{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T04:03:36Z","timestamp":1743912216450,"version":"3.40.3"},"reference-count":54,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2025,4,1]]},"DOI":"10.1587\/transinf.2024edp7117","type":"journal-article","created":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T22:10:39Z","timestamp":1730067039000},"page":"349-359","source":"Crossref","is-referenced-by-count":0,"title":["An Anchor-Free Siamese Tracker with Multi-Attention and Corner Detection Mechanism"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Xiaokang","family":"JIN","sequence":"first","affiliation":[{"name":"Jinhua Advanced Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benben","family":"HUANG","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"SHENG","sequence":"additional","affiliation":[{"name":"Jinhua Advanced Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"WU","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Jinhua Polytechnic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] H. Sheng, K. Cheng, X.K. Jin, T. Han, X.L. Jiang, and C.C. Dong, \u201cAttention-based encoder-decoder network for depth estimation from color-coded light fields,\u201d AIP Advances, vol.13, no.3, 035118, 2023. 10.1063\/5.0140530","DOI":"10.1063\/5.0140530"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] J. Zhang, Y. He, W. Feng, J. Wang, and N.N. Xiong, \u201cLearning background-aware and spatial-temporal regularized correlation filters for visual tracking,\u201d Applied Intelligence, vol.53, no.7, pp.7697-7712, 2023. 10.1007\/s10489-022-03868-8","DOI":"10.1007\/s10489-022-03868-8"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] J. Zhang, Y. Liu, H. Liu, J. Wang, and Y. Zhang, \u201cDistractor-aware visual tracking using hierarchical correlation filters adaptive selection,\u201d Applied Intelligence, vol.52, no.6, pp.6129-6147, 2022. 10.1007\/s10489-021-02694-8","DOI":"10.1007\/s10489-021-02694-8"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] J. Zhang, Y. Liu, H. Liu, and J. Wang, \u201cLearning local-global multiple correlation filters for robust visual track-ing with Kalman filter redetection,\u201d Sensors, vol.21, no.4, 1129, 2021. 10.3390\/s21041129","DOI":"10.3390\/s21041129"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] J. Zhang, X. Jin, J. Sun, J. Wang, and A.K. Sangaiah, \u201cSpatial and semantic convolutional features for robust visual object tracking,\u201d Multimedia Tools and Applications, vol.79, no.21-22, pp.15095-15115, 2020. 10.1007\/s11042-018-6562-8","DOI":"10.1007\/s11042-018-6562-8"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] J. Zhang, W. Feng, T. Yuan, J. Wang, and A.K. Sangaiah, \u201cSCSTCF: Spatial-channel selection and temporal regularized correlation filters for visual tracking\u201d Applied Soft Computing, vol.118, 108485, 2022. 10.1016\/j.asoc.2022.108485","DOI":"10.1016\/j.asoc.2022.108485"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] T. Han, Z. Zhang, M. Ren, C. Dong, X. Jiang, and Q. Zhuang, \u201cSpeech emotion recognition based on deep residual shrinkage network,\u201d Electronics, vol.12, no.11, 2512, 2023. 10.3390\/electronics12112512","DOI":"10.3390\/electronics12112512"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] L. Bertinetto, J. Valmadre, J. Henriques, A. Vedaldi, and P. Torr, \u201cFully-convolutional siamese networks for object tracking,\u201d Proc. 2016 European Conference on Computer Vision, vol.9914, pp.850-865, 2016. 10.1007\/978-3-319-48881-3_56","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] J. Valmadre, L. Bertinetto, J. Henriques, A. Vedaldi, and P.H.S. Torr, \u201cEnd-to-End Representation Learning for Correlation Filter Based Tracking,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.5000-5008, 2017. 10.1109\/cvpr.2017.531","DOI":"10.1109\/CVPR.2017.531"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] A. He, C. Luo, X. Tian, and W. Zeng, \u201cA twofold siamese network for real-time object tracking,\u201d Proc. 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp.4834-4843, 2018. 10.1109\/cvpr.2018.00508","DOI":"10.1109\/CVPR.2018.00508"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu, \u201cHigh performance visual tracking with Siamese region proposal network,\u201d Proc. 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp.8971-8980, 2018. 10.1109\/cvpr.2018.00935","DOI":"10.1109\/CVPR.2018.00935"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] S. Ren, K. He, R. Girshick, and J. Sun, \u201cFaster R-CNN: Towards real-time object detection with region proposal networks,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.39, no.6, pp.1137-1149, 2017. 10.1109\/tpami.2016.2577031","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] Z. Zhu, Q. Wang, B. Li, W. Wu, J. Yan, and W. Hu, \u201cDistractor-aware siamese networks for visual object tracking,\u201d Proc. 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp.101-117, 2018.","DOI":"10.1007\/978-3-030-01240-3_7"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Z. Zhang and H. Peng, \u201cDeeper and wider siamese networks for real-time visual tracking,\u201d Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp.4586-4595, 2019. 10.1109\/cvpr.2019.00472","DOI":"10.1109\/CVPR.2019.00472"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] B. Li, W. Wu, Q. Wang, F. Zhang, J. Xing, and J. Yan, \u201cSiamRPN++: Evolution of siamese visual tracking with very deep networks,\u201d Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.4282-4291, 2019. 10.1109\/cvpr.2019.00441","DOI":"10.1109\/CVPR.2019.00441"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] Z. Tian, C. Shen, H. Chen, and T. He, \u201cFCOS: Fully convolutional one-stage object detection,\u201d Proc. 2019 IEEE International Conference on Computer Vision, pp.9626-9635, 2019. 10.1109\/iccv.2019.00972","DOI":"10.1109\/ICCV.2019.00972"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] H. Law and J. Deng, \u201cCornerNet: Detecting objects as paired keypoints,\u201d Proc. 2018 European Conference on Computer Vision, pp.734-750, 2018. 10.1007\/978-3-030-01264-9_45","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] D. Guo, J. Wang, Y. Cui, Z. Wang, and S. Chen, \u201cSiamCAR: Siamese fully convolutional classification and regression for visual tracking,\u201d Proc. 2020 IEEE Conference on Computer Vision and Pattern Recognition, pp.6268-6276, 2020. 10.1109\/cvpr42600.2020.00630","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] F. Du, P. Liu, W. Zhao, and X. Tang, \u201cCorrelation-guided attention for corner detection based visual tracking,\u201d Proc. 2020 IEEE Conference on Computer Vision and Pattern Recognition, pp.6835-6844, 2020. 10.1109\/cvpr42600.2020.00687","DOI":"10.1109\/CVPR42600.2020.00687"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] J. Hu, L. Shen, and G. Sun, \u201cSqueeze-and-excitation networks,\u201d Proc. 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp.7132-7141, 2018. 10.1109\/cvpr.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] A.G. Roy, N. Nacab, and C. Wachinger, \u201cConcurrent spatial and channel \u2018squeeze & excitation\u2019 in fully convolutional networks,\u201d Proc. 2018 International Conference on Medical Image Computing and Computer Assisted Intervention, pp.421-429, 2018. 10.1007\/978-3-030-00928-1_48","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] E. Real, J. Shlens, S. Mazzocchi, X. Pan, and V. Vanhoucke, \u201cYouTube-BoundingBoxes: A large high-precision human-annotated data set for object detection in video,\u201d Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.5296-5305, 2017. 10.1109\/cvpr.2017.789","DOI":"10.1109\/CVPR.2017.789"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] L. Huang, X. Zhao, and K. Huang, \u201cGOT-10k: A large high-diversity benchmark for generic object tracking in the wild,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.43, no.5, pp.1562-1577, 2021. 10.1109\/tpami.2019.2957464","DOI":"10.1109\/TPAMI.2019.2957464"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and L. Fei-Fei, \u201cImagenet large scale visual recognition challenge,\u201d International Journal of Computer Vision, vol.115, no.3, pp.211-252, 2015. 10.1007\/s11263-015-0816-y","DOI":"10.1007\/s11263-015-0816-y"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll\u00e1r, and C.L. Zitnick, \u201cMicrosoft COCO: Common objects in context,\u201d Proc. 2014 European Conference on Computer Vision, pp.740-755, 2014. 10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] Y. Wu, J. Lim, and M.-H. Yang, \u201cObject tracking benchmark,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.9, pp.1834-1848, 2015. 10.1109\/tpami.2014.2388226","DOI":"10.1109\/TPAMI.2014.2388226"},{"key":"28","unstructured":"[28] M. Kristan, A. Leonardis, J. Matas, M. Felsberg, R. Pfugfelder, L.C. Zajc, T. Vojir, G. Bhat, A. Lukezic, A. Eldesokey, G. Fernandez, et al., \u201cThe visual object tracking VOT2016 challenge results,\u201d Proc. 2016 European Conference on Computer Vision, pp.777-823, 2016."},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] M. Mueller, N. Smith, and B. Ghanem, \u201cA benchmark and simulator for UAV tracking,\u201d Proc. 2016 European Conference on Computer Vision, pp.445-461, 2016. 10.1007\/978-3-319-46448-0_27","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"30","unstructured":"[30] A. Krizhevsky, I. Sutskever, and G. Hinton, \u201cImageNet classification with deep convolutional neural networks,\u201d Proc. 2012 International Conference on Neural Information Processing Systems, vol.25, pp.1106-1114, 2012."},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] Q. Wang, L. Zhang, L. Bertinetto, W. Hu, and P.H.S. Torr, \u201cFast online object tracking and segmentation: a unifying approach,\u201d Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp.1328-1338, 2019. 10.1109\/cvpr.2019.00142","DOI":"10.1109\/CVPR.2019.00142"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, \u201cYou only look once: unifed, real-time object detection,\u201d Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.779-788, 2016. 10.1109\/cvpr.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang, \u201cUnitbox: An advanced object detection network,\u201d ACM International Conference on Multimedia, pp.516-520, 2016. 10.1145\/2964284.2967274","DOI":"10.1145\/2964284.2967274"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, \u201cCenterNet: Keypoint triplets for object detection,\u201d Proc. 2019 IEEE International Conference on Computer Vision, pp.6568-6577, 2019. 10.1109\/iccv.2019.00667","DOI":"10.1109\/ICCV.2019.00667"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] S. Woo, J. Park, J.-Y. Lee, and I.S. Kweon, \u201cCBAM: Convolutional block attention module,\u201d Proc. 2018 European Conference on Computer Vision, pp.3-19, 2018. 10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] Y. Yu, Y. Xiong, W. Huang, and M.R. Scott, \u201cDeformable siamese attention networks for visual object tracking,\u201d Proc. 2020 IEEE Conference on Computer Vision and Pattern Recognition, pp.6727-6736, 2020. 10.1109\/cvpr42600.2020.00676","DOI":"10.1109\/CVPR42600.2020.00676"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] W. Zhou, Y. Liu, N. Wang, D. Liang, and B. Peng, \u201cEfficient Siamese model for visual object tracking with attention-based fusion modules,\u201d Signal Image and Video Processing, vol.18, no.11, pp.7801-7810, 2024. 10.1007\/s11760-024-03429-1","DOI":"10.1007\/s11760-024-03429-1"},{"key":"38","doi-asserted-by":"publisher","unstructured":"[38] Z. Xin, J. Yu, X. He, Y. Song, and H. Li, \u201cSiamRAAN: Siamese residual attentional aggregation network for visual object tracking,\u201d Neural Processing Letters, vol.56, no.2, p.98, 2024. 10.1007\/s11063-024-11556-6","DOI":"10.1007\/s11063-024-11556-6"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] P. Voigtlaender, J. Luiten, P.H.S. Torr, and B. Leibe, \u201cSiam R-CNN: Visual tracking by re-detection,\u201d Proc. 2020 IEEE Conference on Computer Vision and Pattern Recognition, pp.6577-6587, 2020. 10.1109\/cvpr42600.2020.00661","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] X. Li, C. Ma, B. Wu, Z. He, and M.-H. Yang, \u201cTarget-aware deep tracking,\u201d Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp.1369-1378, 2019. 10.1109\/cvpr.2019.00146","DOI":"10.1109\/CVPR.2019.00146"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] F. Li, C. Tian, W. Zuo, L. Zhang, and M.-H. Yang, \u201cLearning spatial-temporal regularized correlation filters for visual tracking,\u201d Proc. 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp.4904-4913, 2018. 10.1109\/cvpr.2018.00515","DOI":"10.1109\/CVPR.2018.00515"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] M. Danelljan, G. Bhat, F. Khan, and M. Felsberg, \u201cEco: Efficient convolution operators for tracking,\u201d Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.6638-6646, 2017. 10.1109\/cvpr.2017.733","DOI":"10.1109\/CVPR.2017.733"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] J. Gao, T. Zhang, and C. Xu, \u201cGraph convolutional tracking,\u201d Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp.4644-4654, 2019. 10.1109\/cvpr.2019.00478","DOI":"10.1109\/CVPR.2019.00478"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[44] M. Danelljan, G. H\u00e4ger, F.S. Khan, and M. Felsberg, \u201cLearning spatially regularized correlation filters for visual tracking,\u201d Proc. 2015 IEEE International Conference on Computer Vision, pp.4310-4318, 2015. 10.1109\/iccv.2015.490","DOI":"10.1109\/ICCV.2015.490"},{"key":"45","doi-asserted-by":"crossref","unstructured":"[45] H.K. Galoogahi, A. Fagg, and S. Lucey, \u201cLearning background-aware correlation filters for visual tracking,\u201d Proc. 2017 IEEE International Conference on Computer Vision, pp.1144-1152, 2017. 10.1109\/iccv.2017.129","DOI":"10.1109\/ICCV.2017.129"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] N. Wang, Y. Song, C. Ma, W. Zhou, W. Liu, and H. Li, \u201cUnsupervised deep tracking,\u201d Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp.1308-1317, 2019. 10.1109\/cvpr.2019.00140","DOI":"10.1109\/CVPR.2019.00140"},{"key":"47","doi-asserted-by":"crossref","unstructured":"[47] Y. Li and J. Zhu, \u201cA scale adaptive kernel correlation filter tracker with feature integration,\u201d Proc. 2014 European Conference on Computer Vision, vol.8926, pp.254-265, 2014. 10.1007\/978-3-319-16181-5_18","DOI":"10.1007\/978-3-319-16181-5_18"},{"key":"48","doi-asserted-by":"crossref","unstructured":"[48] J. Zhang, S. Ma, and S. Sclaroff, \u201cMEEM: Robust tracking via multiple experts using entropy minimization,\u201d Proc. 2014 European Conference on Computer Vision, pp.188-203, 2014. 10.1007\/978-3-319-10599-4_13","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"49","doi-asserted-by":"crossref","unstructured":"[49] Z. Hong, Z. Chen, C. Wang, X. Mei, D. Prokhorov, and D. Tao, \u201cMulti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking,\u201d Proc. 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp.749-758, 2015. 10.1109\/cvpr.2015.7298675","DOI":"10.1109\/CVPR.2015.7298675"},{"key":"50","doi-asserted-by":"crossref","unstructured":"[50] M. Danelljan, G. H\u00e4ger, F. Khan, and M. Felsberg, \u201cAccurate scale estimation for robust visual tracking,\u201d Proc. 2014 British Machine Vision Conference, pp.1-11, 2014. 10.5244\/c.28.65","DOI":"10.5244\/C.28.65"},{"key":"51","doi-asserted-by":"publisher","unstructured":"[51] J.F. Henriques, R. Caseiro, P. Martins, and J. Batista, \u201cHigh-speed tracking with kernelized correlation filters,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.3, pp.583-596, 2015. 10.1109\/tpami.2014.2345390","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"52","doi-asserted-by":"crossref","unstructured":"[52] J. Zheng, C. Ma, H. Peng, and X. Yang, \u201cLearning to track objects from unlabeled videos,\u201d Proc. 2021 IEEE International Conference on Computer Vision, pp.13546-13555, 2021. 10.1109\/iccv48922.2021.01329","DOI":"10.1109\/ICCV48922.2021.01329"},{"key":"53","doi-asserted-by":"crossref","unstructured":"[53] K. Dai, D. Wang, H. Lu, C. Sun, and J. Li, \u201cVisual tracking via adaptive spatially-regularized correlation filters,\u201d Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp.4665-4674, 2019. 10.1109\/cvpr.2019.00480","DOI":"10.1109\/CVPR.2019.00480"},{"key":"54","doi-asserted-by":"publisher","unstructured":"[54] X.-F. Zhu, X.-J. Wu, T. Xu, Z.-H. Feng, and J. Kittler, \u201cRobust visual object tracking via adaptive attribute-aware discriminative correlation filters,\u201d IEEE Trans. Multimedia, vol.24, pp.301-312, 2022. 10.1109\/tmm.2021.3050073","DOI":"10.1109\/TMM.2021.3050073"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/4\/E108.D_2024EDP7117\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T03:25:17Z","timestamp":1743823517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/4\/E108.D_2024EDP7117\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,1]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024edp7117","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2025,4,1]]},"article-number":"2024EDP7117"}}