{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:24:47Z","timestamp":1783009487926,"version":"3.54.5"},"reference-count":19,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,10,4]],"date-time":"2016-10-04T00:00:00Z","timestamp":1475539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1 \u00d7 2 m, and are installed under the surface of the traffic lane.  The wide Point Spread Function (PSF) of such sensors causes the information on chassis geometry, contained in the measurement signal, to be strongly averaged. This significantly limits the effectiveness of the vehicle classification. Restoration of the chassis shape, by solving the inverse problem (deconvolution), is also difficult due to the fact that it is ill-conditioned.  An original approach to solving this problem is presented in this paper. It is a hardware-based solution and involves the use of inductive loops with an asymmetrical PSF. Laboratory experiments and simulation tests, conducted with models of an inductive loop, confirmed the effectiveness of the proposed solution. In this case, the principle applies that the higher the level of sensor spatial asymmetry, the greater the effectiveness of the deconvolution algorithm.<\/jats:p>","DOI":"10.3390\/s16101642","type":"journal-article","created":{"date-parts":[[2016,10,4]],"date-time":"2016-10-04T10:56:50Z","timestamp":1475578610000},"page":"1642","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Eddy-Current Sensors with Asymmetrical Point Spread Function"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4818-7663","authenticated-orcid":false,"given":"Janusz","family":"Gajda","sequence":"first","affiliation":[{"name":"Department of Measurement and Electronics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technologyal, al. A. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marek","family":"Stencel","sequence":"additional","affiliation":[{"name":"Department of Measurement and Electronics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technologyal, al. A. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4315","DOI":"10.1109\/JSEN.2014.2345755","article-title":"Robust Blind Deconvolution Process for Vehicle Reidentification by an Inductive Loop Detector","volume":"14","author":"Guilbert","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Marszalek, Z., Sroka, R., and Zeglen, T. (2015, January 24\u201327). 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