Signal Processing最新文献_第3页

A new Two-Stream Temporal-Frequency transformer network for underwater acoustic target recognition

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-13 DOI: 10.1016/j.sigpro.2025.109891

Dongyao Bi , Lijun Zhang , Jie Chen

引用次数: 0

The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-13 DOI: 10.1016/j.sigpro.2025.109894

Jasin Machkour , Michael Muma , Daniel P. Palomar

{"title":"The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control","authors":"Jasin Machkour , Michael Muma , Daniel P. Palomar","doi":"10.1016/j.sigpro.2025.109894","DOIUrl":"10.1016/j.sigpro.2025.109894","url":null,"abstract":"<div><div>We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original predictors and multiple sets of randomly generated dummy predictors. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations confirm that the FDR is controlled at the target level while allowing for high power. We prove that the dummies can be sampled from any univariate probability distribution with finite expectation and variance. The computational complexity of the proposed method is linear in the number of variables. The T-Rex selector outperforms state-of-the-art methods for FDR control in numerical experiments and on a simulated genome-wide association study (GWAS), while its sequential computation time is more than two orders of magnitude lower than that of the strongest benchmark methods. The open source R package TRexSelector containing the implementation of the T-Rex selector is available on CRAN.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109894"},"PeriodicalIF":3.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138615","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}

引用次数: 0

ASRMF: Adaptive image super-resolution based on dynamic-parameter DNN with multi-feature prior

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-12 DOI: 10.1016/j.sigpro.2024.109880

Zhe Zhang, Ke Wang, Lan Cheng, Xinying Xu

引用次数: 0

A comprehensive survey on stegomalware detection in digital media, research challenges and future directions

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-10 DOI: 10.1016/j.sigpro.2025.109888

Laila Tul Badar, Barbara Carminati, Elena Ferrari

{"title":"A comprehensive survey on stegomalware detection in digital media, research challenges and future directions","authors":"Laila Tul Badar, Barbara Carminati, Elena Ferrari","doi":"10.1016/j.sigpro.2025.109888","DOIUrl":"10.1016/j.sigpro.2025.109888","url":null,"abstract":"<div><div>Stegomalware is a malicious activity that employs steganography techniques to hide malicious code within innocent-looking files. The hidden code can then be executed to launch attacks on the victim’s computer or network. Unlike traditional malware, which performs malicious activities by executing its code, stegomalware is specifically designed to evade detection by hiding its malicious payload within seemingly harmless media files, making it difficult to detect using traditional anti-virus and anti-malware tools. To counter stegomalware, numerous steganalysis techniques have been developed for different digital media, such as images, audio, video, text, and networks. This survey presents a comprehensive and detailed overview of stegomalware, covering its background, techniques, modes of attacks, and evasion techniques in various digital media applications. It also provides notable case studies of stegomalware attacks and in-depth review of recent steganalysis approaches. In addition, the survey reviews widely used stegomalware tools and datasets. Finally, it discusses the limitations of state-of-the-art approaches and outlines related research trends.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109888"},"PeriodicalIF":3.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138412","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}

引用次数: 0

LSCANet: Differential features guided long–short cross attention network for infrared and visible image fusion

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-09 DOI: 10.1016/j.sigpro.2025.109889

Baofeng Guo , Hongtao Huo , Xiaowen Liu , Bowen Zheng , Jing Li

{"title":"LSCANet: Differential features guided long–short cross attention network for infrared and visible image fusion","authors":"Baofeng Guo , Hongtao Huo , Xiaowen Liu , Bowen Zheng , Jing Li","doi":"10.1016/j.sigpro.2025.109889","DOIUrl":"10.1016/j.sigpro.2025.109889","url":null,"abstract":"<div><div>Infrared and visible image fusion can generate images that not only highlight prominent targets, but also contain rich details and texture information. However, directly fusing the features of infrared and visible images can diminish the correlation information between source images. To address this, we propose a differential features guided long–short cross attention network for infrared and visible image fusion (LSCANet). Specifically, a differential feature cross attention network (DCAN) is designed to achieve cross modal multi-scale interaction of differential features in the feature extraction process. Cross modal feature interaction before infrared and visible features fusion can enhance deep feature relationships between cross modal features, thereby preserving more correlation information between source images. Besides, a long–short differential feature attention network (LSDAN) is proposed to achieve the integration of multi-scale cross-modal differential features, which can preserve details and texture information while reducing the artifacts and noise introduced during the integration process. Moreover, the loss function is introduced to impel the network retain more details and texture information while preserving thermal radiation information. Ablation experiments were conducted to validate the effectiveness of LSCANet. Extensive qualitative and quantitative experiments conducted on cross dataset benchmarks have demonstrated that LSCANet outperforms eight state-of-the-art methods. The source code is available at <span><span>https://github.com/Pinot-30/LSCANet/tree/main</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109889"},"PeriodicalIF":3.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138411","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

High-order chirplet transform for efficient reconstruction of multicomponent signals with amplitude modulated crossing ridges

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-08 DOI: 10.1016/j.sigpro.2025.109887

Timothée Maison , Fabrice Silva , Nathalie Henrich Bernardoni , Philippe Guillemain

引用次数: 0

QP-adaptive compressed video super-resolution with coding priors

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-06 DOI: 10.1016/j.sigpro.2024.109878

Tingrong Zhang, Zhengxin Chen, Xiaohai He, Chao Ren, Qizhi Teng

{"title":"QP-adaptive compressed video super-resolution with coding priors","authors":"Tingrong Zhang, Zhengxin Chen, Xiaohai He, Chao Ren, Qizhi Teng","doi":"10.1016/j.sigpro.2024.109878","DOIUrl":"10.1016/j.sigpro.2024.109878","url":null,"abstract":"<div><div>Video super-resolution algorithms have found widespread applications as post-processing techniques in down-sampling based coding methods. With the advancements in deep learning techniques, video super-resolution has achieved remarkable success. However, applying existing video super-resolution methods to compressed videos requires training specific models for various quantization parameters (QPs), significantly increasing the resource consumption for model training and compromising their practical utility. To address this issue, we propose a QP-adaptive network for compressed video super-resolution based on coding priors (QPAN). Firstly, we design a QP modulation module (QPMM), which can utilize the frame-wise QP to recalibrate feature maps. Then, on the basis of QPMM, an adaptive multi-scale prior fusion module (Ada-MSPFM) and an adaptive enhancement modulation module (Ada-EMM) are constructed. The former effectively integrates multi-scale features from spatial coding priors in the bitstream and multi-scale features from the decoded video frames. And the latter improves the expressive ability of the network by leveraging QP modulation and reinforcing feature flow adaptively. Extensive experiments demonstrate the highly flexible and adaptive of our proposed method, which exhibits superior reconstruction performance compared to state-of-the-art video super-resolution algorithms.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"230 ","pages":"Article 109878"},"PeriodicalIF":3.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131789","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

Adaptive order synchrosqueezing transform

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-06 DOI: 10.1016/j.sigpro.2024.109881

Marcelo A. Colominas , Sylvain Meignen

引用次数: 0

Hurst exponent estimation using natural visibility graph embedding in Fisher–Shannon plane

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-04 DOI: 10.1016/j.sigpro.2024.109884

T. Averty, A.O. Boudraa, D. Daré-Emzivat

{"title":"Hurst exponent estimation using natural visibility graph embedding in Fisher–Shannon plane","authors":"T. Averty, A.O. Boudraa, D. Daré-Emzivat","doi":"10.1016/j.sigpro.2024.109884","DOIUrl":"10.1016/j.sigpro.2024.109884","url":null,"abstract":"<div><div>In this article, two important stochastic processes, namely fractional Brownian motions (fBm) and fractional Gaussian noises (fGn) are analyzed, within a Fisher–Shannon framework. These processes are well suited for the realistic modeling of phenomena occurring across various domains in science and engineering. An unique feature that characterizes both fBm and fGn, is the Hurst parameter <span><math><mi>H</mi></math></span>, that measures the long/short range dependence of such stochastic processes. In this paper, we show that these processes, from which we extract the degree distribution of the associated natural visibility graph (NVG), can be located in an informational plane, defined by normalized Shannon entropy <span><math><mi>S</mi></math></span> and Fisher information measure <span><math><mi>F</mi></math></span>, in order to estimate their Hurst exponents. The aim of this work is to map signals onto this informational plane, in which a reference backbone is built using generated fBm and fGn processes with known Hurst exponents. To show the effectiveness of the developed graphical estimator, some real-world data are analyzed, and it found that the <span><math><mi>H</mi></math></span> estimated by our method are quite comparable to those obtained from four well-known estimators of the literature. Besides, estimation of <span><math><mi>H</mi></math></span> parameter is very fast and requires a reduced number of samples of the input signal. Using the constructed reference backbone in the Fisher–Shannon plane, the associated <span><math><mi>H</mi></math></span> exponent can be easily estimated by a simple orthogonal projection of the point <span><math><mrow><mo>(</mo><mi>S</mi><mo>,</mo><mi>F</mi><mo>)</mo></mrow></math></span> extracted from the truncated degree distribution of the considered signal NVG representation.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"230 ","pages":"Article 109884"},"PeriodicalIF":3.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132727","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

Supervised online multi-modal discrete hashing

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-04 DOI: 10.1016/j.sigpro.2024.109872

Yun Liu , Qiang Fu , Shujuan Ji , Xianwen Fang

{"title":"Supervised online multi-modal discrete hashing","authors":"Yun Liu , Qiang Fu , Shujuan Ji , Xianwen Fang","doi":"10.1016/j.sigpro.2024.109872","DOIUrl":"10.1016/j.sigpro.2024.109872","url":null,"abstract":"<div><div>Multi-modal hashing has been proposed due to its flexibility and effectiveness in multimedia retrieval tasks. Nevertheless, the majority of multi-modal hashing methods now in use acquire hash codes and hash functions through batch-based learning, which is unsuitable to handle streaming data. Online learning can be used for multi-modal hashing, but still exists in some issues that need to be addressed, such as how to properly employ the modal semantic information and reduce hash learning loss. To address these issues mentioned above, we propose a multi-modal hashing method, called Supervised Online Multi-modal Discrete Hashing (SOMDH). SOMDH first imposes a multi-modal weight to obtain the integrated multi-modal feature representation and then leverages matrix factorization to directly obtain hash codes. In addition, the correlations between the new data and existing data are established with a similarity matrix. Finally, SOMDH can learn the hash codes by discrete optimization strategy. Experimental results on two benchmark datasets demonstrate that SOMDH outperforms state-of-the-art offline and online multi-modal hashing methods in terms of retrieval accuracy.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109872"},"PeriodicalIF":3.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138408","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