Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Population & Feature extraction. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Phosphorene nanoribbon as a promising candidate for thermoelectric applications.

Abstract: In this work, the electronic properties of phosphorene nanoribbons with different width and edge configurations are studied by using density functional theory It is found that the armchair phosphorene nanoribbons are semiconducting while the zigzag nanoribbons are metallic The band gaps of armchair nanoribbons decrease monotonically with increasing ribbon width By passivating the edge phosphorus atoms with hydrogen, the zigzag series also become semiconducting, while the armchair series exhibit a larger band gap than their pristine counterpart The electronic transport properties of these phosphorene nanoribbons are then investigated using Boltzmann theory and relaxation time approximation We find that all the semiconducting nanoribbons exhibit very large values of Seebeck coefficient and can be further enhanced by hydrogen passivation at the edge Taking pristine armchair nanoribbons and hydrogen-passivated zigzag naoribbons with width N = 7, 8, 9 as examples, we calculate the lattice thermal conductivity with the help of phonon Boltzmann transport equation and evaluate the width-dependent thermoelectric performance Due to significantly enhanced Seebeck coefficient and decreased thermal conductivity, we find that at least one type of phosphorene nanoribbons can be optimized to exhibit very high figure of merit (ZT values) at room temperature, which suggests their appealing thermoelectric applications

PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval

Abstract: Benchmark datasets are critical for developing, evaluating, and comparing remote sensing image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in that (1) they were originally collected for land use/land cover classification instead of RSIR; (2) they are relatively small in terms of the number of classes as well as the number of images per class which makes them unsuitable for developing deep learning based approaches; and (3) they are not appropriate for RSIR due to the large amount of background present in the images. These limitations restrict the development of novel approaches for RSIR, particularly those based on deep learning which require large amounts of training data. We therefore present a new large-scale remote sensing dataset termed “PatternNet” that was collected specifically for RSIR. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class. Significantly, PatternNet’s large scale makes it suitable for developing novel, deep learning based approaches for RSIR. We use PatternNet to evaluate the performance of over 35 RSIR methods ranging from traditional handcrafted feature based methods to recent, deep learning based ones. These results serve as a baseline for future research on RSIR.

Temporally Grounding Natural Sentence in Video

Abstract: We introduce an effective and efficient method that grounds (ie, localizes) natural sentences in long, untrimmed video sequences Specifically, a novel Temporal GroundNet (TGN) is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frame-by-word interactions, and finally grounds the segment corresponding to the sentence Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass We extensively evaluate our proposed TGN on three public datasets with significant improvements over the state-of-the-arts We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test