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.

On Capacity of Large-Scale MIMO Multiple Access Channels with Distributed Sets of Correlated Antennas

Abstract: In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input multiple-output (MIMO) antenna channels are proposed. We consider a large-scale MIMO system consisting of multiple users and one base station with several distributed antenna sets. Each link between a user and an antenna set forms a two-sided spatially correlated MIMO channel with line-of-sight (LOS) components. Our derivations are based on novel techniques from large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the terminals approach to infinity with a fixed ratio. The deterministic equivalent results (the deterministic equivalent of ergodic sum rate and the capacity-achieving input covariance matrices) are easy to compute and shown to be accurate for realistic system dimensions. In addition, they are shown to be invariant to several types of fading distribution.

Pinning impulsive stabilization of nonlinear dynamical networks with time-varying delay

Abstract: In this paper, a new impulsive control strategy, namely pinning impulsive control, is proposed for the stabilization problem of nonlinear dynamical networks with time-varying delay. In this strategy, only a small fraction of nodes is impulsively controlled to globally exponentially stabilize the whole dynamical network. By employing the Lyapunov method combined with the mathematical analysis approach as well as the comparison principle for impulsive systems, some criteria are obtained to guarantee the success of the global exponential stabilization process. The obtained criteria are closely related to the proportion of the controlled nodes, the impulsive strength, the impulsive interval and the time-delay. Numerical examples are given to demonstrate the effectiveness of the designed pinning impulsive controllers.

Transformerless Split-Inductor Neutral Point Clamped Three-Level PV Grid-Connected Inverter

Abstract: Characterized by low leakage current and low voltage stress of the power device, a neutral point clamped three-level inverter (NPCTLI) is suitable for a transformerless photovoltaic (PV) grid-connected system. Unfortunately, the shoot-through problem of bridge legs still exists in an NPCTLI, so its operation reliability is degraded. An improved three-level grid-connected inverter is proposed based on the NPCTLI and the dual-buck half-bridge inverter (DBHBI), and which avoids the shoot-through problem. The proposed topology guarantees no switching-frequency common-mode voltage and no shoot-through risk. Furthermore, the freewheeling diode of bridge legs of the DBHBI can be removed taking into consideration the unity power factor of grid current, and a straightforward topology is thus derived. The new topology is referred to as split-inductor NPCTLI (SI-NPCTLI). The operation mode, common-mode characteristic, and control strategy are analyzed. Finally, both the simulation and the experimental results of a 1-kW SI-NPCTLI prototype verify the analysis.

Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising

Abstract: Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.