The combustion of candles exhibits a variety of dynamical behaviors. Binding several candles together will result in flickering of candle flames, which is generally described as a nonlinear oscillator. The impact on the frequency of the flame by several factors, such as the arrangement, the number and the asymmetry of the oscillators, is discussed. Experimental results show that the frequency gradually decreases as the number of candles increases in the case of an isolated oscillator, while alternation between the in-phase and the anti-phase synchronization appears in a coupled system of two oscillators. Moreover, envelopes in the amplitude of the oscillatory luminance are displayed when candles are coupled asymmetrically. Since the coupling between oscillators is dominated by thermal radiation, a “overlapped peaks model” is proposed to phenomenologically explain the relationship between temperature distribution, coupling strength and the collective behavior in coupled system of candle oscillators in both symmetric and asymmetric cases.

Three-Dimensional (3D) light-field display plays a vital role in realizing 3D display. However, the real-time high quality 3D light-field display is difficult, because super high-resolution 3D light field images are hard to be achieved in real-time. Although extensive research has been carried out on fast 3D light-field image generation, no single study exists to satisfy real-time 3D image generation and display with super high-resolution such as 7680×4320. To fulfill real-time 3D light-field display with super high-resolution, a two-stage 3D image generation method based on path tracing and image super-resolution (SR) is proposed, which takes less time to render 3D images than previous methods. In the first stage, path tracing is used to generate low-resolution 3D images with sparse views based on Monte-Carlo integration. In the second stage, a lite SR algorithm based on a generative adversarial network (GAN) is presented to up-sample the low-resolution 3D images to high-resolution 3D images of dense views with photo-realistic image quality. To implement the second stage efficiently and effectively, the elemental images (EIs) are super-resolved individually for better image quality and geometry accuracy, and a foreground selection scheme based on ray casting is developed to improve the rendering performance. Finally, the output EIs from CNN are used to recompose the high-resolution 3D images. Experimental results demonstrate that real-time 3D light-field display over 30fps at 8K resolution can be realized, while the structural similarity (SSIM) can be over 0.90. It is hoped that the proposed method will contribute to the field of real-time 3D light-field display.

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The selection of clear sky data in space-borne remote sensing data is very important for its data application. For FY-3D satellite microwave humidity and temperature sounder (MWHTS), an inversion system of atmospheric cloud water content by MWHTS is established based on neural network. The cloud water content inversion value is used to select clear sky data from MWHTS observation data. The experimental results show that FY-3D/MWHTS clear sky data selection method based on neural network can effectively select MWHTS observation data, thus improving the simulation brightness temperatures accuracy of MWHTS by radiative transfer model. This method can be used to select clear sky data by using space-borne observation data itself. It is easy to operate and has important practical value for climate change research, numerical weather forecast, etc., based on space-borne observation data.

Light field (LF) image super-resolution (SR) can improve the limited spatial resolution of LF images by using complementary information from different perspectives. However, current LF image SR methods only use the RGB data to implicitly exploit the information among different perspectives, without paying attention to the information loss from raw data to RGB data and the explicit structure information utilization. To address the first issue, a data generation pipeline is developed to collect LF raw data for LF image SR. In addition, to make full use of the multiview information, an end-to-end convolutional neural network architecture (namely, LF-RawSR) is proposed for LF image SR. Specifically, an aggregated module is first used to fuse the angular information based on a volume transformer with plane sweep volume. Then the aggregated feature is warped to all LF views using a cross-view transformer for nonlocal dependencies utilization. The experimental results demonstrate that our method outperforms existing state-of-the-art methods with a comparative computational cost, and fine details and clear structures can be restored.