{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T18:14:06Z","timestamp":1769192046038,"version":"3.49.0"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Center for Advances in Reliability and Safety (CAiRS) admitted under AIR@InnoHK Research Cluster"},{"name":"Department of EEE, The Hong Kong Polytechnic University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tim.2025.3551824","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T17:33:25Z","timestamp":1742232805000},"page":"1-16","source":"Crossref","is-referenced-by-count":2,"title":["High-Dimensional Radio Frequency Fingerprint Synthesis for Indoor Positioning"],"prefix":"10.1109","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0616-9029","authenticated-orcid":false,"given":"Zhongyuan","family":"Lyu","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0244-0491","authenticated-orcid":false,"given":"Tom T. L.","family":"Chan","sequence":"additional","affiliation":[{"name":"Centre for Advances in Reliability and Safety, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2543-703X","authenticated-orcid":false,"given":"Gary C. M.","family":"Leung","sequence":"additional","affiliation":[{"name":"Blue Pin (HK) Ltd., Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1473-094X","authenticated-orcid":false,"given":"Y. L.","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3891-1363","authenticated-orcid":false,"given":"Daniel P. K.","family":"Lun","sequence":"additional","affiliation":[{"name":"Centre for Advances in Reliability and Safety, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1126-8662","authenticated-orcid":false,"given":"Michael G.","family":"Pecht","sequence":"additional","affiliation":[{"name":"Centre for Advances in Reliability and Safety, Hong Kong, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3163391"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2019.2948058"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SENSORS47087.2021.9639241"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2015.2430281"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3149048"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2558659"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3349623.3355475"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2016.2622799"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3196748"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CSNDSP54353.2022.9907908"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2019.8767421"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC49053.2021.9417246"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN51156.2021.9662590"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685548"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref19","article-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","author":"Chen","year":"2014","journal-title":"arXiv:1412.7062"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647261"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3174600"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2979413"},{"key":"ref23","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref24","article-title":"Understanding diffusion models: A unified perspective","author":"Luo","year":"2022","journal-title":"arXiv:2208.11970"},{"key":"ref25","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sohl-Dickstein"},{"key":"ref26","article-title":"Denoising diffusion implicit models","author":"Song","year":"2020","journal-title":"arXiv:2010.02502"},{"key":"ref27","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref29","first-page":"21696","article-title":"Variational diffusion models","volume-title":"Proc. NIPS","volume":"34","author":"Kingma"},{"key":"ref30","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020","journal-title":"arXiv:2011.13456"},{"key":"ref31","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. NIPS","volume":"34","author":"Dhariwal"},{"key":"ref32","article-title":"Classifier-free diffusion guidance","author":"Ho","year":"2022","journal-title":"arXiv:2207.12598"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00091"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00686"},{"key":"ref35","article-title":"PAIR-diffusion: A comprehensive multimodal object-level image editor","author":"Goel","year":"2023","journal-title":"arXiv:2303.17546"},{"key":"ref36","first-page":"1","article-title":"A framework for multiple-instance learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"10","author":"Maron"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-4742-3_23"},{"key":"ref38","article-title":"Quantitatively evaluating GANs with divergences proposed for training","author":"Jiwoong Im","year":"2018","journal-title":"arXiv:1803.01045"},{"key":"ref39","article-title":"Revisiting classifier two-sample tests","author":"Lopez-Paz","year":"2016","journal-title":"arXiv:1610.06545"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.06.034"},{"key":"ref41","first-page":"687","article-title":"Model adaptation with synthetic and real data for semantic dense foggy scene understanding","volume-title":"Proc. Eur. Conf. Comput. Vis. (ECCV)","author":"Sakaridis"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460982"},{"key":"ref43","first-page":"1","article-title":"Deep learning based wireless localization for indoor navigation","volume-title":"Proc. 26th Annu. Int. Conf. Mobile Comput. Netw.","author":"Ayyalasomayajula"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2024.3381077"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2013.6555311"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2012.214"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3181592"},{"key":"ref48","first-page":"1","article-title":"Generative modeling by estimating gradients of the data distribution","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Song"},{"key":"ref49","first-page":"26565","article-title":"Elucidating the design space of diffusion-based generative models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Karras"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref51","first-page":"1","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Heusel"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/19\/10764799\/10929707.pdf?arnumber=10929707","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T03:24:20Z","timestamp":1743218660000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10929707\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/tim.2025.3551824","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}