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ارائه ابزاری کارآمد برای سنتز سطح بالای مبدل‌های دیجیتال مدارهای VLSI An efficient CAD tool for High-Level Synthesis of VLSI digital transformers 1 1 امروزه مبدل­‌های دیجیتال از مهم‌­ترین ادوات یک سامانه پردازش سیگنال و داده به‌شمار رفته و به­‌صورت گسترده در زمینه پردازش صوت، تصویر و سیگنال­‌های حیاتی به‌­کار گرفته می‌­شوند. در طراحی مبدل‌های دیجیتال VLSI، سنتز سطح بالا (HLS) یکی از مراحل مهم و تأثیرگذار به شمار می رود. هدف اصلی از انجام این کار، کمینه‌کردن واحدهای پایه دیجیتالی مورد استفاده در پروژه مفروض جهت بهبود توان، تأخیر، و سطح مصرفی آن­ها است. این کار عمدتاً با تحلیل گراف مسیر داده (DFG) اتفاق می­افتد. بهبود در این مرحله، علاوه‌بر بازدهی بیشتر باعث کاهش زمان طراحی در مراحل پایین­‌تر می‌شود. ماهیت پیچیده، گسترده و گسسته مسائل سنتز سطح بالا، باعث شده است که آنها در زمره مسائل بسیار دشوار در مهندسی مدارات VLSI به‌­شمار آیند؛ از این‌­رو استفاده از روش‌های فراابتکاری و هوش­جمعی جهت حل پروژه‌­های مرتبط با سنتز سطح بالا، گزینه‌­ای مطلوب به نظر می‌­رسد. در این مقاله روشی مبتنی­بر الگوریتم فراابتکاری "شعله و پروانه"(MFO) جهت یافتن بهترین طرح سخت‌­افزاری برای انواع مبدل‌­های دیجیتال ارائه شده است. نتایج مقایسه‌­ای در کنار نتایج حاصل از روش مبتنی ­بر الگوریتم ژنتیک (GA) نشان داد که روش پیشنهادی از توانایی بالاتری در ارائه ساختار سخت‌­افزاری مناسب و سنتز سطح بالای انواع مبدل‌ها برخوردار است. همچنین ویژگی دیگر روش پیشنهادی، سرعت بالای آن در یافتن پاسخ بهینه است (میانگین برتری بیش از 20% نسبت­ به GA). 2 Digital transformers are considered as one of the digital circuits being widely used in signal and data processing systems, audio and video processing, medical signal processing as well as telecommunication systems. Transforms such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) are among the ones being commonly used in this area. As an illustration, the DCT is employed in compressing the images. Moreover, the FFT can be utilized in separating the signal spectrum in signal processing systems as fast as possible. The DWT is used in separating the signal spectrum in a variety of applications from signal processing to telecommunication systems, as well. In order to build a VLSI circuit, several steps have to be taken from chip design to final construction. The first step in the synthesis of the integrated circuits is called high-level synthesis (HLS), in which a structural characteristic is obtained from a behavioral or algorithmic description. The resulting structural characteristic is equivalent to the one being considered in the behavioral description and it somehow represents the method for implementing the behavioral description as a result several structural descriptions could be implementable for each behavioral description. Therefore, depending on the intended use, the characteristic will be selected that outperforms the others. The main purpose of the HLS is to optimize the power consumption, the chip occupied area and delayed and is fulfilled by selecting the appropriate number of operating units and how they are implemented to the operators. This is generally accomplished through a graph analysis called the data flow graph (DFG) which is a graphical representation of the type and how the operators connect. In the DFG, each node is equivalent to an operator while the edges represent the relationship between these operators. Experience has proved that if the level of design optimization is high, in addition to higher efficiency, the design time will be lower, which is why the researchers are far more interested in optimization at higher levels of design than the lower levels. The complex, extensive, and discrete nature of the HLS problems have been ranked them among the most complex problems in VLSI circuits engineering. Bearing this mind, using meta-heuristic and Swarm intelligence methods to solve high-level synthesis projects seems to be a favored option. In this paper, a heuristic method called Moth-Flame Optimization (MFO) has been used to solve the HLS problem in the design of digital transformer to find the optimal response. The MFO is a population-based heuristic algorithm that optimizes the problems using the laws of nature. The leading notion behind the MFO algorithm inspired from the moths’ movements and their instinctive navigation during the night. In the MFO algorithm, the moths are like chromosomes in the GA and like the particles in the PSO algorithm. In order to compare and prove the efficiency of the proposed method, it was applied on the test data with the GA-based method separately but with the same initial conditions. The comparative results along with the results of the GA-based method demonstrated that the proposed method exhibits a higher ability to provide the appropriate hardware structure and high-level synthesis of various types of transformers. Another outstanding feature of the proposed method is its high speed of finding an optimal response with an average of more than 20% greater than the GA based method. 3 18 2019/03/5 1397/12/14 2020/01/22 1398/11/2 محمدرضا اسماعیلی Mohammad Reza Esmaeili دانشگاه بیرجند ایران mr.esmaeili@birjand.ac.ir سید حمید ظهیری Seyed Hamid Zahiri دانشگاه بیرجند ایران hzahiri@birjand.ac.ir سید محمد رضوی Seyed Mohammad Razavi دانشگاه بیرجند ایران smrazavi@birjand.ac.ir High-Level Synthesis Datapath Digital Transformers Meta-heuristic Algorithm MFO Algorithm سنتز سطح بالا مسیرداده مبدل‌های دیجیتال الگوریتم‌های فراابتکاری الگوریتم بهینه‌سازی شعله و پروانه [1] M. C. Bhuvaneswari, Application of Evolutionary Algorithms for Multi-Objective Optimization in VLSI and Embedded Systems. Springer, India 2015.##[2] S. Das, R. Maity, N. P. Maity, "VLSI-Based Pipeline Architecture for Reversible Image Watermarking by Difference Expansion with High-Level Synthesis Approach," Circuits, Systems, and Signal processing, vol. 37, no. 4, pp. 1575-1593, April 2018.##[3] D. Thomas, E. Lagnese, R.Walker, J. Nestor, J. Rajan, and R. Blackburn, Algorithmic and Register-Transfer Level Synthesis: The System Architect's Workbench. Kluwer, 1990.##[4] D. S. Harish Ram, M. C. Bhuvaneswari, S. S. Prabhu, "A Novel Framework for Applying Multiobjective GA and PSO Based Approaches for Simultaneous Area, Delay, and Power Optimization in High Level Synthesis of Datapaths," VLSI Design, vol. 2012, 2012.##[5] X. Tang, T. Jiang, A. Jones, P. Banerjee, "Behavioral synthesis of data-dominated circuits for minimal energy implementation," in Proc. of 18th the International Conference on VLSI Design,Jan. 2005, pp.3-7.##[6] N. Chabini, W. Wolf, "Unification of scheduling, binding, and retiming to reduce power consumption under timings and resources constraints," IEEE Transactions on VLSI Systems, vol. 13, no. 10, pp. 1113-1126, 2005.##[7] S. P. Mohanty, N. Ranganathan, S. K. Chappidi, "ILP models for simultaneous energy and transient power minimization during behavioral synthesis," ACM Transaction on Design Automation of Electronic Systems, vol. 11, no. 1, pp. 186-212, 2006.##[8] W. T. Shiue, "Peak power minimization using novel scheduling algorithm based on an ILP model," in Proc. of the 10th NASA Symposium on VLSI Design, 2002.##[9] A. Kumar, M. Bayoumi, "Multiple voltage-based scheduling methodology for low power in the high level synthesis," in Proc. of the International Symposium on Circuits and Systems (ISCAS), 1999, pp. 371-379.##[10] A. K. Murugavel and N. Ranganathan, "A game theoretic approach for power optimization during behavioral synthesis," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 11, no. 6, pp. 1031-1043, 2003.##[11] R. K. Brayton, R. Camposano, G. De Micheli, R. Otten, J. van Eijndhoven, "The Yorktown silicon compiler system," in Silicon Compilation, D. D. Gajski, Ed. Reading, MA: Addison-Wesley, pp. 204-310, 1988.##[12] O. V. Nepomnyashchiy, I. V. Ryjenko, V. V. Shaydurov, N. Y. Sirotinina A. I. Postnikov, "The VLSI High-Level Synthesis for Building Onboard Spacecraft Control Systems," in Anisimov K. et al. (eds) Proceedings of the Scientific-Practical Conference "Research and Development 2016", Springer, Cham, 2018, pp. 229-238.##[13] R. Gopalan, C. Gopalakrishnan, S. Katkoori, "Leakage power driven behavioral synthesis of pipelined datapaths," in IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pp. 167-172, 11-12May 2005.##[14] S. P. Mohanty, R. Velagapudi, E. Kougianos, "Physical-aware simulated annealing optimization of gate leakage in nanoscale datapath circuits," in Proc. of the Conference on Design, Automation and Test in Europe, 6-10 March 2006, pp. 1191-1196.##[15] F. Su, K. Chakrabarty, "Unified high-level synthesis and module placement for defecttolerant microfluidic biochips," in Proc. of the 42nd Annual Conference on Design automation, 13-17 June 2005, pp. 825-830.##[16] S. Devadas, A. R. Newton, "Algorithms for hardware allocation in data path synthesis," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 8, no. 7, pp. 768-781, 1989.##[17] J. A. Nestor, G. Krishnamoorthy, "SALSA: A new approach to scheduling with timing constraints," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 12, pp. 1107-1122, 1993.##[18] G. Krishnamoorthy, J. A. Nestor, "Data path allocation using extended binding model," in Proc. 29nd ACM/IEEE Design Automation Conf., 8-12 June 1992, pp. 279-284.##[19] T. A. Ly, J. T. Mowchenko, "Applying simulated evolution to high level synthesis," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 12, no. 3, pp. 389-409, 1993.##[20] S. Lucia, D. Navarro, O. Lucia, P. Zometa, R. Findeisen, "Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool," IEEE Trans. on Industrial Informatics, vol. 14, no. 1, pp. 137-145, Jan. 2018.##[21] G. De Micheli, Synthesis and Optimization of Digital Circuits. McGraw-Hill, New York 1994.##[22] R. Camposano, "Path-based scheduling for synthesis," IEEE Trans. Comput. -Aided Des., vol. 10, pp. 85-93, 1991.##[23] P. G. Paulin, J. P. Knight, "Force-directed scheduling for the behavioral synthesis of ASICs," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 8, no. 6, pp. 661-679, 1989.##[24] S. Gupta, S. Katkoori, "Force-directed scheduling for dynamic power optimization," in Proc. of the IEEE Computer Society Annual Symposium on VLSI, 25-26 April 2002, pp. 68-73.##[25] S. H. Gerez, Algorithms for VLSI Design Automation. Wiley, 2004.##[26] S. Katkoori, R. Vemuri, "Scheduling for low power under resource and latency constraints," in Proc. of the IEEE International Symposium on Circuits and Systems, 28-31 May 2000, pp. 53-56.##[27] A. C. Parker, J. T. Pizarro, M. Mlinar, "Maha: A program for datapath synthesis," in Proc. 23rd ACM/IEEE Design Automation Conf., 29 June- 2 July 1986, pp. 461-466.##[28] M. McFarland, T. J. Kowalski, "Incorporating bottom-up design into high-level synthesis," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 9, no. 9, pp. 938-950, Sep. 1990.##[29] M. A. Elgamel, M. Bayoumi, "On low-power high-level synthesis using genetic algorithms," in Proc. of the 9th International Conference on Electronics, Circuits and Systems, 15-18 Sept. 2002, pp. 725-728.##[30] G. W. Grewal, T. C. Wilson, "An enhanced genetic algorithm for solving the high-level synthesis problems of scheduling, allocation, and binding," International Journal of Computational Intelligence and Applications, vol. 1, pp. 91-110, 2001.##[31] V. Krishnan, S. Katkoori, "A genetic algorithm for the design space exploration of datapaths during high-level synthesis," IEEE Trans. Evol. Comput, vol. 10, no. 3, pp. 213-229, 2006.##[32] A. Sengupta, R. Sedaghat, "Integrated scheduling, allocation and binding in high level synthesis using multi structure Genetic Algorithm based design space exploration," in Proc. of the 12th International Symposium on Quality Electronic Design, 14-16 March 2011, pp. 1-9.##[33] C. Pilato, D. Loiacono, A. Tumeo, F. Ferrandi, P. L. Lanzi, D. Sciuto, "Speeding-up expensive evaluations in high-level synthesis using solution modeling and fitness inheritance," in Computational Intelligence in Expensive Optimization Problems, vol. 2, 2010, pp. 701-723.##[34] R. F. Abdel-kader, "Particle Swarm Optimization for Constrained Instruction Scheduling," VLSI design, vol. 2008, no. 4, January 2008.##[35] S. A. Hashemi, B. Nowrouzian, "A novel particle swarm optimization for high-level synthesis of digital filters," In Proc. of the 25th IEEE International Symposium on Circuits and Systems, 20-23 May 2012, pp. 580-583.##[36] A. Sengupta, S. Bhadauria, S. P. Mohanty, "TL-HLS: Methodology for Low Cost Hardware Trojan Security Aware Scheduling with Optimal Loop Unrolling Factor during High Level Synthesis," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 4, pp. 655-668, April 2017.##[37] S. Bhadauria, A. Sengupta, "Adaptive bacterial foraging driven datapath optimization: Exploring power-performance tradeoff in high level synthesis," Applied Mathematics and Computation, vol. 269, pp. 265-278, Oct. 2015.##[38] S. Rajmohan, N. Ramasubramanian, "A Memetic Algorithm based Design Space Exploration for Datapath Resource Allocation during High Level Synthesis," Journal of Circuits, Systems and Computers, 2019.##[39] S. Rajmohan, N. Ramasubramanian, "Group influence based improved firefly algorithm for Design Space Exploration of Datapath resource allocation," Applied Intelligence, vol. 49, no. 6, pp. 2084-2100, June 2019.##[40] C. Pilato, S. Garg, K. Wu, R. Karri, F. Regazzoni, "Securing Hardware Accelerators: A New Challenge for High-Level Synthesis," IEEE Embedded Systems Letters, vol. 10, no. 3, pp. 77-80, Sept. 2018.##[41] R. Nane et al., "A Survey and Evaluation of FPGA High-Level Synthesis Tools," IEEE Transactions on Computer-Aided Designe of Integrated Circuits and Systems, vol. 35, no. 10, pp. 1591-1604, Oct. 2016.##[42] N. S. Kim, J. Xiong, W. W. Hwu, "Heterogeneous Computing Meets Near-Memory Acceleration and High-Level Synthesis in the Post-Moore Era," IEEE Micro, vol. 37, no. 4, pp. 10-18, 2017.##[43] D. R. R. Freias, A. V. M. Inocencio, L. T. Lins, G. J. Alves, M. A. Bendetti, "A Parallel Implementation of the Discrete Wavelet Transform Applied to Real-Time EEG Signal Filtering," in XXVI Brazilian Congress on Biomedical Engineering, May 2019, pp. 17-23.##[44] C. Y. Pang, R. G. Zhou, B. Q. Hu, W. Hu, A. El-Rafei, "Signal and image compression using quantum discrete cosine transform," Information Science, vol. 473, pp. 121-141, January 2019.##[45] V. Mahale, M. M. H. Ali, P. L. Yannawar, A. Gaikwad, "Analysis of Image Inconsistency Based on Discrete Cosine Transform (DCT)," in Proc. of Information and Communictuin Technology for Intelligent Systems, Dec. 2018, vol. 1, pp. 563-571.##[46] A. N. Serov, A. A. Shatokhin, G. V. Antipov, "Sample Rate Converter As a Means of Reducing Measurement Error of the Voltage Spectrum by Application of FFT," in Proc. of 29th International Conference Radioelektronika (RADIOELEKTRONIKA), June. 2019.##[47] S. G. Maghrebi, F. K. Deylamani, "Using WPT as a New Method Instead of FFT for Improving the Performance of OFDM Modulation," Signal and Data Processing, vol. 16, no. 2, pp. 121-136, 2019.##[48] M. R. Esmaeili, SH. Zahiri, "Epileptic seizure detection using Inclined Planes system Optimization algorithm (IPO)," Signal and Data Processing, vol. 13, no. 4, pp. 29-42, 2017.##[49] S. P. Mohanty, N. Ranganathan, E. Kougianos, P. Patra, Low-Power High-Level Synthesis for Nanoscale CMOS Circuits. Springer US, India 2008.##[50] S. Mirjalili, "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm," in Knowledge-Based Systems, vol. 89, pp. 228-249, Nov. 2015.##[51] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. of IEEE Neural Networks, 1995, pp. 1942-1948.##[52] M. Jhamb, Garima, H. Loohani, "Design, implementation and performance comparison of multiplier topologies in power-delay space," in Engineering Science and Technology, an International Journal, vol. 19, no. 1, pp. 355-363, March 2016.##[1] M. C. Bhuvaneswari, Application of Evolutionary Algorithms for Multi-Objective Optimization in VLSI and Embedded Systems. Springer, India 2015.##[2] S. Das, R. Maity, N. P. Maity, "VLSI-Based Pipeline Architecture for Reversible Image Watermarking by Difference Expansion with High-Level Synthesis Approach," Circuits, Systems, and Signal processing, vol. 37, no. 4, pp. 1575-1593, April 2018.##[3] D. Thomas, E. Lagnese, R.Walker, J. Nestor, J. Rajan, and R. Blackburn, Algorithmic and Register-Transfer Level Synthesis: The System Architect's Workbench. Kluwer, 1990.##[4] D. S. Harish Ram, M. C. Bhuvaneswari, S. S. Prabhu, "A Novel Framework for Applying Multiobjective GA and PSO Based Approaches for Simultaneous Area, Delay, and Power Optimization in High Level Synthesis of Datapaths," VLSI Design, vol. 2012, 2012.##[5] X. Tang, T. Jiang, A. Jones, P. Banerjee, "Behavioral synthesis of data-dominated circuits for minimal energy implementation," in Proc. of 18th the International Conference on VLSI Design,Jan. 2005, pp.3-7.##[6] N. Chabini, W. Wolf, "Unification of scheduling, binding, and retiming to reduce power consumption under timings and resources constraints," IEEE Transactions on VLSI Systems, vol. 13, no. 10, pp. 1113-1126, 2005.##[7] S. P. Mohanty, N. Ranganathan, S. K. Chappidi, "ILP models for simultaneous energy and transient power minimization during behavioral synthesis," ACM Transaction on Design Automation of Electronic Systems, vol. 11, no. 1, pp. 186-212, 2006.##[8] W. T. Shiue, "Peak power minimization using novel scheduling algorithm based on an ILP model," in Proc. of the 10th NASA Symposium on VLSI Design, 2002.##[9] A. Kumar, M. Bayoumi, "Multiple voltage-based scheduling methodology for low power in the high level synthesis," in Proc. of the International Symposium on Circuits and Systems (ISCAS), 1999, pp. 371-379.##[10] A. K. Murugavel and N. Ranganathan, "A game theoretic approach for power optimization during behavioral synthesis," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 11, no. 6, pp. 1031-1043, 2003.##[11] R. K. Brayton, R. Camposano, G. De Micheli, R. Otten, J. van Eijndhoven, "The Yorktown silicon compiler system," in Silicon Compilation, D. D. Gajski, Ed. Reading, MA: Addison-Wesley, pp. 204-310, 1988.##[12] O. V. Nepomnyashchiy, I. V. Ryjenko, V. V. Shaydurov, N. Y. Sirotinina A. I. Postnikov, "The VLSI High-Level Synthesis for Building Onboard Spacecraft Control Systems," in Anisimov K. et al. (eds) Proceedings of the Scientific-Practical Conference "Research and Development 2016", Springer, Cham, 2018, pp. 229-238.##[13] R. Gopalan, C. Gopalakrishnan, S. Katkoori, "Leakage power driven behavioral synthesis of pipelined datapaths," in IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pp. 167-172, 11-12May 2005.##[14] S. P. Mohanty, R. Velagapudi, E. Kougianos, "Physical-aware simulated annealing optimization of gate leakage in nanoscale datapath circuits," in Proc. of the Conference on Design, Automation and Test in Europe, 6-10 March 2006, pp. 1191-1196.##[15] F. Su, K. Chakrabarty, "Unified high-level synthesis and module placement for defecttolerant microfluidic biochips," in Proc. of the 42nd Annual Conference on Design automation, 13-17 June 2005, pp. 825-830.##[16] S. Devadas, A. R. Newton, "Algorithms for hardware allocation in data path synthesis," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 8, no. 7, pp. 768-781, 1989.##[17] J. A. Nestor, G. Krishnamoorthy, "SALSA: A new approach to scheduling with timing constraints," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 12, pp. 1107-1122, 1993.##[18] G. Krishnamoorthy, J. A. Nestor, "Data path allocation using extended binding model," in Proc. 29nd ACM/IEEE Design Automation Conf., 8-12 June 1992, pp. 279-284.##[19] T. A. Ly, J. T. Mowchenko, "Applying simulated evolution to high level synthesis," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 12, no. 3, pp. 389-409, 1993.##[20] S. Lucia, D. Navarro, O. Lucia, P. Zometa, R. Findeisen, "Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool," IEEE Trans. on Industrial Informatics, vol. 14, no. 1, pp. 137-145, Jan. 2018.##[21] G. De Micheli, Synthesis and Optimization of Digital Circuits. McGraw-Hill, New York 1994.##[22] R. Camposano, "Path-based scheduling for synthesis," IEEE Trans. Comput. -Aided Des., vol. 10, pp. 85-93, 1991.##[23] P. G. Paulin, J. P. Knight, "Force-directed scheduling for the behavioral synthesis of ASICs," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 8, no. 6, pp. 661-679, 1989.##[24] S. Gupta, S. Katkoori, "Force-directed scheduling for dynamic power optimization," in Proc. of the IEEE Computer Society Annual Symposium on VLSI, 25-26 April 2002, pp. 68-73.##[25] S. H. Gerez, Algorithms for VLSI Design Automation. Wiley, 2004.##[26] S. Katkoori, R. Vemuri, "Scheduling for low power under resource and latency constraints," in Proc. of the IEEE International Symposium on Circuits and Systems, 28-31 May 2000, pp. 53-56.##[27] A. C. Parker, J. T. Pizarro, M. Mlinar, "Maha: A program for datapath synthesis," in Proc. 23rd ACM/IEEE Design Automation Conf., 29 June- 2 July 1986, pp. 461-466.##[28] M. McFarland, T. J. Kowalski, "Incorporating bottom-up design into high-level synthesis," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 9, no. 9, pp. 938-950, Sep. 1990.##[29] M. A. Elgamel, M. Bayoumi, "On low-power high-level synthesis using genetic algorithms," in Proc. of the 9th International Conference on Electronics, Circuits and Systems, 15-18 Sept. 2002, pp. 725-728.##[30] G. W. Grewal, T. C. Wilson, "An enhanced genetic algorithm for solving the high-level synthesis problems of scheduling, allocation, and binding," International Journal of Computational Intelligence and Applications, vol. 1, pp. 91-110, 2001.##[31] V. Krishnan, S. Katkoori, "A genetic algorithm for the design space exploration of datapaths during high-level synthesis," IEEE Trans. Evol. Comput, vol. 10, no. 3, pp. 213-229, 2006.##[32] A. Sengupta, R. Sedaghat, "Integrated scheduling, allocation and binding in high level synthesis using multi structure Genetic Algorithm based design space exploration," in Proc. of the 12th International Symposium on Quality Electronic Design, 14-16 March 2011, pp. 1-9.##[33] C. Pilato, D. Loiacono, A. Tumeo, F. Ferrandi, P. L. Lanzi, D. Sciuto, "Speeding-up expensive evaluations in high-level synthesis using solution modeling and fitness inheritance," in Computational Intelligence in Expensive Optimization Problems, vol. 2, 2010, pp. 701-723.##[34] R. F. Abdel-kader, "Particle Swarm Optimization for Constrained Instruction Scheduling," VLSI design, vol. 2008, no. 4, January 2008.##[35] S. A. Hashemi, B. Nowrouzian, "A novel particle swarm optimization for high-level synthesis of digital filters," In Proc. of the 25th IEEE International Symposium on Circuits and Systems, 20-23 May 2012, pp. 580-583.##[36] A. Sengupta, S. Bhadauria, S. P. Mohanty, "TL-HLS: Methodology for Low Cost Hardware Trojan Security Aware Scheduling with Optimal Loop Unrolling Factor during High Level Synthesis," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 4, pp. 655-668, April 2017.##[37] S. Bhadauria, A. Sengupta, "Adaptive bacterial foraging driven datapath optimization: Exploring power-performance tradeoff in high level synthesis," Applied Mathematics and Computation, vol. 269, pp. 265-278, Oct. 2015.##[38] S. Rajmohan, N. Ramasubramanian, "A Memetic Algorithm based Design Space Exploration for Datapath Resource Allocation during High Level Synthesis," Journal of Circuits, Systems and Computers, 2019.##[39] S. Rajmohan, N. Ramasubramanian, "Group influence based improved firefly algorithm for Design Space Exploration of Datapath resource allocation," Applied Intelligence, vol. 49, no. 6, pp. 2084-2100, June 2019.##[40] C. Pilato, S. Garg, K. Wu, R. Karri, F. Regazzoni, "Securing Hardware Accelerators: A New Challenge for High-Level Synthesis," IEEE Embedded Systems Letters, vol. 10, no. 3, pp. 77-80, Sept. 2018.##[41] R. Nane et al., "A Survey and Evaluation of FPGA High-Level Synthesis Tools," IEEE Transactions on Computer-Aided Designe of Integrated Circuits and Systems, vol. 35, no. 10, pp. 1591-1604, Oct. 2016.##[42] N. S. Kim, J. Xiong, W. W. Hwu, "Heterogeneous Computing Meets Near-Memory Acceleration and High-Level Synthesis in the Post-Moore Era," IEEE Micro, vol. 37, no. 4, pp. 10-18, 2017.##[43] D. R. R. Freias, A. V. M. Inocencio, L. T. Lins, G. J. Alves, M. A. Bendetti, "A Parallel Implementation of the Discrete Wavelet Transform Applied to Real-Time EEG Signal Filtering," in XXVI Brazilian Congress on Biomedical Engineering, May 2019, pp. 17-23.##[44] C. Y. Pang, R. G. Zhou, B. Q. Hu, W. Hu, A. El-Rafei, "Signal and image compression using quantum discrete cosine transform," Information Science, vol. 473, pp. 121-141, January 2019.##[45] V. Mahale, M. M. H. Ali, P. L. Yannawar, A. Gaikwad, "Analysis of Image Inconsistency Based on Discrete Cosine Transform (DCT)," in Proc. of Information and Communictuin Technology for Intelligent Systems, Dec. 2018, vol. 1, pp. 563-571.##[46] A. N. Serov, A. A. Shatokhin, G. V. Antipov, "Sample Rate Converter As a Means of Reducing Measurement Error of the Voltage Spectrum by Application of FFT," in Proc. of 29th International Conference Radioelektronika (RADIOELEKTRONIKA), June. 2019.##[47] سعید قاضی مغربی، فربیان خردادپور دیلمانی، "استفاده از تبدیل موجک در بهبود عملکرد OFDM به جای روش مرسوم مبتنی بر "FFT، پردازش علائم و داده‌ها، دوره 16، شماره 2، ص. 121-136، سال 1398.##[47] S. G. Maghrebi, F. K. 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یک مدل تحلیلی برای پیش‌بینی رفتار همگرایی الگوریتم حداقل میانگین ترکیب نُرم (LMMN) An Analytical Model for Predicting the Convergence Behavior of the Least Mean Mixed-Norm (LMMN) Algorithm 1 1 الگوریتم کمینه میانگین ترکیب نُرم (LMMN)، الگوریتمی مبتنی بر شیب تصادفی خطا است که هدف آن کمینه‌‌سازی ترکیبی از توابع هزینه الگوریتم‌های کمینه میانگین مربعات (LMS) و کمینه میانگین چهارم (LMF) است. این الگوریتم بسیاری از ویژگی‌ها و مزایای الگوریتم‌های LMS و LMF را با خود به ارث برده است و از جهاتی ضعف‌های این دو الگوریتم را هم برطرف کرده است. بزرگ‌ترین مشکل الگوریتم LMMN فقدان یک مدل تحلیلی برای پیش‌بینی رفتار آن است، به‌طوری‌که کاربرد عملی آن‌را محدود کرده است. ما در این مقاله باهدف حل این مشکل، مدلی تحلیلی را ارائه می‌کنیم که قادر است رفتار میانگین مربعات خطا و میانگین خطای وزن‌ها را با دقت بالایی پیش‌بینی کند. دقت مدل استخراج‌شده از طریق آزمایش‌های متعددی تأیید می‌شود. 2 Stochastic gradient-based adaptation algorithms have received a great attention in various applications. The most well-known algorithm in this category is the Least Mean Squares (LMS) algorithm that tries to minimize the second-order criterion of mean squares of the error signal. On the other hand, it has been shown that higher-order adaptive filtering algorithms based on higher-order statistics can perform better in many applications, particularly in the presence of intense noises. However, these algorithms are more prone to instability and also their convergence rates decline in the vicinity of their optimum solutions. In attempt to make use of the useful aspects of these algorithms, it has been proposed to combine the second-order criterion with higher-order ones, e.g. that of the Least Mean Fourth (LMF) algorithm. The Least Mean Mixed-Norm (LMMN) algorithm is a stochastic gradient-based algorithm which aim is to minimize an affine combination of the cost functions of the LMS and LMF algorithms. This algorithm has inherited many properties and advantages of the LMS and the LMF algorithms and mitigated their weaknesses in some ways. These advantages are achieved at the cost of the additional computation burden of just one addition and four multiplications per iteration. The main issue of the LMMN algorithm is the lack of an analytical model for predicting its behaviour, the fact that has restricted its practical application. To address this issue, an analytical model is presented in the current paper that is able to predict the mean-square-error and the mean-weights-error behaviour with a high accuracy. This model is derived using the Isserlis’ theorem, based on two mild and practically valid assumptions; namely the input signal is stationary, zero-mean Gaussian and the measurement noise are additive zero-mean with an even probability distribution function (pdf). The accuracy of the derived model is verified using several simulation tests. These results show that the model is of a high accuracy in various settings for the noise’s power level and distribution as well as the unknown filter characteristics. Furthermore, since the LMF and the LMS algorithms are special cases of the more general LMMN algorithm, the proposed model can also be used for predicting the behaviour of these algorithms. 19 28 2019/03/52019/04/28 1398/2/8 2020/01/222020/01/22 1398/11/2 میثم کاظمی اقبال Mesyam Kazemi Eghbal دانشگاه صنعتی همدان ایران m.eghbal@stu.hut.ac.ir قاسم علی‌پور Ghasem Alipoor دانشگاه صنعتی همدان ایران alipoor@hut.ac.ir Adaptive Algorithms LMMN Algorithm Analytical Model الگوریتم‌های وفقی الگوریتم LMMN مدل تحلیلی [1] B. Farhang-Boroujeny, Adaptive filters: theory and applications: John Wiley & Sons, 2013.##[2] S. S. Haykin, Adaptive filter theory: Pearson Education India, 2008.##[3] D. G. Manolakis, V. K. Ingle, and S. M. Kogon, "Statistical and adaptive signal processing: spectral estimation," signal modeling, adaptive filtering, and array processing: McGraw-Hill Boston, 2000.##[4] S. D. J. T. i. s. i. E. Paulo, and C. Scienc, "Adaptive filtering: algorithms and practical implementation," pp. 23-50, 2008.##[5] N. J. Bershad, and J. C. J. D. S. P. Bermudez, "Stochastic analysis of the least mean kurtosis algorithm for Gaussian inputs," vol. 54, pp. 35-45, 2016.##[6] J. Chambers, O. Tanrikulu, and A. J. E. l. Constantinides, "Least mean mixed-norm adaptive filtering," vol. 30, no. 19, pp. 1574-1575, 1994.##[7] B. Chen, L. Xing, J. Liang, N. Zheng, and J. C. J. I. s. p. l. Principe, "Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion," vol. 21, no. 7, pp. 880-884, 2014.##[8] E. Eweda, N. J. Bershad, J. C. J. S. Bermudez, "Stochastic analysis of the least mean fourth algorithm for non-stationary white Gaussian inputs," Image, and V. Processing, vol. 8, no. 1, pp. 133-142, 2014.##[9] P. I. Hübscher, J. C. M. Bermudez, and V. H. J. I. T. S. P. Nascimento, "A mean-square stability analysis of the least mean fourth (LMF) adaptive algorithm," vol. 55, no. 8, pp. 4018-4028, 2007.##[10] S.-C. Pei, and C.-C. J. I. J. o. S. A. i. C. Tseng, "Least mean p-power error criterion for adaptive FIR filter," vol. 12, no. 9, pp. 1540-1547, 1994.##[11] Y. Zou, S.-C. Chan, T.-S. J. I. T. o. C. Ng, S. I. "Least mean M-estimate algorithms for robust adaptive filtering in impulse noise," Analog, and D. S. Processing,vol. 47, no. 12, pp. 1564-1569, 2000.##[12] O. Tanrikulu, J. J. I. P.-V. Chambers, "Convergence and steady-state properties of the least-mean mixed-norm (LMMN) adaptive algorithm," Image, and S. Processing, vol. 143, no. 3, pp. 137-142, 1996.##[1] B. Farhang-Boroujeny, Adaptive filters: theory and applications: John Wiley & Sons, 2013.##[2] S. S. Haykin, Adaptive filter theory: Pearson Education India, 2008.##[3] D. G. Manolakis, V. K. Ingle, and S. M. Kogon, "Statistical and adaptive signal processing: spectral estimation," signal modeling, adaptive filtering, and array processing: McGraw-Hill Boston, 2000.##[4] S. D. J. T. i. s. i. E. Paulo, and C. Scienc, "Adaptive filtering: algorithms and practical implementation," pp. 23-50, 2008.##[5] N. J. Bershad, and J. C. J. D. S. P. Bermudez, "Stochastic analysis of the least mean kurtosis algorithm for Gaussian inputs," vol. 54, pp. 35-45, 2016.##[6] J. Chambers, O. Tanrikulu, and A. J. E. l. Constantinides, "Least mean mixed-norm adaptive filtering," vol. 30, no. 19, pp. 1574-1575, 1994.##[7] B. Chen, L. Xing, J. Liang, N. Zheng, and J. C. J. I. s. p. l. Principe, "Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion," vol. 21, no. 7, pp. 880-884, 2014.##[8] E. Eweda, N. J. Bershad, J. C. J. S. Bermudez, "Stochastic analysis of the least mean fourth algorithm for non-stationary white Gaussian inputs," Image, and V. Processing, vol. 8, no. 1, pp. 133-142, 2014.##[9] P. I. Hübscher, J. C. M. Bermudez, and V. H. J. I. T. S. P. Nascimento, "A mean-square stability analysis of the least mean fourth (LMF) adaptive algorithm," vol. 55, no. 8, pp. 4018-4028, 2007.##[10] S.-C. Pei, and C.-C. J. I. J. o. S. A. i. C. Tseng, "Least mean p-power error criterion for adaptive FIR filter," vol. 12, no. 9, pp. 1540-1547, 1994.##[11] Y. Zou, S.-C. Chan, T.-S. J. I. T. o. C. Ng, S. I. "Least mean M-estimate algorithms for robust adaptive filtering in impulse noise," Analog, and D. S. Processing,vol. 47, no. 12, pp. 1564-1569, 2000.##[12] O. Tanrikulu, J. J. I. P.-V. Chambers, "Convergence and steady-state properties of the least-mean mixed-norm (LMMN) adaptive algorithm," Image, and S. Processing, vol. 143, no. 3, pp. 137-142, 1996.## ##

شناسایی باج‌افزارها و خانواده آن‌ها با بهره‌گیری از روش کاوش الگوهای متوالی در تحلیل پویا Detecting Ransomware and Identifying their Families Using Sequence Mining in Dynamic Analysis 1 1 امروزه باج‌افزارهای رمز‌کننده تبدیل به یکی از مهم‌ترین تهدیدات حوزه سایبری شده است. یک باج‌افزار رمزکننده با رمز‌کردن داده‌های با ارزش قربانی، دسترسی به داده‌ها را از بین می‌برد و در ازای رمزگشایی آن‌ها درخواست پرداخت باج می‌کند. به‌علت نوظهور‌بودن باج‌افزارهای رمزکننده، پژوهش چندانی در جهت شناسایی آن‌ها انجام نشده است و بیش‌تر پژوهش‌های مرتبط روی سیستم فایل و نظارت بر رفتار فرآیندها روی فایل‌ها انجام شده است. از آن‌جایی که سرعت در تشخیص باج‌افزارها اهمیت فراوانی دارد، تمرکز این مقاله روی تشخیص دقیق و زودهنگام باج‌افزارها بر اساس تحلیل لاگ‌های رفتاری است. در این مقاله، ابتدا محیط آزمایشی مناسبی را ایجاد می‌کنیم تا بتوانیم رفتار 572 نمونه باج‌افزار از خانواده TeslaCrypt، 535 نمونه باج‌افزار از خانواده Cerber و 517 نمونه باج‌افزار از خانواده Locky را ثبت کنیم که محیط مهیا شده قابلیت کاربرد در سایر پروژه‌ها و پژوهش‌های مشابه را دارد. برای دسته‌بندی و شناسایی نمونه‌های باج‌افزار، با بهره‌گیری از روش کاوش الگوهای متوالی، ویژگی‌هایی را به‌دست می‌آوریم تا قابل استفاده برای الگوریتم‌های دسته‌بندی‌کننده یادگیری ماشین باشد. دقت 99% در تشخیص نمونه‌های باج‌افزار و همین طور دقت 96.5% در شناسایی و دسته‌بندی خانواده آن‌ها روی الگوریتم‌های متداول یادگیری ماشین نشان از کیفیت  بالای ویژگی‌های پیشنهادی دارد. 2 Nowadays, crypto-ransomware is considered as one of the most threats in cybersecurity. Crypto ransomware removes data access by encrypting valuable data and requests a ransom payment to allow data decryption. The number of Crypto ransomware variants has increased rapidly every year, and ransomware needs to be distinguished from the goodware types and other types of ransomware to protect users' machines from ransomware-based attacks. Most published works considered System File and process behavior to identify ransomware which depend on how quickly and accurately system logs can be obtained and mined to detect abnormalities. Due to the severity of irreparable damage of ransomware attacks, timely detection of ransomware is of great importance. This paper focuses on the early detection of ransomware samples by analyzing behavioral logs of programs executing on the operating system before the malicious program destroy all the files. Sequential Pattern Mining is utilized to find Maximal Sequential Patterns of activities within different ransomware families as candidate features for classification. First, we prepare our test environment to execute and collect activity logs of 572 TeslaCrypt samples, 535 Cerber ransomware, and 517 Locky ransomware samples. Our testbed has the capability to be used in other projects where the automatic execution of malware samples is essential. Then, we extracted valuable features from the output of the Sequence Mining technique to train a classification algorithm for detecting ransomware samples. 99% accuracy in detecting ransomware instances from benign samples and 96.5% accuracy in detecting family of a given ransomware sample proves the usefulness and practicality of our proposed methods in detecting ransomware samples. 29 44 2019/03/52019/04/282019/04/28 1398/2/8 2020/01/222020/01/222020/08/18 1399/5/28 حمید دارابیان hamid darabian دانشگاه شیراز ایران h.darabian@cse.shirazu.ac.ir ستار هاشمی sattar Hashemi دانشگاه شیراز ایران s_hashemi@shirazu.ac.ir سجاد همایون sajad Homayoon دانشگاه صنعتی شیراز ایران s.homayoun@sutech.ac.ir کرم الله باقری فرد Karamollah Bagherifard باشگاه پژوهش‌گران جوان و نخبگان، واحد یاسوج، دانشگاه آزاد اسلامی ایران k.bagheri@iauyasooj.ac.ir malware ransomware crypto ransomware ransomware detection ransomware family detection بدافزار باج‌افزار باج‌افزار رمز‌کننده شناسایی باج‌افزار شناسایی خانواده باج‌افزار [1] M. Hopkins and A. Dehghantanha, "Exploit Kits: The production line of the Cybercrime economy?," in 2015 2nd International Conference on Information Security and Cyber Forensics, InfoSec 2015, 2016, pp. 23-27.##[2] Hosseini F, Mirzarezaee M, Sharifi A, "Malware Detection using Classification of Variable-Length Sequences," JSDP, vol. 16 (2), pp.137-146, 2019##[3] Symantec, "Internet Security Threat Report (ISTR)," no. April. p. 10, 2017.##[4] D. Palmer, "How Bitcoin helped fuel an explosion in ransomware attacks," 2016. [Online]. Available: http://www.zdnet.com/article/how-bitcoin-helped-fuel-an-explosion-in-ransomware-attacks/.##[5] A. Azmoodeh, A. Dehghantanha, M. Conti, and K.-K. R. Choo, "Detecting crypto-ransomware in IoT networks based on energy consumption footprint," J. Ambient Intell. Humaniz. Comput., Aug. 2017.##[6] R. Richardson and M. M. North, "Ransomware : Evolution , Mitigation and Prevention," Int. Manag. Rev., vol. 13, no. 1, pp. 10-21, Jan. 2017.##[7] K. Savage, P. Coogan, and H. Lau, "The Evolution of Ransomware," Res. Manag., vol. 54, no. 5, pp. 59-63, 2015.##[8] Monika, P. Zavarsky, and D. Lindskog, "Experimental Analysis of Ransomware on Windows and Android Platforms: Evolution and Characterization," in Procedia Computer Science, 2016, vol. 94, pp. 465-472.##[9] E. Kirda, "UNVEIL: A large-scale, automated approach to detecting ransomware (keynote)," in usenix.org, 2017, pp. 1-1.##[10] N. Scaife, H. Carter, P. Traynor, and K. R. B. Butler, "CryptoLock (and Drop It): Stopping Ransomware Attacks on User Data," in Proceedings - International Conference on Distributed Computing Systems, 2016, vol. Aug2016, pp. 303-312.##[11] A. Continella et al., "ShieldFS," in Proceedings of the 32nd Annual Conference on Computer Security Applications - ACSAC 16, 2016, pp. 336-347.##[12] A. Palisse, A. Durand, H. Le Bouder, C. Le Guernic, and J. L. Lanet, "Data aware defense (DaD): Towards a generic and practical ransomware countermeasure," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10674 LNCS, pp. 192-208.##[13] D. Sgandurra, L. Muñoz-González, R. Mohsen, and E. C. Lupu, "Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection," undefined, 2016.##[14] A. Kharraz and E. Kirda, "Redemption: Real-Time Protection Against Ransomware at End-Hosts," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10453 LNCS, pp. 98-119.##[15] Z. He, X. Xu, J. Z. Huang, and S. Deng, "A Frequent Pattern Discovery Method for Outlier Detection," Springer, Berlin, Heidelberg, 2010, pp. 726-732.##[16] R. Agrawal and R. Srikant, "Mining Sequential Patterns," in Proceedings of the Eleventh International Conference on Data Engineering, 1995, pp. 3-14.##[17] C. H. Mooney and J. F. Roddick, "Sequential pattern mining -- approaches and algorithms," ACM Comput. Surv., vol. 45, no. 2, pp. 1-39, Feb. 2013.##[18] Andrej Karpathy, "The Unreasonable Effectiveness of Recurrent Neural Networks," 2015. [Online]. Available: http://karpath-y.github.io/2015/05/21/rnn-effectiveness/. [Accessed: 30-May-2019].##[19] "What is Apache MapReduce? | IBM." [Online]. Available: https://www.ibm.com/-analytics/hadoop/mapreduce. [Accessed: 30-May-2019].##[20] J. A. K. Suykens, "Introduction to Machine Learning," 2014, pp. 765-773.##[21] M. Sohrabi, M. M. Javidi, and S. Hashemi, "Detecting intrusion transactions in database systems: A novel approach," J. Intell. Inf. Syst., vol. 42, no. 3, pp. 619-644, Jun. 2014.##[22] S. Boughorbel, F. Jarray, and M. El-Anbari, "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLoS One, vol. 12, no. 6, pp. e0177678, Jun. 2017.##[23] D. M. W. Powers, "Evaluation: From precision, recall and fmeasure to roc, informedness, markedness and correlation," J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37-63, 2007.##[24] A. Hall, Mark, "Correlation-based feature selection for machine learning‌," 1999.##[1] M. Hopkins and A. Dehghantanha, "Exploit Kits: The production line of the Cybercrime economy?," in 2015 2nd International Conference on Information Security and Cyber Forensics, InfoSec 2015, 2016, pp. 23-27.##[2] Hosseini F, Mirzarezaee M, Sharifi A, "Malware Detection using Classification of Variable-Length Sequences," JSDP, vol. 16 (2), pp.137-146, 2019##[2] حسینی فاطمه، میرزارضایی میترا، شریفی آرش. آشکارسازی بدافزارها با استفاده از دسته‌بندی دنباله‌های با طول متغیر. پردازش علائم و داده‌ها. 1398;(2)16;137-146##[3] Symantec, "Internet Security Threat Report (ISTR)," no. April. p. 10, 2017.##[4] D. Palmer, "How Bitcoin helped fuel an explosion in ransomware attacks," 2016. [Online]. Available: http://www.zdnet.com/article/how-bitcoin-helped-fuel-an-explosion-in-ransomware-attacks/.##[5] A. Azmoodeh, A. Dehghantanha, M. Conti, and K.-K. R. Choo, "Detecting crypto-ransomware in IoT networks based on energy consumption footprint," J. Ambient Intell. Humaniz. Comput., Aug. 2017.##[6] R. Richardson and M. M. North, "Ransomware : Evolution , Mitigation and Prevention," Int. Manag. Rev., vol. 13, no. 1, pp. 10-21, Jan. 2017.##[7] K. Savage, P. Coogan, and H. Lau, "The Evolution of Ransomware," Res. Manag., vol. 54, no. 5, pp. 59-63, 2015.##[8] Monika, P. Zavarsky, and D. Lindskog, "Experimental Analysis of Ransomware on Windows and Android Platforms: Evolution and Characterization," in Procedia Computer Science, 2016, vol. 94, pp. 465-472.##[9] E. Kirda, "UNVEIL: A large-scale, automated approach to detecting ransomware (keynote)," in usenix.org, 2017, pp. 1-1.##[10] N. Scaife, H. Carter, P. Traynor, and K. R. B. Butler, "CryptoLock (and Drop It): Stopping Ransomware Attacks on User Data," in Proceedings - International Conference on Distributed Computing Systems, 2016, vol. Aug2016, pp. 303-312.##[11] A. Continella et al., "ShieldFS," in Proceedings of the 32nd Annual Conference on Computer Security Applications - ACSAC 16, 2016, pp. 336-347.##[12] A. Palisse, A. Durand, H. Le Bouder, C. Le Guernic, and J. L. Lanet, "Data aware defense (DaD): Towards a generic and practical ransomware countermeasure," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10674 LNCS, pp. 192-208.##[13] D. Sgandurra, L. Muñoz-González, R. Mohsen, and E. C. Lupu, "Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection," undefined, 2016.##[14] A. Kharraz and E. Kirda, "Redemption: Real-Time Protection Against Ransomware at End-Hosts," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10453 LNCS, pp. 98-119.##[15] Z. He, X. Xu, J. Z. Huang, and S. Deng, "A Frequent Pattern Discovery Method for Outlier Detection," Springer, Berlin, Heidelberg, 2010, pp. 726-732.##[16] R. Agrawal and R. 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آنالیز و بررسی ویژگی‌های ساختاری در تشخیص مکالمه‌های شایعه توییتر Analysis of Structural Features in Rumor Conversations Detection in Twitter 1 1 توییتر یکی از محبوب‌ترین و مشهورترین شبکه‌های اجتماعی برخط برای گسترش اطلاعات است که در عین قابل ‌اعتماد‌بودن، می‌تواند به‌عنوان منبعی برای گسترش شایعات باشد. شایعاتی غیرواقعی و فریبنده که می‌تواند تأثیرات جبران‌ناپذیری برروی افراد و جامعه به‌وجود بیاورد. در این پژوهش مجموعه کاملی از ویژگی‌های جدید ساختاری مربوط به درخت پاسخ و گراف کاربران در تشخیص مکالمه‌های شایعه توییتر استخراج شدند. این ویژگی‌ها با توجه به معیارهای سنتی گراف‌ها و معیارهای مخصوص انتشار شایعه، در بازه‌های زمانی مختلف به مدت 24 ساعت از زمان شروع مکالمه‌ها در‌خصوص رویدادهای بحرانی در توییتر استخراج شده‌اند.  نتایج حاصل از بررسی ویژگی‌های جدید، دیدگاه عمیقی از ساختار انتشار اطلاعات در مکالمه‌ها را فراهم می‌کند. بر‌اساس نتایج به‌دست‌آمده، ویژگی‌های جدید ساختاری در تشخیص مکالمه‌های شایعه در رویدادهای توییتر مؤثر هستند؛ ازاین‌رو، الگوریتم دسته‌بند شایعه مبتنی بر ویژگی‌های جدید ساختاری، زبانی و کاربران در تشخیص مکالمه‌های شایعه زبان انگلیسی توییتر ، پیشنهاد داده شد. روش پیشنهادی در مقایسه با روش‌های پایه، عملکرد بهتری دارد. همچنین، با توجه به اهمیت کاربر توییت منبع در مکالمه‌ها، این کاربر از جنبه‌های مختلفی موردبررسی و آنالیز قرار گرفت. 2 Today, online social media with numerous users from ordinary citizens to top government officials, organizations, artists and celebrities, etc. is one of the most important platforms for sharing information and communication. These media provide users with quick and easy access to information so that the content of shared posts has the potential to reach millions of users in a matter of seconds. Twitter is one of the most popular and practical/used online social networks for spreading information, which, while being reliable, can also, be a source for spreading unrealistic and deceptive rumors as a result can have irreversible effects on individuals and society. Recently, several studies have been conducted in the field of rumor detection and verify using models based on deep learning and machine learning methods. Previous research into rumor detection has focused more on linguistic, user, and structural features. Concerning structural features, they examined the retweet propagation graph. However, in this study, unlike the previous studies, new structural features of the reply tree and user graph in extracting rumored conversations were extracted and analyzed from different aspects. In this study, the effectiveness of new structural features related to reply tree and user graph in detecting rumored conversations in Twitter events were evaluated from different aspects. First, the structural features of the reply tree and user graph were extracted at different time intervals, and important features in these intervals were identified using the Sequential Forward Selection approach. To evaluate the usefulness of valuable new structural features, these features have been compared with consideration of linguistic and user-specific features. Experiments have shown that combining new structural features with linguistic and user-specific features increases the accuracy of the rumor detection classification. Therefore, a rumor classification algorithm based on new structural, linguistic, and user-specific features in rumor conversation detection was proposed.  This algorithm performs better than the basic methods and detects rumored conversations with greater accuracy. In addition, due to the importance of the source tweet user in conversations, this user was examined and analyzed from different aspects. The results showed that most rumored conversations were started by a small number of users. Rumors can be prevented by early identification of these users on Twitter events. 45 64 2019/03/52019/04/282019/04/282020/04/3 1399/1/15 2020/01/222020/01/222020/08/182021/03/1 1399/12/11 سروه لطفی Serveh Lotfi دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران ایران serveh.lotfi@srbiau.ac.ir میترا میرزارضایی Mitra Mirzarezaee دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران ایران mirzarezaee@srbiau.ac.ir مهدی حسین زاده mehdi Hosseinzadeh دانشگاه علوم پزشکی ایران ایران hosseinzadeh.m@iums.ac.ir وحید صیدی Vahid Seydi دانشگاه آزاد اسلامی، واحد تهران جنوب ایران v_seydi@azad.ac.ir Conversion Rumor detection Twitter Reply tree User graph مکالمه تشخیص شایعه توییتر درخت پاسخ گراف کاربران [1] A. Java, X. Song, T. Finin, and B. Tseng, "Why we twitter: understanding microblogging usage and communities," in Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, 2007, pp. 56-65.##[2] K. Starbird, L. Palen, A. L. Hughes, and S. Vieweg, "Chatter on the red: what hazards threat reveals about the social life of microblogged information," in Proceedings of the 2010 ACM conference on Computer supported cooperative work, 2010, pp. 241-250.##[3] G. Cai, H. Wu, and R. Lv, "Rumors detection in chinese via crowd responses," in 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014, pp. 912-917.##[4] A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, "Detection and resolution of rumours in social media: A survey," ACM Computing Surveys (CSUR), vol. 51, pp. 1-36, 2018.##[5] A. Ritter, C. Cherry, and B. Dolan, "Unsupervised modeling of twitter conversations," in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010, pp. 172-180.##[6] J. A. Reshi and R. Ali, "Rumor proliferation and detection in Social Media: A Review," in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019, pp. 1156-1160.##[7] S. M. Alzanin and A. M. Azmi, "Detecting rumors in social media: A survey," Procedia computer science, vol. 142, pp. 294-300, 2018.##[8] A. Bondielli and F. Marcelloni, "A survey on fake news and rumour detection techniques," Information Sciences, vol. 497, pp. 38-55, 2019.##[9] J. Cao, J. Guo, X. Li, Z. Jin, H. Guo, and J. Li, "Automatic rumor detection on microblogs: A survey," arXiv preprint arXiv:1807.03505, 2018.##[10] A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, "Detection and resolution of rumours in social media: A survey," arXiv preprint arXiv:1704.00656, 2017.##[11] C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on twitter," in Proceedings of the 20th international conference on World wide web, 2011, pp. 675-684.##[12] S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, "Prominent features of rumor propagation in online social media," in Data Mining (ICDM), 2013 IEEE 13th International Conference on, 2013, 2013, pp. 1103-1108.##[13] F. Yang, Y. Liu, X. Yu, and M. Yang, "Automatic detection of rumor on sina weibo," in Proceedings of the ACM SIGKDD workshop on mining data semantics, 2012, pp. 1-7.##[14] G. Cai, H. Wu, and R. Lv, "Rumors detection in Chinese via crowd responses," in Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, 2014, pp. 912-917.##[15] S. Goel, D. J. Watts, and D. G. Goldstein, "The structure of online diffusion networks," in Proceedings of the 13th ACM conference on electronic commerce, 2012, pp. 623-638.##[16] R. Cowan and N. Jonard, "Network structure and the diffusion of knowledge," Journal of economic Dynamics and Control, vol. 28, pp. 1557-1575, 2004.##[17] A. Ganesh, L. Massoulié, and D. Towsley, "The effect of network topology on the spread of epidemics," in Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., 2005, pp. 1455-1466.##[18] V. Lampos, T. De Bie, and N. Cristianini, "Flu detector-tracking epidemics on Twitter," in Joint European conference on machine learning and knowledge discovery in databases, 2010, pp. 599-602.##[19] M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi, "Measuring user influence in twitter: The million follower fallacy," Icwsm, vol. 10, p. 30, 2010.##[20] M. Dash and H. 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Lv, "Rumors detection in chinese via crowd responses," in 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014, pp. 912-917.##[4] A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, "Detection and resolution of rumours in social media: A survey," ACM Computing Surveys (CSUR), vol. 51, pp. 1-36, 2018.##[5] A. Ritter, C. Cherry, and B. Dolan, "Unsupervised modeling of twitter conversations," in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010, pp. 172-180.##[6] J. A. Reshi and R. Ali, "Rumor proliferation and detection in Social Media: A Review," in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019, pp. 1156-1160.##[7] S. M. Alzanin and A. M. Azmi, "Detecting rumors in social media: A survey," Procedia computer science, vol. 142, pp. 294-300, 2018.##[8] A. Bondielli and F. Marcelloni, "A survey on fake news and rumour detection techniques," Information Sciences, vol. 497, pp. 38-55, 2019.##[9] J. Cao, J. Guo, X. Li, Z. Jin, H. Guo, and J. Li, "Automatic rumor detection on microblogs: A survey," arXiv preprint arXiv:1807.03505, 2018.##[10] A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, "Detection and resolution of rumours in social media: A survey," arXiv preprint arXiv:1704.00656, 2017.##[11] C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on twitter," in Proceedings of the 20th international conference on World wide web, 2011, pp. 675-684.##[12] S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, "Prominent features of rumor propagation in online social media," in Data Mining (ICDM), 2013 IEEE 13th International Conference on, 2013, 2013, pp. 1103-1108.##[13] F. Yang, Y. Liu, X. Yu, and M. Yang, "Automatic detection of rumor on sina weibo," in Proceedings of the ACM SIGKDD workshop on mining data semantics, 2012, pp. 1-7.##[14] G. Cai, H. Wu, and R. Lv, "Rumors detection in Chinese via crowd responses," in Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, 2014, pp. 912-917.##[15] S. Goel, D. J. Watts, and D. G. Goldstein, "The structure of online diffusion networks," in Proceedings of the 13th ACM conference on electronic commerce, 2012, pp. 623-638.##[16] R. Cowan and N. Jonard, "Network structure and the diffusion of knowledge," Journal of economic Dynamics and Control, vol. 28, pp. 1557-1575, 2004.##[17] A. Ganesh, L. Massoulié, and D. Towsley, "The effect of network topology on the spread of epidemics," in Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., 2005, pp. 1455-1466.##[18] V. Lampos, T. De Bie, and N. Cristianini, "Flu detector-tracking epidemics on Twitter," in Joint European conference on machine learning and knowledge discovery in databases, 2010, pp. 599-602.##[19] M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi, "Measuring user influence in twitter: The million follower fallacy," Icwsm, vol. 10, p. 30, 2010.##[20] M. Dash and H. Liu, "Feature selection for classification," Intelligent data analysis, vol. 1, pp. 131-156, 1997.##[21] D. Shah and T. Zaman, "Rumors in a network: Who's the culprit?," IEEE Transactions on information theory, vol. 57, pp. 5163-5181, 2011.##[22] D. J. Watts and P. S. Dodds, "Influentials, networks, and public opinion formation," Journal of consumer research, vol. 34, pp. 441-458, 2007.##[23] P. Cogan, M. Andrews, M. Bradonjic, W. S. Kennedy, A. Sala, and G. Tucci, "Reconstruction and analysis of twitter conversation graphs," in Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, 2012, pp. 25-31.##[24] R. Nishi, T. Takaguchi, K. Oka, T. Maehara, M. Toyoda, K.-i. Kawarabayashi, et al., "Reply trees in twitter: data analysis and branching process models," Social Network Analysis and Mining, vol. 6, p. 26, 2016.##[25] M. Mendoza, B. Poblete, and C. Castillo, "Twitter Under Crisis: Can we trust what we RT?," in Proceedings of the first workshop on social media analytics, 2010, pp. 71-79.##[26] F. Jin, W. Wang, L. Zhao, E. R. Dougherty, Y. Cao, C.-T. Lu, et al., "Misinformation propagation in the age of twitter," IEEE Computer, vol. 47, pp. 90-94, 2014.##[27] S. Vosoughi, M. N. Mohsenvand, and D. Roy, "Rumor gauge: predicting the veracity of rumors on twitter," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 11, pp. 50, 2017.##[28] S. Vosoughi, D. Roy, and S. Aral, "The spread of true and false news online," Science, vol. 359, pp. 1146-1151, 2018.##[29] S. Kwon, M. Cha, and K. Jung, "Rumor detection over varying time windows," PloS one, vol. 12, p. e0168344, 2017.##[30] A. Gupta and P. Kumaraguru, "Credibility ranking of tweets during high impact events," in Proceedings of the 1st workshop on privacy and security in online social media, 2012, pp. 2.##[31] J. Ma, W. Gao, and K.-F. Wong, "Detect rumors on Twitter by promoting information campaigns with generative adversarial learning," in The World Wide Web Conference, 2019, pp. 3049-3055.##[32] A. Alsaeedi and M. Al-Sarem, "Detecting Rumors on Social Media Based on a CNN Deep Learning Technique," Arabian Journal for Science and Engineering, pp. 1-32, 2020.##[33] S. Santhoshkumar and L. D. Babu, "Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks," Social Network Analysis and Mining, vol. 10, pp. 1-17, 2020.##[34] M. Z. Asghar, A. 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الگوریتم توزیع‌شده و مشارکتی به‌منظور بازسازی سیگنال‌های تنک در شبکه‌های حس‌گری بی‌سیم با توپولوژی افزایشی دو‌جهته Distributed and Cooperative Compressive Sensing Recovery Algorithm for Wireless Sensor Networks with Bi-directional Incremental Topology 1 1 مسأله دریافت فشرده در همین اواخر توجه زیادی در پردازش سیگنال به خود جلب کرده به‌طوری که بخش اعظمی از پژوهش‌ها در این حوزه به این مسأله معطوف شده است. از جمله حوزه کاربردی دریافت فشرده، کاربرد آن در شبکه‌های حس‌گری بی‌سیم است. ساختمان این شبکه‌ها که متشکل از حس‌گرهای بی‌سیم با توان محدود است، ایجاب می‌کند تا الگوریتم‌هایی که برای این کاربرد ارتقا داده می‌شوند، به لحاظ مصرف انرژی بهینه باشند. به عبارتی، الگوریتم‌های طراحی‌شده برای این زمینه می‌بایست پیچیدگی‌های محاسباتی کمتری داشته و نیازمند کمترین تبادلات بین حس‌گرها باشند. بر همین اساس، در این مقاله الگوریتم بازسازی دریافت فشرده توزیع‌شده‌ای برحسب مد مشارکتی افزایشی دوجهته پیشنهاد شده ‌است؛ در حقیقت، نخست یک چهارچوب جامع توزیع‌شده برای بازسازی سیگنال‌های تنک در شبکه‌های حس‌گری ارائه شده و سپس این چهارچوب برای مسائل بهینه‌سازی متفاوتی پیاده شده است. پیچیدگی پایین محاسباتی و عملکرد حالت دائم بهتر مهم‌ترین مشخصه الگوریتم‌های پیشنهادی است. 2 Recently, the problem of compressive sensing (CS) has attracted lots of attention in the area of signal processing. So, much of the research in this field is being carried out in this issue. One of the applications where CS could be used is wireless sensor networks (WSNs). The structure of WSNs consists of many low power wireless sensors. This requires that any improved algorithm for this application must be optimized in terms of energy consumption. In other words, the computational complexity of algorithms must be as low as possible and should require minimal interaction between the sensors. For such networks, CS has been used in data gathering and data persistence scenario, in order to minimize the total number of transmissions and consequently minimize the network energy consumption and to save the storage by distributing the traffic load and storage throughout the network. In these applications, the compression stage of CS is performed in sensor nodes, whereas the recovering duty is done in the fusion center (FC) unit in a centralized manner. In some applications, there is no FC unit and the recovering duty must be performed in sensor nodes in a cooperative and distributed manner which we have focused on in this paper. Indeed, the notable algorithm for this purpose is distributed least absolute shrinkage and selection operation (D-LASSO) algorithm which is based on diffusion cooperation structure. This algorithm that compete to the state-of-the-art CS algorithms has a major disadvantage; it involves matrix inversion that may be computationally demanding for sufficiently large matrices. On this basis, in this paper, we have proposed a distributed CS recovery algorithm for the WSNs with a bi-directional incremental mode of cooperation. Actually, we have proposed a comprehensive distributed framework for the recovery of sparse signals in WSNs.  Here, we applied this comprehensive structure to three problems with different constraints which results in three completely distributed solutions named as distributed bi-directional incremental basis pursuit (DBIBP), distributed bi-directional incremental noise-aware basis pursuit (DBINBP) and distributed bi-directional incremental regularized least squares (DBIRLS). The proposed algorithms solely involve linear combinations of vectors and soft thresholding operations. Hence, the computational load is significantly reduced in each sensor. In the proposed method each iteration consists of two phases; clockwise and anti-clockwise phases. At each iteration, in anti-clockwise phase, each node receives the local estimate from its previous neighbor and updates an auxiliary variable. Then in the clockwise phase, each node receives the updated auxiliary variable from its next neighbors to update the local estimate. On the other hand, information exchange in two directions in an incremental manner which we called it bi-directional incremental structure. In an incremental strategy, information flows in a sequential manner from one node to the adjacent node. Unlike the diffusion structure (like as D-LASSO) where each node communicates with all of their neighbors, the incremental mode of cooperation requires the least amount of communication and power. The low computational complexity and better steady state performance are the important features of the proposed methods. 65 76 2019/03/52019/04/282019/04/282020/04/32019/06/1 1398/3/11 2020/01/222020/01/222020/08/182021/03/12020/08/18 1399/5/28 قنبر آذرنیا Ghanbar Azarnia دانشگاه ارومیه ایران g.azarnia@tabrizu.ac.ir محمدعلی طینتی Mohammad Ali Tinati دانشگاه تبریز ایران tinati@tabriz.ac.ir توحید یوسفی رضایی Tohid Yousefi Rezaii دانشگاه تبریز ایران yousefi@tabriz.ac.ir Wireless sensor networks Sparse signal Bi-directional incremental topology Compressive sensing Recovery algorithm شبکه‌های حس‌گری بی‌سیم سیگنال تنک توپولوژی افزایشی دوجهته دریافت فشرده الگوریتم بازسازی [1] E.J. Candès, and M.B. Wakin, "An introduction to compressive sampling", IEEE signal processing magazine, vol. 25(2), pp.21-30, 2008.##[2] H. Shiri, M. A. Tinati, M. Codreanu, and G. Azarnia, "Distributed sparse diffusion estimation with reduced communication cost", IET Signal Processing, vol. 12(8), pp. 1043-1052, 2018.##[3] G. Azarnia, M.A. Tinati, and T.Y. Rezaii, "Cooperative and distributed algorithm for compressed sensing recovery in WSNs", IET Signal Processing, vol. 12(3), pp.346-357, 2017.##[4] B.K. Natarajan, "Sparse approximate solutions to linear systems", SIAM Journal on Computing, vol. 24(2), pp. 227-234, 1995.##[5] S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIREV, vol. 43(1), pp.129-159, 2001.##[6] E. J. Candès and T. Tao, "The Dantzig selector: Statistical estimation when p is much larger than n," The annals of Statistics, vol. 35(6), pp. 2313-2351, 2007.##[7] R. Tibshirani, "Regression shrinkage and selection via the Lasso," J. Roy. Statist. Soc. Ser. B, vol. 58(1), pp. 267-288, 1996.##[8] D. Estrin, L. Girod, G. Pottie, and M. Srivastava, "Instrumenting the world with wireless sensor networks', In 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221), vol. 4, pp. 2033-2036, 2001.##[9] G. Azarnia, M. A. Tinati, and T. Y. Rezaii, "Generic cooperative and distributed algorithm for recovery of signals with the same sparsity profile in wireless sensor networks: a non-convex approach," The Journal of Supercomputing, vol. 75(5), pp. 2315-2340, 2019.##[10] I. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "A survey on sensor networks", IEEE Communications Magazine, Vol. 40(8), pp.102-114, 2002.##[11] C. Luo, F. Wu, J. Sun, and C. W. Chen, "Compressive data gathering for large-scale wireless sensor networks", In Proceedings of the 15th annual international conference on Mobile computing and networking, 2009, 145-156,##[12] C. Luo, F. Wu, J. Sun, and C. Chen, "Efficient measurement generation and pervasive sparsity for compressive data gathering", IEEE Trans Wireless Commun., vol. 9(12), pp. 3728-38, 2010.##[13] Y. Zhu and X. Wang, "Multi-session data gathering with compressive sensing for large-scale wireless sensor networks," in Proc. Global Telecommunications conf, 2010, pp. 1-5.##[14] A. Abrardo, C. M. Carretti, and A. Mecocci, "A compressive sampling data gathering approach for wireless sensor networks using a sparse acquisition matrix with abnormal values", Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on, 2012, pp. 1-4.##[15] J. Wang, S. Tang, B. Yin, X. and Li, "Data gathering in wireless sensor networks through intelligent compressive sensing", In 2012 Proceedings IEEE INFOCOM, pp. 603-611, 2012.##[16] R. Xie and X. Jia, "Minimum transmission data gathering trees for compressive sensing in wireless sensor networks," in Proc. IEEE GlobeCom, pp. 1-5, 2011.##[17] X. Wu, Y. Xiong, W. Huang, H. Shen, and M. Li, "An efficient compressive data gathering routing scheme for large-scale wireless sensor networks," Comput. Electr, Eng, vol. 39(6), pp. 1935-1946, 2013.##[18] H. Zheng, F. Yang, X. Tian, X. Gan, X. Wang, and S. Xiao, "Data gathering with compressive sensing in wireless sensor networks: A random walk based approach," IEEE Trans. Parallel Distrib. Syst., vol. 26(1), pp. 35-44, 2015.##[19] D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham, and R. G. Baraniuk, "Distributed compressed sensing", Dept. Elect. Eng., Rice University, Houston, TX, Tech. Rep. TREE-0612, 2006.##[20] J. Park, S. Hwang, J. Yang, and D. Kim, "Generalized distributed compressive sensing" appeared in Rice University Compressive Sensing Resources, http://www.dsp.rice.ed-u/cs.##[21] H. Xu, N. Fu, L. Qiao, and X. Peng, "Fast pursuit method for greedy algorithms in distributed compressive sensing," in Conf. Rec., IEEE Instrumentation and Measurement Technology Conf., pp. 1118-1122, 2015.##[22] W. Chen, I. Wassell, and M. Rodrigues, "Dictionary design for distributed compressive sensing," IEEE Signal Processing Letters, vol. 22(1), pp. 95-99, 2015.##[23] M. Rabbat, J. Haupt, A. Singh, and R. Nowak, "Decentralized compression and predistribution via random gossiping", in Proc. of IPSN, pp. 51-59, 2006.##[24] W. Wang, M. Garofalakis, and K. Ramchandran, "Distributed sparse random projections for refinable approximation," in Proc. of IPSN, pp. 331-339, 2007.##[25] A. Talari and N. Rahnavard, "Cstorage: Distributed data storage in wireless sensor networks employing compressive sensing", in Proc. IEEE Global Telecommunications Conf., 2012, pp. 1-5.##[26] M. Lin, C. Luo, F. Liu, and F. Wu. "Compressive data persistence in large-scale wireless sensor networks", In Global Telecommunications Conference, 2010, pp. 1-5.##[27] F. Liu, M. Lin, Y. Hu, C. Luo, and F. Wu, "Design and analysis of compressive data persistence in large-scale wireless sensor networks," IEEE Trans. Parallel Distrib. Syst., vol. 26(10), pp. 2685-2698, 2015.##[28] G. Azarnia and M. A. Tinati, "Steady-state analysis of the deficient length incremental LMS adaptive networks," Circuits, Syst. Signal Process., vol. 34(9), pp. 2893-2910, 2015.##[29] G. Azarnia and M. A. Tinati, "Steady-state analysis of the deficient length incremental LMS adaptive networks with noisy links," AEU Int. J. Electron. Commun., vol. 69(1), pp. 153-162, 2015.##[30] Z. Zhao, J. Feng and B. Peng "A green distributed signal reconstruction algorithm in wireless sensor networks", IEEE Access, pp. 5908-5917, 2016.##[31] D. Sundman , S. Chatterjee , and M. Skoglund, "Design and analysis of a greedy pursuit for distributed compressed sensing", IEEE Trans. Sig. Process. Vol. 64 (11), pp. 2803-2818, 2016.##[32] W. Chen , and I.J. Wassell , "A decentralized bayesian algorithm for distributed compressive sensing in networked sensing systems", IEEE Trans. Wireless Commu, Vol. 15 (2), pp. 1282-1292, 2016.##[33] G. Mateos, J. A. Bazerque, and G. B. Giannakis, "Distributed sparse linear regression," IEEE Transactions on Signal Processing, vol. 58(10), pp. 5262-5276, 2010.##[34] S. Foucart, H. Rauhut, "A Mathematical Introduction to Compressive Sensing", Springer, New York, August 2013.##[1] E.J. Candès, and M.B. Wakin, "An introduction to compressive sampling", IEEE signal processing magazine, vol. 25(2), pp.21-30, 2008.##[2] H. Shiri, M. A. Tinati, M. Codreanu, and G. Azarnia, "Distributed sparse diffusion estimation with reduced communication cost", IET Signal Processing, vol. 12(8), pp. 1043-1052, 2018.##[3] G. Azarnia, M.A. Tinati, and T.Y. Rezaii, "Cooperative and distributed algorithm for compressed sensing recovery in WSNs", IET Signal Processing, vol. 12(3), pp.346-357, 2017.##[4] B.K. Natarajan, "Sparse approximate solutions to linear systems", SIAM Journal on Computing, vol. 24(2), pp. 227-234, 1995.##[5] S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIREV, vol. 43(1), pp.129-159, 2001.##[6] E. J. Candès and T. Tao, "The Dantzig selector: Statistical estimation when p is much larger than n," The annals of Statistics, vol. 35(6), pp. 2313-2351, 2007.##[7] R. Tibshirani, "Regression shrinkage and selection via the Lasso," J. Roy. Statist. Soc. Ser. B, vol. 58(1), pp. 267-288, 1996.##[8] D. Estrin, L. Girod, G. Pottie, and M. Srivastava, "Instrumenting the world with wireless sensor networks', In 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221), vol. 4, pp. 2033-2036, 2001.##[9] G. Azarnia, M. A. Tinati, and T. Y. Rezaii, "Generic cooperative and distributed algorithm for recovery of signals with the same sparsity profile in wireless sensor networks: a non-convex approach," The Journal of Supercomputing, vol. 75(5), pp. 2315-2340, 2019.##[10] I. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "A survey on sensor networks", IEEE Communications Magazine, Vol. 40(8), pp.102-114, 2002.##[11] C. Luo, F. Wu, J. Sun, and C. W. Chen, "Compressive data gathering for large-scale wireless sensor networks", In Proceedings of the 15th annual international conference on Mobile computing and networking, 2009, 145-156,##[12] C. Luo, F. Wu, J. Sun, and C. Chen, "Efficient measurement generation and pervasive sparsity for compressive data gathering", IEEE Trans Wireless Commun., vol. 9(12), pp. 3728-38, 2010.##[13] Y. Zhu and X. Wang, "Multi-session data gathering with compressive sensing for large-scale wireless sensor networks," in Proc. Global Telecommunications conf, 2010, pp. 1-5.##[14] A. Abrardo, C. M. Carretti, and A. Mecocci, "A compressive sampling data gathering approach for wireless sensor networks using a sparse acquisition matrix with abnormal values", Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on, 2012, pp. 1-4.##[15] J. Wang, S. Tang, B. Yin, X. and Li, "Data gathering in wireless sensor networks through intelligent compressive sensing", In 2012 Proceedings IEEE INFOCOM, pp. 603-611, 2012.##[16] R. Xie and X. Jia, "Minimum transmission data gathering trees for compressive sensing in wireless sensor networks," in Proc. IEEE GlobeCom, pp. 1-5, 2011.##[17] X. Wu, Y. Xiong, W. Huang, H. Shen, and M. Li, "An efficient compressive data gathering routing scheme for large-scale wireless sensor networks," Comput. Electr, Eng, vol. 39(6), pp. 1935-1946, 2013.##[18] H. Zheng, F. Yang, X. Tian, X. Gan, X. Wang, and S. Xiao, "Data gathering with compressive sensing in wireless sensor networks: A random walk based approach," IEEE Trans. Parallel Distrib. Syst., vol. 26(1), pp. 35-44, 2015.##[19] D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham, and R. G. Baraniuk, "Distributed compressed sensing", Dept. Elect. Eng., Rice University, Houston, TX, Tech. Rep. TREE-0612, 2006.##[20] J. Park, S. Hwang, J. Yang, and D. Kim, "Generalized distributed compressive sensing" appeared in Rice University Compressive Sensing Resources, http://www.dsp.rice.ed-u/cs.##[21] H. Xu, N. Fu, L. Qiao, and X. Peng, "Fast pursuit method for greedy algorithms in distributed compressive sensing," in Conf. Rec., IEEE Instrumentation and Measurement Technology Conf., pp. 1118-1122, 2015.##[22] W. Chen, I. Wassell, and M. Rodrigues, "Dictionary design for distributed compressive sensing," IEEE Signal Processing Letters, vol. 22(1), pp. 95-99, 2015.##[23] M. Rabbat, J. Haupt, A. Singh, and R. Nowak, "Decentralized compression and predistribution via random gossiping", in Proc. of IPSN, pp. 51-59, 2006.##[24] W. Wang, M. Garofalakis, and K. Ramchandran, "Distributed sparse random projections for refinable approximation," in Proc. of IPSN, pp. 331-339, 2007.##[25] A. Talari and N. Rahnavard, "Cstorage: Distributed data storage in wireless sensor networks employing compressive sensing", in Proc. IEEE Global Telecommunications Conf., 2012, pp. 1-5.##[26] M. Lin, C. Luo, F. Liu, and F. Wu. "Compressive data persistence in large-scale wireless sensor networks", In Global Telecommunications Conference, 2010, pp. 1-5.##[27] F. Liu, M. Lin, Y. Hu, C. Luo, and F. Wu, "Design and analysis of compressive data persistence in large-scale wireless sensor networks," IEEE Trans. Parallel Distrib. Syst., vol. 26(10), pp. 2685-2698, 2015.##[28] G. Azarnia and M. A. Tinati, "Steady-state analysis of the deficient length incremental LMS adaptive networks," Circuits, Syst. Signal Process., vol. 34(9), pp. 2893-2910, 2015.##[29] G. Azarnia and M. A. Tinati, "Steady-state analysis of the deficient length incremental LMS adaptive networks with noisy links," AEU Int. J. Electron. Commun., vol. 69(1), pp. 153-162, 2015.##[30] Z. Zhao, J. Feng and B. Peng "A green distributed signal reconstruction algorithm in wireless sensor networks", IEEE Access, pp. 5908-5917, 2016.##[31] D. Sundman , S. Chatterjee , and M. Skoglund, "Design and analysis of a greedy pursuit for distributed compressed sensing", IEEE Trans. Sig. Process. Vol. 64 (11), pp. 2803-2818, 2016.##[32] W. Chen , and I.J. Wassell , "A decentralized bayesian algorithm for distributed compressive sensing in networked sensing systems", IEEE Trans. Wireless Commu, Vol. 15 (2), pp. 1282-1292, 2016.##[33] G. Mateos, J. A. Bazerque, and G. B. Giannakis, "Distributed sparse linear regression," IEEE Transactions on Signal Processing, vol. 58(10), pp. 5262-5276, 2010.##[34] S. Foucart, H. Rauhut, "A Mathematical Introduction to Compressive Sensing", Springer, New York, August 2013. ##

آشکار‌سازی پلاک خودروهای ایرانی مبتنی بر طبقه‌بندی‌کننده سلسله‌مراتبی Iranian Vehicle License Plate Detection based on Cascade Classifier 1 1 یک سامانه تشخیص پلاک خودرو شامل سه بخش: شناسایی پلاک‌(های) تصویر، استخراج کاراکترها و تشخیص نویسه‌ها است. اولین و مهم‌‌ترین مرحله در یک سامانه تشخیص پلاک، شناسایی ناحیه پلاک در تصویر است. در این مقاله یک روش کارا و مبتنی بر طبقه‌بندی‌کننده سلسله‌مراتبی برای شناسایی پلاک‌‌(های) خودروهای ایرانی در تصویر پیشنهاد شده و رویکردهایی جهت بهبود کارایی سامانه پیشنهاد  شده است. در ابتدا با بازخورد‌گرفتن از نمونه‌های منفی (نواحی فاقد پلاک)، یک رویکرد آموزشی دو مرحله‌ای و سپس برای کاهش تعداد هشدار غلط و افزایش دقت شناسایی ناحیه، یک رویکرد آزمایش دو‌مرحله‌ای ارائه شده است. در‌نهایت روشی ترکیبی با ادغام دو رویکرد آموزش و آزمایش دو‌‌مرحله‌ای معرفی شده است. این سامانه بر روی تصاویر رنگی و خاکستری قابل اعمال است و توانایی شناسایی چند پلاک در تصویر، از نمای جلو و عقب خودرو، در شرایط نوری مختلف، در محیط‌های شلوغ و پلاک‌ها با مقیاس‌های مختلف‌ را دارد و به محل نصب پلاک و یا ناحیه خاصی از پلاک و همچنین رنگ پس‌زمینه پلاک حساس نیست. برای ارزیابی رویکردهای ارائه‌شده، مجموعه‌داده‌هایی از تصاویر پلاک‌های ایرانی در شرایط مختلف جمع‌آوری شده است. نتایج ارزیابی­‌ها نشان می­‌دهد که رویکردهای ارائه‌شده به‌خوبی توانسته است، نرخ بازیابی نواحی پلاک و دقت ناحیه یافته‌شده را بهبود داده و نرخ هشدار غلط را نیز به‌خوبی کاهش دهد. 2 Automatic license plate recognition is widely used in intelligent transport systems to automatically and quickly read license plates of vehicles. A license plate recognition is a computer vision system containing three main steps: plate detection, character segmentation, and character recognition. The first and foremost step of this system is the plate detection stage where the plate is located from the input image. This step has many challenges, including low-resolution images, illumination change, complex background, multiple plates, and different plate sizes. The plate detection methods can be categorized into connected component-based, color-based, and classifier-based methods. The two first approaches are not reliable in real-world environments, and they are too sensitive to illumination change and plate sizes compared to the classifier-baed techniques. In this context, the Cascade classifier has successfully been applied to various object detection problems. This classifier sequentially combines several weak classifiers based on the AdaBoost Algorithm. In this paper, an effective Iranian vehicle license plate detection approach is developed based on a cascade classifier. A two-phase training approach is proposed to enhance the cascade classifier by getting feedback from the negative data. Then, a new testing approach is suggested to reduce false-positive detection and improve detection precision. We also collected an Iranian license plate dataset which is publicly available for research purposes. The dataset covers images in different real-world conditions. The proposed system evaluated on this dataset is able to be applied on gray as well as color images, in a way that can detect multiple plates at front or rear of the cars in different illuminations. Moreover, the proposed method is invariant to the size, position or where the plates are located in the image. The experimental results provided in different conditions show that the proposed approach can improve the precision and recall rate while reducing the false-positive rate. 77 90 2019/03/52019/04/282019/04/282020/04/32019/06/12019/05/28 1398/3/7 2020/01/222020/01/222020/08/182021/03/12020/08/182021/02/1 1399/11/13 فهمیه رمضانخانی Fahimeh Ramazankhani دانشگاه یزد ایران framazankhani@stu.yazd.ac.ir مهدی یزدیان دهکردی Mahdi Yazdian-Dehkordi دانشگاه یزد ایران yazdian@yazd.ac.ir Iranian license Plate Plate detection Cascade classifier Two-phase training/testing پلاک‌های ایرانی شناسایی پلاک طبقه‌بندی‌کننده سلسله‌مراتبی آموزش/آزمایش دو‌مرحله‌ای [1] S. M. Silva and C. R. Jung, "Real-time license plate detection and recognition using deep convolutional neural networks," Journal of Visual Communication and Image Representation, vol. 71, pp. 102773-102781, 2020.##[2] L. Zhang, P. Wang, H. Li, Z. Li, C. Shen and Y. Zhang, "A Robust Attentional Framework for License Plate Recognition in the Wild," IEEE Transactions on Intelligent Transportation Systems, vol. 22, pp. 6967-6976, 2021.##[3] G. Rabbani, M. Aminul Islam, M. Anwarul Azim, M. Khairul Islam and M. M. Rahman, "Bangladeshi License Plate Detection and Recognition with Morphological Operation and Convolution Neural Network," in 21st Internat-ional Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2018.##[4] G. Lin, B. Xue, B. Xu and C. Chen, "License plate recognition based on mathematical morphology and template matching," in Chinese Automation Congress (CAC), Hangzhou, Chi-na, 2019.##[5] W. Sh. Chowdhury, A. R. Khan and J. Uddin, ''Vehicle License Plate Detection Using Image Segmentation and Morphological Image Pro-cessing," in 3rd International Symposium on Signal Processing and Intelligent Recognition Systems, Springer International Publishing, vol. 678, pp. 142-154, 2018.##[6] S. S. Tabrizi and N. Cavus, "A hybrid KNN-SVM model for Iranian license plate recognition," in Procedia Computer Science, vol. 102, pp. 588-594, 2016.##[7] M. Abdollahi and H. Khosravi, "Design and Implementation of Real-Time License Plate Recognition System in Video Sequences," Journal of Signal and Data Processing (JSDP), vol. 15, no. 4, pp.41-56, 2019.##[8] A. H. Ashtari, "An Iranian License Plate Recognition System Based on Color Features," IEEE Transactions on Intelligent Transporta-tion Systems, vol. 15, no. 4, pp. 1690-1705, 2014.##[9] M. R. Asif, QiChun, S. Hussain, M. S. Fareed and S. Khan, "Multinational vehicle license plate detection in complex backgrounds," Journal of Visual Communication and Image Representation, vol. 46, pp. 176-186, 2017.##[10] H. Li, P. Wang and Ch. Shen, "Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks," IEEE Transactions on Intelligent Transportation Systems, vol. 20, pp. 1126-1136, 2019.##[11] G. Ning, Z. Zhang, C. Huang, X. Ren, H. Wang, C. Cai, and Z. He, "Spatially supervised recurrent convolutional neural networks for visual object tracking," in IEEE International Symposium on Circuits and Systems, pp. 1-4, May 2017.##[12] B. Wu, F. Iandola, P. H. Jin, and K. Keutzer, "SqueezeDet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving," in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 2017, pp. 446-454.##[13] Joshua, J. Hendryli and D. E. Herwindiati, "Automatic License Plate Recognition for Parking System using Convolutional Neural Networks," in International Conference on Information Management and Technology (ICIMTech), Bandung, Indonesia, 2020.##[14] W. Riaz, A. Azeem, G. Chenqiang, Z. Yuxi, Saifullah and W. Khalid, "YOLO Based Recognition Method for Automatic License Plate Recognition," in IEEE International Conference on Advances in Electrical Engi-neering and Computer Applications (AEE-CA), Dalian, China, 2020.##[15] U. Masud, F. Jeribi, M. Alhameed, A. Tahir, Q. Javaid and F. Akram, "Traffic Congestion Avoidance System Using Foreground Estima-tion and Cascade Classifier," IEEE Access, vol. 8, pp. 178859-178869, 2020.##[16] B. S. Bayu Dewantara and D. Twinda Rhamadhaningrum, "Detecting Multi-Pose Masked Face Using Adaptive Boosting and Cascade Classifier," in International Elec-tronics Symposium (IES), Surabaya, Indon-esia, 2020.##[17] A. Wang, L. Li and B. Dong, "Research on Pedestrian Intelligent Recognition Method Based on Cascade Classifier Structure," in IEEE 5th International Conference on Intellig-ent Transportation Engineering (ICITE), Beijing, China, 2020.##[18] X. Chen, L. Liu, Y. Deng and X. Kong , "Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems," Optical and Quantum Electronics, vol. 51, no. 8, pp. 1-18, 2019.##[19] I. Gangopadhyay, A. Chatterjee and I. Das, "Face Detection and Expression Recognition Using Haar Cascade Classifier and Fisherface Algorithm," in Recent Trends in Signal and Image Processing. Advances in Intelligent Systems and Computing, Singapore, 2019.##[20] Z. Hanifelou, A.H. Monadjemi, P. Moallem, "Robust method of changes of light to detect and track vehicles in traffic scenes," Journal of Signal and Data Processing (JSDP), vol 13, no. 3, pp. 79-98, 2016.##[21] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Conference on Computer Vision and Pattern Recognition (CVPR), December 2001.##[22] Y. N. Chen, C. C. Han, G. sF. Ho and K. Fan, "Facial/License Plate Detection Using a Two-Level Cascade Classifier and a Single Convolutional Feature Map", International Journal of Advanced Robotic Systems (IJARS), vol. 12, no. 09, 2015.##[23] A. Elbamby, E. E. Hemayed, D. Helal and M. Rehan, "Real-time automatic multi-style license plate detection in videos," in Computer Engineering Conference, 2016, pp. 148-153.##[24]Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol. 55, pp. 119-139, 1997.##[1] S. M. Silva and C. R. Jung, "Real-time license plate detection and recognition using deep convolutional neural networks," Journal of Visual Communication and Image Representation, vol. 71, pp. 102773-102781, 2020.##[2] L. Zhang, P. Wang, H. Li, Z. Li, C. Shen and Y. Zhang, "A Robust Attentional Framework for License Plate Recognition in the Wild," IEEE Transactions on Intelligent Transportation Systems, vol. 22, pp. 6967-6976, 2021.##[3] G. Rabbani, M. Aminul Islam, M. Anwarul Azim, M. Khairul Islam and M. M. Rahman, "Bangladeshi License Plate Detection and Recognition with Morphological Operation and Convolution Neural Network," in 21st Internat-ional Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2018.##[4] G. Lin, B. Xue, B. Xu and C. Chen, "License plate recognition based on mathematical morphology and template matching," in Chinese Automation Congress (CAC), Hangzhou, Chi-na, 2019.##[5] W. Sh. Chowdhury, A. R. Khan and J. Uddin, ''Vehicle License Plate Detection Using Image Segmentation and Morphological Image Pro-cessing," in 3rd International Symposium on Signal Processing and Intelligent Recognition Systems, Springer International Publishing, vol. 678, pp. 142-154, 2018.##[6] S. S. Tabrizi and N. Cavus, "A hybrid KNN-SVM model for Iranian license plate recognition," in Procedia Computer Science, vol. 102, pp. 588-594, 2016.##[7] M. Abdollahi and H. Khosravi, "Design and Implementation of Real-Time License Plate Recognition System in Video Sequences," Journal of Signal and Data Processing (JSDP), vol. 15, no. 4, pp.41-56, 2019.##[7] م. عبداللهی، ح. خسروی. "طراحی و پیاده‌سازی سامانۀ بی‌درنگ آشکارسازی و شناسایی پلاک خودرو در تصاویر ویدئویی،"مجله پردازش علائم و داده‌ها، جلد ۱۵، شماره ۴، صفحات ۴۱-۵۶، 1397.##[8] A. H. Ashtari, "An Iranian License Plate Recognition System Based on Color Features," IEEE Transactions on Intelligent Transporta-tion Systems, vol. 15, no. 4, pp. 1690-1705, 2014.##[9] M. R. Asif, QiChun, S. Hussain, M. S. Fareed and S. Khan, "Multinational vehicle license plate detection in complex backgrounds," Journal of Visual Communication and Image Representation, vol. 46, pp. 176-186, 2017.##[10] H. Li, P. Wang and Ch. Shen, "Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks," IEEE Transactions on Intelligent Transportation Systems, vol. 20, pp. 1126-1136, 2019.##[11] G. Ning, Z. Zhang, C. Huang, X. Ren, H. Wang, C. Cai, and Z. He, "Spatially supervised recurrent convolutional neural networks for visual object tracking," in IEEE International Symposium on Circuits and Systems, pp. 1-4, May 2017.##[12] B. Wu, F. Iandola, P. H. Jin, and K. Keutzer, "SqueezeDet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving," in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 2017, pp. 446-454.##[13] Joshua, J. Hendryli and D. E. Herwindiati, "Automatic License Plate Recognition for Parking System using Convolutional Neural Networks," in International Conference on Information Management and Technology (ICIMTech), Bandung, Indonesia, 2020.##[14] W. Riaz, A. Azeem, G. Chenqiang, Z. Yuxi, Saifullah and W. Khalid, "YOLO Based Recognition Method for Automatic License Plate Recognition," in IEEE International Conference on Advances in Electrical Engi-neering and Computer Applications (AEE-CA), Dalian, China, 2020.##[15] U. Masud, F. Jeribi, M. Alhameed, A. Tahir, Q. Javaid and F. Akram, "Traffic Congestion Avoidance System Using Foreground Estima-tion and Cascade Classifier," IEEE Access, vol. 8, pp. 178859-178869, 2020.##[16] B. S. Bayu Dewantara and D. Twinda Rhamadhaningrum, "Detecting Multi-Pose Masked Face Using Adaptive Boosting and Cascade Classifier," in International Elec-tronics Symposium (IES), Surabaya, Indon-esia, 2020.##[17] A. Wang, L. Li and B. Dong, "Research on Pedestrian Intelligent Recognition Method Based on Cascade Classifier Structure," in IEEE 5th International Conference on Intellig-ent Transportation Engineering (ICITE), Beijing, China, 2020.##[18] X. Chen, L. Liu, Y. Deng and X. Kong , "Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems," Optical and Quantum Electronics, vol. 51, no. 8, pp. 1-18, 2019.##[19] I. Gangopadhyay, A. Chatterjee and I. Das, "Face Detection and Expression Recognition Using Haar Cascade Classifier and Fisherface Algorithm," in Recent Trends in Signal and Image Processing. Advances in Intelligent Systems and Computing, Singapore, 2019.##[20] Z. Hanifelou, A.H. Monadjemi, P. Moallem, "Robust method of changes of light to detect and track vehicles in traffic scenes," Journal of Signal and Data Processing (JSDP), vol 13, no. 3, pp. 79-98, 2016.##[20] ز. حنیفه‌لو، ا.ح. منجمی، پ. معلم، "ارائه‌ی روشی مقاوم نسبت به تغییرات روشنایی در آشکارسازی و ردیابی خودروها در صحنه‌های ترافیکی،" مجله پردازش علائم و داده‌ها، جلد ۱۳، شماره ۳، صفحات ۷۹-۹۸، 1395.##[21] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Conference on Computer Vision and Pattern Recognition (CVPR), December 2001.##[22] Y. N. Chen, C. C. Han, G. sF. Ho and K. Fan, "Facial/License Plate Detection Using a Two-Level Cascade Classifier and a Single Convolutional Feature Map", International Journal of Advanced Robotic Systems (IJARS), vol. 12, no. 09, 2015.##[23] A. Elbamby, E. E. Hemayed, D. Helal and M. Rehan, "Real-time automatic multi-style license plate detection in videos," in Computer Engineering Conference, 2016, pp. 148-153.##[24]Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol. 55, pp. 119-139, 1997. ##

افزایش گستره پویایی رادار با فیلتر فشرده‌سازی پالس وفقی Extending the Radar Dynamic Range using Adaptive Pulse Compression 1 1 فیلتر منطبق در گیرنده رادار فقط با نسخه سیگنال ارسالی تطبیق دارد و به‌­دلیل عدم تطبیق با سیگنال دریافتی از محیط، خروجی آن دچار تلف می­شود. دامنه گلبرگ­‌های جانبی خروجی فیلتر منطبق در رادارهای مجهز به فشرده‌سازی پالس به شکل‌­موج کدشده ارسالی وابسته است که به‌اندازه طول کد در دو طرف موقعیت هدف گسترده می­‌شوند. برای آشکارسازی یک هدفِ ضعیف در مجاورت یک هدف قوی، گلبرگ‌­های جانبی خروجی فیلترِ منطبقِ ناشی از هدفِ قوی سبب پوشانندگی هدف ضعیف و عدم آشکارسازی آن می­‌شود. به‌طورمعمول گستره پویایی رادار براساس نسبت بیشینه توان دریافتی به کمینه توان قابل آشکارسازی تعریف می­شود که به سطح آستانه و گلبرگ­‌های جانبی وابسته هستند. الگوریتم­‌های وفقی با شرط حفظ تفکیک‌­پذیری برد موجب کاهش سطح گلبرگ­‌های جانبی تا سطح نوفه شده و در نتیجه گستره پویایی را افزایش می‌­دهند. در این مقاله یک الگوریتم وفقی بهبودیافته (از نظر بارمحاسباتی و مقاومت در برابر دوپلر) مبتنی بر تخمین­‌گر کمینه میانگین مربعات خطا (MMSE) به نام الگوریتم مرمت فشرده‌سازی پالس وفقی با طول فیلتر منعطف (FFL-APCR) پیشنهاد می­‌شود، که طول فیلتر در آن وابسته به طول کد ارسالی است. همچنین نشان داده می‌شود که طول کد ارسالی در تعیین حد مجانبی پیک گلبرگ­‌های جانبی و گستره پویایی تأثیرگذار است؛ به‌­علاوه تأثیر سرعت زیاد هدف بر پهن­‌شدگی گلبرگ اصلی و تنزل عملکرد فیلترهای وفقی بررسی می‌شود؛ در‌نهایت افزایش گستره پویایی رادار با الگوریتم پیشنهادی FFL-APCR در شرایط مختلف نشان داده و عملکرد آن با معیار میانگین مجذور خطا (MSE) ارزیابی می­‌شود. 2 The matched filter in the radar receiver is only adapted to the transmitted signal version and its output will be wasted due to non-matching with the received signal from the environment. The sidelobes amplitude of the matched filter output in pulse compression radars are depended on the transmitted coded waveforms that extended as much as the length of the code on both sides of the target location. In order to detect a weak target in vicinity of strong target, the sidelobes of the matched filter output resulting from the strong target masked the weak target and didn’t detect its. Generally, the radar dynamic range is defined by the maximum power ratio to the minimum detectable power that is depended on the level of the threshold and the sidelobe levels. Adaptive algorithms suppress the sidelobe levels to noise level with condition of maintain the range resolution and therefore increase the dynamic range. In this paper, an improved algorithm (in terms of computational cost and Doppler robustness) is proposed based on the minimum mean square error (MMSE) estimator denoted as Flexible Filter Length-Adaptive Pulse Compression Repair (FFL-APCR), which filter length depends on the length of transmitted code. It is also shown that the length of the code is influenced by determining the asymptotic peak sidelobe level and the dynamics range. In addition, the influence of the high-speed target on main lobe broadening and the performance degradation of adaptive filters is investigated. Finally, extending of radar dynamic range with the proposed FFL-APCR algorithm is shown in various conditions and its performance evaluated by mean square error criteria. Where return signals coincide with the transmission of a pulse, pulse eclipsing can occur which results in detection performance loss. The mismatches (Doppler phase shift and pulse eclipsing) degrades performance of sidelobes suppression algorithms. The FFL-APCR algorithm suppresses range sidelobes by using a smaller filter length and reduces the computational cost. Consequently, this algorithm should be computationally efficient (real-time) to enable the practical application of RMMSE. 91 108 2019/03/52019/04/282019/04/282020/04/32019/06/12019/05/282019/04/7 1398/1/18 2020/01/222020/01/222020/08/182021/03/12020/08/182021/02/12021/05/22 1400/3/1 رضا کیوان شکوه Reza Kayvan shokooh دانشکده برق، دانشگاه جامع امام حسین (ع) ایران rkayvanshokooh@ihu.ac.ir مجید اخوت Majid Okhovvat دانشکده برق، دانشگاه جامع امام حسین (ع) ایران mokhovvat@ihu.ac.ir میثم رئیس دانایی Meisam Raees Danaee دانشکده برق، دانشگاه جامع امام حسین (ع) ایران Dynamic range Matched Filter Adaptive Pulse Compression Minimum Mean Square Error (MMSE) Flexible Filter Length-Adaptive Pulse Compression Repair (FFL-APCR) algorithm گستره پویایی فیلتر منطبق فشرده‌سازی پالس وفقی حداقل میانگین مربعات خطا تکراری الگوریتم مرمت فشرده‌سازی پالس وفقی با طول فیلتر منعطف [1] M. I. Skolnik, Introduction to Radar Systems, (3rd ed.), New York: McGraw-Hill, 2001, pp. 339-369.##[2] R. Kayvan Shokooh and M. Okhovvat, "Design and implementation of parallel matched filter bank in pulse compression radars," JOURNAL OF PASSIVE DEFENCE SCIENCE AND TECHNOLOGY, vol. 1, no. 2, pp. 75-85, WINTER 2011.##[3] S.D. Blunt and K. Gerlach, "Adaptive pulse compression via MMSE estimation," IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 2, pp. 572-584, Apr. 2006.##[4] S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory., Upper Saddle River, NJ: Prentice-Hall, 1993, pp. 219-286 and 344-350.##[5] N. Levanon, "Creating Sidelobe-Free Range Zone Around Detected Radar Target," in IEEE 28-th Convention of Electrical and Electronics Engineers in Israel, 2014.##[6] Kayvan shokooh, R., Okhovvat, M., "Efficient Masked Target Detection by Fast Adaptive Pulse Compression Algorithm with Flexible Filter Length," Tabriz Journal of Electrical Engineering (in persian), vol. 49, no. 2, pp. 819-831, 2019.##[7] R. Kayvan shokooh and M. Okhovvat, "An Integrated Algorithm for Optimal Detection of Radar Weak Targets Masked by the Sidelobes of a Strong Target," ECDJ Journal (In Persian), vol. 6, no. 4, 2018.##[8] Kayvan shokooh, R., Okhovvat, M., "Modified-adaptive pulse compression repair algorithm based on post-processing for eclipsing effects," IET Radar, Sonar & Navigation, vol. 12, no. 12, pp. 1527-1534, 2018.##[9] T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing, Upper Saddle River, NJ: Prentice-Hall, 1999.##[10] S.D. Blunt, K. Gerlach, and E. Mokole, "Pulse compression eclipsing repair," in IEEE Radar Conf, Rome, Italy, 26-30 May 2008.##[11] Wai Ho Mow and S. R. Li, "Aperiodic autocorrelation and crosscorrelation of polyphase sequences," IEEE Transactions on Information Theory, vol. 43, no. 3, pp. 1000-1007, 1997.##[12] M. Antweiler and L. Bomer, "Merit Factor of Chu and Frank sequences," Electron. Letter, vol. 26, pp. 2068-2070, 1990.##[13] W. H. 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روش یادگیری گروهی چند‌‌وجهی برای کدگشایی اشیاء دیداری از دادگان fMRI مغزی An Ensemble Multiview learning method for visual object decoding from fMRI brain data 1 1 با توجه به گسترش روزافزون پژوهش‌های علوم شناختی، کدگشایی مغز انسان یک موضوع داغ در حوزه علوم عصب‎شناسی محاسباتی است. در این راستا پژوهش‌های متعددی جهت ارائه روشی کارا و مؤثر برای کدگشایی فعالیت مغز انسان با پردازش دادگان fMRI در حال انجام است. خروجی این روش‎ها به‌طورعمومی معطوف به ارائه یک مدل محاسباتی تعمیم‎یافته است که امکان تشخیص سیگنال مغزی و تعلق آن به عامل محرک (شیء دیداری) را ارائه می‎دهد. دادگان مغزی دارای ابعاد زمانی و فضایی زیادی هستند که سبب افزایش تعداد ویژگی‌ها نیز می‌شود. همچنین استخراج ویژگی‎های مفید از تصاویر مغزی مانند fMRI کاری پیچیده است. این امر سبب طولانی‌شدن عمل هم‌گرایی در الگوریتم‎های یادگیری برای ایجاد مدل مناسب می‌شود؛ با توجه به چالش‌های یادشده، روش‌های یادگیری گروهی چندوجهی یک پیشنهاد مناسب برای حل مسأله کدگشایی مغز محسوب می‌شود که تلفیقی مناسب بین ویژگی‎های عملکردی متفاوت در دادگان مغزی ایجاد می‌کند. در روش پیشنهادی داده‌های آموزشی بر اساس اطلاعات متقابل در فضای ویژگی خوشه‌بندی می‌شوند، به‌صورتی که فضای ویژگی به چند وجه تفکیک می‌شود؛ سپس روی هر وجهِ ویژگی یک مدل ماشین بردار پشتیبان به‌صورت موازی آموزش داده می‌شود. در مرحله آزمون، فضای ویژگی دادگان آزمون نیز به‌صورت مشابه دادگان آموزش تقسیم می‌شود؛ و هر بردار ویژگی به مدل مربوطه تخصیص داده خواهد شد. از هر مدل یک بردار احتمالاتی تولید می‌شود و با هم‌جوشی این بردارها ماتریس پروفایل تصمیم‌گیری ساخته خواهد شد؛ درنهایت عملگرهای وزن‌دار مرتب‌شده اعمال می‌‌شود. جهت بررسی کارایی رویکرد پیشنهادی از رویکرد اعتبار سنجی متقابل با سناریوی درون فردی استفاده ‌شده است. معیارهایی مانند صحت و ماتریسِ درهم‌ریختگی برای ارزیابی مدل، به‌کار برده شده است. با بررسی عملکرد مدل بر روی هر وجه ویژگی، می‌توان دقت دسته‎بندی با میانگین  بیش از 50% را به‌دست آورد؛ اما در مدل گروهی نظارتی متوسط صحت تشخیص به بیش از 90 درصد می‌رسد. 2 In the past two decades, the applications of computational neuroscience have been increasingly growing. Breaking the neural code is a crucial open problem in computational neuroscience. Various research groups attempt to provide an efficient method to decode human brain activity using fMRI data. The output of these methods is a computational model that can assign brain signals to an external stimulus; in this study, visual object recognition has been investigated. The brain decoders are used in many applications, such as the brain-computer interface or detecting specific mental illnesses. In general, brain fMRI data have a high spatial and temporal resolution that increases the number of features of the problem. Proper feature extraction from brain images is a challenging and time-consuming process. Consequently, the convergence of learning algorithms takes a long time to create an appropriate model. So, breaking down the feature space is highly recommended. We proposed new multi-view learning to solve the brain decoding problem. This approach splits the feature space based on mutual information and finds an appropriate ensemble classification model that detects the related visual object to neural activities in the brain. The proposed method clusters the feature space based on mutual information and splits it into coherent sub-spaces, views. For each feature view, a support vector machine model is learned in parallel; the used SVM version can generate a vector of probabilities for each class. At the test phase, the feature space of test data is divided similarly to the training data, and each model generates a probabilistic vector for the test instances. Then, these vectors are combined in the decision profile matrix. The decision fusion is employed by the ordered weighted averaging (OWA) approach. The proposed multi-view learning methods achieved higher accuracy rates than the single view model. The main advantage of the MV model is that it can run in parallel, making it counterproductive to deal with the high-dimensional problems based on the divide and conquer strategy. The optimization phase to detect the most acceptable parameters for each model is obtained using the simulated annealing, SA, algorithm. We have employed three real fMRI datasets of the human brain to assess the proposed method, obtained from the Openneuro website. Also, the leave-one-run-out cross-validation approach has been carried out to evaluate the proposed method in the intra-subject scenario. Criteria such as accuracy rate and confusion matrix have been undertaken to analyze the results. The single feature view obtains an accuracy rate of more than 50%. While in the ensemble model, the accuracy rate in most subjects is more than 90%. 109 126 2019/03/52019/04/282019/04/282020/04/32019/06/12019/05/282019/04/72019/05/28 1398/3/7 2020/01/222020/01/222020/08/182021/03/12020/08/182021/02/12021/05/222021/09/4 1400/6/13 اسامه حورانی Osama Hourani دانشکده برق و کامپیوتر، دانشگاه تربیت مدرس ایران hourani@modares.ac.ir نصر اله مقدم چرکری Nasrollah Moghadam Charkari دانشکده برق و کامپیوتر، دانشگاه تربیت مدرس ایران charkari@modares.ac.ir سعید جلیلی Saeed Jalili دانشکده برق و کامپیوتر، دانشگاه تربیت مدرس ایران sjalili@modares.ac.ir Brain Decoding Decision Fusion Ensemble learning fMRI Mutual Information اطلاعات متقابل تصویربرداری تشدید مغناطیسی عملکردی fMRI کدگشایی مغز همجوشی تصمیم‌‎ها یادگیری گروهی [1] J. V. Haxby, M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten, and P. Pietrini, "Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex," Science (80-. )., vol. 293, no. 5539, pp. 2425-2430, 2001.##[2] T. 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الگوریتم فرا‌ابتکاری دسته والد-فرزند مبتنی بر حافظه و خوشه‌بندی جهت بهینه‌‌سازی پویا Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization 1 1 تاکنون روش­های مختلفی برای بهینه­سازی ارایه شده است و یکی از معروف­ترین روش­های بهینه­سازی، الگوریتم­های هوش­جمعی‌ هستند. بسیاری از مسائل بهینه‌­سازی اخیر در دنیای واقعی طبیعت پویا دارند؛ بنابراین، الگوریتم بهینه‌­سازی برای حل مسائل در محیط­‌های پویا مورد نیاز است. الگوریتم دستۀ والد-فرزند مبتنی بر حافظه و خوشه­‌بندی (CMPCS)، گونه‌­ای از الگوریتم‌­های هوش­جمعی و برگرفته شده از طبیعت است، که در این مقاله ارایه شده است. این روش به رفتار فردی و گروهی وابسته است، در این الگوریتم برای افزایش کارآیی از یک حافظه با خوشه‌بندی و دافعه استفاده شده است. روش CMPCS پیشنهاد شده بر روی محک قله‌های متحرک (MPB) آزمایش شده است. MPB یک محک خوب برای ارزیابی کارایی الگوریتم‌­های بهینه‌­سازی در محیط­‌های پویا است. نتایج تجربی در MPB نشان می‌­دهد که روش پیشنهادی CMPCS کارایی مناسب­‌تری نسبت به روش‌­های دیگر حل مسائل بهینه‌سازی پویا دارد. 2 In the real world, we face some complex and important problems that should be optimized, most of the real-world problems are dynamic. Solving dynamic optimization problems are very difficult due to possible changes in the location of the optimal solution. In dynamic environments, we are faced challenges when the environment changes. To respond to these changes in the environment, any change can be considered as the input of a new optimization problem that should be solved from the beginning, which is not suitable because it is time consuming. One technique for improving optimization and learning in dynamic environments is by using information from the past. By using solutions from previous environments, it is often easier to find promising solutions in a new environment. A common way to maintain and exploit information from the past is the use of memory, where solutions are stored periodically and can be retrieved and refined at the time that the environment changes. Memory can help search respond quickly and efficiently to change in a dynamic problem. Given that a memory has a finite size, if one wishes to store new information in the memory, one of the existing entries must be discarded. The mechanism used to decide whether the candidate entry should be included in the memory or not, and if so, which of the old entries should be replaced it, is called the replacement strategy. This paper explores ways to improve memory for optimization and learning in dynamic environments. In this paper, a memory with clustering and new replacement strategy for storing and restoring memory solutions has been used to enhance memory performance. The evolutionary algorithms that have been presented so far have the problem of rebuilding populations when multiple populations converge to an optimum. For this reason, we proposed algorithm with exclution mechanism that have the ability to explore the environment (Exploration) and extraction (Explitation). Thus, an optimization algorithm is required to solve the problems in dynamic environments well. In this paper, a novel collective optimization algorithm, namely the Clustering and Memory-based Parent-Child Swarm Algorithm (CMPCS), is presented. This method relies on both individual and group behavior. The proposed CMPCS method has been tested on the moving peaks benchmark (MPB). The MPB is a good Benchmark to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMPCS method compared to the other state-of-the-art methods in solving the dynamic optimization problems. 127 146 2019/03/52019/04/282019/04/282020/04/32019/06/12019/05/282019/04/72019/05/282019/05/28 1398/3/7 2020/01/222020/01/222020/08/182021/03/12020/08/182021/02/12021/05/222021/09/42020/01/22 1398/11/2 محسن مرادی mohsen moradi دانشگاه آزاد اسلامی، واحد یاسوج ایران Mohsen2145@yahoo.com صمد نجاتیان samad nejatian دانشگاه آزاد اسلامی، واحد یاسوج ایران samad.nej.2007@gmail.com حمید پروین hamid parvin دانشگاه آزاد اسلامی، واحد نورآباد ممسنی ایران parvin@iust.ac.ir کرم الله باقری فرد karamolla bagherifard دانشگاه آزاد اسلامی، واحد یاسوج ایران k.bagheri@iauyasooj.ac.ir وحیده رضایی vahideh rezaei دانشگاه آزاد اسلامی، واحد یاسوج ایران vahidehrezaie80@gmail.com Dynamic Optimization Dynamic Environments Memory Moving Peaks Benchmark بهینه‌سازی پویا محیط‌های پویا حافظه محک قله‌‌های متحرک [1] S. Saremi, S. 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کشف اهداف دریایی در تصاویر حرارتی نو‌فه‌ای با استفاده از یک الگوریتم بازشناسی ترکیبی Marine Target Detection in Noisy Infrared Images using a Hybrid Recognition Algorithm 1 1 سامانه‌های حمل و نقل دریایی بخش مهمی از ترابری جهانی را شامل می‌شوند. سامانه‌های نظارتی در صنایع دریانوردی و کشف اهداف دریایی از اهمیت به‌سزایی در کاربردهای نظامی و تجاری برخوردار است. افزایش روزافزون ترابری دریایی موجب علاقه پژوهش‌گران به توسعه روش‌های نظارتی هوشمند در زمینه ترابری دریایی شده است. به‌دلیل وجود کلاترها، مه و گرد و غبار در دریا، تصاویر حرارتی نسبت به تصاویر مرئی در این زمینه، از کارایی و دقت تشخیص بالاتری برخوردار هستند. در این مقاله، یک روش برای کشف اهداف دریایی در تصاویر نوفه‌ای حرارتی ارائه شده است. روش پیشنهاد‌شده شامل دو مرحله آشکارسازی خط افق در تصویر و سپس کشف اهداف است. ابتدا خط افق با استفاده از روش بیشینه‌‌گیری از تصویر گرادیان و برازش خط آشکار، سپس یک ناحیه مشخص برای جستجوی اهداف دریایی حول خط افق انتخاب می‌‌شود. محدود‌کردن ناحیه جستجو باعث افزایش سرعت روش پیشنهادی و کاهش هشدارهای کاذب می‌شود. در مرحله دوم، ناحیه انتخابی بلوک‌بندی می‌شود و از هر بلوک تعدادی ویژگی استخراج می‌شود. این ویژگی‌ها به چندین دسته‌بند متعارف داده و نتایج آنها به یک تصمیم‌ساز فازی نوع دوم بازه‌ای داده می‌شود تا با ترکیب این نتایج در مورد تعلق بلوک به ناحیه هدف یا پس‌زمینه تصمیم‌گیری نهایی را انجام دهد؛ در‌نهایت اهداف مورد نظر از تجمیع این بلوک‌ها و حذف موارد ناخواسته کشف می‌شوند. مقادیر شاخصه‌های ارزیابی دقت، صحت و فراخوان سیستم پیشنهادی روی پایگاه داده به‌ترتیب 59/97%، 19/96% و 92/97% بوده که نسبت به سایر روش‌های مقایسه شده، مقادیر بالاتری را گزارش داده است. نتایج به‌دست‌آمده نشان می‌دهد که در روش پیشنهادی، خط افق با حجم محاسباتی کم و با دقت خوبی آشکار شده و در‌نهایت اهداف دریایی مورد نظر با دقت بالایی کشف می‌شوند. 2 Maritime transportation system is a vital part of the world conveyance. The surveillance in maritime industry and detecting marine targets have a great impact on military and commercial applications. Daily increase in maritime zone encourages the researchers to develop intelligent surveillance approaches in the maritime transportation. The sensing methods generally include visual and infrared cameras, sensors, and radars. Cameras are widely used since they capture high resolution images than sensors and traditional radars. Also, applying complex pattern recognition techniques and decision-making processes to the camera images provides more accurate detection results. Due to the clutters, dust, and weather changes in the sea including the rainfall, snowfall, and heavy fog, the image quality taken by the visual cameras is drastically deteriorated. Also, detecting the sea targets -specially the small ones- and similarly the sea-sky horizon line becomes more challenging. In such situations, the infrared images reveal higher performance and accuracy in comparison with visible images. The sea-sky horizon line detection of noisy infrared images in small target detection algorithms with high intensity and low SNR is of great importance in maritime surveillance. Determining the horizon line simplifies the target detection by restricting the search area for the targets in the image. This task decreases the computation time and mistakes in the detection. This paper presents a method for detecting marine targets in noisy infrared images. The proposed method includes two steps of detecting the sea-sky horizon line and finding the targets. In the first step, the two-dimensional gradient of the image is computed, from which it is observed that the most variations are appeared at the edge points. With respect to this remark, the maximum of each column of the gradient image is found and the obtained values for all columns and corresponding rows’ numbers are kept in a set, namely the maximum pixels set. Then, to find the sea-sky horizon line, on the first and the last 75 pixels in the mentioned set, a straight line is fitted along the image width. Afterwards, to search for the objects, a region of interest is selected around the detected line. Restricting the search region increases the speed of the proposed method and decreases the number of false alarms. In the second step, this region is partitioned into some separate blocks; from each, multiple features are extracted. These features are fed into multiple classifiers whose outputs are given to a decision-making algorithm based on the interval type-II fuzzy fusion system. This system decides to which class (target or background) that block belongs. Finally, the objects are found by integrating the target blocks and removing the unwanted ones. To evaluate the proposed method, first an image dataset was generated using an infrared camera with medium wavelength in different situations. This was done due to no access to a complete infrared sea image bank. Sea infrared images were commonly corrupted by a combination of noises including the salt-and-pepper, Gaussian noise or electronic noises due to the detector of camera image supply. In order to attenuate these noises, a 3×3 median filter was applied to the raw image. Afterwards, to increase the image contrast, the histogram equalization method was performed. Finally, the proposed approach was run to find the marine targets in the enhanced image. The results demonstrated that the sea-sky horizon line was detected with low computational complexity and high accuracy while targets were also found with desirable detection rates. 147 160 2019/03/52019/04/282019/04/282020/04/32019/06/12019/05/282019/04/72019/05/282019/05/282019/08/18 1398/5/27 2020/01/222020/01/222020/08/182021/03/12020/08/182021/02/12021/05/222021/09/42020/01/222021/05/10 1400/2/20 آذر محمودزاده Azar Mahmoodzadeh دانشگاه آزاد اسلامی، واحد شیراز ایران mahmoodzadeh@iaushiraz.ac.ir Targets detection Sea-sky horizon line detection Feature extraction Classifier fuzzy inference system کشف اهداف آشکارسازی خط افق استخراج ویژگی دسته‌بند سیستم استنتاج فازی [1] Jiao J, Lu H, Wang Z, Zhang W, Qi L. "L0 Gradient Smoothing and Bimodal Histogram Analysis: A Robust Method for Sea-sky-line Detection", InProceedings of the ACM Multi-media Asia, 2019 Dec 15, pp. 1-6##[2] Li F, Zhang J, Sun W, Jin J, Li L, Dai Y, "Sea-sky line detection using gray variation differences in the time domain for unmanned surface vehicles", Signal, Image and Video Processing, no.8, pp.1-8, 2020.##[3] B. Wang, Y. Su and L. Wan, "A sea-sky line detection method for unmanned surface vehicles based on gradient saliency," Sensors, vol. 16, no. 4, pp. 543, 2016.##[4] I. Lipschutz, E. Gershikov and B. Milgrom, "New methods for horizon line detection in infrared and visible sea images," Int. J. Comput. Eng. Res, vol. 3, no. 3, pp. 1197-1215, 2013.##[5] Zhu M, Liu B, Wang M, Lu Y. Design and Analysis of Switch Median Filters for Salt and Pepper Noise. InAdvances in Graphic Communication, Printing and Packaging 2019 (pp. 220-226). Springer, Singapore.##[6] Abdallah MB, Malek J, Azar AT, Belmabrouk H, Monreal JE, Krissian K, "Adaptive noise-reducing anisotropic diffusion filter", Neural Computing and Applications, vol.1;27(5), pp.1273-300. 2016##[7] Z. C. Wang, "A bilateral filtering based image de-noising algorithm for night time infrared monitoring images," in International Con-ference on Computational Science and Compu-tational Intelligence, pp. 199-203, 2014.##[8] Bartyzel K. Adaptive kuwahara filter. Signal, Image and Video Processing. 2016 Apr 1;10(4):663-70.##[9] Z. Liu, C. Sun, X. Bai and F. Zhou, "Infrared ship target image smoothing based on adaptive mean shift," International Conference on Digital lmage Computing: Techniques and Applications, Nov201, pp.425-27.##[10] L. Dong, D. Ma, G. Qin, T. Zhang,W. Xu, "Infrared target detection in backlighting maritime environment based on visual attention model", Infrared Physics & Technology, no,1, pp. 193-200, 2019.##[11] W.Yang, H. Li, J. Liu, S. Xie, J. Luo , "A sea-sky-line detection method based on Gaussian mixture models and image texture features", International Journal of Advanced Robotic Systems,2019 Dec ;16(6):1729881419892116.##[12] X. Bai, F. Zhou, Y. Xie, T. Jin, "Adaptive morphological method for clutter elimination to enhance and detect infrared small target," International Conference on Internet Comput-ing in Science and Engineering, pp. 47 - 52, 2008.##[13] X. Chen and Y. Wang, "Detection of low contrast targets based on lifting scheme wavelet transform," International Conference on Mechatronics and Automation, pp. 4349 - 4354, 2009.##[14] X. Bai and F. Zhou, "Analysis of new top-hat transformation and the application for infrared dim small target detection," Pattern Recog-nition, pp. 2145-2156, 2010.##[15] X. Kong, L. Liu, Y. Qian and M. Cui, "Automatic detection of sea-sky horizon line and small targets in maritime infrared imagery," Infrared Physics & Technology, pp. 185-199, 2016.##[16] H. Deng, X. Sun, and X. Zhou, "A multiscale fuzzy metric for detectingsmall infrared targets against chaotic cloudy/sea-sky backgrounds," IEEE Trans. Cybern., vol. 49, no. 5, pp. 1694-1707, May 2019.##[17] L. Zhang, L. Peng, T. Zhang, S. Cao, and Z. Peng, "Infrared small tar-get detection via non-convex rank approximation minimization jointl2,1norm," Remote Sens., vol. 10, no. 11, p. 1821, 2018.##[18] P. Du and A. Hamdulla, "Infrared moving small-target detection usingspatial-temporal local difference measure," IEEE Geosci. Remote Sens.Lett., vol. 17, no. 10, pp. 1817-1821, Oct. 2020##[19] S. Kim, "Analysis of small infrared target features and learning-based false detection removal for infrared search and track," Pattern Analysis and Applications, vol. 17, no. 4, pp. 883-900, 2014.##[20] Scherreik MD, Rigling BD. Open set recognition for automatic target classification with rejection. IEEE Transactions on Aerospace and Electronic Systems. 2016 May 26;52(2):632-42.##[21] R.O. Duda, P.E. Hart, and D.G. 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Gaulton, "Mean shift segmentation assessment for individual forest tree delineation from airborne lidar data", Remote Sensing, vol. 11(11), pp.1263, 2019.##[27] P. Melin , E.Ontiveros-Robles ,CI. Gonzalez, JR. Castro, O. Castillo, "An approach for parameterized shadowed type-2 fuzzy membership functions applied in control applications", Soft Computing, vol.1, no-.23(11), pp.3887-901, 2019.##[28] V. Gandhi, V. Mendiratta, S. Thakur, Ms. Choudhry, "Morphological Operations For denoising of White Gaussian Noise Corrupted MR Images", In2020 International Con-ference on Electronics and Sustainable Co-mmunication Systems (ICESC), 2020 Jul 2 ,pp. 228-234.##[29] L. Ltti, C. Koch and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 1254 - 1259, 1998.##[1] Jiao J, Lu H, Wang Z, Zhang W, Qi L. "L0 Gradient Smoothing and Bimodal Histogram Analysis: A Robust Method for Sea-sky-line Detection", InProceedings of the ACM Multi-media Asia, 2019 Dec 15, pp. 1-6##[2] Li F, Zhang J, Sun W, Jin J, Li L, Dai Y, "Sea-sky line detection using gray variation differences in the time domain for unmanned surface vehicles", Signal, Image and Video Processing, no.8, pp.1-8, 2020.##[3] B. Wang, Y. Su and L. Wan, "A sea-sky line detection method for unmanned surface vehicles based on gradient saliency," Sensors, vol. 16, no. 4, pp. 543, 2016.##[4] I. Lipschutz, E. Gershikov and B. Milgrom, "New methods for horizon line detection in infrared and visible sea images," Int. J. Comput. Eng. Res, vol. 3, no. 3, pp. 1197-1215, 2013.##[5] Zhu M, Liu B, Wang M, Lu Y. Design and Analysis of Switch Median Filters for Salt and Pepper Noise. InAdvances in Graphic Communication, Printing and Packaging 2019 (pp. 220-226). Springer, Singapore.##[6] Abdallah MB, Malek J, Azar AT, Belmabrouk H, Monreal JE, Krissian K, "Adaptive noise-reducing anisotropic diffusion filter", Neural Computing and Applications, vol.1;27(5), pp.1273-300. 2016##[7] Z. C. Wang, "A bilateral filtering based image de-noising algorithm for night time infrared monitoring images," in International Con-ference on Computational Science and Compu-tational Intelligence, pp. 199-203, 2014.##[8] Bartyzel K. Adaptive kuwahara filter. Signal, Image and Video Processing. 2016 Apr 1;10(4):663-70.##[9] Z. Liu, C. Sun, X. Bai and F. Zhou, "Infrared ship target image smoothing based on adaptive mean shift," International Conference on Digital lmage Computing: Techniques and Applications, Nov201, pp.425-27.##[10] L. Dong, D. Ma, G. Qin, T. Zhang,W. Xu, "Infrared target detection in backlighting maritime environment based on visual attention model", Infrared Physics & Technology, no,1, pp. 193-200, 2019.##[11] W.Yang, H. Li, J. Liu, S. Xie, J. Luo , "A sea-sky-line detection method based on Gaussian mixture models and image texture features", International Journal of Advanced Robotic Systems,2019 Dec ;16(6):1729881419892116.##[12] X. Bai, F. Zhou, Y. Xie, T. Jin, "Adaptive morphological method for clutter elimination to enhance and detect infrared small target," International Conference on Internet Comput-ing in Science and Engineering, pp. 47 - 52, 2008.##[13] X. Chen and Y. Wang, "Detection of low contrast targets based on lifting scheme wavelet transform," International Conference on Mechatronics and Automation, pp. 4349 - 4354, 2009.##[14] X. Bai and F. Zhou, "Analysis of new top-hat transformation and the application for infrared dim small target detection," Pattern Recog-nition, pp. 2145-2156, 2010.##[15] X. Kong, L. Liu, Y. Qian and M. Cui, "Automatic detection of sea-sky horizon line and small targets in maritime infrared imagery," Infrared Physics & Technology, pp. 185-199, 2016.##[16] H. Deng, X. Sun, and X. Zhou, "A multiscale fuzzy metric for detectingsmall infrared targets against chaotic cloudy/sea-sky backgrounds," IEEE Trans. Cybern., vol. 49, no. 5, pp. 1694-1707, May 2019.##[17] L. Zhang, L. Peng, T. Zhang, S. Cao, and Z. Peng, "Infrared small tar-get detection via non-convex rank approximation minimization jointl2,1norm," Remote Sens., vol. 10, no. 11, p. 1821, 2018.##[18] P. Du and A. Hamdulla, "Infrared moving small-target detection usingspatial-temporal local difference measure," IEEE Geosci. Remote Sens.Lett., vol. 17, no. 10, pp. 1817-1821, Oct. 2020##[19] S. Kim, "Analysis of small infrared target features and learning-based false detection removal for infrared search and track," Pattern Analysis and Applications, vol. 17, no. 4, pp. 883-900, 2014.##[20] Scherreik MD, Rigling BD. Open set recognition for automatic target classification with rejection. IEEE Transactions on Aerospace and Electronic Systems. 2016 May 26;52(2):632-42.##[21] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification, John Wiley & Sons, 2012.##[22] J. Gou, H. Ma, w. Ou, S. Zeng, Y. Rao, H. Yang , A generalized mean distance-based k-nearest neighbor classifier. Expert Systems with Applications. 2019 Jan 1;115:356-72.##[23] M. Bilal, MS. Hanif, "High performance real-time pedestrian detection using light weight features and fast cascaded kernel SVM classification", Journal of Signal Processing Systems, vol.19, pp.117-29, 2019.##[24] M. Khishe, A. Safari , "Classification of sonar targets using an MLP neural network trained by dragonfly algorithm", Wireless Personal Communication, vol.1, pp.2241-60, 2019.##[25] Z. Liu, X. Bai, C. Sun, F. Zhou, Y. Li , "Multi-modal ship target image smoothing based on adaptive mean shift", IEEE Access, vol. 18;6, pp. 12573-86, 2018.##[26] W. Xiao, A. Zaforemska, M. Smigaj, Y. Wang, R. Gaulton, "Mean shift segmentation assessment for individual forest tree delineation from airborne lidar data", Remote Sensing, vol. 11(11), pp.1263, 2019.##[27] P. Melin , E.Ontiveros-Robles ,CI. Gonzalez, JR. Castro, O. Castillo, "An approach for parameterized shadowed type-2 fuzzy membership functions applied in control applications", Soft Computing, vol.1, no-.23(11), pp.3887-901, 2019.##[28] V. Gandhi, V. Mendiratta, S. Thakur, Ms. Choudhry, "Morphological Operations For denoising of White Gaussian Noise Corrupted MR Images", In2020 International Con-ference on Electronics and Sustainable Co-mmunication Systems (ICESC), 2020 Jul 2 ,pp. 228-234.##[29] L. Ltti, C. Koch and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 1254 - 1259, 1998.## ##