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14th IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Yang, Binghao Wang, Dongliang Wen, Ziling Wei, Wu Li, Wenji Fan, Zhun Shantou University Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong 515063 China Shantou University School of Department of Electronic and Information Engineering Guangdong 515063 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Guangzhou510640 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9798331506056

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment. © 2024 IEEE.

关键词： Mobile robots

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Clutter Compensation for Space-Air Bistatic Radar Based on Unitary Subspace Transformation

Clutter Compensation for Space-Air Bistatic Radar Based on U...

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IEEE International Conference on Radar

作者： Qingyun Kan Jingwei Xu Guisheng Liao Keyi Wang Yanhong Xu National Key Laboratory of Radar Signal Processing Xidian University Xi’an China The School of Communication and Information Engineering Xi’an University of Science and Technology Xi’an China

Since the space-air bistatic radar (SABR) system works in down-looking mode, it encounters a severely broadened clutter spectrum and the clutter is range dependent. Thereby diminishing the performance of space-time adaptive processing (STAP) technology through covariance matrix estimation error. It is necessary to perform clutter compensation before STAP clutter suppression. Inspired by the thinking of coherent signal subspace method for estimating the directions of arrival of wideband signals, we propose the range dependence compensation method which transforms the clutter subspace of training samples into the clutter subspace of reference range bin by unitary matrix transformation. Simulation examples are conducted to validate the effectiveness of the proposed algorithm.

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Image Watermarking Technique Using Convolutional Autoencoder

Image Watermarking Technique Using Convolutional Autoencoder

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Advances in Electronics, Control and Communication Systems (ICAECCS), International Conference on

作者： Elchaimae Rebahi Mustapha Hemis Bachir Boudraa Speech Communication and Signal Processing Laboratory Faculty of Electrical Engineering University of science and technology Houari BOUMEDIENE (USTHB) BP 32 El Alia Algiers Algeria

Due to the rapid growth in communication networks and data sharing, protecting copyrights has become increasingly important. Digital watermarking is seen as a promising solution to resolve this issue. A new blind digital watermarking based on deep convolutional auto-encoders is presented in this paper. The system facilitates the embedding and extraction of a watermark in an image for its protection. The proposed architecture comprises four modules: an “encoder” that compresses the original image and watermark, an “embedder” that combines the two, a “decoder” that upsamples the combined result to reconstruct the watermarked image, and an “extractor” for watermark retrieval. The experimental results indicate that the watermarked images maintain their visual quality after insertion, and the extracted watermark remains robust against various various attacks such as filtering, rotation, and noise.

关键词： Visualization Image resolution Image coding Filtering Watermarking Control systems Robustness

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MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection

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Computers in Biology and Medicine 2024年 179卷 108835-108835页

作者： Wei, Pi-Jing Bao, Jin-Jin Gao, Zhen Tan, Jing-Yun Cao, Rui-Fen Su, Yansen Zheng, Chun-Hou Deng, Li Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University 111 Jiulong Road HefeiAnhui230601 China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University 111 Jiulong Road HefeiAnhui230601 China Shenzhen Key Laboratory of Microbial Genetic Engineering College of Life Sciences and Oceanology Shenzhen University Guangdong Shenzhen518055 China School of Artificial Intelligence Anhui University 111 Jiulong Road HefeiAnhui230601 China

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study

关键词： Gene expression

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Multi-View Speaker Embedding Learning for Enhanced Stability and Discriminability

Multi-View Speaker Embedding Learning for Enhanced Stability...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Liang He Zhihua Fang Zuoer Chen Minqiang Xu Ying Meng Penghao Wang School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China iFly Digital Technology Hefei China

Deep neural network models based on x-vector have become the most popular framework for speaker recognition, and the quality of speaker features (embeddings) is important for open-set tasks such as speaker verification and speaker diarization. Currently, the most popular loss function is based on margin penalty, however, it only considers enlarging the inter-class distance while neglecting to reduce the intra-class feature differences. Therefore, we propose a multi-view learning approach that divides the training process into two views from the speaker embedding level. The classification view focuses on distinguishing the discriminability of different speakers, while the clustering view focuses on shrinking the feature boundaries of the same speaker, making intra-class differences smaller. The combined effect of the two perspectives achieves large inter-class distance and small intra-class distances, resulting in the extraction of more discriminative and stable speaker embeddings. We test the performance of the method on both speaker verification and speaker diarization tasks, and the results demonstrate the effectiveness of our approach.

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A signal Coherence Recovery Method for Dechirp Sampling Radar

A Signal Coherence Recovery Method for Dechirp Sampling Rada...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jixiang Fu Xinyi He Zijun Kang Wangshuo Tang Yidi Wang Min Xue Zhixin Wu Yuexin Gao Dan Xu Academy of Advanced Interdisciplinary Research Xidian University Xi’an China National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Aerospace Science and Technology Xidian University Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Due to the range gate hopping and Doppler frequency modulation, the coherence of the high-speed targets radar echoes received via dechirp sampling is disrupted, leading to a significant decrease in the accuracy and robustness of radar imaging. To address this issue, a new method for echo coherence recovery in dechirp sampling radars is proposed. Assuming the range gate hops with a fixed step size, the radar’s reference range can be accurately estimated by combining range alignment (RA) and range unwrapping technique. Simultaneously, the target’s range and velocity can also be estimated by using polynomial smoothing. By deriving and analyzing the accurate dechip signal model, the coherence of the echo is restored by compensating for high-speed Doppler modulation, residual video phase (RVP) terms, and residual motion errors through the construction of corresponding functions. Finally, the effectiveness of this method is validated by simulation experiments.

关键词： Analytical models Accuracy Smoothing methods Coherence Radar imaging Logic gates Polynomials

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MP3Net:Multi-scale Patch Parallel Prediction Networks for Multivariate Time Series Forecasting

MP3Net:Multi-scale Patch Parallel Prediction Networks for Mu...

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International Joint Conference on Neural Networks (IJCNN)

作者： Daishun Cui Jiwei Qin Fang He Fei Shi Qiang Li Dezhi Sun Jiachen Xie School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China HongYou Software Co. Ltd Karamay China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Transformers have emerged as a crucial technology in long-term time series forecasting (LTSF). Recent studies have demonstrated that Transformers segment time series into patches, effectively capturing temporal patterns. However, single-scale patches still fail to adequately capture the complex short-term fluctuations and long-term trends in time series. To tackle this issue, we propose a novel framework, the Multi-scale Patch Parallel Prediction Network (MP3Net). Firstly, we propose a multi-scale patch module to extract local features and long-term correlations from the time series to effectively capture and identify data characteristics across different time scales. Secondly, we propose an adaptive fusion module to dynamically balance the weight of local detail features and long- term correlations from each branch. Thirdly, we introduce a hybrid loss function for model training aimed at enhancing the sensitivity and adaptability of the network towards different types of errors, consequently improving overall performance. Experiments conducted on five benchmark datasets demonstrate that MP3Net surpasses existing methods in LTSF and MP3Net presents an effective approach for addressing the challenge of modeling long-term dependencies in time series analysis.

关键词： Training Adaptation models Correlation Sensitivity Time series analysis Neural networks Transformers

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Distributed solving Sylvester equations with fractional order dynamics

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Control Theory and technology 2021年第2期19卷 249-259页

作者： Songsong Cheng Shu Liang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and AutomationAnhui UniversityHefei230601AnhuiChina Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083China

This paper addresses distributed computation Sylvester equations of the form AX+XB=C with fractional order *** partitioning parameter matrices A,B and C,we transfer the problem of distributed solving Sylvester equations as two distributed optimization models and design two fractional order continuous-time algorithms,which have more design freedom and have potential to obtain better convergence performance than that of existing first order ***,rewriting distributed algorithms as corresponding frequency distributed models,we design Lyapunov functions and prove that proposed algorithms asymptotically converge to an exact or least squares ***,we validate the effectiveness of proposed algorithms by providing a numerical example.

关键词： Fractional order calculus Distributed optimization Sylvester equation Frequency distributed model Convergence

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SSFam: Scribble Supervised Salient Object Detection Family

arXiv

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arXiv 2024年

作者： Liu, Zhengyi Deng, Sheng Wang, Xinrui Wang, Linbo Fang, Xianyong Tang, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China

Scribble supervised salient object detection (SSSOD) constructs segmentation ability of attractive objects from surroundings under the supervision of sparse scribble labels. For the better segmentation, depth and thermal infrared modalities serve as the supplement to RGB images in the complex scenes. Existing methods specifically design various feature extraction and multi-modal fusion strategies for RGB, RGB-Depth, RGB-Thermal, and Visual-Depth-Thermal image input respectively, leading to similar model flood. As the recently proposed Segment Anything Model (SAM) possesses extraordinary segmentation and prompt interactive capability, we propose an SSSOD family based on SAM, named SSFam, for the combination input with different modalities. Firstly, different modal-aware modulators are designed to attain modal-specific knowledge which cooperates with modal-agnostic information extracted from the frozen SAM encoder for the better feature ensemble. Secondly, a siamese decoder is tailored to bridge the gap between the training with scribble prompt and the testing with no prompt for the stronger decoding ability. Our model demonstrates the remarkable performance among combinations of different modalities and refreshes the highest level of scribble supervised methods and comes close to the ones of fully supervised methods. https://***/liuzywen/SSFam Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Ship Target Micro-Doppler Feature Extracted by Multi-Synchrosqueezing Transform and Inverse Radon Transform

Ship Target Micro-Doppler Feature Extracted by Multi-Synchro...

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Ruonan Yang, Zhiwei He, Shun Liao, Guisheng Xidian University National Laboratory of Radar Signal Processing Xi'an China Communication and Information Engineering College Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9781665498142

Ship targets with oceanic waves appear a unique micro-Doppler (m-D) feature, which is important for target identification. In this paper, we propose a method to extract the features of ship target based on Multi-Synchrosqueezing Transform (MSST) and Inverse radon transform (IRT). Firstly, we analyze the complex relative motion between radar and the ship target. After the envelope correction, the echo is a sinusoidal frequency modulation (SFM) signal mixed with a remained time-varying phase. However, it is a challenge to extract the m-D feature directly. Thus, the MSST is used to enhance the TF spectrum concentration, in order to extract the remained phase with m-D characteristics accurately by IRT. The experimental results prove the effectiveness of the proposed method and the accurate estimation of micro motion features. © 2021 IEEE.

关键词： Inverse radon transform (IRT) Micro-Doppler (m-D) Multi-Synchrosqueezing Transform (MSST) Ship Target

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