6,083 research outputs found
Rashba spin splitting based on trilayer graphene systems
We establish a general Rashba Hamiltonian for trilayer graphene (TLG) by
introducing an extrinsic layer-dependent Rashba spin-orbit coupling (SOC)
arising from the off-plane inversion symmetry breaking. Our results indicate
that the band spin splitting depends strongly on the layer-distribution and
sign of Rashba SOC as well as the ABA or ABC stacking order of TLG. We find
that spin splitting is significantly enhanced as the number of layers of the
Rashba SOC with the same sign and magnitude increases. For the
spatially-separated two Rashba SOCs of the same magnitude but the opposite
sign, no spin splitting arises in ABC-TLG due to the preservation of inversion
symmetry that ensures the complete cancellation of contributions from the
opposite layers, whereas nonzero spin splitting is observed for ABA-TLG due to
its own lack of inversion symmetry. We further illustrate that gate voltage is
effective to modulate the spin-polarized states near the band edges. Moreover,
we use density functional theory calculations to verify the Rashba splitting
effect in the example of TLG interfaced by Au layer(s), which induce
simultaneously the effective terms of Rashba SOC and gate voltage. Our results
demonstrate the significance of layer and symmetry in manipulating spin and can
be extended to multilayer graphene or other van der Waals interface systems.Comment: 9 page
Real-time Accurate Runway Detection based on Airborne Multi-sensors Fusion
Existing methods of runway detection are more focused on image processing for remote sensing images based on computer vision techniques. However, these algorithms are too complicated and time-consuming to meet the demand for real-time airborne application. This paper proposes a novel runway detection method based on airborne multi-sensors data fusion which works in a coarse-to-fine hierarchical architecture. At the coarse layer, a vision projection model from world coordinate system to image coordinate system is built by fusing airborne navigation data and forward-looking sensing images, then a runway region of interest (ROI) is extracted from a whole image by the model. Furthermore, EDLines which is a real-time line segments detector is applied to extract straight line segments from ROI at the fine layer, and fragmented line segments generated by EDLines are linked into two long runway lines. Finally, some unique runway features (e.g. vanishing point and runway direction) are used to recognise airport runway. The proposed method is tested on an image dataset provided by a flight simulation system. The experimental results show that the method has advantages in terms of speed, recognition rate and false alarm rate
A Review of Smart Materials in Tactile Actuators for Information Delivery
As the largest organ in the human body, the skin provides the important
sensory channel for humans to receive external stimulations based on touch. By
the information perceived through touch, people can feel and guess the
properties of objects, like weight, temperature, textures, and motion, etc. In
fact, those properties are nerve stimuli to our brain received by different
kinds of receptors in the skin. Mechanical, electrical, and thermal stimuli can
stimulate these receptors and cause different information to be conveyed
through the nerves. Technologies for actuators to provide mechanical,
electrical or thermal stimuli have been developed. These include static or
vibrational actuation, electrostatic stimulation, focused ultrasound, and more.
Smart materials, such as piezoelectric materials, carbon nanotubes, and shape
memory alloys, play important roles in providing actuation for tactile
sensation. This paper aims to review the background biological knowledge of
human tactile sensing, to give an understanding of how we sense and interact
with the world through the sense of touch, as well as the conventional and
state-of-the-art technologies of tactile actuators for tactile feedback
delivery
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