ISSN 1210-2512 (Print)

ISSN 1805-9600 (Online)

Radioengineering

Radioeng

Proceedings of Czech and Slovak Technical Universities

About the Journal Editorial Board Publishing Department Sponsors and Supporters Opened special issues Společnost pro radioelektronické inženýrství [CZ]
About the Journal
Feature Articles
Editorial Board
Publishing Department
Society [CZ]
Log out
Your Profile
Administration

September 2025, Volume 34, Number 3 [DOI: 10.13164/re.2025-3]

Show all Hide all
RIS File
BibTeX

T. C. Jermin Jeaunita, T. Ramesh, C. V. S. R. Manjushree, P. T. Shantala [references] [full-text] [DOI: 10.13164/re.2025.0381] [Download Citations]
A Decentralized and Efficient Crowdfunding Framework for Secure Transactions and User Engagement

Crowdfunding has become essential for financing entrepreneurial projects, innovative projects, and social initiatives. However, existing platforms face critical challenges, including a lack of transparency, low user engagement, data privacy concerns, and ineffective personalization of user experiences. To address these limitations, this study introduces a novel decentralized crowdfunding framework that integrates Federated Learning (FL), blockchain technology, and Q-learning to enhance security, transparency, and user engagement. The framework leverages FL to collaboratively train models across distributed datasets while ensuring privacy preservation by eliminating the need to share raw user data. Blockchain technology is utilized to ensure tamper-proof transaction records and automate trustless interactions through smart contracts, effectively preventing fraud while increasing transparency. Additionally, a Q-learning-based incentive mechanism is incorporated to predict and stimulate user engagement, ensuring dynamic long-term engagement. The experimental evaluation illustrates that the designed framework attains state-of-the-art performance with an accuracy rate of 99.39%, surpassing existing methodologies. The results demonstrate the effectiveness of the framework in providing a secure, decentralized, and highly personalized crowdfunding system, raising trust and engagement among stakeholders and resolving long-standing issues in crowdfunding platforms.

  1. KAMARUDIN, M. K., MOHAMAD NORZILAN, N. I., MUSTAFFA, F. N. A., et al. Why do donors donate? A study on donation-based crowdfunding in Malaysia. Sustainability, 2023, vol. 15, no. 5, p. 1–16. DOI: 10.3390/su15054301
  2. GUPTA, S., RAJ, S., GUPTA, S., et al. Prioritizing crowdfunding benefits: A fuzzy-AHP approach. Quality & Quantity, 2023, vol. 57, no. 1, p. 379–403. DOI: 10.1007/s11135-022-01359-z
  3. ABDUL HALIM, M. Does crowdfunding contribute to digital financial inclusion? Research in Globalization, 2024, vol. 9, no. 1, p. 1–13. DOI: 10.1016/j.resglo.2024.100238
  4. TALUKDER, S. C., LAKNER, Z. Exploring the landscape of social entrepreneurship and crowdfunding: A bibliometric analysis. Sustainability, 2023, vol. 15, no. 12, p. 1–22. DOI: 10.3390/su15129411
  5. DINH, J. M., ISAAK, A. J., WEHNER, M. C. Sustainability oriented crowdfunding: An integrative literature review. Journal of Cleaner Production, 2024, vol. 448, no. 3, p. 1–38. DOI: 10.1016/j.jclepro.2024.141579
  6. LIVINGSTONE, A., SERVAIS, L., WILKINSON, D. J. C. The ethics of crowdfunding in paediatric neurology. Developmental Medicine & Child Neurology. 2023, vol. 65, no. 4, p. 450–455. DOI: 10.1111/dmcn.15442
  7. HUO, H., WANG, C., HAN, C., et al. Risk disclosure and entrepreneurial resource acquisition in crowdfunding digital platforms: Evidence from digital technology ventures. Information Processing & Management, 2024, vol. 61, no. 3, p. 1–15. DOI: 10.1016/j.ipm.2024.103655
  8. JIANG, Z. Y., ZHANG, J. W., YANG, H. J., et al. Secure power data sharing with fine-grained control: A multi-strategy access tree approach. Radioengineering, 2024, vol. 33, no. 4, p. 704–712. DOI: 10.13164/re.2024.0704
  9. KAVITHA, P., KAVITHA, K. Hybrid NOMA for latency minimization in wireless federated learning for 6G networks. Radioengineering, 2023, vol. 32, no. 4, p. 594–602. DOI: 10.13164/re.2023.0594
  10. CARDONA, L. F., GUZMAN-LUNA, J. A., RESTREPO CARMONA, J. A. Bibliometric analysis of the machine learning applications in fraud detection on crowdfunding platforms. Journal of Risk and Financial Management, 2024, vol. 17, no. 8, p. 1–23. DOI: 10.3390/jrfm17080352
  11. LU, B., XU, T., FAN, W. How do emotions affect giving? Examining the effects of textual and facial emotions in charitable crowdfunding. Financial Innovations, 2024, vol. 10, no. 1, p. 1–44. DOI: 10.1186/s40854-024-00630-6
  12. FENG, Y., LUO, Y., PENG, N., et al. Crowdfunding performance prediction using feature‐selection‐based machine learning models. Expert Systems, 2024, vol. 41, no. 10, p. 1–15. DOI: 10.1111/exsy.13646
  13. TIGANOAIA, B, ALEXANDRU, G-M. Building a blockchain based decentralized crowdfunding platform for social and educational causes in the context of sustainable development. Sustainability. 2023, vol. 15, no. 23, p. 1–19. DOI: 10.3390/su152316205
  14. PARK, J., NA, H. J., KIM, H. Development of a success prediction model for crowdfunding based on machine learning reflecting ESG information. IEEE Access, 2024, vol. 12, no. 3, p. 197275–197289. DOI: 10.1109/ACCESS.2024.3519219
  15. LEE, S., PARK, H., KIM, H. C. Fraud detection on crowdfunding platforms using multiple feature selection methods. IEEE Access, 2025, vol. 13, no. 3,
  16. p. 40133–40148. DOI: 10.1109/ACCESS.2025.3547396
  17. USMAN, S. M., BUKHARI, F. A. S., YOU, H., et al. The effect and impact of signals on investing decisions in reward-based crowdfunding: A comparative study of China and the United Kingdom. Journal of Risk and Financial Management, 2020, vol. 13, no. 12, p. 1–20. DOI:10.3390/jrfm13120325
  18. KHURANA, I. Legitimacy and reciprocal altruism in donation based crowdfunding: Evidence from India. Journal of Risk and Financial Management, 2021, vol. 14, no. 5, p. 1–16. DOI: 10.3390/jrfm14050194
  19. FANEA-IVANOVICI, M., BABER, H. Crowdfunding model for financing movies and web series. International Journal of Innovation Studies, 2021, vol. 5, no. 2, p. 99–105. DOI: 10.1016/j.ijis.2021.06.001
  20. PENG, N., ZHOU, X., NIU, B., et al. Predicting fundraising performance in medical crowdfunding campaigns using machine learning. Electronics, 2021, vol. 10, no. 2, p. 1–16. DOI: 10.3390/electronics10020143
  21. LEOŃSKI, W. Crowdfunding as an innovative source of financing business initiatives in Poland. Procedia Computer Science, 2022, vol. 207, no 4, p. 2921–2929. DOI: 10.1016/j.procs.2022.09.350
  22. RAJWA, P., HOPEN, P., WOJNAROWICZ, J., et al. Online crowdfunding for urologic cancer care. Cancers, 2022, vol. 14, no. 17, p. 1–11. DOI: 10.3390/cancers14174104
  23. NAN, L., TANG, C., WANG, X., et al. The real effects of transparency in crowdfunding. Contemporary Accounting Research, 2024, vol. 41, no. 1, p. 39–68. DOI: 10.1111/1911-3846.12903
  24. YEH, J. Y., WANG, Z. L. Mining ESG semantic features for success prediction in green-oriented crowdfunding campaigns. In International Conference on Consumer Electronics-Taiwan (ICCE Taiwan). Ping Tung (Taiwan), 2023, p. 819–820. DOI: 10.1109/ICCE-Taiwan58799.2023.10226784
  25. CORSINI, F., FREY, M. Crowdfunding sustainable products with the product search matrix: niche markets vs. mass markets. Electronic Commerce Research, 2023, vol. 24, no. 4, p. 2327–2352. DOI: 10.1007/s10660-023-09674-9
  26. PETCHHAN, J., PHANICHRAKSAPHONG, V., DOUNGTAP, S., et al. Toward project success forecasting in reward-based crowdfunding through wide-and-deep computational learning. In 15th IEEE International Conference on Industry Applications (INDUSCON). São Bernardo do Campo (Brazil), 2023, p. 1489 to
  27. 1493. DOI: 10.1109/INDUSCON58041.2023.10374665
  28. LI, Z., LIU, J., HAO, J., et al. CrowdSFL: A secure crowd computing framework based on blockchain and federated learning. Electronics, 2020, vol. 9,
  29. no. 5, p. 1–21. DOI: 10.3390/electronics9050773
  30. YADAV, N., SARASVATHI, V. Venturing crowdfunding using smart contracts in blockchain. In Proceedings of the Third International Conference on Smart Systems and Inventive Technology (ICSSIT). Tirunelveli (India), 2020, p. 192–197. DOI: 10.1109/ICSSIT48917.2020.9214295
  31. LEE, S., SHAFQAT, W., KIM, H. C. Backers beware: Characteristics and detection of fraudulent crowdfunding campaigns. Sensors, 2022, vol. 22, no. 19, p. 1–16. DOI: 10.3390/s22197677
  32. SAMBARE, S. S., KHANDAIT, K., KOLAMBE, K., et al. Crowdfunding using blockchain for startup ventures. In 2023 7th International Conference on Computing, Communication, Control and Automation (ICCUBEA). Pune (India), 2022, p. 1–6. DOI: 10.1109/ICCUBEA58933.2023.10392107
  33. VENSLAVIENĖ, S., STANKEVICIENĖ, J., LESCAUSKIENĖ, I. Evaluation of blockchain-based crowdfunding campaign success factors based on VASMA-L criteria weighting method. Administrative Sciences, 2023, vol. 13, no. 6, p. 1–16. DOI: 10.3390/admsci13060144
  34. WAN, X., TENG, Z., LI, Q., et al. Blockchain technology empowers the crowdfunding decision-making of marine ranching. Expert Systems with Applications, 2023, vol. 221, p. 1–23. DOI: 10.1016/j.eswa.2023.119685
  35. GUGGENBERGER, T., SCHELLINGER, B., VON WACHTER, V., et al. Kickstarting blockchain: Designing blockchain-based tokens for equity crowdfunding. Electronic Commerce Research, 2024, vol. 24, p. 239–273. DOI: 10.1007/s10660-022-09634-9
  36. MUKHERJEE, K., RANA, A., RANI, S. Crowdfunding platform using blockchain. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT). Pune (India), 2024, p. 1–6. DOI: 10.1109/I2CT61223.2024.10544161
  37. CPNCF120 Campaign Dataset. [Online] Cited 2025-03-05. Available at: https://data.world/manjushree012/cpncf120
  38. ORG_CF165 Organization Dataset. [Online] Cited 2025-03-05. Available at: https://data.world/manjushree012/orgcf165
  39. KYC_CF133 Individual Dataset. [Online] Cited 2025-03-05. Available at: https://data.world/manjushree012/kyccf133
  40. ALAGHBARI, K. A., LIM, H. S., SAAD, M. H. M., et al. Deep autoencoder-based integrated model for anomaly detection and efficient feature extraction in IoT networks. IoT, 2023, vol. 4, no. 3, p. 345–365. DOI: 10.3390/iot4030016
  41. TAN, Y., TIAN, J. A method for processing static analysis alarms based on deep learning. Applied Sciences, 2024, vol. 14, no. 13, p. 1–23. DOI: 10.3390/app14135542
  42. SHANMUGAM, D., ARUMUGAM, C. Effective communication compression framework using federated learning privacy model for smart grid. Procedia Computer Science, 2025, vol. 254, p. 164–170. DOI: 10.1016/j.procs.2025.02.075
  43. GHOLIZADEH, N., KAZEMI, N., MUSILEK, P. A comparative study of reinforcement learning algorithms for distribution network reconfiguration with deep Q-learning-based action sampling. IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3243549

Keywords: Bidirectional recurrent neural network, blockchain, crowdfunding, Federated Learning (FL), MetaMask wallet, Q-learning, smart contract

Rahman M., Wang, W., Wang J., Wang Y. [references] [full-text] [DOI: 10.13164/re.2025.0393] [Download Citations]
Fast Brain Tumor Segmentation with Model Optimization

Segmenting brain tumors is important for effective diagnosis and treatment planning. Conventional 3D segmentation models achieve high accuracy but are computationally intensive, often limiting real-time applicability. In this study, pseudo-3D convolutions, which consist of spatial and depthwise convolutions, are used in place of traditional 3D convolutions. Adaptive Dilated Multi-Fiber (DMF) units dynamically extract multi-scale features and parallel Multi-Fiber (MF) units combine them with weighted sum. Efficient Channel Attention (ECA) and Cross Attention improve feature selection and fusion in decoder and encoder. Structured pruning reduces superfluous parameters and Quantization Aware Training (QAT) increases the speed of inference with the model converted to INT8 precision. Combination of Dice Loss and Boundary Loss enhances the precision of tumor boundaries. The framework has been evaluated on the BraTS 2021 data validation set and achieved high Dice scores of Whole Tumor 91.85%, Tumor Core 88.52%, and Enhancing Tumor 85.55%, with Hausdorff95 values of 2.58 mm, 3.53 mm, and 3.65 mm. Our proposed model requires only 3.57M parameters and 21.26 GFLOPs, achieving an inference time of 0.016 seconds per 3D volume while maintaining precision alongside efficiency to clinical application.

  1. ZAKERI, Y., KARASFI, B., JALALIAN, A. A review of brain tumor segmentation using MRIs from 2019 to 2023 (statistical information, key achievements, and limitations). Journal of Medical and Biological Engineering, 2024, vol. 44, p. 1–26. DOI: 10.1007/s40846-024-00860-0
  2. AGGARWAL, M., TIWARI, A. K., SARATHI, M., et al. An early detection and segmentation of brain tumor using deep neural network. BMC Medical Informatics and Decision Making, 2023, vol. 23, p. 1–12. DOI: 10.1186/s12911-023-02174-8
  3. MAAS, B., ZABEH, E., ARABSHAHI, S. QuickTumorNet: Fast automatic multi-class segmentation of brain tumors. In Proceedings of the 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). Italy, May 2021, p. 81–85. DOI: 10.1109/NER49283.2021.9441286
  4. DE VERDIER, M. C., SALUJA, R., GAGNON, L., et al. The 2024 Brain Tumor Segmentation (BraTS) challenge: Glioma segmentation on post-treatment MRI. 10 pages. [Online] Cited 2024-05-28. Available at: https://arxiv.org/abs/2405.18368. DOI: 10.48550/arXiv.2405.18368
  5. 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
  6. ÇIÇEK, O., ABDULKADIR, A., LIENKAMP, S. S., et al. 3D UNet: Learning dense volumetric segmentation from sparse annotation. In Proceedings of the 19 th Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016). Athens (Greece), 2016, Part II, p. 424–432. DOI: 10.1007/978-3-319-46723-8_49
  7. MILLETARI, F., NAVAB, N., AHMADI, S. A., et al. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV). Stanford (CA, USA), 2016, p. 565–571. DOI: 10.1109/3DV.2016.79
  8. CASAMITJANA, A., PUCH, S., ADURIZ, A., et al. 3D convolutional neural networks for brain tumor segmentation: A comparison of multi-resolution architectures. In Proceedings of the Second International Workshop on Brain lesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Athens (Greece), 2016, p. 150–161. DOI: 10.1007/978-3-319-55524-9_15
  9. FENG, X., TUSTISON, N. J., PATEL, S. H., et al. Brain tumor segmentation using an ensemble of 3D U-Nets and overall survival prediction using radiomic features. Frontiers in Computational Neuroscience, 2020, vol. 14, p. 1–12. DOI: 10.3389/fncom.2020.00025
  10. LIU, D., SHENG, N., HAN, Y., et al. SCAU-net: 3D self-calibrated attention U-Net for brain tumor segmentation. Neural Computing and Applications, 2023, vol. 35, no. 33, p. 23973–23985. DOI: 10.1007/s00521-023-08872-8
  11. GAMAL, A., BEDDA, K., ASHRAF, N., et al. Brain tumor segmentation using 3D U-Net with hyperparameter optimization. In Proceedings of the 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES). Giza (Egypt), 2021, p. 269–272. DOI: 10.1109/NILES53778.2021.9600556
  12. LIU, L., XIA, K. BTIS-Net: Efficient 3D U-Net for brain tumor image segmentation. IEEE Access, 2024, vol. 12, p. 133392 to 133405. DOI: 10.1109/ACCESS.2024.3460797
  13. NUECHTERLEIN, N., MEHTA, S. 3D-ESPNet with pyramidal refinement for volumetric brain tumor image segmentation. In Proceedings of Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018. Held in Conjunction with MICCAI 2018. Granada (Spain), 2018, Part II, p. 245–253. DOI: 10.1007/978-3-030-11726-9_22
  14. CHEN, W., LIU, B., PENG, S., et al. S3D-UNet: Separable 3D U-Net for brain tumor segmentation. In Proceedings of Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018. Held in Conjunction with MICCAI 2018. Granada (Spain), 2018, Part II, p. 358–368. DOI: 10.1007/978-3-030-11726-9_32
  15. ALI, S., KHURRAM, R., REHMAN, K. U., et al. An improved 3D U-Net-based deep learning system for brain tumor segmentation using multi-modal MRI. Multimed Tools and Applications, 2024, vol. 83, p. 85027–85046. DOI: 10.1007/s11042-024-19406-2
  16. AHMED, S. F., RAHMAN, F. S., TABASSUM, T., et al. 3D U-Net: Fully convolutional neural network for automatic brain tumor segmentation. In Proceedings of the 2019 22nd International Conference on Computer and Information Technology (ICCIT). Dhaka (Bangladesh), 2019, p. 1–6. DOI: 10.1109/ICCIT48885.2019.9038237
  17. WU, Q., PEI, Y., CHENG, Z., et al. SDS-Net: A lightweight 3D convolutional neural network with multi-branch attention for multimodal brain tumor accurate segmentation. Mathematical Biosciences and Engineering, 2023, vol. 20, no. 9, p. 17384–17406. DOI: 10.3934/mbe.2023773
  18. SANKAR, M., BAIJU, B. V., PREETHI, D., et al. Efficient brain tumor grade classification using ensemble deep learning models. BMC Medical Imaging, 2024, vol. 24, no. 1, p. 1–22. DOI: 10.1186/s12880-024-01476-1
  19. CHEN, C., LIU, X., DING, M., et al. 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI 2019). Shenzhen (China), 2019, p. 314–322. DOI: 10.1007/978-3-030-32248-9_21
  20. LIU, S. A., XU, H., LIU, Y., et al. Improving brain tumor segmentation with dilated pseudo-3D convolution and multi direction fusion. In Proceedings of the 26th International Conference on Multi Media Modeling (MMM 2020). Daejeon (South Korea), 2020, p. 727–738. DOI: 10.1007/978-3-030-37731-1_59
  21. WANG, K., LI, B., TAO, R. Pseudo-3D fully convolutional DenseNets for brain tumor segmentation. In Proceedings of the Tenth International Conference on Digital Image Processing (ICDIP 2018). Shanghai (China), 2018, p. 1462–1468. DOI: 10.1117/12.2502863
  22. WANG, Q., WU, B., ZHU, P., et al. ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle (WA, USA), 2020, p. 11531–11539. DOI: 10.1109/CVPR42600.2020.01155
  23. LI, H., KADAV, A., DURDANOVIC, I., et al. Pruning filters for efficient convnets. In International Conference on Learning Representations (ICLR). Toulon (France), 2016, p. 1–13. ArXiv Preprint. DOI: 10.48550/arXiv.1608.08710
  24. JACOB, B., KLIGYS, S., CHEN, B., et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City (UT, USA), 2018, p. 2704–2713. DOI: 10.1109/CVPR.2018.00286
  25. PATRO, S. G. K., SAHU, K. K. Normalization: A preprocessing stage. International Advanced Research Journal in Science, Engineering and Technology, 2015, vol. 2, no. 3, p. 20–22. DOI: 10.17148/IARJSET.2015.2305
  26. 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
  27. BAID, U., GHODASARA, S., MOHAN, S., et al. The RSNA ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint, 2021. DOI: 10.48550/arXiv.2107.02314
  28. CAO, Y., SONG, Y. New approach for brain tumor segmentation based on Gabor convolution and attention mechanism. Applied Sciences, 2024, vol. 14,
  29. no. 11, p. 1–16. DOI: 10.3390/app14114919
  30. LIU, L., XIA, K. BTIS-Net: Efficient 3D U-Net for brain tumor image segmentation. IEEE Access, 2024, vol. 12, p. 133392 to 133405. DOI: 10.1109/ACCESS.2024.3460797
  31. JIANG, Y., ZHANG, Y., LIN, X., et al. SwinBTS: A method for 3D multimodal brain tumor segmentation using swin transformer. Brain Sciences, 2022, vol. 12, no. 6, p. 1–15. DOI: 10.3390/brainsci12060797
  32. ZHOU, Z., WANG, P., YU, X., et al. GSFormer: A gated skip connection and feature fusion transformer-based neural network for 3D MRI brain tumor segmentation. In Proceedings of the 2024 International Conference on Intelligent Computing and Data Mining (ICDM). Chaozhou (China), 2024, p. 67–71. DOI: 10.1109/ICDM63232.2024.10762256
  33. HOU, Q., PENG, Y., WANG, Z., et al. MFD-Net: Modality fusion diffractive network for segmentation of multimodal brain tumor image. IEEE Journal of Biomedical and Health Informatics, 2023, vol. 27, no. 12, p. 5958–5969. DOI: 10.1109/JBHI.2023.3318640
  34. SUN, J., HU, M., WU, X., et al. MVSI-Net: Multi-view attention and multi-scale feature interaction for brain tumor segmentation. Biomedical Signal Processing and Control, 2024, vol. 95, Part A, p. 1–14. DOI: 10.1016/j.bspc.2024.106484
  35. AL-FAKIH, A., SHAZLY, A., MOHAMMED, A., et al. FLAIR MRI sequence synthesis using squeeze attention generative model for reliable brain tumor segmentation. Alexandria Engineering Journal, 2024, vol. 99, p. 108–123. DOI: 10.1016/j.aej.2024.05.008
  36. LIU, D., SHENG, N., HE, T., et al. SGEResU-Net for brain tumor segmentation. Mathematical Biosciences and Engineering, 2022, vol. 19, no. 6, p. 5576–5590. DOI: 10.3934/mbe.2022261
  37. ALWADEE, E. J., SUN, X., QIN, Y., et al. LATUP-Net: A lightweight 3D attention U-Net with parallel convolutions for brain tumor segmentation. Computers in Biology and Medicine, 2025, vol. 184, p. 1–16. DOI: 10.1016/j.compbiomed.2024.109353
  38. MAGADZA, T., VIRIRI, S. Efficient nnU-Net for brain tumor segmentation. IEEE Access, 2023, vol. 11, p. 126386–126397. DOI:
  39. 10.1109/ACCESS.2023.3329517
  40. ISENSEE, F., JAGER, P. F., FULL, P. M., et al. nnU-Net for brain tumor segmentation. In Proceedings of Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop (BrainLes 2020). Held in Conjunction with MICCAI 2020. Lima (Peru), 2020, Part II, p. 118–132. DOI: 10.1007/978-3-030-72087-2_11
  41. YANG, H., ZHOU, T., ZHOU, Y., et al. Flexible fusion network for multi-modal brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 2023, vol. 27, no. 7, p. 3349–3359. DOI: 10.1109/JBHI.2023.3271808
  42. PAN, D., SHEN, J., AL-HUDA, Z., et al. VcaNet: Vision transformer with fusion channel and spatial attention module for 3D brain tumor segmentation. Computers in Biology and Medicine, 2025, vol. 186, p. 1–12. DOI: 10.1016/j.compbiomed.2025.109662
  43. DIAO, Y., FANG, H., YU, H., et al. Multimodal invariant feature prompt network for brain tumor segmentation with missing modalities. Neurocomputing, 2025, vol. 616, p. 1–13. DOI: 10.1016/j.neucom.2024.128847
  44. ZHAO, J., XING, Z., CHEN, Z., et al. Uncertainty-aware multi-dimensional mutual learning for brain and brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 2023, vol. 27, no. 9, p. 4362–4372. DOI: 10.1109/JBHI.2023.3274255
  45. GAO, T., HU, W., CHEN, M., et al. MSDMAT-BTS: Multi-scale diffusion model and attention mechanism for brain tumor segmentation. Biomedical Signal Processing and Control, 2025, vol. 104, p. 1–12. DOI: 10.1016/j.bspc.2025.107505
  46. WANG, W., CHEN, C., DING, M., et al. TransBTS: Multimodal brain tumor segmentation using transformer. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2021). Strasbourg (France), 2021, Part I, p. 109–119. DOI: 10.1007/978-3-030-87199-4_11
  47. CHEN, J., LU, Y., YU, Q., et al. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint, 2021, p. 1–13. DOI: 10.48550/arXiv.2102.04306
  48. HATAMIZADEH, A., NATH, V., TANG, Y., et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. In Proceedings of Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021). Held in conjunction with MICCAI 2021.Virtual conference, Cham (Switzerland), 2021, p. 272–284. DOI: 10.1007/978-3-031-08999-2_22
  49. RAZA, R., IJAZ BAJWA, U., MEHMOOD, Y., et al. dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI. Biomedical Signal Processing and Control, 2023, vol. 79, no. 1, p. 1–12. DOI: 10.1016/j.bspc.2022.103861
  50. JIA, H., BAI, C., CAI, W., et al. HNF-Netv2 for brain tumor segmentation using multi-modal MR imaging. In Proceedings of Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021). Held in conjunction with MICCAI 2021. Virtual conference, Cham (Switzerland), 2021, vol. 12963, p. 106–115. DOI: 10.1007/978-3-031-09002-8_10

Keywords: Brain tumor segmentation, pseudo-3D convolutions, adaptive dilation, computational efficiency, inference time

Y. F. Shen, Q. Gao [references] [full-text] [DOI: 10.13164/re.2025.0407] [Download Citations]
DS-YOLO: A SAR Ship Detection Model for Dense Small Targets

Detecting dense small targets in Synthetic Aperture Radar (SAR) images has always been a challenge in ship target detection. To address this issue, this paper proposes a ship target detection model for SAR images, named DS-YOLO, which is based on the YOLO11 network architecture. The model introduces Space-to-Depth Convolution (SPDConv) module to enhance the detection capability of small targets. Additionally, a new module, Cross Stage Partial-Partial Pyramid Attention (CSP-PPA), is incorporated to improve the model's ability to extract features at multiple scales and suppress confusing backgrounds. The loss function is optimized using a bounding box loss based on Adaptive Weighted Normalized Wasserstein distance (AWNWD), enhancing the model's adaptability to images of varying quality. Finally, experiments were conducted on the standard datasets HRSID and SAR-Ships dataset to validate the robustness and reliability of the DS-YOLO model. The experimental results show that, compared to YOLO11n, DS-YOLO achieved an mAP0.5:0.95 of 68.6% on the SAR-Ships dataset and 69.9% on the HRSID, representing improvements of 1.6% and 0.8%, respectively. Additionally, on these small-target datasets, DS-YOLO achieved an mAP0.5:0.95 of 50.8% and 60.4%, representing improvements of 4.2% and 1.2%, respectively, demonstrating higher detection accuracy.

  1. HUMAYUN, M. F., NASIR, F. A., BHATTI, F. A., et al. YOLOOSD: Optimized ship detection and localization in multiresolution SAR satellite images using a hybrid data-model centric approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, vol. 17, no. 1, p. 5345–5363. DOI: 10.1109/JSTARS.2024.3365807
  2. DENG, Z., SUN, H., ZHOU, S., et al. Learning deep ship detector in SAR images from scratch. IEEE Transactions on Geoscience and Remote Sensing, 2019, vol. 57, no. 1, p. 4021–4039. DOI: 10.1109/TGRS.2018.2889353
  3. WANG, Y., WANG, C., ZHANG, H., et al. Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery. Remote Sensing, 2019, vol. 11, no. 5, p. 1–14. DOI: 10.3390/rs11050531
  4. ZHU, M., HU, G., ZHOU, H., et al. Rapid ship detection in SAR images based on YOLOv3. In Proceedings of the 5th International Conference on Communication, Image and Signal Processing (CCISP). Chengdu (China), 2020, p. 214–218. DOI: 10.1109/CCISP51026.2020.9273476
  5. REDMON, J., DIVVALA, S., GIRSHICK, R., et al. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas (USA), 2016, p. 779–788. DOI: 10.1109/CVPR.2016.91
  6. LIU, W., ANGUELOV, D., ERHAN, D., et al. SSD: Single shot multibox detector. In Proceedings of the European Conference on Computer Vision (ECCV). Amsterdam (Netherlands), 2016, p. 21 to 37. DOI: 10.1007/978-3-319-46448-0_2
  7. LIN, T., GOYAL, P., GIRSHICK, R., et al. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Venice (Italy), 2017, p. 2999–3007. DOI: 10.1109/ICCV.2017.324
  8. GIRSHICK, R., DONAHUE, J., DARRELL, T., et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus (USA), 2014, p. 580–587. DOI: 10.1109/CVPR.2014.81
  9. GIRSHICK, R. Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Santiago (Chile), 2015, p. 1440–1448. DOI: 10.1109/ICCV.2015.169
  10. REN, S., HE, K., GIRSHICK, R., et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, vol. 39, no. 6, p. 1137–1149. DOI: 10.1109/TPAMI.2016.2577031
  11. LV, Y., LI, M., HE, Y. A novel twin branch network based on mutual training strategy for ship detection in SAR images. Complex Intelligent Systems, 2024, vol. 10, p. 2387–2400. DOI: 10.1007/s40747-023-01240-y
  12. CHAI, B., NIE, X., ZHOU, Q., et al. Enhanced cascade R-CNN for multi-scale object detection in dense scenes from SAR images. IEEE Sensors Journal, 2024, vol. 24, no. 12, p. 20143–20153. DOI: 10.1109/JSEN.2024.3393750
  13. WANG, K., QIAO, Q., ZHANG, G., et al. Few-shot SAR target recognition based on deep kernel learning. IEEE Access, 2022, vol. 10, no. 1, p. 89534–89544. DOI: 10.1109/ACCESS.2022.3193773
  14. ZHOU, Z., CUI, Z., TANG, K., et al. Gaussian meta-feature balanced aggregation for few-shot synthetic aperture radar target detection. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, vol. 208, p. 89–106. DOI: 10.1016/j.isprsjprs.2024.01.003
  15. ZHANG, X., LI, Y., LI, F., et al. Ship-Go: SAR ship images inpainting via instance-to-image generative diffusion models. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, vol. 207, no. 1, p. 203–217. DOI: 10.1016/j.isprsjprs.2023.12.002
  16. ANANDHI, D., VALLI, S. An enhanced approach to despeckle SAR images. Radioengineering, 2018, vol. 27, no. 3, p. 864-875. DOI: 10.13164/re.2018.0864
  17. ZHANG, C., GAO, G., LIU, J., et al. Oriented ship detection based on soft thresholding and context information in SAR images of complex scenes. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–15. DOI: 10.1109/TGRS.2023.3340891
  18. WANG, C., GUO, B., SONG, J., et al. A novel CFAR-based ship detection method using range-compressed data for spaceborne SAR system. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, no. 1, p. 1–15. DOI: 10.1109/TGRS.2024.3419893
  19. LEE, K., LEE, S., CHANG, J. A study on ship detection and classification using KOMPSAT optical and SAR images. Ocean Science Journal, 2024, vol. 59. DOI: 10.1007/s12601-024-00134-5
  20. LI, X., CHEN, P., YANG, J., et al. TKP-NET: A three keypoint detection network for ships using SAR imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, vol. 17, no. 1, p. 364–376. DOI: 10.1109/JSTARS.2023.3329252
  21. AMIN, B., RIAZ, M., GHAFOOR, A. Automatic image matting of Synthetic Aperture Radar target chips. Radioengineering, 2020, vol. 29, no. 1, p. 228–234. DOI: 10.13164/re.2020.0228
  22. REN, X., BAI, Y., LIU, G., et al. YOLO-Lite: An efficient lightweight network for SAR ship detection. Remote Sensing, 2023, vol. 15, no. 15, p. 1–21. DOI: 10.3390/rs15153771
  23. WU, F., HU, T., XIA, Y., et al. WDFA-YOLOX: A wavelet driven and feature-enhanced attention YOLOX network for ship detection in SAR images. Remote Sensing, 2024, vol. 16, no. 10, p. 1–24. DOI: 10.3390/rs16101760
  24. FENG, Y., CHEN, J., HUANG, Z., et al. A lightweight position enhanced anchor-free algorithm for SAR ship detection. Remote Sensing, 2022, vol. 14, no. 8, p. 1–19. DOI: 10.3390/rs14081908
  25. YU, J., WU, T., ZHANG, X., et al. An efficient lightweight SAR ship target detection network with improved regression loss function and enhanced feature information expression. Sensors, 2022, vol. 22, no. 9, p. 1–26. DOI: 10.3390/s22093447
  26. WANG, H., HAN, D., CUI, M., et al. NAS-YOLOX: A SAR ship detection using neural architecture search and multi-scale attention. Connection Science, 2023, vol. 35, no. 1, p. 1–32. DOI: 10.1080/09540091.2023.2257399
  27. CHEN, J., SHEN, Y., LIANG, Y., et al. YOLO-SAD: An efficient SAR aircraft detection network. Applied Sciences-Basel, 2024, vol. 14, no. 7, p. 1–24. DOI: 10.3390/app14073025
  28. SUNKARA, R., LUO, T. No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects. In Proceedings of the Machine Learning and Knowledge Discovery in Databases. Grenoble (France), 2022, part III, p. 443–459. DOI: 10.1007/978-3-031-43320-2_27
  29. XU, C., WANG, J., YANG, W., et al. Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, vol. 190, p. 79–93. DOI: 10.1016/j.isprsjprs.2022.06.002
  30. DRAELOS, R. L., CARIN, L. HiResCAM: Explainable multiorgan multi-abnormality prediction in 3d medical images. 11 pages. [Online] Cited 2020-11-17.
  31. Available at: https://arxiv.org/pdf/2011.08891v1
  32. VASWANI, A., SHAZEER, N., PARMAR, N., et al. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). Long Beach (USA), 2017, p. 5998–6008. DOI: 10.48550/arXiv.1706.03762
  33. SHI, D. TransNeXt: Robust foveal visual perception for vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle (USA), 2024, p. 17773–17783. DOI: 10.1109/CVPR52733.2024.01683
  34. WANG, Y., WANG, C., ZHANG, H., et al. A SAR dataset of ship detection for deep learning under complex backgrounds. Remote Sensing, 2019, vol. 11, no. 7, p. 1–14. DOI: 10.3390/rs11070765
  35. WEI, S., ZENG, X., QU, Q., et al. A SAR dataset of ship detection for deep learning under complex backgrounds. IEEE Access, 2020, vol. 8, p. 120234–120254. DOI: 10.1109/ACCESS.2020.3005861
  36. TIAN, Z., SHEN, C., CHEN, H., et al. FCOS: Fully convolutional one-stage object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Seoul (South Korea), 2019, p. 9626–9635. DOI: 10.1109/ICCV.2019.00972
  37. LI, Y., LI, X., DAI, Y., et al. LSKNET: A foundation lightweight backbone for remote sensing. International Journal of Computer Vision, 2025, vol. 133, p. 1410–1431. DOI: 10.1007/s11263-02402247-9
  38. WANG, C., BOCHKOVSKIY, A., LIAO, H. M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver (Canada), 2023, p. 7464–7475. DOI: 10.1109/CVPR52729.2023.00721
  39. WANG, C., YEH, I., LIAO, H. M. YOLOv9: Learning what you want to learn using programmable gradient information. In Proceedings of the European Conference on Computer Vision (ECCV). Milan (Italy), 2024, p. 1–21. DOI: 10.1007/978-3-03172751-1_1
  40. WANG, A., CHEN, H., LIU, L., et al. YOLOv10: Real-time end to-end object detection. 21 pages. [Online] Cited 2024-5-23. Available at: https://arxiv.org/abs/2405.14458
  41. FENG, Y., HUANG, J., DU, S., et al. Hyper-YOLO: When visual object detection meets hypergraph computation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, vol. 47, no. 4, p. 2388–2401. DOI: 10.1109/TPAMI.2024.3524377

Keywords: DS-YOLO, SAR image, ship detection, Space-to-Depth Convolution (SPDConv), Cross Stage Partial-Partial Pyramid Attention (CSP-PPA), Adaptive Weighted Normalized Wasserstein Distance (AWNWD)

D. Zhang, K. Zhu, S. Qi [references] [full-text] [DOI: 10.13164/re.2025.0422] [Download Citations]
A Novel High-Gain Circularly Polarized Filtenna Based on Coaxial Structure

A novel omnidirectional slot array filtenna with a circularly polarized (CP) radiation beam is presented in this paper. This filtenna utilizes the coaxial cylinder structure, and the antenna’s filtering function is primarily achieved through the direct synthesis method of a tubular bandpass filter. Four perpendicular slot pairs form the basic omnidirectional CP radiation element, which is cut into the sleeve and added to the outer conductor of the coaxial cylinder to implement the compact performance. To simplify the design, this work utilizes the high and low impedance conversion of the inner conductor, as well as isolation through dielectric materials. This filtenna achieves high gain by forming an array along the z-axis direction and placing the antenna elements reasonably for radiation in phase. A prototype was designed and fabricated to validate its practicality. The results indicate a fractional impedance bandwidth (S11 < -10 dB) of 5.8%, from 8.87 GHz to 9.39 GHz, and a 3 dB axial-ratio (AR) bandwidth of 6.1%, from 8.85 GHz to 9.40 GHz. The realized gain of the antenna is consistently higher than 5.29 dBi over the operating bandwidth, and its out-of-roundness is less than 1.5 dB in the radiation direction.

  1. LUO, Y., CHEN, Z. N., MA, K. Enhanced bandwidth and directivity of a dual-mode compressed high-order mode stub loaded dipole using characteristic mode analysis. IEEE Transactions on Antennas and Propagation, 2019, vol. 67, no. 3, p. 1922–1925. DOI: 10.1109/TAP.2018.2889025
  2. YANG, Y., LI, Z., WANG, S., et al. Miniaturized high-order-mode dipole antennas based on spoof surface plasmon polaritons. IEEE Antennas and Wireless Propagation Letters, 2018, vol. 17, no. 12, p. 2409–2413. DOI: 10.1109/LAWP.2018.2876691
  3. WEN, D., HAO, Y., WANG, H., et al. Design of a wideband antenna with stable omnidirectional radiation pattern using the theory of characteristic modes. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 5, p. 2671–2676. DOI: 10.1109/TAP.2017.2679767
  4. MA, Z. L. Inductively loaded segmented loop antenna by using multiple radiators. IEEE Antennas and Wireless Propagation Letters, 2017, vol. 16,
  5. p. 109–112. DOI: 10.1109/LAWP.2016.2558544
  6. WU, J., SARABANDI, K. Compact omnidirectional circularly polarized antenna. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 4, p. 1550–1557. DOI: 10.1109/TAP.2017.2669959
  7. LIU, J., XUE, Q., WONG, H., et al. Design and analysis of a low profile and broadband microstrip monopolar patch antenna. IEEE Transactions on Antennas and Propagation, 2013, vol. 61, no. 1, p. 11–18. DOI: 10.1109/TAP.2012.2214996
  8. WEN, S., DONG, Y. A low-profile wideband antenna with monopole like radiation characteristics for 4g/5g indoor micro base station application. IEEE Antennas and Wireless Propagation Letters, 2020, vol. 19, no. 12, p. 2305–2309. DOI: 10.1109/LAWP.2020.3030968
  9. LIN, S., LIAO, S., YANG, Y., et al. Gain enhancement of low profile omnidirectional antenna using annular magnetic dipole directors. IEEE Antennas and Wireless Propagation Letters, 2021, vol. 20, no. 1, p. 8–12. DOI: 10.1109/LAWP.2020.3035819
  10. CAO, Y. F., CHEUNG, S. W., YUK, T. I. A multiband slot antenna for GPS/WiMAX/WLAN systems. IEEE Transactions on Antennas and Propagation, 2015, vol. 63, no. 3, p. 952–958. DOI: 10.1109/TAP.2015.2389219
  11. LU, W. J., ZHU, L. Wideband stub-loaded slot line antennas under multi-mode resonance operation. IEEE Transactions on Antennas and Propagation, 2015, vol. 63, no. 2, p. 818–823. DOI: 10.1109/TAP.2014.2379921
  12. ZHANG, Y., ZHANG, X. Y., PAN, Y. M. Compact single- and dual-band filtering patch antenna arrays using novel feeding scheme. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 8, p. 4057–4066. DOI: 10.1109/TAP.2017.2717046
  13. ZHANG, X. Y., ZHANG, Y., PAN, Y. M., et al. Low-profile dual band filtering patch antenna and its application to LTE MIMO system. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 1, p. 103–113. DOI: 10.1109/TAP.2016.2631218
  14. SAKAGUCHI, K., HASEBE, N. A circularly polarized omnidirectional antenna. In Proceedings of 1993 Eighth International Conference on Antennas and Propagation. Edinburgh (UK), 1993, p. 477–480. ISBN: 0-85296-572-9
  15. SUN, G. H., WONG, S. W., ZHU, L., et al. A compact printed filtering antenna with good suppression of upper harmonic band. IEEE Antennas and Wireless Propagation Letters, 2016, vol. 15, p. 1349–1352. DOI: 10.1109/LAWP.2015.2508918
  16. HU, H. T., CHEN, F. C., CHU, Q. X. Novel broadband filtering slot line antennas excited by multimode resonators. IEEE Antennas and Wireless Propagation Letters, 2017, vol. 16, p. 489–492. DOI: 10.1109/LAWP.2016.2585524
  17. SOLTANPOUR, M., FAKHARIAN, M. M. Compact filtering slot antenna with frequency agility for Wi-Fi/LTE mobile applications. Electronics Letters, 2016, vol. 52, no. 7, p. 491–492. DOI: 10.1049/el.2015.3198
  18. CHENG, W., LI, D. Circularly polarised filtering monopole antenna based on miniaturised coupled filter. Electronics Letters, 2017, vol. 53, no. 11, p. 700–702. DOI: 10.1049/el.2017.1094
  19. CHEN, X., ZHAO, F., YAN, L., et al. A compact filtering antenna with flat gain response within the passband. IEEE Antennas and Wireless Propagation Letters, 2013, vol. 12, p. 857–860. DOI: 10.1109/LAWP.2013.2271972
  20. FAKHARIAN, M. M., REZAEI, P., OROUJI, A. A., et al. A wideband and reconfigurable filtering slot antenna. IEEE Antennas and Wireless Propagation Letters, 2016, vol. 15, p. 1610–1613. DOI: 10.1109/LAWP.2016.2518859
  21. TANG, M. C., WANG, H., DENG, T., et al. Compact planar ultrawideband antennas with continuously tunable, independent band-notched filters. IEEE Transactions on Antennas and Propagation, 2016, vol. 64, no. 8, p. 3292–3301. DOI: 10.1109/TAP.2016.2570254
  22. AGHDAM, S. A. Reconfigurable antenna with a diversity filtering band feature utilizing active devices for communication systems. IEEE Transactions on Antennas and Propagation, 2013, vol. 61, no. 10, p. 5223–5228. DOI: 10.1109/TAP.2013.2273812
  23. ZHANG, Y., ZHANG, X. Y., PAN, Y. M. Low-profile planar filtering dipole antenna with omnidirectional radiation pattern. IEEE Transactions on Antennas and Propagation, 2018, vol. 66, no. 3, p. 1124–1132. DOI: 10.1109/TAP.2018.2790169
  24. HU, P. F., PAN, Y. M., LEUNG, K. W., et al. Wide-/dual-band omnidirectional filtering dielectric resonator antennas. IEEE Transactions on Antennas and Propagation, 2018, vol. 66, no. 5, p. 2622–2627. DOI: 10.1109/TAP.2018.2809706
  25. HU, P. F., PAN, Y. M., ZHANG, X. Y., et al. A compact filtering dielectric resonator antenna with wide bandwidth and high gain. IEEE Transactions on Antennas and Propagation, 2016, vol. 64, no. 8, p. 3645–3651. DOI: 10.1109/TAP.2016.2565733
  26. HU, P. F., PAN, Y. M., ZHANG, X. Y., et al. Broadband filtering dielectric resonator antenna with wide stopband. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 4, p. 2079–2084. DOI: 10.1109/TAP.2017.2670438
  27. WU, T. L., PAN, Y. M., HU, P. F., et al. Design of a low profile and compact omnidirectional filtering patch antenna. IEEE Access, 2017, vol. 5, p. 1083–1089. DOI: 10.1109/ACCESS.2017.2651143
  28. PAL, P., SINHA, R., KUMAR MAHTO, S. A compact wideband circularly polarized planar filtenna using synthesis technique for 5 GHz WLAN application. AEU - International Journal of Electronics and Communications, 2022, vol. 148, p. 1–9. DOI: 10.1016/j.aeue.2022.154180
  29. ZHANG, G., WANG, J., WU, W. Wideband balun bandpass filter explored for a balanced dipole antenna with high selectivity. Electronics Letters, 2016, vol. 52, no. 13, p. 1153-1155. DOI: 10.1049/el.2016.0716
  30. TANG, M. C., CHEN, Y., ZIOLKOWSKI, R. W. Experimentally validated, planar, wideband, electrically small, monopole filtennas based on capacitively loaded loop resonators. IEEE Transactions on Antennas and Propagation, 2016, vol. 64, no. 8, p. 3353–3360. DOI: 10.1109/TAP.2016.2576499
  31. ZHOU, B., GENG, J., BAI, X., et al. An omnidirectional circularly polarized slot array antenna with high gain in a wide bandwidth. IEEE Antennas and Wireless Propagation Letters, 2015, vol. 14, p. 666–669. DOI: 10.1109/LAWP.2014.2376961
  32. CHENHU, G., GENG, J., ZHOU, H., et al. Truncated circular cone slot antenna array that radiates a circularly polarized conical beam. IEEE Antennas and Wireless Propagation Letters, 2017, vol. 16, p. 2574–2577. DOI: 10.1109/LAWP.2017.2734820
  33. SU, T., WU, B., LIANG, C.-H. The designing and tuning methods of tubular filter. In Proceedings of 2005 Asia-Pacific Microwave Conference Proceedings. Suzhou (China), 2005, p. 1–3. DOI: 10.1109/APMC.2005.1607030
  34. WU, B., SU, T., LI, B., et al. Design of tubular filter based on curve-fitting method. Journal of Electromagnetic Waves and Applications, 2006, vol. 20, no. 8, p. 1071–1080. DOI: 10.1163/156939306776930231
  35. MEJILLONES, S. C., OLDONI, M., MOSCATO, S., et al. Circularly polarized coaxial horn filtenna for electromagnetic interference mitigation. IEEE Transactions on Antennas and Propagation, 2023, vol. 71, no. 12, p. 9487–9496. DOI: 10.1109/TAP.2023.3321422
  36. BARBUTO, M., TROTTA, F., BILOTTI, F., et al. Design and experimental validation of dual-band circularly polarised horn filtenna. Electronics Letters, 2017, vol. 53, no. 10, p. 641–642. DOI: 10.1049/el.2017.0145
  37. BARBUTO, M., TROTTA, F., BILOTTI, F., et al. Filtering chiral particle for rotating the polarization state of antennas and waveguides components. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 3, p. 1468–1471. DOI: 10.1109/TAP.2016.2640143
  38. KUMAR CHOUDHARY, D., KUMAR CHAUDHARY, R. Compact filtering antenna using asymmetric CPW-fed based CRLH structure. AEU - International Journal of Electronics and Communications, 2020, vol. 126, p. 1–6. DOI: 10.1016/j.aeue.2020.153462
  39. ZHANG, S. Q., WONG, S. W., ZHANG, Z., et al. Full-metal omnidirectional filtenna array using 3-D metal printing technology. IEEE Transactions on Antennas and Propagation, 2024, vol. 72, no. 5, p. 4095–4106. DOI: 10.1109/TAP.2024.3385914

Keywords: Circular polarized (CP) filtenna, filtering antenna, axial ratio (AR), omnidirectional antenna, tubular bandpass filter

Y. Choi, G. Kim, B. Kim, S. Kim [references] [full-text] [DOI: 10.13164/re.2025.0429] [Download Citations]
An Enhanced Noise Removal-based SAR Image Recognition Using DnCNN and Wavelet Transform

This paper presents an enhanced method for noise removal and target detection in Synthetic Aperture Radar (SAR) images using a Denoising Convolutional Neural Network (DnCNN) combined with wavelet trans¬form. Unlike conventional method, the proposed frame¬work focuses on remove the Speckle Noise through residu¬al learning and wavelet transform. The DnCNN architecture, consisting of 29 layers, efficiently removes noise while preserving high-frequency image features. The integration of wavelet transform further enhances noise removal and feature preservation. Experimental results demonstrate that the recognition rate of the proposed method improves by about 94% compared to original method under 10 dB Speckle Noise conditions. This method outperforms conventional algorithm in SAR image pro¬cessing, making it highly suitable for applications in noisy environments.

  1. ALBABA, A., BAUDUIN, M., SAKHNINI, A., et al. Sidelobes and ghost targets mitigation technique for high-resolution forward looking MIMO-SAR. IEEE Transactions on Radar Systems, 2024, vol. 2, p. 237–250. DOI: 10.1109/TRS.2024.3366779
  2. ZHAI, Y., LIAO, J., SUN, B., et al. Dual consistency alignment based self-supervised learning for SAR target recognition with speckle noise resistance. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, vol. 16, p. 3915–3928. DOI: 10.1109/JSTARS.2023.3267824
  3. AI, J., WANG, G., FAN, G., et al. A trilateral filter for video SAR speckle noise reduction. IEEE Geoscience and Remote Sensing Letters, 2023, vol. 19, p. 1–5. DOI: 10.1109/LGRS.2022.3174834
  4. CHOI, H., CHANG, J. Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform. Remote Sensing, 2019, vol. 11, no. 10, p. 1–27. DOI: 10.3390/rs11101184
  5. MOHAN, E., RAJESH, A., SUNITHA, G., et al. A deep neural network learning-based speckle noise removal technique for enhancing the quality of synthetic-aperture radar images. Concurrency and Computation: Practice and Experience, 2021, vol. 33, no. 13, p. 1–12. DOI: 10.1002/cpe.6239
  6. ZHANG, K., ZUO, W., CHEN, Y., et al. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 2017, vol. 26, no. 7, p. 3142–3155. DOI: 10.1109/TIP.2017.2662206
  7. ZHAO, Y., CELIK, T., LIU, N., et al. A comparative analysis of GAN-based methods for SAR-to-optical image translation. IEEE Geoscience and Remote Sensing Letters, 2022, vol. 19, p. 1–5. DOI: 10.1109/LGRS.2022.3177001
  8. SUN, Y., YAN, K., LI, W. CycleGAN-based SAR-optical image fusion for target recognition. Remote Sensing, 2023, vol. 15, no. 23, p. 1–24. DOI: 10.3390/rs15235569
  9. HAQUE, M. F., LIM, H.-Y., KANG, D.-S. Object detection based on VGG with ResNet network. In Proceedings of the 2019 International Conference on Electronics, Information, and Communication (ICEIC). Auckland (New Zealand), 2019, p. 1–3. DOI: 10.23919/ELINFOCOM.2019.8706476
  10. LI, W., ZHANG, Z., ZHANG, R. Newton time-reassigned multi synchro squeezing wavelet transform. IEEE Signal Processing Letters, 2024, vol. 31, p. 2390–2394. DOI: 10.1109/LSP.2024.3455990
  11. PHAM, T.-H., LI, X., NGUYEN, K.-D. SeUNet-Trans: A simple yet effective UNet-transformer model for medical image segmentation. IEEE Access, 2024, vol. 12, p. 122139–122154. DOI: 10.1109/ACCESS.2024.3451304
  12. WANG, L., SUN, Z., LI, Z., et al. TDWCNet: Triple UNet with dual-window convolution for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–15. DOI: 10.1109/TGRS.2024.3444191
  13. DOSOVITSKIY, A., BEYER, L., KOLESNIKOV, A., et al. An image is worth 1616 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations (ICLR). Virtual (Online), 2021. DOI: 10.48550/arXiv.2010.11929

Keywords: Navy SAR, noise, Convolutional Neural Network (CNN), Denoising Convolutional Neural Network (DnCNN), wavelet transform

X. L. Kong, D. X. Zhao, N. Wang, D. Z. Xu [references] [full-text] [DOI: 10.13164/re.2025.0438] [Download Citations]
Performance Evaluation for Unknown Deterministic Target Signals Detection via Detection Information

The detection method, such as Newman-Pearson (NP) method, can provide an accurate prediction for the performance of target detection under all signal-to-noise ratio regions. However, the performance limits of radar signal detection have not been extensively studied yet. In this paper, we propose a novel detection method for unknown deterministic signals in radar system, which utilizes mutual information to characterize the uncertainty of the existence state of signal. The a posteriori probability density function of existence state of signal can be directly obtained via the Bayesian framework, ensuring that there is no loss of information regarding to the target's existence state during the mutual information computation process. Numerical simulations show that the proposed method exhibits superior detection performance compared to the NP detection method.

  1. DING, C, CHEN, S. Y., LIU, H., et al. Infrared small target detection, high-precision localization and segmentation: using TDU kernel. Radioengineering, 2024, vol. 33, no. 4, p. 721–732. DOI: 10.13164/re.2024.0721
  2. XIAO, L. H., RAO, X., HE, W. B., et al. Weak target integration detection based on radar communication integrated signal via constructed step-LFM model. Radioengineering, 2024, vol. 33, no. 1, p. 195–203. DOI: 10.13164/re.2024.0195
  3. ADDABBO, P., HAN, S., BIONDI, F., et al. Adaptive radar detection in the presence of multiple alternative hypotheses using Kullback-Leibler information criterion-part i: Detector designs. IEEE Transactions on Signal Processing, 2021, vol. 69, p. 3730 to 3741. DOI: 10.1109/TSP.2021.3089440
  4. BOZOVIC, R., SIMIC, M., PEJOVIC, P., et al. The analysis of closed-form solution for energy detector dynamic threshold adaptation in cognitive radio. Radioengineering, 2017, vol. 26, no. 4, p. 1104–1109. DOI: 10.13164/re.2017.1104
  5. MIDDLETON, R. J. C. Dechirp-on-receive linearly frequency modulated radar as a matched-filter detector. IEEE Transactions on Aerospace and Electronic Systems, 2012, vol. 48, no. 3, p. 2716 to 2718. DOI: 10.1109/TAES.2012.6237622
  6. LIM, C. H., GUIMARÃES, D. A. GLRT-based spectrum sensing techniques for pulse radar signals. IEEE Communications Letters, 2024, vol. 24, no. 2,
  7. p. 447–450. DOI: 10.1109/LCOMM.2019.2954307
  8. ADHIKARI, K., KAY, S. An exact solution for sparse sampling for optimal detection of known signals in Gaussian noise. IEEE Signal Processing Letters, 2023, vol. 30, p. 369–373. DOI: 10.1109/LSP.2023.3264106
  9. SALT, J. E., NGUYEN, H. H. Performance prediction for energy detection of unknown signals. IEEE Transactions on Vehicular Technology, 2008, vol. 57, no. 6, p. 3900–3904. DOI: 10.1109/TVT.2008.921617
  10. URKOWITZ, H. Energy detection of unknown deterministic signals. Proceedings of the IEEE, 1967, vol. 55, no. 4, p. 523–531. DOI: 10.1109/PROC.1967.5573
  11. KONG, X. L., XU, D. Z., HUA, B. Y., et al. Target parameter estimation method based on information theory. In Proceedings of the 11th International Conference on Communications, Signal Processing, and Systems (ICCSPS). Changbaishan (China), 2023, p. 285–292. DOI: 10.1007/978-981-99-1260-5_36
  12. CHEN, Y. F. Improved energy detector for random signals in Gaussian noise. IEEE Transactions on Wireless Communications, 2010, vol. 9, no. 2, p. 558–563. DOI: 10.1109/TWC.2010.5403535
  13. JACOBS, I. Energy detection of Gaussian communication signals. In Proceedings of 10th National Communication Symposium, 1965, p. 440–448.
  14. GRENANDER, U., POLLAK, H. O., SLEPIAN, D. The distribution of quadratic forms in normal variates: A small sample theory with applications to spectral analysis. Journal of the Society for Industrial and Applied Mathematics, 1959, vol. 7, no. 4, p. 374 to 401.
  15. KONDO, M. An evaluation and the optimum threshold for radar return signal applied for a mutual information. In Record of the IEEE 2000 International Radar Conference (IRC). Alexandria (VA, USA), 2000, p. 226–230. DOI: 10.1109/RADAR.2000.851836
  16. LUO, J. H., HE, X. T. A soft-hard combination decision fusion scheme for a clustered distributed detection system with multiple sensors. Sensors, 2018, vol. 18, no. 12, p. 1–18. DOI: 10.3390/s18124370
  17. BANTA, E. D. Energy detection of unknown deterministic signals in the presence of jamming. IEEE Transactions on Aerospace and Electronic Systems, 1978, vol. AES-14, no. 2, p. 384–386. DOI: 10.1109/TAES.1978.308664.
  18. KOSTYLEV, V. I. Characteristics of energy detection of quasideterministic radio signals. Radiophysics and Quantum Electronics, 2020, vol. 43,
  19. p. 833–839. DOI: 10.1023/A:1010387403443
  20. HU, C., XU, D. Z., PAN, D., et al. Radar target detection based on information theory. In 5th International Conference on Machine Learning and Intelligent Communications (MLICOM 2020). Shenzhen (China), 2020, p. 320–322. DOI: 10.1007/978-3-03066785-6_35
  21. TAN, J., SHI, C. G., ZHOU J. J. Novel power control scheme for target tracking in radar network with passive cooperation. Radioengineering, 2018, vol. 27, no. 1, p. 234–248. DOI: 10.13164/re.2018.0234

Keywords: Energy detection, detection information, information theory, probability density function, unknown deterministic signals

M. V. Ta, K. K. Phuong, N. D. Trong, N. T. Linh, T. T. T. Huong, N. T. Hung, L. D. Manh [references] [full-text] [DOI: 10.13164/re.2025.0445] [Download Citations]
A Novel Design of a Low-loss and Low-cost Ku-Band Bandpass Filter for VSAT Applications

This paper proposes a novel method to design low-loss and low-cost Ku-band bandpass filters for VSAT applications based on substrate-integrated-waveguide technology. Narrow bandpass filters employed high-order resonant mode TE301 exhibited high selectivity. However, its bandwidth is not enough for VSAT applications. In this paper, we proposed a method to widen the bandwidth of narrow-band filters to meet the bandwidth requirement of VSAT applications. This approach maintains high selectivity while still achieves low insertion loss. The proposed filter was fabricated using a low-cost material. Measurement shows a good agreement with simulated results. Mid-band measured insertion loss and return loss were -1.8 dB and -19.4 dB, respectively. Such low losses were obtained owing to taking advantage of a high-quality factor of high-order mode TE301 of oversized rectangular cavities

  1. DESLANDES, D., WU, K. Single-substrate integration technique of planar circuits and waveguide filters. IEEE Transactions on Microwave Theory and Techniques, 2003, vol. 51, no. 2, p. 593–596. DOI: 10.1109/TMTT.2002.807820
  2. BOZZI, M., GEORGIADIS, A., WU, K. Review of substrate integrated waveguide circuits and antennas. IET Microwaves, Antennas and Propagation, 2011, vol. 5, no. 8, p. 909–920. DOI: 10.1049/iet-map.2010.0463
  3. PRADHAN, N. C., KOZIEL, S., BARIK, R. K., et al. Bandwidth controllable third-order bandpass filter using substrate-integrated full- and semi-circular cavities. Sensors, 2023, vol. 23, p. 1–15. DOI: 10.3390/s23136162
  4. CHENG, F., GU, C., ZHANG, B., et al. High isolation substrate integrated waveguide diplexer with flexible transmission zeros. IEEE Microwave and Wireless Components Letters, 2020, vol. 30, no. 11, p. 1029–1032. DOI: 10.1109/LMWC.2020.3025698
  5. XIE, H. W., ZHOU, K., ZHOU, C. X., et al. Compact SIW diplexers and dual-band bandpass filter with wide-stopband performances. IEEE Transactions on Circuits and Systems. II, Express Briefs, 2020, vol. 67, no. 12, p. 2933–2937. DOI: 10.1109/TCSII.2020.2992059
  6. DONG, Y. D., YANG, T., ITOH, T. Substrate integrated waveguide loaded by complementary split-ring resonators and its applications to miniaturized waveguide filters. IEEE Transactions on Microwave Theory and Techniques, 2009, vol. 57, no. 9, p. 2211–2223. DOI: 10.1109/TMTT.2009.2027156
  7. LI, D., WANG, J., YU, Y., et al. Substrate integrated waveguide based complementary split-ring resonator and its arrays for compact dual-wideband bandpass filter design. International Journal of RF and Microwave Computer-Aided Engineering, 2020, vol. 31, no. 2, p. 1–9. DOI: 10.1002/mmce.22504
  8. NOURI, K., HADDADI, K., BENZAÏM, O., et al. Substrate integrated waveguide (SIW) inductive window band-pass filter based on post-wall irises. European Physical Journal: Applied Physics, 2011, vol. 53, no. 3, p. 1–5. DOI: 10.1051/epjap/2010100057
  9. ADABI, A., TAYARANI, M. Substrate integration of dual inductive post waveguide filter. Progress In Electromagnetics Research B, 2008, vol. 7, p. 321–329. DOI: 10.2528/PIERB08051002
  10. LIU, Q., ZHOU, D., ZHANG, Y., et al. Substrate integrated waveguide bandpass filters in box-like topology with bypass and direct couplings in diagonal cross-coupling path. IEEE Transactions on Microwave Theory and Techniques, 2019, vol. 67, no. 3, p. 1014 to 1022. DOI: 10.1109/TMTT.2018.2889450
  11. LIU, Q., ZHANG, D., TANG, M., et al. A class of box-like bandpass filters with wide stopband based on new dual-mode rectangular SIW cavities. IEEE Transactions on Microwave Theory and Techniques, 2021, vol. 69, no. 1, p. 101–110. DOI: 10.1109/TMTT.2020.3037497
  12. LI, R., TANG, X., XIAO, F. Design of substrate integrated waveguide transversal filter with high selectivity. IEEE Microwave and Wireless Components Letters, 2010, vol. 20, no. 6, p. 328–330. DOI: 10.1109/LMWC.2010.2047518
  13. LIU, H., CHEN, L., WU, S., et al. A new electric coupling and its applications on millimeter-wave SIW filter with high selectivity and controllable TZs. AEU - International Journal of Electronics and Communication, 2024, vol. 173, p. 1–8. DOI: 10.1016/j.aeue.2023.154989
  14. ZHU, F., WU, Y., CHU, P., et al. Single-layer substrate-integrated waveguide inline filters with flexible transmission zeros. IEEE Microwave and Wireless Components Letters, 2022, vol. 32, no. 6, p. 495–498. DOI: 10.1109/LMWC.2022.3141994
  15. CHEN, X. P., WU, K. Substrate integrated waveguide cross coupled filter with negative coupling structure. IEEE Transactions on Microwave Theory and Techniques, 2008, vol. 56, no. 1, p. 142–149. DOI: 10.1109/TMTT.2007.912222
  16. HE, Z., YOU, C. J., LENG, S., et al. Compact inline substrate integrated waveguide filter with enhanced selectivity using new non resonating node. Electronics Letters, 2016, vol. 52, no. 21, p. 1778–1780. DOI: 10.1049/el.2016.2712
  17. AMARI, S., ROSENBERG, U. Characteristics of cross (bypass) coupling through higher/lower order modes and their applications in elliptic filter design. IEEE Transactions on Microwave Theory and Techniques, 2005, vol. 53, no. 10, p. 3135–3141. DOI: 10.1109/TMTT.2005.855359
  18. KHAN, A. A., MANDAL, M. K. Narrowband substrate integrated waveguide bandpass filter with high selectivity. IEEE Microwave and Wireless Components Letters, 2018, vol. 28, no. 5, p. 416 to 418. DOI: 10.1109/LMWC.2018.2820605
  19. LIU, Q., ZHOU, D., LV, D., et al. Ultra-compact highly selective quasi-elliptic filters based on combining dual-mode SIW and coplanar waveguides in a single cavity. IET Microwaves, Antennas and Propagation, 2018, vol. 12, no. 3, p. 360–366. DOI: 10.1049/iet-map.2017.0516
  20. RHBANOU, A., BRI, S., SABBANE, M. Analysis of substrate integrated waveguide (SIW) resonator and design of miniaturized SIW bandpass Filter. International Journal of Electronics and Telecommunications, 2017, vol. 63, no. 3, p. 255–260. DOI: 10.1515/eletel-2017-0034
  21. SIRCI, S., SANCHEZ-SORIANO, M. A., MARTINEZ, J. D., et al. Advanced filtering solutions in coaxial SIW technology based on singlets, cascaded singlets, and doublets. IEEE Access, 2019, vol. 7, p. 29901–29915. DOI: 10.1109/ACCESS.2019.2902956
  22. ZHANG, P. J., LI, M. Q. Cascaded trisection substrate-integrated waveguide filter with high selectivity. Electronics Letters, 2014, vol. 50, no. 23, p. 1717–1719. DOI: 10.1049/el.2014.3456
  23. LEE, B., LEE, T.-H., LEE, K., et al. K-band substrate-integrated waveguide resonator filter with suppressed higher-order mode. IEEE Microwave and Wireless Components Letters, 2015, vol. 25, no. 6, p. 367–369. DOI: 10.1109/LMWC.2015.2421313

Keywords: High/low order mode coupling, substrate integrated waveguide, fractional bandwidth (FBW)

H. Jia, F. Wan, X. Cheng, V. Mordachev, E. Sinkevich, J. Rossignol, X. Chen, B. Ravelo [references] [full-text] [DOI: 10.13164/re.2025.0452] [Download Citations]
Characterization of Four-Layer Microwave Magnetic Probe Design for Integrated Circuit Emission Measurement

With the increase of microwave circuit and system integration design density, the test method to assess the electromagnetic compatibility (EMC) undesirable effect remains a challenging task. To tackle this issue for example with radiated emission analysis, a relevant EMC measurement notably for integrated circuits (IC) and printed circuit board (PCB) is necessary. A four-layer magnetic (H) near-field (NF) probe in miniature technology is designed, fabricated and tested. The H-NF probe works in the challenging frequency band up to 20 GHz. The proposed probe has the advantages of miniaturization, high sensitivity, high flatness, and high electric field suppression. The designed and fabricated H-NF probe characterization is validated with respect to IEC-61967 EMC standard. The device under test (DUT) IC radiation was tested and characterized. Experimental results have shown that the H-NF probe can be used for measuring IC EMC radiation emission.

  1. WU, T.-L., BUESINK, F., CANAVERO, F. Overview of signal integrity and EMC design technologies on PCB: Fundamentals and latest progress. IEEE Transactions on Electromagnetic Compatibility, 2013, vol. 55, no. 4, p. 624–638. DOI: 10.1109/TEMC.2013.2257796
  2. HUANG, Q., ZHANG, L., RAJAGOPALAN, J., et al. A novel RFI mitigation method using source rotation. IEEE Transactions on Electromagnetic Compatibility, 2021, vol. 63, no. 1, p. 11–18. DOI: 10.1109/TEMC.2020.2995828
  3. WEI, X., DING, L., WEN, J., et al. Review of near-field EMI measurement. In International Conference on Microwave and Millimeter Wave Technology (ICMMT). Shanghai (China), 2020, p. 1–3. DOI: 10.1109/ICMMT49418.2020.9387013
  4. XU, Y., FU, H., XUANYUAN, Y., et al. Wireless coexistence testing and EMC risk evaluation for wireless medical applications. In Advanced Science and Industry Research Center. Proceedings of 2018 International Conference on Energy, Power, Electrical and Environmental Engineering (EPEEE 2018). Wuhan (China), 2018, p. 241–245.
  5. MIRANDA, J., CABRAL, J., RAVELO, B., et al. Radiated EMC immunity investigation of Common Recognition Identification Platform for medical applications. European Physical Journal, Applied Physics, 2015, vol. 69, no. 1, p. 1–8. DOI: 10.1051/epjap/2014140230
  6. SONG, E., PARK, H., PARK, H. H. An evaluation method for radiated emissions of components and modules in mobile devices. IEEE Transactions on Electromagnetic Compatibility, 2014, vol. 56, no. 5, p. 1020–1026. DOI: 10.1109/TEMC.2014.2307915
  7. LEMAN, S., DEMOULIN, B., MAURICE, O., et al. New approaches in electromagnetic compatibility: Use of the circuit approach to solve large EMC problems. C. R. Physique, 2019, vol. 10, no. 1, p. 70–82. DOI: 10.1016/j.crhy.2009.01.006
  8. AUNCHALEEVARAPAN, K., PAITHOONWATANAKIJ, K., KHAN-NGERN, W., et al. Novel method for predicting PCB configurations for near-field and far-field radiated EMI using a neural network. IEICE Transactions on Communications, 2003, vol. E86-B, no. 4, p. 1364–1376. ISSN: 0916-8516
  9. MUNIC, N., NIKOLIC STEVANOVIC, M., DJORDJEVIC, A., et al. Evaluation of radiating-source parameters by measurements in Faraday cages and sparse processing. Measurement, 2017, vol. 104, p. 105–116. DOI: 10.1016/j.measurement.2017.03.008
  10. BOYER, A., NOLHIER, N., CAIGNET, F., et al. On the correlation between near-field scan immunity and radiated immunity at printed circuit board level - Part I. IEEE Transactions on Electromagnetic Compatibility, 2022, vol. 64, no. 4, p. 1230–1242. DOI: 10.1109/TEMC.2022.3169183
  11. BOYER, A., NOLHIER, N., CAIGNET, F., et al. On the correlation between near-field scan immunity and radiated immunity at printed circuit board level - Part II. IEEE Transactions on Electromagnetic Compatibility, 2022, vol. 64, no. 5, p. 1493–1505. DOI: 10.1109/TEMC.2022.3172601
  12. HUANG, Q., CHEN, R., FANG, W., et al. Radiation emission source localization by magnetic near-field mapping along the surface of a large-scale IC with BGA package. IEEE Transactions on Electromagnetic Compatibility, 2022, vol. 64, no. 2, p. 495–505. DOI: 10.1109/TEMC.2021.3123536
  13. DING, L., XU, Z. EMI source positioning by phase inversion and near-field scanning. In Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC). Beijing (China), 2022, p. 69–71. DOI: 10.1109/APEMC53576.2022.9888587
  14. WHITESIDE, H., KING, R. The loop antenna as a probe. IEEE Transactions on Antennas and Propagation, 1964, vol. 12, no. 3, p. 291–297. DOI: 10.1109/TAP.1964.1138213
  15. CHO, G. Y., JIN, J., PARK, H., et al. Assessment of integrated circuits emissions with an equivalent dipole-moment method. IEEE Transactions on Electromagnetic Compatibility, 2017, vol. 59, no. 2, p. 633–638. DOI: 10.1109/TEMC.2016.2633332
  16. RAVELO, B. Non-unicity of the electric near-field planar emission model with dipole array. IET Microwaves, Antennas & Propagation, 2017, vol. 11, no. 5, p. 584–592. DOI: 10.1049/ietmap.2016.0910
  17. LIU, Y., RAVELO, B., JASTRZEBSKI, A. K. Time-domain magnetic dipole model of PCB near-field emission. IEEE Transactions on Electromagnetic Compatibility, 2016, vol. 58, no. 5, p. 1561–1569. DOI: 10.1109/TEMC.2016.2578953
  18. RAVELO, B., LIU, Y., JASTRZEBSKI, A. K. PCB near-field transient emission time-domain model. IEEE Transactions on Electromagnetic Compatibility, 2015, vol. 57, no. 6, p. 1320–1328. DOI: 10.1109/TEMC.2015.2438053
  19. KIM, J., KIM, W., YOOK, J. Resonance-suppressed magnetic field probe for EM field-mapping system. IEEE Transactions on Microwave Theory and Techniques, 2005, vol. 53, no. 9, p. 2693–2699. DOI: 10.1109/TMTT.2005.854203
  20. YANG, S., HUANG, Q., LI, G., et al. Differential E-field coupling to shielded H-field probe in near-field measurements and a suppression approach. IEEE Transactions on Instrumentation and Measurement, 2018, vol. 67, no. 12, p. 2872–2880. DOI: 10.1109/TIM.2018.2831398
  21. LIU, Y., RAVELO, B. Fully time-domain scanning of EM near field radiated by RF circuits. Progress In Electromagnetics Research B, 2014, vol. 57,
  22. p. 21–46. DOI: 10.2528/PIERB13072903
  23. YAN, Z., WANG, J., ZHANG, W., et al. A simple miniature ultrawideband magnetic field probe design for magnetic near-field measurements. IEEE Transactions on Antennas and Propagation, 2016, vol. 64, no. 12, p. 5459–5465. DOI: 10.1109/TAP.2016.2606556
  24. LIU, W., YAN, Z., MIN, Z., et al. Design of miniature active magnetic probe for near-field weak signal measurement in ICs. IEEE Microwave and Wireless Components Letters, 2020, vol. 30, no. 3, p. 312–315. DOI: 10.1109/LMWC.2020.2967384
  25. YANG, R., WEI, X., SHU, Y., et al. A miniature multi-component probe for near-field scanning. IEEE Transactions on Antennas and Propagation, 2019, vol. 67, no. 11, p. 6821–6828. DOI: 10.1109/TAP.2019.2927814
  26. INTERNATIONAL ELECTROTECHNICAL COMISSION. Integrated Circuits–Measurement of Electromagnetic Emissions, IEC-61967, 2011.
  27. SUDO, T. Predicting common mode radiation of power bus structure excited by IC’s switching current. In Asia-Pacific Symposium on Electromagnetic Compatibility and 19th International Zurich Symposium on Electromagnetic Compatibility. Singapore, 2008, p. 160–163. DOI: 10.1109/APEMC.2008.4559836
  28. CHEN, L., WU, J., ZHANG, H., et al. An optimized test method based on IC-Stripline TEM Cell. In Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC). Nusa Dua–Bali (Indonesia), 2021, p. 1–4. DOI: 10.1109/APEMC49932.2021.9596990
  29. FANG, W., EN, Y., HUANG, Y., et al. Extracting the electromagnetic radiated emission source of an integrated circuit by rotating the test board in a TEM cell measurement. IEEE Transactions on Electromagnetic Compatibility, 2019, vol. 61, no. 3, p. 933–841. DOI: 10.1109/TEMC.2018.2836698
  30. JIA, H., WAN, F., CHENG, X., et al. Electric near-field scanning for electronic PCB electromagnetic radiation measurement. Measurement, 2024, vol. 228, p. 1–9. DOI: 10.1016/j.measurement.2024.114355
  31. SHAO, W., FANG, W., CHEN, R., et al. Magnetic near-field measurement with a differential probe to suppress common mode noise. IEEE Sensors Journal, 2019, vol. 19, no. 21, p. 9697–9703. DOI: 10.1109/JSEN.2019.2928853
  32. YAN, Z., LIU, W., WANG, J., et al. Noncontact wideband current probes with high sensitivity and spatial resolution for noise location on PCB. IEEE Transactions on Instrumentation and Measurement, 2018, vol. 67, no. 12, p. 2881–2891. DOI: 10.1109/TIM.2018.2830859
  33. YANG, R., WEI, X., SHU, Y., et al. A high-frequency and high spatial resolution probe design for EMI prediction. IEEE Transactions on Instrumentation and Measurement, 2019, vol. 68, no. 8, p. 3012–3019. DOI: 10.1109/TIM.2018.2869181
  34. WANG, L., LIU, X., LU, G., et al. A new differential magnetic-field probe with parasitic elements for near-field scanning. IEEE Photonics Journal, 2024, vol. 16, no. 3, p. 1–5. DOI: 10.1109/JPHOT.2024.3391012

Keywords: Electromagnetic compatibility (EMC), near-field (NF) analysis, four-layer technology, magnetic NF microwave probe, design method, EMC characterization.