检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Using Monotonic Neural Networks for Accurate and Efficient Passive Localization Performance Modeling

Using Monotonic Neural Networks for Accurate and Efficient P...

引用

IEEE Conference on Wireless Communications and Networking

作者： Hanyue Guo Rui Zhou Wenqiang Put Ming-Yi You JunKun Yan Hangzhou Institute of Technology Xidian University Hangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Integrated Sensing and Communication (ISAC) systems are at the forefront of next-generation wireless technologies, enhancing high-precision target localization. Accurate prediction of localization performance is crucial for the design and optimization of these systems. Traditionally, the Cramer- Rao Lower Bound (CRLB) has been used as a theoretical benchmark for estimating localization errors, but it often does not reflect actual errors encountered in practice. The Monte Carlo simulation method, while accurate, is computationally intensive and less adaptable to varying parameters. To bridge this gap, we introduce LocNet-Mono, a novel approach based on monotonic neural networks, designed specifically for predicting localization errors. This approach maintains a consistent, monotonic relationship between input and output features, addressing the shortcomings of traditional methods. Our numerical experiments validate the high accuracy and efficiency of LocNet-Mono, confirming its potential as a superior tool for performance prediction in ISAC systems.

关键词： Location awareness Wireless communication Wireless sensor networks Monte Carlo methods Accuracy Neural networks Predictive models Integrated sensing and communication Numerical models Optimization

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

引用

2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

MN-Net: Speech Enhancement Network via Modeling the Noise

引用

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

来源：评论

学校读者我要写书评

暂无评论

A 3D Wideband High-Gain Circularly-Polarized Array Antenna with Low RCS Using Simplified Sequential Rotation Topology

引用

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Wang, Min Li, Xuan Ren, Yi Chen, Zhengchuan Zhao, Shiwei Chongqing University of Posts and Telecommunications School of Optoelectronic Engineering Chongqing400065 China Xidian University National Key Laboratory of Radar Detection and Sensing Shaanxi Xi'an710071 China Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Guilin University of Electronic Technology Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin541004 China

A 3D wideband high-gain circularly-polarized array antenna (CPAA) with low radar cross section (RCS) is presented in this manuscript. Its element contains four 3D radiation structures fed by a low-loss CP feeding structure and four polarimetric rotating-reflecting staircases (PRRSs) designed in a quasi-I shape. Radiation structure is designed as palm-leaf shaped radiator excited by a fan-shaped line and dispersive bilayer director for bandwidth enhancement. Current inversion method is realized by mirroring fan-shaped line to enable a 180° phase change to be compensated by CP feeding structure. Then, a simplified sequential rotation topology is realized to allow CP feeding structure to reduce loss and space occupancy, further enhancing gain and AR bandwidth. By rotating PRRS 90°, current inversion method is achieved to obtain 1-bit phase quantization. Finally, 16 identical elements with unequal phase states of any two adjacent PRRSs are cascaded by a 1-to-16 power divider. The measured results exhibit that a wide -10 dB impendence bandwidth of 32.8% and a gain of 21.1 dBi at 5.8 GHz are achieved. The 3-dB gain bandwidth reaches 31.9% while 3-dB axial ratio bandwidth is 26.7%. Moreover, the measured 10-dB RCS reduction band covers 5.1 GHz – 11.0 GHz. © 2002-2011 IEEE.

关键词： Circular polarization

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

GateM2Former: Gated Feature Selection and Expert Modeling in Multimodal Emotion Recognition

GateM2Former: Gated Feature Selection and Expert Modeling in...

引用

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

作者： Weixiang Xu Zhongren Dong Runming Wang Xinzhou Xu Zixing Zhang College of Computer Science and Electronic Engineering Hunan University China School of Information Science and Engineering Hunan Normal University China Signal Processing and Speech Communication Laboratory Graz University of Technology Austria Shenzhen Research Institute Hunan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, multimodal emotion recognition (MER) has gained significant attention due to its potential to integrate information from diverse signals. However, existing methods often struggle to effectively capture complex interactions and contextual information both inter- and intra-modalities, and even to extract the salient representations from pre-trained models. To address these issues, we propose a novel model, gated Mixture of Multimodal Experts (MoME) and Mixtral of Experts (MixMoE) models, namely GateM 2 Former. The gate mechanism is used to select the most relevant representations from pre-trained models. The MoME and MixMoE expert modules respectively learn the individual characteristics of each modality and the intrinsic alignment and potential interactions between modalities. Besides, we design a hierarchical merge structure to better suit the long sequence scenario (i. e., speech in our case). To verify the effectiveness of the introduced model, we conducted extensive experiments on the IEMOCAP and MELD datasets. The results show that GateM 2 Former, with a universal multimodal structure, is able to achieve the best results on IEMOCAP and MELD compared with other latest approaches.

关键词： Adaptation models Emotion recognition Semantics Speech recognition Logic gates signal processing Transformers Feature extraction Robustness Speech processing

来源：评论

学校读者我要写书评

暂无评论

Dynamics of Masked Image Modeling in Hyperspectral Image Classification

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Ma, Chen Li, Huayi Jiang, Junjun Aybar, Cesar Yao, Jiaqi Camps-Valls, Gustau Harbin Institute of Technology Research Center of Satellite Technology State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster School of Astronautics Harbin150001 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China University of València Image Signal Processing Laboratory València46980 Spain Tianjin Normal University Tianjin300387 China

Masked image modeling (MIM), a common self-supervised learning (SSL) technique, has been extensively studied for remote sensing (RS) image processing. Nevertheless, its effectiveness for hyperspectral imagery (HSI) remains underexplored due to the distinct data structures and high dimensionality. This article aims to provide a detailed understanding of MIM from different perspectives of representation learning and statistical analysis for HSI classification tasks. Our study reveals that the MIM paradigm injects inductive bias in the attention mechanism of the transformer model, which is advantageous for capturing the local discrepancies between the spectra. We also show that MIM can increase the diversity of the attention heads in every layer, which is beneficial for the model in extracting more discriminative features from different spectral bands. The similarity of representations from various layers further proves this. Furthermore, our investigation highlights how MIM introduces a dynamic perspective to spectral representations, enabling the model to learn more robust and discriminative features. The final numerical experiments indicate that a moderate mask ratio can enhance the performance of downstream tasks. This suggests that designing a more targeted masking strategy might be necessary to achieve higher and more stable gains in downstream task performance. Without bells and whistles, the vanilla MIM improves the overall classification accuracy by an average of 2.69% over its supervised learning (SL) counterpart. We hope that our findings can advance the understanding of MIM in HSI and inspire the design of a more stable SSL paradigm for HSI processing. © 1980-2012 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：