Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
AbstractRadio-frequency (RF) tomographic imaging is a promising technique for inferring multi-dimensional physical space by processing RF signals traversed across a region of interest. Tensor-based approaches for tomographic imaging are superior at detecting the objects within higher dimensional spaces. The recently-proposed tensor sensing approach based on the transform tensor model achieves a lower error rate and faster speed than the previous tensor-based compress sensing approach. However, the running time of the tensor sensing approach increases exponentially with the dimension of tensors, thus not being very practical for big tensors. In this paper, we address this problem by exploiting massively-parallel GPUs. We design, implement, and optimize the tensor sensing approach on an NVIDIA Tesla GPU and evaluate the performance in terms of the running time and recovery error rate. Experimental results show that our GPU tensor sensing is as accurate as the CPU counterpart with an average of
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Xu, D.; Zhang, T. Efficient Tensor Sensing for RF Tomographic Imaging on GPUs. Future Internet 2019, 11, 46.
Xu D, Zhang T. Efficient Tensor Sensing for RF Tomographic Imaging on GPUs. Future Internet. 2019; 11(2):46.Chicago/Turabian Style
Xu, Da; Zhang, Tao. 2019. "Efficient Tensor Sensing for RF Tomographic Imaging on GPUs." Future Internet 11, no. 2: 46.
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