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.

Broadband amplification of spoof surface plasmon polaritons at microwave frequencies

Abstract: Efficient amplification of spoof surface plasmon polaritons (SPPs) is proposed at microwave frequencies by using a subwavelength-scale amplifier For this purpose, a special plasmonic waveguide composed of two ultrathin corrugated metallic strips on top and bottom surfaces of a dielectric substrate with mirror symmetry is presented, which is easy to integrate with the amplifier It is shown that spoof SPPs are able to propagate on the plasmonic waveguide in broadband with low loss and strong subwavelength effect By loading a low-noise amplifier chip produced by the semiconductor technology, the first experiment is demonstrated to amplify spoof SPPs at microwave frequencies (from 6 to 20GHz) with high gain (around 20dB), which can be directly used as a SPP amplifier device The features of strong field confinement, high efficiency, broadband operation, and significant amplification of the spoof SPPs may advance a big step towards other active SPP components and integrated circuits

Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis

Abstract: In this paper, a novel group sparse canonical correlation analysis (GSCCA) method is proposed for simultaneous electroencephalogram (EEG) channel selection and emotion recognition. GSCCA is a group sparse extension of the conventional CCA method to model the linear correlationship between emotional EEG class label vectors and the corresponding EEG feature vectors. In contrast to conventional CCA method or previous GSCCA methods, a major advantage of our GSCCA method is the ability of handling the group feature selection problem from raw EEG features, which makes it very suitable for simultaneously coping with both EEG emotion recognition and automatic channel selection issues where each EEG channel is associated with a group of raw EEG features. To deal with EEG emotion recognition problem, we adopt the popularly used frequency feature to describe the EEG signal by dividing the full EEG frequency band into five parts, i.e., $\boldsymbol {\delta }$ , $\boldsymbol {\theta }$ , $\boldsymbol {\alpha }$ , $\boldsymbol {\beta }$ , and $\boldsymbol {\gamma }$ frequency bands, and then extract the frequency band features from each band for GSCCA model learning and emotion recognition. Finally, we conduct extensive experiments on EEG-based emotion recognition based on the SJTU emotion EEG dataset and experimental results demonstrate that the proposed GSCCA method would outperform the state-of-the-art EEG-based emotion recognition approaches.

Nonsmooth Finite-Time Synchronization of Switched Coupled Neural Networks

Abstract: This paper is concerned with the finite-time synchronization (FTS) issue of switched coupled neural networks with discontinuous or continuous activations. Based on the framework of nonsmooth analysis, some discontinuous or continuous controllers are designed to force the coupled networks to synchronize to an isolated neural network. Some sufficient conditions are derived to ensure the FTS by utilizing the well-known finite-time stability theorem for nonlinear systems. Compared with the previous literatures, such synchronization objective will be realized when the activations and the controllers are both discontinuous. The obtained results in this paper include and extend the earlier works on the synchronization issue of coupled networks with Lipschitz continuous conditions. Moreover, an upper bound of the settling time for synchronization is estimated. Finally, numerical simulations are given to demonstrate the effectiveness of the theoretical results.

Direct writing on paper of foldable capacitive touch pads with silver nanowire inks.

Abstract: Paper-based capacitive touch pads can be fabricated utilizing high-concentration silver nanowire inks needle-printed directly onto paper substrates through a 2D programmable platform. Post deposition, silver nanowire tracks can be photonically sintered using a camera flash to reduce sheet resistance similar to thermal sintering approaches. Touch pad sensors on a variety of paper substrates can be achieved with optimized silver nanowire tracks. Rolling and folding trials, which yielded only modest changes in capacitance and no loss of function, coupled with touch pad functionality on curved surfaces, suggest sufficient flexibility and durability for paper substrate touch pads to be used in diverse applications. A simplified model to predict touch pad capacitance variation ranges with differing touch conditions was developed, with good agreement against experimental results. Such paper-based touch pads have the advantage of simple structure, easy fabrication, and fast sintering, which holds promise for numerous commercial applications including low-cost portable devices where ultrathin and lightweight features, coupled with reliable bending stability are desirable.

Bioinspired Multicompartmental Microfibers from Microfluidics

Abstract: Bioinspired multicompartmental microfibers are generated by novel capillary microfluidics. The resultant microfibers possess multicompartment body-and-shell compositions with specifically designed geometries. Potential use of these microfibers for tissue-engineering applications is demonstrated by creating multifunctional fibers with a spatially controlled encapsulation of cells.