IEEE Signal Processing Letters最新文献_第6页

On the Design of Robust Differential Beamformers From the Beampattern Error Perspective 从振型误差角度看稳健差分波束成形器的设计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-20 DOI: 10.1109/LSP.2024.3465894

Jingli Xie;Xudong Zhao;Junqing Zhang;Jacob Benesty;Jingdong Chen

引用次数: 0

Nonnegative Tensor Representation With Cross-View Consensus for Incomplete Multi-View Clustering 针对不完全多视图聚类的跨视图共识非负张量表示法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-20 DOI: 10.1109/LSP.2024.3466011

Guo Zhong;Juanchun Wu;Xueming Yan;Xuanlong Ma;Shixun Lin

{"title":"Nonnegative Tensor Representation With Cross-View Consensus for Incomplete Multi-View Clustering","authors":"Guo Zhong;Juanchun Wu;Xueming Yan;Xuanlong Ma;Shixun Lin","doi":"10.1109/LSP.2024.3466011","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466011","url":null,"abstract":"Tensors capture the multi-dimensional structure of multi-view data naturally, resulting in richer and more meaningful data representations. This produces more accurate clustering results for challenging incomplete multi-view clustering (IMVC) tasks. However, previous tensor learning-based IMVC (TLIMVC) methods often build a tensor representation by simply stacking view-specific representations. Consequently, the learned tensor representation lacks good interpretability since each entry of it could not directly reveals the similarity relationship of the corresponding two samples. In addition, most of them only focus on exploring the high-order correlations among views, while the underlying consensus information is not fully exploited. To this end, we propose a novel TLIMVC method named Nonnegative Tensor Representation with Cross-view Consensus (NTRC\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000) in this paper. Specifically, a nonnegative constraint and view-specific consensus are jointly integrated into the framework of the tensor based self-representation learning, which enables the method to simultaneously explore the consensus and complementary information of multi-view data more fully. An Augmented Lagrangian Multiplier based optimization algorithm is derived to optimize the objective function. Experiments on several challenging benchmark datasets verify our NTRC\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000 method's effectiveness and competitiveness against state-of-the-art methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial-Frequency-Based Selective Fixed-Filter Algorithm for Multichannel Active Noise Control 用于多通道有源噪声控制的基于空间频率的选择性固定滤波器算法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-20 DOI: 10.1109/LSP.2024.3465889

Xiruo Su;Dongyuan Shi;Zhijuan Zhu;Woon-Seng Gan;Lingyun Ye

{"title":"Spatial-Frequency-Based Selective Fixed-Filter Algorithm for Multichannel Active Noise Control","authors":"Xiruo Su;Dongyuan Shi;Zhijuan Zhu;Woon-Seng Gan;Lingyun Ye","doi":"10.1109/LSP.2024.3465889","DOIUrl":"https://doi.org/10.1109/LSP.2024.3465889","url":null,"abstract":"The multichannel active noise control (MCANC) approach is widely regarded as an effective solution to achieve a large noise cancellation zone in a complicated acoustic environment. However, the sluggish convergence and massive computation of traditional adaptive multichannel active control algorithms typically impede the MCANC system's practical applications. The recently developed selective fixed-filter method offers a way to decrease the computational load in real-time scenarios and enhance the reaction time. Nevertheless, this method is specifically designed for the single-channel ANC system and only considers the frequency information of the noise. This inevitably impacts the effectiveness of reducing noise from various directions, particularly in the MCANC system. Therefore, we proposed a spatial-frequency-based selective fixed-filter ANC technique that adopts the Bhattacharyya Distance Matching (SFANC-BdM). In our work, the BdM is a one-step spectra and is designed by calculating similarity of different data distribution. According to the most similar case, the corresponding control filter is then selected. By avoiding separately extracting the direction and frequency information, the proposed method significantly increases the algorithm's efficiency. Compared to the conventional SFANC method, it enables a more accurate filter choice and achieves better noise reduction.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parametric Binaural Beamforming Based on Auditory Perception 基于听觉感知的参数双耳波束成形

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-20 DOI: 10.1109/LSP.2024.3465895

De Hu;Xinzhe Zhang

引用次数: 0

BLSAN: A Brain Lateralization-Guided Subject Adaptive Network for Motor Imagery Classification BLSAN：用于运动图像分类的大脑侧化引导的受试者自适应网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-20 DOI: 10.1109/LSP.2024.3465348

Fulin Wei;Xueyuan Xu;Qing Li;Xiuxing Li;Xia Wu

引用次数: 0

Robust Hashing for Neural Network Models via Heterogeneous Graph Representation 通过异构图表示神经网络模型的鲁棒散列法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-20 DOI: 10.1109/LSP.2024.3465898

Lin Huang;Yitong Tao;Chuan Qin;Xinpeng Zhang

{"title":"Robust Hashing for Neural Network Models via Heterogeneous Graph Representation","authors":"Lin Huang;Yitong Tao;Chuan Qin;Xinpeng Zhang","doi":"10.1109/LSP.2024.3465898","DOIUrl":"https://doi.org/10.1109/LSP.2024.3465898","url":null,"abstract":"How to protect the intellectual property (IP) of neural network models has become a hot topic in current research. Model hashing as an important model protection scheme, which achieves model IP protection by extracting model feature-based, compact hash codes and calculating the hash distance between original and suspicious models. To realize model IP protection across platforms and environments, we propose a robust hashing scheme for neural network models via heterogeneous graph representation, which can effectively detect the illegal copy of neural network models and doesn't degrade the model performance. Specifically, we first convert the neural network model into a heterogeneous graph and analyze its node attribute data. Then, a graph embedding learning method is used to extract the feature vectors of the model based on different attribute data of graph nodes. Finally, the hash code that can be used for model copy detection is generated based on the designed hash networks with quantization and triplet losses. Experimental results show that our scheme not only exhibits satisfactory robustness to different types of robustness graph attacks but also achieves satisfactory performances of discrimination and generalizability.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Hiding Based on Compressive Autoencoders and Normalizing Flow 基于压缩自动编码器和归一化流的图像隐藏技术

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-19 DOI: 10.1109/LSP.2024.3465350

Liang Chen;Xianquan Zhang;Chunqiang Yu;Zhenjun Tang

{"title":"Image Hiding Based on Compressive Autoencoders and Normalizing Flow","authors":"Liang Chen;Xianquan Zhang;Chunqiang Yu;Zhenjun Tang","doi":"10.1109/LSP.2024.3465350","DOIUrl":"https://doi.org/10.1109/LSP.2024.3465350","url":null,"abstract":"Image hiding aims to hide the secret data in the cover image for secure transmission. Recently, with the development of deep learning, some deep learning-based image hiding methods were proposed. However, most of them do not achieve outstanding hiding performance yet. To address this issue, we propose a new image hiding framework called CAE-NF, which consists of compressive autoencoders (CAE) and normalizing flow (NF). Specifically, CAE's encoder respectively maps the secret image and cover image into the corresponding feature vectors. Image hiding and recovery can be modelled as the forward and backward processes of NF since NF is an invertible neural network. NF maps two feature vectors to a stego-image by its forward process. On the recovery side, the stego-images are mapped to two feature vectors by NF's backward process. Finally, the secret image is recovered by CAE's decoder. The proposed framework can achieve a good trade-off between the stego-image quality and recovered secret image quality, and meanwhile, improve the hiding and recovery performances. The experimental results demonstrate that the proposed framework significantly outperforms some state-of-the-art methods in terms of invisibility, security, and recovery accuracy on various datasets.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ETFormer: An Efficient Transformer Based on Multimodal Hybrid Fusion and Representation Learning for RGB-D-T Salient Object Detection ETFormer：基于多模态混合融合和表征学习的高效变换器，用于 RGB-D-T 突出物体检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-19 DOI: 10.1109/LSP.2024.3465351

Jiyuan Qiu;Chen Jiang;Haowen Wang

引用次数: 0

Spatial-Frequency Feature Fusion Network for Lightweight and Arbitrary-Sized JPEG Steganalysis 用于轻量级和任意大小 JPEG 隐藏分析的空间-频率特性融合网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-17 DOI: 10.1109/LSP.2024.3462174

Xulong Liu;Weixiang Li;Kaiqing Lin;Bin Li

{"title":"Spatial-Frequency Feature Fusion Network for Lightweight and Arbitrary-Sized JPEG Steganalysis","authors":"Xulong Liu;Weixiang Li;Kaiqing Lin;Bin Li","doi":"10.1109/LSP.2024.3462174","DOIUrl":"10.1109/LSP.2024.3462174","url":null,"abstract":"Current deep learning-based JPEG image steganalysis methods typically rely on decompressed pixels for steganalytic feature extraction, without fully leveraging the inherent information in JPEG images. Additionally, they often face limitations such as large parameter counts and restricted image sizes for detection. In this letter, we propose a spatial-frequency feature fusion network (SF3Net) for lightweight and arbitrary-sized JPEG steganalysis. SF3Net introduces a PReLU activation function and a multi-view convolutional module to capture refined residual features from decompressed pixels, while also integrating original DCT coefficients and quantization tables to extract additional modal features. The spatial-frequency multi-modality features are then fused using a coordinate attention mechanism. And a patch splitting scheme is designed to be compatible with any feature resolution, enabling the detection of arbitrary-sized images with a Swin Transformer block. Experimental results demonstrate that SF3Net outperforms existing methods in detecting both fixed-sized and arbitrary-sized images, while significantly reducing the number of parameters.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Optimality of Inverse Gaussian Approximation for Lognormal Channel Models 论对数正态信道模型的反高斯逼近最优性

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-17 DOI: 10.1109/LSP.2024.3462292

Taoshen Li;Shuping Dang;Zhihui Ge;Zhenrong Zhang

{"title":"On the Optimality of Inverse Gaussian Approximation for Lognormal Channel Models","authors":"Taoshen Li;Shuping Dang;Zhihui Ge;Zhenrong Zhang","doi":"10.1109/LSP.2024.3462292","DOIUrl":"10.1109/LSP.2024.3462292","url":null,"abstract":"Because of the equilibrium between mathematical tractability and approximation accuracy maintained by the inverse Gaussian (IG) distributional model, it has been regarded as the most appropriate approximation substitute for the lognormal distributional model for shadowed and atmospheric turbulence induced (ATI) fading in the past decades. In this paper, we conduct an in-depth information-theoretic analysis for the lognormal-to-IG channel model substitution (CMS) technique and study its parametric mapping optimality achieved by minimizing the Kullback-Leibler (K-L) divergence between the two distributional models. In this way, we rigorously prove that the moment matching criterion produces the optimal IG substitute for lognormal reference distributions, which has never been observed in other CMS techniques. In addition, we clarify a myth in the realm of CMS that the IG substitute outperforms the gamma substitute for approximating lognormal reference distributions; instead, the substitution superiority shall depend on the parametric mapping criterion and the scale parameter of the lognormal reference distribution. All analytical insights presented in this paper are validated by simulation results.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0