Wenhao Dong

University of Electronic Science and Technology of China

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i-10 index

Research Trends

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Co-Authors

Jiahao Tian

China University of Mining and Technology

Nizhuan Wang

Hong Kong Polytechnic University

Tianren Sun

National University of Defense Technology

Wai Ting Siok

Hong Kong Polytechnic University

Weiming Zeng

Shanghai Maritime University

Hongyu Chen

Shanghai Maritime University

Chunqin Mao

Nanjing University of Chinese Medicine

Mingxuan Li

Nantong University

Yu Li

Jilin Agricultural University

Jingyi Wang

Shenzhen University

Cooperative Institutions

National University of Defense Technology

Nanjing University of Chinese Medicine

Shanghai Maritime University

University of Electronic Science and Technology of China

Fudan University

Nanjing University

Central South University

North China Electric Power University

Chinese Academy of Sciences

China University of Mining and Technology

Author Statistics

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Research Field

Integrated Multispectral and Multilevel Data Optimization for Rapid Origin Tracing and Quality Assessment of Salvia Miltiorrhiza

Rao Fu Chen Peng Jia Qiao Wenhao Dong Yu Li

Salvia miltiorrhiza

Tracing

10.2139/ssrn.5038744

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A Target Location Algorithm Based on Millimeter Wave Radar

2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC) (2022)

Wenhao Dong Tianren Sun Jiahao Tian Jingyi Wang Zhiyong Song

How to find the trapped rescue workers in the fire disaster is the first problem to be solved. This paper designs the signal waveform, receives the echo data based on the AWR1642 millimeter wave radar sensor produced by TI company. The radar has high speed resolution, is prone to Across Doppler Unit (ADU) phenomenon, and the target signal-to-noise ratio and signal-to-clutter ratio are small in the fire environment, so it is difficult to realize the focus detection of the target. In addition, the radar has low angular resolution and is difficult to achieve accurate target positioning. The range and velocity of the target are estimated through signal processing processes such as Moving Target Indication (MTI), Radon-Fourier Transform (RFT) focusing and cell average constant false alarm rate (CA-CFAR) detection, and then obtains the angle information through multiple signal classification (MUSIC) algorithm processing to complete the target positioning.

Moving target indication

Stationary target indication

Space-Time Adaptive Processing

SIGNAL (programming language)

10.1109/itoec53115.2022.9734670

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A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions

arXiv (Cornell University) (2024)

Yueyang Li Weiming Zeng Wenhao Dong Di Han Lei Chen

Single-channel electroencephalogram (EEG) is a cost-effective, comfortable, and non-invasive method for monitoring brain activity, widely adopted by researchers, consumers, and clinicians. The increasing number and proportion of articles on single-channel EEG underscore its growing potential. This paper provides a comprehensive review of single-channel EEG, focusing on development trends, devices, datasets, signal processing methods, recent applications, and future directions. Definitions of bipolar and unipolar configurations in single-channel EEG are clarified to guide future advancements. Applications mainly span sleep staging, emotion recognition, educational research, and clinical diagnosis. Ongoing advancements of single-channel EEG in AI-based EEG generation techniques suggest potential parity or superiority over multichannel EEG performance.

SIGNAL (programming language)

10.48550/arxiv.2407.14850

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Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding

arXiv (Cornell University) (2024)

Yueyang Li Zhan Kang Shusheng Gong Wenhao Dong Weiming Zeng

Decoding neural visual representations from electroencephalogram (EEG)-based brain activity is crucial for advancing brain-machine interfaces (BMI) and has transformative potential for neural sensory rehabilitation. While multimodal contrastive representation learning (MCRL) has shown promise in neural decoding, existing methods often overlook semantic consistency and completeness within modalities and lack effective semantic alignment across modalities. This limits their ability to capture the complex representations of visual neural responses. We propose Neural-MCRL, a novel framework that achieves multimodal alignment through semantic bridging and cross-attention mechanisms, while ensuring completeness within modalities and consistency across modalities. Our framework also features the Neural Encoder with Spectral-Temporal Adaptation (NESTA), a EEG encoder that adaptively captures spectral patterns and learns subject-specific transformations. Experimental results demonstrate significant improvements in visual decoding accuracy and model generalization compared to state-of-the-art methods, advancing the field of EEG-based neural visual representation decoding in BMI. Codes will be available at: https://github.com/NZWANG/Neural-MCRL.

Neural decoding

Representation

10.48550/arxiv.2412.17337

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Joint Detection and State Estimation of Doppler Radar

2021 CIE International Conference on Radar (Radar) (2021)

Wenhao Dong Jiahao Tian Jingyi Wang Zhiyong Song Qiang Fu

In order to improve the performance of Doppler echo signal's detection and parameter estimation under low signal-to-noise ratio (SNR), a joint detection and state estimation algorithm based on random finite set is proposed. Under the framework of formal Bernoulli filter, the target state model and observation model are designed at the Doppler echo signal level, which overcomes the problem that the traditional Detect-Before-track (DBT) method fails due to the loss of target information in the case of low SNR. The time update and observation update of state vector are realized by sequential Monte Carlo (SMC), and the joint detection and state parameter estimation of single target are realized. Simulation results show that the algorithm can effectively improve the performance of detection and state estimation.

Echo (communications protocol)

SIGNAL (programming language)

Detection theory

Matched filter

10.1109/radar53847.2021.10028643

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Matérn Kernel Adaptive Filtering With Nyström Approximation for Indoor Localization

IEEE Transactions on Instrumentation and Measurement (2023)

Wenhao Dong Xifeng Li Dongjie Bi Yongle Xie

The position identification is one of the most important tasks for many real-time location-oriented applications in Internet of Things (IoT). However, the precise and robust indoor localization always suffers a lot from the high complexity of the indoor environment. In order to attack this problem, a new kernel learning paradigm named kernel adaptive filtering has come to our attention. Kernel adaptive filters (KAFs) have achieved the great success except for its growing network structure which leads to the heavy storage burden. To balance the accuracy with the size of neural networks, sparsification methods are usually initiated into the KAFs to produce a sparse structure of neural networks. Different from traditional sparse approaches, Nyström method employs a subcollection of data sampling points to generate a fixed-size filtering structure which can effectively approximate the space spanned by whole samples. In this work, in order to efficiently fight against the large abnormalities in the indoor environment such as impulsive noise, the Matérn kernel is applied to KAFs for the first time. Based on it, a so-called exponential weighted Matérn kernel recursive maximum correntropy (mKRMC) and its Nyström approximation version, exponential weighted Nyström Matérn kernel recursive maximum correntropy (Nys-mKRMC), are proposed to obtain the desired accuracy performance with a sparse filter structure. In addition, the convergence proof of the proposed Nys-mKRMC has also been given. At last, extensive experimental results demonstrate that both the proposed mKRMC and Nys-mKRMC can provide the high accuracy and the strong robustness with the compact-size of the filter structure compared with the state-of-the-art KAFs and traditional machine learning methods.

Robustness

Kernel (algebra)

10.1109/tim.2023.3291800

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Citations (2)

Research on the Radar Signal Classification Method Based on the Deep Faith Network Model

Smart innovation, systems and technologies (2022)

Tianren Sun Wenhao Dong

SIGNAL (programming language)

Basis (linear algebra)

10.1007/978-981-19-7842-5_6

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