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IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang He Zhida Song Shuanghong Liu Mengqi Niu Ying Hu Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China School of Intelligence 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

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In this paper, we propose a novel loss by integrating a deep clustering (DC) loss at the frame-level and a speaker recognition loss at the segment-level into a single network without additional data requirements and exhaustive computation. The DC loss implicitly generates soft pseudo-phoneme labels for each frame-level feature, which facilitates extracting more discriminant speaker representation by suppressing phonetic content information. We study the DC loss not only on the acoustic feature, but also on the features extracted by the pre-trained models, such as wav2vec 2.0, HuBERT and WavLM. Experimental results on the VoxCeleb dataset shows that the overall system performance based on the pre-trained model features are better than the one on the acoustic feature. The proposed loss is significantly effective for systems on the acoustic feature and has a marginal improvement for systems on the pre-trained model feature.

关键词： Training Costs Computational modeling System performance Phonetics Multimedia databases Feature extraction

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Digital image biological detection technology based on the porous silicon periodic crystals film

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Optoelectronics Letters 2021年第9期17卷 552-557页

作者： YANG Jianfeng JIA Zhenhong LüXiaoyi HUANG Xiaohui WANG Jiajia School of Physical Science and Technology Xinjiang UniversityUrumqi 830046China School of Information Science and Engineering Xinjiang UniversityUrumqi 830046China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous RegionXinjiang UniversityUrumqi 830046China

In order to solve this problem,a new biological detection method is proposed,which makes use of the characteristics of optical transmission at the edge of the spectral band gap and sensitive to refractive index *** the probe light with wavelength at the edge of the Bragg band gap of porous silicon is incident on the surface of porous silicon,the change of refractive index caused by deoxyribonucleic acid(DNA)reaction in porous silicon will affect the detection light intensity transmitted from the porous silicon *** analyzing the change of the average gray value of the transmitted light image,the concentration of the DNA can be obtained.

关键词： porous image refractive

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Extended State Observer Based Fault-Tolerant Predictive Control for a Four-Mecanum Wheels Mobile Robot 14

Extended State Observer Based Fault-Tolerant Predictive Cont...

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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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Complementary Mismatch Filter Group Design for Joint radar and Communications Waveforms

Complementary Mismatch Filter Group Design for Joint Radar a...

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2021 CIE International Conference on radar, radar 2021

作者： Li, Haichuan Liu, Yongjun Liao, Guisheng Chen, Yufeng Yin, Wei Chen, Chilian Xidian University National Laboratory of Radar Signal Processing Xi'an China The 54th Research Institute of CETC Shijiazhuang China The 20th Research Institute of CETC Key Laboratory of Technology on Datalink Xi'an China

ISBN: (纸本)9781665498142

In the traditional joint radar and communications (JRC) system, the receive filter outputs for unambiguous returns have high sidelobe level (SLL), and the outputs of range ambiguity returns have high energy. These problems degrade the performance of the traditional JRC system. To solve these problems, we design the complementary mismatch filter group (CMFG) for the JRC system in this paper. Compared with the matched filter, the output of the CMFG has signal to noise ratio (SNR) loss. Hence, in this method, the SNR loss and the SLL of the summation of filter outputs of unambiguous returns as well as the energy of summation of filter outputs of range ambiguity returns in each CMFG are jointly minimized. To achieve this, the multi-objective optimization problem for CMFG design is established, and the Block Coordinate Descent algorithm is employed to solve this optimization problem. Finally, simulation results show that the output of the designed CMFG has low SLL, low energy of the range ambiguity output, and low SNR loss. © 2021 IEEE.

关键词： complementary mismatch filter group Joint radar and communications range ambiguity sidelobe level signal to noise ratio loss

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A Spatial Variant Phase Compensation Algorithm for Bistatic ISAR Imaging of Maneuvering Targets Based on Optimal Parameter Estimation

A Spatial Variant Phase Compensation Algorithm for Bistatic ...

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2021 CIE International Conference on radar, radar 2021

作者： Ding, Jiabao Wang, Jiadong Li, Yachao Sun, Ziyue Xidian University National Laboratory of Radar Signal Processing Xi'an China Xidian University Academy of Advanced Interdisciplinary Researsh Xi'an China School of Engineering and Computer Science University of Exeter Exeter United Kingdom

ISBN: (纸本)9781665498142

When imaging a maneuvering target with complex three-dimensional (3-D) non-uniform rotational motion, the traditional range Doppler (RD) algorithm for bistatic inverse synthetic aperture radar (Bi-ISAR) will fail due to the presence of phase errors. Moreover, due to the time-varying property of the imaging projection plane (IPP), the phase errors are two-dimensional (2-D) spatial variant. In order to deal with these problems, we propose a spatial variant phase compensation (SVPC) method based on parametric minimun entropy optimization to compensate for the 2-D spatial variant phase errors. The main contribution of the proposed method is to give an accurate expression of the 2-D spatial variant model and optimally compensate the 2-D spatial variant phase errors in the Bi-ISAR configuration. Simulation results verify the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Bistatic inverse synthetic aperture radar (Bi-ISAR) minimun entropy optimization spatial variant phase compensation (SVPC) two-dimensional (2-D) spatial variant

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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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Anti-jamming performance analysis of multi-OAM-mode vortex radar based on waveform diversity

Anti-jamming performance analysis of multi-OAM-mode vortex r...

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International Conference on radar Systems (radar 2022)

作者： D. Hu L. Liu Y. Duan X. Jiang K. Lv H. Ma National Laboratory of Radar Signal Processing Xidian University Xi'an People's Republic of China Beijing Institute of Remote-sensing Equipment Science and Technology on Millimeter-wave Laboratory Beijing People's Republic of China

This paper exploits the electromagnetic vortex (EMV) radar system in anti-jamming applications of digital radio frequency memory (DRFM), considering the orthogonal-waveform-based uniform circular array (UCA) system specifically. Compared to conventional multiple-input-multiple-output (MIMO) system, the EMV radar has a novel dimension of EMV mode proportional to the orbital angular momentum (OAM), providing a new dimension for the orthogonal decomposition of the transmitted waveform, as well as a new dimension for distinguish the DRFM and a real target.

关键词： angular momentum antenna arrays digital radio jamming MIMO communication radar signal processing

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Federated Client-tailored Adapter for Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Hu, Guyue Song, Siyuan Kang, Yukun Yin, Zhu Zhao, Gangming Li, Chenglong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Internet Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Department of Computer Science The University of Hong Kong Hong Kong

Medical image segmentation in X-ray images is beneficial for computer-aided diagnosis and lesion localization. Existing methods mainly fall into a centralized learning paradigm, which is inapplicable in the practical medical scenario that only has access to distributed data islands. Federated Learning has the potential to offer a distributed solution but struggles with heavy training instability due to client-wise domain heterogeneity (including distribution diversity and class imbalance). In this paper, we propose a novel Federated Client-tailored Adapter (FCA) framework for medical image segmentation, which achieves stable and client-tailored adaptive segmentation without sharing sensitive local data. Specifically, the federated adapter stirs universal knowledge in off-the-shelf medical foundation models to stabilize the federated training process. In addition, we develop two client-tailored federated updating strategies that adaptively decompose the adapter into common and individual components, then globally and independently update the parameter groups associated with common client-invariant and individual client-specific units, respectively. They further stabilize the heterogeneous federated learning process and realize optimal client-tailored instead of sub-optimal global-compromised segmentation models. Extensive experiments on three large-scale datasets demonstrate the effectiveness and superiority of the proposed FCA framework for federated medical segmentation. © 2025, CC BY.

关键词： Federated learning

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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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