ijece-202605192320260519230932CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagNashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iranijece168237455112020181shahriargholami mehrabadiyasserattar izisoroushakhlaghi5112020677410.66224/ijece.28359.18.1.67http://ijece.org/en/Article/28359http://ijece.org/en/Article/Download/28359http://ijece.org/en/Article/Download/28359http://ijece.org/en/Article/Download/28359http://ijece.org/en/Article/Download/28359http://ijece.org/en/Article/Download/28359http://ijece.org/en/Article/Download/28359http://ijece.org/en/Article/Download/28359[1] X. Liu and N. Ansari, "Green relay assisted D2D communications with dual batteries in heterogeneous cellular networks for IoT," IEEE Internet of Things J., vol. 4, no. 5, pp. 1707-1715, Jun. 2017. [2] X. Lin, J. Andrews, A. Ghosh, and R. Ratasuk, "An overview of 3GPP device-to-device proximity services," IEEE Communications Magazine, vol. 52, no. 4, pp. 40-48, May 2014. [3] G. Yu, Z. Zhang, F. Qu, and G. Y. Li, "Ultra-dense heterogeneous networks with full-duplex small cell base stations," IEEE Network, vol. 31, no. 6, pp. 108-114, Aug. 2017. [4] H. A. Mustafa, et. al., "Spectral efficiency improvements in HetNets by exploiting device-to-device communications," in Proc. IEEE Globecom Workshops, GC Wkshps’14, pp. 857-862, Austin, TX, USA, 12-14 Dec. 2014. [5] H. Boostanimehr and V. K. Bhargava, "Unified and distributed QoS-driven cell association algorithms in heterogeneous networks," IEEE Trans. on Wireless Communications, vol. 14, no. 3, pp. 1650-1662, Nov. 2014. [6] L. Liang, G. Feng, W. Wang, Y. Jia, and D. Liu, "A hierarchical resource allocation game for heterogeneous networks with relays," in Proc. IEEE 17th In. Conf. on High Performance Computing and Communications, pp. 727-733, New York, NY, USA, 24-26 Aug. 2015. [7] Z. Chen, H. Zhao, Y. Cao, and T. Jiang, "Load balancing for D2D-based relay communications in heterogeneous network," in Proc. 13th Int. Symp. on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt’15, pp. 23-29, Mumbai, India, 25-29 May 2015. [8] T. N. Le, D. Jayalath, and J. Coetzee, "Low complexity throughput optimisation in OFDMA HetNets with heterogeneous services," in Proc. IEEE 85th Vehicular Technology Conf., VTC Spring’17, 5 pp., Sydney, Australia, 4-7 Jun. 2017. [9] S. A. Khan, A. Kavak, K. Kucuk, and M. Asshad, "A new fractional frequency reuse method for interference management in LTE-A HetNets," in Proc. 27th Signal Processing and Communications Applications Conf., SIU’19, 4 pp., Sivas, Turkey, 24-26 Apr. 2019. [10] M. Elhattab, M. M. Elmesalawy, F. M. Salem, and I. I. Ibrahim, "Device-aware cell association in heterogeneous cellular networks: a matching game approach," IEEE Trans. on Green Communications and Networking, vol. 3, no. 1, pp. 57-66, Nov. 2018. [11] M. J. Alam, et al., "Improved joint cell association and interference mitigation for LTE-A heterogeneous networks," in Proc. IEEE 4th Int. Symp. on Telecommunication Technologies, ISTT’18, 4 pp., Selangor, Malaysia, 26-28 Nov. 2018. [12] Z. Luan, H. Qu, J. Zhao, and B. Chen, "Low complexity distributed max-throughput algorithm for user association in heterogeneous network," Wireless Personal Communications, vol. 87, no. 4, pp. 1147-1156, Apr. 2016. [13] M. N. Tehrani, M. Uysal, and H. Yanikomeroglu, "Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions," IEEE Communications Magazine, vol. 52, no. 5, pp. 86-92, May 2014. [14] P. Gandotra and R. K. Jha, "Device-to-device communication in cellular networks: a survey," J. of Network and Computer Applications, vol. 71, no. 1, pp. 99-117, Nov. 2016. [15] Z. Kuang, G. Liu, G. Li, and X. Deng, "Energy efficient resource allocation algorithm in energy harvesting-based D2D heterogeneous networks," IEEE Internet of Things J., vol. 6, no. 1, pp. 557-567, Feb. 2019. [16] Z. Chen, H. Zhao, Y. Cao, and T. Jiang, "Load balancing for D2D-based relay communications in heterogeneous network,"in Proc. 13th Int. Symp. on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt’15, pp. 23-29, Mumbai, India. 25-29 May 2015. [17] M. Ali, S. Qaisar, M. Naeem, S. Mumtaz, and J. J. Rodrigues, "Combinatorial resource allocation in D2D assisted heterogeneous relay networks," Future Generation Computer Systems, vol. 107, pp. 956-964, Jun. 2017. [18] A. Omran, L. Sboui, B. Rong, H. Rutagemwa, and M Kadoch, "Joint relay selection and load balancing using D2D communications for 5G HetNet MEC," in Proc. IEEE Int. Conf. on Communications Workshops, ICC Workshops'18, 5 pp., Shanghai, China, 20-24 May. [19] J. Dai, W. Lv, and F. Ye, "Spectrum allocation scheme based on Stackelberg game in two-tier HetNet," in Proc. IEEE 13th Int. Conf. on Signal Processing, ICSP’16, pp. 1270-1274, Chengdu, China, 6-10 Nov. 2016. [20] H. Zhang, S. Chen, X. Li, H. Ji, and X. Du, "Interference management for heterogeneous networks with spectral efficiency improvement," IEEE Wireless Communications, vol. 22, no. 2, pp. 101-107, Apr. 2015. [21] F. Jiang, Y. Liu, B. Wang, and X. Wang, "A relay-aided device-to-device-based load balancing scheme for multitier heterogeneous networks," IEEE Internet of Things J., vol. 4, no. 5, pp. 1537-1551, Mar. 2017. [22] H. Zhang, L. Song, and Y. J. Zhang, "Load balancing for 5G ultra-dense networks using device-to-device communications," IEEE Trans. on Wireless Communications, vol. 17, no. 6, pp. 4039-4050, Apr. 2018. [23] E. U. T. R. Access, Further Advancements for E-UTRA Physical Layer Aspects, 3GPP TS 36.814, V9. 0.0, Mar. 2010. [24] H. Boostanimehr and V. K. Bhargava, "Unified and distributed QoS-driven cell association algorithms in heterogeneous networks," IEEE Trans. on Wireless Communications, vol. 14, no. 3, pp. 1650-1662, Nov. 2014. [25] Y. Han, L. Jiang, and C. He, "Resource sharing optimization for device-to-device wireless system with femtocells," in Proc. IEEE Int. Conf. on Communications, ICC15’, pp. 2535-2540, London, UK, 8-12 Jun. 2015. [26] Y. Shi, H. Osman, E. Hepsaydir, and J. Wang, "Indoor wireless femtocell measurements," in Proc. IEEE Int. Conf. on Communications, ICC15’, pp. 2166-2171, London, UK, 8-12 Jun. 2015. [27] Y. Han, L. Jiang, and C. He, "Resource sharing optimization for device-to-device wireless system with femtocells," in Proc. IEEE Int. Conf. on Communications, ICC15’, pp. 2535-2540, London, UK, 8-12 Jun. 2015. Mohammad MahdiArzaniM.FathyAhmadAkbari511202011410.66224/ijece.28394.18.1.1http://ijece.org/en/Article/28394http://ijece.org/en/Article/Download/28394http://ijece.org/en/Article/Download/28394http://ijece.org/en/Article/Download/28394http://ijece.org/en/Article/Download/28394http://ijece.org/en/Article/Download/28394http://ijece.org/en/Article/Download/28394http://ijece.org/en/Article/Download/28394[1] A. Schwing, T. Hazan, M. Pollefeys, and R. Urtasun, Efficient Structured Prediction with Latent Variables for General Graphical Models, arXiv preprint arXiv:1206.6436, 2012. [2] A. G. Schwing, T. Hazan, M. Pollefeys, and R. Urtasun, "Distributed structured prediction for big data," in Proc. NIPS Workshop on Big Learning, 5 pp., 2012. [3] A. Schwing, T. Hazan, M. Pollefeys, and R. Urtasun, "Distributed message passing for large scale graphical models," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR’11, pp. 1833-1840, Providence, RI, USA, 20-25 Jun. 2011. [4] J. Piger, "Econometrics: models of regime changes," Complex Systems in Finance and Econometrics, pp. 190-202, Jul. 2009. [5] H. Tong, Threshold Models in Non-Linear Time Series Analysis, Lecture Notes in Statistics, vol. 21, Springer Science & Business Media, 2012. [6] J. D. Hamilton, "A new approach to the economic analysis of nonstationary time series and the business cycle," Econometrica: J. of the Econometric Society, vol. 57, no. 2, pp. 357-384, Mar. 1989. [7] F. Han, B. Reily, W. Hoff, and H. Zhang, "Space-time representation of people based on 3d skeletal data: a review," Computer Vision and Image Understanding, vol. 158, pp. 85-105, May 2017. [8] J. K. Aggarwal and M. S. Ryoo, "Human activity analysis: a review," ACM Computing Surveys, vol. 43, no. 3, Article No. 16, 43 pp., Apr. 2011. [9] J. Sung, C. Ponce, B. Selman, and A. Saxena, "Unstructured human activity detection from RGBD images," in Proc. IEEE Int. Conf. on Robotics and Automation, ICRA’12, pp. 842-849, Saint Paul, MN, USA, 14-18 May. 2012. [10] N. Hu, G. Englebienne, Z. Lou, and B. Krose, "Learning to recognize human activities using soft labels," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 39, no. 10, pp. 1973-1984, Oct. 2016. [11] H. S. Koppula, R. Gupta, and A. Saxena, "Learning human activities and object affordances from RGB-D videos," The International J. of Robotics Research, vol. 32, no. 8, pp. 951-970, Jul. 2013. [12] M. M. Arzani, et al., "Structured prediction with short/long-range dependencies for human activity recognition from depth skeleton data," in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS’17, pp. 560-567, Vancouver, BC, Canada, 24-28 Sept. 2017. [13] C. N. J. Yu and T. Joachims, "Learning structural svms with latent variables," in Proc. of the ACM 26th Annual Int. Conf. on Machine Learning, pp. 1169-1176, Montreal,Canada, Jun. 2009. [14] N. Shapovalova, A. Vahdat, K. Cannons, T. Lan, and G. Mori, "Similarity constrained latent support vector machine: an application to weakly supervised action classification," Computer Vision-ECCV, vol. 7578, pp. 55-68, Oct. 2012. [15] M. Khodabandeh, et al., "Discovering human interactions in videos with limited data labeling," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, pp. 9-18, Boston, MA, USA, 7- 12 Jun. 2015. [16] B. Ni, Y. Pei, P. Moulin, and S. Yan, "Multilevel depth and image fusion for human activity detection," IEEE Trans. on Cybernetics, vol. 43, no. 5, pp. 1383-1394, Aug. 2013. [17] T. Lan, Y. Wang, W. Yang, S. N. Robinovitch, and G. Mori, "Discriminative latent models for recognizing contextual group activities," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1549-1562, Dec. 2012. [18] X. Zhang, Y. Wang, M. Gou, M. Sznaier, and O. Camps, "Efficient temporal sequence comparison and classification using gram matrix embeddings on a Riemannian manifold," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 4498-4507, Las Vegas, NV, USA, 27- 30 Jun. 2016. [19] C. Wang, J. Flynn, Y. Wang, and A. L. Yuille, "Recognizing actions in 3D using action-snippets and activated simplices," in Proc. 31st. AAAI Conf. on Artificial Intelligence, AAAI’16, pp. 3604-3610, Phoenix, USA, 12-17 Feb. 2016. [20] R. Anirudh, P. Turaga, J. Su, and A. Srivastava, "Elastic functional coding of riemannian trajectories," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 39, no. 5, pp. 922-936, May 2017. [21] M. Devanne, H. Wannous, S. Berretti, P. Pala, M. Daoudi, and A. Del Bimbo, "3-d human action recognition by shape analysis of motion trajectories on riemannian manifold," IEEE Trans. on Cybernetics, vol. 45, no. 7, pp. 1340-1352, Sept. 2015. [22] J. Liu, A. Shahroudy, D. Xu, and G. Wang, "Spatio-temporal lstm with trust gates for 3d human action recognition," in Proc. European Conf. on Computer Vision, ECCV'16, pp. 816-833, Amsterdam, The Netherlands, 8-16 Oct. 2016. [23] J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, "Joint training of a convolutional network and a graphical model for human pose estimation," Advances in Neural Information Processing Systems, vol. 1, pp. 1799-1807, Dec. 2014. [24] A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, "Structural-rnn: deep learning on spatio-temporal graphs," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 5308-5317, Las Vegas , NV, USA, 27-30 Jun. 2016. [25] Z. Shi and T. K. Kim, Learning and Refining of Privileged Information-Based RNNs for Action Recognition from Depth Sequences, arXiv preprint arXiv:1703.09625, 2017. [26] J. Liu, G. Wang, L. Y. Duan, P. Hu, and A. C. Kot, Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks, arXiv preprint arXiv:1707.05740, 2017. [27] W. Chen and G. Guo, "Triviews: a general framework to use 3d depth data effectively for action recognition," J. of Visual Communication and Image Representation, vol. 26, pp. 182-191, Jan. 2015. [28] A. Eweiwi, M. S. Cheema, C. Bauckhage, and J. Gall, "Efficient pose-based action recognition," in Proc. Asian Conf. on Computer Vision, pp. 428-443, Singapore, Singapore, 1-5 Nov. 2014. [29] R. Slama, H. Wannous, and M. Daoudi, "Grassmannian representation of motion depth for 3d human gesture and action recognition," in Proc. 22nd IEEE Int. Conf. on Pattern Recognition, ICPR’14, pp. 3499-3504, Stockholm, Sweden, 24-28 Aug. 2014. [30] Y. Zhu, W. Chen, and G. Guo, "Evaluating spatiotemporal interest point features for depth-based action recognition," Image and Vision Computing, vol. 32, no. 8, pp. 453-464, Aug. 2014. [31] Y. Kong and Y. Fu, "Bilinear heterogeneous information machine for RGB-D action recognition," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1054-1062, Boston, MA, USA, 8-10 Jun. 2015. [32] R. Vemulapalli, F. Arrate, and R. Chellappa, "Human action recognition by representing 3d skeletons as points in a lie group," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 588-595, Columbus, Ohio, USA, 24-27 Jun. 2014. [33] G. Zhu, L. Zhang, P. Shen, and J. Song, "Human action recognition using multi-layer codebooks of key poses and atomic motions," Signal Processing: Image Communication, vol. 42, pp. 19-30, Mar. 2016. [34] B. Ni, P. Moulin, and S. Yan, "Order-preserving sparse coding for sequence classification," in Proc. European Conf. on Computer Vision, ECCV'12, pp. 173-187, Firenze, Italy , 7-13 Oct. 2012. [35] G. I. Parisi, C. Weber, and S. Wermter, "Self-organizing neural integration of pose-motion features for human action recognition," Frontiers in Neurorobotics, vol. 9, 3 pp., 2015. [36] P. Koniusz, A. Cherian, and F. Porikli, Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (Extended Version), arXiv preprint arXiv:1604.00239, 2016. [37] D. R. Faria, C. Premebida, and U. Nunes, "A probabilistic approach for human everyday activities recognition using body motion from rgb-d images," in Proc. 23rd IEEE Int. Symp. on Robot and Human Interactive Communication, RO-MAN’14, pp. 732-737, Edinburgh, UK, 25-29 Aug. 2014. [38] J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore, "Real-time human pose recognition in parts from single depth images," Communications of the ACM, vol. 56, no. 1, pp. 116-124, Jun. 2013. [39] A. Manzi, P. Dario, and F. Cavallo, "A human activity recognition system based on dynamic clustering of skeleton data," Sensors, vol. 17, no. 5, p. 1100, May 2017. [40] P. Wang, C. Yuan, W. Hu, B. Li, and Y. Zhang, "Graph based skeleton motion representation and similarity measurement for action recognition," in Proc. European Conf. on Computer Vision, ECCV'16, pp. 370-385, Amsterdam, The Netherlands, 8-16 Oct. 2016. [41] J. Shan and S. Akella, "3D human action segmentation and recognition using pose kinetic energy," in Proc. IEEE Workshop on Advanced Robotics and Its Social Impacts, ARSO’14, pp. 69-75, Evanston, IL, USA, 11-13 Sept. 2014. [42] M. I. Jordan and Y. Weiss, Probabilistic Inference in Graphical Models, Handbook of Neural Networks and Brain Theory, 2002. [43] A. Quattoni, S. Wang, L. P. Morency, M. Collins, and T. Darrell, "Hidden conditional random fields," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 1848-1852, Oct. 2007. [44] S. Nowozin, C. H. Lampert, et al., "Structured learning and prediction in computer vision," Foundations and Trends in Computer Graphics and Vision, vol. 6, no. 3-4, pp. 185-365, May 2011. [45] T. Hazan and R. Urtasun, "A primal-dual message-passing algorithm for approximated large scale structured prediction," in Proc. of the 23rd In. Conf. on Neural Information Processing Systems, NIPS’10, vol. 1, pp. 838-846, Dec. 2010. [46] H. Tong, Non-Linear Time Series: A Dynamical System Approach, Oxford University Pres, 1990. [47] L. Xia, C. C. Chen, and J. Aggarwal, "View invariant human action recognition using histograms of 3d joints," in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, CVPRW’12, pp. 20-27, Rhode Island, USA, 18-20 Jun. 2012. [48] L. Seidenari, V. Varano, S. Berretti, A. Bimbo, and P. Pala, "Recognizing actions from depth cameras as weakly aligned multi-part bag-of-poses," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, CVPRW’13, pp. 479-485, Portland, ON, USA, 23-24 Jun. 2013. [49] J. Ye, K. Li, G. J. Qi, and K. A. Hua, "Temporal order-preserving dynamic quantization for human action recognition from multimodal sensor streams," in Proc. of the 5th ACM on Int. Conf. on Multimedia Retrieval, pp. 99-106, Shanghai, China, 23-26 Jun. 2015. [50] X. Zhang, Y. Wang, M. Gou, M. Sznaier, and O. Camps, "Efficient temporal sequence comparison and classification using gram matrix embeddings on a Riemannian manifold," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 4498-4507, Las Vegas, NV, USA, 27-30 Jun 2016. [51] C. Zhang and Y. Tian, "RGB-D camera-based daily living activity recognition," J. of Computer Vision and Image Processing, vol. 2, no. 4, p. 12, Dec. 2012. [52] X. Yang and Y. Tian, "Effective 3d action recognition using eigenjoints," J. of Visual Communication and Image Representation, vol. 25, no. 1, pp. 2-11, Jan. 2014. [53] L. Piyathilaka and S. Kodagoda, "Gaussian mixture based hmm for human daily activity recognition using 3d skeleton features," in Proc. 8th IEEE Conf. on Industrial Electronics and Applications, ICIEA’13, , pp. 567-572, Melbourne, Australia, 19-21 Jun. 2013. [54] R. Gupta, A. Y. S. Chia, and D. Rajan, "Human activities recognition using depth images," in Proc. of the 21st ACM Int. Conf. on Multimedia, pp. 283-292, Barcelona, Spain, 21-23 Oct. 2013. [55] J. Wang, Z. Liu, and Y. Wu, "Learning actionlet ensemble for 3D human action recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 36, no. 5, pp. 914 - 927, May 2014. [56] S. Gaglio, G. L. Re, and M. Morana, "Human activity recognition process using 3-d posture data," IEEE Trans. on Human-Machine Systems, vol. 45, no. 5, pp. 586-597, Dec. 2015. [57] C. Wang, Y. Wang, and A. L. Yuille, "Mining 3d key-pose-motifs for action recognition," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2639-2647, Las Vegas, NV, USA, 27-30 Jun. 2016. [58] R. Vemulapalli, F. Arrate, and R. Chellappa, "R3dg features: relative 3d geometry-based skeletal representations for human action recognition," Computer Vision and Image Understanding, vol. 152, pp. 155-166, Nov. 2016. [59] C. Luo, C. Ma, C. Y. Wang, and Y. Wang, "Learning discriminative activated simplices for action recognition," in Proc. 32st. AAAI Conf. on Artificial Intelligence, AAAI’17, pp. 4211-4217, Feb. 2017. AdnanNasriM.FathyAliBroumandnia5112020758210.66224/ijece.28437.18.1.75http://ijece.org/en/Article/28437http://ijece.org/en/Article/Download/28437http://ijece.org/en/Article/Download/28437http://ijece.org/en/Article/Download/28437http://ijece.org/en/Article/Download/28437http://ijece.org/en/Article/Download/28437http://ijece.org/en/Article/Download/28437http://ijece.org/en/Article/Download/28437[1] Q. Zhang, L. Cheng, and R. Boutaba, "Cloud computing: state-of-the-art and research challenges," J. of Internet Services and Applications, vol. 1, no. 1, pp. 7-18, May 2010. [2] A. Uchechukwu, K. Li, and Y. Shen, "Energy consumption in cloud computing data centers," International J. of Cloud Computing and Services Science, vol. 3, no. 3, pp. 31-48, Jun. 2014. [3] H. Rong, H. Zhang, S. Xiao, C. Li, and C. Hu, "Optimizing energy consumption for data centers," Renewable and Sustainable Energy Reviews, vol. 58, no. 1, pp. 674-691, May 2016. [4] M. Ferdman, et al., "Clearing the clouds: a study of emerging scale-out workloads on modern hardware," in Proc. of the 17th Conf. on Architectural Support for Programming Languages and Operating Systems, ASPLOS’12, pp. 37-48, Mar. 2012. [5] P. Lotfi-Kamran, B. Grot, and B. Falsafi, "NOC-Out: microarchitecting a scale-out processor," in Proc. of the 45th Annual IEEE/ACM Int. Symp. on Microarchitecture, pp. 177-187, Vancouver, BC, Canada, 1-5 Dec. 2012. [6] M. R. Jokar, M. Arjomand, and H. Sarbazi-Azad, "Sequoia: a high-endurance NVM-based cache architecture," IEEE Trans. on Very Large Scale Integration (VLSI) Systems, vol. 24, no. 3, pp. 954-967, Apr. 2015. [7] P. Lotfi-Kamran, M. Modarressi, and H. Sarbazi-Azad, "An efficient hybrid-switched network-on-chip for chip multiprocessors," IEEE Trans. on Computers, vol. 65, no. 5, pp. 1656-1662, Jun. 2016. [8] V. Karakostas, O. S. Unsal, M. Nemirovsky, A. Cristal, and M. Swift, "Performance analysis of the memory management unit under scale-out workloads," in Proc. IEEE Int. Symp. on Workload Characterization, IISWC'14, 12 pp., Raleigh, NC, USA, 26-28 Oct. 2014. [9] J. Wang, J. Zhang, W. Zhang, K. Qiu, T. Li, and M. Wu, "Near threshold cloud processors for dark silicon mitigation: the impact on emerging scale-out workloads," in Proc. of the 12th ACM Int, Conf. on Computing Frontiers, 8 pp., Ischia, Italy, 10-12 May 2015. [10] A. Pahlevan, et al., "Towards near-threshold server processors," in Proc. IEEE Design, Automation & Test in Europe Conf. & Exhibition, DATE'16, pp. 7-12, Dresden, Germany,14-18 Mar. 2016. [11] Z. Wang, D. A. Jimenez, C. Xu, G. Sun, and Y. Xie, "Adaptive placement and migration policy for an STT-RAM-based hybrid cache," in Proc IEEE 20th Inte. Symp. on High Performance Computer Architecture, HPCA’14, pp. 13-24, Orlando, FL, USA, 15-19 Feb. 2014. [12] Y. T. Chen, J. Cong, H. Huang, B. Liu, C. Liu, M. Potkonjak, and G. Reinman, "Dynamically reconfigurable hybrid cache: an energy efficient last-level cache design," Proc. IEEE Design, Automation & Test in Europe Conf. & Exhibition, DATE’12, pp. 45-50, Dresden, Germany, 12-16 Mar. 2012. [13] J. Ahn, S. Yoo, and K. Choi, "Prediction hybrid cache: an energy-efficient STT-RAM cache architecture," IEEE Trans. on Computer, vol. 65, no. 3, pp. 940-951, May 2015. [14] A. Valero, J. Sahuquillo, S. Petit, P. Lopez, and J. Duato, "Design of hybrid second-level caches," IEEE Trans. on Computers, vol. 64, no. 7, pp. 1884-1897, Aug. 2015. [15] Z. Zhou, L. Ju, Z. Jia, and X. Li, "Managing hybrid on-chip scratchpad and cache memories for multi-tasking embedded systems," in Proc. 20th Asia and South Pacific Design Automation Conf., ASP-DAC’15, pp. 423-428, Chiba, Japan, 19-22 Jan. 2015. [16] D. Jevdjic, G. H. Loh, C. Kaynak, and B. Falsafi, "Unison cache: a scalable and effective die-stacked DRAM cache," in Proc. 47th Annual IEEE/ACM Int. Symp. on Microarchitecture, , pp. 25-37, Cambridge, UK, 13-17 Dec.? Dec. 2014. [17] S. Onsori, A. Asad, K. Raahemifar, and M. Fathy, "Notice of violation of IEEE publication principles: An energy-efficient heterogeneous memory architecture for future dark silicon embedded chip-multiprocessors," IEEE Trans. on Emerging Topics in Computing, vol. 4, no. 2, p. 1, May 2015. [18] A. Asad, A. Dorostkar, and F. Mohammadi., "A novel power model for future heterogeneous 3D chip-multiprocessors in the dark silicon age," EURASIP Journal on Embedded Systems, vol. 12, no. 1, pp. 1-16, Dec. 2016. [19] S. Onsori, A. Asad, K. Raahemifar, and M. Fathy, "OptMem: dark-silicon aware low latency hybrid memory design," in Proc IEEE. International Conf. on VLSI Systems, Architectures, Technology and Applications, VLSI-SATA'16, 5 pp., Bangalore, India, 10-12 Jan. 2016. [20] S. Onsori, A. Asad, O. Ozturk, and M. Fathy, "Hybrid stacked memory architecture for energy efficient embedded chip-multiprocessors based on compiler directed approach," in Proc. IEEE 6th Int. Green Computing Conf. and Sustainable Computing Conf., IGSC'15, 7 pp., Las Vegas, NV, USA, 14-16 Dec. 2015. [21] S. Senni, L. Torres, G. Sassatelli, A. Gamatie, and B. Mussard, "Exploring MRAM technologies for energy efficient systems-on-chip," IEEE J. on Emerging and Selected Topics in Circuits and Systems, vol.6, no. 3pp. 279-292, Apr. 2016. [22] -, CloudSuite 1.0, 2012. [Online]. Available: http://parsa.epfl.ch/cloudsuite [23] J. S. Vetter and S. Mittal, "Opportunities for nonvolatile memory systems in extreme-scale high-performance computing," Computing in Science & Engineering, vol. 17, no. 2, pp. 73-82, Jan. 2015. [24] D. Jevdjic, S. Volos, and B. Falsafi, "Die-stacked DRAM caches for servers: hit ratio, latency, or bandwidth? Have it all with footprint cache," ACM SIGARCH Computer Architecture News, vol. 41, no. 3, pp. 404-415, Jun. 2013. [25] J. Park, J. Jung, K. Yi, and C. M. Kyung, "Static energy minimization of 3D stacked L2 cache with selective cache compression," in Proc. IFIP/IEEE 21st Int. Conf. on Very Large Scale Integration, VLSI-SoC'13, pp. 228-233, Istanbul, Turkey, 7-9 Oct. 2013. [26] M. H. Samavatian, H. Abbasitabar, M. Arjomand, and H. Sarbazi-Azad, "An efficient STT-RAM last level cache architecture for GPUs," in Proc. of the 51st Annual Design Automation Conf., 6 pp., San Francisco, CA, USA, 1-5 Jun. 2014. [27] M. Bakhshalipour, et al., "Reducing writebacks through in-cache displacement," ACM Trans. on Design Automation of Electronic Systems, vol. 24, no. 2, pp. 1-21, Jan. 2019. [28] S. Rashidi, M. Jalili, and H. Sarbazi-Azad, "A survey on pcm lifetime enhancement schemes," ACM Computing Surveys, vol. 52, no. 4, pp. 1-38, Aug. 2019. [29] S. Rashidi, M. Jalili, and H. Sarbazi-Azad, "Improving MLC PCM performance through relaxed write and read for intermediate resistance levels," ACM Trans. on Architecture and Code Optimization, vol. 15, no. 1, pp. 1-31, Mar. 2018. [30] C. Qian, L. Huang, P. Xie, N. Xiao, and Z. Wang, "A study on non-volatile 3d stacked memory for big data applications," in Proc. Int. Conf. on Algorithms and Architectures for Parallel Processing, pp. 103-118, Zhangjiajie, China, 18-20 Nov. 2015. [31] M. Hosomi, et al., "A novel non-volatile memory with spin torque transfer magnetization switching: spin-ram," in Proc. IEEE Int. Electron Devices Meeting, IEDM Technical Digest., pp. 459-462, Washington, DC, USA, 5-5 Dec. 2005. [32] A. K. Mishra, X. Dong, G. Sun, Y. Xie, N. Vijaykrishnan, and C. R. Das, "Architecting on-chip interconnects for stacked 3D STT-RAM caches in CMPs," ACM SIGARCH Computer Architecture News, ACM, vol. 39, no. 3, pp. 69-80, Jun. 2011. [33] Q. Wang, L. Shen, and Z. Wang, "Research on scale-out workloads and optimal design of multicore processors," in Proc. of Int. Conf. on Soft Computing Techniques and Engineering Application, pp. 157-166, Kunming, China, 25-27 Sept. 2013. [34] E. Chen, D. Lottis, A. Driskill-Smith, D. Druist, V. Nikitin, S. Watts, X. Tang, and D. Apalkov, "Nonvolatile spin-transfer torque RAM (STT-RAM)," in Proc. IEEE Device Research Conf., DRC'10, pp. 249-252, South Bend, IN, USA, 21-23 Jun. 2010. [35] T. Wenisch, et al., "SimFlex: statistical sampling of computer system simulation," IEEE Micro, vol. 26, no. 4, pp. 18-31, Jul./Aug. 2006. [36] M. Palesi, S. Kumar, and D. Patti, Noxim: Network-on-Chip Simulator, http://noxim.sourceforge.net, 2010. [37] S. Li, J. H. Ahn, R. D. Strong, J. B. Brockman, D. M. Tullsen, and N. P. Jouppi, "McPAT: an integrated power, area, and timing modeling framework for multicore and manycore architectures," in Proc. Annual IEEE/ACM Int. Symp. on MICRO-42, pp. 469-480, New York, NY, USA, 12-16 Dec. 2009. [38] N. Muralimanohar, R. Balasubramonian, and N. P. Jouppi, "CACTI 6.0: a tool to model large caches," HP Laboratories, Technical Report, 2009. [39] X. Dong, C. Xu, N. Jouppi, and Y. Xie, "NVSim: a circuit-level performance, energy, and area model for emerging non-volatile memory," IEEE Trans, on Computer-Aided Design of Integrated Circuits and Systems, vol. 31, no. 7, pp. 994-1007, Jun. 2012. vajihehsabetiSepidefaiazihadiseshirinkhah5112020495810.66224/ijece.28441.18.1.49http://ijece.org/en/Article/28441http://ijece.org/en/Article/Download/28441http://ijece.org/en/Article/Download/28441http://ijece.org/en/Article/Download/28441http://ijece.org/en/Article/Download/28441http://ijece.org/en/Article/Download/28441http://ijece.org/en/Article/Download/28441http://ijece.org/en/Article/Download/28441[1] Y. J. Chanu, T. Tuithung, and K. M. Singh, "A short survey on image steganography and steganalysis techniques," in Proc. IEEE 3rd National Conf. on Emerging Trends and Applications in Computer Science, pp. 52-55, Shillong, India, 30-31 Mar. 2012. [2] V. Sabeti, S. Samavi, M. Mahdavi, and S. Shirani, "Steganalysis and payload estimation of embedding in pixel differences using neural networks," Pattern Recognition, vol. 43, no. 1, pp. 405-415, Jan. 2010. [3] M. Hussain, A. W. A. Wahab, Y. I. B. Idris, A. T. Ho, and K. H. Jung, "Image steganography in spatial domain: a survey," Signal Processing: Image Communication, vol. 65, pp. 46-66, Jul. 2018. [4] J. Kadhim, P. Premaratne, P. J. Vial, and B. Halloran, "Comprehensive survey of image steganography: techniques, evaluations, and trends in future research," Neurocomputing, vol. 335, pp. 299-326, Mar. 2019. [5] A. Westfeld and A. Pfitzman, "Attacks on steganographic systems," Lecture Notes in Computer Science, vol. 1768, pp. 61-75, Springer, 2000. [6] J. Fridrich, M. Goljan, and R. Du, "Detecting LSB steganography in color and gray-scale images," Magazine of IEEE Multimedia, vol. 4, no. 8, pp. 22-28, Oct-Dec. 2001. [7] S. Dumitrescu, X. Wu, and Z. Wang, "Detection of LSB steganography via sample pair analysis," IEEE Trans. on Signal Processing, vol. 51, no. 7, pp. 1995-2007, Jul. 2003. [8] A. Ker and R. Bohme, "Revisiting weighted stego-image steganalysis," Proc. of SPIE Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, vol. 6819, pp. 1-17, 27-31 Jan. 2008. [9] A. Westfeld, "Detecting low embedding rates," in Proc. Int. Workshop on Information Hiding, pp. 324-339, Oct. 2002. [10] J. J. Harmsen and W. A. Pearlman, "Steganalysis of additive noise modelable information hiding," in Proc. of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents, vol. 5020, pp. 131-142, Santa Clara, CA, USA, 20-20 Jun. 2003. [11] A. Ker, "Steganalysis of LSB matching in grayscale images," IEEE Signal Processing Letters, vol. 12, no. 6, pp. 441-444, Jun. 2005. [12] A. Ker, "Resampling and the detection of LSB matching in color bitmaps," in Proc. of SPIE Security, Steganography and Watermarking of Multimedia Contents VII, pp. 1-15, 2005. [13] J. Zhang, I. J. Cox, and G. Doerr, "Steganalysis for LSB matching in images with high-frequency noise," in Proc. of the IEEE Workshop on Multimedia Signal Processing, pp. 385-388, Crete, Greece, 1-3 Oct. 2007. [14] G. Cancelli, I. J. Cox, and G. Doerr, "Improved LSB matching steganalysis based on the amplitude of local extrema," in Proc. IEEE Int. Conf. on Image Processing, ICIP’08, Oct. 2008. [15] F. Huang, B. Li, and J. Huang, "Attack LSB matching steganography by counting alteration rate of the number of neighbourhood gray levels," in Proc. IEEE Int. Conf. on Image Processing, ICIP’07, vol. 1, pp. 401-404, San Antonio, TX, USA, 16-19 Sept. 2007. [16] M. Goljan, J. Fridrich, and T. Holotyak, "New blind steganalysis and its implications," in Proc. of SPIE Security, Steganography, and Watermarking of Multimedia Contents VIII, vol. 6072, pp. 1-13, San Jose, CA, USA, 2006. [17] G. Cancelli, G. Doerr, I. J. Cox, and M. Barni, "A comparative study of +1 steganalyzers," in Proc. IEEE Int. Workshop on Multimedia Signal Processing, pp. 791-96, Cairns, Australia, 8-10 Oct. 2008. [18] N. Provos, "Defending against statistical steganalysis," in Proc. 10th USENIX Security Symp., pp. 323-335, Washington, DC, USA, 13-17 Oct. 2001. [19] C. K. Chan and L. M. Cheng, "Hiding data in images by simple LSB substitution," Pattern Recognition, vol. 37, pp. 469-474, 2004. [20] م. مهدوي، ش. سماوي، م. اخوت و ص. اكرمي، "روش پنهان‌نگاري تطبيقي بر اساس اغتشاش جمع‌شونده،" پانزدهمين كنفرانس مهندسي برق ايران، جلد مخابرات، 9 صص.، تهران، ایران، ارديبهشت 1386. [21] C. Liu, X. Li, X. Lu, and B. Yang, "A content-adaptive approach for reducing embedding impact in steganography," in Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP’10, pp. 1762-1765, Dallas, TX, USA, 14-19 Mar. 2010. [22] R. Rinita and S. Laha, "Optimization of stego image retaining secret information using Genetic Algorithm with 8-connected PSNR," Procedia Computer Science, vol. 60, pp. 468-477, 2015. [23] H. R. Kanan and B. Nazeri, "A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm," Expert Systems with Applications, vol. 41, no. 14, pp. 6123-6130, Oct. 2014. [24] R. Wang, C. Lin, and J. Lin, "Image hiding by optimal LSB substitution and genetic algorithm," Pattern Recognition, vol. 34, no. 3, pp. 671-683, Mar. 2001. [25] P. D. Shah and R. S. Bichkar, "A secure spatial domain image steganography using genetic algorithm and linear congruential generator," in Dash S., Das S., Panigrahi B. (eds) Int. Conf. on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 632. Springer, Singapore, 2018. [26] D. C. Wu and W. H. Tsai, "A steganographic method for images by pixel-value differencing," Pattern Recognition Letters, vol. 24, no. 9-10, pp. 1613-1626, Jun. 2003. [27] V. Sabeti, S. Samavi, and S. Shirani, "An adaptive LSB matching steganography based on octonary complexity measure," Multimedia Tools and Applications, vol. 64, no. 3, pp. 777-793, 2013. [28] S. Cho, B. H. Cha, M. Gawecki, and C. C. J. Kuo, "Block-based image steganalysis: algorithm and performance evaluation," J. of Visual Communication and Image Representation, vol. 24, no. 7, pp. 846-856, Oct. 2013. F.Ansari RamM.KhademiAbbasEbrahimi moghadamH.Sadoghi Yazdi5112020152810.66224/ijece.28453.18.1.15http://ijece.org/en/Article/28453http://ijece.org/en/Article/Download/28453http://ijece.org/en/Article/Download/28453http://ijece.org/en/Article/Download/28453http://ijece.org/en/Article/Download/28453http://ijece.org/en/Article/Download/28453http://ijece.org/en/Article/Download/28453http://ijece.org/en/Article/Download/28453[1] E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information," IEEE Trans. on Inform.Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006. [2] D. L. Donoho, "Compressed sensing," IEEE Trans. on Inform. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006. [3] S. Qaisar, R. M. Bilal, W. Iqbal, M. Naureen, and S. Lee, "Compressive sensing: from theory to applications, a survey," J. of Communications and Networks, vol. 15, no. 5, pp. 443-456, Oct. 2013. [4] A. Maleki, Approximate Message Passing Algorithms for Compressed Sensing, PhD Thesis, Stanford University, 2011. [5] S. Chen, D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit," SIAM Rev., vol. 43, no. 1, pp. 129-159, Mar. 2001. [6] W. Lu and N. Vaswani, "Modified basis pursuit denoising (modified BPDN) for noisy compressive sensing with partially known support," in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP'10, pp. 3926-3929, Dallas, TX, USA, 14-19 Mar. 2010. [7] B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, "Least angle regression," Annals of Statistics, vol. 32, no. 2, pp. 407-499, Apr. 2004. [8] S. Chen, S. A. Billing, and W. Lue, "Orthogonal least squares methods andtheir application to nonlinear system identification," International J. of Control, vol. 50, no. 5, pp. 1873-1896, 1989. [9] Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, "Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition," in Proc. 27th Asilomar Conf. on Signals, Systems and Computers, vol. 1, pp. 40-44, Pacific Grove, CA, USA, 1-3 Nov. 1993. [10] D. Needell and J. A. Tropp, "CoSaMP: iterative signal recovery from incomplete and inaccurate samples," Applied and Computational Harmonic Analysis, vol. 26, no. 3, pp. 301-321, May 2008. [11] S. Ji, Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. on Signal Processing, vol. 56, no. 6, pp. 2346-2356, Jun. 2008. [12] H. Zayyani, M. Babaie-Zadeh, and C. Jutten, "An iterative Bayesian algorithm for sparse component analysis in presence of noise," IEEE Trans. on Signal Processing, vol. 57, no. 11, pp. 4378-4390, Nov. 2009. [13] S. Derin Babacan, R. Molina, and A. K. Katsaggelos, "Bayesian compressive sensing using Laplace priors," IEEE Trans. on Image Processing, vol. 19, no. 1, pp. 53-63, Jan. 2010. [14] D. Baron, S. Sarvotham, and R. G. Baraniuk, "Bayesian compressive sensing via belief propagation," IEEE Trans. on Signal Processing, vol. 58, no. 1, pp. 269-280, Jan. 2010. [15] R. Giri and B. D. Rao, Type I and Type II Bayesian Methods for Sparse Signal Recovery Using Scale Mixtures, arXiv preprint arXiv:1507.05087, 2015. [16] E. C. Marques, N. Maciel, L. A. B. Naviner, and H. Caie, "A review of sparse recovery algorithms," IEEE Access, vol. 7, pp. 1300-1322, Dec. 2018. [17] T. Blumensath and M. E. Davies, "Iterative hard thresholding for compressed sensing," Applied and Computational Harmonic Analysis, vol. 27, no. 3, pp. 265-274, Nov. 2009. [18] A. Chambolle, R. A. DeVore, N. Lee, and B. J. Lucier, "Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage," IEEE Trans. Image Process., vol. 7, no. 3, pp. 319-335, Mar. 1998. [19] D. L. Donoho, A. Maleki, and A. Montanari, "Message passing algorithms for compressed sensing," in Proc. Nat. Acad. Sci., vol. 106, pp. 18914-18919, Nov. 2009. [20] M. Bayati and A. Montanari, "The dynamics of message passing on dense graphs, with applications to compressed sensing," IEEE Trans. Inf. Theory, vol. 57, no. 2, pp. 764-785, Feb. 2011. [21] J. Vila, P. Schniter, S. Rangan, F. Krzakala, and L. Zdeborov'a, "Adaptive damping and mean removal for the generalized approximate message passing algorithm," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., ICASSP'15, pp. 2021-2025, Brisbane, QLD, Australia, 19-24 Apr. 2015. [22] S. Rangan, P. Schniter, and A. Fletcher, "On the convergence of approximate message passing with arbitrary matrices," in Proc. IEEE Int. Symp. on Information Theory, ISIT'14, pp. 236-240, Honolulu, HI, USA, 29 Jun.-4 Jul. 2014. [23] F. Caltagirone, L. Zdeborov'a, and F. Krzakala, "On convergence of approximate message passing," in Proc. IEEE Int. Symp. on Information Theory, ISIT'14, pp. 1812-1816, Honolulu, HI, USA, 29 Jun.-4 Jul. 2014. [24] A. Manoel, F. Krzakala, E. W. Tramel, and L. Zdeborov'a, "Swept approximate message passing for sparse estimation," in Proc 32nd Int. Conf. on Machine Learning, ICML'15, vol. 37, pp. 1123-1132, Jul. 2015. [25] S. Rangan, A. K. Fletcher, P. Schniter, and U. S. Kamilov, "Inference for generalized linear models via alternating directions and Bethe free energy minimization," in Proc. IEEE Int. Symp. on Information Theory, ISIT'15, pp. 1640-1644, Hong Kong, China, 14-19 Jun. 2015. [26] J. Ma and L. Ping, "Orthogonal AMP," IEEE Access, vol. 5, pp. 2020-2033, Jan. 2017. [27] S. Rangan, P. Schniter, and A. K. Fletcher, "Vector approximate message passing," IEEE Trans. on Information Theory, vol. 65, no. 10, pp. 6664-6684, May 2019. [28] A. Maleki and D. L. Donoho, "Optimally tuned iterative thresholding algorithm for compressed sensing," IEEE J. Select. Top. Signal Processing, vol. 4, no. 2, pp. 330-341, Apr. 2010. [29] I. Daubechies, M. Defrise, and C. D. Mol, "An iterative thresholding algorithm for linear inverse problems with a sparsity constraint," Commun. Pure Appl. Math., vol. 57, no. 11, pp. 1413-1457, Nov. 2004. [30] A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM J. Imag. Sci., vol. 2, no. 1, pp. 183-202, Mar. 2009. [31] A. Montanari, Graphical Models Concepts in Compressed Sensing, in Compressed Sensing: Theory and Applications, (Y. C. Eldar and G. Kutyniok, Eds.), Cambridge Univ. Press, 2012. [32] D. L. Donoho, A. Maleki, and A. Montanari, "Noise sensitivity phase transition," IEEE Trans. Inform. Theory, vol. 57, no. 10, pp. 6920-6941, Oct. 2011. [33] A. Mousavi, A. Maleki, and R. G. Baraniuk, "Parameterless optimal approximate message passing," arXiv preprint, 2013. [34] H. A. van der Vorst, Iterative Krylov Methods for Large Linear Systems, vol. 13, Cambridge, U.K.: Cambridge Univ. Press, 2003. [35] Alyson K. Fletcher and Philip Schniter, "Learning and Free Energies for Vector Approximate Message Passing," arXive preprint 2016. [36] S. Akhlaghi, N. Zhou, and Z. Huang, "Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation," ArXiv preprints, Feb. 2017. [37] "Generalized approximate message passing," SourceForge.net project GAMPmatlab, Available on-line at http://gampmatlab.sourceforge.net/Seyedeh SaraMirmobinMohammadGhasemzadehAminNezarat5112020424810.66224/ijece.28481.18.1.42http://ijece.org/en/Article/28481http://ijece.org/en/Article/Download/28481http://ijece.org/en/Article/Download/28481http://ijece.org/en/Article/Download/28481http://ijece.org/en/Article/Download/28481http://ijece.org/en/Article/Download/28481http://ijece.org/en/Article/Download/28481http://ijece.org/en/Article/Download/28481[1] ا. سادات علوی، ﻫ. مشایخی، ح. حسن‌پور و ب. رحیم‌پور کامی، "استفاده از خوشه‌بندی تکاملی برای تشخیص موضوع در بلاگ‌نویسی کوچک با لحاظ‌نمودن اطلاعات شبکه اجتماعی،" نشريه مهندسي برق و مهندسي كامپيوتر ايران، ب- مهندسي كامپيوتر، جلد 17، شماره 4، صص. 286-277، زمستان 1398. [2] دبيرخانه شورای عالی اطلاع‌رسانی، ‌جمع‌آوری اطلاعات چالش‌ها و روش‌های ترجمه ماشینی زبان انگلیسی به فارسی و بالعکس،‌ شورای عالی اطلاع‌رسانی، مستند شماره 1/1/2537/190، دانشگاه علم و صنعت ايران، تهران، 1388. [3] م. عاصی، "پردازش دستوري زبان فارسي با رايانه،" نامه فرهنگستان، جلد 1، شماره 1، صص. 51-29، اسفند 1383. [4] ش. عباسی، "داده‌های عظیم تعاریف و چالش‌ها،" مجموعه مقالات کنفرانس بین‌المللی سیستم‌های غیر خطی و بهینه‌سازی کامپیوتر، 13 صص.، شیراز، دبی، امارات متحده عربی، خرداد 1394. [5] م. جهانی، "نو پرداز"، شرکت نوپرداز، 19/03/1397. [درون‌خطی]. Available: https://nopardazco.com. [دستيابی در 22/ 05/ 1398]. [6] C. Dyer, A. Cordova, A. Mont, and J. Lin, "Fast, easy, and cheap: construction of statistical machine translation models with mapreduce," in Proc. of the 3rd Workshop on Statistical Machine Translation, pp. 199-207, Columbus, OH, USA, Jun. 2008. [7] ا. نظارات و ط. موسوی میانگاه، "طراحی و پیاده‌سازی یک سامانه بازیابی اطلاعات دوزبانه با استفاده از پیکره‌های زبانی،" پژوهش‌نامه پردازش و مدیریت اطلاعات (علوم و فناوری اطلاعات سابق)، جلد 27، شماره 2، صص. 211-198، زمستان 1390. [8] T. Mousavimiyangah, "Constructing a large-scale English-Persian parallel corpus," Meta, vol. 54, no. 1, pp. 181-188, Jan. 2009. [9] Y. Zhou, C. Zong, and B. Xu, "Bilingual chunk alignment in statical machine translation," in Proc. Int. Conf. on System Man and Cybernetics, pp. 1401-1406, Hague, The Netherlands, 10-13 Oct. 2004. [10] M. Murata, T. Ohno, S. Matsubara, and Y. Inagaki, "Construction of chunk-aligned bilingual lecture corpus for simultaneous machine translation," in Proc. of the 7th Conf. on International Language Resources and Evaluation, LREC'10, pp. 1765-1770, Valletta, Malta, 19-21 May 2010. MohammadSabriMohammadMoinFarbodRazzazi5112020294110.66224/ijece.28897.18.1.29http://ijece.org/en/Article/28897http://ijece.org/en/Article/Download/28897http://ijece.org/en/Article/Download/28897http://ijece.org/en/Article/Download/28897http://ijece.org/en/Article/Download/28897http://ijece.org/en/Article/Download/28897http://ijece.org/en/Article/Download/28897http://ijece.org/en/Article/Download/28897[1] K. D. Mitnick and W. L. Simon, The Art of Deception: Controlling the Human Element of Security, John Wiley & Sons, 2011. [2] D. V. Klein, "Foiling the cracker: a survey of, and improvements to, password security," in Proc. of the 2nd USENIX Security Workshop, pp. 5-14, Berkeley, CA, USA, Aug. 1990. [3] R. Brunelli and D. Falavigna, "Person identification using multiple cues," Pattern Analysis and Machine Intelligence, IEEE Trans. on, vol. 17, no. 10, pp. 955-966, Apr. 1995. [4] A. K. Jain, et al., "Integrating faces, fingerprints, and soft biometric traits for user recognition," in Proc. ECCV Workshop BioAW, pp. 259-269 Prague, Czech Republic, 15-15 May 2004. [5] R. Bolle, S. Pankanti, and A. K. Jain, Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society, Kluwer Academic Publishers, 2006. [6] K. Nandakumar, Multibiometric Systems: Fusion Strategies and Template Security, Michigan State, p. 250, 2008. [7] D. A. Cooper, et al., Interfaces for Personal Identity Verification, (including updates as of 02-08-2016) 2016. [8] C. L. Wilson, P. J. Grother, and R. Chandramouli, Biometric data specification for personal identity verification, 2006. [9] D. Isenor and S. G. Zaky, "Fingerprint identification using graph matching," Pattern Recognition, vol. 19, no. 2, pp. 113-122, Jan. 1986. [10] ISO/IEC 19794-2:2011, Information Technology-Biometric Data Interchange Formats-Part 2: Finger Minutiae Data, 2011. [11] P. J. Grother and W. J. Salamon, MINEX II Performance of Fingerprint Match-on-Card Algorithms-Phase II/III Report, 2009. [12] M. Sabri, M. S. Moin, and F. Razzazi, "A new framework for match on card and match on host quality based multimodal biometric authentication," J. of Signal Processing Systems, vol. 91, no. 2, pp. 163-177, Feb. 2018 2018. [13] N. Poh, T. Bourlai, and J. Kittler, "A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms," Pattern Recognition, vol. 43, no. 3, pp. 1094-1105, Mar. 2010. [14] M. Vatsa, et al., "On the dynamic selection of biometric fusion algorithms," IEEE Trans. on Information Forensics and Security, vol. 5, no. 3, pp. 470-479, Sept. 2010. [15] M. A. Olsen, V. Smida, and C. Busch, "Finger image quality assessment features-definitions and evaluation," IET Biometrics, vol. 5, no. 2, pp. 47-64, May 2016. [16] S. Bharadwaj, M. Vatsa, and R. Singh, "Biometric quality: a review of fingerprint, iris, and face," EURASIP J. on Image and Video Processing, vol. 2014, no. 1, pp. 34-62, Jul. 2014. [17] L. Best-Rowden and A. K. Jain, Automatic Face Image Quality Prediction, arXiv preprint arXiv:1706.09887, 2017. [18] J. Chen, et al., "Face image quality assessment based on learning to rank," IEEE Signal Processing Letters, vol. 22, no. 1, pp. 90-94, Aug. 2015. [19] C. S. Mlambo and M. B. Shabalala, "Distortion analysis on binary representation of minutiae based fingerprint matching for match-on-card," in Proc. IEEE Symp. Series on Computational Intelligence, pp. 349-353, Cape Town, South Africa, 7-10 Dec. 2015. [20] M. Govan and T. Buggy, "A computationally efficient fingerprint matching algorithm for implementation on smartcards," in Proc. First IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems, BTAS, 6 pp., Crystal City, VA, USA, 27-29 Sept. 2007. [21] B. Vibert, C. Rosenberger, and A. Ninassi, "Security and performance evaluation platform of biometric match on card," in Proc. IEEE World Congress on Computer and Information Technology, WCCIT'13, 6 pp., Sousse, Tunisia, 22-24 Jun. 2013. [22] K. K. Nair, A. Helberg, and J. Van der Merwe, "An approach to improve the match-on-card fingerprint authentication system security," in Proc. IEEE 6th Int. Conf. on Digital Information and Communication Technology and Its Applications, DICTAP'16, pp. 119-125, Konya, Turkey, 21-23 Jul. 2016. [23] A. Nagar, K. Nandakumar, and A. K. Jain, "Biometric template transformation: a security analysis," Media Forensics and Security II. 2010. International Society for Optics and Photonics. [24] A. Lumini and L. Nanni, "Overview of the combination of biometric matchers," Information Fusion, , vol. 33, no. 4, pp. 71-85, Jan. 2017. [25] R. Raghavendra, et al., "Designing efficient fusion schemes for multimodal biometric systems using face and palmprint," Pattern Recognition, vol. 44, no. 5, pp. 1076-1088, May 2011. [26] M. Vatsa, et al., "On the dynamic selection of biometric fusion algorithms," IEEE Trans. on Information Forensics and Security, vol. 5, no. 3, pp. 470-479, Jul. 2010. [27] A. Baig, et al., "Cascaded multimodal biometric recognition framework," IET Biometrics, vol. 3, no. 1, pp. 16-28, Aug. 2013. [28] S. Bharadwaj, et al., "QFuse: online learning framework for adaptive biometric system," Pattern Recognition, vol. 48, no. 11, pp. 3428-3439, Aug. 2015. [29] D. Peralta, et al., "On the use of convolutional neural networks for robust classification of multiple fingerprint captures," International J. of Intelligent Systems, vol. 33, no. 1, pp. 213-230, Jan. 2018. [30] H. U. Jang, et al., "DeepPore: fingerprint pore extraction using deep convolutional neural networks," IEEE Signal Processing Letters, vol. 24, no. 12, pp. 1808-1812, Oct. 2017. [31] K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint arXiv:1409.1556, 2014. [32] Y. Tang, F. Gao, and J. Feng, Latent Fingerprint Minutia Extraction Using Fully Convolutional Network, arXiv preprint arXiv:1609.09850, 2016. [33] Y. Tang, et al., FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction, arXiv preprint arXiv:1709.02228, 2017. [34] H. Qin and M. A. El Yacoubi, "Deep representation for finger-vein image quality assessment," IEEE Trans. on Circuits and Systems for Video Technology, vol. 28, no. 8, pp. 1677-1693, Mar. 2017. [35] F. Marra, et al., "A deep learning approach for iris sensor model identification," Pattern Recognition Letters, vol. 113, no. 1, pp. 46-53, Oct. 2017. [36] D. Menotti, et al., "Deep representations for iris, face, and fingerprint spoofing detection," IEEE Trans. on Information Forensics and Security, vol. 10, no. 4, pp. 864-879, Feb. 2015. [37] R. F. Nogueira, R. de Alencar Lotufo, and R. C. Machado, "Fingerprint liveness detection using convolutional neural networks," IEEE Trans. on Information Forensics and Security, vol. 11, no. 6, pp. 1206-1213, Jan. 2016. [38] T. Chugh, K. Cao, and A. K. Jain, "Fingerprint spoof buster: use of minutiae-centered patches," IEEE Trans. on Information Forensics and Security, vol. 13, no. 9, pp. 2190-2202, Mar. 2018. [39] L. Best-Rowden and A. K. Jain, "Learning face image quality from human assessments," IEEE Trans. on Information Forensics and Security, vol. 13, no. 12, pp. 3064-3077, Jan. 2018. [40] ICAO, D., 9303-Machine Readable Travel Documents-Part 9: Deployment of Biometric Identification and Electronic Storage of Data in eMRTDs, International Civil Aviation Organization (ICAO), 2015, [41] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 1, no. 1, pp. 1097-1105, Dec. 2012. [42] X. Glorot and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in Proc. of the 13th International Conf. on Artificial Intelligence and Statistics, vol. 9, pp. 249-256, Sardinia, Italy, 13-15 May 2010. [43] L. Bottou, "Large-scale machine learning with stochastic gradient descent," in Proc. of 19th Int. Symp. on Computational Statistics, COMPSTAT'10, pp. 177-186, Paris, France, 22-27 Aug. 2010. [44] I. Sutskever, et al., "On the importance of initialization and momentum in deep learning," in Proc. Int Conf. on Machine Learning, ICML'13, vol. 28, pp. 1139-1147, Jun. 2013. [45] A. Karpathy, Class Notes for Cs231n: Convolutional Neural Networks for Visual Recognition, Dept. of Comp. Sci., Stanford University, Palo Alto, CA, USA, spring 2017. [Online]. Available: http://cs231n.github.io/python-numpy-tutorial/ [46] C. I. Watson, et al., Fingerprint Vendor Technology Evaluation, 2012, NIST, NIST Interagency/Internal Report (NISTIR), Jan. 2015. [47] M. S. Hossain and K. A. Rahman, "An empirical study on verifier order selection in serial fusion based multi-biometric verification system," in Proc. Int. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 249-258, Arras, France, 27-30 Jun. 2017. [48] A. Jain, K. Nandakumar, and A. Ross, "Score normalization in multimodal biometric systems," Pattern Recognition, vol. 38, no. 12, pp. 2270-2285, Dec. 2005. [49] C. W. Hsu, C. C. Chang, and C. J. Lin, A Practical Guide to Support Vector Classification, Technical Report, Department of Computer Science, National Taiwan University,Taipei, 2003. [50] A. G. Howard, et al., Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv preprint arXiv:1704.04861, 2017. M.RohanianM.SalehiA.Darziوحیدرنجبر5112020596610.66224/ijece.28898.18.1.59http://ijece.org/en/Article/28898http://ijece.org/en/Article/Download/28898http://ijece.org/en/Article/Download/28898http://ijece.org/en/Article/Download/28898http://ijece.org/en/Article/Download/28898http://ijece.org/en/Article/Download/28898http://ijece.org/en/Article/Download/28898http://ijece.org/en/Article/Download/28898[1] S. Greenwood, A. Perrin, and M. Duggan, "Social media update 2016: facebook usage and engagement is on the rise, while adoption of other platforms holds steady," Pew Research Center, 2016. [2] J. Mander, "Daily time spent on social networks rises to 1.72 hours," London: Global Web Index, 2015. [3] B. Liu, "Sentiment analysis and opinion mining," Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1-167, May 2012. [4] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques," in Proc. of the ACL-02 Conf. on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, vol. 10, pp. 79-86, Philadelphia, PA, USA, 6-7 Jul. 2002. [5] A. Tripathy, A. Agrawal, and S. Kumar Rath, "Classification of sentiment reviews using n-gram machine learning approach," Expert Systems with Applications, vol. 57, pp. 117-126, Sept. 2016. [6] T. Mullen and N. Collier, "Sentiment analysis using support vector machines with diverse information sources," in Proc. 9th Conference on Empirical Methods in Natural Language Processing, EMNLP’04, vol. 4, pp. 412-418, Jan. 2004. [7] B. Agarwal and N. Mittal, "Machine learning approach for sentiment analysis," In: Prominent Feature Extraction for Sentiment Analysis, pp. 21-45, Dec. 2016. [8] S. Poria, H. Peng, A. Hussain, N. Howard, and E. Cambria, "Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis," Neurocomputing, vol. 261, pp. 217-230, Oct. 2017. [9] L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu, "Adaptive recursive neural network for target-dependent twitter sentiment classification," in Proc. 52nd Annual Meeting of the Association for Computational Linguistics, ACL’14, vol. 2, pp. 49-54, Baltimore, MD, USA, Jun. 2014. [10] Y. Kim, Convolutional Neural Networks for Sentence Classification, arXiv preprint arXiv:1408.5882, 2014. [11] Y. Zhang and B. Wallace, A Sensitivity Analysis of (and Ppractitioners' Guide to) Convolutional Neural Networks for Sentence Classification, arXiv preprint arXiv:1510.03820, 2015. [12] E. T. Jaynes, "Information theory and statistical mechanics," Physical Review, vol. 106, no. 4, pp. 620, May 1957. [13] M. S. Neethu and R. Rajasree, "Sentiment analysis in twitter using machine learning techniques," in Proc. IEEE 4th Int. Conf. on, Computing, Communications and Networking Technologies, ICCCNT’13, 5 pp., Tiruchengode, India, 4-6 Jul. 2013. [14] C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, Sept.. 1995. [15] م. ع. زارع چاهوکي و س. ح. ر. محمدي، "بهينه‌سازي هسته‌هاي چندگانه در ماشين بردار پشتيبان جفتي براي کاهش شکاف معنايي تشخيص صفحات فريب‌آميز،" مجله مهندسي برق دانشگاه تبريز، جلد 46، شماره 4، صص. 135-145، زمستان 1395. [16] C. N. Dos Santos and M. Gatti, "Deep convolutional neural networks for sentiment analysis of short texts," in Proc. of, the 25th Int. Conf. on Computational Linguistics, COLING'14, pp. 69-78, Dublin, Ireland, 25–29 Aug. 2014. [17] Y. Zhang, M. Chen, L. Liu, and Y. Wang, "An effective convolutional neural network model for Chinese sentiment analysis," in Proc. AIP Conf. Proc., vol. 1836, pp. 020084, Rome, Italy, 27-29 Jan. 2017. [18] M. Cieliebak, J. Deriu, D. Egger, and F. Uzdilli, "A twitter corpus and benchmark resources for german sentiment analysis," in Proc. of the 5th Ine. Workshop on Natural Language Processing for Social Media, SocialNLP, pp. 45-51, Boston, USA, Dec. 2017. [19] R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, "Recursive deep models for semantic compositionality over a sentiment treebank," in Proc. of the Conf. on Empirical Methods in Natural Language Processing, EMNLP'13, vol. 1631, pp. 1631-1642, Seattle, WA, USA, 18-21 Oct. 2013. [20] م. ح. رفان، م. کمرزرين و ع. دمشقي، "بهبود دقت و پايداري RTDGPS با استفاده از مدل ترکيبي RNN و PSO،" مجله مهندسي برق دانشگاه تبريز، جلد 46، شماره 1، صص. 185-196، بهار 1395. [21] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, "Natural language processing (almost) from scratch," J. of Machine Learning Research, vol. 12, no. 76, pp. 2493-2537, Aug. 2011. [22] A. Bagheri and M. Saraee, "Persian sentiment analyzer: a framework based on a novel feature selection method," International J. of Artificial Intelligence™, vol. 12, no. 2, pp. 115-129, Nov. 2014. [23] ح. اکبريان، م. صالحي و ﻫ. ويسي، "تعيين جهت‌گيري نظرات در رسانه‌هاي اجتماعي فارسي‌زبان،" ارائه‌شده در بيست و چهارمين کنفرانس مهندسي برق ايران، 6 صص.، شيراز، ايران، 23-21 ارديبهشت 1395. [24] M. E. Basiri, A. R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A framework for sentiment analysis in Persian," Open Trans. on Information Processing, vol. 1, no. 3, pp. 1-14, Nov. 2014. [25] M. S. Hajmohammadi and R. Ibrahim, "A SVM-based method for sentiment analysis in persian language," in Proc. Int. Conf. on Graphic and Image Processing, ICGIP’12, vol. 8768, 5 pp., Singapore, Singapore, 5-7 Oct. 2013. [26] B. Roshanfekr, S. Khadivi, and M. Rahmati, "Sentiment analysis using deep learning on Persian texts," in Iranian Conf. on Electrical Engineering, ICEE’17, pp. 1503-1508, Tehran, Iran, 2-4 May 2017. [27] K. Wang, X. Wang, L. Lin, M. Wang, and W. Zuo, "3D human activity recognition with reconfigurable convolutional neural networks," in Proc. of the 22nd ACM Int Conf. on Multimedia, pp. 97-106, Orland, FL, USA, 18-19 Jun. 2014. [28] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," in Proc. Advances in Neural Information Processing Systems, NIPS'13, pp. 3111-3119, Lake Tahoe, CA, USA, 5-10 Dec. 2013. [29] Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, "A neural probabilistic language model," J. of Machine Learning Research, vol. 3, no. 6, pp. 1137-1155, Feb. 2003. [30] پ. حسيني، ع. احمديان رامکي، ح. ملکي، م. انواري و س. ا. ميرروشن‌دل، "پيکره فارسي تحليل احساس سنتي‌پرس،" مجموعه مقالات سومين همايش ملي زبان‌شناسي رايانشي، ، 8 صص.، تهران، ایران، آبان 1393. [31] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching Word Vectors with Subword Iinformation, arXiv preprint arXiv:1607.04606, 2016. [32] J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio, "Theano: a CPU and GPU math compiler in python," in Proc. 9th Python in Science Conf., 7 pp., Austin, Texas, 28 Jun.-3 Jul. 2010. [33] F. Pedregosaet al., "Scikit-learn: machine learning in python," J. of Machine Learning Research, vol. 12, pp. 2825-2830, Oct. 2011.