IEEE open journal of signal processing最新文献_第7页

A Family of Swish Diffusion Strategy Based Adaptive Algorithms for Distributed Active Noise Control 基于 Swish 扩散策略的分布式主动噪声控制自适应算法系列

IEEE open journal of signal processing Pub Date : 2024-02-01 DOI: 10.1109/OJSP.2024.3360860

Rajapantula Kranthi;Vasundhara;Asutosh Kar;Mads Græsbøll Christensen

{"title":"A Family of Swish Diffusion Strategy Based Adaptive Algorithms for Distributed Active Noise Control","authors":"Rajapantula Kranthi;Vasundhara;Asutosh Kar;Mads Græsbøll Christensen","doi":"10.1109/OJSP.2024.3360860","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3360860","url":null,"abstract":"The conventional filtered-x least mean square (F-xLMS) algorithm based distributed active noise control (DANC) system's performance suffers in the presence of outliers and impulse like disturbances. In an attempt to reduce noise in such an environment Swish function based algorithms for DANC systems have been proposed presently. The Swish function makes use of the smoothness and unboundedness properties for faster convergence and eliminating vanishing gradient issue. The intention is to employ the smooth approximation of Softplus and the non-convex property of Geman-McClure estimator to propose a Softplus Geman-McClure function. In addition, the bounded nonlinearity of Welsch function which is insensitive to the outliers is utilized with the regularization property of Softsign formulating Softsign Welsch method. Henceforth, this paper proposes a family of robust algorithms employing the Swish diffusion strategy for filtered-x sign, filtered-x LMS, filtered-x Softplus Geman-McClure and filtered-x Softsign Welsch algorithms for DANC systems. The weight update rules are derived for the proposed algorithms and convergence analysis is also carried out. The suggested methods achieve faster convergence in comparison with existing techniques and approximately 1–5 dB improvement in noise cancellation for various noise inputs and impulsive noise interferences.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"503-519"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE open journal of signal processing Pub Date : 2024-01-31 DOI: 10.1109/OJSP.2024.3360914

Didem Dogan;Geert Leus

{"title":"Correlated Sparse Bayesian Learning for Recovery of Block Sparse Signals With Unknown Borders","authors":"Didem Dogan;Geert Leus","doi":"10.1109/OJSP.2024.3360914","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3360914","url":null,"abstract":"We consider the problem of recovering complex-valued block sparse signals with unknown borders. Such signals arise naturally in numerous applications. Several algorithms have been developed to solve the problem of unknown block partitions. In pattern-coupled sparse Bayesian learning (PCSBL), each coefficient involves its own hyperparameter and those of its immediate neighbors to exploit the block sparsity. Extended block sparse Bayesian learning (EBSBL) assumes the block sparse signal consists of correlated and overlapping blocks to enforce block correlations. We propose a simpler alternative to EBSBL and reveal the underlying relationship between the proposed method and a particular case of EBSBL. The proposed algorithm uses the fact that immediate neighboring sparse coefficients are correlated. The proposed model is similar to classical sparse Bayesian learning (SBL). However, unlike the diagonal correlation matrix in conventional SBL, the unknown correlation matrix has a tridiagonal structure to capture the correlation with neighbors. Due to the entanglement of the elements in the inverse tridiagonal matrix, instead of a direct closed-form solution, an approximate solution is proposed. The alternative algorithm avoids the high dictionary coherence in EBSBL, reduces the unknowns of EBSBL, and is computationally more efficient. The sparse reconstruction performance of the algorithm is evaluated with both correlated and uncorrelated block sparse coefficients. Simulation results demonstrate that the proposed algorithm outperforms PCSBL and correlation-based methods such as EBSBL in terms of reconstruction quality. The numerical results also show that the proposed correlated SBL algorithm can deal with isolated zeros and nonzeros as well as block sparse patterns.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"421-435"},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10417118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy 脉冲噪声场景下嵌套阵列的 DOA 估计：自适应阶矩策略

IEEE open journal of signal processing Pub Date : 2024-01-31 DOI: 10.1109/OJSP.2024.3360896

Xudong Dong;Jun Zhao;Jingjing Pan;Meng Sun;Xiaofei Zhang;Peihao Dong;Yide Wang

引用次数: 0

Constrained Weighted Least-Squares Algorithms for 3-D AOA-Based Hybrid Localization 基于 AOA 的三维混合定位的受限加权最小二乘法算法

IEEE open journal of signal processing Pub Date : 2024-01-31 DOI: 10.1109/OJSP.2024.3360901

Yanbin Zou;Wenbo Wu;Jingna Fan;Huaping Liu

引用次数: 0

Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System 雷达-通信联合系统中目标状态估计的最佳波形选择

IEEE open journal of signal processing Pub Date : 2024-01-29 DOI: 10.1109/OJSP.2024.3359997

Ashoka Chakravarthi Mahipathi;Bethi Pardha Pardhasaradhi;Srinath Gunnery;Pathipati Srihari;John d'Souza;Paramananda Jena

{"title":"Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System","authors":"Ashoka Chakravarthi Mahipathi;Bethi Pardha Pardhasaradhi;Srinath Gunnery;Pathipati Srihari;John d'Souza;Paramananda Jena","doi":"10.1109/OJSP.2024.3359997","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3359997","url":null,"abstract":"The widespread usage of the Radio Frequency (RF) spectrum for wireless and mobile communication systems generated a significant spectrum scarcity. The Joint Radar-Communication System (JRCS) provides a framework to simultaneously utilize the allocated radar spectrum for sensing and communication purposes. Generally, a Successive Interference Cancellation (SIC) based receiver is applied to mitigate mutual interference in the JRCS configuration. However, this SIC receiver model introduces a communication residual component. In response to this issue, the article presents a novel measurement model based on communication residual components for various radar waveforms. The radar system's performance within the JRCS framework is then evaluated using the Fisher Information Matrix (FIM). The radar waveforms considered in this investigation are rectangular pulse, triangular pulse, Gaussian pulse, Linear Frequency Modulated (LFM) pulse, LFM-Gaussian pulse, and Non-Linear Frequency Modulated (NLFM) pulse. After that, the Kalman filter is deployed to estimate the target kinematics (range and range rate) of a single linearly moving target for different waveforms. Additionally, range and range rate estimation errors are quantified using the Root Mean Square Error (RMSE) metric. Furthermore, the Posterior Cramer-Rao Lower Bound (PCRLB) is derived to validate the estimation accuracy of various waveforms. The simulation results show that the range and range rate estimation errors are within the PCRLB limit at all time instants for all the designated waveforms. The results further reveal that the NLFM pulse waveform provides improved range and range rate error performance compared to all other waveforms.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"459-477"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10416352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust and Simple ADMM Penalty Parameter Selection 稳健而简单的 ADMM 惩罚参数选择

IEEE open journal of signal processing Pub Date : 2024-01-10 DOI: 10.1109/OJSP.2023.3349115

MICHAEL T. MCCANN;Brendt Wohlberg

{"title":"Robust and Simple ADMM Penalty Parameter Selection","authors":"MICHAEL T. MCCANN;Brendt Wohlberg","doi":"10.1109/OJSP.2023.3349115","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3349115","url":null,"abstract":"We present a new method for online selection of the penalty parameter for the alternating direction method of multipliers (ADMM) algorithm. ADMM is a widely used method for solving a range of optimization problems, including those that arise in signal and image processing. In its standard form, ADMM includes a scalar hyperparameter, known as the penalty parameter, which usually has to be tuned to achieve satisfactory empirical convergence. In this work, we develop a framework for analyzing the ADMM algorithm applied to a quadratic problem as an affine fixed point iteration. Using this framework, we develop a new method for automatically tuning the penalty parameter by detecting when it has become too large or small. We analyze this and several other methods with respect to their theoretical properties, i.e., robustness to problem transformations, and empirical performance on several optimization problems. Our proposed algorithm is based on a theoretical framework with clear, explicit assumptions and approximations, is theoretically covariant/invariant to problem transformations, is simple to implement, and exhibits competitive empirical performance.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"402-420"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10387940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery Dagma-DCE：可解释的非参数差异因果发现

IEEE open journal of signal processing Pub Date : 2024-01-09 DOI: 10.1109/OJSP.2024.3351593

Daniel Waxman;Kurt Butler;Petar M. Djurić

引用次数: 0

Face Reflection Removal Network Using Multispectral Fusion of RGB and NIR Images 利用多光谱融合 RGB 和近红外图像的人脸反射去除网络

IEEE open journal of signal processing Pub Date : 2024-01-09 DOI: 10.1109/OJSP.2024.3351472

Hui Lan;Enquan Zhang;Cheolkon Jung

{"title":"Face Reflection Removal Network Using Multispectral Fusion of RGB and NIR Images","authors":"Hui Lan;Enquan Zhang;Cheolkon Jung","doi":"10.1109/OJSP.2024.3351472","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3351472","url":null,"abstract":"Images captured through glass are usually contaminated by reflections, and the removal of them from images is a challenging task. Since the primary concern on photos is face, the face images with reflections annoy viewers severely. In this article, we propose a face reflection removal network using multispectral fusion of color (RGB) and near infrared (NIR) images, called FRRN. Due to the different spectral wavelengths of visible light [380 nm, 780 nm] and near infrared [780 nm, 2526 nm], NIR cameras are not sensitive to the visible light and thus NIR images are less corrupted by reflections. NIR images preserve structure information well and can guide the restoration process from reflections on the RGB images. Thus, we adopt multispectual fusion of RGB and NIR images for reflection removal from a face image. FRRN consists of one encoder model (contextual encoder model (CEM)) and two decoder models (NIR inference decoder model (NIDM) and image inference decoder model (IIDM)). CEM captures features from shallow to deep layers on the scene information while suppressing the sparse reflection component. NIDM infers NIR image to facilitate multi-scale guidance for reflection removal, while IIDM estimates the transmission layer with the guidance of NIDM. Besides, we present the reflection confidence generation module (RCGM) based on Laplacian convolution and channel attention-based residual block (CARB) to represent the reflection confidence in a region for reflection removal. To train FRRN, we construct a large-scale training dataset with face image and reflection layer (RGB and NIR images) and its corresponding test dataset using JAI AD-130 GE camera. Various experiments demonstrate that FRRN outperforms state-of-the-art methods for reflection removal in terms of visual quality and quantitative measurements.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"383-392"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10384724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Representation Learning for Robust Privacy Preservation in Audio 逆向表征学习实现稳健的音频隐私保护

IEEE open journal of signal processing Pub Date : 2024-01-01 DOI: 10.1109/OJSP.2023.3349113

Shayan Gharib;Minh Tran;Diep Luong;Konstantinos Drossos;Tuomas Virtanen

{"title":"Adversarial Representation Learning for Robust Privacy Preservation in Audio","authors":"Shayan Gharib;Minh Tran;Diep Luong;Konstantinos Drossos;Tuomas Virtanen","doi":"10.1109/OJSP.2023.3349113","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3349113","url":null,"abstract":"Sound event detection systems are widely used in various applications such as surveillance and environmental monitoring where data is automatically collected, processed, and sent to a cloud for sound recognition. However, this process may inadvertently reveal sensitive information about users or their surroundings, hence raising privacy concerns. In this study, we propose a novel adversarial training method for learning representations of audio recordings that effectively prevents the detection of speech activity from the latent features of the recordings. The proposed method trains a model to generate invariant latent representations of speech-containing audio recordings that cannot be distinguished from non-speech recordings by a speech classifier. The novelty of our work is in the optimization algorithm, where the speech classifier's weights are regularly replaced with the weights of classifiers trained in a supervised manner. This increases the discrimination power of the speech classifier constantly during the adversarial training, motivating the model to generate latent representations in which speech is not distinguishable, even using new speech classifiers trained outside the adversarial training loop. The proposed method is evaluated against a baseline approach with no privacy measures and a prior adversarial training method, demonstrating a significant reduction in privacy violations compared to the baseline approach. Additionally, we show that the prior adversarial method is practically ineffective for this purpose.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"294-302"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139488162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Signal Processing in the Retina: Interpretable Graph Classifier to Predict Ganglion Cell Responses 视网膜中的信号处理：预测神经节细胞反应的可解释图形分类器

IEEE open journal of signal processing Pub Date : 2024-01-01 DOI: 10.1109/OJSP.2023.3349111

Yasaman Parhizkar;Gene Cheung;Andrew W. Eckford

{"title":"Signal Processing in the Retina: Interpretable Graph Classifier to Predict Ganglion Cell Responses","authors":"Yasaman Parhizkar;Gene Cheung;Andrew W. Eckford","doi":"10.1109/OJSP.2023.3349111","DOIUrl":"10.1109/OJSP.2023.3349111","url":null,"abstract":"It is a popular hypothesis in neuroscience that ganglion cells in the retina are activated by selectively detecting visual features in an observed scene. While ganglion cell firings can be predicted via data-trained deep neural nets, the networks remain indecipherable, thus providing little understanding of the cells' underlying operations. To extract knowledge from the cell firings, in this paper we learn an interpretable graph-based classifier from data to predict the firings of ganglion cells in response to visual stimuli. Specifically, we learn a positive semi-definite (PSD) metric matrix \u0000<inline-formula><tex-math>${mathbf {M}}succeq 0$</tex-math></inline-formula>\u0000 that defines Mahalanobis distances between graph nodes (visual events) endowed with pre-computed feature vectors; the computed inter-node distances lead to edge weights and a combinatorial graph that is amenable to binary classification. Mathematically, we define the objective of metric matrix \u0000<inline-formula><tex-math>${mathbf {M}}$</tex-math></inline-formula>\u0000 optimization using a graph adaptation of large margin nearest neighbor (LMNN), which is rewritten as a semi-definite programming (SDP) problem. We solve it efficiently via a fast approximation called Gershgorin disc perfect alignment (GDPA) linearization. The learned metric matrix \u0000<inline-formula><tex-math>${mathbf {M}}$</tex-math></inline-formula>\u0000 provides interpretability: important features are identified along \u0000<inline-formula><tex-math>${mathbf {M}}$</tex-math></inline-formula>\u0000’s diagonal, and their mutual relationships are inferred from off-diagonal terms. Our fast metric learning framework can be applied to other biological systems with pre-chosen features that require interpretation.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"303-311"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139388541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0