{"id":12336,"date":"2014-06-22T09:33:26","date_gmt":"2014-06-22T06:33:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=12336"},"modified":"2014-06-22T09:33:26","modified_gmt":"2014-06-22T06:33:26","slug":"the-fast-and-wideband-mom-based-on-gpu-and-two-path-afs-acceleration","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12336","title":{"rendered":"The Fast and Wideband MoM Based on GPU and Two-Path AFS Acceleration"},"content":{"rendered":"<p>In this paper, a General Purpose Unit (GPU) accelerated full-wave method of moment (MoM) is combined with a two-path adaptive frequency sampling (AFS) approach to analyze the wideband characteristic of the body-wire structures. An equivalent principle is employed to treat the wire as surface so that the model which is analyzed based on the electric-field integral equation (EFIE) could be purely discretized by triangles, avoiding adopting three different basis functions. Numerical results for a monopole mounted on the square ground plane show the efficiency and accuracy of the proposed methods.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, a General Purpose Unit (GPU) accelerated full-wave method of moment (MoM) is combined with a two-path adaptive frequency sampling (AFS) approach to analyze the wideband characteristic of the body-wire structures. An equivalent principle is employed to treat the wire as surface so that the model which is analyzed based on the electric-field [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,3,41],"tags":[14,707,20,1789,1226],"class_list":["post-12336","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-signal-processing","tag-cuda","tag-integral-equations","tag-nvidia","tag-signal-processing","tag-tesla-c2075"],"views":2796,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12336","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=12336"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12336\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12336"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12336"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12336"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}