{"id":168450,"date":"2015-06-10T00:00:00","date_gmt":"2015-06-10T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/rate-adaptive-compressed-sensing-and-sparsity-variance-of-biomedical-signals\/"},"modified":"2018-10-26T14:45:43","modified_gmt":"2018-10-26T21:45:43","slug":"rate-adaptive-compressed-sensing-and-sparsity-variance-of-biomedical-signals","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/rate-adaptive-compressed-sensing-and-sparsity-variance-of-biomedical-signals\/","title":{"rendered":"Rate-Adaptive Compressed-Sensing and Sparsity Variance of Biomedical Signals"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Biomedical signals exhibit substantial variance in sparsity. This variance can be exploited to save power in compressed-sensing systems. In this paper, we propose and implement an adaptive compressed-sensing system wherein the compression factor is modified automatically depending on the sparsity of the input signal. Experimental results based on our embedded sensor platform show a 16.2% improvement in power consumption when compared with a traditional compressed-sensing system with a fixed compression factor. We also demonstrate the potential to improve this number to 24% through the use of an ultra low power processor in our embedded system.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Biomedical signals exhibit substantial variance in sparsity. This variance can be exploited to save power in compressed-sensing systems. In this paper, we propose and implement an adaptive compressed-sensing system wherein the compression factor is modified automatically depending on the sparsity of the input signal. Experimental results based on our embedded sensor platform show a 16.2% [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[243062,13552,13553,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-168450","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-audio-acoustics","msr-research-area-hardware-devices","msr-research-area-medical-health-genomics","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"IEEE - Institute of Electrical and Electronics Engineers","msr_edition":"","msr_affiliation":"","msr_published_date":"2015-6-10","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"274290","msr_publicationurl":"http:\/\/ieeexplore.ieee.org\/xpls\/abs_all.jsp?arnumber=7299419","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2015\/06\/Shoaib_BSN_2015.pdf","id":"274290","title":"Shoaib_BSN_2015","label_id":"243109","label":0},{"type":"url","viewUrl":"false","id":"false","title":"http:\/\/ieeexplore.ieee.org\/xpls\/abs_all.jsp?arnumber=7299419","label_id":"243109","label":0},{"type":"doi","viewUrl":"false","id":"false","title":"10.1109\/BSN.2015.7299419","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/ieeexplore.ieee.org\/xpls\/abs_all.jsp?arnumber=7299419"}],"msr-author-ordering":[{"type":"text","value":"Vahid Behravan","user_id":0,"rest_url":false},{"type":"text","value":"Neil E. Glover","user_id":0,"rest_url":false},{"type":"text","value":"Rutger Farry","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Shuayb Zarar","user_id":36563,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Shuayb Zarar"},{"type":"text","value":"Patrick Y. 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