Digital Signal Processing最新文献_第6页

GRdepth: Enrich feature with global information and self-iterative regulation network for monocular depth estimation GRdepth：利用全局信息和自迭代调节网络丰富特征，实现单目深度估计

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-01 DOI: 10.1016/j.dsp.2025.105434

Chenxing Xia , Aoqi Zhang , Xiuju Gao , Bin Ge , Kuan-Ching Li , Xianjin Fang , Xingzhu Liang , Yan Zhang

{"title":"GRdepth: Enrich feature with global information and self-iterative regulation network for monocular depth estimation","authors":"Chenxing Xia , Aoqi Zhang , Xiuju Gao , Bin Ge , Kuan-Ching Li , Xianjin Fang , Xingzhu Liang , Yan Zhang","doi":"10.1016/j.dsp.2025.105434","DOIUrl":"10.1016/j.dsp.2025.105434","url":null,"abstract":"<div><div>Monocular depth estimation (MDE) seeks to infer pixel-wise dense depth maps from a single RGB image. Recent methodologies predominantly utilize the encoder-decoder architecture to effectively extract and analyze multi-scale features. However, they tend to ignore the important role that high-level features with rich global information play in MDE, resulting in a poor understanding of the overall structure of the scene by the model. Based on this, we propose a novel encoder-decoder framework called GRdepth, which includes a cross large scale feature enhancement (CLSE) module and an iterative regulation decoder (IRD). Specifically, the CLSE module is designed to use high-level features, enriched with global information extracted by a global information aggregation (GIA) unit, to guide the enhancement of multi-scale feature maps produced by the encoder. This enhancement is achieved through a cross large scale feature fusion (CLSF) unit built from channel attention and spatial attention to refine low-level features with high-level information. The IRD is tailored for MDE based on classification-regression which mainly utilizes a bin width self-regulation (SRbins) unit to adjust the width of the initial bins predicted with the bottleneck features. This adjustment is guided by bin width predicted by an iterative adaptive feature fusion (IAFF) unit at each level, effectively combining global information and local information for more accurate bin width and bin centers. Extensive experiments on the indoor dataset NYU-Depth-v2 and SUN-RGBD and on the outdoor dataset KITTI demonstrate that our method can achieve comparable state-of-the-art (SOTA) results.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105434"},"PeriodicalIF":2.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534880","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}

引用次数: 0

ACENet: Adaptive correlation-enhanced network for multivariate time series forecasting ACENet：多变量时间序列预测的自适应相关增强网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-01 DOI: 10.1016/j.dsp.2025.105424

Yupeng Wu , Muzhou Hou , Haokun Hu

{"title":"ACENet: Adaptive correlation-enhanced network for multivariate time series forecasting","authors":"Yupeng Wu , Muzhou Hou , Haokun Hu","doi":"10.1016/j.dsp.2025.105424","DOIUrl":"10.1016/j.dsp.2025.105424","url":null,"abstract":"<div><div>A multitude of practical applications necessitate the utilization of multivariate time series forecasting techniques, including the issuance of extreme weather warnings and the formulation of energy consumption plans. However, time series data frequently display intricate intra- and inter-series correlations, rendering modelling and forecasting particularly challenging due to these complex dependencies. The comprehension and representation of these multi-level interactions represent a fundamental research challenge, one that is also of paramount importance in numerous application domains. The extant literature has a restricted focus on capturing correlations within periodic time intervals at disparate time scales and between these intervals. To address these challenges, we propose the Adaptive Correlation-Enhanced Network (ACENet). The model begins by extracting multiple significant period lengths through Fast Fourier Transform (FFT) and segmenting the time series accordingly. At each temporal scale, three dedicated correlation matrices - capturing feature-wise correlations within periods, timestamp-wise correlations within periods, and cross-period correlations respectively - work in concert to enhance periodic pattern learning. The framework then employs an adaptive weighting mechanism to dynamically balance intra-period and inter-period correlations, ultimately generating the final prediction through this hierarchical integration of multi-scale temporal dependencies. Finally, experiments on several real-world datasets demonstrate the effectiveness of ACENet on MST datasets.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105424"},"PeriodicalIF":2.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549078","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}

引用次数: 0

A novel color image encryption algorithm based on infinite collapse map and hierarchical strategy 一种基于无限折叠映射和分层策略的彩色图像加密算法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-01 DOI: 10.1016/j.dsp.2025.105428

Yonghui Huang, Qilin Zhang, Yongbiao Zhao

{"title":"A novel color image encryption algorithm based on infinite collapse map and hierarchical strategy","authors":"Yonghui Huang, Qilin Zhang, Yongbiao Zhao","doi":"10.1016/j.dsp.2025.105428","DOIUrl":"10.1016/j.dsp.2025.105428","url":null,"abstract":"<div><div>Chaos-based image encryption algorithms are important for information security, but current chaotic systems and encryption algorithms still have optimization potential. This paper proposes a novel one-dimensional improved composite chaotic map (1D-ICCM) to enhance chaos performance and the efficiency of generating chaotic sequences. The dynamic characteristics of the 1D-ICCM are analyzed in depth, demonstrating favorable chaotic properties. Based on this, we introduce a hierarchical strategy image encryption algorithm (HS-IEA) that combines 1D-ICCM and the Logistic map to improve the robustness and security of image encryption algorithms. The algorithm begins by integrating the image at the pixel level and performing secondary diffusion on the integrated sequence. After restoring the color channel matrices, the image is encrypted at the pixel level using bidirectional dynamic scrambling. Then, bit-plane decomposition is applied. To handle large amounts of data at the bit level, high-order and low-order bit planes are processed separately: high-order planes are encrypted using bit-level diffusion, while low-order planes use bit-plane rotation. Finally, the encrypted bit-planes and color channel matrices are merged for two-layer encryption. Experimental results and security evaluations confirm that the HS-IEA significantly improves image encryption's robustness, security, and performance.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105428"},"PeriodicalIF":2.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524005","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}

引用次数: 0

Design and performance analysis of high-data-rate multi-carrier three-dimensional index modulation quadrature chaos shift keying system 高数据速率多载波三维指数调制正交混沌移键控系统的设计与性能分析

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-01 DOI: 10.1016/j.dsp.2025.105429

Lifang He, Yanan Hu, Xibiao Chen

引用次数: 0

Robust adaptive beamforming for cylindrical conformal arrays with sidelobe suppression 具有旁瓣抑制的圆柱共形阵列鲁棒自适应波束形成

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-30 DOI: 10.1016/j.dsp.2025.105431

Mingcheng Fu , Zhi Zheng , Wen-Qin Wang

{"title":"Robust adaptive beamforming for cylindrical conformal arrays with sidelobe suppression","authors":"Mingcheng Fu , Zhi Zheng , Wen-Qin Wang","doi":"10.1016/j.dsp.2025.105431","DOIUrl":"10.1016/j.dsp.2025.105431","url":null,"abstract":"<div><div>Robust adaptive beamforming (RAB) using conformal arrays has recently attracted lots of interest, because they can provide enhanced beam coverage and increased robustness against the mismatch problem. However, the conventional RAB methods for conformal arrays usually cause a high sidelobe level. In this paper, we develop a RAB algorithm for conformal arrays with sidelobe suppression. In this algorithm, we devise an adaptive beamformer with sidelobe suppression using cylindrical uniform conformal arrays. To enhance the robustness of adaptive beamformer, we reconstruct the interference-plus-noise covariance matrix (INCM) by estimating the interference steering vectors (SVs), and formulate a constrained optimization problem to correct the signal-of-interest SV. Moreover, we analyze the influence of penalty factor on the proposed beamformer, and offer the method to find the optimal penalty factor. In contrast to the existing RAB methods, our algorithm achieves higher output signal-to-interference-plus-noise ratio as well as a lower sidelobe level. Numerical results illustrate the superiority of the proposed algorithm over the existing RAB techniques for conformal arrays.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105431"},"PeriodicalIF":2.9,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518634","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}

引用次数: 0

Performance analysis of non-linear energy harvesting cognitive two-way relay network based on NOMA 基于NOMA的非线性能量收集认知双向中继网络性能分析

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-30 DOI: 10.1016/j.dsp.2025.105427

Yi Luo , Chuanying Xu , Bingzhen Li , Jian Dong , Lijia Wang , Xiangxiang Li , Qingyun Liu , Ying Lin

引用次数: 0

FDFNet: An efficient detection network for small-size surface defect based on feature differentiated fusion FDFNet：基于特征差异化融合的小尺寸表面缺陷高效检测网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-27 DOI: 10.1016/j.dsp.2025.105432

Jiajian Liu , Zhipeng Zhang , M.D. Kawsar Alam , Qing Cai , Chengyi Xia , Youhong Tang

{"title":"FDFNet: An efficient detection network for small-size surface defect based on feature differentiated fusion","authors":"Jiajian Liu , Zhipeng Zhang , M.D. Kawsar Alam , Qing Cai , Chengyi Xia , Youhong Tang","doi":"10.1016/j.dsp.2025.105432","DOIUrl":"10.1016/j.dsp.2025.105432","url":null,"abstract":"<div><div>In industrial production, real-time detection of steel surface defects is a crucial factor in quality assurance. Furthermore, steel surface defects are diverse and complex, particularly when additive manufacturing of metallic structures have been widely used in the industries. They are easily disturbed by background interference. The current defects detection algorithm requires further enhancement in terms of speed and accuracy. This work investigates the problem of fast and high-precision steel surface defects detection by a lightweight inspection model, FDFNet, based on YOLOv9. First, for the contrast between the defects and the background, a Contrast Limited Adaptive Histogram Equalization (CLAHE) data enhancement strategy is proposed. Second, a SPace-to-Depth Convolution (SPD-Conv) is constructed in the backbone, which retains more texture information and reduce the model parameters. Additionally, a Differentiated Fusion (DF) module is designed at the neck to highlight both the consistency and heterogeneity of feature maps across disparate scales. Finally, the findings of the experiment conducted on the data set of NEU-DET show that the proposed defects detection algorithm can improve the detecting speed and accuracy compared to those of the existing approaches including YOLOv9. To sum up, the proposed model demonstrates an optimal balance between detection efficiency and accuracy.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105432"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534878","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}

引用次数: 0

Covariance reformulations of the dynamic second-order smooth variable structure filter with applications to target tracking 动态二阶光滑变结构滤波器的协方差重构及其在目标跟踪中的应用

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-27 DOI: 10.1016/j.dsp.2025.105421

Salman Akhtar, Peyman Setoodeh, Ryan Ahmed, Saeid Habibi

{"title":"Covariance reformulations of the dynamic second-order smooth variable structure filter with applications to target tracking","authors":"Salman Akhtar, Peyman Setoodeh, Ryan Ahmed, Saeid Habibi","doi":"10.1016/j.dsp.2025.105421","DOIUrl":"10.1016/j.dsp.2025.105421","url":null,"abstract":"<div><div>A popular filter in target tracking is the Kalman Filter (KF). However, its performance degrades when modeling error is present and it may become unstable. Target maneuvers introduce modeling errors. The Smooth Variable Structure Filter (SVSF) is a robust filter formulated based on variable structure system theory to address modeling errors, which are common in practice. This paper reformulates the covariance of an SVSF variant known as the Dynamic Second-Order Smooth Variable Structure Filter (DSO-SVSF). It is reformulated because the current covariance of that filter is approximate, and in this work, an exact covariance is derived. An accurate filter covariance is necessary for data association in target tracking. Although the DSO-SVSF does not require a covariance to update the state, the covariance is necessary for target tracking to perform data association. This paper involves the following original theoretical contributions: i) covariance reformulation of the DSO-SVSF for linear systems with square and non-square output matrices, and ii) formulation of a Probabilistic Data Association Filter (PDAF) that uses the reformulated covariance. The applied contributions are: iii) application of the proposed covariance for data association in target tracking, and iv) comparison of the target tracking performance of the proposed PDAF to other PDAFs in simulations. The proposed covariance is referred to as Stochastic Gain Covariance (SGC). The proposed PDAF is applied to perform target tracking in simulations. The baselines include the KF-based formulation of PDA, a PDAF that employs the DSO-SVSF and its approximate covariance, and a PDAF that uses the original Second-Order SVSF (SO-SVSF) and its approximate covariance.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105421"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518633","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}

引用次数: 0

Beam convergence and 2-D imaging using orthogonal frequency diversity-based synthetic vortex waves 基于正交频率分集的合成涡旋波的波束收敛和二维成像

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-27 DOI: 10.1016/j.dsp.2025.105419

Sihui Chen, Yi Liao, Haonan Tan

引用次数: 0

MSHF-YOLO: Cotton growth detection algorithm integrated multi-semantic and high-frequency features MSHF-YOLO：融合多语义和高频特征的棉花生长检测算法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-27 DOI: 10.1016/j.dsp.2025.105423

Jiahuan Luo , Qunyong Wu , Yuhang Wang , Zhan Zhou , Zihao Zhuo , Hengyu Guo

{"title":"MSHF-YOLO: Cotton growth detection algorithm integrated multi-semantic and high-frequency features","authors":"Jiahuan Luo , Qunyong Wu , Yuhang Wang , Zhan Zhou , Zihao Zhuo , Hengyu Guo","doi":"10.1016/j.dsp.2025.105423","DOIUrl":"10.1016/j.dsp.2025.105423","url":null,"abstract":"<div><div>Accurate monitoring of cotton growth is essential for precision agriculture. However, existing deep learning-based object detection models often underperform in complex field environments due to challenges such as occlusion and low contrast. To address these limitations, we propose MSHF-YOLO, an improved detection framework based on YOLOv8. The model incorporates a Multi-Semantic Spatial and Channel Attention (MSCA) module in the backbone to enhance feature representation. Additionally, we replace traditional upsampling and downsampling operations in the neck with DySample and Adaptive Wavelet Down (AWD) modules to preserve high-frequency information. A High-frequency boost (HB) module is further introduced in the detection head to enhance detail sensitivity. Experimental results demonstrate that MSHF-YOLO achieves [email protected] of 86.0% and [email protected] of 68.2%, outperforming the baseline by 5.5% and 3.5%, respectively, while reducing model size by 12.5%. These results highlight the model's effectiveness and potential for robust cotton growth monitoring.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105423"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524006","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}

引用次数: 0