{"id":4921,"date":"2011-07-29T16:37:26","date_gmt":"2011-07-29T13:37:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=4921"},"modified":"2011-07-29T16:37:26","modified_gmt":"2011-07-29T13:37:26","slug":"toward-real-time-kernel-density-estimate-display-for-instrumentation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4921","title":{"rendered":"Toward real-time kernel density estimate display for instrumentation"},"content":{"rendered":"<p>Histograms are commonly used in instrumentation to produce a visual representation of the probability density of a random signal from repeated measurements. However, histograms have a number of shortcomings as a method of data visualization. We propose using kernel density estimation as a replacement for histograms in instrumentation. Kernel density estimation has a number of advantages as a means of visualizing the probability density of a waveform or derived measurement. However, kernel density estimates have been considered too computationally burdensome for inclusion in instruments and virtual instruments. In this paper, we demonstrate that a graphics processing unit (GPU) can be used to compute and display kernel density estimates of actual measured data at a full video rate.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Histograms are commonly used in instrumentation to produce a visual representation of the probability density of a random signal from repeated measurements. However, histograms have a number of shortcomings as a method of data visualization. We propose using kernel density estimation as a replacement for histograms in instrumentation. Kernel density estimation has a number of [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3],"tags":[1782,963,134],"class_list":["post-4921","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-probability","tag-visualization"],"views":2060,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4921","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=4921"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4921\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}