{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:43:37Z","timestamp":1764996217906,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,17]],"date-time":"2019-09-17T00:00:00Z","timestamp":1568678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["TEC2016-77618-R"],"award-info":[{"award-number":["TEC2016-77618-R"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["TEC2016-77618-R"],"award-info":[{"award-number":["TEC2016-77618-R"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications.<\/jats:p>","DOI":"10.3390\/s19184011","type":"journal-article","created":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T04:50:21Z","timestamp":1569214221000},"page":"4011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1468-6280","authenticated-orcid":false,"given":"\u00d3scar","family":"Mata-Carballeira","sequence":"first","affiliation":[{"name":"Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country UPV\/EHU, 48940 Leioa, Spain"}]},{"given":"Jon","family":"Guti\u00e9rrez-Zaballa","sequence":"additional","affiliation":[{"name":"Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country UPV\/EHU, 48940 Leioa, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6378-5357","authenticated-orcid":false,"given":"In\u00e9s","family":"del Campo","sequence":"additional","affiliation":[{"name":"Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country UPV\/EHU, 48940 Leioa, Spain"}]},{"given":"Victoria","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country UPV\/EHU, 48940 Leioa, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"40674","DOI":"10.1109\/ACCESS.2019.2907261","article-title":"Deep Neural Network Hardware Implementation Based on Stacked Sparse Autoencoder","volume":"7","author":"Coutinho","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MITS.2014.2336271","article-title":"Three Decades of Driver Assistance Systems: Review and Future Perspectives","volume":"6","author":"Bengler","year":"2014","journal-title":"IEEE Intell. Trans. Syst. Mag."},{"key":"ref_3","first-page":"65","article-title":"Antiblockiersystem (ABS) f\u00fcr Personenkraftwagen","volume":"2","author":"Leiber","year":"1980","journal-title":"BOSCH TECH BER"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bleckmann, H.W., Fennel, H., Gr\u00e4ber, J., and Seibert, W.W. (1986). Traction Control System with Teves ABS Mark II, SAE. Technical Report, SAE Technical Paper.","DOI":"10.4271\/860506"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1080\/15389580490896951","article-title":"Effect of electronic stability control on automobile crash risk","volume":"5","author":"Farmer","year":"2004","journal-title":"Traffic Inj. Prev."},{"key":"ref_6","unstructured":"National Highway Traffic Safety Administration (NHTSA), U.S. Department of Transportation (2008). 49 CFR 571.126\u2014Standard No. 126, Electronic Stability Control Systems."},{"key":"ref_7","unstructured":"Council of European Union (2009). REGULATION (EC) No 661\/2009, Council of European Union."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3141\/1724-07","article-title":"Behavioral adaptation, safety, and intelligent transportation systems","volume":"1724","author":"Smiley","year":"2000","journal-title":"Trans. Res. Rec."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1016\/j.aap.2010.12.023","article-title":"The influence of Cruise Control and Adaptive Cruise Control on driving behaviour\u2014A driving simulator study","volume":"43","author":"Markvollrath","year":"2011","journal-title":"Accid. Anal. Prev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"63","DOI":"10.4271\/2016-01-8013","article-title":"Current approaches in HiL-based ADAS testing","volume":"9","author":"Feilhauer","year":"2016","journal-title":"SAE Int. J. Commer. Veh."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1518\/001872007X249965","article-title":"Multisensory in-car warning signals for collision avoidance","volume":"49","author":"Ho","year":"2007","journal-title":"Hum. Factors"},{"key":"ref_12","first-page":"120","article-title":"Lane departure warning system","volume":"3","author":"Mahajan","year":"2015","journal-title":"Int. J. Eng. Technol. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"38","DOI":"10.15199\/48.2015.01.06","article-title":"Comparative survey on traffic sign detection and recognition: A review","volume":"91","author":"Wali","year":"2015","journal-title":"Przegl\u0105d Elektrotechniczny"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Caber, N., Langdon, P., and Clarkson, P.J. (2019). Designing Adaptation in Cars: An Exploratory Survey on Drivers\u2019 Usage of ADAS and Car Adaptations. International Conference on Applied Human Factors and Ergonomics, Springer.","DOI":"10.1007\/978-3-030-20503-4_9"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1049\/et.2017.1007","article-title":"Inside the future cars [Technology Driverless Cars]","volume":"12","author":"Murray","year":"2017","journal-title":"Eng. Tech."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fleming, J.M., Allison, C.K., Yan, X., Stanton, N.A., and Lot, R. (2018). Adaptive driver modelling in ADAS to improve user acceptance: A study using naturalistic data. Saf. Sci.","DOI":"10.1016\/j.ssci.2018.08.023"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.trf.2015.10.005","article-title":"Learning and development of trust, acceptance and the mental model of ACC. A longitudinal on-road study","volume":"35","author":"Beggiato","year":"2015","journal-title":"Trans. Res. Part F Traffic Psychol. Behav."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s12544-018-0324-6","article-title":"Intelligent personalized ADAS warnings","volume":"10","author":"Panou","year":"2018","journal-title":"Eur. Trans. Res. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"77.e1","DOI":"10.1016\/j.jsr.2014.02.010","article-title":"Driver\u2019s behavioral adaptation to Adaptive Cruise Control (ACC): The case of speed and time headway","volume":"49","author":"Piccinini","year":"2014","journal-title":"J. Saf. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hasenj\u00e4ger, M., and Wersing, H. (2017, January 16\u201319). Personalization in advanced driver assistance systems and autonomous vehicles: A review. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317803"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"D\u00f6rr, D., Grabengiesser, D., and Gauterin, F. (2014, January 8\u201311). Online driving style recognition using fuzzy logic. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957822"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bifulco, G.N., Simonelli, F., and Di Pace, R. (2008, January 4\u20136). Experiments toward an human-like Adaptive Cruise Control. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621213"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.trc.2011.07.001","article-title":"Development and testing of a fully adaptive cruise control system","volume":"29","author":"Bifulco","year":"2013","journal-title":"Trans. Res. Part C Emerg. Tech."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TITS.2012.2205143","article-title":"An adaptive longitudinal driving assistance system based on driver characteristics","volume":"14","author":"Wang","year":"2012","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1080\/00423114.2015.1062899","article-title":"Driver models for personalised driving assistance","volume":"53","author":"Carvalho","year":"2015","journal-title":"Veh. Syst. Dyn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1764","DOI":"10.1109\/TITS.2013.2267799","article-title":"Statistical behavior modeling for driver-adaptive precrash systems","volume":"14","author":"Muehlfeld","year":"2013","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/15472450.2014.889960","article-title":"Learning drivers\u2019 behavior to improve adaptive cruise control","volume":"19","author":"Rosenfeld","year":"2015","journal-title":"J. Intell. Trans. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"357","DOI":"10.3182\/20020721-6-ES-1901.01613","article-title":"Personalization of ACC Stop and Go task based on human driver behaviour analysis","volume":"35","author":"Canale","year":"2002","journal-title":"IFAC Proc. Vol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"De Gelder, E., Cara, I., Uittenbogaard, J., Kroon, L., van Iersel, S., and Hogema, J. (2016, January 19\u201322). Towards personalised automated driving: Prediction of preferred ACC behaviour based on manual driving. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535544"},{"key":"ref_30","unstructured":"(2019, June 29). SHRP 2\u2014Strategic Highway Research Program 2 (SHRP 2). Available online: http:\/\/www.trb.org\/StrategicHighwayResearchProgram2SHRP2\/Blank2.aspx."},{"key":"ref_31","unstructured":"Xilinx (2018). Zynq-7000 SoC Data Sheet: Overview (DS190), v1.11.1, Xilinx."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"30653","DOI":"10.3390\/s151229822","article-title":"A review of intelligent driving style analysis systems and related artificial intelligence algorithms","volume":"15","author":"Meiring","year":"2015","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Abut, H., Erdo\u011fan, H., Er\u00e7il, A., \u00c7\u00fcr\u00fckl\u00fc, B., Koman, H.C., Ta\u015f, F., Argun\u015fah, A.\u00d6., Co\u015far, S., Akan, B., and Karabalkan, H. (2009). Real-world data collection with \u201cUYANIK\u201d. In-Vehicle Corpus and Signal Processing for Driver Behavior, Springer.","DOI":"10.1007\/978-0-387-79582-9_3"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Angkititrakul, P., Petracca, M., Sathyanarayana, A., and Hansen, J.H. (2007, January 13\u201315). UTDrive: Driver behavior and speech interactive systems for in-vehicle environments. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey.","DOI":"10.1109\/IVS.2007.4290175"},{"key":"ref_35","unstructured":"Regan, M., Williamson, A., Grzebieta, R., and Tao, L. (2012, January 9\u201310). Naturalistic driving studies: Literature review and planning for the Australian naturalistic driving study. Proceedings of the Australasian College of Road Safety Conference 2012, Sydney, Australia."},{"key":"ref_36","unstructured":"Eenink, R., Barnard, Y., Baumann, M., Augros, X., and Utesch, F. (2014, January 14\u201317). UDRIVE: The European naturalistic driving study. Proceedings of the IFSTTAR Transport Research Arena, Paris, France."},{"key":"ref_37","first-page":"0400","article-title":"An overview of the 100-car naturalistic study and findings","volume":"5","author":"Neale","year":"2005","journal-title":"Natl. Highw. Traffic Saf. Adm."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dingus, T.A., Hankey, J.M., Antin, J.F., Lee, S.E., Eichelberger, L., Stulce, K.E., McGraw, D., Perez, M., and Stowe, L. (2015). Naturalistic Driving Study: Technical Coordination and Quality Control, TRID. Number SHRP 2 Report S2-S06-RW-1.","DOI":"10.17226\/22362"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, V., del Campo, I., Echanobe, J., and Basterretxea, K. (2015, January 15\u201318). Driving behavior signals and machine learning: A personalized driver assistance system. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, Spain.","DOI":"10.1109\/ITSC.2015.470"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Del Campo, I., Asua, E., Mart\u00ednez, V., Mata-Carballeira, \u00d3., and Echanobe, J. (2018, January 4\u20137). Driving Style Recognition based on Ride Comfort Using a Hybrid Machine Learning Algorithm. Proceedings of the IEEE 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569722"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Murphey, Y.L., Milton, R., and Kiliaris, L. (April, January 30). Driver\u2019s style classification using jerk analysis. Proceedings of the 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, Nashville, TN, USA.","DOI":"10.1109\/CIVVS.2009.4938719"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/JPROC.2006.888405","article-title":"Driver modeling based on driving behavior and its evaluation in driver identification","volume":"95","author":"Miyajima","year":"2007","journal-title":"Proc. IEEE"},{"key":"ref_43","unstructured":"Chen, Y., and Li, L. (2014). Chapter 6\u2014Comparative Analysis and Modeling of Driver Behavior Characteristics. Advances in Intelligent Vehicles, Academic Press."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/TITS.2014.2326082","article-title":"Segmentation and clustering of car-following behavior: Recognition of driving patterns","volume":"16","author":"Higgs","year":"2014","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"key":"ref_45","unstructured":"Custer, K., Sudweeks, J., Perez, M.A., del Campo, I., Echanobe, J., Martinez, V., Asua, E., Basterretxea, K., Bosque, G., and Martinez, U. (2019). PSoC for Real-Time Driver Assistance Based on Machine Learning IP Cores, VTTI. Dataset, SHRP2 Naturalistic Driving Study."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/MITS.2014.2328673","article-title":"Driver behavior profiling using smartphones: A low-cost platform for driver monitoring","volume":"7","author":"Castignani","year":"2015","journal-title":"IEEE Intell. Trans. Syst. Mag."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5933","DOI":"10.1109\/TIP.2016.2616302","article-title":"Real-time superpixel segmentation by DBSCAN clustering algorithm","volume":"25","author":"Shen","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/79.543975","article-title":"The expectation-maximization algorithm","volume":"13","author":"Moon","year":"1996","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhao, Y., and Karypis, G. (2002, January 4\u20139). Evaluation of hierarchical clustering algorithms for document datasets. Proceedings of the Eleventh International Conference on Information and Knowledge Management (ACM\u2013CIKM\u201902): McLean, VA, USA.","DOI":"10.1145\/584874.584877"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kalsoom, R., and Halim, Z. (2013, January 19\u201320). Clustering the driving features based on data streams. Proceedings of the IEEE INMIC, Lahore, Pakistan.","DOI":"10.1109\/INMIC.2013.6731330"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bhoraskar, R., Vankadhara, N., Raman, B., and Kulkarni, P. (2012, January 3\u20137). Wolverine: Traffic and road condition estimation using smartphone sensors. Proceedings of the IEEE 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012), Bangalore, India.","DOI":"10.1109\/COMSNETS.2012.6151382"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Brombacher, P., Masino, J., Frey, M., and Gauterin, F. (2017, January 22\u201325). Driving event detection and driving style classification using artificial neural networks. Proceedings of the 2017 IEEE International Conference on Industrial Technology (ICIT), Toronto, ON, Canada.","DOI":"10.1109\/ICIT.2017.7915497"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kurt, A., and \u00d6zg\u00fcner, \u00dc. (2011, January 5\u20137). A probabilistic model of a set of driving decisions. Proceedings of the IEEE 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA.","DOI":"10.1109\/ITSC.2011.6082911"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/2.53","article-title":"Fuzzy logic","volume":"21","author":"Zadeh","year":"1988","journal-title":"Computer"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Aljaafreh, A., Alshabatat, N., and Al-Din, M.S.N. (2012, January 24\u201327). Driving style recognition using fuzzy logic. Proceedings of the 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012), Istanbul, Turkey.","DOI":"10.1109\/ICVES.2012.6294318"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Choudhary, A.K., and Ingole, P.K. (2014, January 7\u20139). Smart phone based approach to monitor driving behavior and sharing of statistic. Proceedings of the IEEE 2014 Fourth International Conference on Communication Systems and Network Technologies, Bhopal, India.","DOI":"10.1109\/CSNT.2014.61"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive-network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1109\/5.364486","article-title":"Neuro-fuzzy modeling and control","volume":"83","author":"Jang","year":"1995","journal-title":"Proc. IEEE"},{"key":"ref_59","unstructured":"Xilinx (2018). ZC706 Evaluation Board for the Zynq-7000 XC7Z045 SoC (UG954), v1.7, Xilinx."},{"key":"ref_60","unstructured":"Xilinx (2018). 7 Series DSP48E1 Slice (UG479), v1.10, Xilinx."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mata-Carballeira, \u00d3., del Campo, I., Mart\u00ednez, V., and Echanobe, J. (2019, January 27\u201330). A Hardware\/Software Extreme Learning Machine Solution for Improved Ride Comfort in Automobiles. Proceedings of the IEEE 2019 International Joint Conference on Neural Networks (IJCNN), Auckland, New Zealand.","DOI":"10.1109\/IJCNN.2019.8852435"},{"key":"ref_62","unstructured":"Xilinx (2016). Divider Generator v5.1 (PG151), Xilinx."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Salda\u00f1a, H.J.B., and Silva-C\u00e1rdenas, C. (March, January 29). A digital hardware architecture for a three-input one-output zero-order ANFIS. Proceedings of the 2012 IEEE 3rd Latin American Symposium on Circuits and Systems (LASCAS), Playa del Carmen, Mexico.","DOI":"10.1109\/LASCAS.2012.6180304"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Darvill, J., Tisan, A., and Cirstea, M. (2017, January 19\u201321). A novel ANFIS algorithm architecture for FPGA implementation. Proceedings of the 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK.","DOI":"10.1109\/ISIE.2017.8001423"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"18223","DOI":"10.3390\/s141018223","article-title":"Parallel fixed point implementation of a radial basis function network in an FPGA","volume":"14","author":"Fernandes","year":"2014","journal-title":"Sensors"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lopes, F.F., Ferreira, J.C., and Fernandes, M.A. (2019). Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent. Electronics, 8.","DOI":"10.3390\/electronics8060631"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/18\/4011\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:21:03Z","timestamp":1760188863000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/18\/4011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,17]]},"references-count":66,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["s19184011"],"URL":"https:\/\/doi.org\/10.3390\/s19184011","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,9,17]]}}}