Qiaoyu Chen

dblp:76/10627 · DBLP profile ↗
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18ranked-venue papers
0as first author
15since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 14 · 11 since 2021Systems, architecture and hardware · 1 · 1 since 2021Computer networks · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
YearPublicationVenuePosition
2026 Event-triggered fixed-time synchronization and energy consumption prediction for coupled neural networks with stochastic disturbances
Qinjie Jiang, Dongbing Tong, Qiaoyu Chen, Wuneng Zhou
Eng. Appl. Artif. Intell.3
2025 EEG2Mesh: High-Quality 3D Mesh Generation from EEG
Ying Fu 0003, Qiaoyu Chen, Chenggang Song, Taorui Li, Dongrui Gao
PRCV (10)2
2025 Fixed/Preassigned-time synchronization of delayed fuzzy memristive neural networks with reaction-diffusion terms
Hanrui Chen, Dongbing Tong, Qiaoyu Chen
Neurocomputing3
2025 Analysis and Design of Wideband Active Single-Sideband Time Modulator in 0.13- μm CMOS
abstract
A wideband active single-sideband time modulator (STM) is proposed in this paper, which achieves high-resolution frequency-independent phase shifting performance through high-precision time delay, eliminating the need for calibrations. The analysis starts with N-step time modulation sequences for the active STM, followed by discussions on enhancing the sideband suppression ratio (SSR) and the effects of quadrature mismatch on SSR. The proposed active STM is based on a periodically controlled active vector modulator with regularly scalable gate-widths, and its timing sequences for control bits feature identical duty cycles and modulation frequency ($f_{\mathrm {P}}$). For verification, a wideband active STM is implemented using 0.13-$\mu $m CMOS technology, which is composed of an input balun and quadrature generator, a periodically controlled 4-bit active vector modulator and a variable-gain stage. Measurement results indicate root mean square (RMS) phase and gain errors ranging from 0.1 to 0.4° and less than 0.2 dB respectively, within a 3-dB frequency range of 13.0 to 20.6 GHz at the maximum gain state. The active STM provides a peak gain of −0.1 dB, an equivalent 10-bit phase control across a 360° range, and a 3-bit gain control spanning 21.0 dB. The measured SSR is below −23.3 dBc, and the instantaneous bandwidth is expanded to$16f_{\mathrm {P}}$. Additionally, the input 1-dB compression point (IP$_{\mathrm {1dB}}$) ranges from 6.1 to 9.3 dBm. The chip occupies a 2.4 mm2 area and consumes 58.8 mW from a 1.2 V supply voltage.
Guoxiao Cheng, Jin-Dong Zhang, Qiaoyu Chen, Wen Wu 0005
IEEE Trans. Circuits Syst. I Regul. Pap.3
2025 Fixed/Prescribed-Time Synchronization and Energy Consumption for Kuramoto-Oscillator Networks
abstract
To evaluate the energy-saving effect of the controller, obtaining upper bounds on energy consumption and control time has become a worthwhile and meaningful issue to study. This article mainly discusses three contents about the Kuramoto oscillator network, including fixed-time synchronization (FxTS), prescribed-time synchronization (PTS), and energy consumption estimation. First, to reach FxTS, two sufficient conditions are proposed to guarantee that the Kuramoto oscillator network can reach fixed-time phase agreement and frequency synchronization. Unlike finite/fixed-time controllers, the prescribed-time controller in this article includes a time-varying function term, which is essential to ensure that the system achieves the prescribed-time phase agreement and frequency synchronization. At the same time, the setting-time for PTS is independent of the system initial values or controller parameters, which expands the application prospects of the system. Then, with limited setting-time as a premise, the energy consumed during the fixed/prescribed-time control process is obtained, which helps to evaluate the working time of the system. Finally, an example of a 5-node network is used to illustrate the effectiveness of FxTS and PTS in Kuramoto-oscillator networks.
Zhenfeng Ma, Dongbing Tong, Qiaoyu Chen, Wuneng Zhou
IEEE Trans. Cybern.3
2025 Enhanced dual lane detection in automotive radar systems using harmonic coordinated beamforming of time-modulated arrays
Yue Ma 0010, Weijun Long, Chen Miao, Qiaoyu Chen, Jin-Dong Zhang, Yingrui Yu, Wen Wu 0005
Wirel. Networks4
2024 Fixed-time synchronization of interconnected memristive neural networks with energy consumption via switched control
Dongbing Tong, Qiaoyu Chen, Wuneng Zhou
Neurocomputing3
2023 Cluster Synchronization and Finite-Time Bounded for Complex Networks Under DoS Attacks and Encoding-Decoding Communication Protocol
Zhennan Shi, Qiaoyu Chen, Dongbing Tong, Hongqian Lu
Neural Process. Lett.2
2023 Cluster Synchronization for Stochastic Coupled Neural Networks with Nonidentical Nodes via Adaptive Pinning Control
Yongkai Xie, Dongbing Tong, Qiaoyu Chen, Wuneng Zhou
Neural Process. Lett.3
2022 Observer-based adaptive finite-time prescribed performance NN control for nonstrict-feedback nonlinear systems
Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Kaili Liao
Neural Comput. Appl.3
2022 Observer-based Adaptive Funnel Dynamic Surface Control for Nonlinear Systems with Unknown Control Coefficients and Hysteresis Input
Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Shigen Shen
Neural Process. Lett.3
2021 Adaptive NN control for nonlinear systems with uncertainty based on dynamic surface control
Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Yuhua Xu 0002
Neurocomputing3
2021 Observer-Based Adaptive NN Tracking Control for Nonstrict-Feedback Systems with Input Saturation
Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Kaili Liao
Neural Process. Lett.3
2021 Exponential Synchronization of Stochastic Neural Networks with Time-Varying Delays and Lévy Noises via Event-Triggered Control
Danni Lu, Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Jun Zhou 0003, Shigen Shen
Neural Process. Lett.3
2021 Exponential Stability of Markovian Jumping Systems via Adaptive Sliding Mode Control
abstract
In this paper, the exponential stability in mean square for Markovian jumping systems (MJSs) is discussed. A new dynamic model, which involves parameters uncertainties, nonlinearities, and Lévy noises, is proposed. Moreover, an adaptive sliding mode controller is built to study the stability of such a complex model. First, an integral-type sliding mode surface (SMS) is established to obtain the sliding mode motion dynamics of MJSs. By the generalized Itô formula and the Lyapunov stability theory, some sufficient conditions are obtained to make sure the exponential stability in mean square for the sliding mode motion dynamics. Second, an adaptive sliding mode control law is provided to assure the reachability of the specified SMS. Furthermore, corresponding parameters of the sliding mode controller and the SMS can be got by solving the convex optimization problem. Finally, the validity of the stability results obtained is illustrated by a numerical simulation and a practical simulation.
Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Peng Shi 0001
IEEE Trans. Syst. Man Cybern. Syst.3
2019 Multi-Delay-Dependent Exponential Synchronization for Neutral-Type Stochastic Complex Networks with Markovian Jump Parameters via Adaptive Control
Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Jun Zhou 0003, Yuhua Xu 0002
Neural Process. Lett.2
2019 Adaptive State Estimation of Stochastic Delayed Neural Networks with Fractional Brownian Motion
Xuechao Yan, Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Yuhua Xu 0002
Neural Process. Lett.3
2018 Exponential synchronization and phase locking of a multilayer Kuramoto-oscillator system with a pacemaker
Dongbing Tong, Pengchun Rao, Qiaoyu Chen, Maciej Ogorzalek, Xiang Li 0010
Neurocomputing3