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This paper proposes an end-to-end emotional speech synthesis (ESS) method which adopts global style tokens (GSTs) for semi-supervised training. This model is built based on the GST-Tacotron framework. The style tokens are defined to present emotion categories. A cross entropy loss function between token weights and emotion labels is designed to obtain the interpretability of style tokens utilizing the small portion of training data with emotion labels. Emotion recognition experiments confirm that this method can achieve one-to-one correspondence between style tokens and emotion categories effectively. Objective and subjective evaluation results show that our model outperforms the conventional Tacotron model for ESS when only 5% of training data has emotion labels. Its subjective performance is close to the Tacotron model trained using all emotion labels.

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Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of sensors to guarantee the full-view coverage for the given region of interest (ROI). To tackle this issue, we derive the constraint condition of the sensor positions for full-view neighborhood coverage with the minimum number of nodes around the point. Next, we prove that the full-view area coverage can be approximately guaranteed, as long as the regular hexagons decided by the virtual grid are seamlessly stitched. Then we present two solutions for camera sensor networks in two different deployment strategies. By computing the theoretically optimal length of the virtual grids, we put forward the deployment pattern algorithm (DPA) in the deterministic implementation. To reduce the redundancy in random deployment, we come up with a local neighboring-optimal selection algorithm (LNSA) for achieving the full-view coverage. Finally, extensive simulation results show the feasibility of our proposed solutions.