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2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017

作者： Kuzmin, Andrey Mikushin, Dmitry Lempitsky, Victor Skolkovo Institute of Science and Technology Russia Applied Parallel Computing LLC Russia

ISBN: (纸本)9781509063413

We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employing very fast classical matching scores instead. At the same time, our approach uses a deep convolutional network to predict the local parameters of cost volume aggregation process, which in this paper we implement using differentiable domain transform. By treating such transform as a recurrent neural network, we are able to train our whole system that includes cost volume computation, cost-volume aggregation (smoothing), and winner-takes-all disparity selection end-to-end. The resulting method is highly efficient at test time, while achieving good matching accuracy. On the KITTI 2012 and KITTI 2015 benchmark, it achieves a result of 5.08% and 6.34% error rate respectively while running at 29 frames per second rate on a modern GPU. © 2017 IEEE.

关键词： Recurrent neural networks

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The GPU-based Phase Tracking method for Planetary Radio Science applications

arXiv

引用

arXiv 2019年

作者： Jian, Nianchuan Mikushin, Dmitry Yan, Jianguo Barriot, Jean-Pierre Wu, Yajun Shanghai Astronomical Observatory Chinese Academy of Science Shanghai200030 China Applied Parallel Computing LLC Switzerland State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430070 China University of French Polynesia Geodesy Observatory of Tahiti Laboratoire GEPASUD BP 6570 98702 Faaa airport Tahiti French Polynesia

This paper introduces a phase tracking method for planetary radio science research with computational algorithm implemented fo r NVIDIA GPUs. In contrast to the phase-locked loop (PPL) phase counting method used in traditional Doppler data processing, this method fits the tracking data signal into the shape expressed by the Taylor polynomial with optimal phase and amplitude coefficients. The Differential Evolution (DE) algorithm is employed for polynomial fitting. In order to cope with high computational intensity of the proposed phase tracking method, the graphics processing units (GPUs) are employed. As a result, the method estimates the instantaneous phase, frequency, derivative of frequency (line-of-sight acceleration) and the total count phase of different integration scales. This data can be further used in planetary radio science research to analyze the planetary occultation and gravitational fields. The method has been tested on MEX (Mars Express, ESA) and Chang'E 4 relay satellite (China) tracking data. In a real experiment with 400K data block size and 80,000 DE solver objective function evaluations we were able to acheive the target convergence threshold in 6.5 seconds and do real-time processing on NVIDIA GTX580 and 2 NVIDIA K80 GPUs, respectively. The precision of integral Doppler (60s) is 2 mrad/s and 4 mrad/s for MEX(3-way) and Chang'E 4 relay satellite(3-way) respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Program processors

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