{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T11:51:29Z","timestamp":1726055489978},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322151"},{"type":"electronic","value":"9783030322168"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32216-8_57","type":"book-chapter","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T16:14:33Z","timestamp":1571847273000},"page":"588-597","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Simulated Traffic Sign Classification Using Cross-Connected Convolution Neural Networks Based on Compressive Sensing Domain"],"prefix":"10.1007","author":[{"given":"Jiping","family":"Xiong","sequence":"first","affiliation":[]},{"given":"Lingfeng","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Ye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"key":"57_CR1","doi-asserted-by":"crossref","unstructured":"Chhabra, R., Verma, S., Rama Krishna, C.: A survey on driver behavior detection techniques for intelligent transportation systems. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence. IEEE (2017)","DOI":"10.1109\/CONFLUENCE.2017.7943120"},{"issue":"99","key":"57_CR2","first-page":"1","volume":"PP","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Huo, L., Lv, Z., Song, H., Qin, W.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. PP(99), 1\u201315 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2015","key":"57_CR3","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.jnca.2015.06.010","volume":"57","author":"D Jiang","year":"2015","unstructured":"Jiang, D., Xu, Z., Wang, W., Wang, Y., Han, Y.: A collaborative multi-hop routing algorithm for maximum achievable rate. J. Netw. Comput. Appl. 57(2015), 182\u2013191 (2015)","journal-title":"J. Netw. Comput. Appl."},{"issue":"19","key":"57_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.comnet.2015.04.003","volume":"84","author":"D Jiang","year":"2015","unstructured":"Jiang, D., Wang, Y., Yao, C., Han, Y.: An effective dynamic spectrum access algorithm for multi-hop cognitive wireless networks. Comput. Netw. 84(19), 1\u201316 (2015)","journal-title":"Comput. Netw."},{"key":"57_CR5","doi-asserted-by":"crossref","unstructured":"Haloi, M., Jayagopi, D.B.: A robust lane detection and departure warning system. In: IEEE Intelligent Vehicles Symposium (IV), pp. 126\u2013131 (2015)","DOI":"10.1109\/IVS.2015.7225674"},{"issue":"2015","key":"57_CR6","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jss.2015.03.006","volume":"104","author":"D Jiang","year":"2015","unstructured":"Jiang, D., Xu, Z., Li, W., Chen, Z.: Network coding-based energy-efficient multicast routing algorithm for multi-hop wireless networks. J. Syst. Softw. 104(2015), 152\u2013165 (2015)","journal-title":"J. Syst. Softw."},{"issue":"2017","key":"57_CR7","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.neucom.2016.07.056","volume":"220","author":"D Jiang","year":"2017","unstructured":"Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 220(2017), 160\u2013169 (2017)","journal-title":"Neurocomputing"},{"issue":"2017","key":"57_CR8","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.neucom.2016.05.102","volume":"220","author":"D Jiang","year":"2017","unstructured":"Jiang, D., Wang, Y., Han, Y., et al.: Maximum connectivity-based channel allocation algorithm in cognitive wireless networks for medical applications. Neurocomputing 220(2017), 41\u201351 (2017)","journal-title":"Neurocomputing"},{"issue":"5","key":"57_CR9","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1109\/JCN.2016.000101","volume":"18","author":"D Jiang","year":"2016","unstructured":"Jiang, D., Xu, Z., Li, W., et al.: An energy-efficient multicast algorithm with maximum network throughput in multi-hop wireless networks. J. Commun. Netw. 18(5), 713\u2013724 (2016)","journal-title":"J. Commun. Netw."},{"issue":"6","key":"57_CR10","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/JIOT.2016.2613111","volume":"3","author":"D Jiang","year":"2016","unstructured":"Jiang, D., Zhang, P., Lv, Z., et al.: Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things J. 3(6), 1437\u20131447 (2016)","journal-title":"IEEE Internet of Things J."},{"key":"57_CR11","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1007\/978-3-642-12101-2_28","volume-title":"Intelligent Information and Database Systems","author":"Tam T. Le","year":"2010","unstructured":"Le, T.T., Tran, S.T., Mita, S., Nguyen, T.D.: Real time traffic sign detection using color and shape-based features. In: Intelligent Information and Database Systems, pp. 268-278. IEEE (2010)"},{"key":"57_CR12","unstructured":"Haloi, M.: A novel pLSA based Traffic Signs Classification System. https:\/\/arxiv.org\/ . Accessed 2015"},{"issue":"3","key":"57_CR13","first-page":"638","volume":"22","author":"ZH Zhao","year":"2010","unstructured":"Zhao, Z.H., Yang, S.P., Ma, Z.Q.: The study of license character recognition based on the convolution neural network LeNet-5. J. Syst. Simul. 22(3), 638\u2013641 (2010)","journal-title":"J. Syst. Simul."},{"issue":"2","key":"57_CR14","first-page":"23","volume":"43","author":"SS Xu","year":"2013","unstructured":"Xu, S.S., Liu, Y.A., Xu, S.: Wood defect recognition based on the convolution neural network. J. Shandong Univ.: Eng. Sci. 43(2), 23\u201328 (2013)","journal-title":"J. Shandong Univ.: Eng. Sci."},{"key":"57_CR15","unstructured":"Mrinal, H.: Traffic Sign Classification Using Deep Inception Based Convolutional Networks. https:\/\/arxiv.org\/ . Accessed 2016"},{"key":"57_CR16","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1109\/TITS.2015.2482461","volume":"17","author":"Y Yang","year":"2016","unstructured":"Yang, Y., Luo, H., Xu, H., Wu, F.: Towards real-time traffic sign detection and classification. IEEE Trans. Intell. Transp. Syst. 17, 2022\u20132031 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"57_CR17","doi-asserted-by":"crossref","unstructured":"Zhong, S.H., Liu, Y., Ren, F.F., Zhang, J.H., Ren, T.W.: Video saliency detection via dynamic consistent spatio-temporal attention modelling. In: Proceedings of the 2013 AAAI Conference on Artificial Intelligence, pp. 1063\u20131069. AAAI, Bellevue (2013)","DOI":"10.1609\/aaai.v27i1.8642"},{"key":"57_CR18","doi-asserted-by":"publisher","first-page":"3046","DOI":"10.1109\/ACCESS.2016.2573264","volume":"4","author":"D Jiang","year":"2016","unstructured":"Jiang, D., Nie, L., Lv, Z., et al.: Spatio-temporal Kronecker compressive sensing for traffic matrix recovery. IEEE Access 4, 3046\u20133053 (2016)","journal-title":"IEEE Access"},{"issue":"2","key":"57_CR19","first-page":"299","volume":"42","author":"M-B Qi","year":"2016","unstructured":"Qi, M.-B., Tan, S.-S., Wang, Y.-X., Liu, H., Jiang, J.-G.: Multi-feature subspace and kernel learning for person reidentication. Acta Automatica Sinica 42(2), 299\u2013308 (2016)","journal-title":"Acta Automatica Sinica"},{"key":"57_CR20","unstructured":"Suhas, L., Kuldeep, K., Pavan, T.: Direct inference on compressive measurements using convolutional neural networks. In: Image Processing (ICIP), pp. 1913\u20131917 (2016)"},{"issue":"8","key":"57_CR21","first-page":"2807","volume":"29","author":"ZJ Sun","year":"2012","unstructured":"Sun, Z.J., Xue, L., Xu, Y.M.: Review of deep learning research. Comput. Appl. Res. 29(8), 2807\u20132810 (2012)","journal-title":"Comput. Appl. Res."},{"issue":"2","key":"57_CR22","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MSP.2007.914731","volume":"25","author":"EJ Cand\u00e8s","year":"2008","unstructured":"Cand\u00e8s, E.J., Wakin, M.B.: An introduction to compressive sampling. Signal Process. Mag. 25(2), 21\u201330 (2008)","journal-title":"Signal Process. Mag."},{"issue":"8","key":"57_CR23","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1002\/cpa.20124","volume":"59","author":"EJ Cand\u00e8s","year":"2006","unstructured":"Cand\u00e8s, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207\u20131223 (2006)","journal-title":"Commun. Pure Appl. Math."},{"issue":"4","key":"57_CR24","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"DL Donoho","year":"2006","unstructured":"Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289\u20131306 (2006)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"12","key":"57_CR25","doi-asserted-by":"publisher","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","volume":"53","author":"JA Tropp","year":"2007","unstructured":"Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655\u20134666 (2007)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"1","key":"57_CR26","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1137\/S1064827596304010","volume":"20","author":"SS Chen","year":"1998","unstructured":"Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20(1), 33\u201361 (1998)","journal-title":"SIAM J. Sci. Comput."},{"key":"57_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-642-15549-9_10","volume-title":"Computer Vision \u2013 ECCV 2010","author":"AC Sankaranarayanan","year":"2010","unstructured":"Sankaranarayanan, A.C., Turaga, P.K., Baraniuk, R.G., Chellappa, R.: Compressive acquisition of dynamic scenes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 129\u2013142. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15549-9_10"},{"key":"57_CR28","unstructured":"Duarte, M.F., Davenport, M.A., Wakin, M.B., Baraniuk, R.G.: Sparse signal detection from incoherent projections. In: Acoustics, Speech and Signal Processing, vol. 3, pp. 305\u2013308 (2006)"},{"issue":"3","key":"57_CR29","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1109\/TAES.2009.5259191","volume":"45","author":"A Mahalanobis","year":"2009","unstructured":"Mahalanobis, A., Muise, R.: Object specific image reconstruction using a compressive sensing architecture for application in surveillance systems. IEEE Trans. Aerosp. Electron. Syst. 45(3), 1167\u20131180 (2009)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"57_CR30","doi-asserted-by":"crossref","unstructured":"Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: Neural Networks (IJCNN), pp. 2809\u20132813. IEEE (2011)","DOI":"10.1109\/IJCNN.2011.6033589"},{"key":"57_CR31","doi-asserted-by":"crossref","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: Neural Networks (IJCNN), pp. 1453\u20131458. IEEE (2011)","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"57_CR32","doi-asserted-by":"crossref","unstructured":"Zaklouta, F., Stanciulescu, B., Hamdoun, O.: Traffic sign classification using KD trees and random forests. In: Neural Networks (IJCNN), pp. 2151\u20132155. IEEE (2011)","DOI":"10.1109\/IJCNN.2011.6033494"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Simulation Tools and Techniques"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32216-8_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T11:41:09Z","timestamp":1664710869000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-32216-8_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322151","9783030322168"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32216-8_57","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMUtools","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Simulation Tools and Techniques","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simutools2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/simutools.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"156","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":"97","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":"62% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}