Hongliang Yuan

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In this paper, by explicitly considering a dynamic model of the robots, the coefficients of trajectories are determined by boundary conditions, optimal performance index and collision avoidance conditions. The planned trajectory is feasible and has a closed loop expression, which is efficient for real-time updating. There are two main improvements compared(More)
We propose an adaptive sampling and reconstruction method based on the robust principal component analysis (PCA) to denoise Monte Carlo renderings. Addressing spike noise is a challenging problem in adaptive rendering methods. We adopt the robust PCA as a pre-processing step to efficiently decompose spike noise from rendered image after the image space is(More)
In this study, a novel adaptive rendering approach is proposed to remove Monte Carlo noise while preserving image details through a feature-based reconstruction. First, noise in the additional features is removed using a guided image filter that reduces the impact of noisy features involving strong motion blur or depth of field. The Sobel operator is then(More)
For the problems that we can't monitor abnormal conditions of heart rate continuously, a clouding based pulse monitoring and data analysis framework has been proposed. Source of the framework is composed of multiple ZigBee based pulse monitoring sensors, customized gateways and back-end system. Individuals' pulse information are collected by sensors and(More)
In this work, we study the bistability of an active nonlinear microring resonator and design a flip-flop based on the active microring resonator. In the presence of nonlinear and linear loss, we use Er-doped gain medium in the microring to obtain gain to compensate for the loss of the resonator. Both analytical and numerical methods are used to solve the(More)
In this paper, we propose a novel adaptive rendering method to robustly handle noise artifacts and outliers of Monte Carlo ray tracing by combining the Nadaraya–Watson and robust local linear estimators while efficiently preserving fine details. Our method first constructs a sparse robust local linear estimator in feature space (normal,texture,etc.), while(More)
With the rapid development of Internet of Things and Big Data analysis, the computing mode of the 21st century is undergoing profound reform. But these technologies bring great challenges such as more multiple-dimensional and more numerous information with wide-area and heterogeneous sensor networks to classical context-aware frameworks at the same time.(More)
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