. 1, pp. 39-59, 1994.##[6] A. Amid, S. Ghodsypour, and C. O'Brien, "A weighted max-min model for fuzzy multi-objective supplier selection in a supply chain", International Journal of Production Economics, vol. 131, no. 1, pp. 139-145, 2011.##[7] R.-H. Lin, "An integrated model for supplier selection under a fuzzy situation", International Journal of Production Economics, vol. 138, no. 1, pp. 55-61, 2012.##[8] F. Arikan, "A fuzzy solution approach for multi objective supplier selection", Expert Systems with Applications, vol. 40, no. 3, pp. 947-952, 2013.##[9] J. Rezaei, P. B. M. Fahim and L. Tavasszy, "Supplier Selection in the airline retail industry using a funnel methodology: Conjuncative screening method and fuzzy AHP", Expert Systems with Applications, vol. 41, no. 18, pp. 679-693, 2014.##[10] J. Chai, J. N. Liu, and Z. Xu, "A new rule-based SIR approach to supplier selection under intuitionistic fuzzy environments", International Journal of Uncertainty, Fuzziness and Know-ledge-Based Systems, vol. 20, no. 03, pp. 451-471, 2012.##[11] A. Makui, M. R. Gholamian, and E. Mohammadi, "A hybrid intuitionistic fuzzy multi-criteria group decision making approach for supplier selection", Journal of Optimization in Industrial Engineering, vol. 9, no. 20, pp. 61-73, 2016.##[12] M. Izadikhah, "Group decision making process for supplier selection with TOPSIS method under interval-valued intuitionistic fuzzy numbers", Advances in Fuzzy Systems, vol. 12, p. 2, 2012.##[13] Z. Y. Bai, "An interval-valued intuitionistic fuzzy TOPSIS method based on an improveed score function", The Scientific World Journal, vol. 13, no.1, 2013.##[14] C. Yu, Y. Shao, K. Wang, L. Zhang, "A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environ-ment", Expert Systems with Applications, vol. 121, pp. 1-17, 2019.##[15] S. E. Omosigho and D. E. Omorogbe, "Supplier selection using different metric functions", Yugoslav Journal of Operations Research, vol. 25, no.3, pp. 413-423, 2015.##[16] J. Chai, J. N. Liu, and A. Li, "A new intuitionistic fuzzy rough set approach for decision support", Proceedings of the International Conference on Rough Sets and Knowledge Technology, Chengdu, China, August 17-20, 2012, pp. 71-80.##[17] D. Liang and Z. Xu, "The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets," Applied Soft Computing, vol. 60 , pp. 167-179, 2017.##[18] A. C. Pan, "Allocation of order quality among suppliers", Journal of Purchasing and Materials Management, vol. 25, no.3, pp. 36-39, 1989.##[19] A. A. Gaballa, "Minimum cost allocation of tenders", Journal of the Operational Research Society, vol. 25, no. 3, pp. 389-398, 1974.##[20] C. L. Hwang and K. Yoon, "Multiple Attribute Decision Making: methods and applications:A State-of-the-Art Survey", Springer Science & Business Media, vol. 186, 2012.##[21] I. Igoulalene, L. Benyoucef and M. K. Tiwari, "Novel fuzzy hybrid multi-criteria group decision making approaches for the strategic supplier selection problem", Expert Systems with Applications, vol. 42, no. 7, pp. 3342-3356, 2015.##[22] J. A. Goguen, "L-fuzzy sets", Journal of Mathematical Analysis and Applications, vol. 18, no. 1, pp. 145-174, 1967.##[23] A. Chaudhuri, D. Kajal, "Fuzzy multi-objective linear programming for traveling salesman problem", African Journal of Mathematics and Computer Science Research, vol. 4, no. 2, pp. 64-70, 2011.##[24] A. Keufman and M. Gupta, "Introduction to fuzzy arithmetic: Theory and application", NY: Van Nostrand Reinhold, 1991.##[25] N. E. Alam, A. A. Hasin, "Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)", Knowledge-Based Systems, vol. 24, no. 3, pp. 367-377, 2011. ##

ارائه یک روش جدید بهسازی گفتار بر مبنای یادگیری مدل ناهمدوس به‌کمک ضرایب تبدیل موجک A New Method for Speech Enhancement Based on Incoherent Model Learning in Wavelet Transform Domain 1 1 بهسازی گفتار یکی از زمینه‌های پرکاربرد در پردازش سیگنال است که در حوزه‌های مختلفی مورد استفاده قرار می‌گیرد. در این مقاله از مفاهیم بازنمایی تُنُک و یادگیری واژه‌نامه به‌منظور حذف نوفه از سیگنال گفتار در فضای ویژگی تبدیل موجک استفاده می‌شود. ساختار مورد نیاز جهت بازنمایی هر مؤلفه از سیگنال به‌کمک مفاهیم بازنمایی تُنُک، براساس تعداد کمی از اتم‌های یادگیری‌شده امکان‌پذیر است. به‌منظور دست‌‌یابی به نتایج مطلوب در بهسازی گفتار، از روال یادگیری واژه‌نامه‌ ناهمدوس بهره گرفته می‌شود. به‌‌کمک ضرایب تبدیل موجک، تجزیه سیگنال در زیرباندهای مختلف که شامل اطلاعات دقیقی از محتوای سیگنال هستند، فراهم می‌شود. در روش پیشنهادی، دو سناریوی نظارت‌شده و نیمه‌نظارت‌شده مورد بررسی قرار گرفته و یک الگوریتم آشکارساز فعالیت گفتاری در هر سناریو با توجه به شرط‌های معرفی‌شده بر اساس واژه‌نامه‌های یادگیری‌شده در گام آموزش، پیشنهاد می‌شود. با استفاده از نتایج خروجی آشکارساز پیشنهادی، سیگنال گفتار تخمینی طی یک روال بهسازی در گام بعد به‌دست خواهد آمد. نتایج گزارش‌شده براساس معیارهای مختلف ارزیابی عملکرد، بر توانایی این روش در زمینه کاهش نوفه سیگنال گفتار تأکید می‌کند. روش‌های پیشنهادی، توانایی بالایی را در‌خصوص کاهش نوفه‌های ناایستا به‌خصوص در مقادیر سیگنال به نوفه پایین دارد. 2 Quality of speech signal significantly reduces in the presence of environmental noise signals and leads to the imperfect performance of hearing aid devices, automatic speech recognition systems, and mobile phones. In this paper, the single channel speech enhancement of the corrupted signals by the additive noise signals is considered. A dictionary-based algorithm is proposed to train the speech and noise models for each subband of wavelet decomposition level based on the coherence criterion. Using the presented learning method, the self-coherence measure between different atoms of each dictionary and mutual coherence between the atoms of speech and noise dictionaries are minimized and lower sparse reconstruction error is yielded. In order to reduce the computation time, a composite dictionary is utilized including only the speech dictionary and one of the noise dictionaries selected corresponding to the noise condition in the test environment. The speech enhancement algorithm is introduced in two scenarios, supervised and semi-supervised situations. In each scenario, a voice activity detector (VAD) scheme is employed based on the energy of sparse coefficient matrices when the observed data is coded over the related dictionary. The presented VAD algorithms are based on the energy of the coefficient matrices in the sparse representation of the observation data over the specified dictionaries. These speech enhancement schemes are different in the mentioned scenarios. In the proposed supervised scenario, domain adaptation technique is employed to transform a learned noise dictionary into an adapted dictionary according to the noise conditions of the test environment. Using this step, the observed data is sparsely coded with low sparse approximation error based on the current situation of the noisy environment. This technique has a prominent role to obtain better enhancement results particularly when the noise signal has non-stationary characteristics. In the proposed semi-supervised scenario, adaptive thresholding of wavelet coefficients is carried out based on the variance of the estimated noise for each frame in different subbands. These implementations are carried out in two different conditions, the training and test steps, as speaker dependent and speaker independent scenarios. Also, different measures are applied to evaluate the performance of the presented enhancement procedures. Moreover, a statistical test is used to have a more precise performance evaluation for different considered methods in the various noisy conditions. The experimental results using different measures show that the presented supervised enhancement scheme leads to much better results in comparison with the baseline enhancement methods, learning-based approaches, and earlier wavelet-based algorithms. These results have been obtained for an extensive range of noise types including the structured, unstructured, and periodic noise signals in different SNR values. 17 36 2018/12/232018/02/7 1396/11/18 2019/11/132019/06/19 1398/3/29 سمیرا مودّتی Samira Mavaddati دانشگاه مازندران ایران s.mavaddati@umz.ac.ir Speech enhancement Dictionary learning Sparse representation Domain adaptation Voice activity detector Wavelet transform بهسازی گفتار بازنمایی تُنُک واژه‌نامه ناهمدوس تبدیل موجک آشکارساز فعالیت گفتار [1] M. Klein, P. Kabal, "Signal subspace speech enhancement with perceptual post-filtering", Proc. IEEE Internat. Conf. Acoust. Speech Signal Process. (ICASSP), Vol. 1, pp. 537-540, 2002.##[2] S. Kamath, P. Loizou, "A multi-band spectral subtraction method for enhancing speech corrupted by colored noise", In: Proc. IEEE Internat. Conf. Acoust. Speech Signal Process. (ICASSP), Orlando, Florida, 2002.##[3] D.L. Donoho, "De-noising by soft-thresholding", IEEE Trans. Inf. Theory, Vol. 41, No. 3, pp. 613-627, 1995.##[4] N. Upadhyay, R.K. Jaiswal, "Single Channel Speech Enhancement: Using Wiener Filtering with Recursive Noise Estimation", Procedia Computer Science, Vol. 84, pp. 22-30, 2016.##[5] J. Candes, M.B. Wakin, "An introduction to compressive sampling", IEEE Signal Processing Magazine, pp. 21-30, 2008.##[6] R.G. Baraniuk, "Compressive Sensing", IEEE Signal Processing Magazine, pp. 118-121, 2007.##[7] S. Ayat, R. Dianat, M. Manzuri, "Wavelet Based Speech Enhancement Using a New Thresholding Algorithm", IEEE Intl. Symposium on Intelligent Multimedia, Video & Speech Processing (ISIMP), Hong Kong, 2004.##[8] C.T. Lu, H.C. Wang, "Speech enhancement using wavelet transform with constrained thresholds", In Proc. The 3rd International Symposium on Chinese Spoken Language Processing (ISCSLP), Taipei, Taiwan, pp. 185-188, 2002.##[9] E. Ambikairajah, G. Tattersall, A. Davis, "Wavelet Transform-based Speech Enhancement", Proc. on ICSLP, Vol. 3, 1998.##[10] V.S.R. Kumari, D.K. Devarakonda, "A Wavelet Based Denoising of Speech Signal", International Journal of Engineering Trends and Technology (IJETT), Vol. 5, No. 2, pp. 107-115, 2013.##[11] K. Khaldi, A.O. Boudraa, A. Komaty, "Speech enhancement using empirical mode decomposition and the Teager-Kaiser energy operator", J Acoust Soc Am, Vol. 13, No. 5, pp. 451-459, 2014.##[12] T.F. Sanam, C. Shahnaz, "A semisoft thresholding method based on Teager energy operation on wavelet packet coefficients for enhancing noisy speech", EURASIP Journal on Audio, Speech and Music Processing, Springer, 2013.##[13] S. Hongo, S. Sakamoto, Y. Suzuki, "Binaural speech enhancement method by wavelet transform based on interaural level and argument differences", International Conference on Wavelet Analysis and Pattern Recognition, Xian, 2012, pp. 290-295.##[14] T. V. Pham, "Wavelet analysis for robust speech processing and applications", PHD Thesis, Graz University of Technology, 2007.##[15] I. Pinter, "Perceptual wavelet-representation of speech signals and its application to speech enhancement", Computer Speech and Language, Vol. 10, No. 1, pp. 1-22, 1996.##[16] M.A. Messaoud, A. Bouzid, "Speech enhancement based on wavelet transform and improved subspace decomposition", Journal of Audio Engineering society (JAES), Vol. 63, No.12, pp.1-11, 2015.##[17] C.L. Wu, H.P. Hsu, S.S. Wang, J.W. Hung, Y.H. Lai, H.M. Wang, Y. Tsao, "Wavelet speech enhancement based on robust principal component analysis", Proc. Interspeech , 781, pp. 439-443, 2017.##[18] T.Y. Zuo, L. He, W.D. Sheng, "A new algorithm of the wavelet packet speech denoising based on masking perception model", 7th International conference on natural computation (ICNC), Vol. 1, 2011, pp. 33-37.##[19] H. Zhao, X. Peng, L. Hu, G. Wang, "An improved speech enhancement method based on teager energy operator and perceptual wavelet packet decomposition", Journal of Multimedia, Vol. 6, No. 3, 2011.##[20] R. Gomez, T. Kawahara, "Optimized wavelet-based speech enhancement for speech recognition in noisy and reverberant conditions", APSIPA ASC, 2011.##[21] T.F. Sanam, C. Shahnaz, "Teager energy operation on wavelet packet coefficients for enhancing noisy speech using a hard thresholding function", Published in Signal Processing: An International Journal (SPIJ), Vol. 6, pp. 22-43, 2011.##[22] G. Chen, C. Xiong, J.J Corso, "Dictionary transfer for image denoising via domain adaptation", In Proceedings of IEEE International Conference on Image Processing, 2012.##[23] S. Mavaddati, S.M. Ahadi Sarkani, S. Seyedin, "A novel speech enhancement method by learnable sparse and low-rank decomposition and domain adaptation", Speech Communication, Vol. 76, pp. 42-60, 2016.##[24] A. Agarwal, A. Anandkumar, P. Jain, P. 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بازشناسی خودکار واج‌های فارسی با استفاده از مدل‌سازی واج‌گونه‌ها Allophone-based acoustic modeling for Persian phoneme recognition 1 1 یکی از مراحل زیربنایی در بازشناسی خودکار گفتار، بازشناسی واج‌ها و از موانع جدی برای بازشناسی واج‌ها، هم‌تولیدی است. یک روش برای جبران تأثیر هم‌تولیدی، استفاده از مدل‌های وابسته به بافت در بازشناسی واج‌هاست. در این پژوهش، از یک روش زبان‌شناختی برای مدل‌سازی واج‌گونه‌ها استفاده شده است. بدین‌منظور ابتدا قواعد وقوع واج‌گونه‌ها در زبان فارسی استخراج و مشخص شده است که هر واج چه واج‌گونه‌هایی دارد. برای مدل‌سازی و شناسایی واج‌گونه‌ها، یک پیکره واج‌گونه‌ای لازم است که به‌‌منظور تولید آن، از پیکره فارس‌دات کوچک استفاده و برچسب‌گذاری واج‌گونه‌ای آن انجام و از این پیکره‌، برای مدل‌سازی و سپس شناسایی واج‌گونه‌های مختلف گفتار ورودی استفاده شده است. درنهایت، با قرار‌گرفتن هر یک از واج‌گونه‌های شناسایی‌شده در دسته واجی مربوط به خود، بازشناسی واج‌ها از مسیر واج‌گونه‌ها انجام شده است. با این روش، دقت بازشناسی واج‌ها در زبان فارسی در مقایسه با بهترین نتایج گزارش‌شده تاکنون، بهبود قابل‌ملاحظه‌ای نشان داده است. 2 Phoneme recognition is one of the fundamental phases of automatic speech recognition. Coarticulation which refers to the integration of sounds, is one of the important obstacles in phoneme recognition. In other words, each phone is influenced and changed by the characteristics of its neighbor phones, and coarticulation is responsible for most of these changes. The idea of modeling the effects of speech context, and using the context-dependent models in phoneme recognition is a method which used to compensate the negative effects of coarticulation. According to this method, if two similar phonemes in speech have different contexts, each of them constitute a separate model. In this research, a linguistic method called allophonic modeling has been used to model context effects in Persian phoneme recognition. For this purpose, in the first phase, the rules required for occurrence of various allophones of each phoneme, are extracted from Persian linguistic resources. So each phoneme is considered as a class, consisting of its various context-dependent forms named allophones. The necessary prerequisites for modeling and identifying allophones, is an allophonic corpus. Since there was no such corpus in Persian language, SMALL FARSDAT corpus has been used. This corpus is segmented and labelled manually for each sentence, word and phoneme. So the phonological and lingual context required for the realization of allophones, is implemented in this corpus. For example, the syllabification has been performed on the corpus and then, for each phoneme, its position (first, middle and end) in the word and syllable is specified using different numeric tags. In the next step, allophonic labeling has been performed by searching for each of the allophonic contexts in the corpus. These allophonic corpus is used to model and recognize the allophones of input speech. Finally, each allophone is assigned to a proper phonemic class so phoneme recognition has been done using allophones. The experimental results show a high accuracy of the proposed method in phenome recognition, indicating a significant improvement comparing with other state-of-the-art methods. 37 54 2018/12/232018/02/72018/09/28 1397/7/6 2019/11/132019/06/192019/05/22 1398/3/1 طاهره احمدی Tahere Ahmadi دانشکده زبان‌های خارجی، دانشگاه اصفهان ایران pazhvak.ta@gmail.com حسین کارشناس Hossein Karshenas دانشکده کامپیوتر، دانشگاه اصفهان ایران professor.karshenas@gmail.com باقر باباعلی Bagher Babaali دانشکده ریاضی، آمار و علوم کامپیوتر، دانشگاه تهران ایران bagher.babaali@gmail.com بتول علی‌نژاد Batool Alinejad دانشکده زبان‌های خارجی، دانشگاه اصفهان ایران batool_alinezhad@yahoo.com automatic speech recognition automatic phoneme recognition context-dependent models phoneme allophone coarticulation بازشناسی خودکار گفتار بازشناسی خودکار واج مدل‌های وابسته به بافت واج واج‌گونه هم‌تولیدی [1] M. 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نمونه‌گیری از گراف شبکه‌های اجتماعی براساس ویژگی‌های توپولوژیکی و الگوریتم کلونی زنبور عسل Sampling from social networks’s graph based on topological properties and bee colony algorithm 1 1 با توجه به رشد سریع شبکه‌های اجتماعی در چند سال اخیر، مسأله نمونه‌گیری از گراف‌های بسیار بزرگ شبکه­های اجتماعی با هدف تجزیه و تحلیل سریع شبکه بر اساس نمونه­های کوچک، اهمیت خاصی پیدا کرده است. مطالعات زیادی در این راستا انجام شده است، ولی آنها تا حد زیادی با مشکل انتخاب تصادفی، عدم حفظ ویژگی‌های شبکه­های پیچیده در گراف حاصل و یا صرف هزینه زمانی بالا برای استخراج گراف نمونه مواجه هستند. در این مقاله یک روش نمونه­گیری جدید را برای نخستین‌­بار با ارائه یک رابطه جدید مبتنی بر ویژگی‌های ساختاری برای مشخص‌کردن اهمیت گره‌ها و استفاده از الگوریتم کلونی زنبور عسل پیشنهاد می­کنیم. این روش نمونه­گیری با ارائه یک رویکرد آگاهانه غیرتصادفی در نمونه­گیری سعی دارد تا نمونه حاصله از لحاظ ویژگی‌هایی مانند توپولوژی شبکه، توزیع درجه، تراکم داخلی، درجه ورودی و خروجی و غیره شباهت زیادی با شبکه اصلی داشته باشد. نتایج حاصل، برتری روش پیشنهادی را از لحاظ حفظ ویژگی‌های توزیع درجه، ضریب خوشه­بندی و غیره در نمونه گراف به‌دست‌آمده نشان می­دهد. 2 In recent years, the sampling problem in massive graphs of social networks has attracted much attention for fast analyzing a small and good sample instead of a huge network. Many algorithms have been proposed for sampling of social network’ graph. The purpose of these algorithms is to create a sample that is approximately similar to the original network’s graph in terms of properties such as degree distribution, clustering coefficient, internal density and community structures, etc. There are various sampling methods such as random walk-based methods, methods based on the shortest path, graph partitioning-based algorithms, and etc. Each group of methods has its own pros and cones. The main drawback of these methods is the lack of attention to the high time complexity in making the sample graph and the quality of the obtained sample graph. In this paper, we propose a new sampling method by proposing a new equation based on the structural properties of social networks and combining it with bee colony algorithm. This sampling method uses an informed and non-random approach so that the generated samples are similar to the original network in terms of features such as network topological properties, degree distribution, internal density, and preserving the clustering coefficient and community structures. Due to the random nature of initial population generation in meta-heuristic sampling methods such as genetic algorithms and other evolutionary algorithms, in our proposed method, the idea of ​​consciously selecting nodes in producing the initial solutions is presented. In this method, based on the finding hub and semi-hub nodes as well as other important nodes such as core nodes, it is tried to maintain the presence of these important nodes in producing the initial solutions and the obtained samples as much as possible. This leads to obtain a high-quality final sample which is close to the quality of the main network. In this method, the obtained sample graph is well compatible with the main network and can preserve the main characteristics of the original network such as topology, the number of communities, and the large component of the original graph as much as possible in sample network. Non-random and conscious selection of nodes and their involvement in the initial steps of sample extraction have two important advantages in the proposed method. The first advantage is the stability of the new method in extracting high quality samples in each time. In other words, despite the random behavior of the bee algorithm, the obtained samples in the final phase mostly have close quality to each other. Another advantage of the proposed method is the satisfactory running time of the proposed algorithm in finding a new sample. In fact, perhaps the first question for asking is about time complexity and relatively slow convergence of the bee colony algorithm. In response, due to the conscious selection of important nodes and using them in the initial solutions, it generates high quality solutions for the bee colony algorithm in terms of fitness function calculation. The experimental results on real world networks show that the proposed method is the best to preserve the degree distribution parameters, clustering coefficient, and community structure in comparison to other method. 55 70 2018/12/232018/02/72018/09/282019/05/2 1398/2/12 2019/11/132019/06/192019/05/222020/01/22 1398/11/2 عسگرعلی بویر Asgarali Bouyer گروه مهندسی کامپیوتر، دانشکده فناوری اطلاعات، دانشگاه شهید مدنی آذربایجان ایران a.bouyer@azaruniv.ac.ir سمیه نوروزی Somayeh Norouzi واحد میاندوآب، دانشگاه آزاد اسلامی ایران someiyenorozi2014@gmail.com Sampling Social networks Clustering coefficient Artificial Bee Colony نمونه‌گیری شبکه‌های اجتماعی ضریب خوشه‌بندی کلونی زنبور عسل [1] E. Katz, P. F. Lazarsfeld, and E. Roper, Personal influence: The part played by people in the flow of mass communications. Routledge, 2017.##[2] N. B. Ellison, J. Vitak, R. Gray, and C. Lampe, "Cultivating social resources on social network sites: Facebook relationship maintenance behaviors and their role in social capital processes," Journal of Computer-Mediated Communication, vol. 19, no. 4, pp. 855-870, 2014.##[3] M. Irani and M. Haghighi, "The Impact of Social Networks on the Internet Business Sustainability (With Emphasis on the Intermediary Role of Entrepreneurial Purpose of Online Branches of Mellat Bank's Portal)," Journal of Information Technology Management, vol. 5, no. 4, pp. 23-46, 2013.##[4] M. Emirbayer and J. Goodwin, "Network analysis, culture, and the problem of agency," American journal of sociology, vol. 99, no. 6, pp. 1411-1454, 1994.##[5] J. Tang, J. Sun, C. Wang, and Z. Yang, "Social influence analysis in large-scale networks," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp. 807-816: ACM .##[6] J. Török, Y. Murase, H.-H. Jo, J. Kertész, and K. Kaski, "What big data tells: sampling the social network by communication channels," Physical Review E, vol. 94, no. 5, pp. 052319, 2016.##[7] M. Papagelis, G. Das, and N. Koudas, "Sampling online social networks," IEEE Transactions on knowledge and data engineering, vol. 25, no. 3, pp. 662-676, 2013.##[8] K. Dempsey, K. Duraisamy, H. Ali, and S. Bhowmick, "A parallel graph sampling algorithm for analyzing gene correlation networks," Procedia Computer Science, vol. 4, pp. 136-145, 2011.##[9] J. Leskovec and C. Faloutsos, "Sampling from large graphs," in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, pp. 631-636: ACM.##[10] S. Yoon, S. Lee, S.-H. Yook, and Y. Kim, "Statistical properties of sampled networks by random walks," Physical Review E, vol. 75, no. 4, pp. 046114, 2007.##[11] S.-H. Yoon, K.-N. Kim, J. Hong, S.-W. Kim, and S. Park, "A community-based sampling method using DPL for online social networks," Information Sciences, vol. 306, pp. 53-69, 2015.##[12] E. M. Airoldi, X. Bai, and K. M. Carley, "Network sampling and classification: An investigation of network model representations," Decision support systems, vol. 51, no. 3, pp. 506-518, 2011.##[13] P. Krömer and J. Platoš, "Genetic algorithm for sampling from scale-free data and networks," in Proceedings of the 2014 annual conference on genetic and evolutionary computation, 2014, pp. 793-800: ACM.##[14] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, "Equation of state calculations by fast computing machines," The journal of chemical physics, vol. 21, no. 6, pp. 1087-1092, 1953.##[15] L. A. Goodman, "Snowball sampling," The annals of mathematical statistics, pp. 148-170, 1961.##[16] L. Lovász, "Random walks on graphs: A survey," Combinatorics, Paul erdos is eighty, vol. 2, no. 1, pp. 1-46, 1993.##[17] D. D. Heckathorn, "Respondent-driven sampling: a new approach to the study of hidden populations," Social problems, vol. 44, no. 2, pp. 174-199, 1997.##[18] S. Ye, J. Lang, and F. Wu, "Crawling online social graphs," in 2010 12th International Asia-Pacific Web Conference:IEEEP, pp. 236-242, 2010.##[19] A. Rezvanian and M. R. Meybodi, "Sampling social networks using shortest paths," Physica A: Statistical Mechanics and its Applications, vol. 424, pp. 254-268, 2015.##[20] A. Sevilla, A. Mozo, and A. F. Anta, "Node sampling using random centrifugal walks," Journal of Computational Science, vol. 11, pp. 34-45, 2015.##[21] C. Tong, Y. Lian, J. Niu, Z. Xie, and Y. Zhang, "A novel green algorithm for sampling complex networks," Journal of Network and Computer Applications, vol. 59, pp. 55-62, 2016.##[22] N. Ahmed, J. Neville, and R. R. Kompella, "Network sampling via edge-based node selection with graph induction," 2011.##[23] K. Brádler, P.-L. Dallaire-Demers, P. Rebentrost, D. Su, and C. Weedbrook, "Gaussian boson sampling for perfect matchings of arbitrary graphs," Physical Review A, vol. 98, no. 3, pp. 032310, 09/10/ 2018.##[24] J. Zhao, P. Wang, J. C. S. Lui, D. Towsley, and X. Guan, "Sampling online social networks by random walk with indirect jumps," Data Mining and Knowledge Discovery, journal article vol. 33, no. 1, pp. 24-57, January 01. 2019.##[25] Y. Xie, S. Chang, Z. Zhang, M. Zhang, and L. Yang, "Efficient sampling of complex network with modified random walk strategies," Physica A: Statistical Mechanics and its Applications, vol. 492, pp. 57-64, 2018.##[26] K. Berahmand and A. Bouyer, "LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks," International Journal of Modern Physics B, vol. 32, no. 06, pp. 1850062, 2018.##[1] E. Katz, P. F. Lazarsfeld, and E. Roper, Personal influence: The part played by people in the flow of mass communications. Routledge, 2017.##[2] N. B. Ellison, J. Vitak, R. Gray, and C. Lampe, "Cultivating social resources on social network sites: Facebook relationship maintenance behaviors and their role in social capital processes," Journal of Computer-Mediated Communication, vol. 19, no. 4, pp. 855-870, 2014.##[3] M. Irani and M. Haghighi, "The Impact of Social Networks on the Internet Business Sustainability (With Emphasis on the Intermediary Role of Entrepreneurial Purpose of Online Branches of Mellat Bank's Portal)," Journal of Information Technology Management, vol. 5, no. 4, pp. 23-46, 2013.##[4] M. Emirbayer and J. Goodwin, "Network analysis, culture, and the problem of agency," American journal of sociology, vol. 99, no. 6, pp. 1411-1454, 1994.##[5] J. Tang, J. Sun, C. Wang, and Z. Yang, "Social influence analysis in large-scale networks," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp. 807-816: ACM .##[6] J. Török, Y. Murase, H.-H. Jo, J. Kertész, and K. Kaski, "What big data tells: sampling the social network by communication channels," Physical Review E, vol. 94, no. 5, pp. 052319, 2016.##[7] M. Papagelis, G. Das, and N. Koudas, "Sampling online social networks," IEEE Transactions on knowledge and data engineering, vol. 25, no. 3, pp. 662-676, 2013.##[8] K. Dempsey, K. Duraisamy, H. Ali, and S. Bhowmick, "A parallel graph sampling algorithm for analyzing gene correlation networks," Procedia Computer Science, vol. 4, pp. 136-145, 2011.##[9] J. Leskovec and C. Faloutsos, "Sampling from large graphs," in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, pp. 631-636: ACM.##[10] S. Yoon, S. Lee, S.-H. Yook, and Y. Kim, "Statistical properties of sampled networks by random walks," Physical Review E, vol. 75, no. 4, pp. 046114, 2007.##[11] S.-H. Yoon, K.-N. Kim, J. Hong, S.-W. Kim, and S. Park, "A community-based sampling method using DPL for online social networks," Information Sciences, vol. 306, pp. 53-69, 2015.##[12] E. M. Airoldi, X. Bai, and K. M. Carley, "Network sampling and classification: An investigation of network model representations," Decision support systems, vol. 51, no. 3, pp. 506-518, 2011.##[13] P. Krömer and J. Platoš, "Genetic algorithm for sampling from scale-free data and networks," in Proceedings of the 2014 annual conference on genetic and evolutionary computation, 2014, pp. 793-800: ACM.##[14] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, "Equation of state calculations by fast computing machines," The journal of chemical physics, vol. 21, no. 6, pp. 1087-1092, 1953.##[15] L. A. Goodman, "Snowball sampling," The annals of mathematical statistics, pp. 148-170, 1961.##[16] L. Lovász, "Random walks on graphs: A survey," Combinatorics, Paul erdos is eighty, vol. 2, no. 1, pp. 1-46, 1993.##[17] D. D. Heckathorn, "Respondent-driven sampling: a new approach to the study of hidden populations," Social problems, vol. 44, no. 2, pp. 174-199, 1997.##[18] S. Ye, J. Lang, and F. Wu, "Crawling online social graphs," in 2010 12th International Asia-Pacific Web Conference:IEEEP, pp. 236-242, 2010.##[19] A. Rezvanian and M. R. Meybodi, "Sampling social networks using shortest paths," Physica A: Statistical Mechanics and its Applications, vol. 424, pp. 254-268, 2015.##[20] A. Sevilla, A. Mozo, and A. F. Anta, "Node sampling using random centrifugal walks," Journal of Computational Science, vol. 11, pp. 34-45, 2015.##[21] C. Tong, Y. Lian, J. Niu, Z. Xie, and Y. Zhang, "A novel green algorithm for sampling complex networks," Journal of Network and Computer Applications, vol. 59, pp. 55-62, 2016.##[22] N. Ahmed, J. Neville, and R. R. Kompella, "Network sampling via edge-based node selection with graph induction," 2011.##[23] K. Brádler, P.-L. Dallaire-Demers, P. Rebentrost, D. Su, and C. Weedbrook, "Gaussian boson sampling for perfect matchings of arbitrary graphs," Physical Review A, vol. 98, no. 3, pp. 032310, 09/10/ 2018.##[24] J. Zhao, P. Wang, J. C. S. Lui, D. Towsley, and X. Guan, "Sampling online social networks by random walk with indirect jumps," Data Mining and Knowledge Discovery, journal article vol. 33, no. 1, pp. 24-57, January 01. 2019.##[25] Y. Xie, S. Chang, Z. Zhang, M. Zhang, and L. Yang, "Efficient sampling of complex network with modified random walk strategies," Physica A: Statistical Mechanics and its Applications, vol. 492, pp. 57-64, 2018.##[26] K. Berahmand and A. Bouyer, "LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks," International Journal of Modern Physics B, vol. 32, no. 06, pp. 1850062, 2018. ##

تعیین کنتراست CEST به روش تحلیلی در تصویربرداری مولکولی تشدید مغناطیسی Analytical determination of the chemical exchange saturation transfer (CEST) contrast in molecular magnetic resonance imaging 1 1 تصویربرداری مولکولی به روش تشدید مغناطیسی با ردیابی عامل‌های کنتراست، امکان تشخیص زود‌هنگام بیماری‌ها به شیوه­ای غیر‌تهاجمی را فراهم کرده است. درهمین‌اواخر با طراحی رشته پالس تصویربرداری مناسب بر روی پویش‌گر تشدید مغناطیسی، امکان اندازه‌گیری میزان تبادل شیمیایی بین عامل کنتراست و آب که به پدیده انتقال اشباع به‌واسطه تبادل شیمیایی (CEST) مشهور است، امکان‌پذیر شده است. اثر CEST منجر به کاهش تعداد هیدروژن­های آب و پایین­آمدن شدت روشنایی تصویر تشدید مغناطیسی می‌شود؛ لذا به این اثر کنتراست منفی CEST هم گفته می­شود. وجود رابطه­ای تحلیلی بین نرخ تبادل شیمیایی و شاخص­های بالینی (دما، مصرف گلوکز، pH و موارد دیگر)، علاقه‌مندی به اندازه‌گیری و کمّی­سازی کنتراست CEST را افزایش داده است. این پژوهش یک فرمول ریاضی بسته دقیق از کنتراست CEST در حالت­های گذرا و دایمی ارایه می­دهد. در این مطالعه با شناسایی عوامل تخریبی مزاحم، مانند انتقال مغناطیس شوندگی توسط ماکرومولکول‌ها (MT) و اثر اشباع مستقیم آب، کنتراست CEST در مدل‌های دو و سه‌حوضچه­ای با استفاده از داده­های پارامتری برگرفته از بافت بدن و داده­های ناشی از مشاهدات تجربی، مدل‌سازی می­شود. تطابق کنتراست CEST پیشنهادی با روش اندازه­گیری غیر­متقارن که مورد استناد بسیاری از پژوهش‌گران است، برای عامل‌های کنتراست پارامگنتیک در یک مدل سه‌حوضچه‌ای بررسی شده است. میزان خطای نسبی برازش به‌طور متوسط بر روی سه دسته داده تجربی از چهار درصد کمتر بود؛ علاوه‌بر آن سازگاری مقبولی بین کنتراست CEST پیشنهادی با یک فرمول تجربی بر اساس داده­های مبتنی بر عامل­های دیامگنتیک در مدل دو‌حوضچه­ای هم دیده می­شود. با دست‌یابی به این رابطه تحلیلی از کنتراست CEST، امکان بهینه­سازی و درک نحوه وابستگی آن به پارامترهای اثرگذاری مانند میزان غلظت عامل کنتراست، نرخ تبادل شیمیایی و ویژگی­های پالس الکترومغناطیسی (مانند دامنه و عرض پالس در پالس­های مستطیلی) فراهم می­‌شود. 2 Magnetic resonance based on molecular imaging allows tracing contrast agents thereby facilitating early diagnosis of diseases in a non-invasive fashion that enhances the soft tissue with high spatial resolution. Recently, the exchange of protons between the contrast agent and water, known as the chemical exchange saturation transfer (CEST) effect, has been measured by applying a suitable pulse sequence to the magnetic resonance imaging (MRI) scanner. CEST MRI is increasingly used to probe mobile proteins and microenvironment properties, and shows great promise for tumor and stroke diagnosis. This effect leads to a reduction in magnetic moments of water causing a corresponding decrease in the gray scale intensity of the image, providing a negative contrast in the CEST image. The CEST effect is complex, and it depends on the CEST agent concentration, exchange rates, the characteristic of the magnetization transfer (MT), and the relaxation properties of the tissue. The CEST contrast is different from the inherent MT of macromolecule bounded protons which evidently occurs as a dipole-dipole interaction between water and macromolecular components. Recently it was shown that CEST agents can be strongly affected by the MT and direct saturation effects, so corrections are needed to derive accurate estimates of CEST contrast. Specifically, the existence of an analytical relation between the chemical exchange rate and physiological parameters such as the core temperature, glucose level, and PH has generated more interest in quantification of the CEST contrast. The most important model was obtained by analyzing water saturation spectrum named magnetization transfer ratio spectrum that was quantified by solving Bloch equations. This paper provides an analytical closed-formula of CEST contrast under steady state and transient conditions based on the eigenspace solution of the Bloch-McConnell equations for both of the MT and CEST effects as well as their interactions. In this paper, the CEST contrast has been modeled in two- and three-pool systems using measured (experimental- real data) and fitted data similar to the muscle tissue by considering interfering factors. The resulting error was characterized by an average of relative sum-square between three experimental data and fitted CEST contrast based on the proposed formulation lower than 4 percent. For further validation, these formulations were compared to the empirical formulation of the CEST effect based on a diamagnetic contrast agent introduced in the two-pool system. Using the proposed analytical expression for the CEST contrast, we optimized critical parameters such as concentration contrast agent, chemical exchange rate and characteristics of the electromagnetic radio frequency pulse via amplitude and pulse width in the rectangular pulse. 71 86 2018/12/232018/02/72018/09/282019/05/22019/04/10 1398/1/21 2019/11/132019/06/192019/05/222020/01/222020/01/22 1398/11/2 محمدرضا رضاییان Mohammad Reza Rezaeian گروه مهندسی پزشکی، دانشگاه صنعتی همدان ایران rezaeian@hut.ac.ir Chemical exchange saturation transfer Bloch-McConnell equations Magnetization transfer Numerical solution Z-spectra modeling انتقال اشباع و مغناطیس شوندگی حل عددی طیف Z کنتراست CEST معادلات بلاخ مک کانل [1] P. Zijl, and N. Yadav, "Chemical exchange saturation transfer (CEST): what is in a name and what isn't?," Magnetic Resonance Imaging, vol. 65, no. 4, pp. 927-948, 2011.##[2] E. Vinogradov, A. Dean Sherry, and R. E. Lenkinski, "CEST: from basic principles to applications, challenges and opportunities," Journal of Magnetic Resonance, vol. 229, pp. 155-172, 2012.##[3] P. Zijl, W. W. Lam, J. Xu, L. Knutsson and G. J. StanisZ, "MagnetiZation Transfer Contrast and Chemical Exchange Saturation Transfer MRI. Features and analysis of the field-dependent saturation spectrum," Nuroimage, vol. 168, pp. 222-241, 2018.##[4] K. L. Desmond and G. J. StanisZ, "Understanding quantitative pulsed CEST in the presence of MT," Magnetic Resonance in Medicine, vol. 67, no. 4, pp. 979-990, 2012.##[5] J.S. Lee, R.R. Regatte, A. Jerschow "Isolating chemical exchange saturation transfer contrast from magnetiZation transfer asymmetry under two-frequency RF irradiation, '' J Magn Reson, vol. 215, pp. 56-63, 2011.##[6] M. Zaiss, and P. Bachert,"Chemical exchange saturation transfer (CEST) and MR Z-spectroscopy in vivo: a review of theoretical approaches and methods," Phys. Med. Biol, vol. 58, no. 22, pp 221-269, 2013.##[7] M.R. ReZaeian, G.A. Hossien-Zadeh and H. Soltanian-Zadeh, "Numerical Solutions to the Bloch-McConnell Equations with Radio Frequency", in: Proceedings of the international society of Electrical Engineering, 20th Con-ference on Electrical Engineering, Tehran, Iran, 2012, pp. 1584-89.##[8] M.R. ReZaeian, G.A. Hossien-Zadeh, and H.Soltanian-Zadeh,"Simultaneously ptimiZing power and duration of RF pulse in the paraCEST MRI", Magnetic Resonance Imaging, vol. 34, no. 6, pp. 743-753, 2016.##[9] P. Z. Sun, "Simultaneous determination of labile proton concentration and exchange rate utiliZing optimal RF power: radio frequency power (RFP) dependence of chemical exchange saturation transfer (CEST) MRI," Journal of Magnetic Resonance, vol. 202, no. 2, pp. 155-161, 2010.##[10] P.Z. Sun, P.C.M. van Zijl and J. Zhou, "OptimiZation of the irradiation power in chemical exchange dependent saturation transfer experiments," J Magn Reson, vol. 175, no. 2, pp. 193-200, 2005.##[11] P. Z. Sun, T. Benner, A. Kumar, and A. G. Sorensen, "Investigation of optimiZing and translating pH‐sensitive pulsed‐chemical ex-change saturation transfer (CEST) imaging to a 3T clinical scanner," Magnetic Resonance in Medicine, vol. 60, no. 4, pp. 834-841, 2008.##[12] B. Schmitt, M. Zaiß, J. Zhou, and P. Bachert, "OptimiZation of pulse train presaturation for CEST imaging in clinical scanners," Magnetic Resonance in Medicine, vol. 65, no. 6, pp. 1620-1629, 2011.##[13] D. Woeessner, S. Zhang, M.E. Merritt and A.D. Sherry, "Numerical solution of the Bloch equations provides insights into the optimum design of PARACEST agents for MRI," Magn. Reson.Med, vol. 53, no. 4, pp. 790-799, 2005.##[14] R.V. Mulkern and M. L. Williams, "The general solution to the Bloch equation with constant RF and relaxation terms: application to saturation and slice selection," Med Phys, vol. 20, no.1, pp. 5-13, 1993.##[15] S.A. Roell, W. Dreher and D. LeibfritZ, "A general solution of the standard magnetiZation transfer model," J Magn Reson, vol. 132, no. 1, pp. 96-101, 1998.##[16] P.K. Madhu and A. Kumar "Direct Cartesian- space solutions of generaliZed Bloch equations in the rotating frame," J Magn Reson A, vol. 114, no. 2, pp. 201-211, 1995.##[17] K.Murase and N.Tanki, "Numeric solution to the time-dependent Bloch equations revisited," Magnetic Resonance Imaging, vol. 12, no.5, pp. 1-6, 2010.##[18] A.X. Li, R.H.E. Hudson, J.W. Barrett, C. K. Jones, S. H. Pasternak and R. Bartha, "Four-pool modeling of proton exchange processes in biological systems in the presence of MRI-paramagnetic chemical exchange saturation transfer (PARACEST) agents," Magn. Reson.Med, vol. 60, no. 5, pp. 1197-1206, 2008.##[19] M. Zaiss, Z. Zu, J. Xu, P. Schuenke, D. F. Gochberg, J. C. Gore, M. E. Ladd, and P. Bachert,"A combined analytical solution for chemical exchange saturation transfer and semi-solid magnetiZation transfer," NMR in Biomed, vol. 28, no. 2, pp 217-230, 2014.##[20] S. Goerke, M. Zaiss, and P. Bachert, "CharacteriZation of creatine guanidium proton exchange by water-exchange (WEX) spectroscopy for absolute pH CEST imaging in vitro," NMR Biomed, vol. 27, no . 5, pp 507-518, 2014.##[21] S.A. Smith, J.A.D. Farrell, C.K Jones, D.S. Reich, P.A. Calabresi and P.C.M van Zijl, "Pulsed magnetiZation transfer imaging with body coil transmission at 3 Tesla: feasibility and application," Magn. Reson.Med, vol. 56, no. 4, pp. 866-875, 2006.##[22] W. T. Dixon, J. Ren, A. J. Lubag, J. Ratnakar,E. Vinogradov, I. Hancu, R. E. Lenkinski and A. D. Sherry, "A concentration-independent method to measure exchange rates in PARACEST agents," Magnetic Resonance in Medicine, vol. 63, no. 3, pp. 625-632, 2010.##[23] T. Jin and S. G. Kim, "Approximated analytical characteriZation of the steady-state chemical exchange saturation transfer (CEST) signals", Magnetic Resonance in Medicine, vol. 82, no. 5, pp.1876-1889, 2019.##[24] J. Kim, Y. Wu, Y. Gue, H. Zheng, and P. Z. Sun, "A review of optimiZation and quantification techniques for chemical exchange saturation transfer (CEST) MRI toward sensitive in vivo imaging," Contrast Media Mol Imaging, vol. 10, no.3, pp.163-178, 2015.##[1] P. Zijl, and N. Yadav, "Chemical exchange saturation transfer (CEST): what is in a name and what isn't?," Magnetic Resonance Imaging, vol. 65, no. 4, pp. 927-948, 2011.##[2] E. Vinogradov, A. Dean Sherry, and R. E. Lenkinski, "CEST: from basic principles to applications, challenges and opportunities," Journal of Magnetic Resonance, vol. 229, pp. 155-172, 2012.##[3] P. Zijl, W. W. Lam, J. Xu, L. Knutsson and G. J. StanisZ, "MagnetiZation Transfer Contrast and Chemical Exchange Saturation Transfer MRI. Features and analysis of the field-dependent saturation spectrum," Nuroimage, vol. 168, pp. 222-241, 2018.##[4] K. L. Desmond and G. J. StanisZ, "Understanding quantitative pulsed CEST in the presence of MT," Magnetic Resonance in Medicine, vol. 67, no. 4, pp. 979-990, 2012.##[5] J.S. Lee, R.R. Regatte, A. Jerschow "Isolating chemical exchange saturation transfer contrast from magnetiZation transfer asymmetry under two-frequency RF irradiation, '' J Magn Reson, vol. 215, pp. 56-63, 2011.##[6] M. Zaiss, and P. Bachert,"Chemical exchange saturation transfer (CEST) and MR Z-spectroscopy in vivo: a review of theoretical approaches and methods," Phys. Med. Biol, vol. 58, no. 22, pp 221-269, 2013.##[7] M.R. ReZaeian, G.A. Hossien-Zadeh and H. Soltanian-Zadeh, "Numerical Solutions to the Bloch-McConnell Equations with Radio Frequency", in: Proceedings of the international society of Electrical Engineering, 20th Con-ference on Electrical Engineering, Tehran, Iran, 2012, pp. 1584-89.##[8] M.R. ReZaeian, G.A. Hossien-Zadeh, and H.Soltanian-Zadeh,"Simultaneously ptimiZing power and duration of RF pulse in the paraCEST MRI", Magnetic Resonance Imaging, vol. 34, no. 6, pp. 743-753, 2016.##[9] P. Z. Sun, "Simultaneous determination of labile proton concentration and exchange rate utiliZing optimal RF power: radio frequency power (RFP) dependence of chemical exchange saturation transfer (CEST) MRI," Journal of Magnetic Resonance, vol. 202, no. 2, pp. 155-161, 2010.##[10] P.Z. Sun, P.C.M. van Zijl and J. Zhou, "OptimiZation of the irradiation power in chemical exchange dependent saturation transfer experiments," J Magn Reson, vol. 175, no. 2, pp. 193-200, 2005.##[11] P. Z. Sun, T. Benner, A. Kumar, and A. G. Sorensen, "Investigation of optimiZing and translating pH‐sensitive pulsed‐chemical ex-change saturation transfer (CEST) imaging to a 3T clinical scanner," Magnetic Resonance in Medicine, vol. 60, no. 4, pp. 834-841, 2008.##[12] B. Schmitt, M. Zaiß, J. Zhou, and P. Bachert, "OptimiZation of pulse train presaturation for CEST imaging in clinical scanners," Magnetic Resonance in Medicine, vol. 65, no. 6, pp. 1620-1629, 2011.##[13] D. Woeessner, S. Zhang, M.E. Merritt and A.D. Sherry, "Numerical solution of the Bloch equations provides insights into the optimum design of PARACEST agents for MRI," Magn. Reson.Med, vol. 53, no. 4, pp. 790-799, 2005.##[14] R.V. Mulkern and M. L. Williams, "The general solution to the Bloch equation with constant RF and relaxation terms: application to saturation and slice selection," Med Phys, vol. 20, no.1, pp. 5-13, 1993.##[15] S.A. Roell, W. Dreher and D. LeibfritZ, "A general solution of the standard magnetiZation transfer model," J Magn Reson, vol. 132, no. 1, pp. 96-101, 1998.##[16] P.K. Madhu and A. Kumar "Direct Cartesian- space solutions of generaliZed Bloch equations in the rotating frame," J Magn Reson A, vol. 114, no. 2, pp. 201-211, 1995.##[17] K.Murase and N.Tanki, "Numeric solution to the time-dependent Bloch equations revisited," Magnetic Resonance Imaging, vol. 12, no.5, pp. 1-6, 2010.##[18] A.X. Li, R.H.E. Hudson, J.W. Barrett, C. K. Jones, S. H. Pasternak and R. Bartha, "Four-pool modeling of proton exchange processes in biological systems in the presence of MRI-paramagnetic chemical exchange saturation transfer (PARACEST) agents," Magn. Reson.Med, vol. 60, no. 5, pp. 1197-1206, 2008.##[19] M. Zaiss, Z. Zu, J. Xu, P. Schuenke, D. F. Gochberg, J. C. Gore, M. E. Ladd, and P. Bachert,"A combined analytical solution for chemical exchange saturation transfer and semi-solid magnetiZation transfer," NMR in Biomed, vol. 28, no. 2, pp 217-230, 2014.##[20] S. Goerke, M. Zaiss, and P. Bachert, "CharacteriZation of creatine guanidium proton exchange by water-exchange (WEX) spectroscopy for absolute pH CEST imaging in vitro," NMR Biomed, vol. 27, no . 5, pp 507-518, 2014.##[21] S.A. Smith, J.A.D. Farrell, C.K Jones, D.S. Reich, P.A. Calabresi and P.C.M van Zijl, "Pulsed magnetiZation transfer imaging with body coil transmission at 3 Tesla: feasibility and application," Magn. Reson.Med, vol. 56, no. 4, pp. 866-875, 2006.##[22] W. T. Dixon, J. Ren, A. J. Lubag, J. Ratnakar,E. Vinogradov, I. Hancu, R. E. Lenkinski and A. D. Sherry, "A concentration-independent method to measure exchange rates in PARACEST agents," Magnetic Resonance in Medicine, vol. 63, no. 3, pp. 625-632, 2010.##[23] T. Jin and S. G. Kim, "Approximated analytical characteriZation of the steady-state chemical exchange saturation transfer (CEST) signals", Magnetic Resonance in Medicine, vol. 82, no. 5, pp.1876-1889, 2019.##[24] J. Kim, Y. Wu, Y. Gue, H. Zheng, and P. Z. Sun, "A review of optimiZation and quantification techniques for chemical exchange saturation transfer (CEST) MRI toward sensitive in vivo imaging," Contrast Media Mol Imaging, vol. 10, no.3, pp.163-178, 2015. ##

نمایش باورپذیر بازخورد تصویری در سامانه توان‌بخشی با استفاده از انسداد تصویر در نگاشت ویدیو Believable Visual Feedback in Motor Learning Using Occlusion-based Clipping in Video Mapping 1 1 کیفیت بازخورد تصویری و نمایش مناسب آن تأثیر به‌سزایی در انجام درست حرکات توان‌بخشی با استفاده از سامانه‌­های توان‌بخشی حرکتی دارند. یکی از مشکلات اساسی سامانه­‌های معمول مبتنی بر واقعیت مجازی آن است که محل وقوع حرکت بر روی زمین و یا تردمیل بوده، درحالی‌که محل نمایش بازخورد این حرکت در صفحه‌نمایش روبه­‌روی بیمار است. در این مقاله، روشی جدید برای نمایش بازخوردهای حرکت با استفاده از نگاشت ویدیو بر روی صفحه تردمیل ارائه شده که سعی در کم‌کردن فاصله میان محل وقوع حرکت و محل نمایش بازخورد آن حرکت برای دریافت بهتر بازخورد دارد. در این روش، بازخورد تصویری با استفاده از ویدیو‌پروژکتور بر روی صفحه تردمیل نمایش داده می­‌شود. ویژگی مهم این روش در مقایسه با کارهای قبلی، ارایه روشی برای باورپذیر‌کردن بازخورد با استفاده از انسداد تصویر است. پس از طراحی و پیاده‌­سازی سامانه بازخورد با انسداد تصویر و بدون انسداد تصویر، یک مطالعه کاربری برای ارزیابی سامانه و مقایسه آنها انجام شد. در این مطالعه، از 24 نفر از شرکت‌کنندگان بدون مشکل حرکتی خواسته شد تا تمرین­‌های قدم‌زدن و عبور از موانع بر اساس پروتکل طراحی‌شده انجام دهند. با توجه به اینکه هدف این مقاله تنها ارایه روشی برای بهبود کیفیت نمایش بازخورد حرکتی بوده و نه بررسی تأثیر سامانه ارایه‌شده بر روی بهبود و توان‌بخشی بیماران، از شرکت‌کنندگان بدون مشکل حرکتی برای ارزیابی این مطالعه استفاده شده است. نتایج حاصل از این پژوهش حاکی از آن است که اختلاف معنادار آماری میان نرخ خطای سامانه نگاشت ویدیو مبتنی بر انسداد تصویر و سامانه بدون انسداد تصویر در تمرین قدم‌زدن (p=0.0031) و عبور از موانع (p=0.021) وجود دارد. از لحاظ شهودی‌بودن بازخورد بر اساس خود‌اظهاری شرکت‌کنندگان در پژوهش، نتایج حاصل بیان‌گر برتری معنادار آماری (p=0.011) سامانه نگاشت ویدیو با انسداد تصویر در مقایسه با سامانه بدون انسداد تصویر است. 2 Gait rehabilitation systems provide patients with guidance and feedback that assist them to better perform the rehabilitation tasks. Real-time feedback can guide users to correct their movements. Research has shown that the quality of feedback is crucial to enhance motor learning in physical rehabilitation. Common feedback systems based on virtual reality present interactive feedback in a monitor in front of a user. However, in this technique, there is a gap between where the feedback is presented and where the actual movement occurs. In particular, there is a discrepancy between where the actual movement occurs (e.g., on a treadmill) and the place of presenting feedback (e.g., a screen in front of the user). As a result, the feedback is not provided in the same location, which requires users perform additional cognitive processing to understand and apply the feedback. This discrepancy is misleading and can consequently result in difficulties to adapt the changes in rehabilitation tasks. In addition, the occlusion problem is not well handled in existing feedback systems that results in misleading the users to assume that the obstacle is on the foot. To address this problem, we need to make an illusion of putting a foot on the obstacle. In this paper, we propose a visual feedback system based on video mapping to provide a better understanding of the relationship between body perception and movement kinematics. This system is based on Augmented Reality (AR) in which visual cues in the form of light are projected on the treadmill using video projectors. In this system, occlusion-based clipping is used to enhance the believability of the feedback. We argue that this system contributes to the correct execution of rehabilitation exercises by increasing patients’ awareness of gait speed and step length. We designed and implemented two prototypes including the video projection with occlusion-based clipping (OC) and a prototype with no occlusion-based clipping (NOC). A set of experiments were performed to assess and compare the ability of unimpaired participants to detect real-time feedback and make modifications to gait using our feedback system. In particular, we asked 24 unimpaired participants to perform stepping and obstacle avoidance tasks. Since the focus of the paper is the quality of the feedback than the effect of feedback on training in long-term, unimpaired participants were recruited for this study. In the experiments, a motion capture device was used to measure the performance of participants. We demonstrated that our system is effective in terms of steps to adapt changes, obstacles to adapt changes, normalized accumulative deviation, quality of user experience, and intuitiveness of feedback. The results showed that projection-based AR feedback can successfully guide participants through a rehabilitation exercise. In particular, the results of this study showed statistically significant differences between the fault-rate of participants using OC and NOC prototypes in the stepping (p=0.0031) and obstacle avoidance (0.021) tasks. In addition, participates rated OC more intuitive than NOC in terms of the quality of feedback. Our feedback system showed a significant improvement in participants’ ability to adapt the changes while walking on the treadmill. 87 100 2018/12/232018/02/72018/09/282019/05/22019/04/102018/10/18 1397/7/26 2019/11/132019/06/192019/05/222020/01/222020/01/222020/08/18 1399/5/28 یونس سخاوت Yoones Sekhavat دانشکده چندرسانه‌ای، دانشگاه هنراسلامی تبریز ایران sekhavat@tabriziau.ac.ir محمدصادق نعمانی Mohammad Sadegh Namani دانشکده چندرسانه‌ای، دانشگاه هنراسلامی تبریز ایران ms.namani@tabriziau.ac.ir Video mapping real-time feedback motor learning augmented reality نگاشت ویدیو بازخورد تصویری سامانه‌های توان‌بخشی انسداد تصویر واقعیت مجازی ]1[ M. Hajizadeh, A. Hashemi Oskouei, F. 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Cipriani, " Humans can integrate augmented reality feedback in their sensorimotor control of a robotic hand", IEEE Transactions on Human-Machine Systems, vol.47(4), pp.583-589, 2016.##[1]حاجی زاده مریم, هاشمی اسکویی علیرضا, قالیچی فرزان, قابلیت اطمینان پارامترهای سینماتیکی زانو و نیروی عکس العمل پا در افراد با آسیب لیگامان متقاطع قدامی در بالا رفتن از پله, مجله مهندسی پزشکی زیستی, دوره 11 ،شماره 3، صفحات 31-17 ،1396.##]1[ M. Hajizadeh, A. Hashemi Oskouei, F. Ghalichi, "The Reliability of Knee Kinematics and Ground Reaction Force During Stair Negotiation", Iranian journal of Biomedical Engineering, vol.11. No. 3, pp.17-31, 2017.##[2]زمانی حمید، دادگو مهدی، ابراهیمی تکامجانی اسماعیل، تأثیر دو ماه آموزش راه رفتن همراه با حمایت وزن روی تردمیل بر تعادل و کیفیت زندگی بیماران ضایعه نخاعی ناکامل, مجله توانبخشی، دوره 18 ،شماره 4، صفحات 31-17 ،1396##]2[ H. Zamani, M. Dadgoo, I. Ebrahimi Takamjani, E. Hajouj, A. 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Minge, "Developing motivational visual feedback for a new telerehabilitation system for motor relearning after stroke", In Proceedings of the 31st British Computer Society Human Computer Interaction Conference , BCS Learning & Development Ltd, 2017, pp. 75.##]14[ HK.Wu, H. R. Chen, & C. H.Yu, "Development of posterior walker with adjustable visual cues to improve gait performance for patients with Parkinson's disease", In IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society, 2010, pp. 1512-1516.##]15[ J. Lieberman, & C. Breazeal, "TIKL: Development of a wearable vibrotactile feedback suit for improved human motor learning", IEEE Transactions on Robotics, vol.23 (5), pp.919-926, 2007.##]16[ P. Celnik, K.Stefan, F. Hummel, J. Duque, J. Classen, L.G. Cohen, "Encoding a motor memory in the older adult by action observation", Neuroimage, vol. 29(2), pp. 677-684, 2006.##]17[ R. Saegusa, K. Shigematsu, K. 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Namani, " Projection-based AR: Effective visual feedback in gait rehabilitation", IEEE Transactions on Human-Machine Systems, vol.48(6), pp.626-636, 2018.##[29] F. Clemente, S. Dosen, L. Lonini, M. Markovic, D. Farina, C. Cipriani, " Humans can integrate augmented reality feedback in their sensorimotor control of a robotic hand", IEEE Transactions on Human-Machine Systems, vol.47(4), pp.583-589, 2016. ##

کاهش نوفه در تصویربرداری تشدید مغناطیسی با استفاده از الگوریتم تخمین بیزین A Bayesian approach for image denoising in MRI 1 1 تصویربرداری تشدید مغناطیسی[1] (MRI) که اساس آن بر پایه تشدید مغناطیسی هسته‎ای بنا نهاده شده، به‎عنوان یک روش بارز در زمینه کاربردهای پزشکی مطرح است. به‎دلیل وضوح مناسب و فناوری کم‌ضرر،MRI در کاربردهای بالینی بسیار مورد توجه قرار گرفته است. کیفیت تصاویر MR نقش کلیدی‎ در نحوه تشخیص پزشک ایفا می‎کند؛ اما به‎دلیل ایجاد نوفه حین فرآیند تصویربرداری، اغلب کیفیت تصاویر دریافتی کاهش می‌یابد. از این‌رو حذف نوفه جهت ارتقای قابلیت تشخیص بسیار مورد توجه قرار گرفته است. نوفه موجود در تصاویر MR که منجر به کاهش شدت نور تصویر شده و بایاس وابسته به سیگنال ایجاد می‎کند، به بهترین شکل با تابع توزیع رایسین مدل می‌شود. به‌طور‌کلی هدف از این پژوهش پیداکردن تابع چگالی احتمال پیشین مناسبی برا یسیگنال بدون نوفه[2] MR و استفاده از تخمین بیزین در راستای کاهش نوفه تصویر است که در مقایسه با سایر روش­های گروه آماری روشی کم‌هزینه با پیچیدگی محاسباتی پایین­تر است. [1] Magnetic Resonance Imaging [2] Noiseless signal 2 Magnetic Resonance Imaging (MRI) is a notable medical imaging technique that is based on Nuclear Magnetic Resonance (NMR). MRI is a safe imaging method with high contrast between soft tissues, which made it the most popular imaging technique in clinical applications. MR Image's visual quality plays a vital role in medical diagnostics that can be severely corrupted by existing noise during the acquisition process. Therefore, the denoising of these images has great importance in medical applications. During the last decades, lots of MR denoising approaches from various groups of techniques have been proposed that can be classified into two general groups of acquisition-based noise reduction and post-acquisition denoising methods. The first group's approaches will add imaging time and led to a much time-consuming process. The second group's issues are its complicated mathematical equations required for image denoising, in which stochastic algorithms are usually required to solve these complex equations. This study aims to find an appropriate statical post-acquisition denoising MR imaging method based on the Bayesian technique. Finding the appropriate prior density function also has great importance since the Bayesian technique's performance is related to its prior density function. In this study, the uniform distribution has been applied as the prior density function. The prior uniform distribution function will reduce the Bayesian algorithm to its simplest possible state and lower computational complexity and time consumption. The proposed method can solve the numerical problems with an adequate timing process without complex algorithms and remove noise in less than 120 seconds on average in all cases. To quantitatively assess image improvement, we used the Structural Similarity Function (SSIM) in MATLAB. The similarity with this function shows an average improvement of more than 0.1 in all images. Considering the results, it can be concluded that combining the uniform distribution function as a prior density function and the Bayesian algorithm can significantly reduce the image's noise without the time and computational cost. 101 108 2018/12/232018/02/72018/09/282019/05/22019/04/102018/10/182018/08/28 1397/6/6 2019/11/132019/06/192019/05/222020/01/222020/01/222020/08/182020/08/18 1399/5/28 سید جواد کاظمی تبار Javad Kazemitabar دانشگاه صنعتی نوشیروانی بابل ایران j.kazemitabar@nit.ac.ir میترا توکلی Mitra Tavakkoli دانشگاه صنعتی نوشیروانی بابل ایران kazemita@hotmail.com Bayesian estimation Rician distribution Magnetic Resonance Imaging تخمین بیزین توزیع رایس تصویربرداری تشدید مغناطیسی [1] Z. Hongtu et al, "Regression models for identifying noise sources in magnetic resonance images," Journal of the American Statistical Association, vol.104, no.486, pp.623-637, 2009.##[2] J. Mohan, V.Krishnaveni, and G.Yanhui , "A survey on the magnetic resonance image denoising methods," Biomedical Signal Processing and Control , vol.9, pp. 56-69, 2014.##[3] A.Macovski, "Noise in MRI," Magnetic Resonance in Medicin, vol.36,no.3, pp. 494-497, 1996.##[4] H. Gudbjartsson, P. Samuel Patz, "The Rician distribution of noisy MRI data," Magnetic resonance in medicine, vol.34, no.6, pp. 910-914, 1995.##[5] L. He, R. Ian Greenshields, "A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images," IEEE transactions on medical imaging, vol.28, no.2, pp. 165-172, 2009.##[6] J. Sijbers & et al, "Estimation of the noise in magnitude MR images," Magnetic Resonance Imaging, vol.16, no.1, pp.87-90, 1998.##[7] J. Sijbers & et al, "Maximum-likelihood estimation of Rician distribution parameters," IEEE Transactions on Medical Imaging. Vol.17, no.3, pp.357-361, 1998.##[8] J.Sijbers, A. J. Den Dekker, "Maximum likelihood estimation of signal amplitude and noise variance from MR data," Magnetic Resonance in Medicine, vol.51, no.3, pp.586-594, 2004.##[9] J.Rajan & et al, "Maximum likelihood estimation-based denoising of magnetic resonance images using restricted local neighborhoods," Physics in Medicine & Biology, vol.56, no.16, pp.5221, 2011.##[10] J.Rajan & et al, "Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images," Magnetic Resonance Imaging, vol.30, no.10, pp. 1512-1518, 2012.##[11] A. Tietze & et al, "Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps," PloS one, vol13, no.9, pp. e0202906, 2018.##[12] L.Lauwers & et al, "Analyzing Rice distributed functional magnetic resonance imaging data: a Bayesian approach," Measurement Science and Technology, vol.21, no.11, pp. 115804, 2010.##[13] M. Kay, M. Steven, "Fundamentals of statistical signal processing", vol. I: estimation theory, 1993.##[14] X. Qi, "Compression of Three-Dimensional Magnetic Resonance Brain Images," 2001.##[15] D.Selvathi, and V. Sathananthavathi, "Genetic algorithm based nonlocal maximum likelihood algorithm for MRI denoising," Int. J. Comput. Intell. Telecommun. Syst, vol.2, pp. 21-26, 2011.##[16] https://mr.usc.edu/download/data/##[1] Z. Hongtu et al, "Regression models for identifying noise sources in magnetic resonance images," Journal of the American Statistical Association, vol.104, no.486, pp.623-637, 2009.##[2] J. Mohan, V.Krishnaveni, and G.Yanhui , "A survey on the magnetic resonance image denoising methods," Biomedical Signal Processing and Control , vol.9, pp. 56-69, 2014.##[3] A.Macovski, "Noise in MRI," Magnetic Resonance in Medicin, vol.36,no.3, pp. 494-497, 1996.##[4] H. Gudbjartsson, P. Samuel Patz, "The Rician distribution of noisy MRI data," Magnetic resonance in medicine, vol.34, no.6, pp. 910-914, 1995.##[5] L. He, R. Ian Greenshields, "A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images," IEEE transactions on medical imaging, vol.28, no.2, pp. 165-172, 2009.##[6] J. Sijbers & et al, "Estimation of the noise in magnitude MR images," Magnetic Resonance Imaging, vol.16, no.1, pp.87-90, 1998.##[7] J. Sijbers & et al, "Maximum-likelihood estimation of Rician distribution parameters," IEEE Transactions on Medical Imaging. Vol.17, no.3, pp.357-361, 1998.##[8] J.Sijbers, A. J. Den Dekker, "Maximum likelihood estimation of signal amplitude and noise variance from MR data," Magnetic Resonance in Medicine, vol.51, no.3, pp.586-594, 2004.##[9] J.Rajan & et al, "Maximum likelihood estimation-based denoising of magnetic resonance images using restricted local neighborhoods," Physics in Medicine & Biology, vol.56, no.16, pp.5221, 2011.##[10] J.Rajan & et al, "Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images," Magnetic Resonance Imaging, vol.30, no.10, pp. 1512-1518, 2012.##[11] A. Tietze & et al, "Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps," PloS one, vol13, no.9, pp. e0202906, 2018.##[12] L.Lauwers & et al, "Analyzing Rice distributed functional magnetic resonance imaging data: a Bayesian approach," Measurement Science and Technology, vol.21, no.11, pp. 115804, 2010.##[13] M. Kay, M. Steven, "Fundamentals of statistical signal processing", vol. I: estimation theory, 1993.##[14] X. Qi, "Compression of Three-Dimensional Magnetic Resonance Brain Images," 2001.##[15] D.Selvathi, and V. Sathananthavathi, "Genetic algorithm based nonlocal maximum likelihood algorithm for MRI denoising," Int. J. Comput. Intell. Telecommun. Syst, vol.2, pp. 21-26, 2011.##[16] https://mr.usc.edu/download/data/ ##

مدل کنترل دسترسی پویای حافظ حریم خصوصی با قابلیت وکالت دسترسی درسلامت الکترونیکی Privacy Preserving Dynamic Access Control Model with Access Delegation for eHealth 1 1 گسترش استفاده از فناوری اطلاعات و به‌طور خاص اینترنت اشیا در حوزه سلامت الکترونیکی، مسائل مختلفی را به‌همراه دارد که از مهم‌ترین آنها مسأله امنیت و کنترل دسترسی است. در این راستا نیازمندی‌های مختلفی از جمله مسأله دسترسی پزشک به پرونده بیمار بر اساس موقعیت فیزیکی پزشک، مسأله تشخیص شرایط اضطراری و اعطای پویای دسترسی موقت به پزشک حاضر، حفظ حریم خصوصی بیمار بر اساس ترجیحات وی و مسأله اعطای وکالت دسترسی به حقوق دسترسی پزشک دیگر مطرح است که در مدل‌های ارائه‌شده تاکنون پوشش داده نشده است. در این مقاله یک مدل کنترل دسترسی پویا و حافظ حریم خصوصی با قابلیت وکالت دسترسی در سلامت الکترونیکی با نام TbDAC ارائه شده است؛ به‌طوری‌که هنگام دسترسی پزشکان و پرستاران به پرونده بیمار بتواند چالش‌های امنیتی مطرح در این محیط‌ها را برطرف‌کند. با پیاده‌سازی یک سامانه کنترل دسترسی بر اساس مدل پیشنهادی و بررسی سناریوهایی واقعی در محیط بیمارستانی با استفاده از آن، کاربرد عملی این مدل در محیط واقعی و کارایی آن نشان داده شده است. 2 eHealth is the concept of using the stored digital data to achieve clinical, educational, and administrative goals and meet the needs of patients, experts, and medical care providers. Expansion of the utilization of information technology and in particular, the Internet of Things (IoT) in eHealth, raises various challenges, where the most important one is security and access control. In this regard, different security requirements have been defined; such as the physician’s access to the patient’s EHR (electronic health record) based on the physician’s physical location, detection of emergency conditions and dynamically granting access to the existing physician or nurse, preserving patients’ privacy based on their preferences, and delegation of duties and related permissions. In security and access control models presented in the literature, we cannot find a model satisfying all these requirements altogether. To fill this gap, in this paper, we present a privacy preserving dynamic access control model with access delegation capability in eHealth (called TbDAC). The proposed model is able to tackle the security challenges of these environments when the physicians and nurses access the patients’ EHR. The model also includes the data structures, procedures, and the mechanisms necessary for providing the access delegation capability. The proposed access control model in this paper is in fact a family of models named TbDAC for access control in eHealth considering the usual hospital procedures. In the core model (called TbDAC0), two primitive concepts including team and role are employed for access control in hospitals. In this model, a set of permission-types is assigned to each role and a medical team (including a set of hospital staff with their roles) is assigned to each patient. In fact the role of a person in a team determines his/her permissions on the health information of the patient. Since patients’ vital information is collected from some IoT sensors, a dynamic access control using a set of dynamic and context-aware access rules is considered in this model. Detecting emergency conditions and providing proper permissions for the nearest physicians and nurses (using location information) is a key feature in this model. Since health information is one of the most sensitive individuals’ personal information, the core model has been enhanced to be a privacy preserving access control model (named TbDAC1). To this aim, the purpose of information usage and the privacy preferences of the patients are considered in the access control enforcement procedure. Delegation of duties is a necessity in medical care. Thus, we added access delegation capability to the core model and proposed the third member of the model family, which is named TbDAC2. The complete model that considers all security requirements of these environments including emergency conditions, privacy, and delegation is the last member of this family, named TbDAC3. In each one of the presented models, the therapeutic process carried out in the hospitals, the relational model, and the entities used in the model are precisely and formally defined. Furthermore in each model, the access control process and the dynamic access rules for different situations are defined. Evaluation of the proposed model is carried out using three approaches; comparing the model with the models proposed in related research, assessing the real-world scenarios in a case study, and designing and implementing a prototype of an access control system based on the proposed model for mobile Android devices. The evaluations show the considerable capabilities of the model in satisfying the security requirements in comparison to the existing models which proposed in related research and also its applicability in practice for different simple and complicated access scenarios. 109 140 2018/12/232018/02/72018/09/282019/05/22019/04/102018/10/182018/08/282018/10/19 1397/7/27 2019/11/132019/06/192019/05/222020/01/222020/01/222020/08/182020/08/182020/01/22 1398/11/2 فائقه غفرانی Faegheh Ghofrani دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شریف ایران ghofrani@ce.sharif.edu مرتضی امینی Morteza Amini دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شریف ایران amini@sharif.edu eHealth IoT Dynamic Access Control Privacy Access Delegation سلامت الکترونیکی اینترنت اشیا کنترل دسترسی پویا حفظ حریم خصوصی وکالت دسترسی ]1[ I. B. Ida, A. Jemai, and A. Loukil, "A survey on security of IoT in the context of ehealth and clouds," in Proceedings of 11th International Design Test Symposium (IDT), pp.25-30, Dec2016.##[2] A. J. Jara, A. F. Alcolea, M. A. Zamora, A. F. G. Skarmeta, and M. Alsaedy, "Drugs interaction checker based on iot," in Proceedings of 2010 Internet of Things (IOT), pp.1-8, Nov2010.##[3] A, Kevin, "That 'internet of things' thing," RFID journal, vol. 22, pp.97-114, Jul2009.##[4] D.Lu and T.Liu, "The application of iot in medical system," in Proceedings of 2011 IEEE International Symposium on IT in Medicine and Education, vol.1, pp.272-275, Dec2011.##[5] R. Marti, J. Delgado, and X. Perramon, "Security specification and implementation for mobile e-health services," in Proceedings of 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, March.2004, pp.241-248.##[6] F. Rezaeibagha and Y. Mu, "Distributed clinical data sharing via dynamic access-control policy transformation," International Journal of Medical Informatics, vol.89, no.Supplement C, pp.25-31, 2016.##[7] I. lakovidis, "Towards Personal Health Record: Current Situation, Obstacles and Trends in Implementation of Electronic Healthcare Record in Europe," International Journal of Medical Informatics, vol.52, pp.105-115, 1998.##[8] M.Sicuranza, A.Esposito, and M.Ciampi, "A view-based access control model for her systems," in Proceedings of Intelligent Distributed Computing VIII, pp.443-452, Springer, 2015.##[9] M. Abomhara, H. Yang, G. M. Køien, and M. B. Lazreg, "Work-based access control model for cooperative healthcare environments: Formal specification and verification," Journal of Healthcare Informatics Research, vol.1, pp.19-51, Jun2017.##[10] H. S. G. Pussewalage and V. A. Oleshchuk, "An attribute based access control scheme for secure sharing of electronic health records," in Proceedings of 18th IEEE International Conference on e-Health Networking, Applications and Services (Healthcom), Sept.2016, pp.1-6.##[11] M.Sicuranza and A.Esposito, "An access control model for easy management of patient privacy in her systems," in Proceedings of 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013), Dec.2013, pp.463-470.##[12] A. Ouaddah, H. Mousannif, A. A. Elkalam, and A. A. Ouahman, "Access control in the internet of things: Big challenges and new opportunities," Computer Networks, vol.112, no.Supplement C, pp.237-262, 2017.##[13] M.F.F.Khan and K.Sakamura, "A secure and flexible e-health access control system with provisions for emergency access overrides and delegation of access privileges," in Proceedings of 18th International Conference on Advanced Communication Technology (ICACT), pp.541-546, Jan2016.##[14] M. Jayabalan and T. O'Daniel, "Access control and privilege management in electronic health record: a systematic literature review," Journal of Medical Systems, vol.40, p.261, Oct2016.##[15] M.F.F.Khan and K.Sakamura, "Context-aware access control for clinical information systems," in Proceedings of 2012 International Conference on Innovations in Information Technology (IIT), March.2012, pp.123-128.##[16] C. K. Georgiadis, I. Mavridis, G. Pangalos, and R. K. Thomas, "Flexible team-based access control using contexts," in Proceedings of the Sixth ACM Symposium on Access Control Models and Technologies, SACMAT'01, (NewYork, NY, USA), pp.21-27, 2001.##[17] M. Yarmand, K. Sartipi, and D. Down, "Behavior-based access control for distributed healthcare environment," in Computer-Based Medical Systems, 2008. CBMS'08. 21st IEEE International Symposium on, pp.126-131, June2008.##[18] E. Georgakakis, S. Nikolidakis, D. Vergados, and C. Douligeris, "Spatio temporal emergency role based access control (stem-rbac): A time and location aware role based access control model with a break the glass mechanism," in Computers and Communications (ISCC): 2011 IEEE Symposium on, pp. 764-770, June2011.##[19] Q.Ni, A.Trombetta, E.Bertino, andJ.Lobo, "Privacy-aware role based access control, " in Proceedings of 12th ACM Symposium on Access Control Models and Technologies, SACMAT '07, pp.41-50, ACM, 2007.##[20] N. Yang, H. Barringer, and N. Zhang, "A purpose-based access control model," in Proceedings of Third International Symposium on Information Assurance and Security, pp.143-148, Aug2007.##[21] K. Seol, Y.-G. Kim, E. Lee, Y.-D. Seo, and D.-K. Baik, ''Privacy preserving attribute-based access control model for XML-based electronic health record system,'' IEEE Access, vol. 6, pp. 9114-9128, 2018.##[22] Majeed, Abdul, "Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data," Journal of King Saud University-Computer and Information Sciences, March 2018.##[23] P.Gope and R.Amin, "A novel reference security model with the situation based access policy for accessing ephr data," Journal of Medical Systems, vol.40, p.242, Sep2016.##[24] H. Narayanan and M. Giine, "Ensuring access control in cloud provisioned healthcare systems," in Consumer Communications and Networking Conference (CCNC): 2011 IEEE, Jan.2011, pp.247-251.##[25] US Department of Health and Human Services, "Public Law 104-191: Health Insurance Portability and Accountability Act of 1996," Retrieved November 24 (2003): 2003.##[26] J. Jing, A. Gail-Joon, H. Hongxin, J. Michael, and Z.Xinwen, "Patient-centric authorization framework for electronic healthcare services," computers & security, vol.30, no.2-3, pp.116-127, 2011.##[27] M.A. Doostari, M. Miabi, and M. Momeni, "Proposing a privacy and anonymity protocol in ehealth using public key infrastructure", in Proceedings of the 4th International Conference on Applied Research in Computer Engineering and Signal Processing, Tehran, Iran, 2016.##[28] F. hashemibeni, "Privacy preserving access control in iot for ehealth," Master's thesis, Sharif University of Technology, September 2015.##]1[ I. B. Ida, A. Jemai, and A. Loukil, "A survey on security of IoT in the context of ehealth and clouds," in Proceedings of 11th International Design Test Symposium (IDT), pp.25-30, Dec2016.##[2] A. J. Jara, A. F. Alcolea, M. A. Zamora, A. F. G. Skarmeta, and M. Alsaedy, "Drugs interaction checker based on iot," in Proceedings of 2010 Internet of Things (IOT), pp.1-8, Nov2010.##[3] A, Kevin, "That 'internet of things' thing," RFID journal, vol. 22, pp.97-114, Jul2009.##[4] D.Lu and T.Liu, "The application of iot in medical system," in Proceedings of 2011 IEEE International Symposium on IT in Medicine and Education, vol.1, pp.272-275, Dec2011.##[5] R. Marti, J. Delgado, and X. Perramon, "Security specification and implementation for mobile e-health services," in Proceedings of 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, March.2004, pp.241-248.##[6] F. Rezaeibagha and Y. Mu, "Distributed clinical data sharing via dynamic access-control policy transformation," International Journal of Medical Informatics, vol.89, no.Supplement C, pp.25-31, 2016.##[7] I. lakovidis, "Towards Personal Health Record: Current Situation, Obstacles and Trends in Implementation of Electronic Healthcare Record in Europe," International Journal of Medical Informatics, vol.52, pp.105-115, 1998.##[8] M.Sicuranza, A.Esposito, and M.Ciampi, "A view-based access control model for her systems," in Proceedings of Intelligent Distributed Computing VIII, pp.443-452, Springer, 2015.##[9] M. Abomhara, H. Yang, G. M. Køien, and M. B. Lazreg, "Work-based access control model for cooperative healthcare environments: Formal specification and verification," Journal of Healthcare Informatics Research, vol.1, pp.19-51, Jun2017.##[10] H. S. G. Pussewalage and V. A. Oleshchuk, "An attribute based access control scheme for secure sharing of electronic health records," in Proceedings of 18th IEEE International Conference on e-Health Networking, Applications and Services (Healthcom), Sept.2016, pp.1-6.##[11] M.Sicuranza and A.Esposito, "An access control model for easy management of patient privacy in her systems," in Proceedings of 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013), Dec.2013, pp.463-470.##[12] A. Ouaddah, H. Mousannif, A. A. Elkalam, and A. A. Ouahman, "Access control in the internet of things: Big challenges and new opportunities," Computer Networks, vol.112, no.Supplement C, pp.237-262, 2017.##[13] M.F.F.Khan and K.Sakamura, "A secure and flexible e-health access control system with provisions for emergency access overrides and delegation of access privileges," in Proceedings of 18th International Conference on Advanced Communication Technology (ICACT), pp.541-546, Jan2016.##[14] M. Jayabalan and T. O'Daniel, "Access control and privilege management in electronic health record: a systematic literature review," Journal of Medical Systems, vol.40, p.261, Oct2016.##[15] M.F.F.Khan and K.Sakamura, "Context-aware access control for clinical information systems," in Proceedings of 2012 International Conference on Innovations in Information Technology (IIT), March.2012, pp.123-128.##[16] C. K. Georgiadis, I. Mavridis, G. Pangalos, and R. K. Thomas, "Flexible team-based access control using contexts," in Proceedings of the Sixth ACM Symposium on Access Control Models and Technologies, SACMAT'01, (NewYork, NY, USA), pp.21-27, 2001.##[17] M. Yarmand, K. Sartipi, and D. Down, "Behavior-based access control for distributed healthcare environment," in Computer-Based Medical Systems, 2008. CBMS'08. 21st IEEE International Symposium on, pp.126-131, June2008.##[18] E. Georgakakis, S. Nikolidakis, D. Vergados, and C. Douligeris, "Spatio temporal emergency role based access control (stem-rbac): A time and location aware role based access control model with a break the glass mechanism," in Computers and Communications (ISCC): 2011 IEEE Symposium on, pp. 764-770, June2011.##[19] Q.Ni, A.Trombetta, E.Bertino, andJ.Lobo, "Privacy-aware role based access control, " in Proceedings of 12th ACM Symposium on Access Control Models and Technologies, SACMAT '07, pp.41-50, ACM, 2007.##[20] N. Yang, H. Barringer, and N. Zhang, "A purpose-based access control model," in Proceedings of Third International Symposium on Information Assurance and Security, pp.143-148, Aug2007.##[21] K. Seol, Y.-G. Kim, E. Lee, Y.-D. Seo, and D.-K. Baik, ''Privacy preserving attribute-based access control model for XML-based electronic health record system,'' IEEE Access, vol. 6, pp. 9114-9128, 2018.##[22] Majeed, Abdul, "Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data," Journal of King Saud University-Computer and Information Sciences, March 2018.##[23] P.Gope and R.Amin, "A novel reference security model with the situation based access policy for accessing ephr data," Journal of Medical Systems, vol.40, p.242, Sep2016.##[24] H. Narayanan and M. Giine, "Ensuring access control in cloud provisioned healthcare systems," in Consumer Communications and Networking Conference (CCNC): 2011 IEEE, Jan.2011, pp.247-251.##[25] US Department of Health and Human Services, "Public Law 104-191: Health Insurance Portability and Accountability Act of 1996," Retrieved November 24 (2003): 2003.##[26] J. Jing, A. Gail-Joon, H. Hongxin, J. Michael, and Z.Xinwen, "Patient-centric authorization framework for electronic healthcare services," computers & security, vol.30, no.2-3, pp.116-127, 2011.##[27] M.A. Doostari, M. Miabi, and M. Momeni, "Proposing a privacy and anonymity protocol in ehealth using public key infrastructure", in Proceedings of the 4th International Conference on Applied Research in Computer Engineering and Signal Processing, Tehran, Iran, 2016.##[27] دوستاری, محمدعلی؛ مریم میابی جغال و مسعود مومنی تزنگی، ۱۳۹۵، ارایه پروتکل حفظ حریم خصوصی و گمنامی در سلامت الکترونیک با استفاده از زیرساخت کلید عمومی، چهارمین کنفرانس بین المللی پژوهش های کاربردی درمهندسی کامپیوتر و پردازش سیگنال، تهران، دانشگاه صنعتی مالک اشتر - دانشگاه شهید بهشتی.##[28] F. hashemibeni, "Privacy preserving access control in iot for ehealth," Master's thesis, Sharif University of Technology, September 2015. ##

ارائه یک روش استخراج ویژگی از تصاویر چهره مبتنی بر اعمال تبدیل روی ویژگی‌‌های به‌‌دست‌‌آمده از شبکه‌‌های عصبی کانولوشن Introducing a method for extracting features from facial images based on applying transformations to features obtained from convolutional neural networks 1 1 در این مقاله، یک روش استخراج ویژگی از داده ارائه شده است. ایده پیشنهادی، کلی بوده و قابل به‌کارگیری در استخراج ویژگی از هر نوع داده‏ است. در این روش، بردار ویژگی آزمون، به ویژگی‏‌های موجود در همه دسته‏‌ها اضافه و سپس تبدیل مناسبی روی مجموعه ویژگی‌‏های هر دسته (با احتساب بردار آزمون اضافه‌شده)، اعمال می‏‌شود. نحوه اعمال تبدیل و مجموعه اقدامات بعد از آن، به‌نحوی صورت می‏‌گیرد که موجب می‏‌شود ویژگی‏‌های موجود در دست‌ه‏ای که داده آزمون در‌واقع متعلق به آن است، دچار آسیب چندانی نشود و در مقابل، ویژگی‌‏های دسته‌‏هایی که داده آزمون متعلق به آنها نیست، دچار تخریب شوند. به‌طور شهودی می‏‌توان گفت، این امر، منجر به افزایش نرخ پذیرش به‌درستی (TP‏) در الگوریتم‌‏های دسته‌‏بندی یا شناسایی الگو می‏‌شود. به‌عنوان یک نمونه، ایده پیشنهادی، در مسأله شناسایی چهره با استفاده از شبکه‌‏های عصبی کانولوشن (CNN)، به‌عنوان یک پس‌‏پردازش و ویژگی‏‌های حاصل، به‌‏عنوان ویژگی‌‏های نهایی، برای عملیات شناسایی چهره به‌کار گرفته شد. نتایج پیاده‌سازی، نشان‏‌دهنده بهبود حدود %4/3 روی پایگاه داده LFW است. 2 In pattern recognition, features are denoting some measurable characteristics of an observed phenomenon and feature extraction is the procedure of measuring these characteristics. A set of features can be expressed by a feature vector which is used as the input data of a system. An efficient feature extraction method can improve the performance of a machine learning system such as face recognition in the image field. Most of the feature extraction methods in facial images are categorized as geometric feature extractor methods, linear transformation-based methods and neural network-based methods. Geometric features include some characteristics of the face such as the distance between the eyes, the height of the nose and the width of the mouth. In the second category, a linear transformation is applied to the original data and displaces them to a new space called feature space. In the third category, the last layer in the network, which is used for categorization, is removed, and the penultimate layer output is used as the extracted features. Convolutional Neural Networks (CNNs) are one the most popular neural networks and are used in recognizing and verifying the face images, and also, extracting features. The aim of this paper is to present a new feature extraction method. The idea behind the method can be applied to any feature extraction problem. In the proposed method, the test feature vector is accompanied with the training feature vectors in each class. Afterward, a proper transform is applied on feature vectors of each class (including the added test feature vector) and a specific part of the transformed data is considered. Selection of the transform type and the other processing, such as considering the specific part of the transformed data, is in such a way that the feature vectors in the actual class are encountered with less disturbing than the other ones. To meet this goal, two transformations, Fourier and Wavelet, have been used in the proposed method. In this regard, it is more appropriate to use transformations that concentrate the energy at low frequencies. The proposed idea, intuitively, can lead to improve the true positive (TP) rate. As a realization, we use the idea in CNN-based face recognition problems as a post-processing step and final features are used in identification. The experimental results show up to 3.4% improvement over LFW dataset. 141 156 2018/12/232018/02/72018/09/282019/05/22019/04/102018/10/182018/08/282018/10/192018/04/10 1397/1/21 2019/11/132019/06/192019/05/222020/01/222020/01/222020/08/182020/08/182020/01/222019/09/2 1398/6/11 مرتضی علی احمدی Morteza ali Ahmadi گروه مهندسی IT، دانشکده فنی و مهندسی، دانشگاه قم ایران ma.ahmadi@qom.ac.ir روح الله دیانت Rouhollah Dianat گروه مهندسی IT، دانشکده فنی و مهندسی، دانشگاه قم ایران rdianat@qom.ac.ir Feature extraction - Convolutional neural networks - Wavelet transform - Fourier transform استخراج ویژگی شبکه‌‌های عصبی کانولوشن تبدیل موجک تبدیل فوریه [1] A. 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Solaiman, "Face recognition based on geometric features using Support Vector Machines," in Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of, 2014: IEEE, pp. 89-95.##[10] M. A. Turk and A. P. Pentland, "Face recognition using eigenfaces," in Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on, 1991: IEEE, pp. 586-591.##[11] H. Hotelling, "Analysis of a complex of statistical variables into principal components," Journal of educational psychology, vol. 24, no. 6, p. 417, 1933.##[12] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on pattern analysis and machine intelligence, vol. 19, no. 7, pp. 711-720, 1997.##[13] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE transactions on pattern analysis and machine intelligence, vol. 31, no. 2, pp. 210-227, 2009.##[14] A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, "Toward a practical face recognition system: Robust alignment and illumination by sparse representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 372-386, 2012.##[15] Y. Sun, X. Wang, and X. Tang, "Deep learning face representation from predicting 10,000 classes," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1891-1898.##[16] Y. Sun, Y. Chen, X. Wang, and X. Tang, "Deep learning face representation by joint identification-verification," in Advances in neural information processing systems, 2014, pp. 1988-1996.##[17] Y. Sun, X. Wang, and X. Tang, "Deeply learned face representations are sparse, selective, and robust," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 2892-2900.##[18] Y. Sun, D. Liang, X. Wang, and X. Tang, "Deepid3: Face recognition with very deep neural networks," arXiv preprint arXiv:1502.00873, 2015.##[19] Y. Taigman, M. Yang, M. A. Ranzato, and L. Wolf, "Deepface: Closing the gap to human-level performance in face verification," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1701-1708.##[20] O. M. Parkhi, A. Vedaldi, and A. Zisserman, "Deep Face Recognition," in BMVC, 2015, vol. 1, no. 3, p. 6.##[21] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.##[22] F. S. Samaria and A. C. Harter, "Parameterisation of a stochastic model for human face identification," in Proceedings of 1994 IEEE workshop on applications of computer vision, 1994: IEEE, pp. 138-142.##[23] S. Milborrow, J. Morkel, and F. Nicolls, "The MUCT landmarked face database," Pattern Recognition Association of South Africa, vol. 201, no. 0, 2010.##[24] G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, "Labeled faces in the wild: A database forstudying face recognition in unconstrained environments," in Workshop on faces in'Real-Life'Images: detection, alignment, and recognition, 2008.##[25] A. Vedaldi and K. Lenc, "Matconvnet: Convolutional neural networks for matlab," in Proceedings of the 23rd ACM international conference on Multimedia, 2015: ACM, pp. 689-692.##[26] GTDLBench. The Database of Faces (AT&T) [Online]. Available: https://git-disl.github.io/GTDLBench/datasets/att_face_dataset/.##[27] P. Grother and M. Ngan, "Performance of face identification algorithms," NIST Inter-agency Internal Report8009, 2014.##[28] R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of eugenics, vol. 7, no. 2, pp. 179-188, 1936.##[29] S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K.-R. Mullers, "Fisher discriminant analysis with kernels," in Neural networks for signal processing IX: Proceedings of the 1999 IEEE signal processing society workshop (cat. no. 98th8468), 1999: Ieee, pp. 41-48.##[1] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, pp. 1097-1105, 2012.##[2] R. Brunelli and T. Poggio, "Face recognition: Features versus templates," IEEE transactions on pattern analysis and machine intelligence, vol. 15, no. 10, pp. 1042-1052, 1993.##[3] R. J. 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انتخاب ویژگی‌های مؤثر در ناهنجاری‌های دریچه‌ای قلب با استفاده از الگوریتم ژنتیک بر اساس ارزیابی همبستگی پیرسون Selecting effective features from Phonocardiography by Genetic Algorithm based on Pearson`s Coefficients Correlation 1 1 امروزه اختلالات دریچه​های قلبی یکی از اصلی​ترین عوامل مرگ و میر در جهان هستند. این اختلالات عبارت است از بروز عوارضی در دریچه‌های قلبی به‌نحوی که موجب تغییر شکل و یا تخریب دریچه‌ها شده و به تبع آن صداهای ناشی از باز و بسته‌شدن آنها نسبت به قلب سالم، دچار تغییر شوند. بدیهی است با توجه به پیچیدگی‌های سیگنال‌های صوتی قلبی و ثبت آنها، طراحی سامانه‌ای عاری از خطا و در عین حال دقیق از نظر میزان صحت تشخیص به‌سختی دست‌یافتنی باشد. با این وجود در این پژوهش تلاش شده است، سامانه‌ای هوشمند برای تشخیص نارسایی​های دریچه​ای قلب با استفاده از سیگنال‌های صوتی فونوکاردیوگرافی ارائه شود تا بیشینه قدرت تشخیصی را داشته باشد. هدف اصلی در روش پیشنهادی انتخاب بردارهای ویژگی مؤثر با استفاده از روش بهینه​سازی ژنتیک و نیز بر اساس تابع ارزیابی مبتنی بر ضرایب همبستگی پیرسون است. پیش از انتخاب ویژگی با توجه به ماهیت سیگنال های صوتی قلب، مراحل پیش​پردازشی شامل ثبت داده‌ها، نرمال​سازی، تقسیم‌بندی و فیلتر‌کردن مورد استفاده قرار گرفته تا صحت عملکرد سامانه را افزایش دهد. در گام بعدی سه دسته ویژگی‌های مختلف زمانی، موجک و انرژی سیگنال (هر یک پانزده ویژگی) از روی سیگنال عاری از نوفه استخراج شده که با توجه به تراکم و ناهمبستگی این ویژگی​ها، به‌کمک روش‌های ادغام، همبسته‌سازی و نیز کاهش فضای مسأله شامل تجزیه و تحلیل مؤلفه‌های اصلی، تحلیل جداکننده‌های خطی و تحلیل جداکننده‌های خطی ناهمبسته بردارهای ویژگی در فضای جدیدی استخراج می‌شوند. این بردارها شامل هجده بردار جدید (هر یک شش بردار) بوده که در‌نهایت از شبکه پرسپترون چند​لایه و المن برای طبقه‌بندی آنها استفاده می‌شود. نتایج مدل‌سازی فرآیند انتخاب ویژگی‌های مؤثر و تشخیص بیماری نشان از کارایی روش پیشنهادی دارد. 2 The heart is one of the most important organs in the body, which is responsible for pumping blood into the valvular systems. Beside, heart valve disorders are one of the leading causes of death in the world. These disorders are complications in the heart valves that cause the valves to deform or damage, and as a result, the sounds caused by their opening and closing compared to a healthy heart. Obviously, due to the complexities of cardiac audio signals and their recording, designing an accurate diagnosis system free of noise and fast enough is difficult to achieve. One of the most important issues in designing an intelligent heart disease diagnosis system is the use of appropriate primary data. This means that these data must not only be recorded according to the patient's equipment and clinical condition, but also must be labeled according to the correct diagnosis of the physician. However, in this study, an attempt has been made to provide an intelligent system for diagnosing valvular heart failure using phonocardiographic sound signals to have maximum diagnostic power. For this purpose, the signals are labeled and used under the supervision of a specialist doctor. The main goal is to select the effective feature vectors using the genetic optimization method and also based on the evaluation function by Pearson correlation coefficients. Before extraction feature step, preprocessing from data recording, normalization, segmentation, and filtering were used to increase system performance accuracy. For better result, Signal temporal, wavelet and signal energy components are extracted from the prepared signal as feature extraction step. Whereas extracted problem space were not correlated enough, in next step principal component analysis, linear separator analysis, and uncorrelated linear separator analysis methods were used to make feature vectors in a final correlated space. In selecting step, an efficient and simple method is used inorder to estimate the number of optimal features. In general, correlation is a criterion for determining the relationship between variables. The difference between the correlations of all feature subsets is calculated (for both in-class and out-of-class subsets) and then categorized in descending order according to the evaluation function. As a result, in the feature selection step the evaluation function is based on the Pearson statistical method, which is evaluated by a genetic algorithm with the aim of identifying more effective and correlated features in the final vectors. Eventually In this paper, two widely used neural networks with dynamic and static structure including perceptron and Elman neural networks have been used to evaluate the accuracy of the proposed vectors. The results of modeling the process of selecting effective features and diagnosing the disease show the efficiency of the proposed method. 157 176 2018/12/232018/02/72018/09/282019/05/22019/04/102018/10/182018/08/282018/10/192018/04/102016/04/25 1395/2/6 2019/11/132019/06/192019/05/222020/01/222020/01/222020/08/182020/08/182020/01/222019/09/22020/08/19 1399/5/29 امید مخلصی Omid Mokhlessi دانشکده مهندسی برق، دانشگاه آزاد اسلامی، مشهد ایران omidmokhlessi@yahoo.com سید جواد سید مهدوی چابک Seyedjavad Seyed Mahdavi Chabok دانشکده مهندسی، دانشگاه آزاد اسلامی، مشهد ایران Mahdavi@mshdiau.ac.ir آیدا علیرضائی Aida Alirezaee دانشگاه علوم پزشکی مشهد ایران Aidaalirezaee@gmail.com phonocardiography cardiac valvular disease integration features genetic optimization algorithm Pearson correlation coefficients فونوکاردیوگرافی بیماری‌های دریچه‌ای قلبی ادغام ویژگی روش بهینه‌سازی ژنتیک ضرایب همبستگی پیرسون [1] A. P. Yoganathan, R. Gupta, F. E. Udwadia, J. W. Miller, W. H. Corcoran, R. Sarma, J. L. 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Ahlstrom, "Nonlinear Phonocardiographic Signal Processing", Link¨oping studies in science and technology Dissertations, No. 1168, 2008.##[5] C. J. Liu, and H. Wechsler, "A Shape- and Texture-Based Enhanced Fisher Classifier for Face Recognition," IEEE Trans. Image Processing, vol. 10, no. 4, pp. 598-608, 2001.##[6] C. Yang, L. Wang, J. Feng, "On Feature Extraction via Kernels", IEEE Transaction on and systems, vol. 38, no. 2, 2008.##[7] C.Guyton, and J.E.hall, Texbook of Medical Physiology, 2006.##[8] E. Comak, A. Arslan, I. Turkoglu. "A decision support system based on support vector machines for diagnosis of the heart valve diseases", Computers in Biology and Medicine, No.37, pp. 21-27, 2007.##[9] F. Pattarin, S. Paterlini, T. Minerva, "Clustering financial time series: An application to mutual funds style analysis", Computational Statistics and Data Analysis, 2004.##[10] F. Ye, Z. Shi, Z. Shi, "A Comparative Study of PCA, LDA and Kernel LDA for Image Classification", International Symposium on Ubiquitous Virtual Reality, 2009.##[11] F.Rushmer, C.Morgan, "Meaning of murmurs", American Journal of Cardiology, pp. 722-730, 1968.##[12] Frontiers_in_bioscience,http://www.bioscience.org/atlases/heart/sound/sound.htm##[13] G. Amit, N. Gavriely, N. Intrator, "Cluster analysis and classification of heart sounds", Biomedical Signal Processing and Control 4, pp. 26-36, 2009.##[14] G. Smith, T.C. Fogarty and I.R. Johnson, "Genetic selection of feature for clustering and classification", 1994.##[15] G.Guraksın, U.Ergun and O.Deperlioglu, "The Analysis of Heart Sounds and a Pocket Computer Application via Discrete Fourier Transform, Fourier Transforms", New Analytical Approaches and FTIR Strategies, 2008.##[16] Hall, M.A. "Correlation-based Feature selection for Machine Learning," Ph.D. Thesis, Department of Computer Science. 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