{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T05:27:51Z","timestamp":1766813271656,"version":"3.48.0"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T00:00:00Z","timestamp":1766448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To enhance the monitoring and conservation efforts for China\u2019s Class II endangered species, specifically the wild giant salamander and its ecosystems, this study addresses the urgent need to counteract the rapid decline of its wild population caused by habitat loss and insufficient surveillance. We present an innovative localization system based on passive Ultra-High-Frequency Radio Frequency Identification (UHF RFID) technology, employing a Double-Transform (D-Tr) methodology that integrates an enhanced 3D LANDMARC algorithm with GAIN generative adversarial networks. This system effectively reconstructs missing Received Signal Strength Indicator (RSSI) data due to environmental barriers by applying a log-distance path loss model. The D-Tr framework simultaneously generates RSSI sequences alongside their first-order differential characteristics, allowing for a comprehensive analysis of spatiotemporal signal relationships. Field tests conducted in the Hubei Xianfeng Zhongjian River Giant Salamander National Nature Reserve reveal that the positioning error consistently remains within 10 cm, with average accuracy improvements of 20.075%, 15.331%, and 12.925% along the X, Y, and Z axes, respectively, compared to traditional time-series models such as long short-term memory (LSTM) and gated recurrent unit (GRU). This system, designed to investigate the behavioral patterns and movement paths of farmed giant salamanders, achieves centimeter-level tracking of their cave-dwelling activities. It provides essential technical support for quantitatively assessing their daily activity patterns, habitat choices, and population trends, thereby promoting a shift from passive oversight to proactive monitoring in the conservation of endangered species.<\/jats:p>","DOI":"10.3390\/s26010106","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T14:27:47Z","timestamp":1766500067000},"page":"106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Study on the Localization Technology for Giant Salamanders Using Passive UHF RFID and Incomplete D-Tr Measurement Data"],"prefix":"10.3390","volume":"26","author":[{"given":"Nanqing","family":"Sun","sequence":"first","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Didi","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyao","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1126\/science.abb2375","article-title":"Giant salamanders: Farmed yet endangered","volume":"367","author":"Lu","year":"2020","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1038\/d41586-022-00564-y","article-title":"Protector of giant salamander","volume":"603","author":"Tay","year":"2022","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"R590","DOI":"10.1016\/j.cub.2018.04.004","article-title":"The Chinese giant salamander exemplifies the hidden extinction of cryptic species","volume":"28","author":"Yan","year":"2018","journal-title":"Curr. Biol."},{"key":"ref_4","first-page":"1508","article-title":"Experiment on suitable hydrodynamic conditions for habitat activities of wild Andrias davidianus and construction of suitability curves","volume":"55","author":"Ding","year":"2024","journal-title":"J. Hydraul. Eng."},{"key":"ref_5","unstructured":"Cao, J., Deng, Z., Jiang, W., Li, H., and Wang, C. (2025). Fish Diversity in Zhangjiajie Giant Salamander National Nature Reserve, Hunan Province. J. Hydroecol., 1\u201318."},{"key":"ref_6","first-page":"186","article-title":"Progress and prospects of biodiversity monitoring of amphibians and reptiles in China","volume":"31","author":"Li","year":"2023","journal-title":"Biodivers. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3879","DOI":"10.1002\/ece3.5014","article-title":"Phylogeographic patterns and conservation implications of the endangered Chinese giant salamander","volume":"9","author":"Liang","year":"2019","journal-title":"Ecol. Evol."},{"key":"ref_8","first-page":"87","article-title":"Differences in respiratory behavior between male and female giant salamanders in the pre-breeding period","volume":"42","author":"Tian","year":"2025","journal-title":"J. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1017\/S0030605304000341","article-title":"The decline of the Chinese giant salamander Andrias davidianus and implications for its conservation","volume":"38","author":"Wang","year":"2004","journal-title":"Oryx"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bloch, V., and Pastell, M. (2020). Monitoring of cow location in a barn by an open-source, low-cost, low-energy bluetooth tag system. Sensors, 20.","DOI":"10.3390\/s20143841"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, H., Liu, X., Liu, L., Qin, B., Pan, R., and Pang, S. (2025). UHF RFID Sensing for Dynamic Tag Detection and Behavior Recognition: A Multi-Feature Analysis and Dual-Path Residual Network Approach. Sensors, 25.","DOI":"10.3390\/s25175540"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cheng, C.-F., and Liao, B.-Y. (2025). Coverage-Conflict-Aware RFID Reader Placement with Range Adjustment for Complete Tag Coverage in IIoT. Sensors, 25.","DOI":"10.3390\/s25237400"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lorenzo-L\u00f3pez, J., and Juan-Ll\u00e1cer, L. (2025). Received Signal Strength Indicator Measurements and Simulations for Radio Frequency Identification Tag Identification and Location in Beehives. Sensors, 25.","DOI":"10.3390\/s25113372"},{"key":"ref_14","first-page":"110","article-title":"Research on giant salamander detection model based on improved YOLO v11n-EWL","volume":"56","author":"Tian","year":"2025","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lasantha, L., Ray, B., and Karmakar, N. (2025). Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs. Sensors, 25.","DOI":"10.3390\/s25061653"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Song, C., and Wu, Z. (2025). A Reading Range- and Frequency-Reconfigurable Antenna for Near-Field and Far-Field UHF RFID Applications. Sensors, 25.","DOI":"10.3390\/s25020408"},{"key":"ref_17","first-page":"56","article-title":"Development of on-rail state perception system for orchard monorail transporter","volume":"36","author":"Wei","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pekgor, M., Arablouei, R., Nikzad, M., and Masood, S. (2024). Displacement Estimation via 3D-Printed RFID Sensors for Structural Health Monitoring: Leveraging Machine Learning and Photoluminescence to Overcome Data Gaps. Sensors, 24.","DOI":"10.3390\/s24041233"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8813","DOI":"10.1021\/acs.macromol.3c00776","article-title":"Variation of aliphatic diisocyanates in bio-based TPUs","volume":"56","author":"Forman","year":"2023","journal-title":"Macromolecules"},{"key":"ref_20","first-page":"190","article-title":"Research on consistency testing method for UHF RFID tags","volume":"46","author":"Lou","year":"2025","journal-title":"Packag. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.wneu.2020.01.172","article-title":"Application of improved LANDMARC indoor positioning algorithm in neurology medical care monitoring and positioning system","volume":"138","author":"Huang","year":"2020","journal-title":"World Neurosurg."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5731","DOI":"10.1007\/s10586-017-1485-0","article-title":"The RFID data clustering algorithm for improving indoor network positioning based on LANDMARC technology","volume":"22","author":"Cui","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1550147720907831","DOI":"10.1177\/1550147720907831","article-title":"LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise\u2013genetic algorithm\u2013radial basis function neural network","volume":"16","author":"Ren","year":"2020","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_24","unstructured":"Yoon, J., Jordon, J., and Schaar, M. (2018, January 10\u201315). Gain: Missing data imputation using generative adversarial nets. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105053","DOI":"10.1016\/j.autcon.2023.105053","article-title":"Architectural layout generation using a graph-constrained conditional Generative Adversarial Network (GAN)","volume":"155","author":"Aalaei","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1080\/13658816.2024.2381146","article-title":"STAGE: A spatiotemporal-knowledge enhanced multi-task generative adversarial network (GAN) for trajectory generation","volume":"39","author":"Cao","year":"2025","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ma, W., Zhang, E., and Xia, X. (2024). Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation. Sensors, 24.","DOI":"10.3390\/s24041185"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, F., Kong, Y., and Yang, C. (2024). Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting. Sensors, 24.","DOI":"10.3390\/s24041178"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"19200","DOI":"10.1109\/TNNLS.2025.3584369","article-title":"Long Short-Term Financial Time Series Forecasting Based on Residual Multiscale TCN Sparse Expert Network and Informer","volume":"36","author":"Bao","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"126530","DOI":"10.1016\/j.eswa.2025.126530","article-title":"A transformer-based double-order RFID indoor positioning system","volume":"271","author":"Wu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"264","DOI":"10.3758\/s13428-020-01407-2","article-title":"t-Test and ANOVA for data with ceiling and\/or floor effects","volume":"53","author":"Liu","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105183","DOI":"10.1016\/j.jmva.2023.105183","article-title":"Finite sample t-tests for high-dimensional means","volume":"196","author":"Li","year":"2023","journal-title":"J. Multivar. 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