1. [1] S. H. Yazdani, H. R. Abutalebi, "Adaptive and Smart Beamforming in Ad-hoc Microphone Arrays by Clustering and Ranking of the Microphones," JSDP. 2015; 12 (3) :57-68.
2. [2] S. Majidian, F. Haddadi, "Direction of Arrival (DOA) Estimation Using Kronecker Subspace," JSDP. 2018; 15 (1) :29-40. [
DOI:10.29252/jsdp.15.1.29]
3. [3] J. Benesty, J. Chen, and Y. Huang, "Conventional Beamforming Techniques," in Microphone Array Signal Processing, vol. 1, Berlin, Germany: Springer, 2008.
4. [4] F. Vignon, and M. R. Burcher, "Capon beamforming in medical ultrasound imaging with focused beams," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 55, no. 3, pp. 619-628, 2008. [
DOI:10.1109/TUFFC.2008.686] [
PMID]
5. [5] F. Yan, M. Jin, and X. Qiao, "Low-Complexity DOA Estimation Based on Compressed MUSIC and Its Performance Analysis," IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 1915-1930, 2013. [
DOI:10.1109/TSP.2013.2243442]
6. [6] X. Wu, W. Zhu, and J. Yan, "Direction of Arrival Estimation for Off-Grid Signals Based on Sparse Bayesian Learning," IEEE Sensors Journal, vol. 16, no. 7, 2016. [
DOI:10.1109/JSEN.2015.2508059]
7. [7] J. Dai, and H. Cheung, "Sparse Bayesian Learning Approach for Outlier-Resistant Direction-of-Arrival Estimation," IEEE Transactions on Signal Processing, vol. 66, no. 3, pp. 744-756, 2018. [
DOI:10.1109/TSP.2017.2773420]
8. [8] J. Capon, "High-resolution frequency-wavenumber spectrum analysis," Proceedings of the IEEE, vol. 57, no. 8, August 1969. [
DOI:10.1109/PROC.1969.7278]
9. [9] J. I. Buskenes, J. P. Asen, C. I. C. Nilsen, and A. Austeng, "An Optimized GPU Implementation of the MVDR Beamformer for Active Sonar Imaging," IEEE J. Ocean. Eng., vol. 40, no. 2, pp. 441-451, 2014. [
DOI:10.1109/JOE.2014.2320631]
10. [10] M. Sasso, and C. Cohen-Bacrie, "Medical Ultrasound Imaging Using the Fully Adaptive Beamformer," Philips Research, no. 4, pp. 489-492, 2005.
11. [11] H. Sun, M. Lu, and T. D. Abhayapala, "Ultrasound imaging using modal domain frequency smoothed MVDR beamforming," in 2017, 11th Int. Conf. Signal Process. Commun. Syst. ICSPCS 2017 - Proc., no. 1, pp. 1-5, Janua 2017. [
DOI:10.1109/ICSPCS.2017.8270476]
12. [12] I. K. Holfort, F. Gran, and J. A. Jøensen, "Broadband minimum variance beamforming for ultrasound imaging," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 56, no. 2. pp. 314-325, 2009. [
DOI:10.1109/TUFFC.2009.1040] [
PMID]
13. [13] J. P. Åsen, J. I. Buskenes, C. I. C. Nilsen, A. Austeng, and S. Holm, "Implementing capon beamforming on a GPU for real-time cardiac ultrasound imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 61, no. 1, pp. 76-85, 2014. [
DOI:10.1109/TUFFC.2014.6689777] [
PMID]
14. [14] Y. S. Yoon, M. G. Amin, and F. Ahmad, "MVDR beamforming for through-the-wall radar imaging," IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 347-366, 2011. [
DOI:10.1109/TAES.2011.5705680]
15. [15] C. I. Nilsen and I. Hafizovic, "Beamspace adaptive beamforming for ultrasound imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 56, no. 10, pp. 2187-2197, 2009. [
DOI:10.1109/TUFFC.2009.1301] [
PMID]
16. [16] A. M. Deylami, and B. M. Asl, "A Fast and Robust Beamspace Adaptive Beamformer for Medical Ultrasound Imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2017.
17. [17] J. F. Synnevåg, S. Holm, and A. Austeng, "A Low Complexity Data-dependent Beamformer," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 57, no. 2, 2010. [
DOI:10.1109/TUFFC.2011.1805] [
PMID]
18. [18] K. Kim, S. Park, J. Kim, S. Park, and M. Bae, "A Fast Minimum Variance Beamforming Method Using Principal Component Analysis," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 61, no. 6, pp. 930-945, 2014. [
DOI:10.1109/TUFFC.2014.2989] [
PMID]
19. [19] A. Jakobsson, S. L. Marple, Jr., and P. Stoica, "Computationally Efficient Two-Dimensional Capon Spectrum Analysis," IEEE Trans. Signal Process., vol. 48, no. 9, pp. 2651-2661, 2000. [
DOI:10.1109/78.863072]
20. [20] Q. Huang, L. Zhang, and Y. Fang, "Performance analysis of Low-complexity MVDR beamformer in spherical harmonics domain," Signal Processing, vol. 153, pp. 153-163, 2018. [
DOI:10.1016/j.sigpro.2018.07.016]
21. [21] B. M. Asl and A. Mahloojifar, "A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 59, no. 4, pp. 660-667, 2012. [
DOI:10.1109/TUFFC.2012.2244] [
PMID]
22. [22] H. Sadeghi, A. Akhavan Bitaghsir, "Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks," JSDP. 2018; 14 (4) :19-30. [
DOI:10.29252/jsdp.14.4.19]
23. [23] D. B. Kirk, and W. W. Hwu, Programming Massively Parallel Processors _ A Hands-On Approach. Burlington, MA: Elsevier, 2010.
24. [24] R. Paridar, M. Mozaffarzadeh, M. Nasiriavanaki, and M. Orooji, "Double Minimum Variance Beamforming Method to Enhance Photoacoustic Imaging," Journal of the Optical Society of America A, pp. 1-9, 2018.
25. [25] A. Hakam, R. M. Shubair, and E. Salahat, "Enhanced DOA Estimation Algorithms Using MVDR and MUSIC," in 2013 International Conference on Current Trends in Information Technology (CTIT), Dubai, United Arab Emirates, 2013. [
DOI:10.1109/CTIT.2013.6749497]
26. [26] Y. Fathi, A. Mahloojifar, and B. M. Asl, "Real-time implementation of adaptive beamformer in ultrasound imaging using parallel processing with GPU," Journal of Iran Audio Engineering Society, vol. 1, no. 1, 2013.
27. [27] Nvidia, "cuBLAS," docs.nvidia.com, Oct. 30, 2018. [Online]. Available:https://docs.nvidia.com/cuda/cublas/index.html. [Accessed: Sept. 19, 2018].