136 research outputs found
Actively controlling the topological transition of dispersion based on electrically controllable metamaterials
Topological transition of the iso-frequency contour (IFC) from a closed
ellipsoid to an open hyperboloid, will provide unique capabilities for
controlling the propagation of light. However, the ability to actively tune
these effects remains elusive and the related experimental observations are
highly desirable. Here, tunable electric IFC in periodic structure which is
composed of graphene/dielectric multilayers is investigated by tuning the
chemical potential of graphene layer. Specially, we present the actively
controlled transportation in two kinds of anisotropic zero-index media
containing PEC/PMC impurities. At last, by adding variable capacitance diodes
into two-dimensional transmission-line system, we present the experimental
demonstration of the actively controlled magnetic topological transition of
dispersion based on electrically controllable metamaterials. With the increase
of voltage, we measure the different emission patterns from a point source
inside the structure and observe the phase-transition process of IFCs. The
realization of actively tuned topological transition will opens up a new avenue
in the dynamical control of metamaterials.Comment: 21 pages,8 figure
Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images
This paper presents an efficient neural network model to generate robotic
grasps with high resolution images. The proposed model uses fully convolution
neural network to generate robotic grasps for each pixel using 400 400
high resolution RGB-D images. It first down-sample the images to get features
and then up-sample those features to the original size of the input as well as
combines local and global features from different feature maps. Compared to
other regression or classification methods for detecting robotic grasps, our
method looks more like the segmentation methods which solves the problem
through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the
model and get high accuracy about 94.42% for image-wise and 91.02% for
object-wise and fast prediction time about 8ms. We also demonstrate that
without training on the multiple objects dataset, our model can directly output
robotic grasps candidates for different objects because of the pixel wise
implementation.Comment: Submitted to ROBIO 201
Door and window detection in 3D point cloud of indoor scenes.
This paper proposes a 3D-2D-3D algorithm for doors and windows detection in 3D indoor environment of point cloud data. Firstly, by setting up a virtual camera in the middle of this 3D environment, a set of pictures are taken from different angles by rotating the camera, so that corresponding 2D images can be generated. Next, these images are used to detect and identify the positions of doors and windows in the space. To obtain point cloud data containing the doors and windows position information, the 2D information are then mapped back to the origin 3D point cloud environment. Finally, by processing the contour lines and crossing points, the features of doors and windows through the position information are optimized. The experimental results show that this "global-local" approach is efficient when detecting and identifying the location of doors and windows in 3D point cloud environment
A simple encoder scheme for distributed residual video coding.
Rate-Distortion (RD) performance of Distributed Video Coding (DVC) is considerably less than that of conventional predictive video coding. In order to reduce the performance gap, many methods and techniques have been proposed to improve the coding efficiency of DVC with increased system complexity, especially techniques employed at the encoder such as encoder mode decisions, optimal quantization, hash methods etc., no doubt increase the complexity of the encoder. However, low complexity encoder is a widely desired feature of DVC. In order to improve the coding efficiency while maintaining low complexity encoder, this paper focuses on Distributed Residual Video Coding (DRVC) architecture and proposes a simple encoder scheme. The main contributions of this paper are as follows: 1) propose a bit plane block based method combined with bit plane re-arrangement to improve the dependency between source and Side Information (SI), and meanwhile, to reduce the amount of data to be channel encoded 2) present a simple iterative dead-zone quantizer with 3 levels in order to adjust quantization from coarse to fine. The simulation results show that the proposed scheme outperforms DISCOVER scheme for low to medium motion video sequences in terms of RD performance, and maintains a low complexity encoder at the same time
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