METHOD OF WIRELESS IMAGE TRANSMISSION USING NEURAL NETWORKS
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- Keywords:
- Array, Array, Array, Array, Array, Array
- Abstract
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The research article addresses the problem of improving the efficiency of image transmission from unmanned aerial vehicles by using state-of-the-art image processing techniques along with advanced machine learning algorithms. The research introduces a new methodology that involves the use of split autoencoders to facilitate the compression of images transmitted from UAVs to ground receivers. These sophisticated autoencoders work in tandem with the transmission systems to provide efficient image compression, thereby reducing bandwidth usage and saving energy without degrading the visual information content of the images.
The authors' innovative approach is that they counteract the potential reduction in image quality typically associated with compression by implementing advanced ultra-high resolution technologies at the receiving end of the transmission. As a result, end users receive images that not only closely match the original, but also feature exceptional clarity and an increased level of detail, which is critical for accurate interpretation and analysis.
The findings of this research are particularly relevant to professionals in the field of wireless network engineering, experts specializing in image processing, and the broader research community that is engaged in the continuous development and improvement of unmanned aerial vehicle technology. The article not only outlines the technical aspects of the proposed methods, but also provides insight into practical applications, thereby serving as a resource for those who intend to explore the limits of UAV capabilities and optimize data transfer protocols.
- Author Biography
- References
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Слюсар В.И. Методы передачи изображений сверхвысокой четкости. Первая миля. Last mile (Приложение к журналу «Электроника: наука, технология, бизнес»). 2019. № 2. С. 46—61. https://doi.org/10.22184/2070-8963.2019.79.2.46.61. DOI: https://doi.org/10.22184/2070-8963.2019.79.2.46.61
Davies, L. (2018). Review of unmanned aircraft system technologies to enable beyond visual line of sight (BVLOS) operations. X 537 Intern. Conf. on electrical power drive systems (ICEPDS). https://doi.org/10.1109/ICEPDS.2018.8571665. DOI: https://doi.org/10.1109/ICEPDS.2018.8571665
Human Factors In UAS Research. Available at: http://uasblogger.blogspot.com/.
Bourtsoulatze, E., Burth Kurka, D. & Gunduz, D. (2019). Deep Joint Source-Channel Coding for Wireless Image Transmission. IEEE Transactions on Cognitive Communications and Networking. Vol. 5(3). Pp. 567—579. https://doi.org/10.1109/TCCN.2019.2919300. DOI: https://doi.org/10.1109/TCCN.2019.2919300
Deng, H., Zhang, Y., Li, R., Hu, C., Feng, Z. & Li, H. (2022). Combining residual attention mechanisms and generative adversarial networks for hippocampus segmentation. Tsinghua Science and Technology. Vol. 27(1). Pp. 68—78.
https://doi.org/10.26599/tst.2020.9010056. DOI: https://doi.org/10.26599/TST.2020.9010056
Arun, P., Herrmann, I., Budhiraju, K.M. & Karnieli, A. (2019). Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images. Pattern recognition. Vol. 88(12). Pp. 431—446. https://doi.org/10.1016/j.patcog.2018.11.033. DOI: https://doi.org/10.1016/j.patcog.2018.11.033
Park, S.C., Park, M.K. & Kang, M.G. (2003). Super-resolution image reconstruction: A technical overview. IEEE Signal Processing Magazine. Vol. 20(3). Pp. 21—36. https://doi.org/10.1109/MSP.2003.1203207. DOI: https://doi.org/10.1109/MSP.2003.1203207
Chang, H., Yeung, D. & Xiong, Y. (2004). Super-resolution through Neighbor embedding. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. P. II. https://doi.org/10.1109/CVPR.2004.1315043. DOI: https://doi.org/10.1109/CVPR.2004.1315043
Goodfellow, Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2020). Generative adversarial networks. Commun. ACM. Vol. 63(11). https://doi.org/10.1145/3422622. DOI: https://doi.org/10.1145/3422622
Slyusar, V., Protsenko, M., Chernukha, A., Gornostal, S., Rudakov, S., Shevchenko, S., Chernikov, O., Kolpachenko, N., Timofeyev, V. & Artiukh, R. (2021). Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform. Eastern-European J. of Enterprise Technologies. Vol. 4/9(112). Pp. 65—77. https://doi.org/10.15587/1729-4061.2021.238601. DOI: https://doi.org/10.15587/1729-4061.2021.238601
Slyusar, V., Protsenko, M., Chernukha, A., Kovalov, P., Borodych, P., Shevchenko, S., Chernikov, O., Vazhynskyi, S., Bogatov, O. & Khrustalev, K. (2021). Improvement of the object recognition model on aerophotos using deep conventional neural network. Eastern-European J. of Enterprise Technologies. Vol. 5/2(113). Pp. 6—21. DOI: https://doi.org/10.15587/1729-4061.2021.243094
https://doi.org/10.15587/1729-4061.2021.243094/.
Slyusar, V., Protsenko, M., Chernukha, A., Melkin, V., Biloborodov, O., Samoilenko, M., Kravchenko, O., Kalinichenko, G., Rohovyi, A. & Soloshchuk, M. (2022). Improvement of the model for detecting objects on aerial photos and video in unmanned aerial systems. Eastern-European J. of Enterprise Technologies. Vol. 1/9(115). Pp. 24—34.
https://doi.org/10.15587/1729-4061.2022.252876. DOI: https://doi.org/10.15587/1729-4061.2022.252876
Redmon, J. (2016). You Only Look Once: Unified, Real-Time Object Detection. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR-2016). https://doi.org/10.1109/cvpr.2016.91. DOI: https://doi.org/10.1109/CVPR.2016.91
Badrinarayanan, V., Kendall, A. & Cipolla, R. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 39(12). Pp. 2481—2495. https://doi.org/10.1109/TPAMI.2016.2644615. DOI: https://doi.org/10.1109/TPAMI.2016.2644615
Slyusar, V. (2021). Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs. Eastern-European J. of Enterprise Technologies. Vol. 6/2 (114). Pp. 86—95.
https://doi.org/10.15587/1729-4061.2021.248390. DOI: https://doi.org/10.15587/1729-4061.2021.248390
Применение нейронной сети на основе архитектуры U-NET с целью детектирования искомых объектов на изображениях на примере детектирования активных элементов на фотографиях печатных плат. [Електронный ресурс]. – Режим доступу: https://codeby.school/blog/informacionnaya-bezopasnost/primenenie-neyronnoy-seti-na-osnove-arhitektury-u-net-s-celyu-detektirovaniya-iskomyh-obektov-na-izobragheniyah-na-primere-detektirovaniya-aktivnyh-elementov-na-fotografiyah-pechatnyh-plat.
Yu, C., Han, R., Song, M., Liu, C. & Chang, C.I. (2020). A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial-Spectral Fusion. IEEE J. of Selected Topics in Applied Earth Observations and Remote Sensing. Vol. 13. Pp. 2485—2501. https://doi.org/10.1109/JSTARS.2020.2983224. DOI: https://doi.org/10.1109/JSTARS.2020.2983224
Asadpour, M., Giustiniano, D., Hummel, K.A. & Egli, S. UAV networks in rescue missions. The 8th ACM Intern. Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization − WiNTECH 13. https://doi.org/10.1145/2505469.2506491. DOI: https://doi.org/10.1145/2505469.2506491
Sangjan, W., McGee, R.J. & Sankaran, S. (2022). Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop. Remote Sensing. Vol. 14(10). P. 2396. https://doi.org/10.3390/rs14102396. DOI: https://doi.org/10.3390/rs14102396
Slyusar, V. & Bihun, N. (2022). The Method of Increasing the Immunity of Data Transmission in Communication Channels. IEEE 9th Intern. Conf. on Problems of Infocommunications. Science and Technology (PIC S&T). Kharkiv. Pp. 301—305. https://doi.org/10.1109/PICST57299.2022.10238546. DOI: https://doi.org/10.1109/PICST57299.2022.10238546
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- 2023-12-31
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Copyright (c) 2023 Вадим Слюсар ,Наталія Бігун
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