Two-dimensional (2D) van der Waals ferroelectric materials have emerged as promising candidates for miniaturized devices due to their atomically thin structures and unique ability to maintain ferroelectricity even at reduced dimensions. Recent research indicates that the interfacial barriers between semiconductors and ferroelectrics can be modulated by polarization charges, with ferroelectric polarization—reversible by an external electric field—playing a crucial role in the switchable diode effect. In this work, we investigate a room-temperature switchable ferroelectric diode (Fe-diode) based on a MoS2/α-In2Se3 heterojunction. The out-of-plane ferroelectric properties of the α-In2Se3 layer enable efficient modulation of the Schottky barriers at the MoS2/α-In2Se3 interface through external voltage application, thereby achieving a notable switchable diode effect with a nonlinearity of up to 934. By exploiting the inherent nonlinearity, the ferroelectric diode can effectively generate complex signal waveforms, making it highly suitable for secure communication systems. These findings make the ferroelectric diode a potential candidate for enhancing confidentiality in future communication technologies, protecting data against eavesdropping and unauthorized access.
Low-light image enhancement (LLIE) aims to improve visibility and signal-to-noise ratio in images captured under poor lighting conditions. Despite impressive improvement, deep learning-based LLIE approaches require extensive training data, which is often difficult and costly to obtain. In this paper, we propose a zero-shot LLIE framework leveraging pre-trained latent diffusion models for the first time, which act as powerful priors to recover latent images from low-light inputs. Our approach introduces several components to alleviate the inherent challenges in utilizing pre-trained latent diffusion models, modeling the degradation process in an image-adaptive manner, penalizing the latent outside the manifold of natural images, and balancing the strengths of the guidance from the given low-light image during the denoising process. Experimental results demonstrate that our framework outperforms existing methods, achieving superior performance across various datasets.
随着电影对极致沉浸式视听体验的发展需求,沉浸式声场记录和重放技术日显重要。本文围绕电影音频制作技术中的声场记录和重放问题,介绍了基于球麦克风阵列的高阶高保真立体声(Higher Order Ambisonics,HOA)分析技术,并针对球麦克风阵列球谐分解中的低频噪声与高频混叠问题,以及双耳重放技术中的阶数受限问题,给出了相应解决方案,研究表明所提方案可为观众提供更真实、更具沉浸感的声场重放效果,提升了观影体验,在电影音频制作中具有广阔的应用前景。