ISSN 1210-2512 (Print)

ISSN 1805-9600 (Online)



Proceedings of Czech and Slovak Technical Universities

About the Journal
Feature Articles
Editorial Board
Publishing Department
Society [CZ]

Log out
Your Profile

September 2024, Volume 33, Number 3 [DOI: 10.13164/re.2024-3]

Show all Hide all

A. Atanaskovic, A. Djoric, N. Males Ilic, B. P. Stosic, D. Budimir [references] [full-text] [DOI: 10.13164/re.2024.0339] [Download Citations]
Linearization of the Fifth Generation Power Amplifiers by Injection of the Second Order Digitally Processed Signals

This paper conducts a validation of two linearization approaches that utilize baseband nonlinear linearization signals of the 2nd order, through practical experiments on an asymmetrical two-way microstrip Doherty amplifier (ADA), and simulations on a symmetrical two-way Doherty amplifier (DA) as well as the single stage power amplifier (PA) for the post-OFDM 5G modulation formats. In the first approach, linearization signals are led at the input and output of the carrier transistor in the DA, while in the second approach, they are injected at the outputs of both the carrier and peaking amplifiers. The DA was tested in simulation for the FBMC signal of 20 MHz bandwidth, while the experimental measurements were performed for the FBMC signal on the ADA for different useful signal frequency bandwidths, 5 MHz, 7.5 MHz, and 10 MHz. The maximal improvement of DA linearity obtained in simulation is 10 dB for lower power and 5 dB for maximum amplifier output power, while the second approach gives around 2 dB better results for higher power levels. The experimental test for ADA performed for considered signal bandwidths indicates 3 dB to 5 dB linearity improvement for the implemented approaches and more symmetrical results achieved by the second approach. Additionally, the simulation tests for the PA were carried out for the FBMC, UFMC, and FOFDM signals of 100 MHz bandwidth, with the application of the first linearization approach. The minimal achieved linearization improvement is 13 dB for the FOFDM signal and a maximal of 18 dB for the FBMC signal.

  1. OSSEIRAN, A., MONSERRAT, J. F., MARSCH, P. (eds.) 5G Mobile and Wireless Communications Technology. Cambridge University Press, 2016. DOI: 10.1017/CBO9781316417744
  2. ZAIDI, A., ATHLEY, F., MEDBO, J., et al. 5G Physical Layer: Principles, Models and Technology Components. Elsevier Academic Press, 2018. ISBN: 978-0128145784
  3. BOREL, A., BARZDENAS, V., VASJANOV, A. Linearization as a solution for power amplifier imperfections: A review of methods. Electronics, 2021, vol. 10, p. 1–25. DOI: 10.3390/electronics10091073
  4. HAIDER, F. M., YOU, F., HE, S., et al. Predistortion-based linearization for 5G and beyond millimeter-wave transceiver systems: A comprehensive survey. IEEE Communications Surveys & Tutorials, 2022, vol. 24, no. 4, p. 2029–2072. DOI: 10.1109/COMST.2022.3199884
  5. ATANASKOVIĆ, A., MALES-ILIĆ, N., BLAU, K., et al. RF PA linearization using modified baseband signal that modulates carrier second harmonic. Microwave Review, 2013, vol. 19, no. 2, p. 119 to 124. ISSN: 14505835
  6. ĐORIĆ, A., ATANASKOVIĆ, A., MALES-ILIĆ, N., et al. Linearization of RF PA by even-order nonlinear baseband signal processed in digital domain. International Journal of Electronics, 2019, vol. 106, no. 12, p. 1904–1918. DOI: 10.1080/00207217.2019.1636145
  7. ATANASKOVIĆ, A., BUDIMIR, D., MALES-ILIĆ, N., et al. Combination of digital second-order linearization technique and DPD compensation technique - concept and results. In Proceedings of the 10th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2023). East Sarajevo (Bosnia and Herzegovina), 2023, p. 1–5. DOI: 10.1109/IcETRAN59631.2023.10192197
  8. ATANASKOVIĆ, A., MALES-ILIĆ, N., ĐORIĆ, A., et al. Doherty amplifier linearization by digital injection methods. Facta Universitatis, Series: Electronics and Energetics, December 2022, vol. 35, no. 4, p. 587–601. DOI: 10.2298/FUEE2204587A
  9. CAI, Y., QIN, Z., CUI, F., et al. Modulation and multiple access for 5G networks. IEEE Communications Surveys & Tutorials, 2018, vol. 20, no. 1, p. 629–646. DOI: 10.1109/COMST.2017.2766698
  10. BOZIC, M., BARJAMOVIC, D., CABARKAPA, M., et al. Waveform comparison and PA nonlinearity effects on CP-OFDM and 5G FBMC wireless systems. Microwave and Optical Technology Letters, 2018, vol. 60, no. 8, p. 1952–1956. DOI: 10.1002/mop.31272
  11. PEDRO, J. C., PEREZ, J. Accurate simulation of GaAs MESFET’s intermodulation distortion using a new drain-source current model. IEEE Transactions on Microwave Theory and Techniques, 1994, vol. 42, no. 1, p. 25–33. DOI: 10.1109/22.265524
  12. PENGELLY, R. Large Signal Modeling of GaN HEMT Based Circuits. January 2012, CREE.
  13. KEYSIGHT ADS, 2021. Santa Rosa (United States): Keysight Technologies, Inc.
  14. MACOM TECHNOLOGY SOLUTIONS INC. CGH40010 10 W, DC - 6 GHz, RF Power GaN HEMT by Cree/Wolfspeed/MACOM for General Purpose Broadband Applications. Datasheet, Rev 4.4. [Online] Cited 2022-10-28. Available at:
  15. KEYSIGHT SYSTEMVUE, 2020. Santa Rosa (United States): Keysight Technologies, Inc.
  16. MALES-ILIĆ, N., ĐORIĆ, A., ATANASKOVIĆ, A. Linearization of broadband two-way microstrip Doherty amplifier. Facta Universitatis, Series: Electronics and Energetics, 2016, vol. 29, no. 1, p. 127–138. DOI: 10.2298/FUEE1601127M
  17. FREESCALE SEMICONDUCTOR/NXP. MRF281S, 2000 MHz, 4 W, 26 V Lateral N-channel Broadband RF Power MOSFET. Technical data, rev. 6, October 2008. [Online] Available at:
  18. ANALOG DEVICES. AD-FMCOMMS5-EBZ, Dual AD9361 Evaluation Board AD-FMCOMMS5-EBZ User Guide. [Online]. January 2016. Available at: http: // rev
  19. ANALOG DEVICES. AD9361, RF 2×2 Agile Transceiver. Data sheet, G. [Online]. June 2013. Available at:
  20. Zynq ZC706 FPGA, AMD Zynq 7000 SoC ZC706 Evaluation Kit by AMD, UG954 - ZC706 Evaluation Board for the Zynq-7000 XC7Z045 SoC User Guide. Version v1.8. [Online]. August 2019. Available:

Keywords: The post-OFDM 5G modulations, broadband power amplifier, Doherty amplifier, baseband signal, second harmonic, linearization

S. Kittiwittayapong, D. Torrungrueng, K. Phaebua, K. Sukpreecha, T. Lertwiriyaprapa, P. Janpugdee [references] [full-text] [DOI: 10.13164/re.2024.0349] [Download Citations]
Miniaturization of Power Dividers by Using Asymmetric CMRC Structures and QWLTs with Low-Cost Materials

This paper presents the miniaturization of power dividers using asymmetric compact-microstrip-resonant-cell (CMRC) structures employing low-cost materials based on a quarter-wave-like transformer (QWLT). The proposed CMRC-based QWLT power divider is intended for operation at a frequency of 2.4 GHz, utilizing the FR-4 print circuit board (PCB) with a dielectric constant of 4.3 and a substrate thickness of 1.6 mm. The CMRC dimensions include a width of 5.32 mm and a length of 8.52 mm. It is found that a significant 50% size reduction of length is achieved compared to a conventional power divider, while maintaining an insertion loss (IL) of 3.3 dB, as well as achieving the return loss and isolation loss of 20 dB.

  1. POZAR, D. M. Microwave Engineering. 4th ed. New Jersey: John Wiley & Sons, 2012. ISBN: 0-471-67751-X
  2. YANG, Y., HU, N., XIE, W., et al. A compact tri-band impedance transforming power divider with independent controllable power division ratios and enhanced bandwidths. IEEE Access, 2019, vol. 7, p. 25185–25194. DOI: 10.1109/ACCESS.2019.2900060
  3. ILYAS, S., SHOAIB, N., NIKOLAOU, S., et al. A wideband tunable power divider for SWIPT systems. IEEE Access, 2020, vol. 8, p. 30675–30681. DOI: 10.1109/ACCESS.2020.2970781
  4. MAKTOOMI, M. H., BANERJEE, D., HASHMI, M. S. An enhanced frequency-ratio coupled-line dual-frequency Wilkinson power divider. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2018, vol. 65, no. 7, p. 888–892. DOI: 10.1109/TCSII.2017.2749407
  5. DANG, Z., ZHANG, Y., ZHU, H. L., et al. An isolated out-of phased 3-dB power divider via waveguide-to-microstrip transition. IEEE Microwave and Wireless Components Letters, 2022. vol. 32, no. 1, p. 21–24. DOI: 10.1109/LMWC.2021.3114413
  6. PIACIBELLO, A., PIROLA, M., GHIONE, G. Generalized symmetrical 3 dB power dividers with complex termination impedances. IEEE Access, 2020. vol. 8, p. 38239–38247. DOI: 10.1109/ACCESS.2020.2976153
  7. MOULAY, A., DJERAFI, T. Wilkinson power divider with fixed width substrate-integrated waveguide line and a distributed isolation resistance. IEEE Microwave and Wireless Components Letters, 2018, vol. 28, no. 2, p. 114–116. DOI: 10.1109/LMWC.2018.2790706
  8. SATITCHANTRAKUL, T., CHUDPOOTI, N., AKKARAEK THALIN, P., et al. An implementation of compact quarter-wave like-transformers using multi-section transmission lines. Radioengineering, 2018, vol. 27, no. 1, p. 101–109. DOI: 10.13164/re.2018.0101
  9. KORANANAN, S., JANPUGDEE, P., TORRUNGRUENG, D. Miniaturization of power dividers using quarter-wave-like transformers (QWLTs). In International Symposium in Antennas and Propagation (ISAP). Phuket (Thailand), 2017, p. 1–2. DOI: 10.1109/ISANP.2017.8228878
  10. TORRUNGRUENG, D. Advanced Transmission-Line Modeling in Electromagnetics. 1st ed. Charansanitwong Printing, 2012. ISBN: 978-616-305-017-5
  11. KURGAN, P., FILIPCEWICZ, J., KITLINSKI, M. Development of compact microstrip resonant cell aimed at efficient microwave component size reduction. IET Microwave, Antennas & Propagation, 2012, vol. 6, no. 12, p. 1291–1298. DOI: 10.1049/ietmap.2012.0192
  12. KITTIWITTAYAPONG, S., SATITCHANTRAKUL, T., TORRUNGRUENG, D., et al. Miniaturized low-loss impedance transformers using bi-characteristic impedance transmission lines (BCITLs). In The 9th International Electrical Engineering Congress (iEECON). Pattaya (Thailand), 2021, p. 595–598. DOI: 10.1109/iEECON51072.2021.9440240
  13. KITTIWITTAYAPONG, S., SATITCHANTRAKUL, T., TORRUNGRUENG, D., et al. Design of miniaturized impedance transformers using quarter-wave-like transformers implemented by asymmetric compact microstrip resonant cells. In The 9th International Electrical Engineering Congress (iEECON). Pattaya (Thailand) 2021, p. 607–610. DOI: 10.1109/iEECON51072.2021.9440254
  14. JONGSUEBCHOKE, I., AKKARAEKTHALIN, P., TORRUNGRUENG, D. Theory and design of quarter-wave-like transformer implemented using conjugately characteristic impedanced transmission lines. Microwave and Optical Technology Letters, 2016, vol. 58, no. 11, p. 2614–2619. DOI: 10.1002/mop.30120
  15. MAHOUTI, P., BELEN, M. A., PARTAL, H. P., et al. Miniaturization with dumbbell shaped defected ground structure for power divider designs using Sonnet. In International Review of Progress in Applied Computational Electromagnetics (ACES). Williamsburg (VA, USA), 2015, p. 1–2. ISBN: 978-0-9960-0781-8
  16. BARZDENES, V., VASJANOV, A., GRAZULEVICIUS, G., et al. Design and miniaturization of dual-band Wilkinson power dividers. Journal of Electrical Engineering, 2020, vol. 71, no. 6, p. 423–427. DOI: 10.2478/jee-2020-0058
  17. HAYATI, M., ROSHANI, S. A novel Wilkinson power divider using open stubs for the suppression of harmonics. The Applied Computational Electromagnetics Society Journal (ACES), 2013, vol. 28, no. 6, p. 501–506. ISSN: 1943-5711 (online)
  18. ROSHANI, S., SIAHKAMARI, P. Design of a compact 1:2 and 1:4 power divider with harmonic suppression using resonator. Wireless Personal Communications, 2022, vol. 126, no. 3, p. 2635–2645. DOI: 10.1007/s11277-022-09833-5
  19. PRADHAN N. C., SUBRAMANIAN, K. S., BARIK, R. K., et al. Design of compact substrate integrated waveguide based triple-and quad-band power dividers. Microwave and Wireless Components Letters, 2021, vol. 31, no. 4, p. 365–368. DOI: 10.1109/LMWC.2021.3061693
  20. CST-MW Studio, Comput. Simul. Technol., Framingham, MA, USA, 2017.

Keywords: Power divider, quarter-wave transformer (QWT), quarter-wave-like transformer (QWLT), compact microstrip resonant cell (CMRC)

Z. Wang, D. Zhang, M. Gao, H. Liu, S. Fang [references] [full-text] [DOI: 10.13164/re.2024.0358] [Download Citations]
Microstrip Circularly-Polarized Leaky-Wave Antenna with Wide Axial Ratio Bandwidth for X-Band Application

A microstrip circularly polarized (CP) leaky-wave antenna (LWA) operating in the X-band, and having the characteristics of a broad axis-ratio bandwidth is proposed. The proposed LWA is made up of 13 unit cells in series through microstrip feeding lines. Elliptical and rectangular slots are etched in each unit cell to achieve the radiation of CP waves. The open stopband at the broadside frequency can be suppressed by shifting the feeding line position and etching two circular notches on both sides of each radiation patch. To validate the proposed method, a prototype antenna operating in the X-band is manufactured and measured. The measured result demonstrates that the −10-dB impedance bandwidth of the microstrip CP LWA is 42.2% (7.96-12.22 GHz); the 3-dB axial ratio bandwidth is 26.4% (9.2-12.2 GHz); the gain of the antenna is 16.0 dBic. Besides, the main beam maintains good CP radiation properties while it continues to scan from −22° to +18°.

  1. JACKSON, D. R., CALOZ, C., ITOH, T. Leaky-wave antennas. Proceedings of the IEEE, 2012, vol. 100, no. 7, p. 2194–2206. DOI: 10.1109/JPROC.2012.2187410
  2. JIDI, L., CAO, X., GAO, J., et al. Ultrawide-angle and high scanning-rate leaky wave antenna based on spoof surface plasmon polaritons. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 3, p. 2312–2317. DOI: 10.1109/TAP.2021.3111182
  3. LIU, J., JACKSON, D. R., LONG, Y. Substrate integrated waveguide (SIW) leaky-wave antenna with transverse slots. IEEE Transactions on Antennas and Propagation, 2012, vol. 60, no. 1, p. 20–29. DOI: 10.1109/TAP.2011.2167910
  4. WANG, H., SUN, S., XUE, X. A periodic meandering microstrip line leaky‐wave antenna with consistent gain and wide-angle beam scanning. International Journal of RF and Microwave Computer Aided Engineering, 2022, vol. 32, no. 7, p. 1–9. DOI: 10.1002/mmce.23162
  5. DUAN, J., ZHU, L. A transversal single-beam EH0-mode microstrip leaky-wave antenna on coupled microstrip lines under differential operation. IEEE Antennas and Wireless Propagation Letters, 2021, vol. 20, no. 4, p. 592–596. DOI: 10.1109/LAWP.2021.3058277
  6. SAGHATI, A. P., MIRSALEHI, M. M., NESHATI, M. H. A HMSIW circularly polarized leaky-wave antenna with backward, broadside, and forward radiation. IEEE Antennas and Wireless Propagation Letters, 2014, vol. 13, p. 451–454. DOI: 10.1109/LAWP.2014.2309557
  7. AGARWAL, R., YADAVA, R. L., DAS, S. A multilayered SIW based circularly polarized CRLH leaky wave antenna. IEEE Transactions on Antennas and Propagation, 2021, vol. 69, no. 10, p. 6312–6321. DOI: 10.1109/TAP.2021.3082618
  8. GUAN, D.-F., YOU, P., ZHANG, Q., et al. A wide-angle and circularly polarized beam-scanning antenna based on microstrip spoof surface plasmon polariton transmission line. IEEE Antennas and Wireless Propagation Letters, 2017, vol. 16, p. 2538–2541. DOI: 10.1109/LAWP.2017.2731877
  9. FU, J.-H., LI, A., CHEN, W., et al. An electrically controlled CRLH-inspired circularly polarized leaky-wave antenna. IEEE Antennas and Wireless Propagation Letters, 2017, vol. 16, p. 760 to 763. DOI: 10.1109/LAWP.2016.2601960
  10. XU, S.-D., GUAN, D.-F., LIU, L., et al. A narrow‐band circularly polarized leaky-wave antenna with open stopband suppressed. International Journal of RF and Microwave Computer-Aided Engineering, 2021, vol. 31, no. 7, p. 1–7. DOI: 10.1002/mmce.22647
  11. SANCHEZ-ESCUDEROS, D., FERRANDO-BATALLER, M., HERRANZ, J. I., et al. Low-loss circularly polarized periodic leaky-wave antenna. IEEE Antennas and Wireless Propagation Letters, 2015, vol. 15, p. 614–617. DOI: 10.1109/LAWP.2015.2463672
  12. ZHAO, S., DONG, Y. Circularly polarized beam-steering microstrip leaky-wave antenna based on coplanar polarizers. IEEE Antennas and Wireless Propagation Letters, 2022, vol. 21, no. 11, p. 2259–2263. DOI: 10.1109/LAWP.2022.3202688
  13. RAHMANI, M. H., DESLANDES, D. Backward to forward scanning periodic leaky-wave antenna with wide scanning range. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 7, p. 3326–3335. DOI: 10.1109/TAP.2017.2705021
  14. AHMAD, A., MUKHERJEE, J. Microstrip leaky-wave antenna with circular polarization and broadside radiation. IEEE Antennas and Wireless Propagation Letters, 2023, vol. 22, no. 9, p. 2265 to 2269. DOI: 10.1109/LAWP.2023.3283398
  15. NI, H., GU, X., WU, K., et al. Compact SIW leaky-wave antenna with open-stopband suppression. IEEE Antennas and Wireless Propagation Letters, 2023, vol. 22, no. 10, p. 2467–2471. DOI: 10.1109/LAWP.2023.3291390
  16. PAULOTTO, S., BACCARELLI, P., FREZZA, F., et al. A novel technique for open-stopband suppression in 1-D periodic printed leaky-wave antennas. IEEE Transactions on Antennas and Propagation, 2009, vol. 57, no. 7, p. 1894–1906. DOI: 10.1109/TAP.2009.2019900
  17. ZHANG, P. F., ZHU, L., SUN, S. Microstrip-line EH1/EH2-mode leaky-wave antennas with backward-to-forward scanning. IEEE Antennas and Wireless Propagation Letters, 2020, vol. 19, no. 12, p. 2363–2367. DOI: 10.1109/LAWP.2020.3033064
  18. XU, K., WANG, Q., LV, L., et al. SIW-based-band leaky-wave antenna with improved beam steering performance. IEEE Antennas and Wireless Propagation Letters, 2022, vol. 21, no. 11, p. 2224 to 2228. DOI: 10.1109/LAWP.2022.3195215
  19. OTTO, S., CHEN, Z., AL-BASSAM, A., et al. Circular polarization of periodic leaky-wave antennas with axial asymmetry: Theoretical proof and experimental demonstration. IEEE Transactions on Antennas and Propagation, 2014, vol. 62, no. 4, p. 1817–1829. DOI: 10.1109/TAP.2013.2297169
  20. JIANG, H., XU, K., ZHANG, Q., et al. Backward-to-forward wide-angle fast beam-scanning leaky-wave antenna with consistent gain. IEEE Transactions on Antennas and Propagation, 2021, vol. 69, no. 5, p. 2987–2992. DOI: 10.1109/TAP.2020.3029721
  21. DAHELE, J., LEE, K. Effect of substrate thickness on the performance of a circular-disk microstrip antenna. IEEE Transactions on Antennas and Propagation, 1983, vol. 31, no. 2, p. 358–360. DOI: 10.1109/TAP.1983.1143037
  22. WANG, H., SUN, S., XUE, X., et al. A periodic coplanar strips leaky-wave antenna with horizontal wide-angle beam scanning and stable radiation. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 10, p. 9861–9866. DOI: 10.1109/TAP.2022.3177514
  23. SARKAR, A., PHAM, D. A., LIM, S. Tunable higher order mode based dual-beam CRLH microstrip leaky-wave antenna for V-band backward-broadside-forward radiation coverage. IEEE Transactions on Antennas and Propagation, 2020, vol. 68, no. 10, p. 6912–6922. DOI: 10.1109/TAP.2020.2995300
  24. SUN, L., HOU, Y., LI, Y., et al. An open cavity leaky-wave antenna with vertical-polarization end fire radiation. IEEE Transactions on Antennas and Propagation, 2019, vol. 67, no. 5, p. 3455–3460. DOI: 10.1109/TAP.2019.2902662

Keywords: Leaky-wave antenna, circular polarization, open stopband, axial ratio bandwidth

Z. Wang, Q. Wang, X. Dang [references] [full-text] [DOI: 10.13164/re.2024.0368] [Download Citations]
Altitude Range and Throughput Analysis for Directional UAV-assisted Backscatter Communications Networks

In the realm of Internet of Things (IoT) networks, Backscatter communication (BackCom) is a promising technique that allows devices to send data through the reflecting surrounding radio frequency (RF) signals. Integrating unmanned aerial vehicles (UAVs) with BackCom technology to establish UAV-assisted BackCom networks presents an opportunity to provide self-generated RF signals for backscatter devices, establishing self-sustaining data collection systems. This paper investigates directional UAV-assisted BackCom networks where UAVs are equipped with directional antennas, which differs from previous studies that mainly consider omni-directional antennas. To ensure the quality of BackCom, we develop a theoretical model that analyzes the valid altitude range of UAVs, which is often ignored in previous studies. Based on the altitude range of UAVs, we then derive the throughput of directional UAV-assisted BackCom networks. Extensive simulations are conducted to verify our theoretical model, revealing correlations between the UAV altitude range, the throughput, directional antennas, and other key parameters. Results indicate that UAVs need to set the proper UAV altitude according to multiple parameters to ensure successful communication. In addition, adjusting the beamwidth of directional antennas can enhance both the altitude range of UAVs and the throughput of networks.

  1. JIANG, T., ZHANG, Y., MA, W., et al. Backscatter communication meets practical battery-free internet of things: A survey and outlook. IEEE Communications Surveys & Tutorials, 2023, vol. 25, no. 3, p. 2021–2051. DOI: 10.1109/comst.2023.3278239
  2. REZAEI, F., GALAPPATHTHIGE, D., TELLAMBURA, C., et al. Coding techniques for backscatter communications- A contemporary survey. IEEE Communications Surveys & Tutorials, 2023, vol. 25, no. 2, p. 1020–1058. DOI: 10.1109/comst.2023.3259224
  3. JIANG, X., SHENG, M., ZHAO, N., et al. Outage analysis of UAV aided networks with underlaid ambient backscatter communications. IEEE Transactions on Wireless Communications, 2023, vol. 22, no. 11, p. 7492–7505. DOI: 10.1109/TWC.2023.3251979
  4. TRAN, D.-H., CHATZINOTAS, S., OTTERSTEN, B. Throughput maximization for backscatter- and cache-assisted wireless powered UAV technology. IEEE Transactions on Vehicular Technology, 2022, vol. 71, no. 5, p. 5187–5202. DOI: 10.1109/tvt.2022.3155190
  5. YANG, H., YE, Y., CHU, X., et al. Energy efficiency maximization for UAV-enabled hybrid backscatter-harvest-then-transmit communications. IEEE Transactions on Wireless Communications, 2022, vol. 21, no. 5, p. 2876–2891. DOI: 10.1109/TWC.2021.3116509
  6. JIANG, X., SHENG, M., ZHAO, N., et al. UAV-assisted networks with underlaid ambient backscattering: Modeling and outage analysis. In Proceedings of IEEE Global Communications Conference (GLOBECOM). Rio (Brazil), 2022, p. 4947–4952. DOI: 10.1109/globecom48099.2022.10000797
  7. HUA, M., YANG, L., LI, C., et al. Throughput maximization for UAV-aided backscatter communication networks. IEEE Transactions on Communications, 2020, vol. 68, no. 2, p. 1254–1270. DOI: 10.1109/tcomm.2019.2953641
  8. YANG, G., DAI, R., LIANG, Y.-C. Energy-efficient UAV backscatter communication with joint trajectory design and resource optimization. IEEE Transactions on Wireless Communications, 2021, vol. 20, no. 2, p. 926–941. DOI: 10.1109/TWC.2020.3029225
  9. TANG, G., LI, X., JI, H., et al. Optimization of trajectory scheduling and time allocation in UAV-assisted backscatter communication. In Proceedings of IEEE International Conference on Communications Workshops (ICC Workshops). Montreal (Canada), 2021, p. 1–6. DOI: 10.1109/iccworkshops50388.2021.9473549
  10. AL-HOURANI, A., KANDEEPAN, S., LARDNER, S. Optimal LAP altitude for maximum coverage. IEEE Wireless Communications Letters, 2014, vol. 3, no. 6, p.569–572.DOI:10.1109/lwc.2014.2342736
  11. WANG, Q., ZHOU, Y., DAI, H.-N., et al. Performance on cluster backscatter communication networks with coupled interferences. IEEE Internet of Things Journal, 2022, vol. 9, no. 20, p. 20282–20294. DOI: 10.1109/JIOT.2022.3174002
  12. FARAJZADEH, A., ERCETIN, O., YANIKOMEROGLU, H. UAV data collection over NOMA backscatter networks: UAV altitude and trajectory optimization. In IEEE International Conference on Communications (ICC). Shanghai (China), 2019, p. 1–7. DOI: 10.1109/ICC.2019.8761125
  13. VAEZI, M., AZARI, A., KHOSRAVIRAD, S. R., et al. Cellular, wide-area, and non-terrestrial IoT: A survey on 5G advances and the road towards 6G. IEEE Communications Surveys & Tutorials, 2022, vol. 24, no. 2, p. 1117–1174. DOI: 10.1109/comst.2022.3151028
  14. SCHWARZ, S., PRATSCHNER, S. Multiple antenna systems in mobile 6G: Directional channels and robust signal processing. IEEE Communications Magazine, 2023, vol. 61, no. 4, p. 64–70. DOI: 10.1109/mcom.001.2200258
  15. LIU, J., YU, J., NIYATO, D., et al. Covert ambient backscatter communications with multi-antenna tag. IEEE Transactions on Wireless Communications, 2023, vol. 22, no. 9, p. 6199–6212. DOI: 10.1109/TWC.2023.3240463
  16. WANG, X., YIGITLER, H., JANTTI, R. Gaining from multiple ambient sources: Signaling matrix for multi-antenna backscatter devices. IEEE Wireless Communications Letters, 2023, vol. 12, no. 3, p. 491–495. DOI: 10.1109/lwc.2022.3231907
  17. BALANIS, C. A. Antenna Theory: Analysis and Design. 3rd ed., New York: John Wiley & Sons, 2005. ISBN: 9781118642061
  18. WANG, Q., DAI, H.-N., ZHENG, Z., et al. On connectivity of wireless sensor networks with directional antennas. Sensors, 2017, vol.17, no. 1, p. 1–22. DOI: 10.3390/s17010134
  19. WANG, Q., DAI, H.-N., GEORGIOU, O., et al. Connectivity of underlay cognitive radio networks with directional antennas. IEEE Transactions on Vehicular Technology, 2018, vol. 67, no. 8, p. 7003–7017. DOI: 10.1109/tvt.2018.2825379
  20. YANG, S., DENG, Y., TANG, X., et al. Energy efficiency optimization for UAV-assisted backscatter communications. IEEE Communications Letters, 2019, vol. 23, no. 11, p. 2041–2045. DOI: 10.1109/lcomm.2019.2931900
  21. LIU, Y., WANG, Q., DAI, H.-N., et al. UAV-assisted wireless backhaul networks: Connectivity analysis of uplink transmissions. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 9, p. 12195–12207. DOI: 10.1109/tvt.2023.3268025
  22. KHAWAJA, W., GUVENC, I., MATOLAK, D. W., et al. A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles. IEEE Communications Surveys & Tutorials, 2019, vol. 21, no. 3, p. 2361–2391. DOI: 10.1109/COMST.2019.2915069
  23. SIMON, M. K., ALOUINI, M.-S. Digital Communication over Fading Channels. New York: Wiley, 2004. ISBN: 9780471649533
  24. LU, X., JIANG, H., NIYATO, D., et al. Wireless-powered device-to-device communications with ambient backscattering: Performance modeling and analysis. IEEE Transactions on Wireless Communications, 2018, vol. 17, no. 3, p. 1528–1544. DOI: 10.1109/TWC.2017.2779857
  25. SHI, L., HU, R. Q., YE, Y., et al. Modeling and performance analysis for ambient backscattering underlaying cellular networks. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 6, p. 6563–6577. DOI: 10.1109/tvt.2020.2984529
  26. LI, D. Backscatter communication powered by selective relaying. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 11, p. 14037–14042. DOI: 10.1109/tvt.2020.3029340
  27. VAN HUYNH,N., HOANG,D.T., LU, X., et al. Ambient backscatter communications: A contemporary survey. IEEE Communications Surveys & Tutorials, 2018, vol. 20, no. 4, p. 2889–2922. DOI: 10.1109/comst.2018.2841964
  28. BOSHKOVSKA, E., NG, D. W. K., ZLATANOV, N., et al. Practical non-linear energy harvesting model and resource allocation for SWIPT systems. IEEE Communications Letters, 2015, vol. 19, no. 12, p. 2082–2085. DOI: 10.1109/LCOMM.2015.2478460
  29. MAO, Z., HU, F., WU, W., et al. Joint distributed beamforming and backscattering for UAV-assisted WPSNs. IEEE Transactions on Wireless Communications, 2023, vol. 22, no. 3, p. 1510–1522. DOI: 10.1109/TWC.2022.3204915
  30. SONG, X., CHIN, K. W. A novel hybrid access point channel access method for wireless-powered IoT networks. IEEE Internet of Things Journal, 2021, vol. 8, no. 15, p. 12329–12338. DOI: 10.1109/JIOT.2021.3063375
  31. ZARGARI, S., KHALILI, A., WU, Q., et al. Max-min fair energy efficient beamforming design for intelligent reflecting surface-aided SWIPT systems with non-linear energy harvesting model. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 6, p. 5848–5864. DOI: 10.1109/tvt.2021.3077477
  32. WANG, Q., ZHOU, Y., DAI, H.-N., et al. Modeling and analysis of finite-scale clustered backscatter communication networks. In IEEE International Conference on Communications (ICC). Rome (Italy), 2023, p. 1456–1461. DOI: 10.1109/ICC45041.2023.10279058

Keywords: Backscatter communications, UAV-assisted networks, directional antennas, altitude range, throughput

Y. Feng, J. Nie, G. Xie, H. Lv [references] [full-text] [DOI: 10.13164/re.2024.0376] [Download Citations]
Soil Moisture Content Inversion by Coupling AEA and ARMA

This study aimed to explore the inversion method of soil moisture content by using numerical simulation and field detection. The researchers used the early signal amplitude envelope (AEA) method to directly invert soil moisture in the shallow part of the soil, which avoided the transmission error of the Topp formula. The Auto-Regressive Moving Average Model (ARMA) was used to calculate the power spectrum of radar signals, and the BP neural network was used to train the power spectrum of different Gaussian windows, so as to improve the inversion accuracy. According to the study, the average error of soil moisture content inverted by AEA method was 0.45% in the range of 0-0.41m, while the error of ARMA method in depth range of 0.1-1.0m was less than 1%. The results showed that the combination of the two methods can effectively invert the soil moisture content within the radar detection range.

  1. LUNT, I. A., HUBBARD, S. S., RUBIN, Y. Soil moisture content estimation using ground-penetrating radar reflection data. Journal of Hydrology, 2005, vol. 307, no. 1–4, p. 254–269. DOI: 10.1016/j.jhydrol.2004.10.014
  2. ZHANG, R., CHEN, X., LU, P., et al. The effect of different mulching modes on soil moisture, temperature and yield of potato in dry land (in Chinese). Crops, 2023, no. 5, p. 145–150. DOI: 10.16035/j.issn.1001-7283.2023.05.021
  3. BENEDETTO, A. Water content evaluation in unsaturated soil using GPR signal analysis in the frequency domain. Journal of Applied Geophysics, 2010, vol. 71, no. 1, p. 26–35. DOI: 10.1016/j.jappgeo.2010.03.001
  4. LIU, Y., WEI, L. S., HUANG, A. B., et al. Temporal and spatial evolution of soil water in the source of the Yangtze river under climate change and its environmental response (in Chinese). Hydrogeology & Engineering Geology, 2023, vol. 50, no. 5, p. 39–52. DOI: 10.16030/j.cnki.issn.1000-3665.202301034
  5. DANIELS, D. J. Ground Penetrating Radar. 1st ed. London (UK): The Institution of Electrical Engineers, 2004. ISBN: 9780863413605
  6. ODEN, C. P., POWERS, M. H., WRIGHT, D. L., et al. Improving GPR image resolution in lossy ground using dispersive migration. IEEE Transactions on Geoscience and Remote Sensing, 2007, vol. 45, no. 8, p. 2492–2500. DOI: 10.1109/tgrs.2006.888933
  7. WANG, T., ORISTAGLIO, M. L. 3-D simulation of GPR surveys over pipes in dispersive soils. Geophysics, 2000, vol. 65, no. 5, p. 1560–1568. DOI: 10.1190/1.1444844
  8. HUISMAN, J. A., HUBBARD, S. S., REDMAN, J. D., et al. Measuring soil water content with ground penetrating radar: A review. Vadose Zone Journal, 2003, vol. 2, no. 4, p. 476–491. DOI: 10.2113/2.4.476
  9. KOYAMA, C. N., LIU, H., TAKAHASHI, K., et al. In-situ measurement of soil permittivity at various depths for the calibration and validation of low-frequency SAR soil moisture models by using GPR. Remote Sensing, 2017, vol. 9, no. 6, p. 1–14. DOI: 10.3390/rs9060580
  10. ERCOLI, M., DI MATTEO, L., PAUSELLI, C., et al. Integrated GPR and laboratory water content measures of sandy soils: From laboratory to field scale. Construction and Building Materials, 2018, vol. 159, p. 734–744. DOI: 10.1016/j.conbuildmat.2017.11.082
  11. PETTINELLI, E., VANNARONI, G., DI PASQUO, B., et al. Correlation between near-surface electromagnetic soil parameters and early-time GPR signals: An experimental study. Geophysics, 2007, vol. 72, no. 2, p. A25–A28. DOI: 10.1190/1.2435171
  12. DI MATTEO, A., PETTINELLI, E., SLOB, E. Early-time GPR signal attributes to estimate soil dielectric permittivity: A theoretical study. IEEE Transactions on Geoscience and Remote Sensing, 2012, vol. 51, no. 3, p. 1643–1654. DOI: 386 10.1109/tgrs.2012.2206817
  13. FERRARA, C., BARONE, P. M., STEELMAN, C. M., et al. Monitoring shallow soil water content under natural field conditions using the early-time GPR signal technique. Vadose Zone Journal, 2013, vol. 12, no. 4, p. 1–9. DOI: 10.2136/vzj2012.0202
  14. ALGEO, J., VAN DAM, R. L., SLATER, L. Early‐time GPR: A method to monitor spatial variations in soil water content during irrigation in clay soils. Vadose Zone Journal, 2016, vol. 15, no. 11, p. 1–9. DOI: 10.2136/vzj2016.03.0026
  15. PETTINELLI, E., DI MATTEO, A., BEAUBIEN, S. E., et al. A controlled experiment to investigate the correlation between early-time signal attributes of ground-coupled radar and soil dielectric properties. Journal of Applied Geophysics, 2014, vol. 101, p. 68–76. DOI: 10.1016/j.jappgeo.2013.11.012
  16. COMITE, D., GALLI, A., LAURO, S. E., et al. Analysis of GPR early-time signal features for the evaluation of soil permittivity through numerical and experimental surveys. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, vol. 9, no. 1, p. 178–187. DOI: 10.1109/jstars.2015.2466174
  17. WU, K., DESESQUELLES, H., COCKENPOT, R., et al. Ground penetrating radar full-wave inversion for soil moisture mapping in trench-hill potato fields for precise irrigation. Remote Sensing, 2022, vol. 14, p. 1–16. DOI: 10.3390/rs14236046
  18. LAURENS, S., BALAYSSAC, J. P., RHAZI, J., et al. Nondestructive evaluation of concrete moisture by GPR: Experimental study and direct modeling. Materials and Structures, 2005, vol. 38, no. 9, p. 827–832. DOI: 10.1007/bf02481655
  19. ARDEKANI, M. R., NEYT, X., BENEDETTO, D., et al. Soil moisture variability effect on GPR data. In Proceedings of the 15th International Conference on Ground Penetrating Radar. Brussels (Belgium), 2014, p. 214–217. DOI: 10.1109/ICGPR.2014.6970416
  20. PONGRAC, B., GLEICH, D. Remote monitoring system based on cross-hole GPR and deep learning. In 2023 17th International Conference on Telecommunications (ConTEL). Graz (Austria), 2023, p. 1–5. DOI: 10.1109/ConTEL58387.2023.10198933
  21. YANG, F., ZHANG, Q. S., WANG, P. Y. Research on Geological Radar Detection Technology for Highway Roadbeds (in Chinese). Beijing (China): China Communication Press, 2009. ISBN: 9787114079115
  22. CUI, F., WU, Z. Y., WANG, L., et al. Application of the ground penetrating radar ARMA power spectrum estimation method to detect moisture content and compactness values in sandy loam. Journal of Applied Geophysics, 2015, vol. 120, p. 26–35. DOI: 10.1016/j.jappgeo.2015.06.006
  23. CHENG, Q., ZHANG, S. W., LUO, M., et al. Inversion of reclaimed soil moisture based on ground penetrating radar fly ash filling (in Chinese). Progress in Geophysics, 2021, vol. 36, no. 5, p. 2159 to 2167. DOI: 10.6038/pg2021EE0413
  24. WU, Z. Y., DU, W. F., NIE, J. L., et al. Detection of cohesive soil moisture content based on early signal amplitude envelope values of ground penetrating radar (in Chinese). Transactions of the Chinese Society of Agricultural Engineering, 2019, vol. 35, no. 22, p. 115–121. DOI: 10.11975/j.issn.1002-6819.2019.22.013
  25. XIE, G. Q., NIE, J. L., CHEN, Z. Q., et al. Prediction of soil moisture status based on ground penetrating radar power spectrum attribute parameters (in Chinese). Water Saving Irrigation, 2023, no. 10, p. 28–35. DOI: 10.12396/jsgg.2023147
  26. TANG, M. G., QI, M., WANG, D. J., et al. Application of ARMA model in accuracy analysis of radar dynamic measurement (in Chinese). Modern Radar, 2019, vol. 41, no. 5, p. 77–81. DOI: 10.16592/j.cnki.1004-7859.2019.05.015
  27. WANG, F. Y., ZHANG, L. L. Power spectrum estimation and MATLAB simulation (in Chinese). Microcomputer Information, 2006, no. 31, p. 287–289. DOI: 10.3969/j.issn.10080570.2006.31.102
  28. GIANNOPOULOS, A. Modelling ground penetrating radar by GprMax. Construction and Building Materials, 2005, vol. 19, no. 10, p. 755–762. DOI: 10.1016/j.conbuildmat.2005.06.007
  29. STEELMAN, C. M., ENDRES, A. L. An examination of direct ground wave soil moisture monitoring over an annual cycle of soil conditions. Water Resources Research, 2010, vol. 46, no. 11, p. 1–16. DOI: 10.1029/2009wr008815
  30. DONG, L., WANG, L. J., HAO, Y., et al. Wind power generation capacity prediction based on autoregressive moving average model (in Chinese). Acta Energiae Solaris Sinica, 2011, vol. 32, no. 5, p. 617–622. ISSN: 0254-0096
  31. ZHU, M. T., LIU, J., WANG, G. L. Research on the order determination method of AR model for road surface irregularity reconstruction (in Chinese). Journal of Highway and Transportation Research and Development, 2010, vol. 27, no. 7, p. 25–28+51. DOI: 10.3969/j.issn.1002-0268.2010.07.005

Keywords: Ground penetrating radar, AEA, ARMA, soil moisture content, BP neural network

Y. Wang, H. Tian, M. Liu [references] [full-text] [DOI: 10.13164/re.2024.0387] [Download Citations]
EFU Net: Edge Information Fused 3D Unet for Brain Tumor Segmentation

Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly segment brain tumors using computers because of the mutual infiltration and fuzzy boundary between the focus areas and the normal brain tissue. To address the above issues, a segmentation method which integrates edge features is proposed in this paper. The overall segmentation architecture follows the encoder decoder structure, extracting rich features from the encoder. The first two layers of features are input to the edge attention module, and to extract tumor edge features which are fully fused with the features of the decoder segment. At the same time, an adaptive weighted mixed loss function is introduced to train the network by adaptively adjusting the weights of different loss parts in the training process. Relevant experiments were carried out using the public brain tumor data set. The Dice mean values of the proposed segmentation model in the whole tumor area (WT), the core tumor area (TC), and the enhancing tumor area (ET) reach 91.10%, 87.16%, and 88.86%, respectively, and the mean values of Hausdorff distance are 3.92, 5.12, and 1.92 mm, respectively. The experimental results showed that the proposed method can significantly improve segmentation accuracy, especially the segmentation effect of the edge part.

  1. KLEIHUES, P., BURGER, P. C., SCHEITHAUER, B. W. The new WHO classification of brain tumors. Brain Pathology, 1993, vol. 3, no. 3, p. 255–268. DOI: 10.1111/j.17503639.1993.tb00752.x
  2. SUN, H., YANG, S., CHEN, L., et al. Brain tumor image segmentation based on improved FPN. BMC Medical Imaging, 2023, vol. 23, no. 1, p. 1–10. DOI: 10.1186/s12880-023-01131-1
  3. XU, Y., JIA, Z. P., AI, Y. G., et al. Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brisbane (Australia), 2015, p. 947–951. DOI: 10.1109/ICASSP.2015.7178109
  4. DONG, H., YANG, G., LIU, F., et al. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In Proceedings of Annual Conference on Medical Image Understanding and Analysis. Edinburgh (Scotland), 2017, p. 506 to 517. DOI: 10.1007/978-3-319-60964-5_44
  5. BEERS, A., CHANG, K., BROWN, J., et al. Sequential 3D U-nets for biologically-informed brain tumor segmentation. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Hawaii (USA), 2017, p. 235–242. DOI: 10.48550/arXiv.1709.02967
  6. DIAKOGIANNIS, F. I., WALDNER, F., CACCETTA, P., et al. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, vol. 162, no. 1, p. 94–114. DOI: 10.1016/j.isprsjprs.2020.01.013
  7. ZHOU, Z., SIDDIQUEE, M. M. R., TAJBAKHSH, N., et al. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 2019, vol. 39, no. 6, p. 1856–1867. DOI: 10.1109/TMI.2019.2959609
  8. ISENSEE, F., JAGER, P. F., FULL, P. M., et al. The nnU-net for brain tumor segmentation. In Proceedings of the International Conference on Brainlesion-Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Lima (Peru), 2020, p. 118–132. DOI: 10.48550/arXiv.2011.00848
  9. WADHWA, A., BHARDWAJ, A., VERMA, V. S. A review on brain tumor segmentation of MRI images. Magnetic Resonance Imaging, 2019, vol. 1, no. 61, p. 247–259. DOI: 10.1016/j.mri.2019.05.043
  10. AL NASIM, M. A., AL MUNEM, A., ISLAM, M., et al. Brain tumor segmentation using enhanced u-net model with empirical analysis. In Proceedings of the 25th International Conference on Computer and Information Technology (ICCIT). Cox's Bazar (Bangladesh), 2022, p. 1027–1032. DOI: 10.1109/ICCIT57492.2022.10054934
  11. VADHNANI, S., SINGH, N. Brain tumor segmentation and classification in MRI using SVM and its variants: A survey. Multimedia Tools and Applications, 2022, vol. 81, no. 22, p. 31631–31656. DOI: 10.1007/s11042-022-12240-4
  12. SHEN, H., ZHANG, J., ZHENG, W. Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation. In Proceedings of IEEE International Conference on Image Processing. Beijing (China), 2017, p. 3864–3868. DOI: 10.1109/ICIP.2017.8297006
  13. ZHANG, J., JIANG, Z., DONG, J., et al. Attention gate resU-Net for automatic MRI brain tumor segmentation. IEEE Access, 2020, vol. 8, p. 58533–58545. DOI: 10.1109/access.2020.2983075
  14. ABOELENEIN, N. M., SONGHAO, P., KOUBAA, A., et al. HTTU-Net: Hybrid two track U-Net for automatic brain tumor segmentation. IEEE Access, 2020, vol. 8, p. 101406–101415. DOI: 10.1109/ACCESS.2020.2998601
  15. ZHANG, Z., FU, H., DAI, H., et al. ET-Net: A generic edge attention guidance network for medical image segmentation. In Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Shenzhen (China), 2019, p. 442–450. DOI: 10.1007/978-3-03032239-7_49
  16. LEE, H. J., KIM, J. U., LEE, S., et al. Structure boundary preserving segmentation for medical image with ambiguous boundary. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle (USA), 2020, p. 4817–4825. DOI: 10.1109/CVPR42600.2020.00487
  17. ZHOU, X., LI, X., HU, K., et al. ERV-Net: An efficient 3D residual neural network for brain tumor segmentation. Expert Systems with Applications, 2021, vol. 170, p. 1–13. DOI: 10.1016/j.eswa.2021.114566
  18. LIN, T. Y., GOYAL, P., GIRSHICK, R., et al. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, vol. 42, no. 2, p. 318–327. DOI: 10.1109/TPAMI.2018.2858826
  19. LU, J. L., WANG, Z. Y., BIER, E., et al. Bias field correction in hyperpolarized Xe-129 ventilation MRI using templates derived by RF-depolarization mapping. Magnetic Resonance in Medicine, 2022, vol. 88, no. 2, p. 802–816. DOI: 10.1002/mrm.29254
  20. HE, K., ZHANG, X., REN, S., et al. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas (USA), 2016, p. 770–778. DOI: 10.1109/CVPR.2016.90
  21. HU, J., SHEN, L., SUN, G. Squeeze-and-excitation networks. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City (USA), 2018, p. 7132–7141. DOI: 10.1109/CVPR.2018.00745
  22. MENZE, B. H., JAKAB, A., BAUER, S., et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, 2014, vol. 34, no. 10, p. 1993 to 2024. DOI: 10.1109/TMI.2014.2377694
  23. BAKAS, S., AKBARI, H., SOTIRAS, A., et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 2017, vol. 4, no. 1, p. 1–13. DOI: 10.1038/sdata.2017.117
  24. OKTAY, O., SCHLEMPER, J., LE FOLGOC, L., et al. Attention u-net: Learning where to look for the pancreas. In Proceedings of International Conference on Medical Imaging with Deep Learning (MIDL). Amsterdam (Netherlands), 2018, p. 1–10. DOI: 10.48550/arXiv.1804.03999
  25. HO, N. V., NGUYEN, T., DIEP, G. H., et al. Point-unet: A context-aware point-based neural network for volumetric segmentation. In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Strasbourg (France), 2021, p. 644–655. DOI: 10.1007/978-3-030-87193-2_61
  26. WANG, W., CHEN, C., DING, M., et al. TransBTS: Multimodal brain tumor segmentation using transformer. In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Strasbourg (France), 2021, p. 109–119. DOI: 10.1007/978-3-030-87193-2_11

Keywords: Deep learning, brain tumor segmentation, encoder decoder structure, edge attention mechanism, hybrid loss function