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