Xinru Jiang

Sun Yat-sen University

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Yuxing Li

Northeast Agricultural University

Yingmin Yi

Xi'an University of Technology

Peizeng Lin

Sun Yat-sen University

Lei Sun

Sun Yat-sen University

Lin Zhao

University of Georgia

Feiyue Ning

Xi'an University of Technology

Lei Sun

Sun Yat-sen University

Junxian Wu

Xi'an University of Technology

Hongxuan Li

Shenyang Aerospace University

Bingzhao Tang

Xi'an University of Technology

Cooperative Institutions

Xi'an University of Technology

Sun Yat-sen University

Tsinghua University

Chinese Academy of Sciences

Risun (China)

Zhejiang Sci-Tech University

Tianjin University of Technology

China Institute of Water Resources and Hydropower Research

Beijing Microelectronics Technology Institute

Xijing University

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RCMFRDE: Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy for Feature Extraction of Ship-Radiated Noise

Mathematical Problems in Engineering (2021)

Yuxing Li Shangbin Jiao Bo Geng Xinru Jiang

Dispersion entropy (DE), as a newly proposed entropy, has achieved remarkable results in its application. In this paper, on the basis of DE, combined with coarse-grained processing, we introduce the fluctuation and distance information of signal and propose the refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE). As an emerging complexity analysis mode, RCMFRDE has been used for the first time for the feature extraction of ship-radiated noise signals to mitigate the loss caused by the misclassification of ships on the ocean. Meanwhile, a classification and recognition method combined with K-nearest neighbor (KNN) came into being, namely, RCMFRDE-KNN. The experimental results indicated that RCMFRDE has the highest recognition rate in the single feature case and up to 100% in the double feature case, far better than multiscale DE (MDE), multiscale fluctuation-based DE (MFDE), multiscale permutation entropy (MPE), and multiscale reverse dispersion entropy (MRDE), and all the experimental results show that the RCMFRDE proposed in this paper improves the separability of the commonly used entropy in the hydroacoustic domain.

10.1155/2021/7150921

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Analysis of the synergistic complementarity between bubble entropy and dispersion entropy in the application of feature extraction

Frontiers in Physics (2023)

Xinru Jiang Yingmin Yi Junxian Wu

Most of the existing studies on the improvement of entropy are based on the theory of single entropy, ignoring the relationship between one entropy and another. Inspired by the synergistic relationship between bubble entropy (BE) and permutation entropy (PE), which has been pointed out by previous authors, this paper aims to explore the relationship between bubble entropy and dispersion entropy. Since dispersion entropy outperforms permutation entropy in many aspects, it provides better stability and enhances the computational efficiency of permutation entropy. We also speculate that there should be potential synergy between dispersion entropy and bubble entropy. Through experiments, we demonstrated the synergistic complementarity between BE and DE and proposed a double feature extraction method based on BE and DE. For the single feature extraction experiment, dispersion entropy and bubble entropy have better recognition performance for sea state signals and bearing signals, respectively; in double feature extraction, the combination of bubble entropy and dispersion entropy makes the recognition rate of sea state signals increase by 10.5% and the recognition rate of bearing signals reach 99.5%.

10.3389/fphy.2023.1163767

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Citations (4)

THInImg: Cross-modal Steganography for Presenting Talking Heads in Images

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2024)

Lin Zhao Hongxuan Li Xuefei Ning Xinru Jiang

Cross-modal Steganography is the practice of concealing secret signals in publicly available cover signals (distinct from the modality of the secret signals) unobtrusively. While previous approaches primarily concentrated on concealing a relatively small amount of information, we propose THInImg, which manages to hide lengthy audio data (and subsequently decode talking head video) inside an identity image by leveraging the properties of human face, which can be effectively utilized for covert communication, transmission and copyright protection. THInImg consists of two parts: the encoder and decoder. Inside the encoder-decoder pipeline, we introduce a novel architecture that substantially increase the capacity of hiding audio in images. Moreover, our framework can be extended to iteratively hide multiple audio clips into an identity image, offering multiple levels of control over permissions. We conduct extensive experiments to prove the effectiveness of our method, demonstrating that THInImg can present up to 80 seconds of high quality talking-head video (including audio) in an identity image with 160×160 resolution.

10.1109/wacv57701.2024.00546

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Citations (1)

Hyperspectral Image Denoising Via Cosine Transform-Based Tensor Subspace Representation

Peizeng Lin Xinru Jiang Lei Sun

Hyperspectral images (HSIs) denoising is essential in hyperspectral imaging. The existence of noise seriously affects the subsequent HSIs processing. In this paper, a novel HSI denoising model is proposed. On one hand, the proposed model exploits the tensor subspace representation model for global correlation description in HSIs. The tensor subspace representation model can avoid lossing the information inherent in tensor matricization which indicates that it has the ability in preserving intrinsic structure of HSIs. On the other hand, we introduce 3-D discrete orthogonal cosine transform (3-D D-CT) for spatial-spectral information extraction. To solve the HSI denoising model,an efficient algorithm is developed in terms of block coordinate descent (BCD) and alternating direction method of multipliers (ADMM) framework. Numerical results show that the proposed method achieves a comparatively performance in HSIs denoising.

Representation

10.1109/whispers61460.2023.10431364

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Rating entropy and its multivariate version

Mechanical Systems and Signal Processing (2025)

Yuxing Li Xinru Jiang Junxian Wu

10.1016/j.ymssp.2025.112368

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Bearing Fault Feature Extraction Method Based on GA-VMD and Center Frequency

Mathematical Problems in Engineering (2022)

Yuxing Li Bingzhao Tang Xinru Jiang Yingmin Yi

To promote the effect of variational mode decomposition (VMD) and further enhance the recognition performances of bearing fault signals, genetic algorithm (GA) is applied to optimize the combination of VMD parameters in this paper, and GA-VMD algorithm is put forward to improve the decomposition accuracy of VMD. In addition, combined with the center frequency, a feature extraction method based on GA-VMD and center frequency is proposed to ameliorate the difficulty of bearing fault feature extraction. Firstly, the bearing signal is decomposed into a series of intrinsic mode components (IMFs) by GA-VMD. Then, the Center Frequency of IMFs is extracted, and the recognition rate is calculated by k-nearest neighbor (KNN) algorithm. Simulation signal experiments state clearly that, compared with manual parameter setting-VMD algorithm and parameter optimization VMD algorithm based on particle swarm optimization (PSO), the decomposition result of GA-VMD has a smaller root mean square error and higher decomposition accuracy, which verifies the effectiveness of GA-VMD. The experimental results demonstrate that, by comparison with the feature extraction method based on envelope entropy, the feature extraction method based on center frequency has better inter class separability and higher mean recognition rate (the highest recognition rate of single feature is 94.5%, and in the case of multiple features, the recognition rate reaches 100% when four features are extracted) and can realize the accurate identification of different bearing fault signals.

Center frequency

Feature (linguistics)

10.1155/2022/2058258

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Citations (59)

Hypergraph-regularized low-rank tensor subspace clustering for hyperspectral band selection

International Journal of Remote Sensing (2025)

Kaijun Luo Lei Sun Yu Meng Xinru Jiang

Hypergraph

Rank (graph theory)

10.1080/01431161.2025.2452311

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THInImg: Cross-modal Steganography for Presenting Talking Heads in Images

arXiv (Cornell University) (2023)

Lin Zhao Hongxuan Li Xuefei Ning Xinru Jiang

10.48550/arxiv.2311.17177

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Feature extraction methods of ship-radiated noise: From single feature of multi-scale dispersion Lempel-Ziv complexity to mixed double features

Applied Acoustics (2022)

Yuxing Li Xinru Jiang Bingzhao Tang Feiyue Ning Yilan Lou

Feature (linguistics)

10.1016/j.apacoust.2022.109032

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Citations (14)

Local Sparsity Blocks and Tensor Low Rank Regularized Sparse Unmixing

Xinru Jiang Lei Sun Peizeng Lin

Hyperspectral unmixing has been an important technique in remote sensing application. Sparse unmixing is a type of widely used methods, which assumes the endmembers in a hyperspectral image (HSI) are relatively small portion of the spectral library. The existing methods have considered single sparsity, row sparsity, and joint-sparsity-blocks regularizations to the unmixing model. However, they all reshape the HSI into a two-dimensional matrix, which results in the loss of local spatial information to some extent. So we consider the HSI as a tensor, and construct a new unmixing model based on the block term decomposition. Simultaneously, we propose a slice-sparsity-blocks regularization to fully utilize the local spatial information. In addition, tensor nuclear norm is added to the model to constrain the low rankness of the abundances, which can preserve the spatial structure and the interactions among the three dimensions. Then we design an efficient algorithm to solve the proposed model. And the experiments on the simulated dataset demonstrate the advantage of the proposed method.

Rank (graph theory)

10.1109/whispers61460.2023.10430747

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