Golay sequences with the zero correlation zone (ZCZ), known as Golay-ZCZ sequences, play a pivotal role in reducing intersymbol interference (ISI) during the process of channel estimation in one dimension. Two-dimensional (2-D) Golay complementary array set (GCAS) within their ZCZ has the potential application in multiple input multiple output (MIMO) omnidirectional transmission. In this letter, 2-D Golay-ZCZ array set is constructed by using generalized Boolean function (GBF) without utilizing any kernels. The proposed construction provides 2-D Golay-ZCZ array set with various array sizes and large ZCZ sizes. Also, we get the one dimensional (1-D) Golay- ZCZ sequence set as a special case of the proposed construction.
{"title":"Constructions of Two-Dimensional Golay-ZCZ Array Sets Based on Generalized Boolean Functions","authors":"Aditya Prakash;Tzu-Chieh Kao;Sudhan Majhi;Prashant Kumar Srivastava;Chao-Yu Chen","doi":"10.1109/LSP.2024.3516562","DOIUrl":"https://doi.org/10.1109/LSP.2024.3516562","url":null,"abstract":"Golay sequences with the zero correlation zone (ZCZ), known as Golay-ZCZ sequences, play a pivotal role in reducing intersymbol interference (ISI) during the process of channel estimation in one dimension. Two-dimensional (2-D) Golay complementary array set (GCAS) within their ZCZ has the potential application in multiple input multiple output (MIMO) omnidirectional transmission. In this letter, 2-D Golay-ZCZ array set is constructed by using generalized Boolean function (GBF) without utilizing any kernels. The proposed construction provides 2-D Golay-ZCZ array set with various array sizes and large ZCZ sizes. Also, we get the one dimensional (1-D) Golay- ZCZ sequence set as a special case of the proposed construction.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"321-325"},"PeriodicalIF":3.2,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890311","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 : 2024-12-11DOI: 10.1109/LSP.2024.3515817
Ruijie Zhao;Pinyan Tang;Sihui Luo
Despite remarkable advancements, mainstream gaze estimation techniques, particularly appearance-based methods, often suffer from performance degradation in uncontrolled environments due to variations in illumination and individual facial attributes. Existing domain adaptation strategies, limited by their need for target domain samples, may fall short in real-world applications. This letter introduces Branch-out Auxiliary Regularization (BAR), an innovative method designed to boost gaze estimation's generalization capabilities without requiring direct access to target domain data. Specifically, BAR integrates two auxiliary consistency regularization branches: one that uses augmented samples to counteract environmental variations, and another that aligns gaze directions with positive source domain samples to encourage the learning of consistent gaze features. These auxiliary pathways strengthen the core network and are integrated into the original branch during training in a smooth, plug-and-play manner, facilitating easy adaptation to various other models without compromising the inference efficiency. Comprehensive experimental evaluations on four cross-dataset tasks demonstrate the superiority of our approach.
{"title":"Improving Domain Generalization on Gaze Estimation via Branch-Out Auxiliary Regularization","authors":"Ruijie Zhao;Pinyan Tang;Sihui Luo","doi":"10.1109/LSP.2024.3515817","DOIUrl":"https://doi.org/10.1109/LSP.2024.3515817","url":null,"abstract":"Despite remarkable advancements, mainstream gaze estimation techniques, particularly appearance-based methods, often suffer from performance degradation in uncontrolled environments due to variations in illumination and individual facial attributes. Existing domain adaptation strategies, limited by their need for target domain samples, may fall short in real-world applications. This letter introduces Branch-out Auxiliary Regularization (BAR), an innovative method designed to boost gaze estimation's generalization capabilities without requiring direct access to target domain data. Specifically, BAR integrates two auxiliary consistency regularization branches: one that uses augmented samples to counteract environmental variations, and another that aligns gaze directions with positive source domain samples to encourage the learning of consistent gaze features. These auxiliary pathways strengthen the core network and are integrated into the original branch during training in a smooth, plug-and-play manner, facilitating easy adaptation to various other models without compromising the inference efficiency. Comprehensive experimental evaluations on four cross-dataset tasks demonstrate the superiority of our approach.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"276-280"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875116","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}
In simulating synthetic aperture radar (SAR) ship wakes, dynamic wake modeling often uses the linear superposition of sea waves and Kelvin wakes. This method, however, overlooks the alterations in sea surface roughness caused by the nonlinear interaction between waves and wakes, thus failing to accurately capture real sea surface variations. In this letter, we introduce a rapid SAR image simulation technique for ship wakes that incorporates sea waves using fluid velocity potential. Firstly, the computational domain and ship grid are constructed, with the grid scale tailored to the ship's surface structure to satisfy boundary conditions for efficient fluid velocity potential calculations. Next, to enhance boundary calculation accuracy, we employ the Taylor expansion boundary element method to swiftly resolve both steady and unsteady velocity potential components. Additionally, our approach not only depicts the interaction between sea waves and ship wakes but also facilitates the simulation analysis of various sea condition parameters. By treating the ship wake as noise and comparing images containing only background sea waves with the simulation images, the results show that the accuracy of the proposed approach is 0.2 SSIM higher than that of the linear superposition method, and the speed is 3 hours faster than that of CFD method.
{"title":"A Rapid SAR Image Simulation Method for Ship Wakes Coupled With Sea Waves Using Fluid Velocity Potential","authors":"Chunhui Zhao;Kaiyu Li;Lu Wang;Tomoaki Ohtsuki;Fumiyuki Adachi","doi":"10.1109/LSP.2024.3514804","DOIUrl":"https://doi.org/10.1109/LSP.2024.3514804","url":null,"abstract":"In simulating synthetic aperture radar (SAR) ship wakes, dynamic wake modeling often uses the linear superposition of sea waves and Kelvin wakes. This method, however, overlooks the alterations in sea surface roughness caused by the nonlinear interaction between waves and wakes, thus failing to accurately capture real sea surface variations. In this letter, we introduce a rapid SAR image simulation technique for ship wakes that incorporates sea waves using fluid velocity potential. Firstly, the computational domain and ship grid are constructed, with the grid scale tailored to the ship's surface structure to satisfy boundary conditions for efficient fluid velocity potential calculations. Next, to enhance boundary calculation accuracy, we employ the Taylor expansion boundary element method to swiftly resolve both steady and unsteady velocity potential components. Additionally, our approach not only depicts the interaction between sea waves and ship wakes but also facilitates the simulation analysis of various sea condition parameters. By treating the ship wake as noise and comparing images containing only background sea waves with the simulation images, the results show that the accuracy of the proposed approach is 0.2 SSIM higher than that of the linear superposition method, and the speed is 3 hours faster than that of CFD method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"271-275"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875012","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 : 2024-12-11DOI: 10.1109/LSP.2024.3515813
Dongyuan Lin;Peng Cai;Xiaofeng Chen;Yunfei Zheng;Shiyuan Wang
Quaternion adaptive filters (QAFs) are extensively used in processing three- or four-dimensional signals effectively. However, their performance can significantly deteriorate or even diverge when system inputs and outputs are contaminated by complex noises. Therefore, this letter addresses the issue of parameter estimation in the quaternion errors-in-variables (QEIV) in asymmetric noise. First, a novel robust criterion, called improved quaternion minimum error entropy criterion with fiducial points (IQMEEF), is constructed. Then, a minimum total quaternion error entropy algorithm with fiducial points (MTQEEF) is proposed by integrating the IQMEEF criterion with the total least squares (TLS) method, leveraging stochastic gradient and quaternion generalized Hamilton-real (GHR) calculus theory. Finally, simulations validate the superior performance of MTQEEF in the QEIV model under asymmetric noise environments.
{"title":"Minimum Total Quaternion Error Entropy Filtering With Fiducial Points Against Asymmetric Noise","authors":"Dongyuan Lin;Peng Cai;Xiaofeng Chen;Yunfei Zheng;Shiyuan Wang","doi":"10.1109/LSP.2024.3515813","DOIUrl":"https://doi.org/10.1109/LSP.2024.3515813","url":null,"abstract":"Quaternion adaptive filters (QAFs) are extensively used in processing three- or four-dimensional signals effectively. However, their performance can significantly deteriorate or even diverge when system inputs and outputs are contaminated by complex noises. Therefore, this letter addresses the issue of parameter estimation in the quaternion errors-in-variables (QEIV) in asymmetric noise. First, a novel robust criterion, called improved quaternion minimum error entropy criterion with fiducial points (IQMEEF), is constructed. Then, a minimum total quaternion error entropy algorithm with fiducial points (MTQEEF) is proposed by integrating the IQMEEF criterion with the total least squares (TLS) method, leveraging stochastic gradient and quaternion generalized Hamilton-real (GHR) calculus theory. Finally, simulations validate the superior performance of MTQEEF in the QEIV model under asymmetric noise environments.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"306-310"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890387","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 : 2024-12-11DOI: 10.1109/LSP.2024.3515818
Rafael Boloix-Tortosa;Juan José Murillo-Fuentes
The Gaussian process (GP) is a well-established Bayesian nonparametric tool for inference in nonlinear estimation problems. When GPs are used for regression, the goal is to estimate a target signal ${y}$ from an input vector $mathbf {x}$ without assuming that they are linearly related, but with a probabilistic model $p({y}|mathbf {x})$ that is Gaussian distributed. Therefore, GPs can be understood as a natural nonlinear extension to MMSE estimation. For real-valued GPs, this has been analyzed in the existing literature, and it is concluded that they are the natural nonlinear Bayesian extension to the linear minimum mean-squared error (LMMSE) estimation. In this letter, we show that, consequently, complex-valued GP regression (GPR) models are the natural nonlinear Bayesian extension of the widely linear minimum mean squared-error (WLMMSE) estimation. As in the real-valued case, complex-valued GPs are able to better model many regression problems by making use of the information that the complementary kernel or pseudo-kernel provides.
{"title":"Complex Gaussian Processes for Regression and Their Connection to WLMMSE","authors":"Rafael Boloix-Tortosa;Juan José Murillo-Fuentes","doi":"10.1109/LSP.2024.3515818","DOIUrl":"https://doi.org/10.1109/LSP.2024.3515818","url":null,"abstract":"The Gaussian process (GP) is a well-established Bayesian nonparametric tool for inference in nonlinear estimation problems. When GPs are used for regression, the goal is to estimate a target signal \u0000<inline-formula><tex-math>${y}$</tex-math></inline-formula>\u0000 from an input vector \u0000<inline-formula><tex-math>$mathbf {x}$</tex-math></inline-formula>\u0000 without assuming that they are linearly related, but with a probabilistic model \u0000<inline-formula><tex-math>$p({y}|mathbf {x})$</tex-math></inline-formula>\u0000 that is Gaussian distributed. Therefore, GPs can be understood as a natural nonlinear extension to MMSE estimation. For real-valued GPs, this has been analyzed in the existing literature, and it is concluded that they are the natural nonlinear Bayesian extension to the linear minimum mean-squared error (LMMSE) estimation. In this letter, we show that, consequently, complex-valued GP regression (GPR) models are the natural nonlinear Bayesian extension of the widely linear minimum mean squared-error (WLMMSE) estimation. As in the real-valued case, complex-valued GPs are able to better model many regression problems by making use of the information that the complementary kernel or pseudo-kernel provides.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"386-390"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10791914","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/LSP.2024.3514798
Lei Cao;Ramanarayanan Viswanathan
In conventional codeword-based distributed detection (CDD), sensors quantize their observations and report codewords to the fusion center (FC) where a final decision is made regarding the truthfulness of the hypotheses. Recently, quantized-but-uncoded DD (QDD) has been proposed, where sensors, after quantization, transmit summarized values instead of codewords to the FC. QDD can adapt well to the power constraint and offers better detection performance than CDD. However, the added degree of freedom in parameter selection in QDD comes with high complexity in optimal system design. The contribution of this letter is a proof showing that in QDD, the optimal sensor decision rules for binary decisions are likelihood-ratio-quantizers (LRQ), regardless of the reporting channel conditions, provided that the sensor observations are conditionally independent given the hypotheses. This property largely simplifies the design of QDD. Performance comparison is presented for CDD, QDD, and a benchmark system that reports original sensor observations, when both sensing and reporting channel noise exist.
{"title":"Optimal Sensor Decision Rules for Quantized-but-Uncoded Distributed Detection","authors":"Lei Cao;Ramanarayanan Viswanathan","doi":"10.1109/LSP.2024.3514798","DOIUrl":"https://doi.org/10.1109/LSP.2024.3514798","url":null,"abstract":"In conventional codeword-based distributed detection (CDD), sensors quantize their observations and report codewords to the fusion center (FC) where a final decision is made regarding the truthfulness of the hypotheses. Recently, quantized-but-uncoded DD (QDD) has been proposed, where sensors, after quantization, transmit summarized values instead of codewords to the FC. QDD can adapt well to the power constraint and offers better detection performance than CDD. However, the added degree of freedom in parameter selection in QDD comes with high complexity in optimal system design. The contribution of this letter is a proof showing that in QDD, the optimal sensor decision rules for binary decisions are likelihood-ratio-quantizers (LRQ), regardless of the reporting channel conditions, provided that the sensor observations are conditionally independent given the hypotheses. This property largely simplifies the design of QDD. Performance comparison is presented for CDD, QDD, and a benchmark system that reports original sensor observations, when both sensing and reporting channel noise exist.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"286-290"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890393","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 : 2024-12-11DOI: 10.1109/LSP.2024.3514796
Tao Chen;Hao Tian;Yingming Liu;Yihan Xiao;Boyi Yang
Aiming at the existing deep learning radar signal modulation recognition methods are mostly based on time-frequency image (TFI) and consequently result in networks with a large number of parameters due to the significant amount of redundant information contained in TFI, this paper proposes a radar signal intra-pulse modulation recognition method based on point cloud which removes redundant information. Radar signals of different modulation types are mapped into point cloud after Smoothed Pseudo Wigner-Ville Distribution (SPWVD) transformation. Then, PointNet++ is used to classify the point cloud data according to its modulation type and output its corresponding modulation type labels. Simulation results show that the proposed method can effectively recognize radar signals of typical modulation types, and show strong effectiveness and reliability at low signal-to-noise ratio (SNR). Besides, the lightweight characteristics of PointNet++ make the operation of the method more efficient.
{"title":"Radar Signal Intra-Pulse Modulation Recognition Based on Point Cloud Network","authors":"Tao Chen;Hao Tian;Yingming Liu;Yihan Xiao;Boyi Yang","doi":"10.1109/LSP.2024.3514796","DOIUrl":"https://doi.org/10.1109/LSP.2024.3514796","url":null,"abstract":"Aiming at the existing deep learning radar signal modulation recognition methods are mostly based on time-frequency image (TFI) and consequently result in networks with a large number of parameters due to the significant amount of redundant information contained in TFI, this paper proposes a radar signal intra-pulse modulation recognition method based on point cloud which removes redundant information. Radar signals of different modulation types are mapped into point cloud after Smoothed Pseudo Wigner-Ville Distribution (SPWVD) transformation. Then, PointNet++ is used to classify the point cloud data according to its modulation type and output its corresponding modulation type labels. Simulation results show that the proposed method can effectively recognize radar signals of typical modulation types, and show strong effectiveness and reliability at low signal-to-noise ratio (SNR). Besides, the lightweight characteristics of PointNet++ make the operation of the method more efficient.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"596-600"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993095","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 : 2024-12-11DOI: 10.1109/LSP.2024.3515811
Qiyang Sun;Alican Akman;Xin Jing;Manuel Milling;Björn W. Schuller
Audio-based kinship verification (AKV) is important in many domains, such as home security monitoring, forensic identification, and social network analysis. A key challenge in the task arises from differences in age across samples from different individuals, which can be interpreted as a domain bias in a cross-domain verification task. To address this issue, we design the notion of an “age-standardised domain” wherein we utilise the optimised CycleGAN-VC3 network to perform age-audio conversion to generate the in-domain audio. The generated audio dataset is employed to extract a range of features, which are then fed into a metric learning architecture to verify kinship. Experiments are conducted on the KAN_AV audio dataset.The results demonstrate that the method markedly enhances the accuracy of kinship verification, while also offering novel insights for future kinship verification research.
{"title":"Audio-Based Kinship Verification Using Age Domain Conversion","authors":"Qiyang Sun;Alican Akman;Xin Jing;Manuel Milling;Björn W. Schuller","doi":"10.1109/LSP.2024.3515811","DOIUrl":"https://doi.org/10.1109/LSP.2024.3515811","url":null,"abstract":"Audio-based kinship verification (AKV) is important in many domains, such as home security monitoring, forensic identification, and social network analysis. A key challenge in the task arises from differences in age across samples from different individuals, which can be interpreted as a domain bias in a cross-domain verification task. To address this issue, we design the notion of an “age-standardised domain” wherein we utilise the optimised CycleGAN-VC3 network to perform age-audio conversion to generate the in-domain audio. The generated audio dataset is employed to extract a range of features, which are then fed into a metric learning architecture to verify kinship. Experiments are conducted on the KAN_AV audio dataset.The results demonstrate that the method markedly enhances the accuracy of kinship verification, while also offering novel insights for future kinship verification research.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"301-305"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890170","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 : 2024-12-11DOI: 10.1109/LSP.2024.3514803
Ning Wang;Minghui Wu;Wenchao Gu;Zhilei Chai
Dementia, particularly Alzheimer's disease (AD), affects millions of elderly individuals worldwide. Traditionally, interview data, including audio recordings and transcripts, is used to train Artificial Intelligence models for the automatic detection of AD patterns. In this work, we introduce a novel attention-weighted image set, where each image integrates text-image relevance with focused areas from the Cookie Theft picture, derived from the corresponding description. Furthermore, we propose a novel multimodal architecture, Hybrid Self-Aligned Fusion with Dual-Weight Attention Network (HSAF-DWAN), to predict AD, using audio recordings, transcripts, and corresponding attention-weighted images. This architecture consists of two key modules: an Intra-Modality Self-Alignment (IMSA) module, which captures relationships within a single modality, and a Dual-Weight Cross-Modality Attention (DW-CMA) module, which effectively fuses cross-modality data through a dual-weight mechanism, incorporating an optimized cross-attention and secondary weighting. Extensive experiments conducted on the Cookie Theft corpus from DementiaBank demonstrate that our method outperforms state-of-the-art models, achieving an accuracy of 86.71% and an F1 score of 88.15%.
{"title":"Hybrid Self-Aligned Fusion With Dual-Weight Attention Network for Alzheimer's Detection","authors":"Ning Wang;Minghui Wu;Wenchao Gu;Zhilei Chai","doi":"10.1109/LSP.2024.3514803","DOIUrl":"https://doi.org/10.1109/LSP.2024.3514803","url":null,"abstract":"Dementia, particularly Alzheimer's disease (AD), affects millions of elderly individuals worldwide. Traditionally, interview data, including audio recordings and transcripts, is used to train Artificial Intelligence models for the automatic detection of AD patterns. In this work, we introduce a novel attention-weighted image set, where each image integrates text-image relevance with focused areas from the Cookie Theft picture, derived from the corresponding description. Furthermore, we propose a novel multimodal architecture, Hybrid Self-Aligned Fusion with Dual-Weight Attention Network (HSAF-DWAN), to predict AD, using audio recordings, transcripts, and corresponding attention-weighted images. This architecture consists of two key modules: an Intra-Modality Self-Alignment (IMSA) module, which captures relationships within a single modality, and a Dual-Weight Cross-Modality Attention (DW-CMA) module, which effectively fuses cross-modality data through a dual-weight mechanism, incorporating an optimized cross-attention and secondary weighting. Extensive experiments conducted on the Cookie Theft corpus from DementiaBank demonstrate that our method outperforms state-of-the-art models, achieving an accuracy of 86.71% and an F1 score of 88.15%.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"346-350"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890385","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 : 2024-12-11DOI: 10.1109/LSP.2024.3515458
Guowei Liu;Engang Tian;Xia Zhao;Huwei Chen
This letter focuses on the design of a novel critical-area-based (CAB) stochastic denial-of-service (DoS) attack strategy aimed at diminishing the quality of remote state estimation in multi-area power systems. This attack strategy features two main characteristics: firstly, unlike most existing DoS attack models which typically overlook system information, the proposed CAB stochastic DoS attack strategy can selectively target critical areas of the power system, and the more severe impacts on system performance are expected. Secondly, the proposed attack strategy is stochastic, offering enhanced concealment compared to continuous or uniformly distributed DoS attack models. Furthermore, when the attacker faces energy constraints, an analytical relationship between the upper bound of the attack parameter and the expected total number of attacks is derived. Simulation results conducted on IEEE 39-bus and 118-bus systems validate that, compared to existing models, the proposed CAB stochastic DoS attack strategy induces more substantial disruptions in power system estimation quality, thus confirming its effectiveness from the attacker's perspective.
{"title":"Critical-Area-Based Stochastic DoS Attack Strategy Design Against Remote State Estimation in Multi-Area Power Systems","authors":"Guowei Liu;Engang Tian;Xia Zhao;Huwei Chen","doi":"10.1109/LSP.2024.3515458","DOIUrl":"https://doi.org/10.1109/LSP.2024.3515458","url":null,"abstract":"This letter focuses on the design of a novel critical-area-based (CAB) stochastic denial-of-service (DoS) attack strategy aimed at diminishing the quality of remote state estimation in multi-area power systems. This attack strategy features two main characteristics: firstly, unlike most existing DoS attack models which typically overlook system information, the proposed CAB stochastic DoS attack strategy can selectively target critical areas of the power system, and the more severe impacts on system performance are expected. Secondly, the proposed attack strategy is stochastic, offering enhanced concealment compared to continuous or uniformly distributed DoS attack models. Furthermore, when the attacker faces energy constraints, an analytical relationship between the upper bound of the attack parameter and the expected total number of attacks is derived. Simulation results conducted on IEEE 39-bus and 118-bus systems validate that, compared to existing models, the proposed CAB stochastic DoS attack strategy induces more substantial disruptions in power system estimation quality, thus confirming its effectiveness from the attacker's perspective.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"291-295"},"PeriodicalIF":3.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890384","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}
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