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