12 research outputs found
Optical bulk-boundary dichotomy in a quantum spin Hall insulator
The bulk-boundary correspondence is a key concept in topological quantum
materials. For instance, a quantum spin Hall insulator features a bulk
insulating gap with gapless helical boundary states protected by the underlying
Z2 topology. However, the bulk-boundary dichotomy and distinction are rarely
explored in optical experiments, which can provide unique information about
topological charge carriers beyond transport and electronic spectroscopy
techniques. Here, we utilize mid-infrared absorption micro-spectroscopy and
pump-probe micro-spectroscopy to elucidate the bulk-boundary optical responses
of Bi4Br4, a recently discovered room-temperature quantum spin Hall insulator.
Benefiting from the low energy of infrared photons and the high spatial
resolution, we unambiguously resolve a strong absorption from the boundary
states while the bulk absorption is suppressed by its insulating gap. Moreover,
the boundary absorption exhibits a strong polarization anisotropy, consistent
with the one-dimensional nature of the topological boundary states. Our
infrared pump-probe microscopy further measures a substantially increased
carrier lifetime for the boundary states, which reaches one nanosecond scale.
The nanosecond lifetime is about one to two orders longer than that of most
topological materials and can be attributed to the linear dispersion nature of
the helical boundary states. Our findings demonstrate the optical bulk-boundary
dichotomy in a topological material and provide a proof-of-principal
methodology for studying topological optoelectronics.Comment: 26 pages, 4 figure
Farmland Obstacle Detection from the Perspective of UAVs Based on Non-local Deformable DETR
In precision agriculture, unmanned aerial vehicles (UAVs) are playing an increasingly important role in farmland information acquisition and fine management. However, discrete obstacles in the farmland environment, such as trees and power lines, pose serious threats to the flight safety of UAVs. Real-time detection of the attributes of obstacles is urgently needed to ensure their flight safety. In the wake of rapid development of deep learning, object detection algorithms based on convolutional neural networks (CNN) and transformer architectures have achieved remarkable results. Detection Transformer (DETR) and Deformable DETR combine CNN and transformer to achieve end-to-end object detection. The goal of this work is to use Deformable DETR for the task of farmland obstacle detection from the perspective of UAVs. However, limited by local receptive fields and local self-attention mechanisms, Deformable DETR lacks the ability to capture long-range dependencies to some extent. Inspired by non-local neural networks, we introduce the global modeling capability to the front-end ResNet to further improve the overall performance of Deformable DETR. We refer to the improved version as Non-local Deformable DETR. We evaluate the performance of Non-local Deformable DETR for farmland obstacle detection through comparative experiments on our proposed dataset. The results show that, compared with the original Deformable DETR network, the mAP value of the Non-local Deformable DETR is increased from 71.3% to 78.0%. Additionally, Non-local Deformable DETR also presents great performance for detecting small and slender objects. We hope this work can provide a solution to the flight safety problems encountered by UAVs in unstructured farmland environments