Xie Xu

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To my family 3 ACKNOWLEDGMENTS I thank my adviser Dr. José C. Príncipe, for giving me the opportunity and funds to pursue my PhD. I specially thank my committee members, who have been a great inspiration. Dr. Murali Rao, helped me through my first steps in sampling theory. Dr. Entezari formally introduced me to the problems in interpolation, sampling and(More)
BACKGROUND TGFbeta has emerged as an attractive target for the therapeutic intervention of glioblastomas. Aberrant TGFbeta overproduction in glioblastoma and other high-grade gliomas has been reported, however, to date, none of these reports has systematically examined the components of TGFbeta signaling to gain a comprehensive view of TGFbeta activation in(More)
In this paper, we investigate compressed sensing principles to devise an in-situ data reduction framework for vi-sualization of volumetric datasets. We exploit the universality of the compressed sensing framework and show that the proposed method offers a refinable data reduction approach for volumetric datasets. The accurate reconstruction is obtained from(More)
We present a variational framework for the reconstruction of irregularly-sampled volumetric data in, nontensor-product, spline spaces. Motivated by the sampling-theoretic advantages of body centered cubic (BCC) lattice, this paper examines the BCC lattice and its associated box spline spaces in a variational setting. We introduce a regularization scheme for(More)
We examine different sampling lattices and their respective bandlimited spaces for reconstruction of irregularly sampled multidimensional images. Considering an irregularly sampled dataset, we demonstrate that the non-tensor-product bandlimited approximations corresponding to the body-centered cubic and face-centered cubic lattices provide a more accurate(More)
—In this paper, we summarize our experiment results of applying various optimization techniques for CUDA application running on NVIDIA Fermi GPUs. Our experiments on matrix multiplication and breadth first search algorithms show that optimization techniques such as coalesced global memory access, conflict-free shared memory access and data pre-fetching(More)
We propose an alternative volumetric data modeling and reduction approach via compressive sensing theory. We provide evidence that with a small set of randomly chosen Fourier samples of a dataset, it is possible to recover the dataset accurately. Our experiments demonstrate that the number of samples necessary for an accurate reconstruction is linearly(More)
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