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Distributed field reconstruction with model-robust basis pursuit

Aurora Schmidt, Jose M. F. Moura
2012 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
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]

Chong Zhang, Yu Zhao, Jochen Triesch, Bertram E. Shi
2014 arXiv   pre-print
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]

Kaichun K. Chang, Carl Barton, Costas S. Iliopoulos, Jyh-Shing Roger Jang
2012 IFIP Advances in Information and Communication Technology  
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

Gergo Bohner, Maneesh Sahani
2016 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)  
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  
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  
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  
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  
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  
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

Marco F. Duarte, Michael B. Wakin, Dror Baron, Richard G. Baraniuk
2006 Proceedings of the fifth international conference on Information processing in sensor networks - IPSN '06  
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

M.R. Duarte, M.B. Wakin, D. Baron, R.G. Baraniuk
2006 2006 5th International Conference on Information Processing in Sensor Networks  
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]

Volkan Cevher, Aswin Sankaranarayanan, Marco F. Duarte, Dikpal Reddy, Richard G. Baraniuk, Rama Chellappa
2008 Lecture Notes in Computer Science  
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

Alexander Lelais, Jonas Mahn, Vikram Narayan, Chong Zhang, Bertram E. Shi, Jochen Triesch
2019 Frontiers in Neurorobotics  
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

Yilong Zhang, Yuehua Li, Shujin Zhu, Yuanjiang Li
2015 Sensors  
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

Musyyab Yousufi, Muhammad Amir, Umer Javed, Muhammad Tayyib, Suheel Abdullah, Hayat Ullah, Ijaz Mansoor Qureshi, Khurram Saleem Alimgeer, Muhammad Waseem Akram, Khan Bahadar Khan
2019 BioMed Research International  
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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