Compressed Sensing With Combinatorial Designs: Theory and Simulations
نویسندگان
چکیده
منابع مشابه
Compressed sensing and designs: theory and simulations
In An asymptotic result on compressed sensing matrices [4], a new construction for compressed sensing matrices using combinatorial design theory was introduced. In this paper, we analyse the performance of these matrices using deterministic and probabilistic methods. We provide a new recovery algorithm and detailed simulations. These simulations suggest that the construction is competitive with...
متن کاملCompressed Sensing: Theory and Applications
Compressed sensing is a novel research area, which was introduced in 2006, and since then has already become a key concept in various areas of applied mathematics, computer science, and electrical engineering. It surprisingly predicts that high-dimensional signals, which allow a sparse representation by a suitable basis or, more generally, a frame, can be recovered from what was previously cons...
متن کاملQuantum Compressed Sensing Using 2-Designs
We develop a method for quantum process tomography that combines the efficiency of compressed sensing with the robustness of randomized benchmarking. Our method is robust to state preparation and measurement errors, and it achieves a quadratic speedup over conventional tomography when the unknown process is a generic unitary evolution. Our method is based on PhaseLift, a convex programming tech...
متن کاملOn combinatorial approaches to compressed sensing
In this paper, we look at combinatorial algorithms for Compressed Sensing from a different perspective. We show that certain combinatorial solvers are in fact recursive implementations of convex relaxation methods for solving compressed sensing, under the assumption of sparsity for the projection matrix. We extend the notion of sparse binary projection matrices to sparse real-valued ones. We pr...
متن کاملINFORMATION THEORY TUTORIAL Compressed sensing
Signal recovery is a very practical and useful concept in both signal processing and communication area. Basically in compressed sensing, we are interested in compressing a signal, which is sparse in some domain and then, construct the original signal from the compressed one by convex optimization. This is very important to collect as less as measurements from the original signal while having t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2017
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2017.2717584