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Existing depth map-based super-resolution (SR) methods cannot achieve satisfactory results in depth map detail restoration. For example, boundaries of the depth map are always difficult to reconstruct effectively from the low-resolution (LR) guided depth map particularly at big magnification factors. In this paper, we present a novel super-resolution method for single depth map by introducing a deep feedback network (DFN), which can effectively enhance the feature representations at depth boundaries that utilize iterative up-sampling and down-sampling operations, building a deep feedback mechanism by projecting high-resolution (HR) representations to low-resolution spatial domain and then back-projecting to high-resolution spatial domain. The deep feedback (DF) block imitates the process of image degradation and reconstruction iteratively. The rich intermediate high-resolution features effectively tackle the problem of depth boundary ambiguity in depth map super-resolution. Extensive experimental results on the benchmark datasets show that our proposed DFN outperforms the state-of-the-art methods.

This work studies loop control composition in continuous chemical reactors with simple structures, due to its large acceptance in chemical industry. A linear cascade composition control (master/slave) is proposed, designed with basic control structures based on Laplace tools. Two configurations are designed, which were evaluated in a dynamic model of continuous stirred tank. From a stability analysis it is noted that, for such configurations, system assent time is 7 to 8 times reduced if compared to the assent time without loop control. Besides, the system shows a good performance when coming to the asked reference. Implementation of such control configurations can solve the problem of loop control composition.