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Radioengineering

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Proceedings of Czech and Slovak Technical Universities

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June 2024, Volume 33, Number 2 [DOI: 10.13164/re.2024-2]

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Y. Xiong, M. X. Luo [references] [full-text] [DOI: 10.13164/re.2024.0223] [Download Citations]
Searchable Encryption Scheme for Large Data Sets in Cloud Storage Environment

Cloud storage has become essential in managing and retrieving extensive volumes of data, providing economical alternatives and adaptability for effective storage environment. However, in light of the rapid expansion of comprehensive datasets in cloud storage, the preservation of security has emerged as a matter of utmost importance for large data sets. Encryption has become a crucial mechanism for protecting confidential large data sets from unauthorized individuals. Encryption is necessary for safeguarding sensitive data by transforming it into indecipherable code so prevent unauthorized entry, and the encryption and decryption process is done at the end-user and cloud server. In the present situation, searchable symmetric encryption assumes a pivotal function by facilitating safe data retrieval while concurrently upholding the principle of secrecy. This research presents the Searchable Encryption Scheme in Cloud Storage Environment (SES-CSE), which offers a resilient solution for tackling the obstacles related to data security and retrieval efficiency for large data sets. The SES-CSE framework effectively incorporates encryption techniques inside a robust search engine, establishing a reliable framework for large data sets protection with Okapi BM25. The approach exhibits significant performance benefits, as shown by an encryption time of 14.85 ms, decryption time of 10.06 ms, memory consumption of 77.87 MB, and search times of 13.5 ms. The SES-CSE model demonstrates remarkable retrieval accuracies of 98.41%, 98.57%, and 97.51% throughout the training, testing, and validation phases. The results underscore the usefulness and security of SES-CSE as a solution for cloud storage, improving both the secrecy of data and the efficiency of retrieval in large-scale settings.

  1. DEEPA, N., PHAM, Q. V., NGUYEN, D. C., et al. A survey on blockchain for big data: Approaches, opportunities, and future directions. Future Generation Computer Systems, 2022, vol. 131, p. 209–226. DOI: 10.1016/j.future.2022.01.017
  2. PRAJAPATI, P., SHAH, P. A review on secure data deduplication: Cloud storage security issue. Journal of King Saud University – Computer and Information Sciences, 2022, vol. 34, no. 7, p. 3996–4007. DOI: 10.1016/j.jksuci. 2020.10.021
  3. LI, F., LU, H., HOU, M., et al. Customer satisfaction with bank services: The role of cloud services, security, e-learning, and service quality. Technology in Society, 2021, vol. 64, p. 1–11. DOI: 10.1016/j. techsoc.2020.101487
  4. WANG, T., YANG, Q., SHEN, X., et al. A privacy-enhanced retrieval technology for the cloud-assisted Internet of Things. IEEE Transactions on Industrial Informatics, 2021, vol. 18, no. 7, p. 4981–4989. DOI: 10.1109/TII.2021.3103547
  5. BELLO, S. A., OYEDELE, L. O., AKINADE, O. O., et al. Cloud computing in the construction industry: Use cases, benefits, and challenges. Automation in Construction, 2021, vol. 122, p. 1–18. DOI: 10.1016/j.autcon.2020. 103441
  6. ZHU, L., LI, F. Agricultural data sharing and sustainable development of ecosystem based on blockchain. Journal of Cleaner Production, 2021, vol. 315, p. 1–9. DOI: 10.1016/j.jclepro.2021.127869
  7. SOWMIYA, B., POOVAMMAL, E., RAMANA, K., et al. Linear elliptical curve digital signature (LECDS) with blockchain approach for enhanced security on cloud server. IEEE Access, 2021, vol. 9, p. 138245–138253. DOI: 10.1109/ACCESS.2021.3115238
  8. ALUVALU, R., UMA MAHESWARI, V., CHENNAM, K. K., et al. Data security in cloud computing using Abe-based access control. Chapter in In: Das, S. K., Samanta, S., Dey, N., et al. (eds.) Architectural Wireless Networks Solutions and Security Issues. Lecture Notes in Networks and Systems, 2021, vol. 196, p. 47–61. Springer, Singapore. DOI: 10.1007/978-981-16-0386-0_4
  9. LI, C., DONG, M., LI, J., et al. Efficient medical big data management with keyword-searchable encryption in health chain. IEEE Systems Journal, 2022, vol. 16, no. 4, p. 5521–5532. DOI: 10.1109/ JSYST.2022.3173538
  10. ZHANG, Y., XU, C., CHENG, N., et al. Secure password protected encryption key for dedicated cloud storage systems. IEEE Transactions on Dependable and Secure Computing, 2021, vol. 19, no. 4, p. 2789–2806. DOI: 10.1109/TDSC.2021.3074146
  11. LAKSHMI, C., THENMOZHI, K., RAYAPPAN, J. B. B., et al. Neural-assisted image-dependent encryption scheme for medical image cloud storage. Neural Computing and Applications, 2021, vol. 33, p. 6671–6684. DOI: 10.1007/ s00521-020-05447-9
  12. MAJUMDAR, A., BISWAS, A., MAJUMDER, A., et al. A novel DNA-inspired encryption strategy for concealing cloud storage. Frontiers of Computer Science, 2021, vol. 15, p. 1–18. DOI: 10.1007/ s11704-019-9015-2
  13. SONG, H., LI, J., LI, H. A cloud-secure storage mechanism based on data dispersion and encryption. IEEE Access, 2021, vol. 9, p. 63745–63751. DOI: 10.1109/ACCESS.2021.3075340
  14. VISWANATH, G., VENKATA KRISHNA, P. Hybrid encryption framework for securing big data storage in a multi-cloud environment. Evolutionary Intelligence, 2021, vol. 14, p. 691–698. DOI: 10.1007/s12065-020-00404-w
  15. PAREEK, G., PURUSHOTHAMA, B. R. KAPRE: Key-aggregate proxy re-encryption for secure and flexible data sharing in cloud storage. Journal of Information Security and Applications, 2021, vol. 63, p. 1–21. DOI: 10.1016/j.jisa.2021.103009
  16. VARRI, U. S., PASUPULETI, S. K., KADAMBARI, K. V. CP-ABSEL: Ciphertext-policy attribute-based searchable encryption from lattice in cloud storage. Peer-to-Peer Networking and Applications, 2021, vol. 14, p. 1290–1302. DOI: 10.1007/s12083-020-01057-3
  17. LI, S., XU, C., ZHANG, Y., et al. Blockchain-based transparent integrity auditing and encrypted deduplication for cloud storage. IEEE Transactions on Services Computing, 2022, vol. 16, no. 1, p. 134–146. DOI: 10.1109/TSC.2022.3144430
  18. LIN, H., ZHAO, G., SONG, S., et al. A new lightweight public key encryption with equality test for cloud storage. Multimedia Tools and Applications, 2024, p. 1–22. DOI: 10.1007/s11042-023-16540-1
  19. ELKANA EBINAZER, S., SAVARIMUTHU, N., MARY SAIRA BHANU, S. ESKEA: Enhanced symmetric key encryption algorithm based on secure data storage in cloud networks with data deduplication. Wireless Personal Communications, 2021, vol. 117, p. 3309–3325. DOI: 10.1007/s11277-020-07989-6
  20. FAN, L., JI, D., LIN, P. Arbitrary surface data patching method based on geometric convolutional neural network. Neural Computing and Applications, 2023, vol. 35, no. 12, p. 8763–8774. DOI: 10.1007/s00521-022-07759-4
  21. MACIAS, A. Integration and application of marine engineering environmental monitoring data based on big data. Frontiers in Ocean Engineering, 2020, vol. 1, no. 1, p. 19–26. DOI: 10.38007/FOE.2020.010103
  22. CHUNG, S. M., SHIEH, M. D., CHIUEH, T. C. FETCH: A cloud‐native searchable encryption scheme enabling efficient pattern search on encrypted data within cloud services. International Journal of Communication Systems, 2023, vol. 36, no. 1, p. 1–26. DOI: 10.1002/dac.4141
  23. BigQuery Public Datasets. [Online] Cited 2023-12-06. Available at: https://cloud.google.com/bigquery/public-data

Keywords: Cloud computing, searchable encryption, data sets, security

R. H. Xiang, S. S. Li, J. L. Pan [references] [full-text] [DOI: 10.13164/re.2024.0236] [Download Citations]
A Novel IoT Intrusion Detection Model Using 2dCNN-BiLSTM

With the continuous advancement of Internet of Things (IoT) intelligence, IoT security issues have become more and more prominent in recent years. The research on IoT security has become a hot spot. A lightweight IoT intrusion detection model fusing a convolutional neural network, bidirectional long short-term memory network is proposed. It aims to improve processed data security and attack detection accuracy. First, sampling is performed by a hybrid sampling algorithm fusing SMOTE and ENN. Its aim is to minimize the impact of imbalanced-data and ensure data quantity in the process. Then, the data features are extracted by 2-dimensional convolutional neural network (2dCNN), and the effect of useless information is reduced by mean pooling and maximum pooling, so it can be adapted to the demanding resource environment of the IoT. On this basis, long-range dependent temporal features are extracted using bidirectional long short-term memory (BiLSTM), which aims to fully extract data features to improve detection accuracy in the limited resource environment. Finally, the algorithm is validated on the UNSW_NB15 dataset, and the results of the experiments reaches 93.5% at Accuracy, 86.4% at Precision, 85.3% at Recall and 85.8% at F1-Score. According to the results, the proposed algorithm can generate higher-quality samples, achieve higher detection rate with faster inference time and spend lower memory costs.

  1. WORTMANN, A. F., FLUCHTER, K. Internet of things. Business & Information Systems Engineering, 2015, vol. 57, no. 3, p. 221–224. DOI: 10.1007/s12599-015-0383-3
  2. CHAHID, Y., BENABDELLAH, M., AZIZI, A. Internet of things security. In Proceedings of 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS). Fez (Morocco), 2017, p. 1–6. DOI: 10.1109/WITS.2017.7934655
  3. KRISHNAN, P., JAIN, K., ACHUTHAN, K., et al. Software defined security-by-contract for blockchain-enabled MUD-aware industrial IoT edge networks. IEEE Internet of Things Journal, 2021, vol. 9, no. 9, p. 6611–6622. DOI: 10.1109/TII.2021.3084341
  4. NASIR, M. H., KHAN, S. A., KHAN, M. M., et al. Swarm intelligence inspired intrusion detection systems a systematic literature review. Computer Networks, 2022, vol. 205, no. 3, p. 1–29. DOI: 10.1016/j.comnet.2021.108708
  5. XU, L. J., WANG, B. L., YANG, M. H., et al. A multi-mode attack detection and anomaly state evaluation method for industrial control networks. (in Chinese) Computer Research and Development, 2021, vol. 58, no. 11, p. 1–17. DOI: 10.7544/issn1000-1239.2021.20210598
  6. WONGSUPHASAWAT, K., SMILKOV, D., WEXLER, J., et al. Visualizing dataflow graphs of deep learning models in tensor flow. IEEE Transactions on Visualization and Computer Graphics, 2017, vol. 24, no. 1, p. 1–12. DOI: 10.1109/TVCG.2017.2744878
  7. YANG, A., ZHUANSUN, Y., LIU, C., et al. Design of intrusion detection system for internet of things based on improved BP neural network. IEEE Access, 2019, vol. 7, p. 106043–106052. DOI: 10.1109/ACCESS.2019.2929919
  8. CHEN, H. S., CHEN, J. J. Construction and optimization of IoT intrusion detection classification model based on ResNet and bidirectional LSTM fusion. Journal of Hunan University: Natural Science Edition, 2020, vol. 47, no. 8, p. 1–8.
  9. CHAWLA, N. V., BOWYER, K. W., HALL, L. O., et al. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, vol. 16, no. 1, p. 321–357. DOI: 10.1613/ jair.953
  10. HAN, H., WANG, W. Y., MAO, B. H. Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In Lecture Notes in Computer Science (International Conference on Intelligent Computing ICIC 2005), 2005, vol. 3644, p. 878–887. DOI: 10.1007/11538059_91
  11. HE, H., BAI, Y., GARCIA, E. A., et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). Hong Kong, 2008, p. 1322–1328. DOI: 10.1109/IJCNN.2008.4633969
  12. ZHANG, J. J. Research on credit risk assessment of listed companies based on category imbalance data. (in Chinese) Hefei University of Technology, 2021.
  13. LI, Y. X., CHAI, Y., HU, Y. Q., et al. A review of imbalance data classification methods. Control and Decision Making, 2019, vol. 34, no. 4, p. 673–688. ISSN:1001-0920
  14. GOODFELLOW, I., POUGET-ABADIE, J., MIRZA, M., et al. Generative adversarial nets. Advances in Neural Information Processing Systems, 2014, p. 2672–2680. DOI: 10.3156/JSOFT.29.5_177_2
  15. ODENA, A. Semi-Supervised Learning with Generative Adversarial Networks. 2016, p. 1–3. [Online] Cited 2024-03-10. DOI: 10.48550/arXiv.1606.01583
  16. LIU, M. Y., BREUEL, T., KAUTZ, J. Unsupervised Image to Image Translation Networks. In 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach (CA, USA), 2017, p. 1–11. DOI: 10.48550/arXiv.1703.00848
  17. RAJESWAR S., SUBRAMANIAN S., DUTIL F., et al. Adversarial generation of natural language. In Proceedings of Meeting of the Association for Computational Linguistics. Canada, 2017, p. 1–11. DOI: 10.18653/V1/W17-2629
  18. DONG, H. W., HSIAO, W. Y., YANG, L. C., et al. MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment. 2017, p. 1–13. [Online] Cited 2024-03-10. DOI: 10.48550/arXiv.1709.06298
  19. YU, L., ZHANG, W., WANG, J., et al. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. 2017, p. 1–11. [Online] Cited 2024-03-10. DOI: 10.48550/arXiv.1609.05473
  20. BRECK, E., CAI, S., NIELSEN, E., et al. The ML test score: A rubric for ML production readiness and technical debt reduction. In Proceedings of 2017 IEEE International Conference on Big Data (Big Data). Boston (MA, USA), 2017, p. 1123–1132. DOI: 10.1109/BigData.2017.8258038
  21. YAN, Y. T., DAI, T., ZHANG, Y. W., et al. Neighborhood-aware oversampling method for unbalanced data sets. Small Microcomputer Systems, 2021, vol. 42, no. 7, p. 1–11.
  22. ZHANG, H., LI, X. J. L., LIU, M., et al. Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection. Future Generation Computer Systems, 2021, vol. 122, p. 130–143. DOI: 10.1016/j.future.2021.03.024
  23. LIPPMANN, R., HAINES, J. W., FRIED, D. J., et al. The 1999 DARPA off-line intrusion detection evaluation. Computer Networks, 2000, vol. 34, no. 4, p. 579–595.
  24. TAVALLAEE, M., BAGHERI, E., LU, W., et al. A detailed analysis of the KDD CUP 99 data set. In Proceedings of 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. Ottawa (Canada), 2009, p. 1–6. DOI: 10.1109/CISDA.2009.5356528
  25. JIA, S. W. Intrusion Detection Based on Imbalanced Classification (in Chinese). Tianjin University of Technology, 2023.
  26. XIAO, Y., XING, C., ZHANG, T., et al. An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access, 2019, vol. 7, p. 42210–42219. DOI: 10.1109/ACCESS.2019.2904620
  27. WANG, W., SHENG, Y., WANG, J., et al. HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access, 2018, vol. 6, p. 1792 to 1806. DOI: 10.1109/ACCESS.2017.2780250
  28. KE, G., MENG, Q., FINLEY, T., et al. LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems 30, 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach (CA, USA), 2017, p. 3149–3157. ISBN: 9781510860964
  29. CHEN, T., GUESTRIN, C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA, 2016, p. 785–794. DOI: 10.1145/2939672.2939785
  30. LIU, J., YANG, D., LIAN, M., et al. Research on intrusion detection based on particle swarm optimization in IoT. IEEE Access, 2021, vol. 9, p. 38254–38268. DOI: 10.1109/ACCESS.2021.3063671
  31. LIU, L., WANG, P., LIN, J., et al. Intrusion detection of imbalanced network traffic based on machine learning and deep learning. IEEE Access, 2020, vol. 9, p. 7550–7563. DOI: 10.1109/ACCESS.2020.3048198
  32. ZHANG, G., WANG, X., LI, R., et al. Network intrusion detection based on conditional Wasserstein generative adversarial network and cost-sensitive stacked autoencoder. IEEE Access, 2020, vol. 8, p. 190431–190447. DOI: 10.1109/ACCESS.2020.3031892
  33. ZHANG, J., LING, Y., FU, X., et al. Model of the intrusion detection system based on the integration of spatial temporal features. Computers & Security, 2020, vol. 89, no. 2, p. 1–9. DOI: 10.1016/j.cose.2019.101681
  34. SHARMA, N. V., YADAV, N. S. An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers. Microprocessors & Microsystems, 2021, no. 85, p. 1–11. DOI: 10.1016/J.MICPRO.2021.104293
  35. SAHU, S. K., MOHAPATRA, D. P., ROUT, J. K., et al. A LSTMFCNN based multi-class intrusion detection using scalable framework. Computers and Electrical Engineering, 2022, vol. 99, p. 1–19. DOI: 10.1016/j.compeleceng.2022.107720
  36. KANNA, P. R., SANTHI, P. Unified deep learning approach for efficient intrusion detection system using integrated spatialtemporal features. Knowledge-Based Systems, 2021, no. 226, p. 1–12. DOI: 10.1016/j.knosys.2021.107132
  37. WANG, Z., XU, Z., HE, D., et al. Deep logarithmic neural network for internet intrusion detection. Soft Computing, 2021, no. 25, p. 10129–10152. DOI: 10.1007/s00500-021-05987-9

Keywords: Internet of Things (IoT), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), intrusion detection

Z. C. Wang, Z. B. Wang, M. Gao, H. Liu, S. Fang [references] [full-text] [DOI: 10.13164/re.2024.0246] [Download Citations]
Balanced Linear-Phase Bandpass Filter Equalized with Negative Group Delay Circuit

A novel balanced linear-phase bandpass filter is proposed to achieve differential-mode linear-phase filtering and common-mode suppression characteristics. The balanced linear-phase bandpass filter consists of a proposed compact balanced bandpass filter and negative group delay circuits, in which the circuits are loaded on the ports of the filter as branches. The linear-phase performance is achieved through negative compensation of group delay fluctuations using negative group delay circuit equalization. In order to verify the design method, a 3-order balanced linear-phase bandpass filter is designed, simulated, manufactured, and measured. The results show that the group delay fluctuation of the balanced bandpass filter has been reduced by 89.6 % from 1.110 to 0.115 ns. The minimum common-mode suppression within the passband is 41.4 dB. The proposed balanced bandpass filter has an excellent differential-mode linear-phase transmission and common-mode suppression performances.

  1. ZHANG, Y., WU, Y., WANG, W., et al. High-performance common- and differential-mode reflection less balanced band-pass filter using coupled ring resonator. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2022, vol. 69, no. 3, p. 974–978. DOI: 10.1109/TCSII.2021.3103535
  2. FENG, W., PAN, B., ZHU, H., et al. High performance balanced bandpass filters with wideband common mode suppression. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2021, vol. 68, no. 6, p. 1897–1901. DOI: 10.1109/TCSII.2020.3044092
  3. SHI, J., XUE, Q. Dual-band and wide-stopband single-band balanced bandpass filters with high selectivity and common-mode suppression. IEEE Transactions on Microwave Theory and Techniques, 2010, vol. 58, no. 8, p. 2204–2212. DOI: 10.1109/TMTT.2010.2052959
  4. MYOUNG, S., KIM, Y., YOOK, J. Impact of group delay in RF BPF on impulse radio systems. In IEEE MTT-S International Microwave Symposium Digest. Long Beach (CA, USA), 2005, p. 1891–1894. DOI: 10.1109/MWSYM.2005.1517103
  5. SZYDLOWSKI, L., LESZCZYNSKA, N., MROZOWSKI, M. A linear phase filter in quadruplet topology with frequency-dependent couplings. IEEE Microwave and Wireless Components Letters, 2014, vol. 24, no. 1, p. 32–34. DOI: 10.1109/LMWC.2013.2288178
  6. CHEN, X., HONG, W., CUI, T., et al. Substrate integrated waveguide (SIW) linear phase filter. IEEE Microwave and Wireless Components Letters, 2005, vol. 15, no. 11, p. 787–789. DOI: 10.1109/LMWC.2005.859021
  7. ZHANG, T., YANG, K., LUO, Z. Development of integration HTSC linear phase filter with external equalization. IEEE Transactions on Applied Superconductivity, 2013, vol. 23, no. 4, p. 1–5. DOI: 10.1109/TASC.2013.2256353
  8. YU, T., LI, C., LI, F., et al. A novel quasi-elliptic HTS filter with group-delay equalization using compact quasi-lumped element resonators in VHF band. IEEE Transactions on Applied Superconductivity, 2009, vol. 19, no. 2, p. 69–75. DOI: 10.1109/TASC.2009.2013819
  9. GAO, L., SUN, L., LI, F., et al. 8-GHz narrowband high-temperature superconducting filter with high selectivity and flat group delay. IEEE Transactions on Microwave Theory and Techniques, 2009, vol. 57, no. 7, p. 1767–1773. DOI: 10.1109/TMTT.2009.2022813
  10. ZHANG, T., ZHOU, L., YANG, K., et al. The research of parallel-coupled linear-phase super-conducting filter. Physica C: Super-conductivity and its Applications, 2015, vol. 519, p. 153–158. DOI: 10.1016/j.physc.2015.10.006
  11. SHAO, T., WANG, Z., FANG, S., et al. A group-delay-compensation admittance inverter for full-passband self-equalization of linear-phase band-pass filter. AEU – International Journal of Electronics and Communications, 2020, vol. 123, p. 1–6. DOI: 10.1016/j.aeue.2020.153297
  12. ZHANG, A., XU, J., LIU, Z. A self-equalized linear-phase absorptive BPF using negative-group-delay admittance inverters. IEEE Microwave and Wireless Components Letters, 2022, vol. 32, no. 7, p. 851–854. DOI: 10.1109/LMWC.2022.3156049
  13. SHAO, T., WANG, Z., FANG, S., et al. A full-passband linear-phase band-pass filter equalized with negative group delay circuits. IEEE Access, 2020, vol. 8, p. 43336–43343. DOI: 10.1109/ACCESS.2020.2977100
  14. CHAUDHARY, G., JEONG, Y. Low signal-attenuation negative group-delay network topologies using coupled lines. IEEE Transactions on Microwave Theory and Techniques, 2014, vol. 62, no. 10, p. 2316–2324. DOI: 10.1109/TMTT.2014.2345352
  15. WANG, Z., MENG, Y., FANG, S., et al. Wideband flat negative group delay circuit with improved signal attenuation. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2022, vol. 69, no. 8, p. 3371–3375. DOI: 10.1109/TCSII.2022.3156537
  16. GU, T., CHEN, J., RAVELO. B., et al. Quad-band NGD investigation on crossed resonator interconnect structure. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2022, vol. 69, no. 12, p. 4789–4793. DOI: 10.1109/TCSII.2022.3192288
  17. VELEZ, P., NAQUI, J., FERNANDEZ–PRIETO, A., et al. Differential bandpass filters with common-mode suppression based on stepped impedance resonators (SIRs). In IEEE MTT-S International Microwave Symposium Digest. Seattle (WA, USA), 2013, p. 1–4. DOI: 10.1109/MWSYM.2013.6697380
  18. SUN, M., CHEN, Z., ZUO, T., et al. A high selectivity dual-band bandpass filter using quadruple-mode multi-stub loaded ring resonator (SLRR). International Journal of RF and Microwave Computer-Aided Engineering, 2021, vol. 31, p. 1–5. DOI: 10.1002/mmce.22667
  19. ZHANG, P., LIU, L., CHEN, D., et al. Application of a stub-loaded square ring resonator for wideband bandpass filter design. Electronics, 2020, vol. 9, p. 1–14. DOI: 10.3390/electronics9010176
  20. AHN, H., KIM, B. Small wideband coupled-line ring hybrids with no restriction on coupling power. IEEE Transactions on Micro-wave Theory and Techniques, 2009, vol. 57, no. 7, p. 1806–1817. DOI: 10.1109/TMTT.2009.2022815

Keywords: Balanced bandpass filter, linear-phase, negative group delay, common-mode suppression

Y. Wang, H. Tian, Y. Ji, M. Liu [references] [full-text] [DOI: 10.13164/re.2024.0253] [Download Citations]
Full-automatic Segmentation Algorithm of Brain Tumor Based on RFE-UNet and Hybrid Focal Loss Function

Semantic segmentation of glioma and its subregions plays a critical role in the entirely clinical workflow of brain cancer diagnosis, monitoring, and treatment planning. Recently, automatic tumor segmentation has attracted a lot of attention, especially supervised learning methods based on neural networks, and the popular “U-shaped” network architecture has achieved state-of-the-art performance in many fields of medical image segmentation. Despite the success of these models, the commonly used small convolution kernel can only extract local features, and more global contextual features cannot be learned, resulting in the disappointed performance of modeling long-range information. At the same time, due to the difficulty of obtaining medical image data, and the imbalance of tumor data in which tumor usually occupies a relatively small volume compared with the background, the adverse influence on the training of the model occurs. In this paper, a novel segmentation framework including TensorMixup data augmentation, improved Receptive Field Expansion UNet (RFE-UNet) and hybrid loss function is designed. Specifically, the TensorMixup algorithm in the data preprocessing phase is used to provide more high-quality training data. In the training phase, both a RFE-UNet network and a hybrid loss function are proposed respectively. RFE-UNet network adds Receptive field expansion module based on Dilated convolution in the first three stages of skip connection, which is used to learn more local and global features. In addition, hybrid loss function is mainly composed of focal loss and focal Tversky loss，focal loss increasing the weight of fewer samples and focal Tversky loss focusing on learning the characteristics of samples with incorrect predictions，which is adopted to alleviate data imbalance. The experimental results on the BraTs2019 dataset show that the average Dice value of the proposed algorithm in the intact tumor, tumor core, and enhanced tumor region can reach 91.55%, 89.23%, and 84.16% respectively, which proves the feasibility and effectiveness of using the proposed architecture.

  1. KLEIHUES, P., BURGER, P. C., SCHEITHAUER, B. W. The new WHO classification of brain tumors. Brain Pathology, 1993, vol. 3, no. 3, p. 255–268. DOI: 10.1111/j.1750-3639.1993.tb00752.x
  2. LUNDERVOLD, A. S., LUNDERVOLD, A. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift Fur Medizinische Physik, 2019, vol. 29, no. 2, p. 102–127. DOI: 10.1016/j.zemedi.2018.11.002
  3. PICCIALLI, F., DI SOMMA, V., GIAMPAOLO, F., et al. A survey on deep learning in medicine: Why, how and when? Information Fusion, 2021, vol. 66, no. 1, p. 111–137. DOI: 10.1016/j.inffus.2020.09.006
  4. DONG, H., YANG, G., LIU, F., et al. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In Proceedings of the International Conference on Medical Image Understanding and Analysis. Cham (Switzerland), 2017, p. 506–517. DOI: 10.1007/978-3-319-60964-5_44
  5. MYRONENKO, A. 3D MRI brain tumor segmentation using autoencoder regularization. In Proceedings of the International Conference on Brainlesion-Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Berlin (German), 2018, p. 311–320. DOI: 10.1007/978-3-030-11726-9_28
  6. 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). Vienna (Austria), 2021, p. 1–22. DOI: 10.48550/arXiv.2010.11929
  7. MAGADZA, T., VIRIRI, S. Deep learning for brain tumor segmentation: A survey of state-of-the-art. Journal of Imaging, 2021, vol. 7, no. 2, p. 1–22. DOI: 10.3390/jimaging7020019
  8. LIU, Z., LIN, Y., CAO, Y., et al. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the International Conference on Computer Vision (ICCV). Montreal (Canada), 2021, p. 10012–10022. DOI: 10.1109/ICCV48922.2021.00986
  9. MORENO LOPEZ, M., VENTURA, J. Dilated convolutions for brain tumor segmentation in MRI scans. In Proceedings of the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI). Granada (Spain), 2018, p. 253–262. DOI: 10.1007/978-3-319-75238-9_22
  10. DING, Y., LI, C., YANG, Q. Q., et al. How to improve the deep residual network to segment multi-modal brain tumor images. IEEE Access, 2019, vol. 7, no. 1, p. 152821–152831. DOI: 10.1109/ACCESS.2019.2948120
  11. BALA, S. A., KANT, S. Dense dilated inception network for medical image segmentation. International Journal of Advanced Computer Science and Applications, 2020, vol. 11, no. 11, p. 785–793. DOI: 10.14569/IJACSA.2020.0111195
  12. SHEN, H., ZHANG, J., ZHENG, W. Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation. In Proceedings of the International Conference on Image Processing (ICIP). Beijing (China), 2017, p. 3864–3868. DOI: 10.1109/ICIP.2017.8297006
  13. MCKINLEY, R., MEIER, R., WIEST, R. Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In Proceedings of the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI). Granada (Spain), 2018, p. 456–465. DOI: 10.1007/978-3-030-11726-9_40
  14. ABRAHAM, N., KHAN, N. M. A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. In Proceedings of the 16th International Symposium on Biomedical Imaging (ISBI). Venice (Italy), 2019, p. 683–687. DOI: 10.48550/arXiv.1810.07842
  15. TAGHANAKI, S. A., ZHENG, Y., ZHOU, S. K., et al. Combo loss: Handling input and output imbalance in multiorgan segmentation. Computerized Medical Imaging and Graphics, 2019, vol. 75, no. 1, p. 24–33. DOI: 10.1016/j.compmedimag.2019.04.005
  16. WANG, G. T., LI, W., OURSELIN, S., et al. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In Proceedings of the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI). Quebec (Canada), 2017, p. 178–190. DOI: 10.1007/978-3-319-75238-9_16
  17. YEUNG, M., SALA, E., SCHONLIEB, C. B., et al. Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy. Computers in Biology and Medicine, 2021, vol. 137, no. 1, p. 1–11. DOI: 10.1016/j.compbiomed.2021.104815
  18. YEUNG, M., SALA, E., SCHONLIEB, C. B., et al. Unified focal loss: Generalizing Dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics, 2022, vol. 95, no. 1, p. 1–13. DOI: 10.1016/j.compmedimag.2021.102026
  19. GARCEA, F., SERRA, A., LAMBERTI, F., et al. Data augmentation for medical imaging: A systematic literature review. Computers in Biology and Medicine, 2023, vol. 152, no. 1, p. 1–20. DOI: 10.1016/j.compbiomed.2022.106391
  20. MOK, T. C., CHUNG, A. C. Learning data augmentation for brain tumor segmentation with coarse-to-fine generative adversarial networks. In Proceedings of the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI). Granada (Spain), 2018, p. 70–80. DOI: 10.1007/978-3-030-11723-8_7
  21. NALEPA, J., MARCINKIEWICZ, M., KAWULOK, M. Data augmentation for brain-tumor segmentation: A review. Frontiers in Computational Neuroscience, 2019, vol. 13, no. 1, p. 1–18. DOI: 10.3389/fncom.2019.00083
  22. DVORNIK, N., MAIRAL, J., SCHMID, C. On the importance of visual context for data augmentation in scene understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, vol. 43, no. 6, p. 2014–2028. DOI: 10.1109/TPAMI.2019.2961896
  23. ZHANG, H. Y., CISSE, M., DAUPHIN, Y. N., et al. Mixup: Beyond empirical risk minimization. In Proceedings of the International Conference on Learning Representations (ICLR). Vancouver (Canada), 2018, p. 1–13. DOI: 10.48550/arXiv.1710.09412
  24. WANG, Y., JI, Y. R., XIAO, H. B. A data augmentation method for fully automatic brain tumor segmentation. Computers in Biology and Medicine, 2022, vol. 149, p. 1–10. DOI: 10.1016/j.compbiomed.2022.106039
  25. RICKMANN, A. M., ROY, A. G., SARASUA, I., et al. Project & excite’ modules for segmentation of volumetric medical scans. In Proceedings of the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI). Shenzhen (China), 2019, p. 39–47. DOI: 10.1007/978-3-030-32245-8_5
  26. ALJABRI, M., ALGHAMDI, M. A review on the use of deep learning for medical images segmentation. Neurocomputing, 2022, vol. 506, p. 311–335. DOI: 10.1016/j.neucom.2022.07.070
  27. LIN, T. Y., GOYAL, P., GIRSHICK, R., et al. Focal loss for dense object detection. In Proceedings of the International Conference on Computer Vision (ICCV). Venice (Italy), 2017, p. 2980–2988. DOI: 10.48550/arXiv.1708.02002
  28. 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
  29. 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
  30. DIAKOGIANNIS, F. I., WALDNER, F., CACCETTA, P., et al. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, vol. 162, p. 94–114. DOI: 10.1016/j.isprsjprs.2020.01.013
  31. ZHOU, Z., SIDDIQUEE, M. M. R., TAJBAKHSH, N., et al. UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 2020, vol. 9, no. 6, p. 1856–1867. DOI: 10.1109/TMI.2019.2959609
  32. ISENSEE, F., JAGER, P. F., KOHL, S. A. A., et al. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 2021, vol. 18, no. 2, p. 203 to 211. DOI: 10.1038/s41592-020-01008-z
  33. ISENSEE, F., JAGER, P. F., FULL, P. M., et al. nnU-Net for brain tumor segmentation. In Proceedings of the International Conference on Brainlesion-Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Lima (Peru), 2020, p. 118–132. DOI: 10.1007/978-3-030-72087-2_11
  34. CHEN, J., LU, Y., YU, Q., et al. TransUnet: Transformers make strong encoders for medical image segmentation. In Proceedings of the International Conference on Machine Learning (ICML). Vienna (Austria), 2021, p. 1–13. DOI: 10.48550/arXiv.2102.04306

Keywords: Segmentation, brain tumor, magnetic resonance imaging, dilated convolutions, three-dimensional CNN

S.Yesil, A. O. Yilmaz [references] [full-text] [DOI: 10.13164/re.2024.0265] [Download Citations]
Identification of the Linear Systems of the Wiener Hammerstein RF Power Amplifier Model Using DFT Analysis

This paper presents a novel method for identification of the sub-system parameters of a Wiener-Hammerstein Nonlinear (WHNL) system that is used for modeling RF Power Amplifier characteristics. The proposed method first isolates the overall linear system from the memoryless nonlinearity by exploiting the Bussgang decomposition method. Then, Discrete Fourier Transform (DFT) analysis is used for the estimation of the inner linear system. Finally, the outer linear system parameters are updated based on the inner system estimation. The estimated systems are then used to model the target system for an In-Band-Full-Duplex (IBFD) scenario. Performance of Self-Interferene Cancellation (SIC) has been evaluated under the existence of Signal-of-Interest (SoI). Error Vector Magnitude (EVM) metric of the SoI is used to compare with a Half-Duplex (HD) receiver under various inner linear system parameters. SIC performance has been examined with respect to the changing power levels of the SoI and self-interference signal for various delay and gain values of a practical two-tap inner linear system. The benefit of modeling the inner linear system has been revealed by comparing the SIC performance with Hammerstein nonlinear model. The performance has also been compared to well known black box models such as Generalized Memory Polynomial (GMP) and Artificial Neural Networks (ANN).

  1. DING, L., RAICH, R., ZHOU, G. T. A Hammerstein predistortion linearization design based on the indirect learning architecture. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Orlando (FL, USA), 2002, p. 2689–2692. DOI: 10.1109/ICASSP.2002.5745202
  2. GILABERT, P., MONTORO, G., BERTRAN, E. On the Wiener and Hammerstein models for power amplifier predistortion. In Asia-Pacific Microwave Conference Proceedings. Suzhou (China), 2005, p. 1–4. DOI: 10.1109/APMC.2005.1606491
  3. KOLODZIEJ, K. E., PERRY, B. T., HERD, J. S. In-band fullduplex technology: Techniques and systems survey. IEEE Transactions on Microwave Theory and Techniques, 2019, vol. 67, no. 7, p. 3025–3041. DOI: 10.1109/TMTT.2019.2896561
  4. TAPIO, V., JUNTTI, M. Non-linear self-interference cancelation for full-duplex transceivers based on Hammerstein-Wiener model. IEEE Communications Letters, 2021, vol. 25, no. 11, p. 3684–3688. DOI: 10.1109/LCOMM.2021.3109669
  5. KIBANGOU, A. Y., FAVIER, G. Wiener-Hammerstein systems modeling using diagonal Volterra kernels coefficients. IEEE Signal Processing Letters, 2006, vol. 13, no. 6, p. 381–384. DOI: 10.1109/LSP.2006.871705
  6. SAFARI, N., FEDORENKO, P., KENNEY, J. S., et al. Spline based model for digital predistortion of wide-band signals for high power amplifier linearization. In IEEE/MTT-S International Microwave Symposium. Honolulu (HI, USA), 2007, p. 1441–1444. DOI: 10.1109/MWSYM.2007.380504
  7. TAIJUN, L., BOUMAIZA, S., GHANNOUCHI, F. M. Augmented Hammerstein predistorter for linearization of broad-band wireless transmitters. IEEE Transactions on Microwave Theory and Techniques, 2006, vol. 54, no. 4, p. 1340–1349. DOI: 10.1109/TMTT.2006.871230
  8. MKADEM, F., BOUMAIZA, S. Extended Hammerstein behavioral model using artificial neural networks. IEEE Transactions on Microwave Theory and Techniques, 2009, vol. 57, no. 4, p. 745–751. DOI: 10.1109/TMTT.2009.2015092
  9. PASCUAL CAMPO, P., ANTTILA, L., KORPI, D., et al. Cascaded spline-based models for complex nonlinear systems: Methods and applications. IEEE Transactions on Signal Processing, 2021, vol. 69, p. 370–384. DOI: 10.1109/TSP.2020.3046355
  10. DEMIR, O. T., BJORNSON, E. The Bussgang decomposition of nonlinear systems: Basic theory and MIMO extensions [Lecture notes]. IEEE Signal Processing Magazine, 2021, vol. 38, no. 1, p. 131–136. DOI: 10.1109/MSP.2020.3025538
  11. ENQVIST, M., LJUNG, L. Linear models of nonlinear FIR systems with Gaussian inputs. In 13th IFAC Symposium on System Identification (SYSID). Rotterdam (Netherlands), 2003, p. 1873–1878. DOI: 10.1016/S1474-6670(17)35033-4
  12. SALEH, A. A. M. Frequency-independent and frequency dependent nonlinear models of TWT amplifiers. IEEE Transactions on Communications, 1981, vol. 29, no. 11, p. 1715–1720. DOI: 10.1109/TCOM.1981.1094911
  13. KURT, A., SALMAN, M. B., SARAC, U. B., et al. An adaptive iterative nonlinear interference cancellation in time-varying fullduplex channels. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 2, p. 1862–1878. DOI: 10.1109/TVT.2022.3208766
  14. MORGAN, D. R., MA, Z., KIM, J., et al. A generalized memory polynomial model for digital predistortion of RF power amplifiers. IEEE Transactions on Signal Processing, 2006, vol. 54, no. 10, p. 3852–3860. DOI: 10.1109/TSP.2006.879264
  15. BOUMAIZA, S., MKADEM, F. Wideband RF power amplifier predistortion using real-valued time-delay neural networks. European Microwave Conference (EuMC). Rome (Italy), 2009, p. 1449–1452. DOI: 10.23919/EUMC.2009.5296072

Keywords: Wiener-Hammerstein nonlinear system, digital self-interference cancellation, in-band full-duplex communications

A. K. Chaudhary, M. Manohar [references] [full-text] [DOI: 10.13164/re.2024.0274] [Download Citations]
A Quadruple Band-Notched SWB MIMO Antenna with Enhanced Isolation Using Wiggly Line

A novel quadruple band-notched spatial diversity/MIMO antenna for super wideband (SWB) application is investigated. The proposed antenna comprises two identical tapered semicircular radiators with two microstrip feedlines and a common slotted ground plane (CSGP), contributing a wide impedance bandwidth from 1.88-30 GHz. Further, a wiggly-line-decoupling-structure (WLDS) is introduced among the radiating ports to maximize the average isolation, more than 24 dB. The first band-notched functionality at 2.4 GHz is produced by etching a meandering slot on the CSGP, while the remaining three notch bands at 3.5, 5.5, and 7.5 GHz are obtained by implanting open-ended-semicircular (OES), complementary-split-ring-resonator (CSRR), and elliptical-split-ring-resonator (ESRR) slots in each radiating patch. The designed and fabricated results for the two and four elements are analyzed, which exhibit wideband characteristics, stable radiation pattern, higher efficiency (above 85%), and reasonably high peak gain within the working frequency, excluding the quadruple notched bands. Moreover, other essential parameters such as ECC, DG, CCL, and TARC have also been analyzed, showing the antenna's usefulness for radar imaging, cognitive radio, military, and long-range RF applications.

  1. KOOHESTANI, M., HUSSAIN, A., MOREIRA, A. A., et al. Diversity gain influenced by polarization and spatial diversity techniques in ultrawideband. IEEE Access, 2015, vol. 3, p. 281–286. DOI: 10.1109/ACCESS.2015.2421505
  2. GENG, J., ZIOLKOWSKI, R. W., WANG, K., et al. Dual CP polarization diversity and space diversity antennas enabled by a compact T-shaped feed structure. IEEE Access, 2019, vol. 7, p. 96284–96296. DOI: 10.1109/ACCESS.2019.2925396
  3. LIU, J., ESSELLE, K. P., HAY, S. G., et al. A compact super wideband antenna pair with polarization diversity. IEEE Antennas and Wireless Propagation Letters, 2015, vol. 12, p. 1472–1475. DOI: 10.1109/LAWP.2013.2287500
  4. ULLAH, H.,RAHMAN, S.U., CAO, Q., et al. Design of SWBMIMO antenna with extremely wideband isolation. Electronics, 2020, vol. 9, no. 1, p. 1–12. DOI: 10.3390/electronics9010194
  5. YU, C., YANG, S., CHEN, Y., et al. A super wideband and high isolation MIMO antenna system using a windmill shaped decoupling structure. IEEE Access, 2020, vol. 8, p. 11567–11577. DOI: 10.1109/ACCESS.2020.3004396
  6. SINGHAL, S. Feather-shaped super wideband MIMO antenna. International Journal of Wireless and Microwave Technologies, 2021, vol. 13, no. 1, p. 94–102. DOI: 10.1017/S1759078720000549
  7. CHAUDHARY, A. K., MANOHAR, M. A modified SWB hexagonal fractal spatial diversity antenna with high isolation using Meander line approach. IEEE Access, 2021, vol. 10, p. 10238–10250. DOI: 10.1109/ACCESS.2022.3144850
  8. KANG, L., LI, H., WANG, X., et al. Compact offset microstrip-fed MIMO antenna for band-notched UWB applications. IEEE Antennas and Wireless Propagation Letters, 2015, vol. 14, p. 1754–1757. DOI: 10.1109/LAWP.2015.2422571
  9. CHANDEL, R., GAUTAM, A. K., RAMBABU, K. Tapered fed compact UWB MIMO-diversity antenna with dual band-notched characteristics. IEEE Transactions on Antennas and Propagation, 2018, vol. 66, no. 4, p. 1677–1684. DOI: 10.1109/TAP.2018.2803134
  10. CHEN, Z., ZHOU, W., HONG, J. A Miniaturized MIMO antenna with triple band-notched characteristics for UWB applications. IEEE Access, 2021, vol. 9, p. 63646–63655. DOI: 10.1109/ACCESS.2021.3074511
  11. TANG, Z., WU, X., ZHAN, J., et al. Compact UWBMIMO antenna with high isolation and triple band-notched characteristics. IEEE Access, 2019, vol. 7, p. 2169–3536. DOI: 10.1109/ACCESS.2019.2897170
  12. ABBAS, A., HUSSAIN, N., LEE, J., et al. Triple rectangular notch UWB antenna using EBG and SRR. IEEE Access, 2020, vol. 9, p. 2508–2515. DOI: 10.1109/ACCESS.2020.3047401
  13. PENG, L., WEN, B. J., LI, X. F., et al. CPW fed UWB antenna by EBGs with wide rectangular notched-band. IEEE Access, 2016, vol. 4, p. 9545–9552. DOI: 10.1109/ACCESS.2016.2646338
  14. MODAK, S., KHAN, T., LASKAR, R. H. Penta-notched UWB monopole antenna using EBG structures and fork-shaped slots. Radio Science, 2020, vol. 55, no. 9, p. 1–11. DOI: 10.1029/2019RS006983
  15. LI, Z., YIN, C., ZHU, X. Compact UWB MIMO Vivaldi antenna with dual band-notched characteristics. IEEE Access, 2019, vol. 7, p. 28696–38701. DOI: 10.1109/ACCESS.2019.2906338
  16. KUMAR, S., KUMAR, R., VISHWAKARMA, R. K., et al. An improved compact MIMO antenna for wireless applications with band-notched characteristics. AEU-International Journal of Electronics and Communications, 2018, vol. 90, p. 20–29. DOI: 10.1016/j.aeue.2018.04.008
  17. LUO, S., CHEN, Y., WANG, D., et al. A monopole UWB antenna with sextuple band-notched based on SRRs and U-shaped parasitic strips. AEU-International Journal of Electronics and Communications, 2020, vol. 120, p. 1–9. DOI: 10.1016/j.aeue.2020.153206
  18. SAXENA, G., JAIN, P., AWASTHI, Y. K. High diversity gain super wideband single band-notch MIMO antenna for multiple wireless applications. IET Microwaves, Antennas & Propagation, 2020, vol. 14, no. 1, p. 109–119. DOI: 10.1049/iet-map.2019.0450
  19. MODAK, S., KHAN, T. A slotted UWB-MIMO antenna with quadruple band-notch characteristics using mushroom EBG structure. AEU - International Journal of Electronics and Communications, 2021, vol. 134, p. 1–6. DOI: 10.1016/j.aeue.2021.153673
  20. RAHEJA, D. K., KUMAR, S., KANAUJIA, B. K. Compact quasielliptical-self-complementary four-port super-wideband MIMO antenna with dual band elimination characteristics. AEU - International Journal of Electronics and Communications, 2020, vol. 114, p. 1–10. DOI: 10.1016/j.aeue.2019.153001
  21. RAHEJA, D. K., KANAUJIA, B. K., KUMAR, S. Low profile four port super-wideband multiple-input-multiple-output antenna with triple band rejection characteristics. International Journal of RF and Microwave Computer-Aided Engineering, 2019, vol. 29, no. 10, p. 1–13. DOI: 10.1002/mmce.21831
  22. KUMAR, P., UROOJ, S., ALROWAIS, F. Design of quad-port MIMO/diversity antenna with triple-band elimination characteristics for super-wideband applications. Sensors, 2020, vol. 20, no. 3, p. 1–13. DOI: 10.3390/s20030624
  23. KUMAR, P., UROOJ, S., MALIBARI, A. Design and implementation of quad-element super-wideband MIMO antenna for IoT applications. IEEE Access, 2020, vol. 8, p. 226697–226704. DOI: 10.1109/ACCESS.2020.3045534
  24. ORAIZI, H., REZAEI, B. Dual-banding and miniaturization of planar triangular monopole antenna by inductive and dielectric loadings. IEEE Antennas and Wireless Propagation Letters, 2013, vol. 12, p. 1594–1597. DOI: 10.1109/LAWP.2013.2293615
  25. CHU, Q. X., YANG, Y. Y. A compact ultrawideband antenna with 3.4/5.5 GHz dual band-notched characteristics. IEEE Transactions on Antennas and Propagation, 2008, vol. 56, no. 12, p. 3637–3644. DOI: 10.1109/TAP.2008.2007368
  26. ZHANG, K., WANG, T., CHENG, L. L. Analysis of band-notched UWB printed monopole antennas using a novel segmented structure. Progress In Electromagnetics Research C, 2013, vol. 34, p. 13–27. DOI: 10.2528/PIERC12082401
  27. ZHU, F., GAO, S., HO, A. T., et al. Multiple band-notched UWB antenna with band-rejected elements integrated in the feed line. IEEE Transactions on Antennas and Propagation, 2013, vol. 61, no. 8, p. 3952–3960. DOI: 10.1109/TAP.2013.2260119
  28. WANG, L., DU, Z., YANG, H., et al. Compact UWB MIMO antenna with high isolation using fence-type decoupling structure. IEEE Antennas and Wireless Propagation Letters, 2019, vol. 18, no. 8, p. 1641–1645. DOI: 10.1109/LAWP.2019.2925857
  29. SINGHAL, S., SINGH, A. K. CPW-fed hexagonal Sierpinski super wideband fractal antenna. IET Microwaves, Antennas & Propagation, 2016, vol. 10, no. 15, p. 1701–1707. DOI: 10.1049/iet-map.2016.0154

Keywords: Diversity antenna, quadruple band-notch, SWB, tapered semicircular patch, WLDS