337 research outputs found
Generalized transfer matrix theory on electronic transport through graphene waveguide
In the effective mass approximation, electronic property in graphene can be
characterized by the relativistic Dirac equation. Within such a continuum model
we investigate the electronic transport through graphene waveguides formed by
connecting multiple segments of armchair-edged graphene nanoribbons of
different widths. By using appropriate wavefunction connection conditions at
the junction interfaces, we generalize the conventional transfer matrix
approach to formulate the linear conductance of the graphene waveguide in terms
of the structure parameters and the incident electron energy. In comparison
with the tight-binding calculation, we find that the generalized transfer
matrix method works well in calculating the conductance spectrum of a graphene
waveguide even with a complicated structure and relatively large size. The
calculated conductance spectrum indicates that the graphene waveguide exhibits
a well-defined insulating band around the Dirac point, even though all the
constituent ribbon segments are gapless. We attribute the occurrence of the
insulating band to the antiresonance effect which is intimately associated with
the edge states localized at the shoulder regions of the junctions.
Furthermore, such an insulating band can be sensitively shifted by a gate
voltage, which suggests a device application of the graphene waveguide as an
electric nanoswitch.Comment: 11 pages, 5 figure
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection
Adversarial attacks in the physical world can harm the robustness of
detection models. Evaluating the robustness of detection models in the physical
world can be challenging due to the time-consuming and labor-intensive nature
of many experiments. Thus, virtual simulation experiments can provide a
solution to this challenge. However, there is no unified detection benchmark
based on virtual simulation environment. To address this challenge, we proposed
an instant-level data generation pipeline based on the CARLA simulator. Using
this pipeline, we generated the DCI dataset and conducted extensive experiments
on three detection models and three physical adversarial attacks. The dataset
covers 7 continuous and 1 discrete scenes, with over 40 angles, 20 distances,
and 20,000 positions. The results indicate that Yolo v6 had strongest
resistance, with only a 6.59% average AP drop, and ASA was the most effective
attack algorithm with a 14.51% average AP reduction, twice that of other
algorithms. Static scenes had higher recognition AP, and results under
different weather conditions were similar. Adversarial attack algorithm
improvement may be approaching its 'limitation'.Comment: CVPR 2023 worksho
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