Southeast University

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

Nitrogen-doped carbon nanotubes: high electrocatalytic activity toward the oxidation of hydrogen peroxide and its application for biosensing.

Abstract: This study compares the electrocatalytic activity of nitrogen-doped carbon nanotubes (NCNTs) with multiwalled carbon nanotubes (MWCNTs). Results indicate that NCNTs possess a marked electrocatalytic activity toward oxygen reduction reaction (ORR) by an efficient four-electron process in the alkaline condition, while the process of MWCNTs is through a two-electron pathway. Meanwhile, NCNTs show a very attractive electrochemical performance for the redox reaction of hydrogen peroxide (H2O2) and could be employed as a H2O2 sensor at a low potential of +0.3 V. The sensitivity of the NCNT-based biosensor reaches 24.5 microA/mM, more than 87 times that of the MWCNT-based one. Moreover, NCNTs exhibit striking analytical stability and reproducibility, which enables a reliable and sensitive determination of glucose by monitoring H2O2 produced by an enzymatic reaction between glucose oxidase/glucose or choline oxidase/choline at +0.3 V without the help of the electron mediator. The NCNT-based glucose biosensor has a linear range from 2 to 140 microM with an extremely high sensitivity of 14.9 microA/mM, and the detection limit is estimated to be 1.2 microM at a signal-to-noise ratio of 3. The results indicate that the NCNTs are good nanostructured materials for potential application in biosensors.

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, multilabel 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 deep label distribution learning (DLDL) method effectively utilizes the label ambiguity in both feature learning and classifier learning, which help prevent the network from overfitting even when the training set is small. Experimental results show that the proposed approach produces significantly better results than the 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.

A tunable metamaterial absorber using varactor diodes

Abstract: We present the design, analysis and measurements of a polarization-insensitive tunable metamaterial absorber with varactor diodes embedded between metamaterial units. The basic unit shows excellent absorptivity in the designed frequency band over a wide range of incident angles. By regulating the reverse bias voltage on the varactor diode, the absorption frequency of the designed unit can be controlled continuously. The absorption mechanism is interpreted using the electromagnetic-wave interference theory. When the metamaterial units are placed along two orthogonal directions, the absorber is insensitive to the polarization of incident waves. The tunability of the absorber has been verified by experimental results with the measured bandwidth of 1.5?GHz (or relative bandwidth of 30%).

Optimal traffic counting locations for origin–destination matrix estimation

Abstract: There has been substantial interest in development and application of methodology for estimating origin–destination (O–D) trip matrices from traffic counts. Generally, the quality of an estimated O–D matrix depends much on the reliability of the input data, and the number and locations of traffic counting points in the road network. The former has been investigated extensively, while the latter has received very limited attention. This paper addresses the problem of how to determine the optimal number and locations of traffic counting points in a road network for a given prior O–D distribution pattern. Four location rules: O–D covering rule, maximal flow fraction rule, maximal flow-intercepting rule and link independence rule are proposed, and integer linear programming models and heuristic algorithms are developed to determine the counting links satisfying these rules. The models and algorithms are illustrated with numerical examples.

On-chip generation and manipulation of entangled photons based on reconfigurable lithium-niobate waveguide circuits.

Abstract: On a single chip, sources of entangled photons are combined with optical elements that can perform complex manipulations of quantum signals.