The Experts below are selected from a list of 16059 Experts worldwide ranked by ideXlab platform
I-hsin Yang - One of the best experts on this subject based on the ideXlab platform.
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Cross-reference weighted Least Square Estimates for positron emission tomography
IEEE transactions on medical imaging, 1998Co-Authors: Chung-ming Chen, I-hsin YangAbstract:An efficient new method, termed as the cross-reference weighted Least Square Estimate (WLSE) [CRWLSE], is proposed to integrate the incomplete local smoothness information to improve the reconstruction of positron emission tomography (PET) images in the presence of accidental coincidence events and attenuation. The algebraic reconstruction technique (ART) is applied to this new Estimate and the convergence is proved. This numerical technique is based on row operations. The computational complexity is only linear in the sizes of pixels and detector tubes. Hence, it is efficient in storage and computation for a large and sparse system. Moreover, the easy incorporation of range limits and spatially variant penalty will not deprive the efficiency. All this makes the new method practically applicable. An automatically data-driven selection method for this new Estimate based on the generalized cross validation is also studied. The Monte Carlo studies demonstrate the advantages of this new method.
Chung-ming Chen - One of the best experts on this subject based on the ideXlab platform.
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Cross-reference weighted Least Square Estimates for positron emission tomography
IEEE transactions on medical imaging, 1998Co-Authors: Chung-ming Chen, I-hsin YangAbstract:An efficient new method, termed as the cross-reference weighted Least Square Estimate (WLSE) [CRWLSE], is proposed to integrate the incomplete local smoothness information to improve the reconstruction of positron emission tomography (PET) images in the presence of accidental coincidence events and attenuation. The algebraic reconstruction technique (ART) is applied to this new Estimate and the convergence is proved. This numerical technique is based on row operations. The computational complexity is only linear in the sizes of pixels and detector tubes. Hence, it is efficient in storage and computation for a large and sparse system. Moreover, the easy incorporation of range limits and spatially variant penalty will not deprive the efficiency. All this makes the new method practically applicable. An automatically data-driven selection method for this new Estimate based on the generalized cross validation is also studied. The Monte Carlo studies demonstrate the advantages of this new method.
Udo Frese - One of the best experts on this subject based on the ideXlab platform.
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Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping
Autonomous Robots, 2006Co-Authors: Udo FreseAbstract:This article presents a very efficient SLAM algorithm that works by hierarchically dividing a map into local regions and subregions. At each level of the hierarchy each region stores a matrix representing some of the landmarks contained in this region. To keep those matrices small, only those landmarks are represented that are observable from outside the region. A measurement is integrated into a local subregion using O(k(2)) computation time for k landmarks in a subregion. When the robot moves to a different subregion a full Least-Square Estimate for that region is computed in only O(k(3) log n) computation time for n landmarks. A global Least Square Estimate needs O(kn) computation time with a very small constant (12.37 ms for n = 11300). The algorithm is evaluated for map quality, storage space and computation time using simulated and real experiments in an office environment.
Gui Yong-xin - One of the best experts on this subject based on the ideXlab platform.
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The Least Square Estimate of covariance matrices in the growth curve model with random effects
Journal of Central China Normal University, 2004Co-Authors: Gui Yong-xinAbstract:In this paper, a Least Square Estimate of the covariance matrices Σ, Γ and their linear function tr(CΣ+DΓ) of the growth curve model with random regression (coefficient) structure has been given respectively by using the projective theory and the spectrum decomposition of matrix. Finally, it gives some optimal properties of tr(CΣ\+*+DΓ\+*).
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The Least Square Estimate of Covariance Matrix in the Restricted Growth Curve Model
Journal of Xianning Teachers College, 2002Co-Authors: Gui Yong-xinAbstract:The paper derives The Least Square Estimate of the unknown Covariance matrix Σ and tr(CΣ) on the extension of Projective theory and the spectrum decomposition of matrix,satisfying the given condition.
Umberto Spagnolini - One of the best experts on this subject based on the ideXlab platform.
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shift invariance algorithms for the angle delay estimation of multipath space time channel
Vehicular Technology Conference, 2001Co-Authors: José Picheral, Umberto SpagnoliniAbstract:In time-division CDMA systems the angle/delay of the multipath propagation channel can be Estimated jointly by exploiting the invariance of angles/delays across multiple slots regardless of fast-variation in faded amplitudes. The joint angle/delay estimation (JADE) is reduced to the estimation of frequency of 2D sinusoids, here the shift-invariance method (2D-ESPRIT) is used. Application of JADE to 3/sup rd/ generation mobile communication system (TDD-UTRA standard) and to realistic propagation environments demonstrates the advantage of the proposed method. Linear multiuser detection that exploits angle/delay Estimates shows a meaningful improvement (approx. 2/spl divide/4 dB in signal to noise ratio) with respect to the conventional Least Square Estimate.