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.

Fixed-time synchronization of delayed memristor-based recurrent neural networks

Abstract: This paper focuses on the fixed-time synchronization control methodology for a class of delayed memristor-based recurrent neural networks. Based on Lyapunov functionals, analytical techniques, and together with novel control algorithms, sufficient conditions are established to achieve fixed-time synchronization of the master and slave memristive systems. Moreover, the settling time of fixed-time synchronization is estimated, which can be adjusted to desired values regardless of the initial conditions. Finally, the corresponding simulation results are included to show the effectiveness of the proposed methodology derived in this paper.

Pinning synchronization of delayed dynamical networks via periodically intermittent control.

Abstract: This paper investigates the synchronization problem for a class of complex delayed dynamical networks by pinning periodically intermittent control. Based on a general model of complex delayed dynamical networks, using the Lyapunov stability theory and periodically intermittent control method, some simple criteria are derived for the synchronization of such dynamical networks. Furthermore, a Barabasi–Albert network consisting of coupled delayed Chua oscillators is finally given as an example to verify the effectiveness of the theoretical results.

Visible-light responsive cerium ion modified titania sol and nanocrystallites for X-3B dye photodegradation

Abstract: The cerium ion (Ce4+) modified titania sol and nanocrystallites were prepared by chemical coprecipitation–peptization and hydrothermal synthesis methods, respectively. XRD patterns show that Ce4+-TiO2 sol particles had anatase semicrystalline structure. And the calcined Ce4+-TiO2 powder was composed of predominant anatase titania and crystalline cerium titanate (11.18 wt.% CexTi(1−x)O2). AFM micrograph shows that ultrafine particles were well dispersed in sol system and average particle size was about 10 nm. Ce4+-TiO2 nanocrystallites have grown into 70 nm in mean size. The difference in calculated particle size (2.41 nm for sol particle and 4.53 nm for crystallite) by XRD Scherrer’s formula was mainly due to aggregation effect of nanoparticles. The experimental results exhibit that Ce4+-TiO2 sol and nanocrystallites can effectively photodegrade reactive brilliant red dye (X-3B) with the dye/Ce4+-TiO2/visible-light reaction system. Moreover, photocatalytic reaction also can carry out in hydrosol reaction system as well as in suspension reaction system. And Ce4+-TiO2 sol has shown higher efficiency than nanocrystallites in respect of photocatalytic activity. Meanwhile, dye photodegradation mechanisms involving photolysis, photocatalysis, photosensitized photocatalysis and interband photocatalysis were proposed regarding different photocatalytic reaction system.

Model-Driven Deep Learning for MIMO Detection

Abstract: In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches.