Exploring the Potential of Integrated Optical Sensing and Communication (IOSAC) Systems with Si Waveguides for Future Networks

Advanced silicon photonic technologies enable integrated optical sensing and communication (IOSAC) in real time for the emerging application requirements of simultaneous sensing and communication for next-generation networks. Here, we propose and demonstrate the IOSAC system on the silicon nitride (SiN) photonics platform. The IOSAC devices based on microring resonators are capable of monitoring the variation of analytes, transmitting the information to the terminal along with the modulated optical signal in real-time, and replacing bulk optics in high-precision and high-speed applications. By directly integrating SiN ring resonators with optical communication networks, simultaneous sensing and optical communication are demonstrated by an optical signal transmission experimental system using especially filtering amplified spontaneous emission spectra. The refractive index (RI) sensing ring with a sensitivity of 172 nm/RIU, a figure of merit (FOM) of 1220, and a detection limit (DL) of 8.2*10-6 RIU is demonstrated. Simultaneously, the 1.25 Gbps optical on-off-keying (OOK) signal is transmitted at the concentration of different NaCl solutions, which indicates the bit-error-ratio (BER) decreases with the increase in concentration. The novel IOSAC technology shows the potential to realize high-performance simultaneous biosensing and communication in real time and further accelerate the development of IoT and 6G networks.

A Free Viewpoint Portrait Generator with Dynamic Styling

Generating portrait images from a single latent space facing the problem of entangled attributes, making it difficult to explicitly adjust the generation on specific attributes, e.g., contour and viewpoint control or dynamic styling. Therefore, we propose to decompose the generation space into two subspaces: geometric and texture space. We first encode portrait scans with a semantic occupancy field (SOF), which represents semantic-embedded geometry structure and output free-viewpoint semantic segmentation maps. Then we design a semantic instance wised(SIW) StyleGAN to regionally styling the segmentation map. We capture 664 3D portrait scans for our SOF training and use real capture photos(FFHQ and CelebA-HQ) for SIW StyleGAN training. Adequate experiments show that our representations enable appearance consistent shape, pose, regional styles controlling, achieve state-of-the-art results, and generalize well in various application scenarios.

Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation

In this work, we demonstrate yet another approach to tackle the amodal segmentation problem. Specifically, we first introduce a new representation, namely a semantics-aware distance map (sem-dist map), to serve as our target for amodal segmentation instead of the commonly used masks and heatmaps. The sem-dist map is a kind of level-set representation, of which the different regions of an object are placed into different levels on the map according to their visibility. It is a natural extension of masks and heatmaps, where modal, amodal segmentation, as well as depth order information, are all well-described. Then we also introduce a novel convolutional neural network (CNN) architecture, which we refer to as semantic layering network, to estimate sem-dist maps layer by layer, from the global-level to the instance-level, for all objects in an image. Extensive experiments on the COCOA and D2SA datasets have demonstrated that our framework can predict amodal segmentation, occlusion and depth order with state-of-the-art performance.