The Experts below are selected from a list of 45552 Experts worldwide ranked by ideXlab platform
Tony Q.s. Quek - One of the best experts on this subject based on the ideXlab platform.
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Cross-Layer Resource Allocation With Elastic Service Scaling in Cloud Radio Access Network
IEEE Transactions on Wireless Communications, 2015Co-Authors: Jianhua Tang, Wee Peng Tay, Tony Q.s. QuekAbstract:Cloud radio access network (C-RAN) aims to improve spectrum and energy efficiency of wireless networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We propose and investigate a cross-layer resource allocation model for CRAN to minimize the overall system power consumption in the BBU pool, fiber links and the remote radio heads (RRHs). We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which jointly considers elastic service scaling, RRH selection, and joint beamforming. The MINLP is however a combinatorial optimization problem and NP-hard. We relax the original MINLP problem into an extended sum-utility maximization (ESUM) problem, and propose two different Solution approaches. We also propose a low-complexity Shaping-and-Pruning (SP) algorithm to obtain a Sparse Solution for the active RRH set. Simulation results suggest that the average sparsity of the Solution given by our SP algorithm is close to that obtained by a recently proposed greedy selection algorithm, which has higher computational complexity. Furthermore, our proposed cross-layer resource allocation is more energy efficient than the greedy selection and successive selection algorithms.
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Cross-layer resource allocation in cloud radio access network
2014 IEEE Global Conference on Signal and Information Processing GlobalSIP 2014, 2014Co-Authors: Jianhua Tang, Wee Peng Tay, Tony Q.s. QuekAbstract:Cloud radio access network (C-RAN) aims to improve the spectrum and energy efficiency of wireless communication networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in both the BBU pool and the remote radio heads (RRHs), while guaranteeing the cross-layer QoS. We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which is however NP-hard. By relaxing the original MINLP problem to a quasi weighted sum-rate maximization (QWSRM) problem, we utilize a branch and bound method to solve the QWSRM problem, and propose a low-complexity bisection search algorithm to obtain a Sparse Solution for RRH selection problem. Simulation results suggest that our cross-layer approach achieves more energy savings than the recently proposed greedy selection and successive selection algorithms for optimal RRH selection.
Xindong Wu - One of the best experts on this subject based on the ideXlab platform.
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Manifold elastic net: a unified framework for Sparse dimension reduction
Data Mining and Knowledge Discovery, 2011Co-Authors: Tianyi Zhou, Xindong WuAbstract:It is difficult to find the optimal Sparse Solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al., Ann Stat 32(2):407–499, 2004), one of the most popular algorithms in Sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the Sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal Sparse Solution of MEN. In particular, MEN has the following advantages for subsequent classification: (1) the local geometry of samples is well preserved for low dimensional data representation, (2) both the margin maximization and the classification error minimization are considered for Sparse projection calculation, (3) the projection matrix of MEN improves the parsimony in computation, (4) the elastic net penalty reduces the over-fitting problem, and (5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.
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Manifold elastic net: A unified framework for Sparse dimension reduction
Data Mining and Knowledge Discovery, 2011Co-Authors: Tianyi Zhou, Dacheng Tao, Xindong WuAbstract:It is difficult to find the optimal Sparse Solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in Sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the Sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal Sparse Solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for Sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.
Yin Tian - One of the best experts on this subject based on the ideXlab platform.
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neuroelectric source imaging using 3sco a space coding algorithm based on particle swarm optimization and l0 norm constraint
NeuroImage, 2010Co-Authors: Yin Tian, Xu Lei, Dezhong YaoAbstract:The electroencephalogram (EEG) neuroelectric sources inverse problem is usually underdetermined and lacks a unique Solution, which is due to both the electromagnetism Helmholtz theorem and the fact that there are fewer observations than the unknown variables. One potential choice to tackle this issue is to solve the underdetermined system for a Sparse Solution. Aiming to the Sparse Solution, a novel algorithm termed 3SCO (Solution Space Sparse Coding Optimization) is presented in this paper. In 3SCO, after the Solution space is coded with some particles, the particle-coded space is compressed by the evolution of particle swarm optimization algorithm, where an l0 constrained fitness function is introduced to guarantee the selection of a suitable Sparse Solution for the underdetermined system. 3SCO was first tested by localizing simulated EEG sources with different configurations on a realistic head model, and the comparisons with minimum norm (MN), LORETA (low reSolution electromagnetic tomography), l1 norm Solution and FOCUSS (focal underdetermined system solver) confirmed that a good Sparse Solution for EEG source imaging could be achieved with 3SCO. Finally, 3SCO was applied to localize the neuroelectric sources in a visual stimuli related experiment and the localized areas were basically consistent with those reported in previous studies.
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equivalent charge source model based iterative maximum neighbor weight for Sparse eeg source localization
Annals of Biomedical Engineering, 2008Co-Authors: Peng Xu, Yin Tian, Xiao HuAbstract:How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative Sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete Solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source Solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration Solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for Sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.
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lp norm iterative Sparse Solution for eeg source localization
IEEE Transactions on Biomedical Engineering, 2007Co-Authors: Peng Xu, Yin Tian, Huafu ChenAbstract:How to localize the neural electric activities effectively and precisely from the scalp EEG recordings is a critical issue for clinical neurology and cognitive neuroscience. In this paper, based on the spatial Sparse assumption of brain activities, proposed is a novel iterative EEG source imaging algorithm, Lp norm iterative Sparse Solution (LPISS). In LPISS, the lp(ples1) norm constraint for Sparse Solution is integrated into the iterative weighted minimum norm Solution of the underdetermined EEG inverse problem, and it is the constraint and the iteratively renewed weight that forces the inverse problem to converge to a Sparse Solution effectively. The conducted simulation studies with comparison to LORETA and FOCUSS for various dipoles configurations confirmed the validation of LPISS for Sparse EEG source localization. Finally, LPISS was applied to a real evoked potential collected in a study of inhibition of return (IOR), and the result was consistent with the previously suggested activated areas involved in an IOR process
Nick S. Jones - One of the best experts on this subject based on the ideXlab platform.
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Sparse bayesian step filtering for high throughput analysis of molecular machine dynamics
arXiv: Quantitative Methods, 2010Co-Authors: Max A. Little, Nick S. JonesAbstract:Nature has evolved many molecular machines such as kinesin, myosin, and the rotary flagellar motor powered by an ion current from the mitochondria. Direct observation of the step-like motion of these machines with time series from novel experimental assays has recently become possible. These time series are corrupted by molecular and experimental noise that requires removal, but classical signal processing is of limited use for recovering such step-like dynamics. This paper reports simple, novel Bayesian filters that are robust to step-like dynamics in noise, and introduce an L1-regularized, global filter whose Sparse Solution can be rapidly obtained by standard convex optimization methods. We show these techniques outperforming classical filters on simulated time series in terms of their ability to accurately recover the underlying step dynamics. To show the techniques in action, we extract step-like speed transitions from Rhodobacter sphaeroides flagellar motor time series. Code implementing these algorithms available from this http URL
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Sparse Bayesian step-filtering for high-throughput analysis of molecular machine dynamics
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Max A. Little, Nick S. JonesAbstract:Nature has evolved many molecular machines such as kinesin, myosin, and the rotary flagellar motor powered by an ion current from the mitochondria. Direct observation of the step-like motion of these machines with time series from novel experimental assays has recently become possible. These time series are corrupted by molecular and experimental noise that requires removal, but classical signal processing is of limited use for recovering such step-like dynamics. This paper reports simple, novel Bayesian filters that are robust to step-like dynamics in noise, and introduce an L1-regularized, global filter whose Sparse Solution can be rapidly obtained by standard convex optimization methods. We show these techniques outperforming classical filters on simulated time series in terms of their ability to accurately recover the underlying step dynamics. To show the techniques in action, we extract step-like speed transitions from Rhodobacter sphaeroides flagellar motor time series.
Jianhua Tang - One of the best experts on this subject based on the ideXlab platform.
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Cross-Layer Resource Allocation With Elastic Service Scaling in Cloud Radio Access Network
IEEE Transactions on Wireless Communications, 2015Co-Authors: Jianhua Tang, Wee Peng Tay, Tony Q.s. QuekAbstract:Cloud radio access network (C-RAN) aims to improve spectrum and energy efficiency of wireless networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We propose and investigate a cross-layer resource allocation model for CRAN to minimize the overall system power consumption in the BBU pool, fiber links and the remote radio heads (RRHs). We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which jointly considers elastic service scaling, RRH selection, and joint beamforming. The MINLP is however a combinatorial optimization problem and NP-hard. We relax the original MINLP problem into an extended sum-utility maximization (ESUM) problem, and propose two different Solution approaches. We also propose a low-complexity Shaping-and-Pruning (SP) algorithm to obtain a Sparse Solution for the active RRH set. Simulation results suggest that the average sparsity of the Solution given by our SP algorithm is close to that obtained by a recently proposed greedy selection algorithm, which has higher computational complexity. Furthermore, our proposed cross-layer resource allocation is more energy efficient than the greedy selection and successive selection algorithms.
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Cross-layer resource allocation in cloud radio access network
2014 IEEE Global Conference on Signal and Information Processing GlobalSIP 2014, 2014Co-Authors: Jianhua Tang, Wee Peng Tay, Tony Q.s. QuekAbstract:Cloud radio access network (C-RAN) aims to improve the spectrum and energy efficiency of wireless communication networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in both the BBU pool and the remote radio heads (RRHs), while guaranteeing the cross-layer QoS. We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which is however NP-hard. By relaxing the original MINLP problem to a quasi weighted sum-rate maximization (QWSRM) problem, we utilize a branch and bound method to solve the QWSRM problem, and propose a low-complexity bisection search algorithm to obtain a Sparse Solution for RRH selection problem. Simulation results suggest that our cross-layer approach achieves more energy savings than the recently proposed greedy selection and successive selection algorithms for optimal RRH selection.
