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The field-emission properties of ordered ZnO nanorod arrays with different morphologies were investigated in detail. After comparison of three different morphologies, it was found that the morphology of the ZnO nanorods has considerable effect on their field emission properties, especially the turn-on field and the emission current density. Among them, the ZnO nanoneedle arrays have the lowest turn-on field, highest current density, and the largest emission efficiency, which is ascribed to the small emitter radius on the nanoscale. On the other hand, high nanorod density remarkably reduces the local field at the emitters owing to the screening effect, which is related to the density of the emitters. The analysis results could be valuable for the application of field-emission-based devices using ZnO nanorod arrays as cathode materials.

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This paper proposes a deep learning method for intra prediction. Different from traditional methods utilizing some fixed rules, we propose using a fully connected network to learn an end-to-end mapping from neighboring reconstructed pixels to the current block. In the proposed method, the network is fed by multiple reference lines. Compared with traditional single line-based methods, more contextual information of the current block is utilized. For this reason, the proposed network has the potential to generate better prediction. In addition, the proposed network has good generalization ability on different bitrate settings. The model trained from a specified bitrate setting also works well on other bitrate settings. Experimental results demonstrate the effectiveness of the proposed method. When compared with high efficiency video coding reference software HM-16.9, our network can achieve an average of 3.4% bitrate saving. In particular, the average result of 4K sequences is 4.5% bitrate saving, where the maximum one is 7.4%.

We present an instance segmentation scheme based on pixel affinity information, which is the relationship of two pixels belonging to a same instance. In our scheme, we use two neural networks with similar structure. One is to predict pixel level semantic score and the other is designed to derive pixel affinities. Regarding pixels as the vertexes and affinities as edges, we then propose a simple yet effective graph merge algorithm to cluster pixels into instances. Experimental results show that our scheme can generate fine grained instance mask. With Cityscapes training data, the proposed scheme achieves 27.3 AP on test set.