University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Engineering graphene and TMDs based van der Waals heterostructures for photovoltaic and photoelectrochemical solar energy conversion

Abstract: Graphene and two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted significant interest due to their unique properties that cannot be obtained in their bulk counterparts. These atomically thin 2D materials have demonstrated strong light–matter interactions, tunable optical bandgap structures and unique structural and electrical properties, rendering possible the high conversion efficiency of solar energy with a minimal amount of active absorber material. The isolated 2D monolayer can be stacked into arbitrary van der Waals (vdWs) heterostructures without the need to consider lattice matching. Several combinations of 2D/3D and 2D/2D materials have been assembled to create vdWs heterojunctions for photovoltaic (PV) and photoelectrochemical (PEC) energy conversion. However, the complex, less-constrained, and more environmentally vulnerable interface in a vdWs heterojunction is different from that of a conventional, epitaxially grown heterojunction, engendering new challenges for surface and interface engineering. In this review, the physics of band alignment, the chemistry of surface modification and the behavior of photoexcited charge transfer at the interface during PV and PEC processes will be discussed. We will present a survey of the recent progress and challenges of 2D/3D and 2D/2D vdWs heterojunctions, with emphasis on their applicability to PV and PEC devices. Finally, we will discuss emerging issues yet to be explored for 2D materials to achieve high solar energy conversion efficiency and possible strategies to improve their performance.

Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition

Abstract: Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial–spectral total variation regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell _1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regularization has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method. Finally, extensive experiments on simulated and real-world noisy HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.

Distribution-Aware Coordinate Representation for Human Pose Estimation

Abstract: While being the de facto standard coordinate representation for human pose estimation, heatmap has not been investigated in-depth. This work fills this gap. For the first time, we find that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for the performance. We further probe the design limitations of the standard coordinate decoding method, and propose a more principled distributionaware decoding method. Also, we improve the standard coordinate encoding process (i.e. transforming ground-truth coordinates to heatmaps) by generating unbiased/accurate heatmaps. Taking the two together, we formulate a novel Distribution-Aware coordinate Representation of Keypoints (DARK) method. Serving as a model-agnostic plug-in, DARK brings about significant performance boost to existing human pose estimation models. Extensive experiments show that DARK yields the best results on two common benchmarks, MPII and COCO. Besides, DARK achieves the 2nd place entry in the ICCV 2019 COCO Keypoints Challenge. The code is available online.