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

作者： Jiachang Sun Xiuhong Li Fuxian Zhu School of Computer Science and Technology XinJiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Economics and Management XinJiang University Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, aspect-based multimodal sentiment analysis (ABMSA) remains a highly hot research field, aiming to leverage various modalities such as images and text to determine the sentiment orientation of viewpoint entities. Although deep learning methods have made significant progress in this field, some challenges still exist: Image information unrelated to the viewpoint entity can introduce noise, insufficient interaction during modal fusion. To solve these problems, we propose a novel ABMSA model with prompt fusion and noise filtration. Specifically, we introduced an Image Noise Filtering (INF) module that can filter the extracted image features to reduce the noise introduced by the irrelevant between viewpoint entity and the image. Additionally, we implemented Multimodal Prompt Fusion (MPF) module to enhance the interaction between modalities, addressing the issue of minimal effective features related to viewpoint entity during the fusion stage of text and image features. Experimental results show that the model outperforms the baseline model on two public datasets, Twitter-2015 and Twitter-2017, demonstrating that our proposed approach effectively enhances the accuracy of aspect-based multimodal sentiment analysis tasks.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Filtration Noise Feature extraction Information filters Acoustics Speech processing

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Blockchain-Assisted Revocable Cross-Domain Authentication for Vehicular Ad-Hoc Networks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Ru Cui, Jie Zhang, Jing Wei, Lu Zhong, Hong He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Wuhan University School of Cyber Science and Engineering 430072 China

With the rapid development of vehicular ad-hoc networks (VANETs) and the increasing diversification of user demands, interactions between different management domains have become more frequent. Identity authentication is an effective way to establish cross-domain trust and secure communication. However, the existing cross-domain authentication schemes of VANETs are limited to the same management or authentication technology for each domain and rely on centralized cross-domain identity management. Even distributed management solutions encounter latency sensitivity, security and privacy challenges. To address these challenges, we propose a blockchain-assisted revocable cross-domain authentication scheme for VANETs. The proposed scheme can establish trust between domain entities by deploying different authentication methods and using distributed management to avoid single-point failures. In addition, the scheme can revoke the identity of malicious vehicles by updating the group public key, thereby ensuring the security and privacy of cross-domain Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. This design avoids the additional impacts of blockchain technology constraints on the high mobility and real-time requirements of VANETs. Security analysis and performance evaluation show that our scheme can resist more attacks and has better security than other related schemes while also achieving a better balance between communication and computational cost. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Modeling and analysis of connected elements interferometry error influencing factors

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Journal of Beijing Institute of technology 2016年第S2期25卷 166-169页

作者：赵培焱王斌王帆 National Key Laboratory of Science and Technology on Blind Signal Processing

Space security is facing unprecedented challenges with more and more crowded *** elements interferometry(CEI) is a new radio interference measurement technology,which can achieve high precision of angle measurement on a short baseline,it is expected to be independent or combined with other method to open up a new area of orbit ***,the influence of time delay measurement and baseline length on angle estimation are analyzed;secondly,the theoretical modeling of the propagation error is carried out,and then the influence of the ionosphere and troposphere are simulated and analyzed;finally,the error of the measurement equipment is modeled,then the clock synchronization error,frequency error and location error are analyzed *** results have important effects on the next research and engineering application of CEI.

关键词： space security connected elements interferometry(CEI) theoretical modeling accuracy analysis

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A space debris detection method based on distributed radar

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Journal of Beijing Institute of technology 2016年第S2期25卷 170-174页

作者：宋科康赵培焱王斌彭华峰 National Key Laboratory of Science and Technology on Blind Signal Processing

The radar echoes of space debris are weak and difficult to be *** at this problem,a distributed radar observation model is built combined with orbit characteristics of space debris,and a centralized CA-CFAR(constant false alarm rate) detection method with incoherent integration is proposed for space debris ***,the space debris scattering property and echoes model are ***,the multistation centralized cooperative detection system is applied,and as the space noise environment is well relatively,a CA-CFAR detector is adopted that follows the traditional incoherent integration,which solves the problem that echoes parameter can’t be estimated correctly and is propitious to space debris *** with four radars shows that the method can improve detection probability of space debris by 40% compared with one radar when the SNR is 10 dB.

关键词： centralized detection constant false alarm rate(CFAR) space debris distributed radar

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A Two-Stage Iteratively Reweighted Least Squares Estimator for Robust Elliptic Localization in the Presence of Impulsive Noise

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhao, Xiaoyu Lu, Zukun Li, Jun Huang, Long Yu, Yi Ou, Gang The College of Electronic Science and Technology National University of Defense Technology Changsha410073 China The Key Laboratory of Satellite Navigation Technology Changsha410073 China The National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Hunan Xinghe Electronics Co.LTD Changsha410073 China

As a range-based localization approach, elliptic localization based on bistatic ranges (BRs) has wide range of applications in multistatic systems. This paper addresses the problem of robust elliptic localization using the minimum `pnorm criterion in scenarios affected by impulsive α-stable noise. To solve the `p-norm formulation, we develop an asymptotically efficient two-stage iteratively reweighted least squares (TSIRLS) estimator via incorporating pseudolinear estimation and multistage weighted least squares estimation into the framework of iteratively reweighted least squares (IRLS) methods. The estimator consists of two cascaded `p-norm minimization estimators. To obtain a closed-form solution at each iteration, we transform nonlinear BR measurement equations into pseudolinear forms through introducing auxiliary variables, thus resulting in an iteratively reweighted pseudolinear least squares (IRPLS) estimator in the first stage. The dependencies of unknown parameters and the estimate error derived from the IRPLS are utilized in the second stage to enhance the performance of target location estimate. The analytical derivation demonstrates that, under the assumptions of a sufficiently large number of measurements and small noise, the final position estimate obtained from the TSIRLS achieves the theoretical covariance of the general `pnorm minimization estimation. Extensive numerical simulations highlight the advantages of TSIRLS over existing least squares and robust estimators in terms of positioning performance and runtime. The TSIRLS is also observed to generate approximately unbiased estimates with mean square errors that closely approach the Cramér-Rao lower bound. © 2025 IEEE.

关键词： Impulse noise

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A Novel Special Emitter Identification Method Based on Improved Subclass Discriminant Analysis 2

A Novel Special Emitter Identification Method Based on Impro...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Guiliang Wang Yuanling Huang Science and Technology on Blind Signal Processing Laboratory

Radio frequency fingerprinting(RFF) is used to uniquely identify individual radios by exploiting the radio frequency characteristics. Often attributing to the phase ambiguity, the features of an emitt

ISBN: (纸本)9781467389808

关键词： Special emitter identification Radio frequency fingerprinting Discriminant analysis Feature extraction Pattern recognition Feature extraction discriminant analysis Pattern recognition emitters phase ambiguity Clustering algorithms sample covariance radiofrequency

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Millimeter-Wave Broadband Dual-Polarized Shorted Patch-Loaded Magnetoelectric-Dipole Antenna and Its Array

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IEEE Transactions on Antennas and Propagation 2025年

作者： Wang, Gan Zhang, Yan Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing210096 China Southeast University School of Information Science and Engineering State Key Laboratory of Millimeter Waves Nanjing210096 China Guilin University of Electronic Technology Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin541004 China

A broadband dual-polarized antenna is proposed for millimeter-wave (mmW) application, which is composed of two pairs of magnetoelectric (ME) dipoles and a shorted patch. The ME dipoles are arranged into a square ring shape to enclose the shorted patch to generate multiple resonances, and they are fed through a cross-shaped slot by two orthogonally placed feeding strip lines for dual-polarization. The proposed antenna is fabricated and measured, which achieves an impedance bandwidth of 65.4% (21.3-42 GHz) for dual-polarization, with an in-band gain variation of less than 1 dB. A 1 × 8 array is further designed and implemented using two substrate-integrated coaxial line (SICL) feeding networks to excite eight uniformly distributed elements. The measured results show that an impedance bandwidth of 58.75 % (22.1-40.48 GHz) is achieved for dual-polarization, accompanying a peak gain of 15 dBi and gain fluctuation smaller than 3 dB. To break through the beam scanning limitation over the achieved octave bandwidth, an offset feeding scheme is introduced to the proposed antenna, and a smallscale 2×2 non-uniformly arranged array is proposed and numerically analyzed. The measured results indicate that a beam scanning range of ±30° can be achieved over the entire operating bandwidth, and the side-lobe levels (SLLs) remain below -10.46 dB, -11.5 dB, and -10.16 dB at 22, 30, and 38 GHz, respectively. The proposed antenna and its array have features of broad bandwidth, flat gain, stable radiation pattern, and wide-angle scanning, which are promising for mmW communication systems. © 1963-2012 IEEE.

关键词： Dipole antennas

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A Weak Target Detection Method Based on Spatial Power Spectrum Map(SPS-MAP) 9th

A Weak Target Detection Method Based on Spatial Power Spectr...

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2017 9th International Conference on Modelling, Identification and Control (ICMIC 2017)

作者： Peng Jin Jinyu Xiong Xiaoyong Zhang Liankun Bo the Science and Technology On Blind Signal Processing Laboratory

ISBN: (纸本)9781509065769;9781509065752

Aiming at the problem that the detection probability of the moving platform using traditional detection method is low and even bad under low SNR condition.A weak target detection method based on spatial power spectrum map(SPS-MAP) is put *** with traditional cross direction finding method,this method omits the step in which the location is determined with the "post processed" SPS peak information and instead maps the SPS curve directly to the search grid to achieve the purpose of location as *** this thesis,the theoretical properties and computational complexity of the method are deduced and *** last,simulation results show that the proposed method can effectively detect targets in low SNR conditions.

关键词： spatial power spectrum direction finding weak target detection passive location mobile platform

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MN-Net: Speech Enhancement Network via Modeling the Noise

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1208-1219页

作者： Ying Hu Qin Yang Wenbing Wei Li Lin Liang He Zhijian Ou Wenzhong Yang School of Computer Science and Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Xinjiang Institute of Electronics Research Shares Company Ltd. Urumqi China Department of Electrical Engineering Tsinghua University Beijing China Beijing National Research Center for information Science and Technology Department of Electronics Tsinghua University Beijing China

Currently, deep learning-based speech enhancement methods generally focus on target speech extraction while neglecting modeling the other sound sources in the mixture. These methods still can't distinguish the target speech from the interference well. In this paper, we present a monaural speech enhancement network via Modeling the Noise (MN-Net), which includes a shared Encoder and three separate Decoders for parallel modeling the magnitude and phase spectrogram of target speech, and the complex spectrogram of noise. Specifically, we propose a Multi-Branch Feature Extractor (MBFE) module to capture the richer contextual information in mixture, and a Spatial Reconstruction Unit (SRU) to remove the redundancy from extracted features. We compared our proposed MN-Net with 18 classical speech enhancement methods on the VoiceBank+DEMAND dataset, and with 9 ones on DNS-Challenge dataset for denoising task, and with 7 ones on the WHAMR! dataset for simultaneous denoising & de-reverberation task. Our proposed MBFE module was applied to two classical speech enhancement methods, DB-AIAT and CMGAN, replacing their DenseBlocks module. The results demonstrate that applying the MBFE module can boost their performances while keeping smaller model size. A series of visualization analysis intuitively verify that modeling the noise can enable the network to distinguish the target speech from noise and other interference more accurately.

关键词： Noise Feature extraction Spectrogram Speech enhancement Decoding Kernel Transformers Noise reduction Data mining Training

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Boosting polyp screening with improved point-teacher weakly semi-supervised

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Computers in Biology and Medicine 2025年 191卷 109998-109998页

作者： Du, Xiuquan Zhang, Xuejun Chen, Jiajia Li, Lei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Department of Neurology Shuyang Affiliated Hospital of Nanjing University of Traditional Chinese Medicine Suqian China School of Computer Science and Technology Anhui University Hefei China

Polyps, like a silent time bomb in the gut, are always lurking and can explode into deadly colorectal cancer at any time. Many methods are attempted to maximize the early detection of colon polyps by screening, however, there are still face some challenges: (i) the scarcity of per-pixel annotation data and clinical features such as the blurred boundary and low contrast of polyps result in poor performance. (ii) existing weakly semi-supervised methods directly using pseudo-labels to supervise student tend to ignore the value brought by intermediate features in the teacher. To adapt the point-prompt teacher model to the challenging scenarios of complex medical images and limited annotation data, we creatively leverage the diverse inductive biases of CNN and Transformer to extract robust and complementary representation of polyp features (boundary and context). At the same time, a novel designed teacher–student intermediate feature distillation method is introduced rather than just using pseudo-labels to guide student learning. Comprehensive experiments demonstrate that our proposed method effectively handles scenarios with limited annotations and exhibits good segmentation performance. All code is available at https://***/dxqllp/WSS-Polyp. © 2025 Elsevier Ltd

关键词： Weakly semi-supervised Medical image segmentation Boundary extraction Feature distillation

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