{"id":10778,"date":"2013-10-24T00:15:05","date_gmt":"2013-10-23T21:15:05","guid":{"rendered":"http:\/\/hgpu.org\/?p=10778"},"modified":"2013-10-24T00:15:05","modified_gmt":"2013-10-23T21:15:05","slug":"a-parallel-pso-algorithm-for-a-watermarking-application-on-a-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10778","title":{"rendered":"A Parallel PSO Algorithm for a Watermarking Application on a GPU"},"content":{"rendered":"<p>In this paper, a research about the usability, advantages and disadvantages of using Compute Unified Device Architecture (CUDA) is presented, implementing an algorithm based on populations called Particle Swarm Optimization (PSO) [5]. In order to test the performance of the proposed algorithm, a hide watermark image application is put into practice. The PSO is used to optimize the positions where a watermark has to be inserted. This application uses the insertion\/extraction algorithm proposed by Shieh et al. [1]. This algorithm was implemented for both sequential and CUDA architectures. The fitness function-used in the optimization algorithm &#8211; has two objectives: fidelity and robustness. The measurement of fidelity and robustness is computed using Mean Squared Error (MSE) and Normalized Correlation (NC), respectively; these functions are evaluated using Pareto dominance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, a research about the usability, advantages and disadvantages of using Compute Unified Device Architecture (CUDA) is presented, implementing an algorithm based on populations called Particle Swarm Optimization (PSO) [5]. In order to test the performance of the proposed algorithm, a hide watermark image application is put into practice. The PSO is used [&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":[36,89,33,3,287],"tags":[1787,14,1786,20,1342,1800,199],"class_list":["post-10778","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","category-security","tag-algorithms","tag-cuda","tag-image-processing","tag-nvidia","tag-particle-swarm-optimization","tag-security","tag-tesla-c1060"],"views":2798,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10778","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=10778"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10778\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10778"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10778"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10778"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}