ijece-140502292014050229202444CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagNashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iranijece16823745372020174kh.Kamarif.rashidia.Khalili37202025326610.66224/ijece.28826.17.4.253http://ijece.org/fa/Article/28826http://ijece.org/fa/Article/Download/28826http://ijece.org/fa/Article/Download/28826http://ijece.org/fa/Article/Download/28826http://ijece.org/fa/Article/Download/28826http://ijece.org/fa/Article/Download/28826http://ijece.org/fa/Article/Download/28826http://ijece.org/fa/Article/Download/28826[1] V. Bolon-Canedo, N. Sanchez-Marono, A. Alonso-Betanzos, J. M. Benitez, and F. Herrera, "A review of microarray datasets and applied feature selection methods," Information Sciences, vol. 282, pp. 111-135, 20 Oct. 2014. [2] M. Dashtban and M. Balafar, "Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts," Genomics, vol. 109, no. 2, pp. 91-107, Mar. 2017. [3] I. Jain, V. K. Jain, and R. Jain, "Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification," Applied Soft Computing, vol. 62, pp. 203-215, Jan. 2018. [4] G. Ditzler, R. Polikar, and G. Rosen, "A sequential learning approach for scaling up filter-based feature subset selection," IEEE Trans. on Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2530-2544, Jun 2017. [5] S. Sayed, M. Nassef, A. Badr, and I. Farag, "A nested genetic algorithm for feature selection in high-dimensional cancer microarray datasets," Expert Systems with Applications, vol. 121, pp. 233-243, May 2019. [6] G. I. Sayed, A. E. Hassanien, and A. T. Azar, "Feature selection via a novel chaotic crow search algorithm," Neural Computing and Applications, vol. 31, pp. 171-188, 2019. [7] B. Cao, J. Zhao, P. Yang, P. Yang, X. Liu, J. Qi, et al., "Multiobjective feature selection for microarray data via distributed parallel algorithms," Future Generation Computer Systems, vol. 100, no. 2, pp. 952-981, Nov. 2019. [8] A. K. Das, S. K. Pati, and A. Ghosh, "Relevant feature selection and ensemble classifier design using bi-objective genetic algorithm," Knowledge and Information Systems, vol. 62, no. 2, pp. 1-33, Feb. 2019. [9] X. Li and M. Yin, "Multiobjective binary biogeography based optimization for feature selection using gene expression data," IEEE Trans. on NanoBioscience, vol. 12, no. 4, pp. 343-353, Dec. 2013. [10] A. Joshi, O. Kulkarni, G. Kakandikar, and V. Nandedkar, "Cuckoo search optimization-a review," in Materials Today: Proc., vol. 4, pp. 7262-7269, Jan. 2017. [11] N. Kwak and C. H. Choi, "Input feature selection for classification problems," IEEE Trans. on Neural Networks, vol. 13, no. 1, pp. 143-159, Jan. 2002. [12] B. K. Singh, K. Verma, and A. Thoke, "Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images," Expert Systems with Applications, vol. 66, pp. 114-123, Dec. 2016. [13] H. Uguz, "A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm," Knowledge-Based Systems, vol. 24, no. 7, pp. 1024-1032, Oct. 2011. [14] C. Lee and G. G. Lee, "Information gain and divergence-based feature selection for machine learning-based text categorization," Information Processing & Management, vol. 42, no. 1, pp. 155-165, Jan. 2006. [15] L. Yu and H. Liu, "Feature selection for high-dimensional data: a fast correlation-based filter solution," in Proc. of the 20th Int. Conf. on Machine Learning, ICML'03, pp. 856-863, Washington DC, USA, ???. 2003. [16] M. Robnik-Sikonja and I. Kononenko, "Theoretical and empirical analysis of ReliefF and RReliefF," Machine Learning, vol. 53, pp. 23-69, Oct. 2003. [17] P. E. Meyer, C. Schretter, and G. Bontempi, "Information-theoretic feature selection in microarray data using variable complementarity," IEEE J. of Selected Topics in Signal Processing, vol. 2, no. 3, pp. 261-274, Jun. 2008. [18] L. Lan and S. Vucetic, "Improving accuracy of microarray classification by a simple multi-task feature selection filter," International J. of Data Mining and Bioinformatics, vol. 5, no. 2, pp. 189-208, Jan. 2011. [19] J. Wang, L. Wu, J. Kong, Y. Li, and B. Zhang, "Maximum weight and minimum redundancy: a novel framework for feature subset selection," Pattern Recognition, vol. 46, no. 6, pp. 1616-1627, Jun. 2013. [20] N. Garcia-Pedrajas and J. Perez-Rodriguez, "Multi-selection of instances: a straightforward way to improve evolutionary instance selection," Applied Soft Computing, vol. 12, no. 11, pp. 3590-3602, Nov. 2012. [21] V. Bolon-Canedo, N. Sanchez-Marono, and A. Alonso-Betanzos, "A review of feature selection methods on synthetic data," Knowledge and Information Systems, vol. 34, no. 1, pp. 483-519, Mar. 2013. [22] D. Keco, A. Subasi, and J. Kevric, "Cloud computing-based parallel genetic algorithm for gene selection in cancer classification," Neural Computing and Applications, vol. 30, no. 5, pp. 1601-1610, Sept. 2018. [23] M. M. Mafarja and S. Mirjalili, "Hybrid whale optimization algorithm with simulated annealing for feature selection," Neurocomputing, vol. 260, pp. 302-312, Oct. 2017. [24] K. H. Chen, K. J. Wang, K. M. Wang, and M. A. Angelia, "Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data," Applied Soft Computing, vol. 24, pp. 773-780, Nov. 2014. [25] L. Y. Chuang, C. S. Yang, K. C. Wu, and C. H. Yang, "Gene selection and classification using Taguchi chaotic binary particle swarm optimization," Expert Systems with Applications, vol. 38, no. 10, pp. 13367-13377, Sept. 2011. [26] A. Ghosh, A. Datta, and S. Ghosh, "Self-adaptive differential evolution for feature selection in hyperspectral image data," Applied Soft Computing, vol. 13, no. 4, pp. 1969-1977, Apr. 2013. [27] S. Nakariyakul, "A hybrid gene selection algorithm based on interaction information for microarray-based cancer classification," PloS ONE, vol. 14, no. 2, Article No.: e0212333, 15 Feb. 2019. [28] X. Han, D. Li, P. Liu, and L. Wang, "Feature selection by recursive binary gravitational search algorithm optimization for cancer classification," Soft Computing, vol. 24, pp. 1-19, 2020. [29] B. Liu, M. Tian, C. Zhang, and X. Li, "Discrete biogeography based optimization for feature selection in molecular signatures," Molecular Informatics, vol. 34, no. 4, pp. 197-215, Apr. 2015. [30] S. Yazdani, J. Shanbehzadeh, and E. Aminian, "Feature subset selection using constrained binary/integer biogeography-based optimization," ISA Trans., vol. 52, no. 3, pp. 383-390, Mar. 2013. [31] D. Rodrigues, L. A. Pereira, R. Y. Nakamura, K. A. Costa, X. S. Yang, A. N. Souza, et al., "A wrapper approach for feature selection based on bat algorithm and optimum-path forest," Expert Systems with Applications, vol. 41, no. 5, pp. 2250-2258, May 2014. [32] A. Sharma, S. Imoto, and S. Miyano, "A top-r feature selection algorithm for microarray gene expression data," IEEE/ACM Trans. on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 754-764, Nov. 2011. [33] L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt, "Feature selection via dependence maximization," J. of Machine Learning Research, vol. 13, pp. 1393-1434, May 2012. [34] P. A. Mundra and J. C. Rajapakse, "SVM-RFE with MRMR filter for gene selection," IEEE Trans. on Nanobioscience, vol. 9, pp. 31-37, Oct. 2009. [35] N. Hoque, D. K. Bhattacharyya, and J. K. Kalita, "MIFS-ND: a mutual information-based feature selection method," Expert Systems with Applications, vol. 41, no. 14, pp. 6371-6385, Oct. 2014. [36] H. H. Hsu, C. W. Hsieh, and M. D. Lu, "Hybrid feature selection by combining filters and wrappers," Expert Systems with Applications, vol. 38, no. 7, pp. 8144-8150, Jul. 2011. [37] Y. Ye, Q. Wu, J. Z. Huang, M. K. Ng, and X. Li, "Stratified sampling for feature subspace selection in random forests for high dimensional data," Pattern Recognition, vol. 46, no. 3, pp. 769-787, Mar. 2013. [38] M. K. Ebrahimpour and M. Eftekhari, "Ensemble of feature selection methods: a hesitant fuzzy sets approach," Applied Soft Computing, vol. 50, no. C, pp. 300-312, Jan. 2017. [39] H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy," IEEE Trans. on Pattern Analysis & Machine Intelligence, vol. 27, no. 8, pp. 1226-1238, Jun. 2005. [40] A. Bai and A. Pradhan, "An efficient approach for feature extraction and classification of microarray cancer data," International J. of Computational Intelligence Studies, vol. 3, no. 4, pp. 339-355, Jan. 2014. [41] X. S. Yang and S. Deb, "Cuckoo search: recent advances and applications," Neural Computing and Applications, vol. 24, no. 1, pp. 169-174, Jan. 2014. [42] L. Lin and M. Gen, "Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation," Soft Computing, vol. 13, no. 2, pp. 157-168, Jan. 2009. [43] H. Rashidi and J. Khorshidi, "Exergoeconomic analysis and optimization of a solar based multigeneration system using multiobjective differential evolution algorithm," J. of Cleaner Production, vol. 170, pp. 978-990, Jan. 2018. [44] X. S. Yang and S. Deb, "Multiobjective cuckoo search for design optimization," Computers & Operations Research, vol. 40, no. 6, pp. 1616-1624, Jun. 2013. [45] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Trans. on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, Aug. 2002. [46] J. A. Olvera-Lopez, J. A. Carrasco-Ochoa, J. F. Martinez-Trinidad, and J. Kittler, "A review of instance selection methods," Artificial Intelligence Review, vol. 34, no. 2, pp. 133-143, Aug. 2010. [47] Y. Chen, J. Bi, and J. Z. Wang, "MILES: multiple-instance learning via embedded instance selection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 1931-1947, Oct. 2006. [48] L. Vendramin, R. J. Campello, and E. R. Hruschka, "Relative clustering validity criteria: a comparative overview," Statistical Analysis and Data Mining: the ASA Data Science J., vol. 3, no. 4, pp. 209-235, Aug.. 2010. [49] R. J. Campello and E. R. Hruschka, "A fuzzy extension of the silhouette width criterion for cluster analysis," Fuzzy Sets and Systems, vol. 157, no. 21, pp. 2858-2875, Nov. 2006. [50] Y. Saeys, I. Inza, and P. Larranaga, "A review of feature selection techniques in bioinformatics," Bioinformatics, vol. 23, no. 19, pp. 2507-2517, Oct. 2007. [51] Z. Zhu, Y. S. Ong, and M. Dash, "Markov blanket-embedded genetic algorithm for gene selection," Pattern Recognition, vol. 40, no. 11, pp. 3236-3248, Nov. 2007. [52] A. A. Aburomman and M. B. I. Reaz, "A novel SVM-kNN-PSO ensemble method for intrusion detection system," Applied Soft Computing, vol. 38, pp. 360-372, Jan. 2016. [53] D. H. Mazumder and R. Veilumuthu, "An enhanced feature selection filter for classification of microarray cancer data," ETRI J., vol. 41, no. 3, pp. 358-370, Jun. 2019. MohammadMomenyM.Agha SarramA. M. LatifR.Sheikhpour37202026727610.66224/ijece.28827.17.4.267http://ijece.org/fa/Article/28827http://ijece.org/fa/Article/Download/28827http://ijece.org/fa/Article/Download/28827http://ijece.org/fa/Article/Download/28827http://ijece.org/fa/Article/Download/28827http://ijece.org/fa/Article/Download/28827http://ijece.org/fa/Article/Download/28827http://ijece.org/fa/Article/Download/28827[1] Z. Zhang, D. Han, J. Dezert, and Y. Yang, "A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning," Signal Processing, vol. 147, pp. 173-189, Jun. 2018. [2] K. H. Jin and J. C. Ye, "Sparse and low-rank decomposition of a hankel structured matrix for impulse noise removal," IEEE Trans. Image Process., vol. 27, no. 3, pp. 1448-1461, Nov. 2018. [3] I. Turkmen, "The ANN based detector to remove random-valued impulse noise in images," J. Vis. Commun. Image Represent., vol. 34, pp. 28-36, Jan. 2016. [4] S. Liang, S. Lu, J. Chang, and C. C. T. Lin, "A novel two-stage impulse noise removal technique based on neural networks and fuzzy decision," IEEE Trans. on Fuzzy Systems, vol. 16, no. 4, pp. 863-873, Aug. 2008. [5] Y. Hou, Z. Li, P. Wang, and W. Li, "Skeleton optical spectra-based action recognition using convolutional neural networks," IEEE Trans. Circuits Syst. Video Technol., vol. 28, no. 3, pp. 807-811, Nov. 2018. [6] C. Ding and D. Tao, "Trunk-branch ensemble convolutional neural networks for video-based face recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 1002-1014, May 2018. [7] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs," IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 834-848, Apr. 2018. [8] G. Lin, Q. Wu, L. Qiu, and X. Huang, "Image super-resolution using a dilated convolutional neural network," Neurocomputing, vol. 275, pp. 1219-1230, Jan. 2018. [9] S. Yu, S. Jia, C. Xu, "Convolutional neural networks for hyperspectral image classification," Neurocomputing, vol. 219, pp. 88-98, Jan. 2016. [10] K. Audhkhasi, O. Osoba, B. Kosko, "Noise-enhanced convolutional neural networks," Neural Networks, vol.78, pp. 15-23, Jun. 2016. [11] W. Zhang, L. Jin, E. Song, and X. Xu, "Removal of impulse noise in color images based on convolutional neural network," Applied Soft Computing, vol. 82, Article No. 105558, Sept. 2019. [12] L. Jin, W. Zhang, G. Ma, and N. Song, "Learning deep CNNs for impulse noise removal in images," Journal of Visual Communication and Image Representation, vol. 62, pp. 193-205, Jul. 2019. [13] J. Yim and K. A. Sohn, "Enhancing the performance of convolutional neural networks on quality degraded datasets," in Int. Conf. Digit. Image Comput. Tech. Appl., DICTA'17, 8 pp., Sydney, NSW, Australia, 29 Nov.-1 Dec. 2017. [14] I. F. Jafar, R. A. Alna'Mneh, and K. A. Darabkh, "Efficient improvements on the BDND filtering algorithm for the removal of high-density impulse noise," IEEE Trans. Image Process., vol. 22, no. 3, pp. 1223-1232, Nov. 2013. [15] S. Q. Yuan and Y. H. Tan, "Impulse noise removal by a global-local noise detector and adaptive median filter," Signal Processing, vol. 86, no. 8, pp. 2123-2128, Aug. 2006. [16] D. Ze-Feng, Y. Zhou-Ping, and X. You-Lun, "High probability impulse noise-removing algorithm based on mathematical morphology," IEEE Signal Process. Lett., vol. 14, no. 1, pp. 31-34, Jan. 2007. [17] K. S. Srinivasan and D. Ebenezer, "A new fast and efficient decision-based algorithm for removal of high-density impulse noises," IEEE Signal Process. Lett., vol. 14, no. 3, pp. 189-192, Mar. 2007. [18] S. Esakkirajan, T. Veerakumar, A. N. Subramanyam, and C. H. PremChand, "Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter," IEEE Signal Process. Lett., vol. 18, no. 5, pp. 287-290, Mar. 2011. [19] G. Yu, S. Niu, and J. Ma, "A hybrid spectral gradient method for removing salt-and-pepper impulse noise," in Proc. 4th Int. Congress on Image and Signal Processing, pp. 765-768, Shanghai, China, 15-17 Oct. 2011. [20] P. Shanmugavadivu and P. S. Eliahim Jeevaraj, "Fixed-value impulse noise suppression for images using PDE based adaptive two-stage median filter," in Proc. Int. Conf. on Computer, Communication and Electrical Technology, ICCCET'11, pp. 290-295, Tamilnadu, India, 18-19 Mar. 2011. [21] X. Zhang and Y. Xiong, "Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter," IEEE Signal Process. Lett., vol. 16, no. 4, pp. 295-298, Apr. 2009. [22] S. Sadeghi, A. Rezvanian, and E. Kamrani, "An efficient method for impulse noise reduction from images using fuzzy cellular automata," AEU-Int. J. Electron. Commun., vol. 66, no. 9, pp. 772-779, Sept. 2012. [23] D. Androutsos, K. N. Plataniotis, and A. N. Venetsanopoulos, "Novel vector-based approach to color image retrieval using a vector angular-based distance measure," Comput. Vis. Image Underst., vol. 75, no. 1, pp. 46-58, Jul. 1999. [24] T. Toffoli and N. Margolus, Cellular Automata Machines: A New Environment for Modeling, 1987. [25] S. S. Wang and C. H. Wu, "A new impulse detection and filtering method for removal of wide range impulse noises," Pattern Recognit., vol. 42, no. 9, pp. 2194-2202, Sep. 2009. [26] P. Y. Chen and C. Y. Lien, "An efficient edge-preserving algorithm for removal of salt-and-pepper noise," IEEE Signal Process. Lett., vol. 15, pp. 833-836, Dec. 2008. [27] C. Szegedy, et al., "Going deeper with convolutions," in Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR'15, 9 pp., Boston, MA, USA, 7-12 Jun. 2015. [28] J. Gu, et al., "Recent advances in convolutional neural networks," Pattern Recognit., vol. 77, pp. 354-377, May 2018. [29] A. K. Samantaray, P. Kanungo, and B. Mohanty, "Neighbourhood decision based impulse noise filter," IET Image Process., vol. 12, no. 7, pp. 1222-1227, Jul. 2018. [30] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," Sep. 2014. [31] O. Russakovsky, et al., "ImageNet large scale visual recognition challenge," Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, Dec. 2015. [32] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedins of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998 [33] Y. Wang, A. Szlam, and G. Lerman, "Robust locally linear analysis with applications to image denoising and blind inpainting," SIAM J. Imaging Sci., vol. 6, no. 1, pp. 526-562, Jan. 2013. [34] B. Dong, H. Ji, J. Li, Z. Shen, and Y. Xu, "Wavelet frame based blind image inpainting," Appl. Comput. Harmon. Anal., vol. 32, no. 2, pp. 268-279, May 2012. [35] M. Yan, "Restoration of images corrupted by impulse noise and mixed gaussian impulse noise using blind inpainting," SIAM J. Imaging Sci., vol. 6, no. 3, pp. 1227-1245, Jan. 2013. [36] J. Salmon, Z. Harmany, C. A. Deledalle, and R. Willett, "Poisson noise reduction with non-local PCA," J. Math. Imaging Vis., vol. 48, no. 2, pp. 279-294, Feb. 2014.E.AlaviH.MashayekhiH.HassanpourB.Rahimpour Kami37202027728610.66224/ijece.28828.17.4.277http://ijece.org/fa/Article/28828http://ijece.org/fa/Article/Download/28828http://ijece.org/fa/Article/Download/28828http://ijece.org/fa/Article/Download/28828http://ijece.org/fa/Article/Download/28828http://ijece.org/fa/Article/Download/28828http://ijece.org/fa/Article/Download/28828http://ijece.org/fa/Article/Download/28828[1] ر. بهرامی و ح. مریم، "ارائه یک الگوریتم تشخیص رویداد جدید و ردیابی موضوع در اخبار فارسی،" مجموعه مقالات دومین همایش ملی پژوهش‌های کاربردی در علوم کامپیوتر و فناوری اطلاعات، 8 صص.، دانشگاه جامع علمی کاربردی، تهران، 1393. [2] J. Allan, "Introduction to topic detection and tracking," In: Allan J. (eds.) Topic Detection and Tracking. The Information Retrieval Series, vol 12, pp.1-16, Springer, Boston, MA, USA, 2002. [3] T. R. Zaman, R. Herbrich, J. Van Gael, and D. Stern, "Predicting information spreading in twitter," in Proc. Workshop on Computational Social Science and the Wisdom of Crowds, Nips, vol. 104, pp. 17599-17601, 2010. [4] V. Krishnan and J. Eisenstein, "Nonparametric Bayesian storyline detection from microtexts," arXiv preprint arXiv:1601.04580, 2016. [5] J. H. Lau, N. Collier, and T. Baldwin, "On-line trend analysis with topic models: # twitter trends detection topic model online," in Proc. of COLING, pp. 1519-1534, Mumbai, India, Dec. 2012. [6] L. AlSumait, D. Barbara, and C. Domeniconi, "On-line lda: adaptive topic models for mining text streams with applications to topic detection and tracking," in Proc. IEEE Int. Conf. on Data Mining, pp. 3-12, Pisa, Italy, 15-19 Dec. 2008.. [7] K. Nur'aini, I. Najahaty, L. Hidayati, H. Murfi, and S. Nurrohmah, "Combination of singular value decomposition and K-means clustering methods for topic detection on Twitter," in Proc. In. Conf. on Advanced Computer Science and Information Systems, ICACSIS’15, pp. 123-128, Depok, Indonesia, 10-11 Oct. 2015. [8] S. Li, X. Lv, T. Wang, and S. Shi, "The key technology of topic detection based on K-means," in Proc. Int. Conf. on Future Information Technology and Management Engineering, vol. 2, pp. 387-390, Changzhou,, China, 9-10 Oct. 2010. [9] L. M. Aiello, et al., "Sensing trending topics in Twitter," IEEE Trans. on Multimedia, vol. 15, no. 6, pp. 1268-1282, Jun. 2015. [10] Y. Xiaolin, Z. Xiao, K. Nan, and Z. Fengchao, "An improved single-pass clustering algorithm internet-oriented network topic detection," in Proc. 4th IEEE Int. Conf. on Intelligent Control and Information Processing, ICICIP’13, pp. 560-564, Beijing, China, 9-11 Jun. 2013. [11] F. Atefeh and W. Khreich, "A survey of techniques for event detection in twitter," Computational Intelligence, vol. 31, no. 1, pp. 132-164, Feb. 2013. [12] L. M. Aiello, et al., "Sensing trending topics in Twitter," IEEE Trans. on Multimedia, vol. 15, no. 6, pp. 1268-1282, Oct. 2013. [13] G. Petkos, S. Papadopoulos, and Y. Kompatsiaris, "Two-level Message Clustering for Topic Detection in Twitter." SNOW-DC@ WWW, pp. 49-56, 2014. [14] R. Ibrahim, A. Elbagoury, M. S. Kamel, and F. Karray, "Tools and approaches for topic detection from Twitter streams: survey," Knowledge and Information Systems, vol. 54, no. 3, pp. 511-539, Mar. 2018. [15] S. Yang, Q. Sun, H. Zhou, Z. Gong, Y. Zhou, and J. Huang, "A topic detection method based on keygraph and community partition," in Proc. of the Int. Conf. on Computing and Artificial Intelligence, ICCAI'18, pp. 30-34, Chengdu, China, Mar. 2018. [16] H. J. Choi and C. H. Park, "Emerging topic detection in twitter stream based on high utility pattern mining," Expert Systems with Applications, vol. 115, pp. 27-36, Jun. 2019. [17] Y. N. Li, Y. Tao, J. N. Wang, and Y. H. Fu, "A new online new event detection algorithm based on event merging and event splitting," Applied Mechanics and Materials, vol. 513, pp. 2024-2030, Feb. 2014. [18] D. T. Nguyen and J. E. Jung, "Real-time event detection for online behavioral analysis of big social data," Future Generation Computer Systems, vol. 66, pp. 137-145, Jun. 2017. [19] Y. Zhang, W. Mao, and J. Lin, "Modeling topic evolution in social media short texts," in Proc. IEEE Int. Conf. on Big Knowledge, ICBK’17, pp. 315-319, Hefei, China, 9-10 Aug. 2017. [20] D. M. Blei and P. I. Frazier, "Distance dependent Chinese restaurant processes," Journal of Machine Learning Research, vol. 12, pp. 2383-2410, 2011. [21] J. H. Lau, D. Newman, and T. Baldwin, "Machine reading tea leaves: automatically evaluating topic coherence and topic model quality," in Proc. of the 14th Conf. of the European Chapter of the Association for Computational Linguistics, EACL'14, pp. 530-539, Gothenburg, Sweden, 26-30 Apr. 2014. [22] J. H. Lau, T. Baldwin, and D. Newman, "On collocations and topic models," ACM Trans. on Speech and Language Processing, Article No.: 10, Jul. 2013. [23] Y. Fang, H. Zhang, Y. Ye, and X. Li, "Detecting hot topics from Twitter: a multiview approach," J. of Information Scienc, vol. 40, no. 5, pp. 578-593, Jul. 2014. [24] J. Tang and H. Liu, "Feature selection with linked data in social media," in Proc. of the SIAM Int. Conf. on Data Mining, pp. 118-128, Anaheim, CA, USA, 26-28 Apr. 2012.MaryamYounesiAbbasHeydarnooriF.Ghanadi37202028729710.66224/ijece.28830.17.4.287http://ijece.org/fa/Article/28830http://ijece.org/fa/Article/Download/28830http://ijece.org/fa/Article/Download/28830http://ijece.org/fa/Article/Download/28830http://ijece.org/fa/Article/Download/28830http://ijece.org/fa/Article/Download/28830http://ijece.org/fa/Article/Download/28830http://ijece.org/fa/Article/Download/28830[1] L. Villarroel, G. Bavota, B. Russo, R. Oliveto, and M. Di Penta, "Release planning of mobile apps based on user reviews," in Proc. of the 38th Int. Conf. on Software Engineering, ICSE'16, pp. 14-24, Austin, TX, USA, 14-22 May 2016. [2] F. Palomba, et al., "User reviews matter! tracking crowdsourced reviews to support evolution of successful apps," in Proc. of the 31st IEEE Int. Conf. on Software Maintenance and Evolution, ICSME'15, pp. 291-300, Bremen, Germany, 29 Sept.-1 Oct. 2015. [3] A. Ciurumelea, A. Schaufelbuhl, S. Panichella, and H. C. Gall, "Analyzing reviews and code of mobile apps for better release planning," in Proc. IEEE 24th Int. Conf. on Software Analysis, Evolution and Reengineering, SANER'17, pp. 91-102, Klagenfurt, Austria, 20-24 Feb. 2017. [4] A. Di Sorbo, S. Panichella, C. V. Alexandru, C. A. Visaggio, and G. Canfora, "SURF: summarizer of user reviews feedback," in IEEE/ACM 39th Int. Conf. on Software Engineering Companion, ICSE-C'17, pp. 55-58, Buenos Aires, Argentina, 20-28 May 2017. [5] S. Scalabrino, et al., "Listening to the crowd for the release planning of mobile apps," Trans. on Software Engineering, vol. 45, no. 1, pp. 1-19, Jan. 2017. [6] S. Panichella, et al., "ARdoc: app reviews development oriented classifier," in Proc. 24th ACM SIGSOFT Int. Symp. Found. Softw. Eng., pp. 1023-1027, Seattle, WA, USA, 15-17 Nov. 2016. [7] N. Chen, J. Lin, S. C. H. Hoi, X. Xiao, and B. Zhang, "AR-miner: mining informative reviews for developers from mobile app marketplace," in Proc. of the 36th Int. Conf. on Software Engineering, ICSE'14, pp. 767-778, Hyderabad, India, 31 May-7 Jun. 2014. [8] C. Gao, J. Zeng, M. R. Lyu, and I. King, "Online app review analysis for identifying emerging issues," in Proc. 40th Int. Conf. on Software Engineering, ICSE'18, pp. 48-58, Gothenburg, Sweden, 27 May-3 Jun. 2018. [9] C. Gao, et al., "Emerging app issue identification from user feedback: experience on WeChat," in Proc. IEEE-ACM 41st Int. Conf. Softw. Eng.: Softw. Eng. In Pract., pp. 279-288, Montreal, QC, Canada,, 25-31 May 2019. [10] T. T. Nguyen, A. T. Nguyen, and T. N. Nguyen, "Topic-based, time-aware bug assignment," ACM SIGSOFT Softw. Eng. Notes, vol. 39, no. 1, pp. 1-4, Feb. 2014. [11] J. Park, M. Lee, J. Kim, S. Hwang, and S. Kim, "CosTriage: a cost-aware triage algorithm for bug reporting systems," in Proc. of the 25th Int. Conf. on Artificial Intelligence, AAAI'11, pp. 139-144, San Francisco, CA, USA, 7-11 Aug. 2011. [12] R. Shokripour, J. Anvik, Z. M. Kasirun, and S. Zamani, "Why so complicated? simple term filtering and weighting for location-based bug report assignment recommendation," in Proc. of the 10th Working Conf. on Mining Software Repositories, MSR'13, pp. 2-11, Hyderabad, India, 31 May-7 Jun. 2014. [13] F. Servant and J. A. Jones, "WhoseFault: automatic developer-to-fault assignment through fault localization," in Proc. of the 34th Int. Conf. on Software Engineering, ICSE'12, pp. 36-46, Zurich, Switzerland, 2-9 Jun. 2012. [14] M. Linares-Vasquez, et al., "Triaging incoming change requests: bug or commit history, or code authorship?," in Proc. of the 28th IEEE Int. Conf. on Software Maintenance, ICSM'12, pp. 451-460, Trento, Italy, 23-28 Sept. 2012. [15] H. Naguib, N. Narayan, B. Br, and D. Helal, "Bug report assignee recommendation using activity profiles," in Proc. of the 10th Working Conf. on Mining Software Repositories, MSR'13, pp. 22-30, San Francisco,, CA, USA, 18-19 May 2013. [16] H. Hu, H. Zhang, J. Xuan, and W. Sun, "Effective bug triage based on historical bug-fix information," in Proc. of the 25th IEEE Int. Symp. on Software Reliability Engineering, ISSRE'14, pp. 122-132, Naples, Italy, 3-6 Nov. 2014. [17] O. Baysal, M. W. Godfrey, and R. Cohen, "A bug you like: a framework for automated assignment of bugs," in Proc. of the 17th IEEE Int. Conf. on Program Comprehension, ICPC'09, pp. 297-298, Vancouver, BC, Canada, 17-19 May 2009. [18] A. Yadav, S. K. Singh, and J. S. Suri, "Ranking of software developers based on expertise score for bug triaging," Inf. Softw. Technol., vol. 112, pp. 1-17, Aug. 2019. [19] R. Shokripour, J. Anvik, Z. M. Kasirun, and S. Zamani, "A time-based approach to automatic bug report assignment," J. Syst. Softw., vol. 102, pp. 109-122, Apr. 2015. [20] X. Xia, D. Lo, X. Wang, and B. Zhou, "Accurate developer recommendation for bug resolution," in Proc. of the 20th Working Conf. on Reverse Engineering, WCRE'13, pp. 72-81, Koblenz, Germany, 14-17 Oct. 2013. [21] R. Wu, H. Zhang, S. Kim, and S. C. Cheung, "ReLink: recovering links between bugs and changes," in Proc. of the 19th ACM Int. Symp. on the Foundations of Software Engineering, FSE'11, pp. 15-25, Szeged, Hungary, 5-9 Sept. 2011. [22] J. Sliwerski, T. Zimmermann, and A. Zeller, "When do changes induce fixes?," ACM SIGSOFT Softw. Eng. Notes, vol. 30, no. 4, pp. 1-5, Jul. 2005. [23] A. T. Nguyen, T. T. Nguyen, H. A. Nguyen, and T. N. Nguyen, "Multi-layered approach for recovering links between bug reports and fixes," in Proc. of the 20th ACM Int. Symp. on the Foundations of Software Engineering, FSE'12, 11pp., Washington, DC, USA, 19-21 Mar. 2012. [24] M. Linares-Vasquez, D. Lo, T. B. Le, and M. Linares-v, "RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information," in Proc. IEEE 23rd Int. Conf. on Program Comprehension, 12 pp., Florence, Italy, 18-19 May, 2015. [25] M. Linares-Vasquez, L. F. Cortes-Coy, J. Aponte, and D. Poshyvanyk, "ChangeScribe: a tool for automatically generating commit messages," in Proc. of the 37th IEEE/ACM Int. Conf. on Software Engineering, ICSE'15, pp. 709-712, Firenze, Italy, 16-24 Mar. 2015. [26] Y. Sun, Q. Wang, and M. Li, "Understanding the contribution of non-source documents in improving missing link recovery," in Proc. of the 10th ACM/IEEE Int. Symp. on Empirical Software Engineering and Measurement, ESEM'16, 10 pp., Ciudad Real Spain, 8-9 Sept. 2016. [27] Y. Sun, Q. Wang, and Y. Yang, "FRLink: improving the recovery of missing issue-commit links by revisiting file relevance," Inf. Softw. Technol., vol. 84, pp. 33-47, Apr. 2017. [28] T. D. B. Le, M. Linares-Vasquez, D. Lo, and D. Poshyvanyk, "RCLinker: automated linking of issue reports and commits leveraging rich contextual information," in Proc. of the 23rd IEEE Int. Conf. on Program Comprehension, ICPC'15, pp. 36-47, Florence, Italy, 18-19 May 2015.S. A.MostafaviM.Agha SarramT.Salimian37202029830510.66224/ijece.28831.17.4.298http://ijece.org/fa/Article/28831http://ijece.org/fa/Article/Download/28831http://ijece.org/fa/Article/Download/28831http://ijece.org/fa/Article/Download/28831http://ijece.org/fa/Article/Download/28831http://ijece.org/fa/Article/Download/28831http://ijece.org/fa/Article/Download/28831http://ijece.org/fa/Article/Download/28831[1] C. M. D. Farias, et al., "A systematic review of shared sensor networks," ACM Computing Surveys, vol. 44, no. 4, Article No.: 51, Feb. 2016. [2] C. Delgado, et al., "An optimization framework for resource allocation in virtual sensor networks," in Proc. IEEE Global Communications Conf,. GLOBECOM'15, 7 pp., San Diego, CA, USA, 6-10 Dec. 2015. [3] N. M. K. Chowdhury and R. Boutaba, "A survey of network virtualization," Computer Networks, vol. 54, no. 5, pp. 862-876, Apr. 2010. [4] A. Wang, M. Iyer, R. Dutta, G. N. Rouskas, and L. Baldinel, "Network virtualization: technologies, perspectives, and frontiers," Lightwave Technology, vol. 31, no. 4, pp. 523-537, 15 Feb. 2013. [5] H. Kim and N. Feamster, "Improving network management with software defined networking," IEEE Communications Magazine, vol. 51, no. 2, pp. 114-119, Feb. 2013. [6] S. Costanzo, L. Galluccio, G. Morabito, and S. Palazzo, "Software defined wireless networks: unbridling SDNs," in Proc. European Workshop on Software Defined Networking, EWSDN'12, 6 pp., Darmstadt, Germany, 25-26 Oct. 2012. [7] A. P. Jayasumana, Q. Han, and T. H. Illangasekare, "Virtual sensor networks-a resource efficient approach for concurrent application," in Proc. 4th Int. Conf. on IEEE Information Technology, ITNG'07, pp. 111-115, Las Vegas, NV, USA, 2-4 Apr. 2007. [8] D. Kreutz, et al., "Software-defined networking: a comprehensive survey," Proceeding of the IEEE, vol. 103, no. 1, pp. 14-76, Jan. 2015. [9] D. Zeng, P. Li, S. Guo, T. Miyazaki, J. Hu, and Y. Xiang, "Energy minimization in multi-task software-defined sensor networks," IEEE Trans. on Computers, vol. 64, no. 11, pp. 3128-3139, Nov. 2015. [10] S. Bousnina, et al., "A greedy approach for resource allocation in virtual sensor networks," 2017 Wireless Days, pp. 15-20, Porto, Portugal, 29-31 Mar. 2017. [11] C. Delgado, et al., "Energy-aware dynamic resource allocation in virtual sensor networks," in Proc. 14th IEEE Annual Consumer Communications Networking Conf., CCNC'17, pp. 264-267, Las Vegas, NV, USA, 8-11 Jan. 2017. [12] S. Tomovic and I. Radusinovic, "Allocation algorithm for handling multiple applications in software-defined WSN," in Proc. 24th Telecommunications Forum, TELFOR'16, 4 pp., Belgrade, Serbia, Nov. 2016. [13] S. Tomovic and I. Radusinovic, "Mapping application requirements to virtualization-enabled software defined WSN," Wireless Personal Communications, vol. 79, pp. 1693-1709, Feb. 2017. [14] A. Wang, M. Iyer, R. Dutta, G. N. Rouskas, and I. Baldine, "Network virtualization: technologies, perspectives, and frontiers," Journal of Lightwave Technology, vol. 31, no. 4, pp. 523-537, 15 Feb. 2013. [15] R. Huang, X. Chu, J. Zhang, and Y. H. Hu, "Energy-efficient monitoring in software defined wireless sensor networks using reinforcement learning: a prototype," International J. of Distributed Sensor Networks, vol. 11, no. 10, pp. 1-12, Aug. 2015. [16] C. M. de Farias, W. Li, F. C. Delicato, L. Pirmez, P. F. Pires, and A. Y. Zomaya, "SERAPH: service allocation algorithm for the execution of multiple applications in heterogeneous shared sensor and actuator networks," Management of Cyber Physical Objects in the Future Internet of Things, Springer, Cham, vol. 1, pp. 93-113, Jan. 2016. [17] A. E. Redondi, Energy-Aware Visual Analysis for Wireless Multimedia Sensor Networks, PhD. Dissertation, Politecnico Di Milano, 2014.HassanNazariM.R. DanaeeM.Sepahvand37202030631010.66224/ijece.28832.17.4.306http://ijece.org/fa/Article/28832http://ijece.org/fa/Article/Download/28832http://ijece.org/fa/Article/Download/28832http://ijece.org/fa/Article/Download/28832http://ijece.org/fa/Article/Download/28832http://ijece.org/fa/Article/Download/28832http://ijece.org/fa/Article/Download/28832http://ijece.org/fa/Article/Download/28832[1] N. Patwari, Location Estimation in Sensor Networks, Ph.D. Disseration, University of Michigan, Ann Arbor, USA, 2005. [2] X. Fang and J. Li, "Frame theory for optimal sensor augmentation problem of AOA localization," IEEE Signal Process. Lett., vol. 25, no. 9, pp. 1310-1314, Sept. 2018. [3] Y. Kang, Q. Wang, J. Wang, and R. Chen, "A high-accuracy TOA-based localization method without time synchronization in 3D space," IEEE Trans. Ind. Informat., vol. 15, no. 1, pp. 173-182, Jan. 2019. [4] S. Salari, F. Chan, Y. T. Chan, and W. Read, "TDOA estimation with compressive sensing measurements and hadamard matrix," IEEE Trans. Aerosp. Electron. Syst., vol. 54, no. 6, pp. 3137-3142, Dec. 2018. [5] X. Tian, et al., "Improve accuracy of fingerprinting localization with temporal correlation of the RSS," IEEE Trans. Mob. Comput., vol. 17, no. 1, pp. 113-126, Jan. 2018. [6] Y. Y. Li, G. Q. Qi, and A. D. Sheng, "Performance metric on the best achievable accuracy for hybrid TOA/AOA target localization," IEEE Commun. Lett., vol. 22, no. 7, pp. 1474-1477, Jul. 2018. [7] R. Sari and H. Zayyani, "RSS localization using unknown statistical path loss exponent model," IEEE Commun. Lett., vol. 22, no. 9, pp. 1830-1833, Sep. 2018. [8] C. Li, Y. Li, Z. Tian, S. L. Weekes, and K. Pahlavan, "Design and performance evaluation of a localization system to locate unwanted drones by using wireless signals," in Proc. IEEE Inte Conf. on Consumer Electronics, ICCE’16, 6 pp., Las Vegas, NV, USA, 12-14 Jan. 2018. [9] S. Tomic, M. Beko, M. Tuba, and V. M. F. Correia, "Target localization in NLOS environments using RSS and TOA measurements," IEEE Wirel. Commun. Lett., vol. 7, no. 6, pp. 1062-1065, Dec. 2018. [10] S. M. Kay, Fundamentals of Statistical Signal Processing, volume i: Estimation Theory, PTR Prentice-Hall, Englewood Cliffs, 1993. [11] H. Lohrasbipeydeh, T. A. Gulliver, and H. Amindavar, "Unknown transmit power RSSD based source localization with sensor position uncertainty," IEEE Trans. on Communications, vol. 63, no. 5, pp. 1784-1797, May 2015. [12] A. Beck, P. Stoica, and J. Li, "Exact and approximate solutions of source localization problems," IEEE Trans. Signal Process., vol. 56, no. 5, pp. 1770-1778, May 2008. [13] R. W. Ouyang, A. K. S. Wong, and C. T. Lea, "Received signal strength-based wireless localization via semidefinite programming: noncooperative and cooperative schemes," IEEE Trans. Veh. Technol., vol. 59, no. 3, pp. 1307-1318, Mar. 2010. [14] R. M. Vaghefi, M. R. Gholami, R. M. Buehrer, and E. G. Strom, "Cooperative received signal strength-based sensor localization with unknown transmit powers," IEEE Trans. Signal Process., vol. 61, no. 6, pp. 1389-1403, Mar. 2013. [15] Y. Hu and G. Leus, "Robust differential received signal strength-based localization," IEEE Trans. Signal Process., vol. 65, no. 12, pp. 3261-3276, Jun. 2017. [16] S. Tomic, M. Beko, R. Dinis, and V. Lipovac, "RSS-based localization in wireless sensor networks using SOCP relaxation," IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, SPAWC’13, pp. 749-753, Darmstadt, Germany, 16-19 Jun. 2013. [17] CVX Research, Inc., CVX: MATLAB Software for Disciplined Convex Programming [Online]. Available: http://cvxr.com/cvx/. [Accessed: 17-Dec-2010]. [18] J. H. Lee and R. M. Buehrer, "Location estimation using differential RSS with spatially correlated shadowing," in Proc. IEEE Glob. Telecommun. Conf., pp. 4613-4618, Honolulu, HI, USA, 30 Nov.- 4 Dec. 2009. [19] N. Salman, Y. J. Guo, A. H. Kemp, and M. Ghogho, "Analysis of linear least square solution for RSS based localization," in Proc. Int. Symp. on Communications and Information Technologies, ISCIT’12, pp. 1051-1054, Gold Coast, QLD, Australia, 2-5 Oct. 2012. [20] M. R. Gholami, R. M. Vaghefi, and E. G. Strom, "RSS-based sensor localization in the presence of unknown channel parameters," IEEE Trans. Signal Process., vol. 61, no. 15, pp. 3752-3759, Aug. 2013. [21] S. Tomic, M. Beko, R. Dinis, M. Tuba, and N. Bacanin, "Bayesian methodology for target tracking using combined RSS and AoA measurements," Phys. Commun., vol. 25, no. 1, pp. 158-166, Dec. 2017. [22] N. Patwari, et al., "Locating the nodes," IEEE Signal Processing Mag., vol. 22, no. 4, pp. 54-69, Jul. 2005. [23] S. Tomic, M. Beko, and R. Dinis, "RSS-based localization in wireless sensor networks using convex relaxation: noncooperative and cooperative schemes," IEEE Trans. Veh. Technol., vol. 64, no. 5, pp. 2037-2050, May 2015. [24] G. Rahimi, M. R. Danaee, and S. Bayat, "A generalized total least squares algorithm for RSS-based localization with unknown path-loss model parameters," in Proc. 24th Iran. Conf. Elec. Eng. ICEE’16, pp. 521-524, Shiraz, Iran, 10-12 May 2016. [25] T. S. Rappaport, Wireless Communications Principles and Practice, vol. 2, Prentice Hall PTR New Jersey, 1996. [26] Z. Li, X. Zhong, and J. Wei, "A novel geometry-based model for localization based on received signal strength," in Proc. IEEE 87th Vehicular Technology Conf., VTC Spring’18, 5 pp., Porto, Portugal, 3-6 Jun. 2018. [27] R. M. Vaghefi and R. M. Buehrer, "Received signal strength-based sensor localization in spatially correlated shadowing," in Proc. IEEE ICASSP’13, pp. 4076-4080, Vancouver, BC, Canada, 26-31 May 2013. [28] S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.ParyaTavoosiقاسمعازمیپگاهزرجام37202031131610.66224/ijece.28833.17.4.311http://ijece.org/fa/Article/28833http://ijece.org/fa/Article/Download/28833http://ijece.org/fa/Article/Download/28833http://ijece.org/fa/Article/Download/28833http://ijece.org/fa/Article/Download/28833http://ijece.org/fa/Article/Download/28833http://ijece.org/fa/Article/Download/28833http://ijece.org/fa/Article/Download/28833[1] G. Camps-Valls, et al., "Foetal ECG recovery using dynamic neural networks," Artificial Intelligence in Medicine, vol. 31, no. 3, pp. 197-209, Jul. 2004. [2] I. Silva, et al., "Noninvasive fetal ECG: the PhysioNet/computing in cardiology challenge 2013," Computing in Cardiology, vol. 35, no. 8, pp. 149-152, Sept. 2013. [3] R. Sameni and G. D. Clifford, "A review of fetal ECG signal processing; issues and promising directions," The Open Pacing, Electrophysiology & Therapy J., vol. 3, no. 1, pp. 4-20, Jan. 2010. [4] M. G. Jafari and J. A. Chambers, "Fetal electrocardiogram extraction by sequential source separation in the wavelet domain," IEEE Trans. on Biomedical Engineering, vol. 52, no. 3, pp. 390-400, Mar. 2005. [5] L. De Lathauwer, B. De Moor, and J. Vandewalle, "Fetal electrocardiogram extraction by blind source subspace separation," IEEE Trans. on Biomedical Engineering, vol. 47, no. 5, pp. 567-572, May 2000. [6] V. Zarzoso and A. K. Nandi, "Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation," IEEE Trans. on Biomedical Engineering, vol. 48, no. 1, pp. 12-18, Jan. 2001. [7] R. Martinek, et al., "Comparative effectiveness of ICA and PCA in extraction of fetal ECG from abdominal signals: towards non-invasive fetal monitoring," Frontiers in Physiology, vol. 9, pp. 1-25, May 2018. [8] A. K. Rahmati, S. Setarehdan, and B. Araabi, "A PCA/ICA based fetal ECG extraction from mother abdominal recordings by means of a novel data-driven approach to fetal ECG quality assessment," J. of Biomedical Physics & Engineering, vol. 7, no. 1, pp. 37-50, Mar. 2017. [9] J. Behar, A. Johnson, J. Oster, and G. Clifford, "An echo state neural network for foetal ECG extraction optimised by random search," in Proc. Adv. Neural Inf. Process. Syst., 5 pp., 2013. [10] J. S. Lee, M. Seo, S. W. Kim, and M. Choi, "Fetal QRS detection based on convolutional neural networks in noninvasive fetal electrocardiogram," in Proc. 4th Int. Conf. on Frontiers of Signal Processing, ICFSP'18, pp. 75-78, Poitiers, France, 15-18 Sept. 2018. [11] R. Sameni, M. B. Shamsollahi, C. Jutten, and G. D. Clifford, "A nonlinear Bayesian filtering framework for ECG denoising," IEEE Trans. on Biomedical Engineering, vol. 54, no. 12, pp. 2172-2185, Dec. 2007. [12] R. Singh and R. Dewan, "Extraction of fetus ECG using adaptive filters: a new approach," IJECCE, vol. 4, no. 5, pp. 1349-1351, 2013. [13] S. G. Lingala and M. Jacob, "Blind compressive sensing dynamic MRI," IEEE Trans. on Medical Imaging, vol. 32, no. 6, pp. 1132-1145, Jun. 2013. [14] M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, "Compressed sensing MRI," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 72-82, Mar. 2008. [15] C. Quinsac, A. Basarab, J. M. Girault, and D. Kouame, "Compressed sensing of ultrasound images: sampling of spatial and frequency domains," in Proc. IEEE Workshop on Signal Processing Systems, pp. 231-236, San Francisco, CA, USA, 6-8 Oct. 2010. [16] G. H. Chen, P. Theriault-Lauzier, J. Tang, B. Nett, S. Leng, J. Zambelli, et al., "Time-resolved interventional cardiac c-arm cone-beam CT: an application of the PICCS algorithm," IEEE Trans. on Medical Imaging, vol. 31, no. 4, pp. 907-923, Apr. 2012. [17] Y. C. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications, Cambridge University Press, 2012. [18] M. M. Abo-Zahhad, A. I. Hussein, and A. M. Mohamed, "Compression of ECG signal based on compressive sensing and the extraction of significant features," International J. of Communications, Network and System Sciences, vol. 8, no. 5, pp. 97-117, May 2015. [19] J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. on Information Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007. [20] Z. Zhang and B. D. Rao, "Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation," IEEE Trans. on Signal Processing, vol. 61, no. 8, pp. 2009-2015, Apr. 2013. [21] H. Mohimani, M. Babaie-Zadeh, and C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed L0 norm," IEEE Trans. on Signal Processing, vol. 57, no. 1, pp. 289-301, Jan. 2008. [22] A. Hyvarinen and E. Oja, "Independent component analysis: algorithms and applications," Neural Networks, vol. 13, no. 4, pp. 411-430, Jun. 2000. [23] M. Varanini, G. Tartarisco, L. Billeci, A. Macerata, G. Pioggia, and R. Balocchi, "A multi-step approach for non-invasive fetal ECG analysis," Computing in Cardiology, vol. 35, no. 8, pp. 281-284, Sept. 2013. [24] I. Silva, et al., "Noninvasive fetal ECG: the PhysioNet/computing in cardiology challenge 2013," Sept. 2013. [Online]. Available:https://archive.physionet.org/challenge/2013/sources/ [25] P. Podziemski and J. Gieraltowski, "Fetal heart rate discovery: algorithm for detection of fetal heart rate from noisy, noninvasive fetal ECG recordings," Computing in Cardiology, vol. 35, no. 8, pp. 333-336, Jul. 2013. [26] C. Liu, P. Li, C. Di Maria, L. Zhao, H. Zhang, and Z. Chen, "A multi-step method with signal quality assessment and fine-tuning procedure to locate maternal and fetal QRS complexes from abdominal ECG recordings," Physiological Measurement, vol. 35, no. 8, pp. 1665-1683, Jul. 2014. [27] L. Y. Di Marco, A. Marzo, and A. Frangi, "Multichannel foetal heartbeat detection by combining source cancellation with expectation-weighted estimation of fiducial points," Computing in Cardiology, vol. 40, no. 8, pp. 329-332, Sept. 2013. [28] J. Kuzilek and L. Lhotska, "Advanced signal processing techniques for fetal ECG analysis," Computing in Cardiology, vol. 40, no. 8, pp. 177-180, Sept. 2013.حمیدرضازرندیSh.Jalilian37202031732110.66224/ijece.28834.17.4.317http://ijece.org/fa/Article/28834http://ijece.org/fa/Article/Download/28834http://ijece.org/fa/Article/Download/28834http://ijece.org/fa/Article/Download/28834http://ijece.org/fa/Article/Download/28834http://ijece.org/fa/Article/Download/28834http://ijece.org/fa/Article/Download/28834http://ijece.org/fa/Article/Download/28834[1] S. Borkar and A. A. Chien, "The future of microprocessors," ACM J. of the Communications, vol. 54, no. 5, p. 67, May 2011. [2] W. M. Holt, "1.1 Moore's law: a path going forward," in Proc. IEEE Int. Solid-State Circuits Conf., ISSCC'16, pp. 8-13, San Francisco, CA, USA, 31 Jan.-4 Feb. 2016. [3] S. Rusu, High-Performance Digital-2015 Trends, 2015. [Online]. Available: http://isscc.org/doc/2015/isscc2015_trends.pdf. [Accessed: 14-Jul-2018]. [4] S. Mittal, "A survey of architectural techniques for improving cache power efficiency," Sustainable Computing: Informatics and Systems, vol. 4, no. 1, pp. 33-43, ???. 2014. [5] F. Hamzaoglu, et al., "A 1 Gb 2 GHz embedded DRAM in 22 nm tri-gate CMOS technology," in Proc. IEEE Int. Solid-State Circuits Conf., ISSCC'14, pp. 230-231, San Francisco, CA, USA, 9-13 Feb. 2014. [6] V. Sridharan, et al., "Memory errors in modern systems the good, the bad, and the ugly," in Proc. of the 20th Int. Conf. on Architectural Support for Programming Languages and Operating Systems, ASPLOS'15, pp. 297-310, Mar. 2015. [7] H. S. Philip et al., "Phase change memory," Proceedings of the IEEE, vol. 98, no. 12, pp. 2201-2227, Dec. 2010. [8] C. Augustine, N. N. Mojumder, X. Fong, S. H. Choday, S. P. Park, and K. Roy, "Spin-transfer torque MRAMs for low power memories: perspective and prospective," IEEE Sensor J., vol. 12, no. 4, pp. 756-766, Apr. 2012. [9] H. F. Liu, S. S. Ali, and X. F. Han, "Perpendicular magnetic tunnel junction and its application in magnetic random access memory," Chinese Physics, vol. 23, no. 7, p. 77501, Jun. 2014. [10] H. Akinaga and H. Shima, "Resistive random access memory (ReRAM) based on metal oxides," Proceedings of the IEEE, vol. 98, no. 12, pp. 2237-2251, Dec. 2010. [11] C. Augustine, et al., "Spin-transfer torque MRAMs for low power memories: perspective and prospective," IEEE Sensor J., vol. 12, no. 4, pp. 756-766, Apr. 2012. [12] S. Mittal and V. Jeffrey, "Reliability tradeoffs in design of volatile and nonvolatile caches," J. of Circuits, Systems and Computers, vol. 25, no. 11, pp. 1650139:1-1650139:14, Jun. 2016. [13] P. Chi, S. Li, Y. Cheng, Y. Lu, S. H. Kang, and Y. Xie, "Architecture design with STT-RAM: opportunities and challenges," in Proc. of Asia South Pacific Design Automation Conf., ASP-DAC'16, , pp. 109-114, Macau, China, 25-28 Jan. 2016. [14] R. Bishnoi, M. Ebrahimi, F. Oboril, and M. B. Tahoori, "Improving write performance for STT-MRAM," IEEE Trans. on Magnetics, vol. 52, no. 8, pp. 1-11, Aug. 2016. [15] H. Farkhani, M. Tohidi, A. Peiravi, J. K. Madsen, and F. Moradi, "STT-RAM energy reduction using self-referenced differential write termination technique," IEEE Trans. on Very Large Scale Integration Systems, vol. 25, no. 2, pp. 476-487, Feb. 2017. [16] Y. Zhang, et al., "Compact modeling of perpendicular-anisotropy CoFeB/MgO magnetic tunnel junctions," IEEE Trans. on Electron Devices, vol. 59, no. 3, pp. 819-826, Mar. 2012. [17] D. Suzuki, M. Natsui, A. Mochizuki, and T. Hanyu, "Cost-efficient self-terminated write driver for spin-transfer-torque RAM and logic," IEEE Trans. on Magnetics, vol. 50, no. 11, pp. 1-4, Nov. 2014.