{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T08:23:19Z","timestamp":1787732599615,"version":"build-2784847793"},"reference-count":58,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Fire Protection Technology for Industry and Public Building, Ministry of Emergency Management","award":["2024KLIB02"],"award-info":[{"award-number":["2024KLIB02"]}]},{"award":["2024KLIB02"],"award-info":[{"award-number":["2024KLIB02"]}],"id":[{"id":"https:\/\/ror.org\/02e6b1s72","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cross-view geo-localization (CVGL) between unmanned aerial vehicle (UAV) imagery and satellite imagery is a key technique for autonomous UAV navigation in Global Navigation Satellite System (GNSS)-denied environments. However, most existing methods rely on energy-intensive Artificial Neural Networks (ANNs), making them difficult to deploy on resource-constrained edge computing platforms. Spiking Neural Networks (SNNs) provide a promising alternative for energy-efficient inference, but their application to CVGL still faces two challenges that remain insufficiently addressed. First, the isotropic computation used by existing SNN backbones is mismatched with the directional characteristics of spike activations. Spike activations tend to form oriented aggregation patterns along elongated geographic structures, and isotropic computation can therefore dilute directional signals. Second, the limited representational capacity of SNNs further increases the sensitivity during training optimization. However, the standard triplet loss adopts a static weighting strategy and assigns the same weight to all triplets that violate the margin constraint, which is unfavorable for learning from hard negatives. To address these challenges, we propose a framework with two core contributions. At the feature extraction level, the Directional Adaptive Convolution Module (DACM) processes spike feature maps by sequentially performing horizontal strip convolution and vertical strip convolution, thereby capturing a more complete geometric structure of directional spike clusters. At the training supervision level, we propose a Dual-dimensional Progressive Reweighting (DPR) loss, which jointly characterizes sample difficulty from pairwise difficulty and positive-pair quality difficulty. A learnable fusion parameter is used to adaptively balance these two types of difficulty information. Experimental results on the University-1652 and SUES-200 benchmarks show that the proposed framework, when equipped with the same representation learning head as its ANN counterparts, achieves competitive and, in many settings, superior performance. In terms of energy efficiency, its estimated theoretical energy consumption is over 8.8\u00d7 lower than that of published ANN methods under their original configurations. Under a more rigorous matched ANN control that shares the identical architecture, the estimated energy is reduced from 29.84 mJ to 6.36 mJ, an approximately 4.7\u00d7 reduction obtained at a cost of only 2.29 percentage points in R@1.<\/jats:p>","DOI":"10.3390\/s26175372","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:01:07Z","timestamp":1787670067000},"page":"5372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Directional Spike Feature Learning with Progressive Reweighting for Energy-Efficient Cross-View Geo-Localization"],"prefix":"10.3390","volume":"26","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Fire Protection Technology for Industry and Public Building, Ministry of Emergency Management, Tianjin 300381, China"},{"name":"Tianjin Fire Science and Technology Research Institute of MEM, Tianjin 300381, China"},{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0719-0896","authenticated-orcid":false,"given":"Yidan","family":"Su","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5501-9011","authenticated-orcid":false,"given":"Yimeng","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"},{"name":"School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Belongie, S., and Hays, J. (2013). Cross-view image geolocalization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2013.120"},{"key":"ref_2","unstructured":"Yu, Q., Wang, C., Cetiner, B., Yu, S.X., Mckenna, F., Taciroglu, E., and Law, K.H. (2019). Building information modeling and classification by visual learning at a city scale. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shi, Y., Yu, X., Campbell, D., and Li, H. (2020). Where am i looking at? joint location and orientation estimation by cross-view matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00412"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1007\/s11263-022-01615-7","article-title":"Investigating the role of image retrieval for visual localization: An exhaustive benchmark","volume":"130","author":"Humenberger","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, A., Hu, H., Mirowski, P., and Farajtabar, M. (2019). Cross-view policy learning for street navigation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2019.00819"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fervers, F., Bullinger, S., Bodensteiner, C., Arens, M., and Stiefelhagen, R. (2023). Uncertainty-aware vision-based metric cross-view geolocalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52729.2023.02071"},{"key":"ref_7","first-page":"5620815","article-title":"Query-Driven Feature Learning for Cross-View Geo-Localization","volume":"63","author":"Hu","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6004405","DOI":"10.1109\/LGRS.2025.3543949","article-title":"Learning robust feature representation for cross-view image geo-localization","volume":"22","author":"Gan","year":"2025","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/TCSVT.2021.3061265","article-title":"Each part matters: Local patterns facilitate cross-view geo-localization","volume":"32","author":"Wang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_10","first-page":"4700912","article-title":"TransFG: A cross-view geo-localization of satellite and UAVs imagery pipeline using transformer-based feature aggregation and gradient guidance","volume":"62","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deuser, F., Habel, K., and Oswald, N. (2023). Sample4geo: Hard negative sampling for cross-view geo-localisation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51070.2023.01545"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","article-title":"Networks of spiking neurons: The third generation of neural network models","volume":"10","author":"Maass","year":"1997","journal-title":"Neural Netw."},{"key":"ref_13","unstructured":"Li, Y., Deng, S., Dong, X., Gong, R., and Gu, S. (2021). A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration. Proceedings of the International Conference on Machine Learning (ICML), Proceedings of Machine Learning Research (PMLR)."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fan, Y., Zhang, W., Liu, C., Li, M., and Lu, W. (2024). Sfod: Spiking fusion object detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.01627"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"129977","DOI":"10.1016\/j.eswa.2025.129977","article-title":"Spiking Depth: Depth estimation from sparse events with spiking neural networks","volume":"299","author":"Liu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1038\/s41586-019-1677-2","article-title":"Towards spike-based machine intelligence with neuromorphic computing","volume":"575","author":"Roy","year":"2019","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sun, Q., Lu, C., Chen, W., Wei, W., Wang, J., Zhang, J., Liu, X., Ye, Y., Yang, Y., and Zhang, M. (2025). Temporal-coded Spiking Transformer. Proceedings of the 33rd ACM International Conference on Multimedia, Association for Computing Machinery.","DOI":"10.1145\/3746027.3754545"},{"key":"ref_18","unstructured":"Fan, Y., Liu, C., Li, M., Liu, D., Liu, Y., and Zhang, W. (2025). SpikeDet: Better firing patterns for accurate and energy-efficient object detection with spiking neuron networks. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Maboudi, M., Amini, J., Hahn, M., and Saati, M. (2016). Road network extraction from VHR satellite images using context aware object feature integration and tensor voting. Remote Sens., 8.","DOI":"10.3390\/rs8080637"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10243","DOI":"10.1109\/TGRS.2020.3034011","article-title":"DiResNet: Direction-aware residual network for road extraction in VHR remote sensing images","volume":"59","author":"Ding","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","unstructured":"Hu, S., Li, Y., Li, Y., and Zhu, Y. (2025). Seeing the Unseen: Mask-Driven Positional Encoding and Strip-Convolution Context Modeling for Cross-View Object Geo-Localization. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hoffer, E., and Ailon, N. (2015). Deep metric learning using triplet network. Proceedings of the International Workshop on Similarity-Based Pattern Recognition, Springer.","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"ref_23","unstructured":"Zheng, G., Guan, J., Xie, M., Zhao, X., Fan, C., Zhang, S., and Feng, P. (2025). Dual-level Progressive Hardness-Aware Reweighting for Cross-View Geo-Localization. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wei, Y., and Yang, Y. (2020). University-1652: A multi-view multi-source benchmark for drone-based geo-localization. Proceedings of the 28th ACM International Conference on Multimedia, Association for Computing Machinery.","DOI":"10.1145\/3394171.3413896"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4825","DOI":"10.1109\/TCSVT.2023.3249204","article-title":"SUES-200: A multi-height multi-scene cross-view image benchmark across drone and satellite","volume":"33","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhu, S., Shah, M., and Chen, C. (2022). Transgeo: Transformer is all you need for cross-view image geo-localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.00123"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ye, J., Lv, Z., Li, W., Yu, J., Yang, H., Zhong, H., and He, C. (2024). Cross-view image geo-localization with Panorama-BEV Co-Retrieval Network. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-72913-3_5"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, S., Nguyen, C., Liu, J., Zhang, Y., Muthu, S., Maken, F.A., Zhang, K., and Li, H. (2024). View from above: Orthogonal-view aware cross-view localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.01406"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mi, L., Xu, C., Castillo-Navarro, J., Montariol, S., Yang, W., Bosselut, A., and Tuia, D. (2024). Congeo: Robust cross-view geo-localization across ground view variations. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-72630-9_13"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Zhu, Y. (2024). Benchmarking the robustness of cross-view geo-localization models. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-73021-4_3"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chu, M., Zheng, Z., Ji, W., Wang, T., and Chua, T.S. (2024). Towards natural language-guided drones: GeoText-1652 benchmark with spatial relation matching. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-73247-8_13"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ji, Y., He, B., Tan, Z., and Wu, L. (2025). MMGeo: Multimodal Compositional Geo-Localization for UAVs. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51701.2025.02334"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ju, H., Huang, S., Liu, S., and Zheng, Z. (2025). Video2bev: Transforming drone videos to bevs for video-based geo-localization. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51701.2025.02513"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xia, Z., and Alahi, A. (2025). FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.00596"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., Han, X., Huang, W., Dong, D., and Scott, M.R. (2019). Multi-similarity loss with general pair weighting for deep metric learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00516"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, Y., Cheng, C., Zhang, Y., Zhang, C., Zheng, L., Wang, Z., and Wei, Y. (2020). Circle loss: A unified perspective of pair similarity optimization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00643"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Han, H., Zheng, Q., Dai, G., Luo, M., and Wang, J. (2024). Learning to rematch mismatched pairs for robust cross-modal retrieval. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.02519"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, S., He, C., Liu, X., Zhou, J.T., Peng, X., and Hu, P. (2025). Learning with noisy triplet correspondence for composed image retrieval. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.01828"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rao, J., Lin, H., Chen, L., Xie, J., Zheng, S., and Yang, Y. (2025). Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.02864"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","article-title":"Surrogate gradient learning in spiking neural networks","volume":"36","author":"Neftci","year":"2019","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_42","unstructured":"Zhou, Z., Zhu, Y., He, C., Wang, Y., Yan, S., Tian, Y., and Yuan, L. (2022). Spikformer: When spiking neural network meets transformer. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"64043","DOI":"10.52202\/075280-2798","article-title":"Spike-driven transformer","volume":"Volume 36","author":"Yao","year":"2023","journal-title":"Proceedings of the Advances on Neural Information Processing Systems (NeurIPS)"},{"key":"ref_44","unstructured":"Yao, M., Hu, J., Hu, T., Xu, Y., Zhou, Z., Tian, Y., Xu, B., and Li, G. (2024). Spike-driven transformer v2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shi, X., Hao, Z., and Yu, Z. (2024). Spikingresformer: Bridging resnet and vision transformer in spiking neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00536"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13074","DOI":"10.52202\/079017-0416","article-title":"Qkformer: Hierarchical spiking transformer using qk attention","volume":"37","author":"Zhou","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","unstructured":"Qiu, X., Zhang, M., Zhang, J., Wei, W., Cao, H., Guo, J., Zhu, R.J., Shan, Y., Yang, Y., and Li, H. (2025). Quantized spike-driven transformer. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Wang, S., Zhang, D., Wei, W., Shan, Y., Liu, X., Jiang, Y., and Zhang, M. (2025). Rethinking spiking self-attention mechanism: Implementing a-XNOR similarity calculation in spiking transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.00512"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Guo, Y., Liu, X., Chen, Y., Peng, W., Zhang, Y., and Ma, Z. (2025). Spiking Transformer: Introducing Accurate Addition-Only Spiking Self-Attention for Transformer. Proceedings of the Computer Vision and Pattern Recognition Conference, IEEE.","DOI":"10.1109\/CVPR52734.2025.02272"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lee, D., Li, Y., Kim, Y., Xiao, S., and Panda, P. (2025). Spiking transformer with spatial-temporal attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.01302"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Huang, Y., Yu, Y., Zhu, Z., Tang, J., Yu, Z., and Jin, Y. (2025). SpiLiFormer: Enhancing spiking transformers with lateral inhibition. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51701.2025.02275"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1109\/TPAMI.2025.3530246","article-title":"Scaling spike-driven transformer with efficient spike firing approximation training","volume":"47","author":"Yao","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"130310","DOI":"10.1016\/j.neucom.2025.130310","article-title":"SU-YOLO: Spiking neural network for efficient underwater object detection","volume":"644","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zheng, N., Du, Y., Xia, H., and Liang, Z. (2025). Signal-SGN: A Spiking Graph Convolutional Network for Skeleton Action Recognition via Learning Temporal-Frequency Dynamics. Proceedings of the 33rd ACM International Conference on Multimedia, Association for Computing Machinery.","DOI":"10.1145\/3746027.3755246"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4376","DOI":"10.1109\/TCSVT.2021.3135013","article-title":"A transformer-based feature segmentation and region alignment method for UAV-view geo-localization","volume":"32","author":"Dai","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"11810","DOI":"10.1109\/TCSVT.2024.3424196","article-title":"SDPL: Shifting-dense partition learning for uav-view geo-localization","volume":"34","author":"Chen","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/TIP.2024.3513157","article-title":"A self-adaptive feature extraction method for aerial-view geo-localization","volume":"34","author":"Lin","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_58","first-page":"21056","article-title":"Deep residual learning in spiking neural networks","volume":"34","author":"Fang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T08:15:04Z","timestamp":1787732104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,25]]},"references-count":58,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175372"],"URL":"https:\/\/doi.org\/10.3390\/s26175372","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,25]]}}}