This letter introduces a novel coalitional game-theoretic power allocation (CGTPA) paradigm, tailored for resource management for communication and netted radar systems. Taking the power allocation for enhancing downlink throughput in a distributed antenna system (DAS) as a clear-cut example, theoretical expositions, and experimental simulations are presented accordingly. As an optimal decision-making method grounded in behavioral rules, the CGTPA method distinguishes itself from the methods yielded by conventional convex optimization and heuristic methods. It evolves around the idea that cooperation among antennas can be modeled as a coalitional game. Then the Sharpley value-based power allocation ensures a Pareto solution for both optimality and fairness. Furthermore, it is mathematically proven that the throughput consistently improves through iterative application of the allocation rule. Mathematical analysis and simulation results validate the effectiveness of the proposed method.
{"title":"Coalitional Game-Theoretic Paradigm for Power Allocation in Distributed Antenna Systems","authors":"Cheng Qi;Junwei Xie;Haowei Zhang;Weijian Liu;Weike Feng","doi":"10.1109/LSP.2024.3468989","DOIUrl":"https://doi.org/10.1109/LSP.2024.3468989","url":null,"abstract":"This letter introduces a novel coalitional game-theoretic power allocation (CGTPA) paradigm, tailored for resource management for communication and netted radar systems. Taking the power allocation for enhancing downlink throughput in a distributed antenna system (DAS) as a clear-cut example, theoretical expositions, and experimental simulations are presented accordingly. As an optimal decision-making method grounded in behavioral rules, the CGTPA method distinguishes itself from the methods yielded by conventional convex optimization and heuristic methods. It evolves around the idea that cooperation among antennas can be modeled as a coalitional game. Then the Sharpley value-based power allocation ensures a Pareto solution for both optimality and fairness. Furthermore, it is mathematically proven that the throughput consistently improves through iterative application of the allocation rule. Mathematical analysis and simulation results validate the effectiveness of the proposed method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2815-2819"},"PeriodicalIF":3.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442993","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}
Pub Date : 2024-09-24DOI: 10.1109/LSP.2024.3466992
Xiao Pu;Ping Yang;Lin Yuan;Xinbo Gao
This letter presents a novel approach to enhance image-text matching by incorporating word sense disambiguation (WSD) within the text encoder. Our method explicitly models the senses of potentially ambiguous words, refining the semantic understanding between images and text. We introduce a sense-aware mechanism for image-text alignment by integrating a lightweight WSD component into the matching framework, optimizing both tasks simultaneously. Our WSD module operates on extensive word contexts, leveraging the power of graph attention networks (GAT), and distills knowledge from a substantially larger pre-trained WSD model through multi-task learning. Our experiments demonstrate the effectiveness of augmenting original word embeddings with sense representations derived from our WSD approach. We systematically evaluate our method against several baselines and state-of-the-art approaches on two widely-used image-text matching benchmarks: MS-COCO and Flickr30K. The results illustrate significant improvements in matching accuracy, highlighting the efficacy of our proposed approach.
{"title":"Improving Image-Text Matching by Integrating Word Sense Disambiguation","authors":"Xiao Pu;Ping Yang;Lin Yuan;Xinbo Gao","doi":"10.1109/LSP.2024.3466992","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466992","url":null,"abstract":"This letter presents a novel approach to enhance image-text matching by incorporating word sense disambiguation (WSD) within the text encoder. Our method explicitly models the senses of potentially ambiguous words, refining the semantic understanding between images and text. We introduce a sense-aware mechanism for image-text alignment by integrating a lightweight WSD component into the matching framework, optimizing both tasks simultaneously. Our WSD module operates on extensive word contexts, leveraging the power of graph attention networks (GAT), and distills knowledge from a substantially larger pre-trained WSD model through multi-task learning. Our experiments demonstrate the effectiveness of augmenting original word embeddings with sense representations derived from our WSD approach. We systematically evaluate our method against several baselines and state-of-the-art approaches on two widely-used image-text matching benchmarks: MS-COCO and Flickr30K. The results illustrate significant improvements in matching accuracy, highlighting the efficacy of our proposed approach.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2695-2699"},"PeriodicalIF":3.2,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397137","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}
The electromagnetic environment in modern battlefields is highly complex, particularly due to active forwarding signals based on digital radio frequency memory (DRFM), which significantly affects radar performance. Active forwarding signals generate jammers in the radar receiver.A jammer with a negative range offset needs to reach the radar with an inter-pulse delay. In addition, the target may also introduce range ambiguity in some cases. This paper establishes a general signal model for inter-pulse initial phase coding and proposes a two-dimensional local low sidelobe initial phase coding algorithm. This algorithm optimizes both the slow-time and Doppler dimensions to make local low sidelobes in both dimensional. The designed initial phase sequence ensures the Doppler resolution of the target while exhibiting locally low sidelobes in the Doppler responses of jammers.Simulation results demonstrate that this algorithm effectively enhances the radar's resistance to jammers and improves target detection performance. Furthermore, it operates independently of prior detection information and maintains low computational complexity.
{"title":"Initial Phase Coding With Two-Dimensional Local Low Sidelobes for Suppression of Active Forwarding Signals","authors":"Qianlan Huang;Sergey Heister;Hongqi Fan;Huaitie Xiao","doi":"10.1109/LSP.2024.3466996","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466996","url":null,"abstract":"The electromagnetic environment in modern battlefields is highly complex, particularly due to active forwarding signals based on digital radio frequency memory (DRFM), which significantly affects radar performance. Active forwarding signals generate jammers in the radar receiver.A jammer with a negative range offset needs to reach the radar with an inter-pulse delay. In addition, the target may also introduce range ambiguity in some cases. This paper establishes a general signal model for inter-pulse initial phase coding and proposes a two-dimensional local low sidelobe initial phase coding algorithm. This algorithm optimizes both the slow-time and Doppler dimensions to make local low sidelobes in both dimensional. The designed initial phase sequence ensures the Doppler resolution of the target while exhibiting locally low sidelobes in the Doppler responses of jammers.Simulation results demonstrate that this algorithm effectively enhances the radar's resistance to jammers and improves target detection performance. Furthermore, it operates independently of prior detection information and maintains low computational complexity.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2755-2759"},"PeriodicalIF":3.2,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408816","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}
Pub Date : 2024-09-23DOI: 10.1109/LSP.2024.3466790
Sebastian J. Schlecht;Matteo Scerbo;Enzo De Sena;Vesa Välimäki
Feedback delay networks (FDNs) are used in audio processing and synthesis. The modal shapes of the system describe the modal excitation by input and output signals. Previously, the Ehrlich-Aberth method was used to find modes in large FDNs. Here, the method is extended to the corresponding eigenvectors indicating the modal shape. In particular, the computational complexity of the proposed analysis method does not depend on the delay-line lengths and is thus suitable for large FDNs, such as artificial reverberators. We show the relation between the compact generalized eigenvectors in the delay state space and the spatially extended modal shapes in the state space. We illustrate this method with an example FDN in which the suggested modal excitation control does not increase the computational cost. The modal shapes can help optimize input and output gains. This letter teaches how selecting the input and output points along the delay lines of an FDN adjusts the spectral shape of the system output.
{"title":"Modal Excitation in Feedback Delay Networks","authors":"Sebastian J. Schlecht;Matteo Scerbo;Enzo De Sena;Vesa Välimäki","doi":"10.1109/LSP.2024.3466790","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466790","url":null,"abstract":"Feedback delay networks (FDNs) are used in audio processing and synthesis. The modal shapes of the system describe the modal excitation by input and output signals. Previously, the Ehrlich-Aberth method was used to find modes in large FDNs. Here, the method is extended to the corresponding eigenvectors indicating the modal shape. In particular, the computational complexity of the proposed analysis method does not depend on the delay-line lengths and is thus suitable for large FDNs, such as artificial reverberators. We show the relation between the compact generalized eigenvectors in the delay state space and the spatially extended modal shapes in the state space. We illustrate this method with an example FDN in which the suggested modal excitation control does not increase the computational cost. The modal shapes can help optimize input and output gains. This letter teaches how selecting the input and output points along the delay lines of an FDN adjusts the spectral shape of the system output.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2690-2694"},"PeriodicalIF":3.2,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397136","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}
Cross-Modal Visible-Infrared Person Re-identification (VI-REID) constitutes a vital application for constructing all-time surveillance systems. However, the current VI-REID model exhibits significant performance deterioration in noisy environments. Existing algorithms endeavor to mitigate this challenge through fine-tuning stages. We contend that, in contrast to fine-tuning stages, the pre-training phase can effectively exploit the attributes of extensive unlabeled data, thereby facilitating the development of a robust VI-REID model. Therefore, in this paper, we propose a pre-training method for VI-REID based on Diffusion Augmentation and Pose Generation (DAPG), aiming to enhance the robustness and recognition rate of VI-REID models in the presence of damaged scenes. Multiple transfer experiments on the SYSU-MM01 and RegDB datasets demonstrate that our method outperforms existing self-supervised methods, as evidenced by the results.
{"title":"Diffusion Augmentation and Pose Generation Based Pre-Training Method for Robust Visible-Infrared Person Re-Identification","authors":"Rui Sun;Guoxi Huang;Ruirui Xie;Xuebin Wang;Long Chen","doi":"10.1109/LSP.2024.3466792","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466792","url":null,"abstract":"Cross-Modal Visible-Infrared Person Re-identification (VI-REID) constitutes a vital application for constructing all-time surveillance systems. However, the current VI-REID model exhibits significant performance deterioration in noisy environments. Existing algorithms endeavor to mitigate this challenge through fine-tuning stages. We contend that, in contrast to fine-tuning stages, the pre-training phase can effectively exploit the attributes of extensive unlabeled data, thereby facilitating the development of a robust VI-REID model. Therefore, in this paper, we propose a pre-training method for VI-REID based on Diffusion Augmentation and Pose Generation (DAPG), aiming to enhance the robustness and recognition rate of VI-REID models in the presence of damaged scenes. Multiple transfer experiments on the SYSU-MM01 and RegDB datasets demonstrate that our method outperforms existing self-supervised methods, as evidenced by the results.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2670-2674"},"PeriodicalIF":3.2,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383361","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}
Pub Date : 2024-09-23DOI: 10.1109/LSP.2024.3466608
Qian Tang;Qikui Zhu;Yuxuan Xiong;Yongchao Xu;Bo Du
Denoising diffusion probabilistic models (DDPMs) exhibit significant potential in the realm of medical image segmentation. Nevertheless, current DDPM implementations rely on original image features as conditional information, thus lacking the ability to specifically emphasize edge information, a critical aspect in addressing the primary challenge of segmentation. Furthermore, the necessary semantic features for conditioning the diffusion process lack effective alignment with the noise embedding. To address the above issues, we propose a novel edge-and-mask integration-driven diffusion model (EMidDiff). Specifically, 1) an edge-and-mask condition strategy is proposed for the segmentation diffusion model to effectively leverage rich semantic features, particularly the edge feature. 2) A novel co-attention guidance block is designed to align the segmentation map and condition features. The experimental results on brain tumor segmentation and optic-cup segmentation underscore the effectiveness of our approach, surpassing the performance of some state-of-the-art segmentation diffusion models.
{"title":"Edge-and-Mask Integration-Driven Diffusion Models for Medical Image Segmentation","authors":"Qian Tang;Qikui Zhu;Yuxuan Xiong;Yongchao Xu;Bo Du","doi":"10.1109/LSP.2024.3466608","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466608","url":null,"abstract":"Denoising diffusion probabilistic models (DDPMs) exhibit significant potential in the realm of medical image segmentation. Nevertheless, current DDPM implementations rely on original image features as conditional information, thus lacking the ability to specifically emphasize edge information, a critical aspect in addressing the primary challenge of segmentation. Furthermore, the necessary semantic features for conditioning the diffusion process lack effective alignment with the noise embedding. To address the above issues, we propose a novel edge-and-mask integration-driven diffusion model (EMidDiff). Specifically, 1) an edge-and-mask condition strategy is proposed for the segmentation diffusion model to effectively leverage rich semantic features, particularly the edge feature. 2) A novel co-attention guidance block is designed to align the segmentation map and condition features. The experimental results on brain tumor segmentation and optic-cup segmentation underscore the effectiveness of our approach, surpassing the performance of some state-of-the-art segmentation diffusion models.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2665-2669"},"PeriodicalIF":3.2,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383431","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}
Pub Date : 2024-09-23DOI: 10.1109/LSP.2024.3466008
Neethu S. Ravi;Rakesh Kumar;Bradley M. Ratliff
Superresolution (SR) methods become essential when an undersampled low-resolution (LR) image is unable to provide accurate target detection. The estimation of an HR image from a single LR image is ill-posed problem, and hence requires prior information. More the constraints, better is the reconstruction accuracy and this forms the basis of most of the contemporary state-of-the-art (SOTA) superresolution methods such as SRCNN and SRGAN which implement prior information as training set. Yet another approach to overcome the ill-posedness of the problem is to have multiple diverse LR images with the potential to reconstruct accurate HR image. Here we present the performance analysis of a generalized sampling theorem (GST) based multi-frame SR method. A simulation study using Gaussian targets is conducted, and a comparative performance analysis of the GST multi-frame SR method with the traditional multi-frame interpolation schemes and SOTA methods is presented using the percentage mean square error ( $%$ MSE) and Structural Similarity Index Measure (SSIM). Our findings indicate that the GST SR method can outperform traditional interpolation and the SOTA methods.
当采样不足的低分辨率（LR）图像无法提供准确的目标检测时，超分辨率（SR）方法就变得至关重要。从单幅低分辨率图像估计高分辨率图像是一个难以解决的问题，因此需要先验信息。约束条件越多，重建精度就越高，这就构成了大多数当代最先进（SOTA）超分辨率方法的基础，如 SRCNN 和 SRGAN，它们都将先验信息作为训练集。另一种克服问题不确定性的方法是使用多幅不同的 LR 图像来重建准确的 HR 图像。在此，我们介绍了基于广义抽样定理（GST）的多帧 SR 方法的性能分析。我们使用高斯目标进行了模拟研究，并使用均方误差百分比（$%$MSE）和结构相似性指数测量（SSIM）对 GST 多帧 SR 方法与传统多帧插值方案和 SOTA 方法进行了性能比较分析。研究结果表明，GST SR 方法的性能优于传统插值法和 SOTA 方法。
{"title":"A Digital Superresolution Method With Minimal Sensitivity to Shift Estimation Error","authors":"Neethu S. Ravi;Rakesh Kumar;Bradley M. Ratliff","doi":"10.1109/LSP.2024.3466008","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466008","url":null,"abstract":"Superresolution (SR) methods become essential when an undersampled low-resolution (LR) image is unable to provide accurate target detection. The estimation of an HR image from a single LR image is ill-posed problem, and hence requires prior information. More the constraints, better is the reconstruction accuracy and this forms the basis of most of the contemporary state-of-the-art (SOTA) superresolution methods such as SRCNN and SRGAN which implement prior information as training set. Yet another approach to overcome the ill-posedness of the problem is to have multiple diverse LR images with the potential to reconstruct accurate HR image. Here we present the performance analysis of a generalized sampling theorem (GST) based multi-frame SR method. A simulation study using Gaussian targets is conducted, and a comparative performance analysis of the GST multi-frame SR method with the traditional multi-frame interpolation schemes and SOTA methods is presented using the percentage mean square error (\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000MSE) and Structural Similarity Index Measure (SSIM). Our findings indicate that the GST SR method can outperform traditional interpolation and the SOTA methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2700-2704"},"PeriodicalIF":3.2,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397132","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}
Pub Date : 2024-09-23DOI: 10.1109/LSP.2024.3466788
Zhuangxin Fang;Zhiyong Luo
In this letter, we address the challenge of employing multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) to simultaneously estimate the range, velocity, and angle of multiple moving targets at the transmitter, while maintaining communication. Specifically, we present a mathematical model for radar sensing signal processing and elaborate on a successive estimation scheme. We reveal that the compensation processing algorithm proposed in existing successive estimation scheme suffers from the problem of ambiguous matching, which fails to properly match the angle and the range-velocity for targets with close angles. To address the ambiguous matching problem, this letter proposes a novel compensation processing algorithm for range-velocity estimation. Based on minimum variance unbiased estimation, the proposed compensation processing algorithm fully exploits the estimated angle information to extract corresponding range-velocity information for eliminating the interference from targets at close angles.
{"title":"A Novel Algorithm for Multi-Target Angle and Range-Velocity Estimation With MIMO-OFDM Communication Waveforms","authors":"Zhuangxin Fang;Zhiyong Luo","doi":"10.1109/LSP.2024.3466788","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466788","url":null,"abstract":"In this letter, we address the challenge of employing multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) to simultaneously estimate the range, velocity, and angle of multiple moving targets at the transmitter, while maintaining communication. Specifically, we present a mathematical model for radar sensing signal processing and elaborate on a successive estimation scheme. We reveal that the compensation processing algorithm proposed in existing successive estimation scheme suffers from the problem of ambiguous matching, which fails to properly match the angle and the range-velocity for targets with close angles. To address the ambiguous matching problem, this letter proposes a novel compensation processing algorithm for range-velocity estimation. Based on minimum variance unbiased estimation, the proposed compensation processing algorithm fully exploits the estimated angle information to extract corresponding range-velocity information for eliminating the interference from targets at close angles.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2750-2754"},"PeriodicalIF":3.2,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408860","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}
Pub Date : 2024-09-20DOI: 10.1109/LSP.2024.3466011
Guo Zhong;Juanchun Wu;Xueming Yan;Xuanlong Ma;Shixun Lin
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 $^{2}$ ) 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 $^{2}$ method's effectiveness and competitiveness against state-of-the-art methods.
{"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":"31 ","pages":"2605-2609"},"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}
Pub Date : 2024-09-20DOI: 10.1109/LSP.2024.3466012
Byeongho Jo;Seungkwon Beack
Complex-valued frequency-domain linear predictive coding (CLPC) has been developed for audio coding. Recently, representations for efficiently quantizing CLPC coefficients have been proposed, including the complex immittance spectral frequency (CISF). The CISF has limitations in that it requires signalling the sequential information to eliminate ambiguity and the highest-order coefficient (HOC) for reconstructing the CLPC coefficients. This study developed a modified CISF-based method that eliminates the need for additional information by utilizing intermediate complex polynomial properties. Furthermore, a perceptual-metric-based codebook search was proposed to improve quantization efficiency. The experimental results show robust quantization performance, while listening tests demonstrate superior audio quality compared to MPEG-D USAC long TCX at 12 kbps.
{"title":"Efficient Complex Immittance Spectral Frequency With the Perceptual-Metric-Based Codebook Search","authors":"Byeongho Jo;Seungkwon Beack","doi":"10.1109/LSP.2024.3466012","DOIUrl":"https://doi.org/10.1109/LSP.2024.3466012","url":null,"abstract":"Complex-valued frequency-domain linear predictive coding (CLPC) has been developed for audio coding. Recently, representations for efficiently quantizing CLPC coefficients have been proposed, including the complex immittance spectral frequency (CISF). The CISF has limitations in that it requires signalling the sequential information to eliminate ambiguity and the highest-order coefficient (HOC) for reconstructing the CLPC coefficients. This study developed a modified CISF-based method that eliminates the need for additional information by utilizing intermediate complex polynomial properties. Furthermore, a perceptual-metric-based codebook search was proposed to improve quantization efficiency. The experimental results show robust quantization performance, while listening tests demonstrate superior audio quality compared to MPEG-D USAC long TCX at 12 kbps.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"2720-2724"},"PeriodicalIF":3.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397082","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：