{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:36:22Z","timestamp":1743096982181,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030805678"},{"type":"electronic","value":"9783030805685"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-80568-5_15","type":"book-chapter","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T17:04:53Z","timestamp":1624467893000},"page":"178-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Neural Networks for Indoor Geomagnetic Field Fingerprinting with Regression Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5504-3014","authenticated-orcid":false,"given":"Mahdi","family":"Abid","sequence":"first","affiliation":[]},{"given":"Gr\u00e9goire","family":"Lefebvre","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,1]]},"reference":[{"issue":"1","key":"15_CR1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/SURV.2009.090103","volume":"11","author":"Y Gu","year":"2009","unstructured":"Gu, Y., Lo, A., Niemegeers, I.: A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutorials 11(1), 13\u201332 (2009)","journal-title":"IEEE Commun. Surv. Tutorials"},{"issue":"11","key":"15_CR2","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1109\/TNSRE.2017.2705285","volume":"25","author":"M Abid","year":"2017","unstructured":"Abid, M., Renaudin, V., Aoustin, Y., Le-Carpentier, E., Robert, T.: Walking gait step length asymmetry induced by handheld device. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 2075\u20132083 (2017)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Romanovas, M., et al.: A study on indoor pedestrian localization algorithms with foot-mounted sensors. In: 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1\u201310. IEEE, Sydney, Australia (2012)","DOI":"10.1109\/IPIN.2012.6418886"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Bae, H.J., Choi, L.: Large-scale indoor positioning using geomagnetic field with deep neural networks. In: 2019 IEEE International Conference on Communications (ICC), pp. 1\u20136. IEEE, Shanghai, China (2019)","DOI":"10.1109\/ICC.2019.8761118"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, Z., Mao, S.: DeepML: deep LSTM for indoor localization with smartphone magnetic and light sensors. In: 2018 IEEE International Conference on Communications (ICC), pp. 1\u20136. IEEE, Kansas City, MO, USA (2018)","DOI":"10.1109\/ICC.2018.8422562"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Lee, N., Han, D.: Magnetic indoor positioning system using deep neural network. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1\u20138. IEEE, Sapporo, Japan (2017)","DOI":"10.1109\/IPIN.2017.8115887"},{"issue":"20","key":"15_CR7","doi-asserted-by":"publisher","first-page":"7472","DOI":"10.1109\/JSEN.2016.2600099","volume":"16","author":"Y Ma","year":"2016","unstructured":"Ma, Y., Dou, Z., Jiang, Q., Hou, Z.: Basmag: an optimized HMM-based localization system using backward sequences matching algorithm exploiting geomagnetic information. IEEE Sens. J. 16(20), 7472\u20137482 (2016)","journal-title":"IEEE Sens. J."},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Ezzati Khatab, Z., Moghtadaiee, V., Ghorashi, S.A.: A fingerprint-based technique for indoor localization using fuzzy least squares support vector machine. In: 2017 Iranian Conference on Electrical Engineering (ICEE), pp. 1944\u20131949. IEEE, Tehran, Iran (2017)","DOI":"10.1109\/IranianCEE.2017.7985373"},{"issue":"5","key":"15_CR9","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.3390\/s18051598","volume":"18","author":"N Lee","year":"2018","unstructured":"Lee, N., Ahn, S., Han, D.: AMID: accurate magnetic indoor localization using deep learning. Sens. (Basel) 18(5), 1598 (2018)","journal-title":"Sens. (Basel)"},{"issue":"1","key":"15_CR10","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1080\/17489725.2020.1856428","volume":"15","author":"M Abid","year":"2021","unstructured":"Abid, M., Lefebvre, G.: Improving indoor geomagnetic field fingerprinting using recurrence plot-based convolutional neural networks. J. Location Based Serv. 15(1), 61\u201387 (2021)","journal-title":"J. Location Based Serv."},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Al-homayani, F., Mahoor, M.: Improved indoor geomagnetic field fingerprinting for smartwatch localization using deep learning. In: 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1\u20138. IEEE, Nantes, France (2018)","DOI":"10.1109\/IPIN.2018.8626558"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Barsocchi, P., Crivello, A., La Rosa, D., Palumbo, F.: Multisource and multivariate dataset for indoor localization methods based on WLAN and geo-magnetic field fingerprinting. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1\u20138. IEEE, Alcala de Henares, Spain (2016)","DOI":"10.1109\/IPIN.2016.7743678"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Jang, H.J., Shin, J.M., Choi, L.: Geomagnetic field based indoor localization using recurrent neural networks. In: GLOBECOM 2017\u20132017 IEEE Global Communications Conference, pp. 1\u20136. IEEE, Singapore (2017)","DOI":"10.1109\/GLOCOM.2017.8254556"},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv:1409.1259 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Bae, H.J., Choi, L.: Large-scale indoor positioning using geomagnetic field with deep neural networks. In: 2019 IEEE International Conference on Communications (ICC 2019), pp. 1\u20136. IEEE, Shanghai (2019)","DOI":"10.1109\/ICC.2019.8761118"},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"33943","DOI":"10.1109\/ACCESS.2019.2902573","volume":"7","author":"B Bhattarai","year":"2019","unstructured":"Bhattarai, B., Yadav, R.K., Gang, H., Pyun, J.: Geomagnetic field based indoor landmark classification using deep learning. IEEE Access 7, 33943\u201333956 (2019)","journal-title":"IEEE Access"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, Z., Mao, S.: DeepML: Deep LSTM for indoor localization with smartphone magnetic and light sensors. In: 2018 IEEE International Conference on Communications (ICC), pp. 1\u20136. IEEE, Kansas City, MO (2018)","DOI":"10.1109\/ICC.2018.8422562"},{"key":"15_CR19","unstructured":"Olah, C.: Understanding LSTM networks (2015). http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/"},{"key":"15_CR20","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. In: 31st Conference on Neural Information Processing Systems (NIPS). arXiv:1706.02515, Long Beach, CA, USA (2017)"},{"key":"15_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv:1706.03762 (2017)"}],"container-title":["Proceedings of the International Neural Networks Society","Proceedings of the 22nd Engineering Applications of Neural Networks Conference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80568-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T07:06:29Z","timestamp":1656399989000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80568-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030805678","9783030805685"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80568-5_15","relation":{},"ISSN":["2661-8141","2661-815X"],"issn-type":[{"type":"print","value":"2661-8141"},{"type":"electronic","value":"2661-815X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Crete","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.eann2021.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}