209 research outputs found
Broadband second harmonic generation in one-dimensional randomized nonlinear photonic crystal
We study experimentally second harmonic generation in a one-dimensional nonlinear photonic crystal with randomized inverted-domain structure. We show that the randomness enables one to realize an efficient broadband emission of high-quality second harmonic beam.The
authors acknowledge financial support from the Australian
Research Council and Australian Academy of Science
NOMA for Multi-Cell RIS Networks: A Stochastic Geometry Model
This paper investigates reconfigurable intelligent surface (RIS) aided multi-cell non-orthogonal multiple access (NOMA) networks with stochastic geometry methods. Under Rayleigh and Nakagami-m fading channels, we provide two types of approximate channel models to depict RIS channels, i.e., the N-fold convolution model and the curve fitting model. The analysis reveals that the N-fold convolution model is accurate and tractable when ignoring inter-cell interference, while the curve fitting model can evaluate the impact of inter-cell interference with a small error. The N-fold convolution model provides accurate diversity orders compared to other existing approaches such as the central limit model. Based on these channel models, we derive the closed-form analytical and asymptotic expressions of coverage probabilities and ergodic rates for two paired NOMA users. The analytical results demonstrate that: i) When we ignore inter-cell interference, the diversity order of the typical user is equal to the number of Rayleigh fading channels; and ii) For Nakagami-m fading channels with coefficient m , the diversity order is equal to m times of the channel number. Numerical results show that: i) RISs are capable of enhancing the coverage performance and ergodic rates of the proposed network; and ii) RISs provide extra flexibility for NOMA decoding orders
End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention
Accurate real-time catheter segmentation is an important pre-requisite for
robot-assisted endovascular intervention. Most of the existing learning-based
methods for catheter segmentation and tracking are only trained on small-scale
datasets or synthetic data due to the difficulties of ground-truth annotation.
Furthermore, the temporal continuity in intraoperative imaging sequences is not
fully utilised. In this paper, we present FW-Net, an end-to-end and real-time
deep learning framework for endovascular intervention. The proposed FW-Net has
three modules: a segmentation network with encoder-decoder architecture, a flow
network to extract optical flow information, and a novel flow-guided warping
function to learn the frame-to-frame temporal continuity. We show that by
effectively learning temporal continuity, the network can successfully segment
and track the catheters in real-time sequences using only raw ground-truth for
training. Detailed validation results confirm that our FW-Net outperforms
state-of-the-art techniques while achieving real-time performance.Comment: ICRA 202
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