SIGNAL VA TASVIRLARNI REAL VAQT REJIMIDA QAYTA ISHLASHNING MUAMMOLARI VA YECHIMLARI
Keywords:
Kalit so'zlar: real vaqt signal ishlash, tasvir qayta ishlash, kechikish, parallel hisoblash, FPGA, GPU, embedded systems, latency, DSP, tibbiy tasvir tahliliAbstract
ANNOTATSIYA
Maqolada signal va tasvirlarni real vaqt rejimida qayta ishlashning zamonaviy
muammolari, ularning yechim yo'llari hamda amaliy qo'llanmalari tahlil qilingan.
Hisoblash resurslarining cheklanganligi, kechikishlarni minimallashtirish, ma'lumotlar
oqimini boshqarish kabi asosiy muammolar ko'rib chiqilgan. Apparat va dasturiy
ta'minot optimizatsiyasi, parallel ishlov berish, sun'iy intellekt algoritmlarining
qo'llanilishi va FPGA, GPU kabi zamonaviy texnologiyalar orqali real vaqt tizimlarini
samaradorligini oshirish usullari taqdim etilgan. Tibbiyot, transport, xavfsizlik va
sanoat sohasidagi amaliy tatbiqotlar misollari keltirilgan.
References
ADABIYOTLAR
1. Liu, J. W. S. (2000). Real-Time Systems. Prentice Hall. 580 p.
2. Marwedel, P. (2018). Embedded System Design: Embedded Systems Foundations
of Cyber-Physical Systems. Springer. 446 p.
3. Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M. (2012). A review of wearable
sensors and systems with application in rehabilitation. Journal of NeuroEngineering
and Rehabilitation, 9(21), 1-17.
4. Gonzalez, R. C., Woods, R. E. (2018). Digital Image Processing. 4th Edition.
Pearson. 1168 p.
5. Woods, R., McAllister, J., Lightbody, G., Yi, Y. (2017). FPGA-based
Implementation of Signal Processing Systems. 2nd Edition. Wiley. 360 p.
6. Kirk, D. B., Hwu, W. W. (2016). Programming Massively Parallel Processors: A
Hands-on Approach. 3rd Edition. Morgan Kaufmann. 544 p.
7. Han, S., Mao, H., Dally, W. J. (2016). Deep Compression: Compressing Deep
Neural Networks with Pruning, Trained Quantization and Huffman Coding.
International Conference on Learning Representations (ICLR).
8. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L. (2016). Edge Computing: Vision and
Challenges. IEEE Internet of Things Journal, 3(5), 637-646.
9. Bradski, G., Kaehler, A. (2008). Learning OpenCV: Computer Vision with the
OpenCV Library. O'Reilly Media. 580 p.
10. LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-
444.
