Squeezed cooling of mechanical motion beyond the resolved-sideband limit

Cavity optomechanics provides a unique platform for controlling micromechanical systems by means of optical fields that crosses the classical-quantum boundary to achieve solid foundations for quantum technologies. Currently, optomechanical resonators have become promising candidates for the development of precisely controlled nano-motors, ultrasensitive sensors and robust quantum information processors. For all these applications, a crucial requirement is to cool the mechanical resonators down to their quantum ground states. In this paper, we present a novel cooling scheme to further cool a micromechanical resonator via the noise squeezing effect. One quadrature in such a resonator can be squeezed to induce enhanced fluctuation in the other, "heated" quadrature, which can then be used to cool the mechanical motion via conventional optomechanical coupling. Our theoretical analysis and numerical calculations demonstrate that this squeeze-and-cool mechanism offers a quick technique for deeply cooling a macroscopic mechanical resonator to an unprecedented temperature region below the zero-point fluctuations.Comment: 5 pages, 4 figure

A new feature-preserving nonlinear anisotropic diffusion method for image denoising

We present a new diffusion method for noise reduction and feature preservation.Presently, denoising methods commonly use a first-order derivative to detect edges inorder to achieve a good balance between noise removal and feature preserving.However, if edges are partly lost to a certain extent or contaminated severely bynoise, these methods may not be able to detect them and thus fail to preserve variousfeatures in images. To overcome this problem, we propose a new and moresophisticated feature detector by combining first- and second-order derivatives for anonlinear anisotropic diffusion model. Numerical experiments show that the newdiffusion filter outperforms many popular filters for denoising images containingedges, blobs and ridges and textures made of these features

PCNN: A Lightweight Parallel Conformer Neural Network for Efficient Monaural Speech Enhancement

Convolutional neural networks (CNN) and Transformer have wildly succeeded in multimedia applications. However, more effort needs to be made to harmonize these two architectures effectively to satisfy speech enhancement. This paper aims to unify these two architectures and presents a Parallel Conformer for speech enhancement. In particular, the CNN and the self-attention (SA) in the Transformer are fully exploited for local format patterns and global structure representations. Based on the small receptive field size of CNN and the high computational complexity of SA, we specially designed a multi-branch dilated convolution (MBDC) and a self-channel-time-frequency attention (Self-CTFA) module. MBDC contains three convolutional layers with different dilation rates for the feature from local to non-local processing. Experimental results show that our method performs better than state-of-the-art methods in most evaluation criteria while maintaining the lowest model parameters.Comment: Accepted at INTERSPEECH 202

A Low Power Single-stage LED Driver Operating between Discontinuous Conduction Mode and Critical Conduction Mode

A novel single-stage single-switch (S4) LED driver is proposed in this paper. The paper focuses on the operation principles of the power stage circuit with an operation switched between Critical Conduction Mode (CRM) and Discontinuous Conduction Mode (DCM), including steady state analysis, simulation and backed up by experimental results. The results verify that this proposed LED driver can obtain a high power factor (PF) and the dc output is relatively stable