The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Andrea Abrardo - One of the best experts on this subject based on the ideXlab platform.
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density evolution based analysis and design of ldpc codes with a Priori Information
Information Theory and Applications, 2010Co-Authors: Marco Martalo, Andrea Abrardo, Gianluigi Ferrari, Michele M Franceschini, R RaheliAbstract:In this paper, we consider multiple access schemes with correlated sources, where a Priori Information, in terms of source correlation, is available at the access point (AP). In particular, we assume that each source uses a proper low-density parity-check (LDPC) code to transmit, through an additive white Gaussian noise (AWGN) channel, its Information sequence to the AP. At the AP, the Information sequences are recovered by an iterative decoder, with component decoders associated with the sources, which exploit the available a Priori Information. In order to analyze the behaviour of the considered multiple access coded system, we propose a density evolution-based approach, which allows to determine a signal-to-noise ratio (SNR) transfer chart and compute the system multi-dimensional SNR feasible region. The proposed technique, besides characterizing the performance of LDPC-coded multiple access scheme, is expedient to design optimized LDPC codes for this application.
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Performance bounds and codes design criteria for channel decoding with a-Priori Information
IEEE Transactions on Wireless Communications, 2009Co-Authors: Andrea AbrardoAbstract:In this article we focus on the channel decoding problem in presence of a-Priori Information. In particular, assuming that the a-Priori Information reliability is not perfectly estimated at the receiver, we derive a novel analytical framework for evaluating the decoder's performance. It is derived the important result that a ldquogood coderdquo, i.e., a code which allows to fully exploit the potential benefit of a-Priori Information, must associate Information sequences with high Hamming distances to codewords with low Hamming distances. Basing on the proposed analysis, we analyze the performance of random codes and turbo codes.
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Decoding with a-Priori Information: Analysis and codes’ design criteria
2008 3rd International Symposium on Communications Control and Signal Processing, 2008Co-Authors: Andrea AbrardoAbstract:An analytical framework for evaluating the performance of decoding in presence of a-Priori is provided. It is shown that a "good code", i.e., a code which allows to fully exploit the potential benefit of a-Priori Information, must be characterized by both a high minimum Hamming weight and a high minimum Information weight. This is a completely novel result which may help the design of good channel codes in presence of a- Priori Information. Basing on the proposed analysis, we derive a measure of the performance gain due to a-Priori Information that can be obtained by random codes with infinite length. It is then shown that turbo codes with very long interleavers approach the behavior of random codes. Finally, an effective joint source-channel decoding scheme is proposed in a wireless sensors network scenario where two nodes detect correlated sources and deliver them to a central collector.
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Performance bounds and codes design criteria for channel decoding with a-Priori Information
arXiv: Information Theory, 2007Co-Authors: Andrea AbrardoAbstract:In this article we focus on the problem of channel decoding in presence of a-Priori Information. In particular, assuming that the a-Priori Information reliability is not perfectly estimated at the receiver, we derive a novel analytical framework for evaluating the decoder's performance. It is derived the important result that a "good code", i.e., a code which allows to fully exploit the potential benefit of a-Priori Information, must associate Information sequences with high Hamming weights to codewords with low Hamming weights. Basing on the proposed analysis, we analyze the performance of convolutional codes, random codes, and turbo codes. Moreover, we consider the transmission of correlated binary sources from independent nodes, a problem which has several practical applications, e.g. in the case of sensor networks. In this context, we propose a very simple joint source-channel turbo decoding scheme where each decoder works by exploiting a-Priori Information given by the other decoder. In the case of block fading channels, it is shown that the inherent correlation between Information signals provide a form of non-cooperative diversity, thus allowing joint source-channel decoding to outperform separation-based schemes.
Arion F Chatziioannou - One of the best experts on this subject based on the ideXlab platform.
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source reconstruction for spectrally resolved bioluminescence tomography with sparse a Priori Information
Optics Express, 2009Co-Authors: Yujie Lu, Jie Tian, Xiaoqun Zhang, Ali Douraghy, David B Stout, Tony F Chan, Arion F ChatziioannouAbstract:Through restoration of the light source Information in small animals in vivo, optical molecular imaging, such as fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT), can depict biological and physiological changes observed using molecular probes. A Priori Information plays an indispensable role in tomographic reconstruction. As a type of a Priori Information, the sparsity characteristic of the light source has not been sufficiently considered to date. In this paper, we introduce a compressed sensing method to develop a new tomographic algorithm for spectrally-resolved bioluminescence tomography. This method uses the nature of the source sparsity to improve the reconstruction quality with a regularization implementation. Based on verification of the inverse crime, the proposed algorithm is validated with Monte Carlo-based synthetic data and the popular Tikhonov regularization method. Testing with different noise levels and single/multiple source settings at different depths demonstrates the improved performance of this algorithm. Experimental reconstruction with a mouse-shaped phantom further shows the potential of the proposed algorithm. (C) 2009 Optical Society of America
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source reconstruction for spectrally resolved bioluminescence tomography with sparse a Priori Information
Optics Express, 2009Co-Authors: Xiaoqun Zhang, Jie Tian, Ali Douraghy, David B Stout, Tony F Chan, Arion F ChatziioannouAbstract:Through restoration of the light source Information in small animals in vivo, optical molecular imaging, such as fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT), can depict biological and physiological changes observed using molecular probes. A Priori Information plays an indispensable role in tomographic reconstruction. As a type of a Priori Information, the sparsity characteristic of the light source has not been sufficiently considered to date. In this paper, we introduce a compressed sensing method to develop a new tomographic algorithm for spectrally-resolved bioluminescence tomography. This method uses the nature of the source sparsity to improve the reconstruction quality with a regularization implementation. Based on verification of the inverse crime, the proposed algorithm is validated with Monte Carlo-based synthetic data and the popular Tikhonov regularization method. Testing with different noise levels and single/multiple source settings at different depths demonstrates the improved performance of this algorithm. Experimental reconstruction with a mouse-shaped phantom further shows the potential of the proposed algorithm. (C) 2009 Optical Society of America
Jie Tian - One of the best experts on this subject based on the ideXlab platform.
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source reconstruction for spectrally resolved bioluminescence tomography with sparse a Priori Information
Optics Express, 2009Co-Authors: Xiaoqun Zhang, Jie Tian, Ali Douraghy, David B Stout, Tony F Chan, Arion F ChatziioannouAbstract:Through restoration of the light source Information in small animals in vivo, optical molecular imaging, such as fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT), can depict biological and physiological changes observed using molecular probes. A Priori Information plays an indispensable role in tomographic reconstruction. As a type of a Priori Information, the sparsity characteristic of the light source has not been sufficiently considered to date. In this paper, we introduce a compressed sensing method to develop a new tomographic algorithm for spectrally-resolved bioluminescence tomography. This method uses the nature of the source sparsity to improve the reconstruction quality with a regularization implementation. Based on verification of the inverse crime, the proposed algorithm is validated with Monte Carlo-based synthetic data and the popular Tikhonov regularization method. Testing with different noise levels and single/multiple source settings at different depths demonstrates the improved performance of this algorithm. Experimental reconstruction with a mouse-shaped phantom further shows the potential of the proposed algorithm. (C) 2009 Optical Society of America
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source reconstruction for spectrally resolved bioluminescence tomography with sparse a Priori Information
Optics Express, 2009Co-Authors: Yujie Lu, Jie Tian, Xiaoqun Zhang, Ali Douraghy, David B Stout, Tony F Chan, Arion F ChatziioannouAbstract:Through restoration of the light source Information in small animals in vivo, optical molecular imaging, such as fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT), can depict biological and physiological changes observed using molecular probes. A Priori Information plays an indispensable role in tomographic reconstruction. As a type of a Priori Information, the sparsity characteristic of the light source has not been sufficiently considered to date. In this paper, we introduce a compressed sensing method to develop a new tomographic algorithm for spectrally-resolved bioluminescence tomography. This method uses the nature of the source sparsity to improve the reconstruction quality with a regularization implementation. Based on verification of the inverse crime, the proposed algorithm is validated with Monte Carlo-based synthetic data and the popular Tikhonov regularization method. Testing with different noise levels and single/multiple source settings at different depths demonstrates the improved performance of this algorithm. Experimental reconstruction with a mouse-shaped phantom further shows the potential of the proposed algorithm. (C) 2009 Optical Society of America
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MICCAI (1) - Spatial Weighed Element Based FEM Incorporating a Priori Information on Bioluminescence Tomography
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2008Co-Authors: Jin Shi, Jie Tian, Wei YangAbstract:Bioluminescence tomography (BLT) is a promising imaging technique which may dynamically and real-timely detect the molecular and cellular activity at the whole-body level in small animal studies. In view of the ill-posedness of the BLT, it is hard to fully reconstruct source density. In addition, the uniqueness theorem on BLT indicates that it is important to employ a Priori Information for accurate source reconstruction. Hence, we adopt diffuse optical tomography (DOT) technique to provide optical parameters of main tissues as a Priori Information. Besides, we restrict the range of real source to a permissible region to raise the numerical stability and reduce the ill-posedness of BLT. Next, we forward Spatial Weighed Element based Finite Element Method and compare it's solutions with analytic formula and MOSE. Numerical simulation of homogeneous and heterogeneous phantom demonstrates the source location and density with prior Information is better than that not using a Priori Information.
Paul L. Carson - One of the best experts on this subject based on the ideXlab platform.
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first arrival traveltime sound speed inversion with a Priori Information
Medical Physics, 2014Co-Authors: Fong Ming Hooi, Paul L. CarsonAbstract:Purpose: A first-arrival travel-time sound speed algorithm presented byTarantola [Inverse Problem Theory and Methods for Model Parameter Estimation (SIAM, Philadelphia, PA, 2005)] is adapted to the medical ultrasonics setting. Through specification of a covariance matrix for the object model, the algorithm allows for natural inclusion of physical a Priori Information of the object. The algorithm's ability to accurately and robustly reconstruct a complex sound speed distribution is demonstrated on simulation and experimental data using a limited aperture. Methods: The algorithm is first demonstrated generally in simulation with a numerical breast phantom imaged in different geometries. As this work is motivated by the authors' limited aperture dual sided ultrasound breast imaging system, experimental data are acquired with a Verasonics system with dual, 128 element, linear L7-4 arrays. The transducers are automatically calibrated for usage in the eikonal forward model.A Priori Information such as knowledge of correlated regions within the object is obtained via segmentation of B-mode images generated from synthetic aperture imaging. Results: As one illustration of the algorithm's facility for inclusion ofa Priori Information, physically grounded regularization is demonstrated in simulation. The algorithm's practicality is then demonstrated through experimental realization in limited aperture cases. Reconstructions of sound speed distributions of various complexity are improved through inclusion of a Priori Information. The sound speed maps are generally reconstructed with accuracy within a few m/s. Conclusions: This paper demonstrates the ability to form sound speed images using two opposed commercial linear arrays to mimic ultrasound image acquisition in the compressed mammographic geometry. The ability to create reasonably good speed of sound images in the compressed mammographic geometry allows images to be readily coregistered to tomosynthesis image volumes for breast cancer detection and characterization studies.
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First‐arrival traveltime sound speed inversion with a Priori Information
Medical physics, 2014Co-Authors: Fong Ming Hooi, Paul L. CarsonAbstract:Purpose: A first-arrival travel-time sound speed algorithm presented byTarantola [Inverse Problem Theory and Methods for Model Parameter Estimation (SIAM, Philadelphia, PA, 2005)] is adapted to the medical ultrasonics setting. Through specification of a covariance matrix for the object model, the algorithm allows for natural inclusion of physical a Priori Information of the object. The algorithm's ability to accurately and robustly reconstruct a complex sound speed distribution is demonstrated on simulation and experimental data using a limited aperture. Methods: The algorithm is first demonstrated generally in simulation with a numerical breast phantom imaged in different geometries. As this work is motivated by the authors' limited aperture dual sided ultrasound breast imaging system, experimental data are acquired with a Verasonics system with dual, 128 element, linear L7-4 arrays. The transducers are automatically calibrated for usage in the eikonal forward model.A Priori Information such as knowledge of correlated regions within the object is obtained via segmentation of B-mode images generated from synthetic aperture imaging. Results: As one illustration of the algorithm's facility for inclusion ofa Priori Information, physically grounded regularization is demonstrated in simulation. The algorithm's practicality is then demonstrated through experimental realization in limited aperture cases. Reconstructions of sound speed distributions of various complexity are improved through inclusion of a Priori Information. The sound speed maps are generally reconstructed with accuracy within a few m/s. Conclusions: This paper demonstrates the ability to form sound speed images using two opposed commercial linear arrays to mimic ultrasound image acquisition in the compressed mammographic geometry. The ability to create reasonably good speed of sound images in the compressed mammographic geometry allows images to be readily coregistered to tomosynthesis image volumes for breast cancer detection and characterization studies.
Xiaoqun Zhang - One of the best experts on this subject based on the ideXlab platform.
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source reconstruction for spectrally resolved bioluminescence tomography with sparse a Priori Information
Optics Express, 2009Co-Authors: Yujie Lu, Jie Tian, Xiaoqun Zhang, Ali Douraghy, David B Stout, Tony F Chan, Arion F ChatziioannouAbstract:Through restoration of the light source Information in small animals in vivo, optical molecular imaging, such as fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT), can depict biological and physiological changes observed using molecular probes. A Priori Information plays an indispensable role in tomographic reconstruction. As a type of a Priori Information, the sparsity characteristic of the light source has not been sufficiently considered to date. In this paper, we introduce a compressed sensing method to develop a new tomographic algorithm for spectrally-resolved bioluminescence tomography. This method uses the nature of the source sparsity to improve the reconstruction quality with a regularization implementation. Based on verification of the inverse crime, the proposed algorithm is validated with Monte Carlo-based synthetic data and the popular Tikhonov regularization method. Testing with different noise levels and single/multiple source settings at different depths demonstrates the improved performance of this algorithm. Experimental reconstruction with a mouse-shaped phantom further shows the potential of the proposed algorithm. (C) 2009 Optical Society of America
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source reconstruction for spectrally resolved bioluminescence tomography with sparse a Priori Information
Optics Express, 2009Co-Authors: Xiaoqun Zhang, Jie Tian, Ali Douraghy, David B Stout, Tony F Chan, Arion F ChatziioannouAbstract:Through restoration of the light source Information in small animals in vivo, optical molecular imaging, such as fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT), can depict biological and physiological changes observed using molecular probes. A Priori Information plays an indispensable role in tomographic reconstruction. As a type of a Priori Information, the sparsity characteristic of the light source has not been sufficiently considered to date. In this paper, we introduce a compressed sensing method to develop a new tomographic algorithm for spectrally-resolved bioluminescence tomography. This method uses the nature of the source sparsity to improve the reconstruction quality with a regularization implementation. Based on verification of the inverse crime, the proposed algorithm is validated with Monte Carlo-based synthetic data and the popular Tikhonov regularization method. Testing with different noise levels and single/multiple source settings at different depths demonstrates the improved performance of this algorithm. Experimental reconstruction with a mouse-shaped phantom further shows the potential of the proposed algorithm. (C) 2009 Optical Society of America
