{"584842":{"#nid":"584842","#data":{"type":"news","title":"Professor David Citrin Publishes Research on Compressive Sensing Conducted with Colleagues at Centrale-Sup\u00e9lec in Metz, France","body":[{"value":"\u003Cp\u003EThe world largely consists of continuous signals.\u0026nbsp; For example, the electromagnetic field in a radio signal varies with time in a continuous fashion.\u0026nbsp; Likewise for audio signals and images.\u0026nbsp; When it comes time to store or process the signal on a computer, it is typical to retain only the signal at a set of discrete successive times separated by an interval that is sufficiently small so that none of the essential rapidly varying information is lost.\u0026nbsp; How to choose this interval is determined by the famous Nyquist-Shannon sampling theorem.\u0026nbsp; Some signals, however, may be very sparse.\u0026nbsp; That is, the signal might be zero most of the time, so sampling at a rate determined by the Nyquist-Shannon limit may result in the retention of lots of data that are just zeros.\u0026nbsp; (More generally, the signal might be sparse in some other domain.)\u0026nbsp; Thus, storage or transmission of sparse signals sampled in the conventional way can be extremely inefficient.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\nCompressive sensing was devised by Justin Romberg of ECE and others to sample a sparse signal below the Nyquist-Shannon limit, but nonetheless to permit its faithful reconstruction, and thus to store and transmit sparse signals in a very efficient fashion.\u0026nbsp; Compression is the process of sampling and storing the sparse signal, while sensing is the process of reconstructing the original signal.\u0026nbsp; Compression relies on having at hand large strings of random (or sufficiently random-looking) numbers to populate the compression matrix needed to compress the data. Such strings of pseudo-random numbers are typically generated on a digital computer.\u0026nbsp; Nevertheless, for the ultimate in high speed and simplicity, it is desirable to generate the string of random-like numbers, and ultimately carry out the compression itself, not only at speeds not readily attained on a conventional computer, but also physically.\u0026nbsp; In recent work published in Scientific Reports [reference here], together with collaborator Damien Rontani of Centrale-Sup\u0026eacute;lec in Metz, France, Profs. David Citrin and Alexandre Locquet of ECE with PhD students Daeyoung Choi (ECE) and C.-Y. Chang (Physics) have used a chaotic optical signal produced by an external-cavity semiconductor laser to generate sufficiently random-like numbers at very high rate, based on the sub-100 picosecond timescale determining the dynamics of the laser.\u0026nbsp; The team demonstrated efficient compression followed by high-fidelity reconstruction of images using this technique.\u0026nbsp; The work at Georgia Tech was conducted at the GT-CNRS UMI 2958 laboratory (\u003Ca href=\u0022http:\/\/gtl-umi.gatech.edu\u0022 target=\u0022_blank\u0022\u003Ehttp:\/\/gtl-umi.gatech.edu\u003C\/a\u003E) at Georgia Tech Lorraine (lorraine.gatech.edu) in Metz, France where the Nonlinear Dynamics and Optics group led by Profs. Citrin and Locquet.\u0026nbsp; According to Citrin, \u0026quot;This work is exciting as it opens the way to ultrahigh-speed compression of sparse signals--and we hope soon in a way to be carried out in the physical layer.\u0026quot;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022http:\/\/rdcu.be\/neD4\u0022\u003EView \u0026quot;Compressive Sensing with Optical Chaos\u0026quot; in Scientific Reports at this link.\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"\u0022Compressive Sensing with Optical Chaos\u0022 discusses the use of  chaotic optical signal for compression produced by an external-cavity semiconductor laser to generate sufficiently random-like numbers at very high rates."}],"uid":"27863","created_gmt":"2016-12-08 18:39:26","changed_gmt":"2016-12-08 18:41:58","author":"Christa Ernst","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2016-12-08T00:00:00-05:00","iso_date":"2016-12-08T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"groups":[{"id":"213791","name":"3D Systems Packaging Research Center"},{"id":"198081","name":"Georgia Electronic Design Center (GEDC)"},{"id":"197261","name":"Institute for Electronics and Nanotechnology"}],"categories":[{"id":"135","name":"Research"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"145","name":"Engineering"},{"id":"149","name":"Nanotechnology and Nanoscience"}],"keywords":[{"id":"172930","name":"David Citrin"},{"id":"29761","name":"Yves Berthelot"},{"id":"7037","name":"Justin Romberg"},{"id":"166855","name":"School of Electrical and Computer Engineering"},{"id":"172931","name":"The Institute for Electronics and Nantechnology"},{"id":"169638","name":"sensing"},{"id":"172932","name":"compression techniques"},{"id":"2768","name":"optics"},{"id":"172933","name":"Nonlinear Dynamics and Optics"}],"core_research_areas":[{"id":"39431","name":"Data Engineering and Science"},{"id":"39451","name":"Electronics and Nanotechnology"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":["christa.ernst@ien.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}