학술논문


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'학술논문' 에서 검색결과 609,717건 | 목록 1~20
[1] A. Moffet, 'Minimum-redundancy linear arrays', IEEE Transactions on Antennas and Propagation, Vol. 16, Iss. 2, pp. 172-175, 1968. DOI: 10.1109/TAP.1968.1139138 [2] G. S. Bloom and S. W. Golomb, 'Applications of numbered undirected graphs', in Proceedings of the IEEE, Vol. 65, Iss. 4, pp. 562-570, 1977. DOI: 10.1109/PROC.1977.10517 [3] P. Pal and P. P. Vaidyanathan, 'Nested arrays in two dimensions, Part I: Geometrical considerations', IEEE Transactions on Signal Processing, Vol. 60, No. 9, pp. 4694-4705, 2012. DOI: 10.1109/TSP.2012.2203814 [4] P. Pal and P. P. Vaidyanathan, 'Nested arrays: A novel approach to array processing with enhanced degrees of freedom', IEEE Transactions on Signal Processing, Vol. 58, No. 8, pp. 4167-4181, 2010. DOI: 10.1109/TSP.2010.2049264 [5] C. Wen, G. Shi, and X. Xie, 'Estimation of directions of arrival of multiple distributed sources for nested array', Signal Processing, Vol. 130, pp. 315-322, 2017. DOI: 10.1016/j.sigpro.2016.07.011 [6] J. Li, Y. He, P. Ma, X. Zhang, and Q. Wu, 'Direction of arrival estimation using sparse nested arrays with coprime displacement', IEEE Sensors Journal, Vol. 21, Iss. 4, pp. 5282-5291, 2020. DOI: 10.1109/JSEN.2020.3034761 [7] P. Zhao et al., 'Generalized nested array configuration family for direction-of-arrival estimation', IEEE Transactions on Vehicular Technology, Vol. 72, Iss. 8, pp. 10380-10392, 2023. DOI: 10.1109/TVT.2023.3260196 [8] Y. Lou, X. Qu, D. Wang, and J. Cheng, 'Direction-of-arrival estimation for nested acoustic vector-sensor arrays using quaternions', IEEE Transactions on Geoscience and Remote Sensing, Vol. 61, pp. 1-14, 2023. DOI: 10.1109/TGRS.2023.3274182 [9] G. Jiang, J. Huang, and Y. Yang, 'Dual-sparse parallel nested array for two-dimensional direction of arrival estimation', Circuits, Systems, and Signal Processing, Vol. 43, No. 12, pp. 8060-8073, 2024. DOI: 10.1007/s00034-024-02816-w [10] L. Zhou, Z. Feng, K. Ye, J. Qi, and S. Hong, 'Design of relocating sparse nested arrays for DOA estimation of non-circular signals', AEU - International Journal of Electronics and Communications, Vol. 173, 2024. DOI: 10.1016/j.aeue.2023.154976 [11] G. Jiang, J. Huang, and Y. Yang, 'High-accuracy 2D DOA estimation with three parallel sparse nested array', AEU - International Journal of Electronics and Communications, Vol. 179, 2024. DOI: 10.1016/j.aeue.2024.155319 [12] C. Qin, L. Yang, J. Dang, B. Dang, and Y. Zhou, 'An enhanced expanding and shift sparse array based on the coprime array and nested array', IET Radar, Sonar & Navigation, Vol. 18, Iss. 3, pp. 493-499, 2024. DOI: 10.1049/rsn2.12495 [13] S. He, N. Sun, and Z. Yang, 'Designing sparse extended nested arrays with high degrees of freedom and low coupling', Signal Processing, Vol. 227, 2025. DOI: 10.1016/j.sigpro.2024.109702 [14] N. V. Son, N. T. Chinh, and N. N. Dong, 'A novel directional finding algorithm applying to underwater passive sonar system based on sparse representation combined adaptive comb filter', The International Conference on Intelligent Systems & Networks, 2024, Springer, pp. 382-391. EXTENDING THE CBSL ALGORITHM FOR DIRECTION OF ARRIVAL ESTIMATION WITH A NESTED ARRAY IN PASSIVE SONAR
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1. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific Reports, *8*(1), 6085. 5. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (pp. 301-318). 6. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, *24*(2), 361-370. 7. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, *9*(8), 1735-1780. 9. Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2016). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677. 10. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., ... Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, *1*(1), 18. 11. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, *22*(5), 1589-1604. 12. Song, H., Rajan, D., Thiagarajan, J. J., & Spanias, A. (2018). Attend and diagnose: Clinical time series analysis using attention models. In Proceedings of the AAAI Conference on Artificial Intelligence, *32*(1). 13. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, *30*. 14. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, *19*(6), 1236-1246. 15. Beaulieu-Jones, B. K., Yuan, W., Brat, G. A., Lai, A., Orzechowski, N., Finn, C., ... & Weber, G. M. (2019). Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?. NPJ Digital Medicine, *2*(1), 62. 16. Yoon, J., Zame, W. R., & van der Schaar, M. (2018). Deep sensing: Active sensing using deep learning. IEEE Transactions on Signal Processing, *66*(20), 5438-5452. 17. Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A. (2020). The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health, *2*(9), e489-e492
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