AI asosida real vaqt rejimida signalni shovqindan tozalash tizimlarini ishlab chiqish
Annotatsiya
Ushbu maqolada sun’iy intellekt texnologiyalari yordamida real vaqt rejimida signalni shovqindan tozalash tizimlarini ishlab chiqish masalasi o‘rganiladi. Zamonaviy aloqa va multimedia tizimlarida signal sifatini oshirish muhim vazifa bo‘lib, shovqinlar uzatilayotgan ma’lumotning aniqligini pasaytiradi. Tadqiqotda Deep Learning va Machine Learning usullaridan foydalangan holda signalni qayta ishlash algoritmlari tahlil qilinadi. Real vaqt sharoitida ishlovchi modellarning samaradorligi baholanib, ularning an’anaviy filtrlash usullariga nisbatan ustunliklari ko‘rsatib beriladi. Olingan natijalar AI asosidagi yondashuvlar signal sifatini sezilarli darajada yaxshilashini tasdiqlaydi.
Kalit so‘zlar: sun’iy intellekt, signalni qayta ishlash, shovqinni kamaytirish, real vaqt tizimlari, neyron tarmoqlar, denoising
Библиографические ссылки
1. Haykin, S. (2019). Neural Networks and Learning Machines. Pearson Education.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
3. Oppenheim, A. V., & Schafer, R. W. (2010). Discrete-Time Signal Processing. Prentice Hall.
4. Proakis, J. G., & Manolakis, D. G. (2007). Digital Signal Processing: Principles, Algorithms, and Applications. Pearson.
5. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
6. Rabiner, L. R., & Gold, B. (1975). Theory and Application of Digital Signal Processing. Prentice Hall.
7. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
8. Zhang, K., Zuo, W., & Zhang, L. (2017). Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing.
9. Mallat, S. (2008). A Wavelet Tour of Signal Processing. Academic Press.
10. Stoica, P., & Moses, R. L. (2005). Spectral Analysis of Signals. Pearson/Prentice Hall.
11. ITU-T Recommendation G.711 (2019). Pulse Code Modulation (PCM) of Voice Frequencies. International Telecommunication Union.
12. Goodfellow, I. et al. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems (NeurIPS).
