[01149] Sparse optimization techniques and applications
Session Time & Room : 4E (Aug.24, 17:40-19:20) @D515
Type : Proposal of Minisymposium
Abstract : Natural data that arise in several applications (such as biomedical imaging) are inherently sparse in suitable transformation domains provided in general by the gradient, wavelets, and their other variants. Such data sets can be stored in terms of a few samples, which in turn can be used for retrieving the original data with minimal or no loss of information via sparsity-seeking optimization techniques. A wealth of recent developments - going by the name of compressive sensing - aim at signal acquisition compressively and sparse (or economical) description of data of certain types. Of late, this area of research has seen some fascinating developments, which include adaptive solvers, sparsity-driven deep learning methods, hardware-friendly algorithms suitable for biomedical imaging and impedance tomography, etc. The symposium aims at discussing some recent developments in sparse representation/optimization theory that pertain to fundamental as well as application-centric topics.
Organizer(s) : K. Z. Najiya, R. Ramu Naidu, Pradip Sasmal, Phanindra Jampana
[05058] A new matrix factorization for sparse representation of over-determined systems
Format : Talk at Waseda University
Author(s) :
NAJIYA K Z (PhD Scholar)
C S Sastry (IIT Hyderabad)
Abstract : This presentation aims at discussing a novel method that finds a sparse approximation of a matrix system y ̴ Ax (where A has a bigger row size compared to its column size). While highlighting the need for such an approximation through some applications, the presentation realizes its objective via a new matrix factorization. Besides, it compares and contrasts the proposed method with established ones that have similar objectives.
[05060] Sparse optimization-based ERT algorithms for multiphase flows
Format : Talk at Waseda University
Author(s) :
NAJIYA K Z (PhD Scholar)
Shantam Gulati (IIT Hyderabad)
Abstract : We discuss applications of the compressed sensing framework mainly in the field of Electrical Impedance tomography (EIT). EIT is a scanning technique that draws a relationship between the impedance inside the domain and the current to voltage map on the boundary at the electrodes. In particular, we wish to address the ill-posed inverse problem in the circular domain. The idea is to draw comparisons and improve upon the existing techniques with L 1 and the weighted-norm approaches.
[05061] Hardware-friendly binary frames for sparse optimization
Format : Talk at Waseda University
Author(s) :
NAJIYA K Z (PhD Scholar)
Prasad Theeda (Vellore Institute of Technology)
Pradip Sasmal
Abstract : Binary matrices are preferred as compressed sensing (CS) matrices because they are hardware-friendly and support low-complexity sparse recovery algorithms. In this talk, we discuss that the disjunctness property of a binary matrix, which has been used in non-adaptive group testing, can also be very useful for recovering sparse signals. Disjunct matrices are particularly well-suited as compressed sensing matrices because they can support a non-iterative, fast sparse recovery algorithm.
