IEEE open journal of signal processing最新文献

List of Reviewers

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-12-13 DOI: 10.1109/OJSP.2024.3498352

引用次数: 0

JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-11-11 DOI: 10.1109/OJSP.2024.3496819

Chang Sun;Bo Qin;Hong Yang

{"title":"JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition","authors":"Chang Sun;Bo Qin;Hong Yang","doi":"10.1109/OJSP.2024.3496819","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3496819","url":null,"abstract":"Visual Speech Recognition (VSR) tasks are generally recognized to have a lower theoretical performance ceiling than Automatic Speech Recognition (ASR), owing to the inherent limitations of conveying semantic information visually. To mitigate this challenge, this paper introduces an advanced knowledge distillation approach using a Joint-Embedding Predictive Architecture (JEPA), JEP-KD, designed to utilize audio features more effectively during model training. Central to JEP-KD is including a generative network within the embedding layer in the knowledge distillation structure, which enhances the video encoder's capacity for semantic feature extraction and brings it closer to the audio features from a pre-trained ASR model's encoder. This approach aims to reduce the performance gap between VSR and ASR progressively. Moreover, a comprehensive multimodal, multistage training regimen for the JEP-KD framework is established, bolstering the robustness and efficacy of the training process. Experiment results demonstrate that JEP-KD significantly improves the performance of VSR models and demonstrates versatility across different VSR platforms, indicating its potential for broader application within other multimodal tasks.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1147-1152"},"PeriodicalIF":2.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810545","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

Atom-Constrained Gridless DOA Refinement With Wirtinger Gradients 使用 Wirtinger 梯度的原子约束无网格 DOA 精化

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-11-11 DOI: 10.1109/OJSP.2024.3496815

Yongsung Park;Peter Gerstoft;Christoph F. Mecklenbräuker

引用次数: 0

Iterative Sparse Identification of Nonlinear Dynamics 非线性动力学的迭代稀疏识别

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-11-11 DOI: 10.1109/OJSP.2024.3495553

Jinho Choi

{"title":"Iterative Sparse Identification of Nonlinear Dynamics","authors":"Jinho Choi","doi":"10.1109/OJSP.2024.3495553","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3495553","url":null,"abstract":"In order to extract governing equations from time-series data, various approaches are proposed. Among those, sparse identification of nonlinear dynamics (SINDy) stands out as a successful method capable of modeling governing equations with a minimal number of terms, utilizing the principles of compressive sensing. This feature, which relies on a small number of terms, is crucial for interpretability. The effectiveness of SINDy hinges on the choice of candidate functions within its dictionary to extract governing equations of dynamical systems. A larger dictionary allows for more terms, enhancing the quality of approximations. However, the computational complexity scales with dictionary size, rendering SINDy less suitable for high-dimensional datasets, even though it has been successfully applied to low-dimensional datasets. To address this challenge, we introduce iterative SINDy in this paper, where the dictionary undergoes expansion and compression through iterations. We also conduct an analysis of the convergence properties of iterative SINDy. Simulation results validate that iterative SINDy can achieve nearly identical performance to SINDy, while significantly reducing computational complexity. Notably, iterative SINDy demonstrates effectiveness with high-dimensional time-series data without incurring the prohibitively high computational cost associated with SINDy.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1107-1118"},"PeriodicalIF":2.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691822","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

Energy Efficient Signal Detection Using SPRT and Ordered Transmissions in Wireless Sensor Networks 在无线传感器网络中使用 SPRT 和有序传输进行高能效信号检测

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-10-30 DOI: 10.1109/OJSP.2024.3488530

Shailee Yagnik;Ramanarayanan Viswanathan;Lei Cao

{"title":"Energy Efficient Signal Detection Using SPRT and Ordered Transmissions in Wireless Sensor Networks","authors":"Shailee Yagnik;Ramanarayanan Viswanathan;Lei Cao","doi":"10.1109/OJSP.2024.3488530","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3488530","url":null,"abstract":"In a distributed detection system with multiple sensors, the ordered transmission scheme (OTS) proposed by Blum and Sadler requires a fewer number of transmissions in comparison with a fixed sample size test with the same probability of error performance. In this work, we propose an ordered transmission scheme using a truncated sequential probability ratio test (SPRT), termed as OSPRT. With a suitable choice of two design parameters, the probability of error of the OSPRT can be upper bounded by no more than a certain percentage above the probability of error of OTS, yet achieving significant savings in both the average number of samples needed to arrive at a decision, and the average energy in signal transmission. The superiority of ordered transmissions over unordered transmissions is quantified in terms of Kullback-Leibler information. Simulation analysis for the detection of a constant signal of moderate strength in Gaussian noise shows that the probability of error of OSPRT, which is substantially below the theoretical upper bound, is only negligibly larger than the OTS error. Analysis also shows that OSPRT is more energy efficient than the original OTS.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1119-1133"},"PeriodicalIF":2.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713893","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 Estimation of the Covariance Matrix From Data With Outliers 从异常值数据中稳健估计协方差矩阵

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-10-02 DOI: 10.1109/OJSP.2024.3473610

Petre Stoica;Prabhu Babu;Piyush Varshney

{"title":"Robust Estimation of the Covariance Matrix From Data With Outliers","authors":"Petre Stoica;Prabhu Babu;Piyush Varshney","doi":"10.1109/OJSP.2024.3473610","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3473610","url":null,"abstract":"The robust estimation of the covariance matrix is a frequent task in practical applications in which, more often than not, some data samples are outliers. There are several methods that can be used to robustly estimate a covariance matrix from corrupted data, a representative example of which is the \u0000<bold>m\u0000inimum \u0000<bold>c\u0000ovariance \u0000<bold>d\u0000eterminant (MCD) method. In this paper we present a maximum conditional likelihood interpretation of MCD that provides a new motivation of as well as further insights into this method. To perform at its best MCD requires information on the number of outliers in the data, which usually is not available. We propose two new methods for covariance matrix estimation from data with outliers that do not suffer from this problem: TEST (multiple-hypothesis \u0000<bold>test\u0000ing method) which uses the FDR (false discovery rate) to test a set of model hypotheses and hence estimate the number of outliers and their locations, and LIKE (penalized \u0000<bold>like\u0000lihood method) that solves the outlier estimation problem using a GIC (generalized information criterion) to penalize the complexity of a high-dimensional data model. We show by means of numerical simulations that the performances of TEST and LIKE are relatively similar to one another as well as to the performance of the oracle MCD (which uses the true number of outliers) and significantly better than the performance of MCD that uses an upper bound on the outlier number.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1061-1072"},"PeriodicalIF":2.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565595","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

Dynamic Sensor Placement Based on Sampling Theory for Graph Signals 基于图形信号采样理论的动态传感器布局

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-09-23 DOI: 10.1109/OJSP.2024.3466133

Saki Nomura;Junya Hara;Hiroshi Higashi;Yuichi Tanaka

{"title":"Dynamic Sensor Placement Based on Sampling Theory for Graph Signals","authors":"Saki Nomura;Junya Hara;Hiroshi Higashi;Yuichi Tanaka","doi":"10.1109/OJSP.2024.3466133","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3466133","url":null,"abstract":"In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 sensor positions from \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 candidates where \u0000<inline-formula><tex-math>$K < N$</tex-math></inline-formula>\u0000. Most existing methods assume that sensor positions are static, i.e., they do not move, however, many mobile sensors like drones, robots, and vehicles can change their positions over time. Moreover, underlying measurement conditions could also be changed, which are difficult to cover with statically placed sensors. We tackle the problem by allowing the sensors to change their positions in their neighbors on the network. We dynamically determine the sensor positions based on graph signal sampling theory such that the non-observed signals on the network can be best recovered from the observations. For signal recovery, the dictionary is learned from a pool of observed signals. It is also used for the sensor position selection. In experiments, we validate the effectiveness of the proposed method via the mean squared error of the reconstructed signals. The proposed dynamic sensor placement outperforms the existing static ones for both synthetic and real data.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1042-1051"},"PeriodicalIF":2.9,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10689379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518001","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 Training for Jamming-Robust Channel Estimation in OFDM Systems 用于 OFDM 系统中干扰-稳健信道估计的对抗训练

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-09-02 DOI: 10.1109/OJSP.2024.3453176

Marcele O. K. Mendonça;Paulo S. R. Diniz;Javier Maroto Morales;Pascal Frossard

{"title":"Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems","authors":"Marcele O. K. Mendonça;Paulo S. R. Diniz;Javier Maroto Morales;Pascal Frossard","doi":"10.1109/OJSP.2024.3453176","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3453176","url":null,"abstract":"Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming is critical as it focuses on corrupting the symbols used for channel estimation and equalization, degrading the system performance. Although neural networks (NNs) can improve channel estimation and mitigate pilot jamming penalty, they are also themselves susceptible to malicious perturbations known as adversarial examples. If the jamming attack is crafted in order to fool the NN, it represents an adversarial example that impairs the proper behavior of OFDM systems. In this work, we explore two machine learning (ML)-based jamming strategies that are especially intended to degrade the performance of ML-based channel estimators, in addition to a traditional Additive White Gaussian Noise (AWGN) jamming attack. These ML-based attacks create noise patterns designed to reduce the precision of the channel estimation process, thereby compromising the reliability and robustness of the communication system. We highlight the vulnerabilities of wireless communication systems to ML-based pilot jamming attacks that corrupts symbols used for channel estimation, leading to system performance degradation. To mitigate these threats, this paper proposes an adversarial training defense mechanism desined to counter jamming attacks. The effectiveness of this defense is validated through simulation results, demonstrating improved channel estimation performance in the presence of jamming attacks. The proposed defense methods aim to enhance the resilience of OFDM systems against pilot jamming attacks, ensuring more robust communication in wireless environments.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1031-1041"},"PeriodicalIF":2.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320495","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

Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods 多目标跟踪中的轨迹聚合和排斥：MHT、JPDA 和信念传播方法分析

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-28 DOI: 10.1109/OJSP.2024.3451167

Thomas Kropfreiter;Florian Meyer;David F. Crouse;Stefano Coraluppi;Franz Hlawatsch;Peter Willett

{"title":"Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods","authors":"Thomas Kropfreiter;Florian Meyer;David F. Crouse;Stefano Coraluppi;Franz Hlawatsch;Peter Willett","doi":"10.1109/OJSP.2024.3451167","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3451167","url":null,"abstract":"Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, while MHT methods suffer from an opposite effect known as track repulsion, i.e., the estimated tracks of targets in close proximity tend to repel each other in the sense that their separation is larger than the actual distance between the targets. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm. We argue that BP-based MTT does not exhibit track repulsion because it is not based on maximum a posteriori estimation, and that it exhibits significantly reduced track coalescence because certain properties of the BP messages related to data association encourage separation of target state estimates. Our theoretical arguments are confirmed by numerical results for four representative simulation scenarios.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1089-1106"},"PeriodicalIF":2.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595863","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

Impact of Varying Distance-Based Fingerprint Similarity Metrics on Affinity Propagation Clustering Performance in Received Signal Strength-Based Fingerprint Databases 基于接收信号强度的指纹数据库中不同距离指纹相似度指标对亲缘传播聚类性能的影响

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-26 DOI: 10.1109/OJSP.2024.3449816

Abdulmalik Shehu Yaro;Filip Maly;Karel Maly;Pavel Prazak

{"title":"Impact of Varying Distance-Based Fingerprint Similarity Metrics on Affinity Propagation Clustering Performance in Received Signal Strength-Based Fingerprint Databases","authors":"Abdulmalik Shehu Yaro;Filip Maly;Karel Maly;Pavel Prazak","doi":"10.1109/OJSP.2024.3449816","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3449816","url":null,"abstract":"The affinity propagation clustering (APC) algorithm is popular for fingerprint database clustering because it can cluster without pre-defining the number of clusters. However, the clustering performance of the APC algorithm heavily depends on the chosen fingerprint similarity metric, with distance-based metrics being the most commonly used. Despite its popularity, the APC algorithm lacks comprehensive research on how distance-based metrics affect clustering performance. This emphasizes the need for a better understanding of how these metrics influence its clustering performance, particularly in fingerprint databases. This paper investigates the impact of various distance-based fingerprint similarity metrics on the clustering performance of the APC algorithm. It identifies the best fingerprint similarity metric for optimal clustering performance for a given fingerprint database. The analysis is conducted across five experimentally generated online fingerprint databases, utilizing seven distance-based metrics: Euclidean, squared Euclidean, Manhattan, Spearman, cosine, Canberra, and Chebyshev distances. Using the silhouette score as the performance metric, the simulation results indicate that structural characteristics of the fingerprint database, such as the distribution of fingerprint vectors, play a key role in selecting the best fingerprint similarity metric. However, Euclidean and Manhattan distances are generally the preferable choices for use as fingerprint similarity metrics for the APC algorithm across most fingerprint databases, regardless of their structural characteristics. It is recommended that other factors, such as computational intensity and the presence or absence of outliers, be considered alongside the structural characteristics of the fingerprint database when choosing the appropriate fingerprint similarity metric for maximum clustering performance.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1005-1014"},"PeriodicalIF":2.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276495","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