2,131 research outputs found
Autocalibrating and Calibrationless Parallel Magnetic Resonance Imaging as a Bilinear Inverse Problem
Modern reconstruction methods for magnetic resonance imaging (MRI) exploit
the spatially varying sensitivity profiles of receive-coil arrays as additional
source of information. This allows to reduce the number of time-consuming
Fourier-encoding steps by undersampling. The receive sensitivities are a priori
unknown and influenced by geometry and electric properties of the (moving)
subject. For optimal results, they need to be estimated jointly with the image
from the same undersampled measurement data. Formulated as an inverse problem,
this leads to a bilinear reconstruction problem related to multi-channel blind
deconvolution. In this work, we will discuss some recently developed approaches
for the solution of this problem.Comment: 3 pages, 3 figures, 12th International Conference on Sampling Theory
and Applications, Tallinn 201
Stable Recovery from the Magnitude of Symmetrized Fourier Measurements
In this note we show that stable recovery of complex-valued signals
up to global sign can be achieved from the magnitudes of
Fourier measurements when a certain "symmetrization and zero-padding" is
performed before measurement ( is possible in certain cases). For real
signals, symmetrization itself is linear and therefore our result is in this
case a statement on uniform phase retrieval. Since complex conjugation is
involved, such measurement procedure is not complex-linear but recovery is
still possible from magnitudes of linear measurements on, for example,
.Comment: 4 pages, will be submitted to ICASSP1
Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks
In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods
- …