Thermal tunability in terahertz metamaterials fabricated on strontium titanate single crystal substrates

We report an experimental demonstration of thermal tuning of resonance frequency in a planar terahertz metamaterial consisting of a gold split-ring resonator array fabricated on a bulk single crystal strontium titanate (SrTiO3) substrate. Cooling the metamaterial starting from 409 K down to 150 K causes about 50% shift in resonance frequency as compare to its room temperature resonance, and there is very little variation in resonance strength. The resonance shift is due to the temperature-dependent refractive index (or the dielectric constant) of the strontium titanate. The experiment opens up avenues for designing tunable terahertz devices by exploiting the temperature sensitive characteristic of high dielectric constant substrates and complex metal oxide materials.Comment: 6 pages, 3 figures, accepted at Optics Letter

Hierarchical-level rain image generative model based on GAN

Autonomous vehicles are exposed to various weather during operation, which is likely to trigger the performance limitations of the perception system, leading to the safety of the intended functionality (SOTIF) problems. To efficiently generate data for testing the performance of visual perception algorithms under various weather conditions, a hierarchical-level rain image generative model, rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on the generative adversarial network (GAN) and can generate images of light, medium, and heavy rain. Different rain intensities are introduced as labels in conditional GAN (CGAN). Meanwhile, the model structure is optimized and the training strategy is adjusted to alleviate the problem of mode collapse. In addition, natural rain images of different intensities are collected and processed for model training and validation. Compared with the two baseline models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the structural similarity (SSIM) is improved by 18% and 8%, respectively. The ablation experiments are also carried out to validate the effectiveness of the model tuning

AGG-Net: Attention Guided Gated-convolutional Network for Depth Image Completion

Recently, stereo vision based on lightweight RGBD cameras has been widely used in various fields. However, limited by the imaging principles, the commonly used RGB-D cameras based on TOF, structured light, or binocular vision acquire some invalid data inevitably, such as weak reflection, boundary shadows, and artifacts, which may bring adverse impacts to the follow-up work. In this paper, we propose a new model for depth image completion based on the Attention Guided Gated-convolutional Network (AGG-Net), through which more accurate and reliable depth images can be obtained from the raw depth maps and the corresponding RGB images. Our model employs a UNet-like architecture which consists of two parallel branches of depth and color features. In the encoding stage, an Attention Guided Gated-Convolution (AG-GConv) module is proposed to realize the fusion of depth and color features at different scales, which can effectively reduce the negative impacts of invalid depth data on the reconstruction. In the decoding stage, an Attention Guided Skip Connection (AG-SC) module is presented to avoid introducing too many depth-irrelevant features to the reconstruction. The experimental results demonstrate that our method outperforms the state-of-the-art methods on the popular benchmarks NYU-Depth V2, DIML, and SUN RGB-D.Comment: 9 pages, 7 figures, ICCV202