{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:14:04Z","timestamp":1740154444010,"version":"3.37.3"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FUI - Scanvision project - r\u00e9gion PACA","award":["F14014U"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The purpose of this work is to perform the joint design of a classification system including both a radar sensor and an image processing software. Experimental data were generated with a three-dimensional scanner. The criterion which rules the design is the false recognition rate, which should be as small as possible. The classifier involved is support vector machines, combined with an error correcting code. We apply the proposed method to optimize security check. For this purpose we retain eight relevant parameters which impact the recognition performances. To estimate the best parameters, we adapt our adaptive mixed grey wolf algorithm. This is a computational technique inspired by nature to minimize a criterion. Our adaptive mixed grey wolf algorithmwas found to outperform comparative methods in terms of computational load on simulations and with real-world data.<\/jats:p>","DOI":"10.3390\/rs12183097","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T13:40:56Z","timestamp":1600782056000},"page":"3097","source":"Crossref","is-referenced-by-count":2,"title":["Joint Design of the Hardware and the Software of a Radar System with the Mixed Grey Wolf Optimizer: Application to Security Check"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8793-816X","authenticated-orcid":false,"given":"Julien","family":"Marot","sequence":"first","affiliation":[{"name":"CNRS, Aix Marseille Universit\u00e9, Centrale Marseille, Institut Fresnel UMR 7249, 13013 Marseille, France"}]},{"given":"Claire","family":"Migliaccio","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2018Electronique, Antennes et T\u00e9l\u00e9communications, Universit\u00e9 C\u00f4te d\u2019Azur, 06560 Nice, France"}]},{"given":"J\u00e9r\u00f4me","family":"Lant\u00e9ri","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2018Electronique, Antennes et T\u00e9l\u00e9communications, Universit\u00e9 C\u00f4te d\u2019Azur, 06560 Nice, France"}]},{"given":"Paul","family":"Lauga","sequence":"additional","affiliation":[{"name":"CNRS, Aix Marseille Universit\u00e9, Centrale Marseille, Institut Fresnel UMR 7249, 13013 Marseille, France"}]},{"given":"Salah","family":"Bourennane","sequence":"additional","affiliation":[{"name":"CNRS, Aix Marseille Universit\u00e9, Centrale Marseille, Institut Fresnel UMR 7249, 13013 Marseille, France"}]},{"given":"Laurent","family":"Brochier","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2018Electronique, Antennes et T\u00e9l\u00e9communications, Universit\u00e9 C\u00f4te d\u2019Azur, 06560 Nice, France"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3390\/rs2010036","article-title":"Application of Microwave Remote Sensing to Dynamic Testing of Stay-Cables","volume":"2","author":"Gentile","year":"2009","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3390\/rs2010166","article-title":"Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture","volume":"2","author":"Chai","year":"2009","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lv, X., Ming, D., Lu, T., Zhou, K., Wang, M., and Bao, H. 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