Results 11 to 20 of about 122,061 (212)
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary In this contribution, we propose a detailed study of interpolation‐based data‐driven methods that are of relevance in the model reduction and also in the systems and control communities. The data are given by samples of the transfer function of the underlying (unknown) model, that is, we analyze frequency‐response data.
Quirin Aumann, Ion Victor Gosea
wiley +1 more source
Advanced Engineering Materials, EarlyView., 2023 A novel method for tracking structural changes in gels using widely accessible microcomputed tomography is presented and validated for various hydro‐, alco‐, and aerogels. The core idea of the method is to track positions of micrometer‐sized tracer particles entrapped in the gel and relate them to the density of the gel network.
Anja Hajnal +3 more
wiley +1 more source
Compressed wavefront sensing [PDF]
Optics Letters, 2014 We report on an algorithm for fast wavefront sensing that incorporates sparse representation for the first time in practice. The partial derivatives of optical wavefronts were sampled sparsely with a Shack-Hartman wavefront sensor (SHWFS) by randomly subsampling the original SHWFS data to as little as 5%.
Ryan P. McNabb +3 more
openaire +3 more sources
International Journal of Engineering Technologies and Management Research, 2020 Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality.
openaire +3 more sources
Perceptual Compressive Sensing [PDF]
, 2018 Accepted by The First Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2018). This is a pre-print version (not final version)
Jiang Du +3 more
openaire +4 more sources
Computational Complexity versus Statistical Performance on Sparse Recovery Problems [PDF]
, 2018 We show that several classical quantities controlling compressed sensing performance directly match classical parameters controlling algorithmic complexity.
Boumal, Nicolas +2 more
core +4 more sources
Blind Compressed Sensing [PDF]
IEEE Transactions on Information Theory, 2011 The fundamental principle underlying compressed sensing is that a signal, which is sparse under some basis representation, can be recovered from a small number of linear measurements. However, prior knowledge of the sparsity basis is essential for the recovery process.
Yonina C. Eldar, Sivan Gleichman
openaire +2 more sources
A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging [PDF]
, 2017 Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling.
Edgar, Matthew P. +4 more
core +1 more source
Experimentally exploring compressed sensing quantum tomography [PDF]
, 2016 In the light of the progress in quantum technologies, the task of verifying the correct functioning of processes and obtaining accurate tomographic information about quantum states becomes increasingly important.
Bell, B. A. +8 more
core +4 more sources
(Compressed) sensing and sensibility [PDF]
Proceedings of the National Academy of Sciences, 2011 For decades, researchers have built computer models of molecular interactions to predict properties of new molecules (1). These models take the form of potential functions, equations that can be used predict the molecular energy of interaction. Potential functions have very broad applications. Other than ab initio quantum mechanics-based approaches (2),
openaire +2 more sources