3,197 research outputs found
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Algorithms for the Construction of Incoherent Frames Under Various Design Constraints
Unit norm finite frames are generalizations of orthonormal bases with many
applications in signal processing. An important property of a frame is its
coherence, a measure of how close any two vectors of the frame are to each
other. Low coherence frames are useful in compressed sensing applications. When
used as measurement matrices, they successfully recover highly sparse solutions
to linear inverse problems. This paper describes algorithms for the design of
various low coherence frame types: real, complex, unital (constant magnitude)
complex, sparse real and complex, nonnegative real and complex, and harmonic
(selection of rows from Fourier matrices). The proposed methods are based on
solving a sequence of convex optimization problems that update each vector of
the frame. This update reduces the coherence with the other frame vectors,
while other constraints on its entries are also imposed. Numerical experiments
show the effectiveness of the methods compared to the Welch bound, as well as
other competing algorithms, in compressed sensing applications
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