Ring-modulator-based RoF system with local SSB modulation and remote carrier reuse

A full-duplex radio-over-fibre (RoF) system based on an integrated silicon ring modulator is proposed and demonstrated. For the downstream link, a coherent dual-wavelength laser source is coupled to a silicon ring modulator in the central office (CO). Since only one of the optical carriers in the dual-wavelength laser source is aligned to the resonance of the ring modulator, a single sideband (SSB) modulated optical downstream signal is obtained, which is able to combat the power fading introduced by the fibre dispersion. Besides, for the upstream link, the unmodulated optical carrier in the SSB-modulated optical downstream signal is reused by using an optical filter in the remote radio head. After being modulated by the upstream data, the optical upstream signal is transmitted back to the CO. A proof-of-concept experiment is carried out. Error vector magnitudes of 21-GHz downstream and 10-GHz upstream signals are measured, which confirms that the proposed architecture is a promising lowcost solution for future high-speed wireless communication systems

Influence of parameters on flame expansion in a high-speed flow : experimental and numerical study

Flameholder-stabilized flames are conventional and also commonly used in propulsion and various power generation fields to maintain combustion process. The characteristics of flame expansion were obtained with various blockage ratios, which were observed to be highly sensitive to inlet conditions such as temperatures and velocities. Experiments and simulations combined methodology was performed; also the approach adopted on image processing was calculated automatically through a program written in MATLAB. It was found that the change of flame expansion angle indicated increasing fuel supply could contribute to the growth of flame expansion angle in lean premixed combustion. Besides, the influence of inlet velocity on flame expansion angle varies with different blockage ratios, i.e. under a small blockage ratio (BR ¼ 0.1), flame expansion angle declined with the increase of velocity; however, under a larger blockage ratio (BR ¼ 0.2 or 0.3), flame expansion angle increased firstly and then decreased with the increasing velocity. Likewise, flame expansion angle increased firstly and then decreased with the increasing temperature under BR ¼ 0.2/0.3. In addition, flame expansion angle was almost the same for BR ¼ 0.2 and BR ¼ 0.3 at a higher temperature (900 K), and both of which were bigger than BR ¼ 0.1. Overall, BR ¼ 0.2 is the best for increasing flame expansion angle and reducing total pressure loss. The influence of velocity and temperature on flame expansion angle found from this research are vital for engineering practice and for developing a further image processing method to measure flame boundary

Neural Interactive Keypoint Detection

This work proposes an end-to-end neural interactive keypoint detection framework named Click-Pose, which can significantly reduce more than 10 times labeling costs of 2D keypoint annotation compared with manual-only annotation. Click-Pose explores how user feedback can cooperate with a neural keypoint detector to correct the predicted keypoints in an interactive way for a faster and more effective annotation process. Specifically, we design the pose error modeling strategy that inputs the ground truth pose combined with four typical pose errors into the decoder and trains the model to reconstruct the correct poses, which enhances the self-correction ability of the model. Then, we attach an interactive human-feedback loop that allows receiving users' clicks to correct one or several predicted keypoints and iteratively utilizes the decoder to update all other keypoints with a minimum number of clicks (NoC) for efficient annotation. We validate Click-Pose in in-domain, out-of-domain scenes, and a new task of keypoint adaptation. For annotation, Click-Pose only needs 1.97 and 6.45 NoC@95 (at precision 95%) on COCO and Human-Art, reducing 31.4% and 36.3% efforts than the SOTA model (ViTPose) with manual correction, respectively. Besides, without user clicks, Click-Pose surpasses the previous end-to-end model by 1.4 AP on COCO and 3.0 AP on Human-Art. The code is available at https://github.com/IDEA-Research/Click-Pose.Comment: Accepted to ICCV 202

Experimental realisations of the fractional Schr\"{o}dinger equation in the temporal domain

The fractional Schr\"{o}dinger equation (FSE) -- a natural extension of the standard Schr\"{o}dinger equation -- is the basis of fractional quantum mechanics. It can be obtained by replacing the kinetic-energy operator with a fractional derivative. Here, we report the experimental realisation of an optical FSE for femtosecond laser pulses in the temporal domain. Programmable holograms and the single-shot measurement technique are respectively used to emulate a \textit{L\'evy waveguide} and to reconstruct the amplitude and phase of the pulses. Varying the L\'evy index of the FSE and the initial pulse, the temporal dynamics is observed in diverse forms, including solitary, splitting and merging pulses, double Airy modes, and ``rain-like'' multi-pulse patterns. Furthermore, the transmission of input pulses carrying a fractional phase exhibits a ``fractional-phase protection'' effect through a regular (non-fractional) material. The experimentally generated fractional time-domain pulses offer the potential for designing optical signal-processing schemes.Comment: This manuscript reports on experimental progress in fractional Schrodinger equations. Welcome to your comments and suggestions

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds ground-truth bounding boxes with noises into Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to a faster convergence. Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement ($+1.9$AP) under the same setting and achieves the best result (AP $43.4$ and $48.6$ with $12$ and $50$ epochs of training respectively) among DETR-like methods with ResNet-$50$ backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with $50\%$ training epochs. Code is available at \url{https://github.com/FengLi-ust/DN-DETR}.Comment: To appear in CVPR 202