Digital Signal Processing最新文献

{"title":"A novel adaptive beamforming for multipath signal reception based on eigenspace","authors":"Rongchen Sun,&nbsp;Zhaoyu Shi,&nbsp;Zhenduo Wang,&nbsp;Zengmao Chen,&nbsp;Zhiguo Sun","doi":"10.1016/j.dsp.2025.105282","DOIUrl":"10.1016/j.dsp.2025.105282","url":null,"abstract":"<div><div>This paper introduces a new beamforming method designed for receiving multipath signals. It addresses the issue of signal cancellation caused by multipath signals in traditional beamformers. First, a bearing response function based on eigenspace is developed for multipath scenarios. This function is utilized to estimate the composite steering vector (CSV). Because it effectively captures multipath fading coefficients. Subsequently, in a multipath environment, the applicability of the power estimation method in the signal subspace scaled multiple signal classification algorithm is analyzed. This method is used to estimate the power of uncorrelated interference. Finally, the CSV is combined with a reconstructed interference-plus-noise covariance matrix to derive the optimal weight vector, achieving reception of multipath signals. Numerical simulations verify the effectiveness and superiority of the proposed beamformer. However, the computational complexity of the proposed beamformer is relatively high.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105282"},"PeriodicalIF":2.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long baseline underwater source localization based on deep K-Means++ clustering in complex underwater environments","authors":"Yawen Dai ,&nbsp;Lei Yang ,&nbsp;Yifei Cao","doi":"10.1016/j.dsp.2025.105281","DOIUrl":"10.1016/j.dsp.2025.105281","url":null,"abstract":"<div><div>Long baseline localization relies on trilateration, with the least squares method being utilized to determine the unique position of underwater sources. However, it is highly sensitive to distance measurements from any of the reference beacons to the source. Furthermore, achieving continuous and stable high-precision distance measurements presents a significant challenge in complex marine environments. To address this practical problem, this paper proposes a long baseline underwater source localization method based on deep K-Means++ clustering. A three-layer stack denoising autoencoder (SDA) was utilized to extract the features of the trilateration results. Subsequently, the K-Means++ algorithm was utilized to conduct multi-source location cluster analysis and fine-tuning. The experimental results demonstrate that, compared to the existing Kmeans-based long baseline localization method, the proposed approach demonstrates a modest enhancement in localization accuracy. Concurrently, it has led to an average increase of 7 percentage points in normalized mutual information (NMI), a rise of 8 percentage points in the adjusted Rand index (ARI), and an improvement of 2.5 percentage points in clustering accuracy (ACC). This not only ensures the stability and robustness of accuracy against ocean noise in long baseline mode but also enhances the operational efficiency and versatility of clustering methods applied within this field.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105281"},"PeriodicalIF":2.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DR-YOLO: An improved multi-scale small object detection model for drone aerial photography scenes based on YOLOv7","authors":"Hongbo Bi,&nbsp;Rui Dai,&nbsp;Fengyang Han,&nbsp;Cong Zhang","doi":"10.1016/j.dsp.2025.105265","DOIUrl":"10.1016/j.dsp.2025.105265","url":null,"abstract":"<div><div>With the advancement of drone technology, detecting and recognizing ground targets from aerial perspectives has become crucial in various drone applications. However, object detection in drone imagery poses several challenges, including the prevalence of small targets, the significant impact of aerial perspectives, variations in target scales, complex backgrounds, and frequent occlusions. To address these issues, we propose DR-YOLO, a multi-scale target detection model specifically designed for aerial drone images, building upon the YOLOv7 framework. We introduce the Spatial Pyramid Pooling with Dilated Convolutions (SPPDSPC) module to enhance dense target feature extraction. Additionally, we incorporate a decoupled detection head tailored for small objects and redesign the number and sizes of detection heads. To handle complex backgrounds and varying target sizes, we embed the Multi-Scale Feature Fusion (HTLF) Module into the feature pyramid network, providing rich spatial information for detection heads of different scales. Furthermore, we utilize the Gaussian Wasserstein Distance (GWD) to refine the regression loss, leading to improved bounding box quality, faster convergence, and higher accuracy in small object detection. Experimental results on the VisDrone2019 dataset demonstrate a 14.8% increase in [email protected] and a 9.8% increase in [email protected] compared to the baseline YOLOv7, validating the effectiveness of DR-YOLO in detecting objects within aerial drone imagery. The code and results of our method are available at <span><span>https://github.com/DRdairuiDR/DR-YOLO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105265"},"PeriodicalIF":2.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Half-quadratic Student's t-based kernel adaptive filter based on multikernel Nyström method","authors":"Mingjing Cui,&nbsp;Dongyuan Lin,&nbsp;Yunfei Zheng,&nbsp;Shiyuan Wang","doi":"10.1016/j.dsp.2025.105260","DOIUrl":"10.1016/j.dsp.2025.105260","url":null,"abstract":"<div><div>Kernel adaptive filters (KAFs) are effective for nonlinear signal processing. However, the performance of KAFs based on the minimum mean square error (MMSE) criterion can significantly deteriorate in non-Gaussian noise environments. In addition, their computational efficiency decreases as the network size grows, unless an effective sparsification method is employed. To address this issue, this paper introduces a novel Student's <em>t</em>-based KAF based on half-quadratic (HQ) method and multikernel Nyström approach. First, the HQ method transforms the non-convex problem of solving the Student's <em>t</em>-based loss function into a globally convex least squares (LS) problem. Then, the LS problem is solved using the dichotomous coordinate descent (DCD) method, achieving an efficient and low-complexity solution. Moreover, the multikernel Nyström method is leveraged to enhance the algorithm stability and mitigate the impact of network growth, resulting in the Student's <em>t</em>-Based-Multikernel Nyström dichotomous coordinate descent algorithm (ST-MNDCD). The energy conservation argument (ECA) and Taylor expansion are utilized to approximate a range of the steady-state characteristics of ST-MNDCD for performance analysis. Finally, simulations on Mackey-Glass time series prediction and nonlinear system identification demonstrate the advantages of ST-MNDCD.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105260"},"PeriodicalIF":2.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AC2Net: Hybrid attention convolution and compression fusion network for multimodal emotion recognition","authors":"Lixun Xie ,&nbsp;Weiqing Sun ,&nbsp;Jingyi Zhang ,&nbsp;Xiaohu Zhao","doi":"10.1016/j.dsp.2025.105261","DOIUrl":"10.1016/j.dsp.2025.105261","url":null,"abstract":"<div><div>The rapid development of human-computer interaction systems can help computers better understand human intentions. How to comprehensively and accurately recognize emotions is a key link in human-computer interaction. However, the current research on multimodal sentiment analysis tends to extract the information of a single modal separately and then simply combine the features of each mode. In this process, there is a lack of effective interaction between the features of each modal extraction, and the close relationship between the multi-modal data and the recognition task is ignored. In addition, the multimodal features are simply added and connected, ignoring the differences in the degree of correlation and contribution of different modal information to the final recognition result. In view of this, this paper proposes the Hybrid Attention Convolution and Compression Fusion Unit for Multimodal Emotion Recognition, namely AC2Net. We have designed a hybrid attention convolution model, which focuses on the interactive extraction of multi-modal features and can accurately capture key emotional perception features among three signal features, namely facial image sequence, EEG, and peripheral physiological signals. A compression fusion unit is designed to aggregate the multi-modal features, and the deep fusion of multi-modal heterogeneous information is realized by compressing and exciting the dense layers in the multi-modal branches. Finally, the proposed model was verified on the DEAP dataset, and the accuracy of valence and arousal dimension two-classification recognition reached 99.39%, 99.37%, and four-classification recognition accuracy reached 99.08%. Compared with the existing single-mode and multi-mode emotion recognition methods, the performance of this model is excellent.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105261"},"PeriodicalIF":2.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AO-TransUNet: A multi-attention optimization network for COVID-19 and medical image segmentation","authors":"Yang Qi ,&nbsp;Jiaxin Cai ,&nbsp;Rongshang Chen","doi":"10.1016/j.dsp.2025.105264","DOIUrl":"10.1016/j.dsp.2025.105264","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic has created a significant demand for accurate and efficient diagnostic tools to support effective disease management. Medical images related to COVID-19 present unique challenges, as the lesions often appear in various forms (e.g., ground glass shadows and consolidation shadows) that vary significantly in size, shape, and distribution. Additionally, these lesions can share similar gray levels or texture features with normal lung tissue, making it difficult to delineate clear boundaries between affected and healthy areas.</div></div><div><h3>Methods and procedures</h3><div>To address these challenges, the paper introduces a novel network called Attention Optimization TransUNet (AO-TransUNet), which builds upon the foundation of TransUNet. The method incorporates multiple attention mechanisms aimed at minimizing the loss of key information during the dimensionality reduction phase of segmentation. AO-TransUNet enhances dense interactions across all pixels, ensuring that morphological details and feature information of the lesions are preserved. This comprehensive approach improves the model's ability to detect subtle structural differences and effectively segment complex COVID-19 lesions.</div></div><div><h3>Results</h3><div>The performance of AO-TransUNet was validated through experimental evaluations on four datasets. The results demonstrated that AO-TransUNet outperformed existing state-of-the-art networks, showcasing its effectiveness in medical image segmentation.</div></div><div><h3>Conclusion:</h3><div>The study underscores the potential of AO-TransUNet to contribute to the field of medical image segmentation by addressing the challenges of complex and variable lesions, such as those seen in COVID-19. The method's ability to maintain morphological details and improve pixel-level interactions suggests broader applicability for other medical image analysis challenges. All code is available at <span><span>https://github.com/xiaqi7/AO-TransUNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105264"},"PeriodicalIF":2.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new gear weak feature extraction method based on modified symplectic geometry mode decomposition","authors":"Yanli Ma ,&nbsp;Wenlong Liu ,&nbsp;Yu Zhang ,&nbsp;Yiyuan Gao ,&nbsp;Zhiyi He","doi":"10.1016/j.dsp.2025.105284","DOIUrl":"10.1016/j.dsp.2025.105284","url":null,"abstract":"<div><div>The symplectic geometry mode decomposition (SGMD) is an effective analysis method applying to nonlinear and non-stationary signal. However, applying SGMD to gear signal, the weak fault feature is hard to be extracted, leading to the fault diagnosis failure. The reason lies in that the embedding dimension selection method of trajectory matrix lacks selection criteria, the construction type of trajectory matrix will result in spectral leakage, and it uses QR factorization tending to error diffusion when decomposing singular matrix. This paper proposes modified symplectic geometry mode decomposition (MSGMD) and concentrates on weak feature abstraction for gear fault diagnosis. First, a new embedding dimension choice strategy is proposed to select the ideal parameter, solving the problem of parameter solidification in SGMD. Then, the trajectory matrix is modified with “wraps around” method, which enhances the oscillation component and reduces the residual energy, and weak state features can be fully explored. Finally, singular value decomposition (SVD) takes the place of QR factorization to enhance the decomposition process, making the original signal feature information more completely. Simulated and experimental analysis demonstrate that MSGMD has excellent feature extractive ability in diagnosing gear fault with weak feature. The proposed method provides an effective way to diagnose gear fault of practical signal.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105284"},"PeriodicalIF":2.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FHR signal analysis using attention-based 1DCNN-BiLSTM neural network for intrapartum fetal monitoring","authors":"Aswathi Mohan P P ,&nbsp;V. Uma ,&nbsp;R. Sasirekha ,&nbsp;V. Hamsika","doi":"10.1016/j.dsp.2025.105259","DOIUrl":"10.1016/j.dsp.2025.105259","url":null,"abstract":"<div><div>The accurate prediction of fetal hypoxia is crucial in reducing fetal mortality rates. The Cardiotocography (CTG) signal is a widely used tool in fetal monitoring, especially for identifying fetal hypoxia. However, manual CTG analysis presents challenges, leading to a reduced diagnostic rate influenced by subjective factors. Automated CTG analysis emerges as a promising solution to these challenges. Numerous studies have been done on fetal hypoxia detection, but data imbalance poses a hurdle in obtaining the desired results. In response, we propose a novel approach integrating signal denoising through Discrete Wavelet Transform (DWT) based techniques, data balancing using Synthetic Minority Over-sampling Technique (SMOTE), and sliding window-based signal segmentation. Subsequently, an attention-based hybrid 1DCNN-BiLSTM model is employed for fetal hypoxia classification. Our proposed approach achieves impressive results with accuracy, sensitivity, specificity, F1 score, and quality index reaching 93.13%, 93.12%, 94.14%, 93.12%, and 93.53%, respectively. The proposed approach advances fetal hypoxia detection by addressing challenges associated with manual interpretation and data imbalance.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105259"},"PeriodicalIF":2.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A signal fingerprint feature extraction method based on decomposition and fusion for radar emitter individual identification","authors":"Wei Quan,&nbsp;Wenjing Cheng,&nbsp;Yike Yang,&nbsp;Haiquan Zhao,&nbsp;Zhaoyu Chen,&nbsp;Yunfan Luo","doi":"10.1016/j.dsp.2025.105257","DOIUrl":"10.1016/j.dsp.2025.105257","url":null,"abstract":"<div><div>Radar emitter individual identification is one of the key technologies of modern electronic countermeasure reconnaissance and electronic intelligence. With the advancement of radar technology and the increasingly complex electromagnetic environment, existing methods for identifying emitter are gradually becoming unable to meet the performance requirements of modern radar individual identification. Aiming at improving the adaptability of feature extraction for non-cooperative radar emitter signals and the robustness of individual identification in the complex modern electronic warfare environment, a signal fingerprint feature extraction method based on decomposition and fusion is proposed. It firstly integrates signal decomposition and scattering convolution networks (SCN) to adaptively extract the multi-scale intra-pulse feature of the signal, while removing the potential noise of the redundant component by energy proportion. And then a deep feature fusion model based on multi-head self-attention and residual connection is proposed to fuse the multi-scale features and the time domain features to further extract signal fingerprint of radar emitter. Experimental results based on the real radar emitter signals demonstrate that the identification method proposed in this paper can more effectively extract signal fingerprint features and the identification accuracy reaches 96.45%, which outperforms other existing identification methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105257"},"PeriodicalIF":2.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uniform allowance model built on the ordered and disordered features of corresponding points","authors":"Jingyu Sun ,&nbsp;Yadong Gong ,&nbsp;Songhua Li ,&nbsp;Chuang Zuo ,&nbsp;Zichen Zhao ,&nbsp;Jibin Zhao ,&nbsp;Hongyao Zhang ,&nbsp;Ming Cai","doi":"10.1016/j.dsp.2025.105256","DOIUrl":"10.1016/j.dsp.2025.105256","url":null,"abstract":"<div><div>Registration is the basis for visual guidance in automated machining processes. This paper focuses on models which are with similar spatial structures. Using bounding boxes to represent outer contours, we extract sparse feature points from point clouds. In this process, matching results are critically affected by erroneous point pairs. Therefore, this paper introduces the Kullback-Leibler (K-L) divergence into the topography evaluation function. A sequential motion-invariant matrix is added to the function to describe the corresponding relationship. To even out the machining allowance, we propose a fine registration method. It considers minimizing variance in the allowance and tangent distance between corresponding points. Meanwhile, the judge criteria of similarity are proposed. They are based on the Hausdorff-Cosine similarity function. This function accounts for angles between neighboring point normals, reducing misidentification and ensuring correct counterparts are included in calculations. Compared with other algorithms, the method improves accuracy, speed of calculations, and ability to resist Gaussian noise. The resulting model ensures uniform allowance distribution. It's a visual prerequisite for further processing.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105256"},"PeriodicalIF":2.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}