Shannon Seminar Room, Place du Levant 3, Maxwell Building, 1st floor -- Wednesday, 28 September 2016 at 11:00 (45 min.)
{
"name":"Restricted Range Space Property Based Theory for 1-Bit Compressive Sensing",
"description":"Plenty works have been devoted to the study of compressive sensing over the past decades. Such a promising development of compressive sensing has a great impact on many aspects of signal and image processing. One of the key mathematical problems addressed in compressive sensing is how to reconstruct a sparse signal from linear/nonlinear measurements via a decoding algorithm. In the classic compressive sensing, it is well known that to exactly reconstruct the sparsest signal from a limited number of linear measurements is possible when the sensing matrix admits certain properties.",
"startDate":"2016-09-28",
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"startTime":"11:00",
"endTime":"11:45",
"location":"Shannon Seminar Room, Place du Levant 3, Maxwell Building, 1st floor",
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Plenty works have been devoted to the study of compressive sensing over the past decades. Such a promising development of compressive sensing has a great impact on many aspects of signal and image processing. One of the key mathematical problems addressed in compressive sensing is how to reconstruct a sparse signal from linear/nonlinear measurements via a decoding algorithm. In the classic compressive sensing, it is well known that to exactly reconstruct the sparsest signal from a limited number of linear measurements is possible when the sensing matrix admits certain properties.
