Digital Signal Processing最新文献

Attention learning with counterfactual intervention based on feature fusion for fine-grained feature learning

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-07 DOI: 10.1016/j.dsp.2025.105215

Ning Yu , Long Chen , Xiaoyin Yi , Jiacheng Huang

{"title":"Attention learning with counterfactual intervention based on feature fusion for fine-grained feature learning","authors":"Ning Yu , Long Chen , Xiaoyin Yi , Jiacheng Huang","doi":"10.1016/j.dsp.2025.105215","DOIUrl":"10.1016/j.dsp.2025.105215","url":null,"abstract":"<div><div>Deep learning models can learn features from a large amount of data and usually localize the overall region of the target object accurately in visual recognition tasks. However, in fine-grained scenarios with inter-class similarities, such as brand recognition in vehicles and subspecies recognition in organisms, there is a need to capture crucial distinct features and provide reliable explanations when tracking decision behavior. Therefore, this paper builds on the idea of counterfactual intervention in causal reasoning and proposes a counterfactual intervention of attention learning to learn feature information that plays an important role in fine-grained recognition tasks. First, we use the iterative feature fusion attention module that learns different levels of features and fuses them to capture the crucial features of the target object and suppress attention to the unimportant features. Second, we perform the counterfactual intervention on the feature fusion-based attention map. The changes produced by the intervening variables serve as monitoring signals for attentional learning to enhance the feature learning that contributes positively for the predicted result. Besides, we use the contrast learning function as a constraint to avoid focusing solely on salient features, thus enabling the network model to learn richer differential features. Finally, we use GradCAM visualization to explain the process of decision-making. The experimental results show that the method in this paper learned important distinguishable features of the target object, weakens the attention to non-critical regions, and offers reliable traceability analysis in tracing back decision-making behaviors.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105215"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Advances in meta-learning and zero-shot learning for multi-label classification: A review

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-07 DOI: 10.1016/j.dsp.2025.105220

Luis-Carlos Quiñonez-Baca , Graciela Ramirez-Alonso , Abimael Guzman-Pando , Javier Camarillo-Cisneros , David R. Lopez-Flores

{"title":"Advances in meta-learning and zero-shot learning for multi-label classification: A review","authors":"Luis-Carlos Quiñonez-Baca , Graciela Ramirez-Alonso , Abimael Guzman-Pando , Javier Camarillo-Cisneros , David R. Lopez-Flores","doi":"10.1016/j.dsp.2025.105220","DOIUrl":"10.1016/j.dsp.2025.105220","url":null,"abstract":"<div><div>Effectively dealing with multi-label classification is a significant challenge. Traditional methods often struggle with issues such as label dependencies, data imbalance, and a limited number of annotated datasets. However, meta-learning and zero-shot learning models offer promising solutions by leveraging previous tasks to enable rapid generalization with minimal data. In this paper, we provide a comprehensive review of meta-learning and zero-shot strategies for multi-label classification in various domains, including audio, text, image, and sensor data, focusing on research published between 2019 and 2025. It presents an overview of commonly used datasets and a detailed description of models designed to capture the relationships inherent in multi-label scenarios. In addition, we propose a novel categorization framework based on neural architecture enhancements, algorithm adaptation, and problem transformation to highlight the main contributions of the reviewed literature. The aim of this review is to provide valuable insights into the current state of meta-learning and zero-shot approaches for multi-label classification, offering guidance for future research and development in addressing the complexities of real-world multi-label tasks.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105220"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Design and analysis of grouping five-dimensional index modulation for high data rate DCSK communication system

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-07 DOI: 10.1016/j.dsp.2025.105211

Fadhil S. Hasan

{"title":"Design and analysis of grouping five-dimensional index modulation for high data rate DCSK communication system","authors":"Fadhil S. Hasan","doi":"10.1016/j.dsp.2025.105211","DOIUrl":"10.1016/j.dsp.2025.105211","url":null,"abstract":"<div><div>In this paper, a new index modulation system termed grouping five-dimensional index modulation differential chaos shift keying (G5DIM-DCSK) is proposed, which is intended to provide ultra-high data rate transmission based on grouping technique. This system uses five different types of index sources: subcarrier, time slot, permutation chaos, Walsh code, and permutation code. There is an equal distribution of mapping and modulating bits among the G groups formed by the input information bits. In all time slots and active subcarriers, the modulating bits are sent via either the chaotic reference sequence or the permutation chaotic reference, which is chosen by the permutation chaotic index bits. The Kronecker product of a chaotic signal and the first row of the Walsh coding matrix yield the chaotic reference sequence. In the unselected time slots and inactive subcarriers, the modulating bits are carried by a sequence created by the Kronecker product between the chaotic signal and the selected row of the Walsh code matrix. This operation is completed by permuting the selected Wlash codes using the permutation code index bits. Our examination looks at the system's information rate, spectral efficiency, and complexity, comparing them to similar systems. Furthermore, the analytical BER for the G5DIM-DCSK system under multipath Rayleigh fading channels and additive white Gaussian noise (AWGN) is derived. Simulation results not only support the performance analysis, but also show that the proposed system outperforms similar systems under equivalent conditions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105211"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Single image deraining using asymmetric feature pyramid context-aware network

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-07 DOI: 10.1016/j.dsp.2025.105216

Shan Gai, Minglei Yin

{"title":"Single image deraining using asymmetric feature pyramid context-aware network","authors":"Shan Gai, Minglei Yin","doi":"10.1016/j.dsp.2025.105216","DOIUrl":"10.1016/j.dsp.2025.105216","url":null,"abstract":"<div><div>Existing image deraining algorithms have challenges of accurately identifying the size and density of rain streaks, which can lead to incomplete removal and difficulties in restoring high resolution images. To address these challenges, we propose a context-aware network based on an asymmetric feature pyramid (CA-AFPN) for effective rain streak removal. The CA-AFPN is composed of feature extraction module, image restoration module, and a multi-scale feature fusion module. In the feature extraction module, the key features are extracted using a channel and self-attention (CS-Attention) module, which can perform downsampling on the image to capture color, semantic, and spatial information from various feature layers. The image restoration module employs the context-aware deep upsampling (CADU) technique to globally and dynamically restore the original features. Additionally, horizontal connections between the two modules integrate shallow physical positioning and deep semantic information, expanding the network receptive field and spatial context. Finally, the multi-scale feature fusion module (MFF) utilizes a residual network and dilated convolution layers to merge feature information across different scales for reconstructing the rain-free image. Extensive experimental results demonstrate that the proposed method is effective not only on synthetic datasets but also achieves superior performance on real-world data.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105216"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Cognitive radar recognition with Kolmogorov-Smirnov test and momentum gradient descent

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-03 DOI: 10.1016/j.dsp.2025.105212

Xiaoyuan Zhang , Shaohang Jing , Jingshu Li , Yechao Bai , Feng Yan

{"title":"Cognitive radar recognition with Kolmogorov-Smirnov test and momentum gradient descent","authors":"Xiaoyuan Zhang , Shaohang Jing , Jingshu Li , Yechao Bai , Feng Yan","doi":"10.1016/j.dsp.2025.105212","DOIUrl":"10.1016/j.dsp.2025.105212","url":null,"abstract":"<div><div>The emission parameters of cognitive radars can adaptively change according to the environment, which poses a challenge to radar electronic countermeasures (ECM). To counter cognitive radars, it is essential to identify the cognitive characteristics. In this paper, a method is proposed to recognize cognitive radars with power allocation function. The signal-to-interference-plus-noise ratio (SINR) distribution of cognitive radars is derived through feature functions, and hypothesis test is used to identify whether the target radar has cognitive function by designing a Kolmogorov-Smirnov (K-S) detector to recognize adaptive optimization power allocation. Subsequently, a momentum gradient descent algorithm is used to optimize the signal of the jamming machine to reduce type II error probability of radar recognition. K-S detector is simulated and compared with Afriat detector, SVM and MLP detector. Results demonstrate that the K-S detector outperforms both the Afriat and MLP detectors in identifying cognitive radars with dynamic power allocation functionality. At the same detection probability, the K-S detector achieves a 2 dB improvement over the MLP detector and a 4 dB improvement over the Afriat detector.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105212"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Fractional lower-order covariance-based measures for cyclostationary time series with heavy-tailed distributions: Application to dependence testing and model order identification

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-03 DOI: 10.1016/j.dsp.2025.105214

Wojciech Żuławiński, Agnieszka Wyłomańska

{"title":"Fractional lower-order covariance-based measures for cyclostationary time series with heavy-tailed distributions: Application to dependence testing and model order identification","authors":"Wojciech Żuławiński, Agnieszka Wyłomańska","doi":"10.1016/j.dsp.2025.105214","DOIUrl":"10.1016/j.dsp.2025.105214","url":null,"abstract":"<div><div>This article introduces new methods for the analysis of cyclostationary time series with infinite variance. Traditional cyclostationary analysis, based on periodically correlated (PC) processes, relies on the autocovariance function (ACVF). However, the ACVF is not suitable for data exhibiting a heavy-tailed distribution, particularly with infinite variance. Thus, we propose a novel framework for the analysis of cyclostationary time series with heavy-tailed distribution, utilizing the fractional lower-order covariance (FLOC) as an alternative to covariance. This leads to the introduction of two new autodependence measures: the periodic fractional lower-order autocorrelation function (peFLOACF) and the periodic fractional lower-order partial autocorrelation function (peFLOPACF). These measures generalize the classical periodic autocorrelation function (peACF) and periodic partial autocorrelation function (pePACF), offering robust tools for analyzing infinite-variance processes. Two practical applications of the proposed measures are explored: a portmanteau test for testing dependence in cyclostationary series and a method for order identification in periodic autoregressive (PAR) and periodic moving average (PMA) models with infinite variance. Both applications demonstrate the potential of new tools, with simulations validating their efficiency. The methodology is further illustrated through the analysis of real-world air pollution data, which showcases its practical utility. The results indicate that the proposed measures based on FLOC provide reliable and efficient techniques for analyzing cyclostationary processes with heavy-tailed distributions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105214"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Slow FAMA under Nakagami-m fading channels

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-02 DOI: 10.1016/j.dsp.2025.105208

Paulo R. de Moura , Hugerles S. Silva , Ugo S. Dias , Higo T.P. Silva , Osamah S. Badarneh , Rausley A.A. de Souza

引用次数: 0

Infrared and visible image fusion based on text-image core-semantic alignment and interaction

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-02 DOI: 10.1016/j.dsp.2025.105203

Xuan Li , Jie Wang , Weiwei Chen , Rongfu Chen , Guomin Zhang , Li Cheng

{"title":"Infrared and visible image fusion based on text-image core-semantic alignment and interaction","authors":"Xuan Li , Jie Wang , Weiwei Chen , Rongfu Chen , Guomin Zhang , Li Cheng","doi":"10.1016/j.dsp.2025.105203","DOIUrl":"10.1016/j.dsp.2025.105203","url":null,"abstract":"<div><div>The text prior can effectively compensate for the limitations of image modality in capturing semantic information, which makes the fusion process more semantic and contextual. However, current fusion methods are not sufficiently adaptive to flexible text inputs and lack the precise alignment between textual semantics and image local regions. To address these issues, an image fusion method based on the text-image core-semantic alignment and interaction is proposed to bridge the gap between cross-modal information. The text-image core-semantic alignment module is designed to refine the close adherence between text and object regions through a pixel-wise coarse-to-fine segmentation mechanism. Meanwhile, a synergistic fusion pipeline is devised to establish a link between a contextual feature extraction unit and a cross-modal affine fusion module. The pipeline directs local attention to text-adherent image regions, while coupling global text features to compensate for the contextual details of whole images. In this way, the fused images enhance the adherence to the flexible text and capture richer contextual details for a more comprehensive visual representation. Extensive experiments on several datasets demonstrate that the proposed text-guided fusion method has obvious advantages over state-of-the-art methods in fusion performance.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105203"},"PeriodicalIF":2.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Phase transitions with structured sparsity

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-02 DOI: 10.1016/j.dsp.2025.105213

Huiguang Zhang, Baoguo Liu

{"title":"Phase transitions with structured sparsity","authors":"Huiguang Zhang, Baoguo Liu","doi":"10.1016/j.dsp.2025.105213","DOIUrl":"10.1016/j.dsp.2025.105213","url":null,"abstract":"<div><div>While phase transition phenomena in compressed sensing have been rigorously established for simple sparse signals by Donoho and others, the behavior of structured sparse signals—such as block or tree patterns common in real-world applications—remains theoretically underexplored. This paper addresses this critical gap by extending phase transition analysis to structured sparsity models through the geometric lens of high-dimensional convex polytope projections.</div><div>Our investigation reveals that weak thresholds, representing the proportion of faces lost after random projection, remain invariant across both simple and structured sparsity frameworks. In contrast, strong thresholds, which determine exact recovery guarantees, vary significantly according to structure type. We derive explicit mathematical expressions for these thresholds in both block-structured and tree-structured signals, demonstrating how additional structural constraints modify recovery boundaries. For block-sparse signals, we prove that the strong threshold rises as the number of blocks increases. Similarly, tree-sparse signals exhibit distinct threshold behaviors depending on whether sparsity falls below or exceeds the thresholds.</div><div>These findings provide theoretical justification for the empirical success of structured sparsity models in applications ranging from medical imaging to radar systems, where they consistently outperform traditional compressed sensing approaches.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105213"},"PeriodicalIF":2.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An enhanced UNet3+ model for accurate identification of COVID-19 in CT images

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-04-01 DOI: 10.1016/j.dsp.2025.105205

Hai Thanh Nguyen, Nhat Minh Nguyen, Thinh Quoc Huynh, Anh Kim Su

{"title":"An enhanced UNet3+ model for accurate identification of COVID-19 in CT images","authors":"Hai Thanh Nguyen, Nhat Minh Nguyen, Thinh Quoc Huynh, Anh Kim Su","doi":"10.1016/j.dsp.2025.105205","DOIUrl":"10.1016/j.dsp.2025.105205","url":null,"abstract":"<div><div>The COVID-19 pandemic has resulted in an out-of-control number of infections worldwide, causing severe and irreparable consequences. Computed Tomography scans show lung damage in patients. In this study, we propose using deep learning techniques, particularly image segmentation techniques on medical data, to facilitate the identification of affected areas, aiding medical professionals in detecting and screening this disease. This research is based on applying the UNet3+ architecture for image segmentation on CT lung images. Additionally, the integration of the UNet3+ architecture with SE-ResNeXt50 and ResNet50 has demonstrated the effectiveness of leveraging the strengths of these architectures together. The proposed methods are evaluated on a dataset that includes 373 out of the total of 829 slices from 9-axis computed tomography images evaluated by experienced radiologists. Experimental results show that the combination of UNet3+ and SE-ResNeXt50 is more effective for identifying COVID-19 infection, with a mean Intersection over Union value of 0.9290 and a mean Dice coefficient value of 0.9619. At the same time, the segmentation efficiency of COVID-19-infected regions achieved quite good results, with the Dice index reaching 0.9111 and IoU reaching 0.8367, which are promising for medical data segmentation and strong support for Healthcare.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"163 ","pages":"Article 105205"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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