IET Signal Processing最新文献

{"title":"Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment","authors":"Haibin Wang,&nbsp;Changshou Deng,&nbsp;Junbo Long,&nbsp;Youxue Zhou","doi":"10.1049/2024/7605121","DOIUrl":"https://doi.org/10.1049/2024/7605121","url":null,"abstract":"<div>\u0000 <p>Mechanical fault vibration signal is a typical non-Gaussian process, they can be characterized by the infinite variance process, and the noise within these signals may also be the process in complex environments. The performance of the traditional cross-term reduction algorithm is compromised, sometimes yielding incorrect results under the infinite variance process environment. Several robust fractional lower order time–frequency representation methods are proposed including fractional low-order smoothed pseudo Wigner (FLOSPW), fractional low-order multi-windowed short-time Fourier transform (FLOMWSTFT), and improved fractional low-order multi-windowed short-time Fourier transform (IFLOMWSTFT) utilizing fractional low-order statistics and short-time Fourier transform (STFT) to mitigate cross-terms, enhance time–frequency resolution, and accommodate the infinite variance process environment. When compared to traditional methods, simulation results indicate that they effectively suppress the pulse noise and function effectively in lower mixed signal noise ratio (MSNR) in an infinite variance process environment. The efficacy of the proposed time–frequency algorithm is validated through its application to mechanical bearing outer ring fault vibration signals contaminated with Gaussian noise and subjected to an <i>α</i> infinite variance process.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/7605121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-Efficiency Maximization in Backscatter Communication-Based Non-Orthogonal Multiple Access System: Dinkelbach and Successive Convex Approximation Approaches","authors":"Dingjia Lin,&nbsp;Tianqi Wang,&nbsp;Kaidi Wang,&nbsp;Zhiguo Ding","doi":"10.1049/2024/4107801","DOIUrl":"https://doi.org/10.1049/2024/4107801","url":null,"abstract":"<div>\u0000 <p>This paper investigates a backscatter communication (BackCom) based non-orthogonal multiple access (NOMA) system in a multiple-input and single-output (MISO) scenario, where two decoding methods are deployed, including the sum-capacity approach and QR decomposition. The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. Two algorithms, Dinkelbach based on penalty semidefinite relaxation (SDR) and successive convex approximation (SCA), are proposed as high-performance and low-complexity solutions, respectively. Simulation results indicate that the combination of the sum-capacity approach and Dinkelbach yields the best performance, though at the highest complexity, while the amalgamation of QR decomposition and SCA offers the lowest performance but with minimal complexity.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/4107801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extended Infrared Target Filtering via Random Finite Set and Low-Rank Matrix Decomposition","authors":"Jian Su,&nbsp;Haiyin Zhou,&nbsp;Qi Yu,&nbsp;Jubo Zhu,&nbsp;Jiying Liu","doi":"10.1049/2024/9914774","DOIUrl":"https://doi.org/10.1049/2024/9914774","url":null,"abstract":"<div>\u0000 <p>Target detection in infrared remote sensing images has important practical applications. Among the current high-performance methods, the deep learning-based methods require training samples, and their generalization ability is also limited by the training set. The separation of low-rank and sparse matrix requires joint processing of multiple images with high computational complexity. The track-before-detect algorithms based on particle filtering also have high computational complexity. In this paper, the low-rank and sparse matrix of a single image are proposed for target detection, and a differentiable objective function is used in the separation. At the same time, an extended multitarget tracking algorithm based on random sets is used for target filtering between frames, and the design of the filters adopts the conjugate distribution under the Bayesian framework. Finally, the practical infrared sequence images containing multiple targets and complex backgrounds were employed to verify the performance of the proposed algorithms by comparing them with state-of-the-art algorithms.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9914774","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Jaccard Coefficient-Based Clustering Approach with Application to Diagnosis and RUL Estimation","authors":"Xiaoqing Li,&nbsp;Hao Tang,&nbsp;Hai Wang,&nbsp;Gangzhong Miao,&nbsp;Mingang Cheng","doi":"10.1049/2024/6586622","DOIUrl":"https://doi.org/10.1049/2024/6586622","url":null,"abstract":"<div>\u0000 <p>Sample clustering techniques play a crucial role in the data-driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of similarity for scalar set-type samples. In this paper, we propose an incremental clustering algorithm for matrix-type samples by defining an improved Jaccard coefficient. First, a new binary relation is formulated to derive a relationship matrix between samples. Second, an undirected graph is given by using the relationship matrix, and an improved pruning operation is provided to simplify the graph by eliminating redundant edges. Then, a new relationship matrix is generated according to the modified graph, which enables the calculation of the improved Jaccard coefficient. By using the improved Jaccard coefficient, the improved incremental clustering algorithm updates cluster centers by selecting a particular sample to maximize the sum of similarities between the selected sample and other samples within the same cluster. Finally, the effectiveness of the proposed incremental clustering algorithm is demonstrated in fault diagnosis and remaining useful life estimation application scenarios, respectively. The experimental results indicate that the improved algorithm outperforms traditional clustering methods.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/6586622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram","authors":"Xu Zhang,&nbsp;Xi Hui,&nbsp;Pengwu Wan,&nbsp;Tengfei Hui,&nbsp;Xiongfei Li","doi":"10.1049/2024/9589239","DOIUrl":"https://doi.org/10.1049/2024/9589239","url":null,"abstract":"<div>\u0000 <p>Automatic modulation recognition is a key technology in the field of signal processing. Conventional recognition methods suffer from low recognition accuracy at low signal-to-noise ratios (SNR), and when the signal frequency is unstable or there is asynchronous sampling, the performance of conventional recognition methods will deteriorate or even fail. To address these challenges, deep learning-based modulation mode recognition technique is investigated in this paper for low-speed asynchronous sampled signals under channel conditions with varying SNR and delay. Firstly, the low-speed asynchronous sampled signals are modeled, and their in-phase quadrature components are used to generate a two-dimensional asynchronous in-phase quadrature histogram. Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. Finally, the accuracy of the method for seven modulation methods is verified by extensive simulations. The experimental results show that under the channel model of additive white Gaussian noise (AWGN), when the SNR of the input signal with low-speed asynchronous sampling is 6 dB, more than 95% of the average recognition accuracy can be achieved, and the effectiveness and robustness of the proposed scheme are verified by comparative experiments.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9589239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141966674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"No-Reference High Dynamic Range Omnidirectional Image Quality Metric: From the Perspective of Global and Local Statistical Characteristics","authors":"Rongyao Yu,&nbsp;Fang Yang,&nbsp;Yi Liu,&nbsp;Jianghui He,&nbsp;Qingjiang Pang,&nbsp;Yang Song","doi":"10.1049/2024/5653845","DOIUrl":"https://doi.org/10.1049/2024/5653845","url":null,"abstract":"<div>\u0000 <p>High dynamic range omnidirectional image (HOI) can provide more real and immersive watching experience for viewers, thus has become an important presentation of virtual reality technology. However, both the system processing and the characteristics of HOI make the design of HOI quality metric (HOIQM) a challenging issue. In this work, considering the difference between whole field of view (FoV) and viewer-selected viewport, distortion features from both global and local perspectives are extracted, and a blind HOIQM is proposed. Specifically, because different regions have different projections in SSP projection, we have constructed the optimal bivariate response pair in the equatorial region and bipolar region according to their projection direction, and parameters in the BGGD based-spatial oriented correlation model are extracted as global statistical features. Meanwhile, combined with the visual perception for HOI, the key blocks are determined in equatorial region, and the local statistical characteristics of the key blocks are extracted by analyzing the distribution of multiscale structure information. Finally, the global and local features are regressed by SVR to obtain the final HOI quality. Experimental results on NBU-HOID database demonstrate that the proposed quality metric is outperformed the existing representative quality metrics and is more consistent with human visual perception for HOI.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/5653845","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gated Spatial–Temporal Merged Transformer Inspired by Multimask and Dual Branch for Traffic Forecasting","authors":"Yongpeng Yang,&nbsp;Zhenzhen Yang,&nbsp;Zhen Yang","doi":"10.1049/2024/8639981","DOIUrl":"https://doi.org/10.1049/2024/8639981","url":null,"abstract":"<div>\u0000 <p>As an essential part of intelligent transportation system (ITS), traffic forecasting has provided crucial role for traffic management and risk assessment. However, complex spatial–temporal dependencies, heterogeneity, dynamicity, and periodicity of traffic data influence the traffic forecasting performance. Consequently, we propose a novel effective gated spatial–temporal merged transformer (GSTMT) inspired by multimask and dual branch for accurate traffic forecasting in this paper. Specifically, we first conduct a concatenation of gated spatial static mask transformer (GSSMT) and gated spatial dynamic mask transformer (GSDMT) with residual network. The GSSMT and GSDMT evolve from the traditional transformer by making preferable modifications that include gated linear unit (GLU), multimask mechanism including static mask matrix (SMM) and dynamic mask matrix (DMM), and spatial attention (SA). Among them, GLU is to promote the performance of capturing spatial dependency, dynamicity, and heterogeneity due to advanced performance for controlling information flow through layers. Additionally, by developing multimask mechanism including two novel SMM and DMM, the proposed GSTMT can precisely model the static and dynamic spatial structure for effectively highlighting static dependency and dynamicity. And SA is injected for enhancing the ability of capturing spatial dependency of GSSMT and GSDMT. Secondly, we develop a dual-branch gated temporal transformer (DBGTT) for capturing temporal dependency, heterogeneity, dynamicity, and periodicity via incorporating the GLU and mixed time series decomposition (MTD) into traditional transformer. Similarly, we also introduce the GLU for empowering DBGTT with capability of capturing temporal dependency, dynamicity, and heterogeneity. In addition, MTD, which brings dual-branch mechanism, can enhance the DBGTT for capturing more detailed temporal information via exploiting global and periodic profile of traffic data. At last, some experiments, which are performed on several real-world traffic datasets, demonstrate the better results over classic traffic forecasting methods.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/8639981","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Effective Strategy of Object Instance Segmentation in Sonar Images","authors":"Pengfei Shi,&nbsp;Huanru Sun,&nbsp;Qi He,&nbsp;Hanren Wang,&nbsp;Xinnan Fan,&nbsp;Yuanxue Xin","doi":"10.1049/2024/1357293","DOIUrl":"https://doi.org/10.1049/2024/1357293","url":null,"abstract":"<div>\u0000 <p>Instance segmentation is a task that involves pixel-level classification and segmentation of each object instance in images. Various CNN-based methods have achieved promising results in natural image instance segmentation. However, the noise interference, low resolution, and blurred edges bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose the Effective Strategy for Sonar Images Instance Segmentation (ESSIIS). We introduce ASception, a new network combining Atrous Spatial Pyramid Pooling (ASPP) and Extreme Inception (Xception). By integrating this with ResNet and transforming traditional convolutions into deformable convolutions, we further improve the ability of the network to extract features from sonar images. Additionally, we incorporate a bidirectional feature fusion module to enhance information fusion. Finally, we evaluate the detection accuracy and segmentation accuracy of the proposed method on the public sonar image dataset and the self-constructed dataset. ESSIIS attains a detection accuracy of 0.981 and a segmentation accuracy of 0.951 on SCTD, further impressively achieving 0.986 in both metrics when appraised on our dataset. The evaluation results demonstrate that the proposed method is more accurate, robust, and considerable for sonar image detection and segmentation.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/1357293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Reinforcement Learning Approach for Portfolio Management in Non-Short-Selling Market","authors":"Ruidan Su,&nbsp;Chun Chi,&nbsp;Shikui Tu,&nbsp;Lei Xu","doi":"10.1049/2024/5399392","DOIUrl":"https://doi.org/10.1049/2024/5399392","url":null,"abstract":"<div>\u0000 <p>Reinforcement learning (RL) has been applied to financial portfolio management in recent years. Current studies mostly focus on profit accumulation without much consideration of risk. Some risk-return balanced studies extract features from price and volume data only, which is highly correlated and missing representation of risk features. To tackle these problems, we propose a weight control unit (WCU) to effectively manage the position of portfolio management in different market statuses. A loss penalty term is also designed in the reward function to prevent sharp drawdown during trading. Moreover, stock spatial interrelation representing the correlation between two different stocks is captured by a graph convolution network based on fundamental data. Temporal interrelation is also captured by a temporal convolutional network based on new factors designed with price and volume data. Both spatial and temporal interrelation work for better feature extraction from historical data and also make the model more interpretable. Finally, a deep deterministic policy gradient actor–critic RL is applied to explore optimal policy in portfolio management. We conduct our approach in a challenging non-short-selling market, and the experiment results show that our method outperforms the state-of-the-art methods in both profit and risk criteria. Specifically, with 6.72% improvement on an annualized rate of return, 7.72% decrease in maximum drawdown, and a better annualized Sharpe ratio of 0.112. Also, the loss penalty and WCU provide new aspects for future work in risk control.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/5399392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DOA Estimation Based on Logistic Function for CD Sources in Impulsive Noise","authors":"Quan Tian,&nbsp;Ruiyan Cai,&nbsp;Yang Luo","doi":"10.1049/2024/7043115","DOIUrl":"https://doi.org/10.1049/2024/7043115","url":null,"abstract":"<div>\u0000 <p>To improve direction of arrival (DOA) estimation for coherently distributed sources under impulsive noise environments, a logistic-based adaptive factor is proposed to suppress the impulsive noise contained in the output signals of the array. The properties of this adaptive factor are derived. Furthermore, this adaptive factor is applied to subspace methods, and a novel DOA estimation algorithm is proposed. This novel algorithm ensures the boundedness of the signal and the noise subspaces while improving the DOA estimation accuracy and robustness. The experimental results demonstrate that the proposed algorithm outperforms existing algorithms in terms of resolution probability and estimation accuracy under impulsive noise environments.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/7043115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}