حسگری فشرده انتشاری

[1]Vadeboncoeur, Ö.D. Akyildiz, I. Kazlauskaite, M. Girolami, and F. Cirak, “Fully probabilistic deep models for forward and inverse problems in parametric PDEs”. Elsevier Journal of Computational Physics, vol. 491, pp.112369, 2023.

[2] Zhao, J.C. Ye, and Y. Bresler, “Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications”, IEEE Signal Processing Magazine, vo. 40, no. 1, pp.148-163, 2023.

[3] Hammernik, T. Küstner, B. Yaman, Z. Huang, D. Rueckert, F. Knoll, and M. Akçakaya, “Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging”, IEEE Signal Processing Magazine, vol. 40, no. 1, pp.98-114, 2023.

[4] Kitić, L. Albera, N. Bertin, and R. Gribonval, “Physics-driven inverse problems made tractable with cosparse regularization”, IEEE Transactions on Signal Processing, vol. 64, no. 2, pp.335-348, 2015.

[5] Ranieri, A. Vincenzi, A. Chebira, D. Atienza, and M. Vetterli, “Near-optimal thermal monitoring framework for manycore systems-on-chip,” IEEE Transactions on Computers, vol. 64, no. 11, pp. 3197-3209, 2015.

[6] Murray-Bruce and P. Dragotti, “Estimating localized sources of diffusion fields using spatiotemporal sensor measurements,” IEEE Transactions on Signal Processing, vol. 63, no. 12, pp. 3018–3031, 2015.

[7] Murray-Bruce, and P. L. Dragotti, “Solving Inverse Source Problems for linear PDEs using Sparse Sensor Measurements”, in 50th Asilomar Conference on Signals, Systems and Computers, pp. 517-521, 2016.

[8] Murray-Bruce, and P. L. Dragotti, “A sampling framework for solving physics-driven inverse source problems”. IEEE Transactions on Signal Processing, vol. 65, no. 24, pp.6365-6380, 2017.

[9] Zhang, W. Li, J. Xiao, and J. Liu, “Thermal field reconstruction based on weighted dictionary learning”, IET Circuits, Devices & Systems, vol. 16, no. 3, pp.228-239, 2022.

[10] C. Chen, H.W. Tang, C.H. Wu, and C.H. Chen, “Thermal sensor placement for multicore systems based on low-complex compressive sensing theory”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol 41. no. 11, pp.5100-5111, 2022.

[11] Alexandru, T. Blu, P. L. Dragotti “Diffusion SLAM: Localizing Diffusion Sources From Samples Taken by Location-Unaware Mobile Sensors”, IEEE Transactions on Signal Processing, vol. 69, pp. 5539-5554, 2021.

[12] Alexandru, T. Blu, and P. L. Dragotti, “D-SLAM: Diffusion Source Localization and Trajectory Mapping”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5600-5604, 2020.

[13] Salgia, and A. Kumar, “Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4574-4578, 2018.

[14] Rostami, N-M Cheung, and T. Quek. “Compressed sensing of diffusion fields under heat equation constraint,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, pp. 4271-4274.

[15] Cobos, M. Pezzoli, F. Antonacci, and A. Sarti, “Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations”, In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, June, 2023.

[16] Othmani, N.S. Dokhanchi, S. Merchel, A. Vogel, M.E.Altinsoy, C. Voelker, and F. Takali, “Acoustic tomographic reconstruction of temperature and flow fields with focus on atmosphere and enclosed spaces: A review”, Applied Thermal Engineering, vol. 223, p.119953, 2023.

[17] Becker, L. Albera, P. Comon, R. Gribonval, F. Wendling, and I. Merlet, “Brain-source imaging: From sparse to tensor models,” IEEE Signal Processing Magazine, vol. 32, no. 6, pp. 100–112, Nov 2015.

[18] H. Eom, “Electroencephalography source localization”. Clinical and Experimental Pediatrics, vol. 66, no. 5, pp.201-209, 2023.

[19] Azarnia, M. A. Tinati, and T. Y. Rezaii, “Cooperative and distributed algorithm for compressed sensing recovery in WSNs”, IET Signal Processing, vol 12, no. 3, pp. 346-357, 2018.

[20] L. Donoho, “Compressed sensing”, IEEE Trans. Inf. Theory, vol. 52, no. 4 , pp. 1289–1306, 2006.

[21] J. Candes, J.K. Romberg, T. Tao, “Stable signal recovery from incomplete and inaccurate measurements”, Commun. Pure Appl. Math, vol. 59, no. 8, pp. 1207–1223, 2006.

[22] G. Baraniuk, “Compressive sensing,” IEEE Signal Processing Magazine, vol.24, no.4, 2007.

[23] J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Processing Magazine, vol.25, no.2, pp.21–30, 2008.

[24] J. Candès et al., “Compressive sampling,” in Proceedings of the international congress of mathematicians, vol.3, Madrid, Spain, 2006, pp.1433–1452.

[25] Strohmer, “Measure what should be measured: progress and chllenges in compressive sensing,” IEEE Signal Processing Letters, vol. 19, no. 12, pp.887-893, 2012.

[26] A. Davenport, M. F. Duarte, Y. C. Eldar, and G. Kutyniok, “Introduction to compressed sensing”, In Compressed Sensing: Theory and Applications (pp. 1-64). Cambridge University Press, 2012.

[27] S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic Decomposition By Basis Pursuit”, SIAM Review, vol. 43, no. 1, pp. 129-159, 2001.

[28] علی­جبار رشیدی، ایمان فرامرزی، رحیم انتظاری، «بازسازی سیگنال تنک رادار دهانه ترکیبی معکوس مبتنی بر همبستگی­های درون خوشه­ای»، مجله مهندسی برق دانشگاه تبریز، جلد 50 ،شماره 2 ، صفحات 709-722، 1399.

[29] شهریار شیروانی مقدّم، راشین جلیلی دانالو، «شناسایی رادیوئی کاربران اولیه بر اساس سنجش فشرده مشارکتی»، مجله مهندسی برق دانشگاه تبریز، جلد 47 ،شماره 4 ، صفحات 1551-1561، 1396.