{"id":5391,"date":"2021-10-07T11:29:39","date_gmt":"2021-10-07T09:29:39","guid":{"rendered":"https:\/\/www.eledia.org\/eledia-unitn\/?post_type=news&#038;p=5391"},"modified":"2021-10-08T11:04:27","modified_gmt":"2021-10-08T09:04:27","slug":"low-frequency-data-prediction-with-iterative-learning-for-highly-nonlinear-inverse-scattering-problems","status":"publish","type":"news","link":"https:\/\/www.eledia.org\/eledia-unitn\/news\/low-frequency-data-prediction-with-iterative-learning-for-highly-nonlinear-inverse-scattering-problems\/","title":{"rendered":"Low-Frequency Data Prediction with Iterative Learning for Highly Nonlinear Inverse Scattering Problems"},"content":{"rendered":"\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.eledia.org\/eledia-unitn\/wp-content\/uploads\/2021\/10\/1628173698575.jpeg\" alt=\"\" class=\"wp-image-5392\" width=\"382\" height=\"382\" srcset=\"https:\/\/www.eledia.org\/eledia-unitn\/wp-content\/uploads\/2021\/10\/1628173698575.jpeg 800w, https:\/\/www.eledia.org\/eledia-unitn\/wp-content\/uploads\/2021\/10\/1628173698575-300x300.jpeg 300w, https:\/\/www.eledia.org\/eledia-unitn\/wp-content\/uploads\/2021\/10\/1628173698575-150x150.jpeg 150w, https:\/\/www.eledia.org\/eledia-unitn\/wp-content\/uploads\/2021\/10\/1628173698575-768x768.jpeg 768w\" sizes=\"auto, (max-width: 382px) 100vw, 382px\" \/><\/figure><\/div>\n\n\n\n<p>The&nbsp;<a href=\"https:\/\/www.linkedin.com\/feed\/hashtag\/?keywords=elediaresearchcenter&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6829055462336413697\" rel=\"noopener\">#ELEDIAResearchCenter<\/a>&nbsp;is pleased to announce that a new paper on&nbsp;&#8220;Low-Frequency Data Prediction with Iterative Learning for Highly Nonlinear Inverse Scattering Problems&#8221; has been published:<\/p>\n\n\n\n<p>Z. Lin&nbsp;et al., &#8220;Low-Frequency Data Prediction With Iterative Learning for Highly Nonlinear Inverse Scattering Problems,&#8221; in&nbsp;IEEE Transactions on Microwave Theory and Techniques, doi: 10.1109\/TMTT.2021.3098769.<\/p>\n\n\n\n<p>The paper can be downloaded at the following link:&nbsp;<a href=\"https:\/\/lnkd.in\/dR2yi4JQ\" rel=\"noopener\">https:\/\/lnkd.in\/dR2yi4JQ<\/a><\/p>\n\n\n\n<p>&#8212;<br><a href=\"https:\/\/www.linkedin.com\/company\/ieee\/\" rel=\"noopener\">IEEE<\/a><a href=\"https:\/\/www.linkedin.com\/feed\/hashtag\/?keywords=ieeemtt&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6829055462336413697\" rel=\"noopener\">#IEEEMTT<\/a><a href=\"https:\/\/www.linkedin.com\/feed\/hashtag\/?keywords=dataprediction&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6829055462336413697\" rel=\"noopener\">#DataPrediction<\/a><a href=\"https:\/\/www.linkedin.com\/feed\/hashtag\/?keywords=newpublication&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6829055462336413697\" rel=\"noopener\">#newpublication<\/a><a href=\"https:\/\/www.linkedin.com\/feed\/hashtag\/?keywords=iterativelearning&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6829055462336413697\" rel=\"noopener\">#iterativelearning<\/a><a href=\"https:\/\/www.linkedin.com\/company\/tsinghua-university\/\" rel=\"noopener\">Tsinghua University<\/a><a href=\"https:\/\/www.linkedin.com\/feed\/hashtag\/?keywords=eledia&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6829055462336413697\" rel=\"noopener\">#ELEDIA<\/a><a href=\"https:\/\/www.linkedin.com\/feed\/hashtag\/?keywords=tsinghuauniversity&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6829055462336413697\" rel=\"noopener\">#TsinghuaUniversity<\/a><a href=\"https:\/\/www.linkedin.com\/feed\/hashtag\/?keywords=antennas&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6829055462336413697\" rel=\"noopener\">#antennas<\/a><\/p>\n","protected":false},"author":1,"featured_media":5392,"template":"","format":"standard","meta":{"footnotes":""},"tags":[],"news_status":[],"news_topic":[91],"class_list":["post","post-type","post-5391","news","type-news","status-publish","format-standard","has-post-thumbnail","hentry","news_topic-news-publications",""],"acf":[],"_links":{"self":[{"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/news\/5391","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/news"}],"about":[{"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/types\/news"}],"author":[{"embeddable":true,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":2,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/news\/5391\/revisions"}],"predecessor-version":[{"id":5394,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/news\/5391\/revisions\/5394"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/media\/5392"}],"wp:attachment":[{"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/media?parent=5391"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/tags?post=5391"},{"taxonomy":"news_status","embeddable":true,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/news_status?post=5391"},{"taxonomy":"news_topic","embeddable":true,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/news_topic?post=5391"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}