{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:52:54Z","timestamp":1743094374200,"version":"3.40.3"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031348983"},{"type":"electronic","value":"9783031348990"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34899-0_2","type":"book-chapter","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T14:06:14Z","timestamp":1686319574000},"page":"17-32","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Channel Attention-Based Deformable Generative Adversarial Network for\u00a0Underwater Image Enhancement"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4514-5866","authenticated-orcid":false,"given":"Tingkai","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1745-1425","authenticated-orcid":false,"given":"Ning","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangjun","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,10]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Akkaynak, D., Treibitz, T.: A revised underwater image formation model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 6723\u20136732 (2018)","DOI":"10.1109\/CVPR.2018.00703"},{"issue":"6","key":"2_CR2","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1109\/LGRS.2020.2990971","volume":"18","author":"T Alipour-Fard","year":"2020","unstructured":"Alipour-Fard, T., Paoletti, M., Haut, J.M., Arefi, H., Plaza, J., Plaza, A.: Multibranch selective kernel networks for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 18(6), 1089\u20131093 (2020)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Ancuti, C.O., Ancuti, C., Bekaert, P.: Effective single image dehazing by fusion. In: International Conference on Image Processing, Hong Kong, China, pp. 3541\u20133544 (2010)","DOI":"10.1109\/ICIP.2010.5651263"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Ancuti, C., Ancuti, C.O., Haber, T., Bekaert, P.: Enhancing underwater images and videos by fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp. 81\u201388 (2012)","DOI":"10.1109\/CVPR.2012.6247661"},{"key":"2_CR5","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the International Conference on Machine Learning, Sydney, Australia, pp. 214\u2013223 (2017)"},{"key":"2_CR6","unstructured":"Berman, D., Treibitz, T., Avidan, S.: Diving into haze-lines: color restoration of underwater images. In: Proceedings of the British Machine Vision Conference, London, UK, vol. 1, pp. 1\u201312 (2017)"},{"issue":"6","key":"2_CR7","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679\u2013698 (1986)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.neunet.2021.08.014","volume":"144","author":"T Chen","year":"2021","unstructured":"Chen, T., Wang, N., Wang, R., Zhao, H., Zhang, G.: One-stage CNN detector-based benthonic organisms detection with limited training dataset. Neural Netw. 144, 247\u2013259 (2021)","journal-title":"Neural Netw."},{"issue":"4","key":"2_CR9","doi-asserted-by":"publisher","first-page":"1756","DOI":"10.1109\/TIP.2011.2179666","volume":"21","author":"JY Chiang","year":"2011","unstructured":"Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756\u20131769 (2011)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Chiang, J.Y., Chen, Y.C., Chen, Y.F.: Underwater image enhancement: using wavelength compensation and image dehazing (WCID). In: International Conference on Advanced Concepts for Intelligent Vision Systems, Ghent, Belgium, pp. 372\u2013383 (2011)","DOI":"10.1007\/978-3-642-23687-7_34"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Drews, P., Nascimento, E., Moraes, F., Botelho, S., Campos, M.: Transmission estimation in underwater single images. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, Sydney, Australia, pp. 825\u2013830 (2013)","DOI":"10.1109\/ICCVW.2013.113"},{"issue":"7","key":"2_CR13","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1016\/j.visres.2010.09.006","volume":"51","author":"DH Ebner","year":"2011","unstructured":"Ebner, D.H.: Color constancy. Vis. Res. 51(7), 674\u2013700 (2011)","journal-title":"Vis. Res."},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Fabbri, C., Islam, M.J., Sattar, J.: Enhancing underwater imagery using generative adversarial networks. In: International Conference on Robotics and Automation, Brisbane, QLD, Australia, pp. 7159\u20137165 (2018)","DOI":"10.1109\/ICRA.2018.8460552"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Feifei, S., Xuemeng, Z., Guoyu, W.: An approach for underwater image denoising via wavelet decomposition and high-pass filter. In: International Conference on Intelligent Computation Technology and Automation, Shenzhen, China, vol. 2, pp. 417\u2013420 (2011)","DOI":"10.1109\/ICICTA.2011.388"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Fu, X., Fan, Z., Ling, M., Huang, Y., Ding, X.: Two-step approach for single underwater image enhancement. In: International Symposium on Intelligent Signal Processing and Communication Systems, Xiamen, China, pp. 789\u2013794 (2017)","DOI":"10.1109\/ISPACS.2017.8266583"},{"key":"2_CR17","first-page":"1","volume":"70","author":"W Gao","year":"2021","unstructured":"Gao, W., Zhang, L., Huang, W., Min, F., He, J., Song, A.: Deep neural networks for sensor-based human activity recognition using selective kernel convolution. IEEE Trans. Instrum. Meas. 70, 1\u201313 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Harris, C., Stephens, M., et al.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, vol. 15, pp. 10\u20135244. Citeseer (1988)","DOI":"10.5244\/C.2.23"},{"key":"2_CR19","unstructured":"He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Miami Beach, FL, USA, pp. 1956\u20131963 (2009)"},{"issue":"12","key":"2_CR20","first-page":"2341","volume":"33","author":"K He","year":"2010","unstructured":"He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341\u20132353 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"2_CR21","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/48.50695","volume":"15","author":"JS Jaffe","year":"1990","unstructured":"Jaffe, J.S.: Computer modeling and the design of optimal underwater imaging systems. IEEE J. Oceanic Eng. 15(2), 101\u2013111 (1990)","journal-title":"IEEE J. Oceanic Eng."},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Jia, D., Ge, Y.: Underwater image de-noising algorithm based on nonsubsampled contourlet transform and total variation. In: International Conference on Computer Science and Information Processing, Xi\u2019an, China, pp. 76\u201380 (2012)","DOI":"10.1109\/CSIP.2012.6308799"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Jian, S., Wen, W.: Study on underwater image denoising algorithm based on wavelet transform. In: International Conference on Control Engineering and Artificial Intelligence, Kuala Lumpur, Malaysia, vol. 806, p. 012006 (2017)","DOI":"10.1088\/1742-6596\/806\/1\/012006"},{"issue":"11","key":"2_CR24","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TVCG.2019.2921336","volume":"26","author":"Y Jing","year":"2019","unstructured":"Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., Song, M.: Neural style transfer: a review. IEEE Trans. Vis. Comput. Graph. 26(11), 3365\u20133385 (2019)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"issue":"1","key":"2_CR25","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13640-016-0104-y","volume":"2016","author":"S Lee","year":"2016","unstructured":"Lee, S., Yun, S., Nam, J.H., Won, C.S., Jung, S.W.: A review on dark channel prior based image dehazing algorithms. EURASIP J. Image Video Process. 2016(1), 4\u201326 (2016)","journal-title":"EURASIP J. Image Video Process."},{"key":"2_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107038","volume":"98","author":"C Li","year":"2020","unstructured":"Li, C., Anwar, S., Porikli, F.: Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn. 98, 107038 (2020)","journal-title":"Pattern Recogn."},{"issue":"3","key":"2_CR27","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/LSP.2018.2792050","volume":"25","author":"C Li","year":"2018","unstructured":"Li, C., Guo, J., Guo, C.: Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal. Proc. Lett. 25(3), 323\u2013327 (2018)","journal-title":"IEEE Signal. Proc. Lett."},{"issue":"1","key":"2_CR28","first-page":"387","volume":"3","author":"J Li","year":"2017","unstructured":"Li, J., Skinner, K.A., Eustice, R.M., Johnson-Roberson, M.: WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot. Autom. Lett. 3(1), 387\u2013394 (2017)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 510\u2013519 (2019)","DOI":"10.1109\/CVPR.2019.00060"},{"issue":"2","key":"2_CR30","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis."},{"issue":"5","key":"2_CR31","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1364\/JOSAA.32.000886","volume":"32","author":"H Lu","year":"2015","unstructured":"Lu, H., Li, Y., Zhang, L., Serikawa, S.: Contrast enhancement for images in turbid water. J. Opt. Soc. Am. A-Opt. Image Sci. 32(5), 886\u2013893 (2015)","journal-title":"J. Opt. Soc. Am. A-Opt. Image Sci."},{"issue":"4","key":"2_CR32","doi-asserted-by":"publisher","first-page":"140","DOI":"10.5670\/oceanog.2007.14","volume":"20","author":"M Ludvigsen","year":"2007","unstructured":"Ludvigsen, M., Sortland, B., Johnsen, G., Singh, H.: Applications of geo-referenced underwater photo mosaics in marine biology and archaeology. Oceanography 20(4), 140\u2013149 (2007)","journal-title":"Oceanography"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 2794\u20132802 (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"2_CR34","unstructured":"McGlamery, B.: A computer model for underwater camera systems. In: Ocean Optics, Monterey, CA, USA, vol. 208, pp. 221\u2013231 (1980)"},{"key":"2_CR35","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014). https:\/\/arxiv.org\/abs\/1411.1784"},{"issue":"3","key":"2_CR36","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/JOE.2015.2469915","volume":"41","author":"K Panetta","year":"2015","unstructured":"Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Ocean. Eng. 41(3), 541\u2013551 (2015)","journal-title":"IEEE J. Ocean. Eng."},{"issue":"4","key":"2_CR37","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1007\/s11045-017-0533-5","volume":"29","author":"R Priyadharsini","year":"2018","unstructured":"Priyadharsini, R., Sharmila, T.S., Rajendran, V.: A wavelet transform based contrast enhancement method for underwater acoustic images. Multidimension. Syst. Signal Process. 29(4), 1845\u20131859 (2018)","journal-title":"Multidimension. Syst. Signal Process."},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Singh, R., Biswas, M.: Adaptive histogram equalization based fusion technique for hazy underwater image enhancement. In: IEEE International Conference on Computational Intelligence and Computing Research, Chennai, India, pp. 1\u20135 (2016)","DOI":"10.1109\/ICCIC.2016.7919711"},{"issue":"3","key":"2_CR39","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1109\/TCST.2014.2359880","volume":"23","author":"N Wang","year":"2014","unstructured":"Wang, N., Er, M.J.: Self-constructing adaptive robust fuzzy neural tracking control of surface vehicles with uncertainties and unknown disturbances. IEEE Trans. Control Syst. Technol. 23(3), 991\u20131002 (2014)","journal-title":"IEEE Trans. Control Syst. Technol."},{"issue":"5","key":"2_CR40","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1109\/TCST.2015.2510587","volume":"24","author":"N Wang","year":"2016","unstructured":"Wang, N., Er, M.J.: Direct adaptive fuzzy tracking control of marine vehicles with fully unknown parametric dynamics and uncertainties. IEEE Trans. Control Syst. Technol. 24(5), 1845\u20131852 (2016)","journal-title":"IEEE Trans. Control Syst. Technol."},{"issue":"7","key":"2_CR41","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1109\/TCYB.2015.2451116","volume":"46","author":"N Wang","year":"2015","unstructured":"Wang, N., Er, M.J., Sun, J.C., Liu, Y.C.: Adaptive robust online constructive fuzzy control of a complex surface vehicle system. IEEE Trans. Cybern. 46(7), 1511\u20131523 (2015)","journal-title":"IEEE Trans. Cybern."},{"issue":"3","key":"2_CR42","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TMECH.2019.2906395","volume":"24","author":"N Wang","year":"2019","unstructured":"Wang, N., Karimi, H.R., Li, H., Su, S.F.: Accurate trajectory tracking of disturbed surface vehicles: a finite-time control approach. IEEE\/ASME Trans. Mechatron. 24(3), 1064\u20131074 (2019)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"issue":"4","key":"2_CR43","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1109\/TCST.2015.2496585","volume":"24","author":"N Wang","year":"2015","unstructured":"Wang, N., Qian, C., Sun, J.C., Liu, Y.C.: Adaptive robust finite-time trajectory tracking control of fully actuated marine surface vehicles. IEEE Trans. Control Syst. Technol. 24(4), 1454\u20131462 (2015)","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"2_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2020.104458","volume":"118","author":"N Wang","year":"2022","unstructured":"Wang, N., Wang, Y., Er, M.J.: Review on deep learning techniques for marine object recognition: architectures and algorithms. Control. Eng. Pract. 118, 104458 (2022)","journal-title":"Control. Eng. Pract."},{"key":"2_CR45","doi-asserted-by":"publisher","first-page":"87884","DOI":"10.1109\/ACCESS.2020.2992749","volume":"8","author":"W Wang","year":"2020","unstructured":"Wang, W., Wu, X., Yuan, X., Gao, Z.: An experiment-based review of low-light image enhancement methods. IEEE Access 8, 87884\u201387917 (2020)","journal-title":"IEEE Access"},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: video restoration with enhanced deformable convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA, pp. 1954\u20131963 (2019)","DOI":"10.1109\/CVPRW.2019.00247"},{"issue":"10","key":"2_CR47","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","volume":"43","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Chen, J., Hoi, S.C.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3365\u20133387 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR48","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/978-1-4471-0765-1_53","volume-title":"Robotics Research","author":"L Whitcomb","year":"2000","unstructured":"Whitcomb, L., Yoerger, D.R., Singh, H., Howland, J.: Advances in underwater robot vehicles for deep ocean exploration: navigation, control, and survey operations. In: Hollerbach, J.M., Koditschek, D.E. (eds.) Robotics Research, pp. 439\u2013448. Springer, London (2000). https:\/\/doi.org\/10.1007\/978-1-4471-0765-1_53"},{"key":"2_CR49","doi-asserted-by":"crossref","unstructured":"Yang, H., Chen, P., Huang, C., Zhuang, Y., Shiau, Y.: Low complexity underwater image enhancement based on dark channel prior. In: International Conference on Innovations in Bio-inspired Computing and Applications, Shenzhen, China, pp. 17\u201320 (2011)","DOI":"10.1109\/IBICA.2011.9"},{"issue":"12","key":"2_CR50","doi-asserted-by":"publisher","first-page":"6062","DOI":"10.1109\/TIP.2015.2491020","volume":"24","author":"M Yang","year":"2015","unstructured":"Yang, M., Sowmya, A.: An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24(12), 6062\u20136071 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2017.03.029","volume":"245","author":"S Zhang","year":"2017","unstructured":"Zhang, S., Wang, T., Dong, J., Yu, H.: Underwater image enhancement via extended multi-scale Retinex. Neurocomputing 245, 1\u20139 (2017)","journal-title":"Neurocomputing"},{"key":"2_CR52","unstructured":"Zhao, J., Mathieu, M., LeCun, Y.: Energy-based generative adversarial network (2016). https:\/\/arxiv.org\/abs\/1609.03126"},{"key":"2_CR53","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"2_CR54","doi-asserted-by":"publisher","unstructured":"Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems, pp. 474\u2013485 (1994). https:\/\/doi.org\/10.1016\/B978-0-12-336156-1.50061-6","DOI":"10.1016\/B978-0-12-336156-1.50061-6"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Sensor Systems and Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34899-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T14:14:35Z","timestamp":1686320075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34899-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031348983","9783031348990"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34899-0_2","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"S-Cube","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Sensor Systems and Software","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dalian","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scube2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}