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.

J. Wu, X. Wang, Y. Zhou, B. Chen, W. Li, L. Zhang [references] [full-text] [DOI: 10.13164/re.2025.0461] [Download Citations]
Intra-pulse Modulation Recognition of LPI Radar Signals Based on Improved MobileNet

Addressing the challenge of feature extraction for Low Probability of Intercept (LPI) radar signals under low signal-to-noise ratio conditions, this study introduces a new method for intra-pulse modulation recognition of LPI radar signals based on an enhanced MobileNet architecture. Initially, a Time-Frequency Image (TFI) preprocessing technique suitable for LPI radar signals is proposed, which significantly improves the recognition accuracy of subsequent networks for intra-pulse modulation of LPI radar signals. Subsequently, the MobileNet network is modified by integrating Hybrid Dilation Convolution (HDC) and Efficient Channel Attention (ECA) modules, resulting in the development of an improved MobileNet. This enhanced network expands the receptive field of feature maps and improves the network's ability to capture channel and positional information. Additionally, a label smoothing strategy is utilized to optimize the network training process, reducing overfitting and enhancing sample clustering performance. Simulation experiments indicate that this method not only yields a high recognition accuracy rate but also outperforms existing comparative networks with fewer parameters.

1. REN, F., QUAN, D., SHEN, L., et al. LPI radar signal recognition based on feature enhancement with deep metric learning. Electronics, 2023, vol. 12, no. 24, p. 1–16. DOI: 10.3390/electronics12244934
2. LIU, Z., WANG, J., WU, T., et al. A method for LPI radar signals recognition based on complex convolutional neural network. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2023, vol. 37, no. 1, p. 1–13. DOI: 10.1002/jnm.3155
3. MI, X., CHEN, X., LIU, Q., et al. Radar signals modulation recognition based on bispectrum feature processing. Journal of Physics: Conference Series, 2021, vol. 1971, no. 1, p. 1–11. DOI: 10.1088/1742-6596/1971/1/012099
4. CHEN, B., WANG, X., ZHU, D., et al. LPI radar signals modulation recognition in complex multipath environment based on improved ResNeSt. IEEE Transactions on Aerospace and Electronic Systems, 2024, vol. 60, no. 6, p. 8887–8900. DOI: 10.1109/TAES.2024.3436634
5. ZHANG, X., LIU, Y., YAN, X., et al. Frequency modulation recognition for radar signal based on Resnet-LSTM network. Artificial Intelligence Technology Research, 2024, vol. 2, no. 3, p. 100–102. DOI: 10.18686/aitr.v2i3.4426
6. QUAN, D., TANG, Z., WANG, X., et al. LPI radar signal recognition based on dual-channel CNN and feature fusion. Symmetry, 2022, vol. 14, no. 3, p. 1–13. DOI: 10.3390/sym14030570
7. WAN, C., SI, W., DENG, Z. Research on modulation recognition method of multi‐component radar signals based on deep convolution neural network. IET Radar, Sonar & Navigation, 2023, vol. 17, no. 9, p. 1313–1326. DOI: 10.1049/rsn2.12421
8. RUAN, G., WANG, Y., WANG, L., et al. Automatic recognition of radar signal types based on CNN-LSTM. Telecommunications and Radio Engineering, 2020, vol. 79, no. 4, p. 305–321. DOI: 10.1615/TelecomRadEng.v79.i4.40
9. JIANG, Y., YIN, Z., SONG, Y. Low probability of intercept radar signal detection algorithm based on convolution neural network (in Chinese). Journal of Electronics and Information Technology, 2022, vol. 44, no. 2, p. 718–725. DOI: 10.11999/JEIT210132
10. HUYNH-THE, T., DOAN, V.-S., HUA, C.-H., et al. Accurate LPI radar waveform recognition with CWD-TFA for deep convolutional network. IEEE Wireless Communications Letters, 2021, vol. 10, no. 8, p. 1638–1642. DOI: 10.1109/LWC.2021.3075880
11. DONG, N., JIANG, H., LIU, Y., et al. Intra-pulse modulation radar signal recognition using CNN with second-order STFT-based synchro squeezing transform. Remote Sensing, 2024, vol. 16, no. 14, p. 1–13. DOI: 10.3390/rs16142582
12. ZHOU, Z., HUANG, G., CHEN, H., et al. Automatic radar waveform recognition based on deep convolutional denoising auto-encoders. Circuits, Systems, and Signal Processing, 2018, vol. 37, no. 9, p. 4034–4048. DOI: 10.1007/s00034-018-0757-0
13. THOMAS, M., JACOB, R., LETHAKUMARY, B. Comparison of WVD based time-frequency distributions. In 2012 International Conference on Power, Signals, Controls and Computation. Thrissur (India), 2012, p. 1–8. DOI: 10.1109/EPSCICON.2012.6175242
14. SI, W., WAN, C., DENG, Z. An efficient deep convolutional neural network with features fusion for radar signal recognition. Multimedia Tools and Applications, 2023, vol. 82, no. 2, p. 2871 to 2885. DOI: 10.1007/s11042-022-13407-9
15. CHEN, J., LI, B. The short-time Wigner–Ville distribution. Signal Processing, 2024, vol. 219, p. 1–13. DOI: 10.1016/j.sigpro.2024.109402
16. SINGH, V. K., PACHORI, R. B. Sliding eigenvalue decomposition-based cross-term suppression in Wigner–Ville distribution. Journal of Computational Electronics, 2021, vol. 20, no. 6, p. 2245–2254. DOI: 10.1007/s10825-021-01781-w
17. LIU, L., LI, X. Radar signal recognition based on triplet convolutional neural network. EURASIP Journal on Advances in Signal Processing, 2021, vol. 2021, no. 1, p. 1–16. DOI: 10.1186/s13634-021-00821-8
18. HEPSIBA, D., JUSTIN, J. Enhancement of single channel speech quality and intelligibility in multiple noise conditions using Wiener filter and deep CNN. Soft Computing, 2022, vol. 26, no. 23, p. 13037–13047. DOI: 10.1007/s00500-021-06291-2
19. SHAHI, T. B., SITAULA, C., NEUPANE, A., et al. Fruit classification using attention-based MobileNetV2 for industrial applications. PloS one, 2022, vol. 17, no. 2. DOI: 10.1371/journal.pone.0264586
20. ZHAO, L., WANG, L., JIA, Y., et al. A lightweight deep neural network with higher accuracy. Plos one, 2022, vol. 17, no. 8. DOI: 10.1371/journal.pone.0271225
21. BIAN, S., HE, X., XU, Z., et al. Hybrid dilated convolution with attention mechanisms for image denoising. Electronics, 2023, vol. 12, no. 18, p. 1–17. DOI: 10.3390/electronics12183770
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. Seattle (WA, USA), 2020, p. 11531–11539. DOI: 10.1109/CVPR42600.2020.01155
23. WANG, Y., LI, R., WANG, Z., et al. E3D: An efficient 3D CNN for the recognition of dairy cow's basic motion behavior. Computers and Electronics in Agriculture, 2023, vol. 205, p. 1–12. DOI: 10.1016/j.compag.2022.107607
24. SHAO, G., CHEN, Y., WEI, Y. Deep fusion for radar jamming signal classification based on CNN. IEEE Access, 2020, vol. 8, no. 1, p. 117236–117244. DOI: 10.1109/ACCESS.2020.3004188
25. ZHANG, Y., PENG, L., MA, G, et al. Dynamic gesture recognition model based on millimeter-wave radar with ResNet-18 and LSTM. Frontiers in Neurorobotics, 2022, vol. 16, p. 1–9. DOI: 10.3389/fnbot.2022.903197
26. SONG, Q., HUANG, S., ZHANG, Y., et al. Radar target classification using enhanced Doppler spectrograms with ResNet34CA in ubiquitous radar. Remote Sensing, 2024, vol. 16, no. 15, p. 1–24. DOI: 10.3390/rs16152860
27. LIANG, J., LUO, Z., LIAO, R. Intra-pulse modulation recognition of radar signals based on efficient cross-scale aware network. Sensors, 2024, vol. 24, no. 16, p. 1–17. DOI: 10.3390/s24165344
28. WANG, G., CHEN, S., HUANG, J., et al. Radar signal sorting and recognition based on transferred deep learning (in Chinese). Computer Science and Application, 2019, vol. 9, no. 9, p. 1761 to 1778. DOI: 10.12677/CSA.2019.99198

Keywords: Low Probability of Intercept (LPI) radar signals, intra-pulse modulation recognition, Time-Frequency Analysis (TFA) technology, attention mechanism, lightweight convolutional neural network

Z. Zhang, Q. Liu, Y. Wang [references] [full-text] [DOI: 10.13164/re.2025.0471] [Download Citations]
LVRT Strategy Considering Reactive Power Support and Fluctuating Power Suppression for Photovoltaic Application

Addressing the insufficient negative sequence dynamic reactive current support and power doubling oscillations present in conventional low-voltage ride through control mechanisms for photovoltaic inverters, this study designs a q-axis command for both positive and negative sequence currents in accordance with recent regulatory requirements for photovoltaic grid integration technologies. The d-axis commands for positive and negative sequence currents are computed to effectively attenuate second harmonic fluctuations in active power output. The proposed approach establishes equilibrium between inverter current carrying capacity and oscillation mitigation, thereby concurrently enhancing dynamic reactive current support for both sequence components while diminishing power doubling fluctuations. The short-circuit current characteristics of photovoltaic installations are examined utilizing this enhanced low-voltage ride through control methodology. Comparative analysis between the suggested approach and current low-voltage ride through control techniques was conducted via simulation models, confirming both the efficiency of the proposed method and the accuracy of analytical expressions for short-circuit current.

1. XUE, Y., CHANG, L., KJAER, S. B., et al. Topologies of single-phase inverters for small distributed power generators: An overview. IEEE Transactions on Power Electronics, 2004, vol. 19, no. 5, p. 1305–1314. DOI: 10.1109/TPEL.2004.833460
2. TEODORESCU, R., LISERRE, M., RODRIGUEZ, P. Grid Converters for Photovoltaic and Wind Power Systems. Hoboken (USA): Wiley, 2011. DOI:10.1002/9780470667057
3. BLAABJERG, F., YANG, Y., YANG, D., et al. Distributed power generation systems and protection. Proceedings of IEEE, 2017. vol. 105, no. 7, p. 1311–1331. DOI: 10.1109/JPROC.2017.2696878
4. TURITSYN, K., SULC, P., BACKHAUS, S., et al. Options for control of reactive power of distributed photovoltaic generators. Proceedings of IEEE, 2011. vol. 99, no. 6, p. 1063–1073. DOI: 10.1109/JPROC.2011.2116750
5. DEMIROK, E., GONZALEZ, P. C., FREDERIKSEN, K. H. B., et al. Local reactive power control methods for overvoltage prevention of distributed solar inverters in low-voltage grids. IEEE Journal of Photovoltaics, 2011, vol. 1, no. 2, p. 174–182. DOI: 10.1109/JPHOTOV.2011.2174821
6. CIOBOTARU, M., AGELIDIS, V. G., TEODORESCU, R., et al. Accurate and less-disturbing active anti-islanding method based on
7. PLL for grid-connected converters. IEEE Transactions on Power Electronics, 2010, vol. 25, no. 6, p. 1576–1584. DOI: 10.1109/TPEL.2010.2040088
8. ENSLIN, J. H. R., HESKES, P. J. M. Harmonic interaction between a large number of distributed power inverters and the distribution network. IEEE Transactions on Power Electronics, 2004, vol. 19, no. 6, p. 1586–1593. DOI: 10.1109/TPEL.2004.836615
9. LI, X. ZHANG, H., SHADMAND, M. B., et al. Model predictive control of a voltage-source inverter with seamless transition between islanded and grid-connected operations. IEEE Transactions on Industrial Electronics, 2017, vol. 64, no. 10, p. 7906–7918. DOI: 10.1109/TIE.2017.2696459
10. SAJADIAN, S., AHMADI, R. Model predictive control of dual-mode operations z-source inverter: Islanded and grid-connected. IEEE Transactions on Power Electronics, 2018, vol. 33, no. 5, p. 4488–4497. DOI: 10.1109/TPEL.2017.2723358
11. HASANIEN, H. M. An adaptive control strategy for low voltage ride through capability enhancement of grid-connected photovoltaic power plants. IEEE Transactions on Power Systems, 2016, vol. 31, no. 4, p. 3230–3237. DOI: 10.1109/TPWRS.2015.2466618
12. DING, G., GAO, F., TIAN, H., et al. Adaptive dc-link voltage control of two-stage photovoltaic inverter during low voltage ride-through operation. IEEE Transactions on Power Electronics, 2016, vol. 31, no. 6, p. 4182–4194. DOI: 10.1109/TPEL.2015.2469603
13. WEN, G., CHEN, Y., ZHONG, Z., et al. Dynamic voltage and current assignment strategies of nine-switch-converter-based DFIG wind power system for low-voltage ride through (LVRT) under symmetrical grid voltage dip. IEEE Transactions on Industry Applications, 2016, vol. 52, no. 4, p. 3422–3434. DOI: 10.1109/TIA.2016.2535274
14. ABBEY, C., JOOS, G. Effect of low voltage ride through (LVRT) characteristic on voltage stability. In IEEE Power Engineering Society General Meeting 2005. San Francisco (CA, USA), 2005, vol. 2, p. 1901–1907. DOI: 10.1109/PES.2005.1489659
15. HU, S., LIN, X., KANG, Y., et al. An improved low-voltage ride-through control strategy of doubly fed induction generator during grid faults. IEEE Transactions on Power Electronics, 2011, vol. 26, no. 12, p. 3653–3665. DOI: 10.1109/TPEL.2011.2161776
16. GOKSU, O., TEODORESCU, R., BAK, C. L., et al. Instability of wind turbine converters during current injection to low voltage grid faults and PLL frequency based stability solution. IEEE Transactions on Power Systems, 2014, vol. 29, no. 4, p. 1683 to 1691. DOI: 10.1109/TPWRS.2013.2295261
17. MOLINAS, M., SUUL, J. A., UNDELAND, T. Low voltage ride through of wind farms with cage generators: STATCOM versus SVC. IEEE Transactions on Power Electronics, 2008, vol. 23, no. 3, p. 1104–1117. DOI: 10.1109/TPEL.2008.921169
18. YANG, Y., BLAABJERG, F. Low-voltage ride-through capability of a single-stage single-phase photovoltaic system connected to the low-voltage grid. International Journal of Photoenergy, 2013, no. 1, p. 1–9. DOI: 10.1155/2013/257487
19. CONROY, J. F., WATSON, R. Low-voltage ride-through of a full converter wind turbine with permanent magnet generator. IET Renewable Power Generation, 2007, vol. 1, no. 2, p. 182–189. DOI: 10.1049/iet-rpg:20070033
20. ANURAG, A., YANG, Y., BLAABJERG, F. Thermal performance and reliability analysis of single-phase PV inverters with reactive power injection outside feed-in operating hours. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2015, vol. 3, no. 4, p. 870–880. DOI: 10.1109/JESTPE.2015.2428432
21. SUBUDHI, B., PRADHAN, R. A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Transactions on Sustainable Energy, 2013, vol. 4, no. 1, p. 89–98. DOI: 10.1109/TSTE.2012.2202294
22. SANGWONGWANICH, A., YANG, Y., BLAABJERG, F. High-performance constant power generation in grid-connected PV systems. IEEE Transactions on Power Electronics, 2016, vol. 31, no. 3, p. 1822–1825. DOI: 10.1109/TPEL.2015.2465151
23. FARAJI, R., REZA NAJI, H., ROUHOLAMINI, A., et al. FPGA-based real time incremental conductance maximum power point tracking controller for photovoltaic systems. IET Power Electronics, 2014, vol. 7, no. 5, p. 1294–1304. DOI: 10.1049/iet-pel.2013.0603
24. MCGRATH, B. P., HOLMES, D. G., GALLOWAY, J. J. H. Power converter line synchronization using a discrete Fourier transform (DFT) based on a variable sample rate. IEEE Transactions on Power Electronics, 2005, vol. 20, no. 4, p. 877 to 884. DOI: 10.1109/TPEL.2005.850944
25. HAN, Y., LUO, M., ZHAO, X., et al. Comparative performance evaluation of orthogonal-signal-generators-based single-phase PLL algorithms - a survey. IEEE Transactions on Power Electronics, 2016, vol. 31, no. 5, p. 3932–3944. DOI: 10.1109/TPEL.2015.2466631
26. SANTOS FILHO, R. M., SEIXAS, P. F., CORTIZO, P. C., et al. Comparison of three single-phase PLL algorithms for UPS applications. IEEE Transactions on Industrial Electronics, 2008, vol. 55, no. 8, p. 2923–2932. DOI: 10.1109/TIE.2008.924205
27. GOLESTAN, S., MONFARED, M., FREIJEDO, F. D., et al. Dynamics assessment of advanced single-phase PLL structures. IEEE Transactions on Industrial Electronics, 2013, vol. 60, no. 6, p. 2167–2177. DOI: 10.1109/TIE.2012.2193863
28. KARIMI-GHARTERMANI, M. Linear and pseudolinear enhanced phased-locked loop (EPLL) structures. IEEE Transactions on Industrial Electronics, 2014, vol. 61, no. 3, p. 1464–1474. DOI: 10.1109/TIE.2013.2261035
29. CIOBOTARU, M., TEODORESCU, R., BLAABJERG, F. A new single-phase PLL structure based on second order generalized integrator. In Proceedings of the 37th IEEE Power Electronics Specialists Conference. Jeju (South Korea), 2006, p. 1–6. DOI: 10.1109/pesc.2006.1711988
30. ZHENG, L., GENG, H., YANG, G. Fast and robust phase estimation algorithm for heavily distorted grid conditions. IEEE Transactions on Industrial Electronics, 2016, vol. 63, no. 11, p. 6845–6855. DOI: 10.1109/TIE.2016.2585078
31. GENG, H., XU, D., WU, B. A novel hardware-based all-digital phase-locked loop applied to grid-connected power converters.
32. IEEE Transactions on Industrial Electronics, 2011, vol. 58, no. 5, p. 1737–1745. DOI: 10.1109/TIE.2010.2053338
33. HUKA, G. B. Y., LI, W., CHAO, P., et al. A comprehensive LVRT strategy of two-stage photovoltaic systems under balanced and unbalanced faults. International Journal of Electrical Power and Energy Systems, 2018, vol. 103, p. 288–301. DOI: 10.1016/j.ijepes.2018.06.014
34. NASIRI, M., ARZANI, A., GUERRERO, J. M. LVRT operation enhancement of single-stage photovoltaic power plants: An analytical approach. IEEE Transactions on Smart Grid, 2021, vol. 12, no. 6, p. 5020–5029. DOI: 10.1109/TSG.2021.3108391
35. JOSHI, J., SWAMI, A. K., JATELY, V., et al. A comprehensive review of control strategies to overcome challenges during LVRT in PV systems. IEEE Access, 2021, vol. 9, p. 121804–121834. DOI: 10.1109/ACCESS.2021.3109050

Keywords: Photovoltaic, low voltage ride through (LVRT), reactive power support, fluctuating power suppression, efficiency

B. B. Shabarinath, P. Muralidhar [references] [full-text] [DOI: 10.13164/re.2025.0482] [Download Citations]
A Streaming Dataflow Accelerator for Sparse SVM Kernel Computation in Hyperspectral Image Classification

Hyperspectral images (HSI) provide extensive spectral information but their high dimensionality and redundancy create substantial challenges for computation and storage while increasing energy demands. The proposed solution combines sparse dictionary learning with Field Programmable gate Array (FPGA)-accelerated Sparse matrix vector multiplication (SpMV) operations and Support Vector Machine (SVM) training to tackle these issues. Spatial patches and spectral blocks partition HSI to enable the extraction of compact discriminative sparse features through the use of a learned sub-dictionary. In contrast to deep learning frameworks which demand large training datasets and generate significant computational overhead, the SVM-based approach achieves efficient real-time training and adaptation. The FPGA accelerator executes intensive SpMV operations through dynamic load balancing. We tested our approach with four varied HSI datasets gathered from aerial and UAV systems as well as terrestrial platforms on the PYNQ-Z2 board. Our design reaches classification accuracies between 98.65% and 99.95% across datasets including Indian Pines,AVRIS-NG, Cubert-UAV, Cubert-Terrestrial with per-pixel classification times below 7 us and inference times up to 36x faster than optimized software baselines which remain under typical sensor acquisition times. The strategy requires less than 0.24 W of on-chip power at maximum load which makes it ideal for deployment on satellites or UAVs. The proposed method outperforms existing FPGA-based SVM architectures in classification accuracy and throughput while enabling on-device incremental learning which makes it ideal for analyzing hyperspectral images in real-time.

1. PATHAK, D. K., KALITA, S. K., BHATTACHARYA, D. K. et al. Hyperspectral image classification using support vector machine: a spectral spatial feature based approach. Evolutionary Intelligence, 2022, vol. 15, p. 1809–1823. DOI: 10.1007/s12065-021-00591-0
2. MACIAS, R., BERNABE, S., BASCONES, D. et al. FPGA implementation of a hardware optimized automatic target detection and classification algorithm for hyperspectral image analysis. IEEE Geoscience and Remote Sensing Letters, 2022, vol. 19, p. 1–5. DOI: 10.1109/LGRS.2022.3189109
3. MARTINS, L. A., VIEL, F., SEMAN, L. O. et al. A real-time SVMbased hardware accelerator for hyperspectral images classification in FPGA. Microprocessors and Microsystems, 2024, vol. 104, p. 1–13. DOI: 10.1016/j.micpro.2023.104998
4. TAJIRI, K., MARUYAMA, T. FPGA acceleration of a composite kernel SVM for hyperspectral image classification. IEEE Access, 2023, vol. 11, p. 214–226. DOI: 10.1109/ACCESS.2022.3230066
5. GYANESHWAR, D., NIDAMANURI, R. R. A real-time FPGA accelerated stream processing for hyperspectral image classification. Geocarto International, 2022, vol. 37, no. 1, p. 52–69. DOI: 10.1080/10106049.2020.1713231
6. SHENMING, Q., XIANG, L., ZHIHUA, G. et al. A new hyperspectral image classification method based on spatial spectral features. Scientific Reports, 2022, vol. 12, p. 1–15. DOI: 10.1038/s41598-022-05422-5
7. KAUL, A., RAINA, S. Support vector machine versus convolutional neural network for hyperspectral image classification: a systematic review. Concurrency and Computation: Practice and Experience, 2022, vol. 34, p. 1–35. DOI: 10.1002/cpe.6945
8. ZHENG, Z., ZHONG, Y., MA, A. et al. FPGA: Fast patch-free global learning framework for fully end-to-end hyperspectral image classification.
9. IEEE Transactions on Geoscience and Remote Sensing, 2020, vol. 58, no. 8, p. 5612–5626. DOI: 10.1109/TGRS.2020.2967821
10. NASCIMENTO, J., MARTIN, G. Nonlinear spectral unmixing. Chapter in: AMIGO, J. M. (ed.). Data Handling in Science and Technology, Elsevier, 2019, vol. 32, p. 151–166. ISBN: 9780444639776. DOI: 10.1016/B978-0-444-63977-6.00008-0
11. RATLE, F., CAMPS-VALLS, G., WESTON, J. et al. Semi supervised neural networks for efficient hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2010, vol. 48, no. 5, p. 2271–2282. DOI: 10.1109/TGRS.2009.2037898
12. FU, H., et al. A novel band selection and spatial noise reduction method for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–13. DOI: 10.1109/TGRS.2022.3189015
13. LI, F., WANG, J., LAN, R., et al. Hyperspectral image classification using multi-feature fusion. Optics & Laser Technology, 2019, vol. 110, p. 176–183. DOI: 10.1016/j.optlastec.2018.08.044
14. FENG, S., ITOH, Y., PARENTE, M., et al. Hyperspectral band selection from statistical wavelet models. IEEE Transactions on Geoscience and Remote Sensing, 2017, vol. 55, no. 4, p. 2111–2123. DOI: 10.1109/TGRS.2016.2636850
15. KUMAR, B., DIKSHIT, O. Hyperspectral image classification based on morphological profiles and decision fusion. International Journal of Remote Sensing, 2017, vol. 38, no. 20, p. 5830–5854. DOI: 10.1080/01431161.2017.1348636
16. BHATTI, U. A., YU, Z., CHANUSSOT, J., et al. Local similarity-based spatial–spectral fusion hyperspectral image classification with deep CNN and Gabor filtering. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–15. DOI: 10.1109/TGRS.2021.3090410
17. MEI, S., LI, X., LIU, X., et al. Hyperspectral image classification using attention-based bidirectional long short-term memory network. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–12. DOI: 10.1109/TGRS.2021.3102034
18. WANG, H., CELIK, T. Sparse representation-based hyperspectral image classification. Signal, Image and Video Processing, 2018, vol. 12, p. 1009–1017. DOI: 10.1007/s11760-018-1249-1
19. IORDACHE, M.-D., BIOUCAS-DIAS, J. M., PLAZA, A. Sparse unmixing of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 2011, vol. 49, no. 6, p. 2014–2039. DOI: 10.1109/TGRS.2010.2098413
20. CHEN, Y., NASRABADI, N. M., TRAN, T. D. Hyperspectral image classification using dictionary-based sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 2011, vol. 49, no. 10, p. 3973–3985. DOI: 10.1109/TGRS.2011.2129595
21. PENG, J., JIANG, X., CHEN, N., et al. Local adaptive joint sparse representation for hyperspectral image classification. Neurocomputing,
22. 2019, vol. 334, p. 239–248. DOI: 10.1016/j.neucom.2019.01.034
23. SOLTANI-FARANI, A., RABIEE, H. R., HOSSEINI, S. A. Spatial aware dictionary learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2015, vol. 53, no. 1, p. 527–541. DOI: 10.1109/TGRS.2014.2325067
24. FU,W., LI, S., FANG, L., et al. Contextual online dictionary learning for hyperspectral image classification. IEEE Transactions on Geoscience
25. and Remote Sensing, 2018, vol. 56, no. 3, p. 1336–1347. DOI: 10.1109/TGRS.2017.2761893
26. XIE, M., JI, Z., ZHANG, G., et al. Mutually exclusive-KSVD: Learning a discriminative dictionary for hyperspectral image classification. Neurocomputing, 2018, vol. 315, p. 177–189. DOI: 10.1016/j.neucom.2018.07.015
27. CASTRODAD, A., VASILESCU, M., SAPIRO, G., et al. Learning discriminative sparse representations for modeling, classification, and reconstruction of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2011, vol. 49, no. 11, p. 4263–4281. DOI: 10.1109/TGRS.2011.2159265
28. CAMPS-VALLS, G., BRUZZONE, L. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2005, vol. 43, no. 6, p. 1351–1362. DOI: 10.1109/TGRS.2005.846154
29. MELGANI, F., BRUZZONE, L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 2004, vol. 42, no. 8, p. 1778–1790. DOI: 10.1109/TGRS.2004.831865
30. GAO, S., TSANG, I. W.-H., CHIA, L.-T. Laplacian sparse coding, hypergraph Laplacian sparse coding, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, vol. 35, no. 1, p. 92–104. DOI: 10.1109/TPAMI.2012.63
31. HUANG, J., HUANG, T., DENG, L., et al. Joint-sparse-blocks and low-rank representation for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2019, vol. 57, no. 4, p. 2419–2438. DOI: 10.1109/TGRS.2018.2873326
32. FU,W., LI, S., FANG, L., et al. Hyperspectral image classification via shape-adaptive joint sparse representation. IEEE Journal of Selected
33. Topics in Applied Earth Observations and Remote Sensing, 2015, vol. 9, no. 2, p. 556–567. DOI: 10.1109/JSTARS.2015.2477364
34. HE, Z., LIU, L., DENG, R., et al. Low-rank group inspired dictionary learning for hyperspectral image classification. Signal Processing, 2016, vol. 120, p. 209–221. DOI: 10.1016/j.sigpro.2015.09.004
35. CHANG, C.-C., LIN, C.-J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, vol. 2, no. 3, p. 1–27. DOI: 10.1145/1961189.1961199
36. BAUMGARDNER, M. F., BIEHL, L. L., LANDGREBE, D. A. 220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3. Purdue University Research Repository, 2015. DOI: 10.4231/R7RX991C
37. BHATTACHARYA, B. K., JAIN, S., PARIHAR, J. S., et al. An overview of AVIRIS-NG airborne hyperspectral science campaign over India. Current Science, 2019, vol. 116, no. 7, p. 1082–1088. DOI: 10.18520/CS/V116/I7/1082-1088
38. ASTOR, T., DAYANANDA, S., NAUTIYAL, S., et al. Vegetable crop biomass estimation using hyperspectral and RGB 3D UAV data. Agronomy, 2020, vol. 10, no. 10, p. 1–19. DOI: 10.3390/agronomy10101600
39. SARMA, A. S., NIDAMANURI, R. R. Active learning-enhanced plant-level crop mapping with drone hyperspectral imaging and evolutionary computing. In Proceedings of the Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Athens (Greece), 2023, p. 1–5. DOI: 10.1109/WHISPERS61460.2023.10430799
40. WU, Z., LIU, J., PLAZA, A., et al. GPU implementation of composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2015, vol. 12, no. 9, p. 1973–1977. DOI: 10.1109/LGRS.2015.2441631
41. THORPE, A. K., ROBERTS, D. A., FRANKLIN, J., et al. Mapping methane concentrations from a controlled release experiment using the next generation airborne visible/infrared imaging spectrometer (AVIRIS-NG). Remote Sensing of Environment, 2016, vol. 179, p. 104–115. DOI: 10.1016/j.rse.2016.03.032

Keywords: Hyperspectral image classification, support vector machine, sparse dictionary learning, sparse matrix-vector multiplication, load balancing

Q. Fang, Y. Zhao, J. Wang, L. Zhang [references] [full-text] [DOI: 10.13164/re.2025.0494] [Download Citations]
Self-Supervised Learning Driven Cross-Domain Feature Fusion Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification faces significant challenges due to the high cost of acquiring labeled samples. To mitigate this, we propose SSCF-Net, a novel self-supervised learning driven cross-domain feature fusion Network. SSCF-Net uniquely leverages readily available labeled natural images (source domain) to aid HSI (target domain) classification by transfer learning. Specifically, we employ rotation-based self-supervision in the source domain to learn transferable features, which are then transferred to the HSI domain. Within SSCF-Net, we effectively fuse local and global features: local features are extracted by a jointly trained module combining VGG and two-dimensional long short-term memory networks (TD-LSTM) networks, while global features capturing long-range dependencies are learned via a Transformer model. Crucially, in the HSI domain, we further employ contrastive learning as a self-supervised strategy to maximally utilize the limited labeled data. Extensive experiments on three benchmark HSI datasets (Salinas, Indian Pines, WHU-Hi-LongKou) demonstrate that SSCF-Net significantly outperforms existing methods, validating its effectiveness in addressing the label scarcity problem. The code is available at https://github.com/6pangbo/SSCF-Net.

1. CHEN, W., OUYANG, S., YANG, J., et al. A framework for complex land cover classification using Gaofen-5 AHSI images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, vol. 15, p. 1591–1603. DOI: 10.1109/JSTARS.2022.3144339
2. PENG, J., ZHOU, Y., CHEN, C. L. P. Region-kernel-based support vector machines for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2015, vol. 53, no. 9, p. 4810–4824. DOI: 10.1109/TGRS.2015.2410991
3. ZHANG, Y., CAO, G., LI, X., et al. Cascaded random forest for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, vol. 11, no. 4, p. 1082–1094. DOI: 10.1109/JSTARS.2018.2809781
4. YU, C., HUANG, J., SONG, M., et al. Edge-inferring graph neural network with dynamic task-guided self-diagnosis for few-shot hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–13. DOI: 10.1109/TGRS.2022.3196311
5. CHEN, Y., LIN, Z., ZHAO, X., et al. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, vol. 7, no. 6, p. 2094–2107. DOI: 10.1109/JSTARS.2014.2329330
6. CHEN, Y., ZHAO, X., JIA, X., et al. Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, vol. 8, no. 6, p. 2381–2392. DOI: 10.1109/JSTARS.2015.2388577
7. HU, W., HUANG, Y., WEI, L., et al. Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015, vol. 2015, no. 1, p. 1–13. DOI: 10.1155/2015/258619
8. CHEN, Y., JIANG, H., LI, C., et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks.
9. IEEE Transactions on Geoscience and Remote Sensing, 2016, vol. 54, no. 10, p. 6232–6251. DOI: 10.1109/TGRS.2016.2584107
10. YE, Z., LI, C., LIU, Q., et al. Computationally lightweight hyperspectral image classification using a multiscale depth wise convolutional network with channel attention. IEEE Geoscience and Remote Sensing Letters, 2023, vol. 20, p. 1–5. DOI: 10.1109/LGRS.2023.3285208
11. ZHANG, C., LI, G., DU, S., et al. Multi-scale dense networks for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 2019, vol. 57, no. 11, p. 9201–9222. DOI: 10.1109/TGRS.2019.2925615
12. FANG, Q., GU, S.,WANG, J., et al. A feature dynamic enhancement and global collaboration guidance network for remote sensing image compression. Radioengineering, 2025, vol. 34, no. 2, p. 324–341. DOI: 10.13164/re.2025.0324
13. DING, Y., CHONG, Y., PAN, S., et al. Diversity-connected graph convolutional network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–18. DOI: 10.1109/TGRS.2023.3298848
14. LIU, C., DONG, A., DONG, D., et al. Contrastive graph convolution network with skip connections for few-shot hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2024, vol. 21, p. 1–5. DOI: 10.1109/LGRS.2024.3355147
15. ZHOU, W., KAMATA, S.-I., WANG, H., et al. Multiscanning-based RNN-transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–19. DOI: 10.1109/TGRS.2023.3277014
16. MEI, S., LI, X., LIU, X., et al. Hyperspectral image classification using attention-based bidirectional long short-term memory network. IEEE Transactions on Geoscience and Remote Sensing, 2021, vol. 60, p. 1–12. DOI: 10.1109/TGRS.2021.3102034
17. SUN, L., ZHAO, G., ZHENG, Y., et al. Spectral-spatial feature tokenization transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–14. DOI: 10.1109/TGRS.2022.3144158
18. MEI, S., SONG, C., MA, M., et al. Hyperspectral image classification using group-aware hierarchical transformer. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–14. DOI: 10.1109/TGRS.2022.3207933
19. ZHAO, Z., XU, X., LI, S., et al. Hyperspectral image classification using group wise separable convolutional vision transformer network. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 61, p. 1–17. DOI: 10.1109/TGRS.2024.3377610
20. LONG, Y., WANG, X., XU, M., et al. Dual self-attention Swin transformer for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–12. DOI: 10.1109/TGRS.2023.3275146
21. QI, W., HUANG, C., WANG, Y., et al. Global-local 3-D convolutional transformer network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–20. DOI: 10.1109/TGRS.2023.3272885
22. ZHAO, F., ZHANG, J., MENG, Z., et al. Multiple vision architectures-based hybrid network for hyperspectral image classification. Expert Systems with Applications, 2023, vol. 234, p. 1–16. DOI: 10.1016/j.eswa.2023.121032
23. WANG, Y., MEI, J., ZHANG, L., et al. Self-supervised feature learning with CRF embedding for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2018, vol. 57, no. 5, p. 2628–2642. DOI: 10.1109/TGRS.2018.2875943
24. BAI, J., ZHOU, Z., CHEN, Z., et al. Cross-dataset model training for hyperspectral image classification using self-supervised learning. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–17. DOI: 10.1109/TGRS.2024.3493969
25. CAO, X., YU, J., XU, R., et al. Mask-enhanced contrastive learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–15. DOI: 10.1109/TGRS.2024.3479220
26. YE, Z., CAO, Z., LIU, H., et al. Self-supervised learning with multiscale densely connected network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–15. DOI: 10.1109/TGRS.2024.3424394
27. HE, K., CHEN, X., XIE, S., et al. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans (USA), 2022, p. 16000–16009. DOI: 10.1109/CVPR52688.2022.01553
28. ZHOU, F., XU, C., YANG, G., et al. Masked spectral-spatial feature prediction for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 62, p. 1–13. DOI: 10.1109/TGRS.2023.3344782
29. CAO, M., ZHANG, X., CHENG, J., et al. Spatial-spectral-semantic cross-domain few-shot learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–19. DOI: 10.1109/TGRS.2024.3434484
30. LI, Z., LIU, M., CHEN, Y., et al. Deep cross-domain few shot learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–18. DOI: 10.1109/TGRS.2021.3057066
31. WANG, Y., LIU, M., YANG, Y., et al. Heterogeneous few shot learning for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2021, vol. 19, p. 1–5. DOI: 10.1109/LGRS.2021.3117577
32. ZHANG, Y., LI, W., ZHANG, M., et al. Graph information aggregation cross-domain few-shot learning for hyperspectral image classification. IEEE Transactions on Neural Networks and Learning Systems, 2022, vol. 35, no. 2, p. 1912–1925. DOI: 10.1109/TNNLS.2022.3185795
33. LI, Z., GUO, H., CHEN, Y., et al. Few-shot hyperspectral image classification with self-supervised learning. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–17. DOI: 10.1109/TGRS.2023.3298851
34. XIAO, F., HAN, X., CAO, C., et al. Neural architecture search-based few-shot learning for hyperspectral image classification. IEEE Transactions
35. on Geoscience and Remote Sensing, 2024, vol. 61, p. 1–15. DOI: 10.1109/TGRS.2024.3385478
36. QIN, B., FENG, S., ZHAO, C., et al. Cross-domain few-shot learning based on feature disentanglement for hyperspectral image classification.
37. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–15. DOI: 10.1109/TGRS.2024.3386256
38. YE, Z., WANG, J., LIU, H., et al. Adaptive domain-adversarial few shot learning for cross-domain hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–17. DOI: 10.1109/TGRS.2023.3334289
39. WANG, F., KONG, T., ZHANG, R., et al. Self-supervised learning by estimating twin class distribution. IEEE Transactions on Image Processing, 2023, vol. 32, p. 2228–2236. DOI: 10.1109/TIP.2023.3266169
40. GRANA, M., VEGANZONS, M. A., AYERDI, B. Hyperspectral Remote Sensing Scenes. Available online: https://www.ehu.eus/ccwintco/index.php/Hyperspectral_
41. Remote_Sensing_Scenes
42. DONG, Z., LIU, T., GU, Y. Spatial and semantic consistency contrastive learning for self-supervised semantic segmentation of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–12. DOI: 10.1109/TGRS.2023.3317016
43. HONG, D., HAN, Z., YAO, J., et al. Spectral Former: Rethinking hyperspectral image classification with transformers. IEEE Transactions
44. on Geoscience and Remote Sensing, 2021, vol. 60, p. 1–15. DOI: 10.1109/TGRS.2021.3130716

Keywords: Hyperspectral image classification, self-supervised learning, transfer learning, feature fusion