Signal Processing最新文献_第10页

Alternative Cholesky Decomposition and family of scale mixture of Normal distribution: A joint modeling approach 正态分布的替代Cholesky分解与尺度族混合：一种联合建模方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-07-25 DOI: 10.1016/j.sigpro.2025.110207

Vinícius Silva Osterne Ribeiro , Lionel Bombrun , Juvêncio Santos Nobre , Charles Casimiro Cavalcante , Yannick Berthoumieu

{"title":"Alternative Cholesky Decomposition and family of scale mixture of Normal distribution: A joint modeling approach","authors":"Vinícius Silva Osterne Ribeiro , Lionel Bombrun , Juvêncio Santos Nobre , Charles Casimiro Cavalcante , Yannick Berthoumieu","doi":"10.1016/j.sigpro.2025.110207","DOIUrl":"10.1016/j.sigpro.2025.110207","url":null,"abstract":"<div><div>In Statistics, the analysis of longitudinal data is essential across various domains, including biomedical and agricultural research. Joint mean-covariance models have been widely used to capture within-subject dependence, often by parametrizing the scatter matrix via the Modified Cholesky Decomposition (MCD). However, the MCD has known drawbacks, such as sensitivity to the ordering of variables and challenges in parameter interpretation. As an alternative, the Alternative Cholesky Decomposition (ACD) offers improved numerical stability and interpretability, yet has been underexplored in robust modeling contexts. Traditional approaches also frequently assume normally distributed residuals, which may not hold in practice. While extensions based on the Student-t and Laplace distributions address heavier tails, they still rely on fixed parametric forms. To overcome both structural and distributional limitations, this paper proposes a novel joint regression model that combines the flexibility of ACD with the robustness of scale mixture of normal (SMN) distributions. We obtain maximum likelihood estimators and compare our model against classical and Student-t-based alternatives. Simulation studies show superior performance in estimation and prediction under outlier contamination. Real data applications further highlight the model’s robustness and practical utility.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110207"},"PeriodicalIF":3.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749580","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

Optimally conditioned sparse semi-orthonormal frames 最佳条件稀疏半标准正交帧

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-07-24 DOI: 10.1016/j.sigpro.2025.110204

Saber Jafarizadeh

{"title":"Optimally conditioned sparse semi-orthonormal frames","authors":"Saber Jafarizadeh","doi":"10.1016/j.sigpro.2025.110204","DOIUrl":"10.1016/j.sigpro.2025.110204","url":null,"abstract":"<div><div>Frame theory has been extensively used for generating over-complete redundant representations of signals. When parts of the frame measurements of a signal are lost, iterative frame reconstruction methods are used for recovering the signal, where its convergence rate is associated with the tightness of the frame and the Second Largest Eigenvalue Modulus (SLEM) of its operator. Scaling is a popular noninvasive method employed for constructing optimally conditioned frames that are as tight as possible. This is possible by optimizing the condition number of the frame operator. In large vector spaces, frame sparsity is critical for constructing frames. Following these design factors, this paper defines particular types of frames, namely Semi-Orthonormal (SO) and Disjoint Semi-Orthonormal (DSO) frames that have a very sparse structure. Optimal conditioning of these frames, using scaling, has been addressed by optimizing the SLEM of the frame operator, which has been solved using its Semi-definite Programming (SDP) formulation. Based on the results derived from the SDP solution, an iterative algorithm has been proposed to determine the optimal scales and SLEM values for DSO frames of any size. These optimal results have been extended to the SO frames using a conjecture developed based on the dual variables in the SDP solution. Furthermore, the erasure robustness of SO and DSO frames has been addressed, where the maximal reconstruction error is minimized with respect to all possible erasure locations with a fixed cardinality. The erasure robustness and the optimal scaling have been examined by simulating the frame algorithm.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110204"},"PeriodicalIF":3.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724148","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

Line spectral estimation with unlimited sensing 线谱估计无限传感

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-07-23 DOI: 10.1016/j.sigpro.2025.110205

Hongwei Wang , Jun Fang , Hongbin Li , Geert Leus , Ruixiang Zhu , Lu Gan

{"title":"Line spectral estimation with unlimited sensing","authors":"Hongwei Wang , Jun Fang , Hongbin Li , Geert Leus , Ruixiang Zhu , Lu Gan","doi":"10.1016/j.sigpro.2025.110205","DOIUrl":"10.1016/j.sigpro.2025.110205","url":null,"abstract":"<div><div>In the paper, we consider the line spectral estimation problem in an unlimited sensing framework (USF), where a modulo analog-to-digital converter (ADC) is employed to fold the input signal back into a bounded interval before quantization. Such an operation is mathematically equivalent to taking the modulo of the input signal with respect to the interval. To overcome the noise sensitivity of higher-order difference-based methods, we explore the properties of the first-order difference of modulo samples, and develop two line spectral estimation algorithms based on the first-order difference, which are robust against noise. Specifically, we show that, with a high probability, the first-order difference of the original samples is equivalent to that of the modulo samples. By utilizing this property, line spectral estimation is solved via a robust sparse signal recovery approach. The second algorithms is built on our finding that, with a sufficiently high sampling rate, the first-order difference of the original samples can be decomposed as a sum of the first-order difference of the modulo samples and a sequence whose elements are confined to three possible values. This decomposition enables us to formulate the line spectral estimation problem as a mixed integer linear program that can be efficiently solved. Simulation results show that both proposed methods are robust against noise and achieve a significant performance improvement over the higher-order difference-based method. methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110205"},"PeriodicalIF":3.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722675","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

Inter-pulse time-varying vibration compensation with a physically-informed deep neural network for synthetic aperture Ladar imaging 基于物理信息深度神经网络的脉冲间时变振动补偿合成孔径雷达成像

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-07-22 DOI: 10.1016/j.sigpro.2025.110213

Rongrong Wang , Jiongge Zhang , Jiarui Li , Long Tian , Junkun Yan , Bingnan Wang , Hongwei Liu

{"title":"Inter-pulse time-varying vibration compensation with a physically-informed deep neural network for synthetic aperture Ladar imaging","authors":"Rongrong Wang , Jiongge Zhang , Jiarui Li , Long Tian , Junkun Yan , Bingnan Wang , Hongwei Liu","doi":"10.1016/j.sigpro.2025.110213","DOIUrl":"10.1016/j.sigpro.2025.110213","url":null,"abstract":"<div><div>Synthetic Aperture Ladar (SAL) provides high-resolution, high-data-rate, and detailed imaging for remote sensing. However, its short wavelength makes SAL systems highly sensitive to vibrations, introducing Doppler frequency shifts and range cell migration that degrade image quality, particularly for extended targets. Traditional vibration compensation methods often face limitations in challenging scenes with severe vibration conditions or when strong scattering points are absent. To address these challenges, a second-order vibration error model is firstly developed to characterize the time-varying errors within each tunable period. Then, a physically-informed deep neural network is designed to estimate the vibration coefficients through its encoder, which are then used by physical layers in the decoder to correct the errors. By combining the physical model with a data-driven approach, the proposed method can mitigate severe vibration-induced errors and reduce range cell migration without relying on strong scattering points. Additionally, integration of the physical layers makes the decoder being non-parametric, thus simplifies the network training. Numerical results validate the method’s effectiveness and its superiority over traditional spectral correlation algorithm, demonstrating its potential for high-resolution SAL imaging.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110213"},"PeriodicalIF":3.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722674","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

Optimizing privacy and utility in one-bit compressive sensing with adaptive local differential privacy 基于自适应局部差分隐私的比特压缩感知中的隐私和效用优化

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-07-22 DOI: 10.1016/j.sigpro.2025.110206

Bi Chen , Xiang Yao , Xianwei Gao , Ye Yuan , Zhufeng Suo

{"title":"Optimizing privacy and utility in one-bit compressive sensing with adaptive local differential privacy","authors":"Bi Chen , Xiang Yao , Xianwei Gao , Ye Yuan , Zhufeng Suo","doi":"10.1016/j.sigpro.2025.110206","DOIUrl":"10.1016/j.sigpro.2025.110206","url":null,"abstract":"<div><div>One-bit compressive sensing (1-bit CS) offers significant hardware and computational cost advantages and has application prospects in low-resource overhead scenarios. However, achieving accurate signal reconstruction while rigorously protecting data privacy in 1-bit CS systems, which are highly sensitive to noise, remains a substantial challenge, as traditional Differential Privacy (DP) methods often struggle to balance this trade-off. To address this critical issue, this paper proposes a comprehensive framework that synergistically integrates an adaptive DP method with Bayesian inference. We propose a novel adaptive DP method that injects noise based on the characteristics of the measurements and adjusts the noise level according to the measurement matrix properties and data statistics. This is complemented by a new Bayesian reconstruction algorithm, specifically designed to effectively handle the heteroscedastic noise introduced by our adaptive DP method, thereby significantly improving signal recovery accuracy. Theoretical analysis confirms that the proposed method satisfies <span><math><mrow><mo>(</mo><mi>ɛ</mi><mo>,</mo><mi>δ</mi><mo>)</mo></mrow></math></span>-DP, while the reconstruction algorithm is proven to converge linearly under Restricted Isometry Property (RIP) conditions and achieves favorable reconstruction error bounds. Extensive experimental results demonstrate that our framework attains superior reconstruction performance under various privacy budgets, signal sparsities, and measurement ratios, consistently outperforming existing methods in privacy-preserving 1-bit CS scenarios.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110206"},"PeriodicalIF":3.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686625","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

Tensor decompositions for signal processing: Theory, advances, and applications 信号处理的张量分解：理论、进展和应用

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-07-20 DOI: 10.1016/j.sigpro.2025.110191

Neriman Tokcan , Shakir Showkat Sofi , Van Tien Pham , Clémence Prévost , Sofiane Kharbech , Baptiste Magnier , Thanh Phuong Nguyen , Yassine Zniyed , Lieven De Lathauwer

{"title":"Tensor decompositions for signal processing: Theory, advances, and applications","authors":"Neriman Tokcan , Shakir Showkat Sofi , Van Tien Pham , Clémence Prévost , Sofiane Kharbech , Baptiste Magnier , Thanh Phuong Nguyen , Yassine Zniyed , Lieven De Lathauwer","doi":"10.1016/j.sigpro.2025.110191","DOIUrl":"10.1016/j.sigpro.2025.110191","url":null,"abstract":"<div><div>In the era of big data, rapid advancements in technology and data collection methods have led to the generation and accessibility of vast amounts of multi-modal, high-dimensional data across a diverse range of disciplines. Tensor methods have emerged as essential tools in signal processing, providing powerful frameworks to model and analyze such complex data effectively. This survey offers a comprehensive overview of tensor factorization techniques and their applications in key areas. We explore their role in remote sensing, focusing on tensor-based methods for analyzing hyperspectral and multispectral images, tackling challenges such as recovering super-resolution images and addressing spectral unmixing. In wireless communication, we examine tensor methods used for signal modulation in unsourced massive random access communication, which achieve strong performance in multi-antenna channel and signal modeling. We also discuss tensor applications in network compression, where they reduce the computational demands of deep neural networks, making them more feasible for edge devices. Additionally, we highlight the use of tensor methods in high-dimensional missing data completion problems, showcasing their versatility across various domains. Furthermore, we explore applications in image analysis and computer vision, where tensors are effectively utilized for motion and object tracking, 3D modeling, satellite image analysis, and medical imaging. By bridging theoretical advancements with practical applications, this survey aims to guide researchers in leveraging tensor methods to tackle emerging challenges in signal processing.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110191"},"PeriodicalIF":3.6,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749626","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

Short-time variational mode decomposition 短时变分模态分解

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-07-19 DOI: 10.1016/j.sigpro.2025.110203

Hao Jia , Pengfei Cao , Tong Liang , Cesar F. Caiafa , Zhe Sun , Yasuhiro Kushihashi , Antoni Grau , Yolanda Bolea , Feng Duan , Jordi Solé-Casals

{"title":"Short-time variational mode decomposition","authors":"Hao Jia , Pengfei Cao , Tong Liang , Cesar F. Caiafa , Zhe Sun , Yasuhiro Kushihashi , Antoni Grau , Yolanda Bolea , Feng Duan , Jordi Solé-Casals","doi":"10.1016/j.sigpro.2025.110203","DOIUrl":"10.1016/j.sigpro.2025.110203","url":null,"abstract":"<div><div>Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies using Fourier transformations. However, since these transformations span the entire time-domain signal, they are suboptimal for analyzing non-stationary time series.</div><div>We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of VMD that incorporates Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows and converts these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of mode bandwidths across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring extraction of modes with narrow bandwidths.</div><div>STVMD is divided into dynamic and non-dynamic types, depending on whether central frequencies vary with time. Our experiments show non-dynamic STVMD matches VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals through reduced mode function errors and tracking of dynamic frequencies. This effectiveness is validated using steady-state visual-evoked potentials in electroencephalogram signals.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110203"},"PeriodicalIF":3.6,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring and reconstructing latent domains for multi-source domain adaptation 基于多源域自适应的潜在域探索与重构

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-07-19 DOI: 10.1016/j.sigpro.2025.110145

Wanjun Liang , Meijuan Tan , Xiangyu Meng , Chengzhe Zhang , Jun Zhou , Chilin Fu , Xiaolu Zhang , Changsheng Li

{"title":"Exploring and reconstructing latent domains for multi-source domain adaptation","authors":"Wanjun Liang , Meijuan Tan , Xiangyu Meng , Chengzhe Zhang , Jun Zhou , Chilin Fu , Xiaolu Zhang , Changsheng Li","doi":"10.1016/j.sigpro.2025.110145","DOIUrl":"10.1016/j.sigpro.2025.110145","url":null,"abstract":"<div><div>Multi-Source Domain Adaptation (MSDA) is receiving increased focus as a technique for reliably transferring knowledge from several source domains to a specific target domain. Traditional approaches generally operate under the assumption that samples in each source conform to a consistent distribution. However, real-world conditions often involve samples derived from diverse distributions, as well as potential data imbalance among different domains. Addressing these challenges, we introduce a novel and trustworthy framework, Multi-source Reconstructed Domain Adaptation (MSRDA), designed to enhance adaptation efficacy while maintaining robust performance and reliability across heterogeneous data sources. To start with,we delve into the latent mixed distributions of each source using clustering techniques, followed by the reconstruction of the latent domains following the original distribution. Additionally, we introduce an adaptive weighting mechanism to mitigate data imbalances.In cases where an latent domain consists of only a few samples, the global features are identified and dominate in that particular domain to help avoid overfitting. Moreover, given the difficulties of optimizing clustering while updating the model,we apply ExpectationMaximization (EM) algorithm to iteratively perform domain reconstruction and domain adaptation.Experiments are performed on two public datasets and one real-world datasets, and experimental results demonstrate that our MSRDA can effectively achieve multi-source domain adaptation through re-. constructing source domain with identified latent domains.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110145"},"PeriodicalIF":3.4,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713455","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

Frames and vertex-frequency representations in graph fractional Fourier domain 图像分数傅里叶域中的帧和顶点频率表示

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-07-19 DOI: 10.1016/j.sigpro.2025.110198

Linbo Shang , Zhichao Zhang

{"title":"Frames and vertex-frequency representations in graph fractional Fourier domain","authors":"Linbo Shang , Zhichao Zhang","doi":"10.1016/j.sigpro.2025.110198","DOIUrl":"10.1016/j.sigpro.2025.110198","url":null,"abstract":"<div><div>Vertex-frequency analysis – particularly the windowed graph Fourier transform (WGFT) – captures the correspondence between vertices and frequencies in graph signal processing. However, existing methods often struggle to accurately extract vertex-frequency features from sparse graph signals encountered in real-world applications, such as those derived from COVID-19 datasets. To address this limitation, we propose the multi-windowed graph fractional Fourier transform (MWGFRFT), a novel framework that enhances vertex localization through multi-windowed analysis and improves (fractional) frequency resolution via fractional-order transforms. Theoretically, MWGFRFT generalizes several existing transforms, including the WGFT, as special cases. To improve computational efficiency, we further develop a fast algorithm, FMWGFRFT. Experimental results demonstrate that FMWGFRFT effectively captures vertex-frequency features in the graph fractional Fourier domain and exhibits strong robustness in practical scenarios. Applications include anomaly detection and adaptive learning of graph fractional Laplacian bases, highlighting its potential for analyzing complex signals over irregular graph domains.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110198"},"PeriodicalIF":3.4,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679362","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

Anomalous activity detection using RF emanations 利用射频辐射进行异常活动检测

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-07-18 DOI: 10.1016/j.sigpro.2025.110201

Venkatesh Sathyanarayanan , Peter Gerstoft

{"title":"Anomalous activity detection using RF emanations","authors":"Venkatesh Sathyanarayanan , Peter Gerstoft","doi":"10.1016/j.sigpro.2025.110201","DOIUrl":"10.1016/j.sigpro.2025.110201","url":null,"abstract":"<div><div>Electronic activity in digital systems unintentionally emits radio frequency (RF) signals called emanations. These emanations compromise data security, which is important for corporate and military establishments. This work focuses on detecting anomalous activity that compromises data security through emanations. An example of such anomalous activity is emanations from damaged peripherals, such as a mouse or keyboard, which can be used to steal digital data. Prior work on emanation detection uses profiling on specific hardware (HW). However, this is not scalable across all types of HW. We propose a HW-agnostic solution for finding anomalous activity using emanations by scanning the signature of harmonics from leakages of clock signals. An algorithm for multi-harmonic pitch estimation is introduced for wireless applications. A preprocessing technique is developed that removes the effect of artifacts. Thorough mathematical derivations demonstrate the algorithm theoretically. In-phase and Quadrature-phase (IQ) data are collected from emanation sources placed in a shielded room from 0.1–1.1 GHz using software-defined radios (SDR). Results are presented for use cases emulating anomalous activity that compromises data security, such as damaged peripherals and unauthorized data copy onto external devices.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110201"},"PeriodicalIF":3.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694666","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