IEEE Signal Processing Letters最新文献_第7页

Fast Beam Pattern Synthesis Based on Vector Accelerated Alternating Direction Multiplier Method 基于矢量加速交替方向乘子法的快速波束方向图合成

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-25 DOI: 10.1109/LSP.2024.3522858

Qiyan Song

引用次数: 0

ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition 目的：在面部情绪识别的准确性和减少损失之间架起桥梁

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-23 DOI: 10.1109/LSP.2024.3521321

Arnab Kumar Roy;Hemant Kumar Kathania;Adhitiya Sharma;Abhishek Dey;Md. Sarfaraj Alam Ansari

{"title":"ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition","authors":"Arnab Kumar Roy;Hemant Kumar Kathania;Adhitiya Sharma;Abhishek Dey;Md. Sarfaraj Alam Ansari","doi":"10.1109/LSP.2024.3521321","DOIUrl":"https://doi.org/10.1109/LSP.2024.3521321","url":null,"abstract":"The human face is a silent communicator, expressing emotions and thoughts through it's facial expressions. With the advancements in computer vision in recent years, facial emotion recognition technology has made significant strides, enabling machines to decode the intricacies of facial cues. In this work, we propose ResEmoteNet, a novel deep learning architecture for facial emotion recognition designed with the combination of Convolutional, Squeeze-Excitation (SE) and Residual Networks. The inclusion of SE block selectively focuses on the important features of the human face, enhances the feature representation and suppresses the less relevant ones. This helps in reducing the loss and enhancing the overall model performance. We also integrate the SE block with three residual blocks that help in learning more complex representation of the data through deeper layers. We evaluated ResEmoteNet on four open-source databases: FER2013, RAF-DB, AffectNet-7 and ExpW, achieving accuracies of 79.79%, 94.76%, 72.39% and 75.67% respectively. The proposed network outperforms state-of-the-art models across all four databases.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"491-495"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937894","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

A Simple and Efficient Method for Hybrid AOA and DTD Localization With Unknown Transmitter Location 一种简单有效的未知发射机位置的AOA和DTD混合定位方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-23 DOI: 10.1109/LSP.2024.3521317

Yanbin Zou;Yangpeng Xiao;Weien Zhang

{"title":"A Simple and Efficient Method for Hybrid AOA and DTD Localization With Unknown Transmitter Location","authors":"Yanbin Zou;Yangpeng Xiao;Weien Zhang","doi":"10.1109/LSP.2024.3521317","DOIUrl":"https://doi.org/10.1109/LSP.2024.3521317","url":null,"abstract":"Recently, joint target and transmitter localization using differential time-delay (DTD) and angle-of-arrival (AOA) measurements has attracted researchers' interest. Due to the fact that three Euclidean norms exist in the DTD equation, the DTD equation is difficult to tackle directly. In this paper, we divide the joint localization problem into three subproblems, respectively, the AOA-only localization problem, the hybrid AOA and time-difference-of-arrival (TDOA) localization problem, and the hybrid AOA and time-delay (TD) localization problem with known transmitter location. Then, a two-stage algorithm is developed. In the first stage, solving the AOA-only localization problem provides initial estimates. In the second stage, alternatively and iteratively solving the problem of hybrid AOA and TDOA localization and the problem of hybrid AOA and TD localization provide the improved solutions. Simulation results validate that the proposed algorithm is superior to the existing constrained weighted least-squares (CWLS) algorithm when AOA noise variance is not sufficiently small. \u0000<italic>Index Term</i>\u0000-Angle-of-arrival (AOA), differential time-delay (DTD), time-delay (TD), time-difference-of-arrival (TDOA), elliptic localization.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"401-405"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925407","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

Gate-Calibrated Double Disentangled Distribution Matching Network for Cross-Domain Pedestrian Trajectory Prediction

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-23 DOI: 10.1109/LSP.2024.3521786

Zhengfa Liu;Ya Wu;Dequan Zeng;Shihang Du;Boyang Peng

{"title":"Gate-Calibrated Double Disentangled Distribution Matching Network for Cross-Domain Pedestrian Trajectory Prediction","authors":"Zhengfa Liu;Ya Wu;Dequan Zeng;Shihang Du;Boyang Peng","doi":"10.1109/LSP.2024.3521786","DOIUrl":"https://doi.org/10.1109/LSP.2024.3521786","url":null,"abstract":"In cross-domain pedestrian trajectory prediction, most existing methods usually focus on learning entangled spatial-temporal domain-invariant features, while ignoring the different contributions of spatial and temporal shifts to the prediction model. To address this issue, we propose a novel gate-Calibrated Double Disentangled Distribution Matching Network (CD<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>MN) that can effectively eliminate cross-trajectory domain shifts at both the spatial and temporal levels while learning robust prediction using a calibrated gated-fusion. The key idea of CD<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>MN is to model domain-invariant features across trajectories as a calibrated gated-fusion of disentangled domain-invariant features at the temporal and spatial levels. We first introduce a spatial-temporal disentanglement module to disentangle the spatial-temporal properties of pedestrian trajectories from the spatial-level and temporal-level. Secondly, we design a domain-invariant disentanglement module for learning domain-invariant sample-level transferable feature representations at the spatial and temporal levels. Finally, to effectively fuse these disentangled temporal and spatial features, we design a calibrated gated-fusion module where both inter-level and intra-level knowledge are introduced to calibrate the fusion gate. Extensive experiments on real datasets demonstrate the effectiveness of CD<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>MN.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"656-660"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184278","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

Energy-Efficient Sensor Scheduling for State Estimation Over Homogeneous Multi-Hop Networks 同质多跳网络状态估计的节能传感器调度

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-23 DOI: 10.1109/LSP.2024.3521378

Yao Li;Rui Song;Jianyong Zheng;Xinping Guan

引用次数: 0

Robust Tensor Completion via Spatial-Spectral Constrained Deep Low-Rank Tensor Factorization for Hyperspectral Image Recovery 基于空间光谱约束的深度低秩张量分解鲁棒张量补全用于高光谱图像恢复

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-23 DOI: 10.1109/LSP.2024.3521382

Jian-Li Zhao;Jian-Feng Gao;Sheng Fang;Tian-Heng Zhang;Jin-Yu Wang

{"title":"Robust Tensor Completion via Spatial-Spectral Constrained Deep Low-Rank Tensor Factorization for Hyperspectral Image Recovery","authors":"Jian-Li Zhao;Jian-Feng Gao;Sheng Fang;Tian-Heng Zhang;Jin-Yu Wang","doi":"10.1109/LSP.2024.3521382","DOIUrl":"https://doi.org/10.1109/LSP.2024.3521382","url":null,"abstract":"Robust tensor completion of hyperspectral image (HSI) is a challenging task in the field of remote sensing. Recently, nuclear norm minimization-based methods have made certain progress in robust tensor completion. However, the tensor nuclear norm applies the same constraint to all singular values, resulting in insufficient capturing power for the global structure of the HSI. In addition, as a convex surrogate of global low-rankness, tensor nuclear norm minimization leads to an overall low-rank approximation that cannot capture the details of the HSI. In this letter, we propose the spatial-spectral constrained deep low-rank tensor factorization (SDLTF). More precisely, the low-rank tensor factorization is used to dynamically assign penalty weights, aiming to preserve the main information and maintain the global structure of the HSI. The spatial-spectral constrained unsupervised deep prior is applied within a deep convolutional neural network to capture spatial-spectral correlations and local details of the HSI. We develop an efficient algorithm to tackle the corresponding model based on the ADMM. Extensive experiments demonstrate that our model has superior performance compared with several state-of-the-art methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"551-555"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993347","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

Exploring Local Sparse Structure Prior for Image Deraining and Desnowing 探索局部稀疏结构先验图像去噪和去噪

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-23 DOI: 10.1109/LSP.2024.3521374

Xin Guo;Xueyang Fu;Zheng-Jun Zha

{"title":"Exploring Local Sparse Structure Prior for Image Deraining and Desnowing","authors":"Xin Guo;Xueyang Fu;Zheng-Jun Zha","doi":"10.1109/LSP.2024.3521374","DOIUrl":"https://doi.org/10.1109/LSP.2024.3521374","url":null,"abstract":"Existing image deraining and desnowing methods are typically trained under specific weather conditions, which limits their effectiveness in locating rain streaks and snowflakes in diverse, open scenes. This restriction often leads to suboptimal restoration performance. To address these limitations, we propose a novel local sparse structure prior for rain and snow, characterized by high pixel intensity and the locally sparse spatial distribution of rain streaks and snowflakes. Leveraging this prior, we developed an algorithm that extracts rain and snow structure masks, enabling precise localization of rain streaks and snowflake regions across open scenes. In addition, we introduce a refinement and compensation process to remove irrelevant information from the masks and correct mask estimation errors. We further construct a Mask-Guided Restoration Network (MGNet) that utilizes the rain and snow structure masks effectively and includes a mask-conditioned attention module to focus restoration efforts on degraded areas affected by rain streaks and snowflakes. Extensive experimental results demonstrate that our method significantly outperforms current state-of-the-art techniques in open scenes, effectively restoring various types of rain streaks and snowflakes with a single model parameter configuration.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"406-410"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925367","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

Fast 3D Gaussian Splatting Rendering via Easily Integrable Improvements 快速3D高斯飞溅渲染通过易于集成的改进

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-23 DOI: 10.1109/LSP.2024.3521379

Laurens Diels;Michiel Vlaminck;Wilfried Philips;Hiep Luong

引用次数: 0

A Delta Operator State Estimation Algorithm for Discrete-Time Systems With State Time-Delay 具有状态时滞的离散系统的Delta算子状态估计算法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-18 DOI: 10.1109/LSP.2024.3519897

Ling Xu;Xiao Zhang;Feng Ding;Quanmin Zhu

引用次数: 0

Novel Mathematical Model of Voltage Waveform Prediction for High-Speed PRBS Signal Transmission 高速PRBS信号传输电压波形预测的新数学模型