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.

Anomalous refraction of airborne sound through ultrathin metasurfaces

Abstract: Similar to their optic counterparts, acoustic components are anticipated to flexibly tailor the propagation of sound. However, the practical applications, e.g. for audible sound with large wavelengths, are frequently hampered by the issue of device thickness. Here we present an effective design of metasurface structures that can deflect the transmitted airborne sound in an anomalous way. This flat lens, made of spatially varied coiling-slit subunits, has a thickness of deep subwavelength. By elaborately optimizing its microstructures, the proposed lens exhibits high performance in steering sound wavefronts. Good agreement has been demonstrated experimentally by a sample around the frequency 2.55 kHz, incident with a Gaussian beam at normal or oblique incidence. This study may open new avenues for numerous daily life applications, such as controlling indoor sound effects by decorating rooms with light metasurface walls.

Global stem cell research trend: Bibliometric analysis as a tool for mapping of trends from 1991 to 2006

Abstract: In this study, we aim to evaluate the global scientific production of stem cell research for the past 16 years and provide insights into the characteristics of the stem cell research activities and identify patterns, tendencies, or regularities that may exist in the papers. Data are based on the online version of SCI, Web of Science from 1991 to 2006. Articles referring to stem cell were assessed by many aspects including exponential fitting the trend of publication outputs during 1991–2006, distribution of source title, author keyword, and keyword plus analysis. Based on the exponential fitting the yearly publicans of the last decade, it can also be calculated that, in 2,011, the number of scientific papers on the topic of stem-cell will be twice of the number of publications in 2006. Synthetically analyzing three kinds of keywords, it can be concluded that application of stem cell transplantation technology to human disease therapy, especially research related on “embryonic stem cell” and “mesenchymal stem cell” is the orientation of all the stem cell research in the 21st century. This new bibliometric method can help relevant researchers realize the panorama of global stem cell research, and establish the further research direction.

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

Abstract: Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great importance since it provides valuable information for diagnosis and surgery. However, accurate polyp segmentation is a challenging task, for two major reasons: (i) the same type of polyps has a diversity of size, color and texture; and (ii) the boundary between a polyp and its surrounding mucosa is not sharp. To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. Specifically, we first aggregate the features in high-level layers using a parallel partial decoder (PPD). Based on the combined feature, we then generate a global map as the initial guidance area for the following components. In addition, we mine the boundary cues using the reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues. Thanks to the recurrent cooperation mechanism between areas and boundaries, our PraNet is capable of calibrating some misaligned predictions, improving the segmentation accuracy. Quantitative and qualitative evaluations on five challenging datasets across six metrics show that our PraNet improves the segmentation accuracy significantly, and presents a number of advantages in terms of generalizability, and real-time segmentation efficiency (\(\varvec{\sim }\)50 fps).

Robust anonymous authentication protocol for health-care applications using wireless medical sensor networks

Abstract: With the fast development of wireless communication technologies and semiconductor technologies, the wireless sensor network (WSN) has been widely used in many applications As an application of the WSN, the wireless medical sensor network (WMSN) could improve health-care quality and has become important in the modern medical system In the WMSN, physiological data are collected by sensors deployed in the patient's body and sent to health professionals' mobile devices through wireless communication Then health professionals could get the status of the patient anywhere and anytime The data collected by sensors are very sensitive and important The leakage of them could compromise the patient's privacy and their malicious modification could harm the patient's health Therefore, both security and privacy are two important issues in WMSNs Recently, Kumar et al proposed an efficient authentication protocol for health-care applications using WMSNs and claimed that it could withstand various attacks However, we find that their protocol is vulnerable to the off-line password guessing attack and the privileged insider attack We also point out that their protocol cannot provide user anonymity In this paper, we will propose a robust anonymous authentication protocol for health-care applications using WMSNs Compared with Kumar et al's protocol, the proposed protocol has strong security and computational efficiency Therefore, it is more suitable for health-care applications using WMSNs