Results 61 to 70 of about 1,177,560 (190)

Nonparametric Statistical Thresholding for Sparse Magnetoencephalography Source Reconstructions

Frontiers in Neuroscience, 2012
Uncovering brain activity from MEG data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources.
Julia Parsons Owen, Julia Parsons Owen, Kensuke eSekihara, Srikantan S. Nagarajan, Srikantan S. Nagarajan   +4 more
doaj   +1 more source

Structure-Based Bayesian Sparse Reconstruction [PDF]

IEEE Transactions on Signal Processing, 2012
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates.
Ahmed Abdul Quadeer, Tareq Y. Al-Naffouri   +1 more
openaire   +4 more sources

Positive Competitive Networks for Sparse Reconstruction

Neural Computation
Abstract We propose and analyze a continuous-time firing-rate neural network, the positive firing-rate competitive network (PFCN), to tackle sparse reconstruction problems with non-negativity constraints. These problems, which involve approximating a given input stimulus from a dictionary using a set of sparse (active) neurons, play a ...
Veronica Centorrino, Anand Gokhale, Alexander Davydov 0001, Giovanni Russo 0002, Francesco Bullo   +4 more
openaire   +3 more sources

Deflection Tomography Reconstruction Based on Diagonal Total Variation

MATEC Web of Conferences, 2018
In view of the shortages of the reconstruction algorithm based on Total Variation (TV) minimum under the framework of measured field compressed sensing, we study the measured field sparse representation method and solving method of optimization equation,
Li Huaxin, Pan Jinxiao
doaj   +1 more source

Sparse representations in audio & music: from coding to source separation [PDF]

, 2010
—Sparse representations have proved a powerful toolin the analysis and processing of audio signals and already lieat the heart of popular coding standards such as MP3 andDolby AAC.
Davies, ME, Gribonval, R, Daudet, Laurent, Plumbley, MD, Davies, M.E.; id_orcid, Daudet, L., Blumensath, T., Plumbley, Mark D., Gribonval, Rémi, Davies, Mike E., Plumbley, M.D., Blumensath, Thomas, Gribonval, R., Blumensath, T, Daudet, L, Davies, M.E.   +15 more
core   +1 more source

Progressive dual-branch adversarial diffusion for sparse-view photoacoustic tomography reconstruction

Photoacoustics
Photoacoustic computed tomography (PACT) combines the high optical absorption contrast of optical excitation with the deep tissue penetration enabled by ultrasonic detection, making it a promising imaging modality.
Xin Li, Jiaming Peng, Yun Zhang, Shuixing Zhang, Liyu Huang, Yubo Wang   +5 more
doaj   +1 more source

Dictionary learning with large step gradient descent for sparse representations [PDF]

, 2012
This is the accepted version of an article published in Lecture Notes in Computer Science Volume 7191, 2012, pp 231-238.
Boris Mailhé, Mailhé, B, Plumbley, MD, Mark D. Plumbley, Mailhé, Boris, Plumbley, Mark, D., Plumbley, Mark,   +6 more
core   +1 more source

Sparse reconstruction methods in x-ray CT

, 2017
International audienceRecent progress in X-ray CT is contributing to the advent of new clinical applications. A common challenge for these applications is the need for new image reconstruction methods that meet tight constraints in radiation dose and ...
Monica Abella, Nicolas Ducros, Juan Felipe Perez Juste Abascal, Marinetto, Eugenio, Eugenio Marinetto, Abella, Monica, Abascal, Juan F P J, Ducros, Nicolas, Desco, Manuel, Manuel Desco, Peyrin, Françoise, Cyril Mory, Françoise Peyrin, Mory, Cyril, Claudia de Molina, de Molina, Claudia   +15 more
core   +1 more source

Bayesian modelling of music : algorithmic advances and experimental studies of shift-invariant sparse coding [PDF]

, 2005
PhDIn order to perform many signal processing tasks such as classification, pattern recognition and coding, it is helpful to specify a signal model in terms of meaningful signal structures.
Blumensath, Thomas
core   +3 more sources

A Low-Cost Computational Spectrometer Based on a Trained Sparse Base Matrix

Sensors
Computational spectrometers based on coded measurement and computational reconstruction have great application prospects. This paper proposes a computational spectrometer that has a low cost, is easy to implement in hardware, and has high reconstruction ...
Yanbo Gao, Hejia Pan, Yajuan Sheng, Rui Wen, Yuanhao Zheng, Lin Yang   +5 more
doaj   +1 more source