Wenkai Lu

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Independent component analysis (ICA) proves to be effective in the removing the ocular artifact from electroencephalogram recordings (EEG). While using ICA in ocular artifact correction, a crucial step is to correctly identify the artifact components among the decomposed independent components. In most previous works, this step of selecting the artifact(More)
The technologies with kilovoltage (kV) and megavoltage (MV) imaging in the treatment room are now available for image-guided radiation therapy to improve patient setup and target localization accuracy. However, development of strategies to efficiently and effectively implement these technologies for patient treatment remains challenging. This study proposed(More)
Nonlinear classification problems are always assumed to be equivalent to a linear classification problem in some higher dimensional feature space. Kernel machines like support vector machines (SVMs) implicitly map the features to higher dimensional feature space by a kernel trick, and use all the mapped features for classification. This paper proposes an(More)
Algebraic reconstruction techniques (ART) are iterative procedures for reconstructing objects from their projections. It is proven that ART can be computationally efficient by carefully arranging the order in which the collected data are accessed during the reconstruction procedure and adaptively adjusting the relaxation parameters. In this paper, an(More)
The following topics are dealt with: neural information processing; supervised learning; unsupervised learning; reinforcement learning; neural modeling; neural architectures; neurodynamics; learning theory; neural networks for image and signal processing; CMOS implementation of neural networks; neural optimization; neural control; hardware implementation;(More)
A joint speech signal enhancement based on singular value decomposition filter after spectral subtraction (SSVD) is proposed in this paper. The residual noise after spectral subtraction, which results for audible musical noise, is reduced further by SVD filter. The matrix size in spectral domain can be reduced half, and larger step-length adopted by SVD(More)