Southeast University

About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: MIMO & Control theory. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.

Bio-inspired self-healing structural color hydrogel

Abstract: Biologically inspired self-healing structural color hydrogels were developed by adding a glucose oxidase (GOX)- and catalase (CAT)-filled glutaraldehyde cross-linked BSA hydrogel into methacrylated gelatin (GelMA) inverse opal scaffolds. The composite hydrogel materials with the polymerized GelMA scaffold could maintain the stability of an inverse opal structure and its resultant structural colors, whereas the protein hydrogel filler could impart self-healing capability through the reversible covalent attachment of glutaraldehyde to lysine residues of BSA and enzyme additives. A series of unprecedented structural color materials could be created by assembling and healing the elements of the composite hydrogel. In addition, as both the GelMA and the protein hydrogels were derived from organisms, the composite materials presented high biocompatibility and plasticity. These features of self-healing structural color hydrogels make them excellent functional materials for different applications.

Bandwidth Enhancement for a 60 GHz Substrate Integrated Waveguide Fed Cavity Array Antenna on LTCC

Abstract: A substrate integrated waveguide fed cavity array antenna using multilayered low temperature co-fired ceramic technology is presented and designed at V-band (60 GHz). The 8 × 8 antenna array is designed with an enhanced bandwidth of 17.1% and a gain up to 22.1 dBi by reconfiguring radiating elements, feeding network, and the transition. The proposed array antenna also features the merits of compact size, stable performance, and high efficiency.

Deep Label Distribution Learning with Label Ambiguity

Abstract: Convolutional Neural Networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most important factors for its success. However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation. Fortunately, there is ambiguous information among labels, which makes these tasks different from traditional classification. Based on this observation, we convert the label of each image into a discrete label distribution, and learn the label distribution by minimizing a Kullback-Leibler divergence between the predicted and ground-truth label distributions using deep ConvNets. The proposed DLDL (Deep Label Distribution Learning) method effectively utilizes the label ambiguity in both feature learning and classifier learning, which help prevent the network from over-fitting even when the training set is small. Experimental results show that the proposed approach produces significantly better results than state-of-the-art methods for age estimation and head pose estimation. At the same time, it also improves recognition performance for multi-label classification and semantic segmentation tasks.

Understanding Kin Relationships in a Photo

Abstract: There is an urgent need to organize and manage images of people automatically due to the recent explosion of such data on the Web in general and in social media in particular. Beyond face detection and face recognition, which have been extensively studied over the past decade, perhaps the most interesting aspect related to human-centered images is the relationship of people in the image. In this work, we focus on a novel solution to the latter problem, in particular the kin relationships. To this end, we constructed two databases: the first one named UB KinFace Ver2.0, which consists of images of children, their young parents and old parents, and the second one named FamilyFace. Next, we develop a transfer subspace learning based algorithm in order to reduce the significant differences in the appearance distributions between children and old parents facial images. Moreover, by exploring the semantic relevance of the associated metadata, we propose an algorithm to predict the most likely kin relationships embedded in an image. In addition, human subjects are used in a baseline study on both databases. Experimental results have shown that the proposed algorithms can effectively annotate the kin relationships among people in an image and semantic context can further improve the accuracy.