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Distributed field reconstruction with model-robust basis pursuit
2012
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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We propose a model-robust adaptation to basis pursuit to control for the error arising from the spatial quantization. ...
Experiments show that the two types of robust estimators successfully address infeasibility and consistency issues that arise in basis pursuit for spatially quantized acoustic sources. ...
MODEL ROBUST BASIS PURSUIT (MRBP) The observation models, (2) and (6) , are a linear approximation of the sensor measurements. ...
doi:10.1109/icassp.2012.6288467
dblp:conf/icassp/SchmidtM12
fatcat:jwwweortxrejnbhctmepstv2ji
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit
[article]
2014
arXiv
pre-print
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Applying this framework to a model system consisting of an active eye behaving in a time varying environment, we find that this generic principle leads to the simultaneous development of both smooth pursuit ...
We suggest that this general principle may form the basis for a unified and integrated explanation of many perception/action loops. ...
Our model is consistent with the behavior in presaccadic pursuit. ...
arXiv:1402.3344v2
fatcat:alq6a4jzlzbfxd5ojgik5d5cqq
Conceptualization and Significance Study of a New Appliation CS-MIR
[chapter]
2012
IFIP Advances in Information and Communication Technology
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it affects the reconstruction of MIR. ...
Numerous researches on Music Information Retrieval (MIR) have been estimated and linked with sparse representation method, few has paid enough attention on the application of compressive sensing and how ...
In the paper [15] authors discussed a streaming CS framework and greedy reconstruction algorithm, the Streaming Greedy Pursuit (SGP), to reconstruct signals with sparse frequency content, in which their ...
doi:10.1007/978-3-642-33409-2_16
fatcat:77bh2z7pbfa2zlsesx3gh5rpku
Convolutional higher order matching pursuit
2016
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)
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The resulting algorithm adapts to varying signal and noise distributions to flexibly recover source signals in a variety of settings. ...
the pursuit in the domain of high-order multivariate cumulant statistics. ...
Signal distributions were modelled as mixtures of Gaussians, with parameters selected by non-linear least-squares to match an intended set of moments. ...
doi:10.1109/mlsp.2016.7738847
dblp:conf/mlsp/BohnerS16
fatcat:5lhrezgwxbb3jiquesyxo7hkh4
FIGURE ACKNOWLEDGEMENTS
[chapter]
1984
The Solar System
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RIC based bounds x − x 2 ≤ 4 √ 1+δ 2k 1−(1+ √ 2)δ 2k ε for the Basis Pursuit with Bernoulli sensing matrices and n = 256 with ε = 1. ...
MC based bound x − x 2 ≤ 2 1−µ(A)(4k−1) ε for the Basis Pursuit for Hadamard sensing matrices with n = 2048. ...
block-sparse vectors and low-rank matrices. • Procedures for optimization of system design. • Computationally efficient algorithms for sensing matrices other than Fourier or Hadamard. • Minimization of modeling ...
doi:10.1016/b978-0-08-026495-0.50023-5
fatcat:o55v5o3cjjbwdnvnqcr3dfbv3e
Figure acknowledgements
[chapter]
2007
Pinch Analysis and Process Integration
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RIC based bounds x − x 2 ≤ 4 √ 1+δ 2k 1−(1+ √ 2)δ 2k ε for the Basis Pursuit with Bernoulli sensing matrices and n = 256 with ε = 1. ...
MC based bound x − x 2 ≤ 2 1−µ(A)(4k−1) ε for the Basis Pursuit for Hadamard sensing matrices with n = 2048. ...
block-sparse vectors and low-rank matrices. • Procedures for optimization of system design. • Computationally efficient algorithms for sensing matrices other than Fourier or Hadamard. • Minimization of modeling ...
doi:10.1016/b978-075068260-2.50005-5
fatcat:pbya65rzfrafrflt25u36rleee
Figure acknowledgements
[chapter]
2007
Marine Rudders and Control Surfaces
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RIC based bounds x − x 2 ≤ 4 √ 1+δ 2k 1−(1+ √ 2)δ 2k ε for the Basis Pursuit with Bernoulli sensing matrices and n = 256 with ε = 1. ...
MC based bound x − x 2 ≤ 2 1−µ(A)(4k−1) ε for the Basis Pursuit for Hadamard sensing matrices with n = 2048. ...
block-sparse vectors and low-rank matrices. • Procedures for optimization of system design. • Computationally efficient algorithms for sensing matrices other than Fourier or Hadamard. • Minimization of modeling ...
doi:10.1016/b978-075066944-3/50003-2
fatcat:h3y6o3qv6fhz5msm4ljntx47du
Figure acknowledgements
[chapter]
2009
Cardiovascular Physiology: Questions for Self Assessment
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RIC based bounds x − x 2 ≤ 4 √ 1+δ 2k 1−(1+ √ 2)δ 2k ε for the Basis Pursuit with Bernoulli sensing matrices and n = 256 with ε = 1. ...
MC based bound x − x 2 ≤ 2 1−µ(A)(4k−1) ε for the Basis Pursuit for Hadamard sensing matrices with n = 2048. ...
block-sparse vectors and low-rank matrices. • Procedures for optimization of system design. • Computationally efficient algorithms for sensing matrices other than Fourier or Hadamard. • Minimization of modeling ...
doi:10.1201/b13483-22
fatcat:jlmbz76hr5axfkrkfn3s2b3xty
Figure acknowledgements
[chapter]
2010
Veterinary Ocular Pathology
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RIC based bounds x − x 2 ≤ 4 √ 1+δ 2k 1−(1+ √ 2)δ 2k ε for the Basis Pursuit with Bernoulli sensing matrices and n = 256 with ε = 1. ...
MC based bound x − x 2 ≤ 2 1−µ(A)(4k−1) ε for the Basis Pursuit for Hadamard sensing matrices with n = 2048. ...
block-sparse vectors and low-rank matrices. • Procedures for optimization of system design. • Computationally efficient algorithms for sensing matrices other than Fourier or Hadamard. • Minimization of modeling ...
doi:10.1016/b978-0-7020-2797-0.00019-9
fatcat:es5m5zjmpvh43k75ly5gupf6ly
Universal distributed sensing via random projections
2006
Proceedings of the fifth international conference on Information processing in sensor networks - IPSN '06
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This paper develops a new framework for distributed coding and compression in sensor networks based on distributed compressed sensing (DCS). ...
DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity; just a few measurements of a jointly sparse signal ensemble contain enough information for reconstruction ...
Greedy pursuit methods have also been proposed for CS reconstruction, including Orthogonal Matching Pursuit (OMP), which tend to require fewer computations but at the expense of slightly more measurements ...
doi:10.1145/1127777.1127807
dblp:conf/ipsn/DuarteWBB06
fatcat:lyhlfnfyhnglvj6myfj54geome
Universal distributed sensing via random projections
2006
2006 5th International Conference on Information Processing in Sensor Networks
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This paper develops a new framework for distributed coding and compression in sensor networks based on distributed compressed sensing (DCS). ...
DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity; just a few measurements of a jointly sparse signal ensemble contain enough information for reconstruction ...
Greedy pursuit methods have also been proposed for CS reconstruction, including Orthogonal Matching Pursuit (OMP), which tend to require fewer computations but at the expense of slightly more measurements ...
doi:10.1109/ipsn.2006.244161
fatcat:bhv3emjuqzcmrf2bwykcz7n45u
Compressive Sensing for Background Subtraction
[chapter]
2008
Lecture Notes in Computer Science
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Moreover, the resulting compressive measurements are robust against packet drops over communication channels with graceful degradation in reconstruction accuracy, as the image information is fully distributed ...
The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse in some basis, e.g., wavelets. ...
We employ Basis Pursuit Denoising methods [14] as well as total variation minimization [5] as convex objectives to process field data. 2. ...
doi:10.1007/978-3-540-88688-4_12
fatcat:utgpijdudrfo3fya7x4sl66hsy
Autonomous Development of Active Binocular and Motion Vision Through Active Efficient Coding
2019
Frontiers in Neurorobotics
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Furthermore, we show that the emerging sensory tuning properties are in line with results on disparity, motion, and motion-in-depth tuning in the visual cortex of mammals. ...
We present a model for the autonomous and simultaneous learning of active binocular and motion vision. ...
Right to the preprocessed images are the respective images reconstructed with random Gabor wavelets at initialization time and the images reconstructed with learned basis functions at the end of training ...
doi:10.3389/fnbot.2019.00049
pmid:31379548
pmcid:PMC6646586
fatcat:waz54mhoefghxiohz324gjv6fe
A Robust Reweighted L1-Minimization Imaging Algorithm for Passive Millimeter Wave SAIR in Near Field
2015
Sensors
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images in near field. ...
However, it cannot always obtain the sparsest solution and may yield outliers with the non-adaptive random measurement matrix adopted by current CS models. ...
are largely incoherent with any fixed basis. ...
doi:10.3390/s151024945
pmid:26404282
pmcid:PMC4634407
fatcat:v4x646zxkrf7zn535che2rqgra
Application of Compressive Sensing to Ultrasound Images: A Review
2019
BioMed Research International
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Compressive sensing (CS) offers compression of data below the Nyquist rate, making it an attractive solution in the field of medical imaging, and has been extensively used for ultrasound (US) compression ...
This paper reviews a number of significant CS algorithms used to recover US images from the undersampled data along with the discussion of CS in 3D US images. ...
In these studies, Field II is used to generate the images with different settings of PSF, TRF, and various distributions of scatterers. ...
doi:10.1155/2019/7861651
pmid:31828130
pmcid:PMC6885152
fatcat:w7f5wojvgrfn7mejqm2z3cipoq
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