On the Performance of Downlink MIMO-ISAC

This letter analyzes the performance of sensing and communications (S\&C) achieved by a multiple-input multiple-output downlink integrated S\&C (ISAC) system. Three typical ISAC scenarios are studied, including the sensing-centric design, communications-centric design, and Pareto optimal design. For each scenario, diversity orders and high signal-to-noise ratio slopes of the sensing rate and communication rate are derived to gain further insights. It is found that ISAC can provide more degrees of freedom and a broader rate region than existing frequency-division S\&C (FDSAC) techniques.Comment: 5 page

Ultrathin Oxide Wrapping of Plasmonic Nanoparticles via Colloidal Electrostatic Self-Assembly and their Enhanced Performances

Ultrathin and uniform oxide layer-wrapped plasmonic nanoparticles (NPs) have been expected in the fields of light energy conversion and optical sensing fields. In this chapter, we proposed a universal strategy to prepare such core-shell plasmonic NPs based on colloidal electrostatic attraction and self-assembly procedures. Based on the self-assembly strategy, laser ablation of metal targets in liquid medium was conducted at room temperature to one-pot fabricate the oxide-wrapped plasmonic NPs. It demonstrates that a series of core-shell nanostructured NPs such as Au@Fe2O3, Au@Al2O3, Au@CuO, Au@ZnO, Pt@TiO2, and Pd@TiO2, have been readily obtained free of contaminations. Technical analyses illustrate that those composite NPs possess uniform and symmetrical oxides layers with several nanometers in thickness. Furthermore, both the thickness and crystallinity of the oxides layer could be precisely tailored simply by controlling hydrolysis of precursors and irradiation durations. Finally, due to ultrathin wrapping of oxides, the as-obtained core-shell plasmonic NPs show excellent surface-enhanced Raman scattering (SERS) and gas-sensing performances compared with bare metal or oxides NPs

Liquid-based infrared optical switch

We report an infrared (IR) optical switch using a position-shifting glycerol droplet. The droplet is surrounded by density-matched oil. In the voltage-on state, the droplet shifts in one direction. Upon removing the voltage, the droplet returns to its original position with the aid of interfacial tensions. Due to the strong absorption of glycerol at 1.55 mu m, our IR optical switch shows similar to 95:1 contrast ratio and similar to 200 ms response time. Such a device is promising for fiber optical switch and various IR optical attenuators

A novel adaptive mechanical-wetting lens for visible and near infrared imaging

We demonstrate an adaptive mechanical-wetting lens with a concentric reservoir to reduce image aberrations and overcome the gravity effect. This lens adopts liquid pressure to change the interface between two immiscible liquids which, in turn, changes the focal length of the resultant liquid lens. Good optical performance, high resolution, and a wide dynamic range of both positive and negative optical power are achieved. Since no PDMS is employed, such lenses can extend their working range to infrared region by choosing proper liquids

Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering

We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene can be directly represented as a 4D spatio-temporal tensor. To tackle the accompanying memory issue, we decompose the 4D tensor hierarchically by projecting it first into three time-aware volumes and then nine compact feature planes. In this way, spatial information over time can be simultaneously captured in a compact and memory-efficient manner. When applying Tensor4D for dynamic scene reconstruction and rendering, we further factorize the 4D fields to different scales in the sense that structural motions and dynamic detailed changes can be learned from coarse to fine. The effectiveness of our method is validated on both synthetic and real-world scenes. Extensive experiments show that our method is able to achieve high-quality dynamic reconstruction and rendering from sparse-view camera rigs or even a monocular camera. The code and dataset will be released at https://liuyebin.com/tensor4d/tensor4d.html

Learning Implicit Templates for Point-Based Clothed Human Modeling

We present FITE, a First-Implicit-Then-Explicit framework for modeling human avatars in clothing. Our framework first learns implicit surface templates representing the coarse clothing topology, and then employs the templates to guide the generation of point sets which further capture pose-dependent clothing deformations such as wrinkles. Our pipeline incorporates the merits of both implicit and explicit representations, namely, the ability to handle varying topology and the ability to efficiently capture fine details. We also propose diffused skinning to facilitate template training especially for loose clothing, and projection-based pose-encoding to extract pose information from mesh templates without predefined UV map or connectivity. Our code is publicly available at https://github.com/jsnln/fite.Comment: Accepted to ECCV 202