4,084 research outputs found
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
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