As a new display technology, electrowetting display (EWD) has many excellent display characteristics, such as paper-like, low power consumption, quick response and full color. These characteristics make EWD devices very suitable for portable devices. However, the gray-scale distortion caused by the contact angle hysteresis of EWDs seriously affects the accuracy of gray-scale display. To improve this phenomenon, the hysteresis curve of an EWD panel was studied according to the motion characteristics of advancing contact angle and receding contact angle of oil in a pixel. Then, a driving scheme for EWDs using alternating current (AC) voltage instead of direct current (DC) voltage was proposed in this paper. And the advantages and disadvantages of the driving scheme at different AC frequencies from 90 to 2,700 Hz were analyzed through experiments. According to the stability of aperture ratio in EWDs, a 470 Hz AC driving scheme was determined. Experimental results showed that the aperture ratio distortion of EWDs could be reduced from 35.82 to 5.97%, which significantly improved the display performance of pixel units.

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Building change detection (BuCD) can offer fundamental data for applications such as urban planning and identifying illegally-built new buildings. With the development of deep neural network-based approaches, BuCD using high-spatial-resolution remote sensing images (RSIs) has significantly advanced. These deep neural network-based methods, nevertheless, typically demand a considerable number of computational resources. Additionally, the accuracy of these algorithms can be improved. Hence, LightCDNet, a lightweight Siamese neural network for BuCD, is introduced in this paper. Specifically, LightCDNet comprises three components: a Siamese encoder, a multi-temporal feature fusion module (MultiTFFM), and a decoder. In the Siamese encoder, MobileNetV2 is chosen as the feature extractor to decrease computational costs. Afterward, the multi-temporal features from dual branches are independently concatenated based on the layer level. Subsequently, multiscale features computed from higher levels are up-sampled and fused with the lower-level ones. In the decoder, deconvolutional layers are adopted to gradually recover the changed buildings. The proposed network LightCDNet was assessed using two public datasets: namely, the LEVIR BuCD dataset (LEVIRCD) and the WHU BuCD dataset (WHUCD). The F1 scores on the LEVIRCD and WHUCD datasets of LightCDNet were 89.6% and 91.5%, respectively. The results of the comparative experiments demonstrate that LightCDNet outperforms several state-of-the-art methods in accuracy and efficiency.

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