Digital Signal Processing最新文献_第9页

WaveU -Net: Multi-scale wavelet framework for robust recovery of continuous pressure signals in mud pulse telemetry WaveU -Net：用于泥浆脉冲遥测中连续压力信号鲁棒恢复的多尺度小波框架

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-05 DOI: 10.1016/j.dsp.2025.105853

Qingfeng Zeng , Yanfeng Geng , Shu Jiang , Weiliang Wang

Mud pulse telemetry (MPT) enables real-time transmission of downhole data during drilling operations. As the transmission distance increases, the received continuous pressure signals undergo significant attenuation. Moreover, strong periodic pump interference, random noise, and complex multipath propagation in the MPT system introduce three major challenges: (1) dynamic spectral overlap between signal and noise, (2) periodic disturbances with spectral drift, and (3) complex multi-scale temporal-frequency characteristics of the noise. These effects severely degrade signal quality, making accurate recovery particularly difficult for traditional model-based and learning-based denoising methods. To address these challenges, a lightweight neural network architecture named WaveU-Net is proposed. It consists of three major aspects: (1) To address dynamic spectral overlap between signal and noise, a learnable wavelet denoising network (LWDNet) is incorporated. By adaptively learning wavelet filters, LWDNet enables the model to track and separate time-varying overlapping frequency bands, thereby enhancing the extraction of weak signals from strong, spectrally mixed interference; (2) To cope with periodic noise and spectral drift, a frequency-domain contrast regularization (FCR) loss is introduced. This loss explicitly enforces separation between signal and noise in the frequency domain, improving the model’s ability to distinguish useful components even under shifting interference; (3) To effectively exploit information at multiple temporal and frequency scales, a compact U-Net architecture with frequency-aware skip connections is employed, which facilitates adaptive multi-scale feature fusion, further improving denoising performance. Experimental results on field-collected datasets demonstrate that WaveU-Net achieves an average reduction of 38.85% in mean squared error (MSE) compared to standard U-Net models. Moreover, WaveU-Net outperforms recent state-of-the-art (SOTA) models in terms of signal reconstruction quality, while requiring significantly fewer parameters and reducing computational complexity.

泥浆脉冲遥测技术（MPT）可以在钻井作业期间实时传输井下数据。随着传输距离的增加，接收到的连续压力信号衰减明显。此外，在MPT系统中，强周期泵浦干扰、随机噪声和复杂多径传播带来了三大挑战：(1)信号与噪声之间的动态频谱重叠；(2)具有频谱漂移的周期性干扰；(3)噪声的复杂多尺度时频特性。这些影响严重降低了信号质量，使得传统的基于模型和基于学习的去噪方法难以准确恢复。为了应对这些挑战，我们提出了一种名为WaveU-Net的轻量级神经网络架构。它主要包括三个方面：(1)为了解决信号和噪声之间的动态频谱重叠，引入了可学习的小波去噪网络（LWDNet）。通过自适应学习小波滤波器，LWDNet使模型能够跟踪和分离时变重叠频带，从而增强从强频谱混合干扰中提取弱信号的能力；(2)为了应对周期性噪声和频谱漂移，引入频域对比正则化（FCR）损失。这种损失明确地加强了频域信号和噪声之间的分离，提高了模型在移位干扰下区分有用成分的能力；(3)为了有效利用多时间和多频率尺度的信息，采用了一种紧凑的U-Net结构，采用频率感知跳跃连接，便于自适应多尺度特征融合，进一步提高了去噪性能。现场采集数据集的实验结果表明，与标准U-Net模型相比，WaveU-Net模型的均方误差（MSE）平均降低了38.85%。此外，WaveU-Net在信号重建质量方面优于最新的最先进（SOTA）模型，同时需要的参数大大减少，计算复杂度也大大降低。

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引用次数: 0

Joint equalization and power allocation for UAV-assisted RSMA-OTFS transmission over doubly dispersive channels 双色散信道无人机辅助RSMA-OTFS传输的联合均衡与功率分配

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-03 DOI: 10.1016/j.dsp.2025.105870

Ghania Khraimech , Fatiha Merazka , Mustapha Benssalah

With the emergence of sixth-generation (6G) networks, unmanned aerial vehicles (UAVs) are expected to play a key role in enhancing coverage, connectivity, and capacity, particularly in highly dynamic environments. However, UAV-based communication systems face significant challenges such as high mobility and severe Doppler effects, interference management, and link reliability, due to the doubly selective nature of wireless channels, characterized by both time and frequency selective variations resulting from high mobility. To address these issues, this paper proposes a downlink transmission framework that integrates rate-splitting multiple access (RSMA) with orthogonal time frequency space (OTFS) modulation. This combination enhances the communication reliability and the spectral efficiency in UAV-assisted networks. The channel is modeled using integer-valued delay-Doppler parameters to reflect realistic high-mobility conditions. To reduce the inter-symbol interference (ISI) and enhance the detection performance, a low-complexity equalization algorithm based on QR decomposition with Givens rotations is introduced, tailored to the sparse structure of the OTFS channel matrix. Additionally, a bi-objective power allocation strategy for RSMA is formulated to simultaneously minimize bit error rate (BER) and maximize throughput. The non-dominated sorting genetic algorithm II (NSGA-II) is used to find Pareto-optimal solutions suited to varying system demands. Comprehensive simulations further compare the proposed OTFS-RSMA system with benchmark schemes, namely OTFS-NOMA and OFDMA-RSMA, under identical conditions, showing superior sum-rate and BER performance.

随着第六代（6G）网络的出现，无人驾驶飞行器（uav）预计将在增强覆盖、连通性和容量方面发挥关键作用，特别是在高动态环境中。然而，基于无人机的通信系统面临着重大挑战，如高移动性和严重的多普勒效应、干扰管理和链路可靠性，这是由于无线信道的双重选择性，其特点是高移动性导致的时间和频率选择性变化。为了解决这些问题，本文提出了一种集成了分频多址（RSMA）和正交时频空间（OTFS）调制的下行传输框架。这种组合提高了无人机辅助网络的通信可靠性和频谱效率。采用整数值延迟多普勒参数对信道进行建模，以反映实际的高迁移率条件。为了减少码间干扰，提高检测性能，针对OTFS信道矩阵的稀疏结构，提出了一种基于给定旋转QR分解的低复杂度均衡算法。此外，提出了RSMA的双目标功率分配策略，以实现误码率最小化和吞吐量最大化。非支配排序遗传算法II （NSGA-II）用于寻找适合不同系统需求的pareto最优解。综合仿真进一步将所提出的OTFS-RSMA系统与基准方案OTFS-NOMA和OFDMA-RSMA在相同条件下进行了比较，显示出优越的和速率和误码率性能。

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引用次数: 0

WHTMLDet: a wind turbine blade defect detection method integrating channel split-and-conquer strategy and a spatial perception mechanism WHTMLDet：一种结合通道分治策略和空间感知机制的风力机叶片缺陷检测方法

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-03 DOI: 10.1016/j.dsp.2025.105878

Feiyang Lv , Yuanyuan Wang , Rugang Wang , Binghe Sun , Feng Zhou , Xuesheng Bian

To address the limitations of existing object detection algorithms in wind turbine blade defect detection–namely insufficient feature representation, high computational complexity, and poor robustness in complex backgrounds-this paper proposes a Wind Turbine Heterogeneous Multi-path Learning-based Defect Detector (WHTMLDet), which leverages a Channel Split-and-Conquer strategy and a Spatial Perception mechanism. The algorithm incorporates a Heterogeneous Fusion-Residual Convolution (HFRConv) module to capture local-to-global features across different receptive fields, effectively mitigating information loss caused by traditional downsampling while reducing computational complexity. A Triple-source Heterogeneous Group Convolution (THGConv) module is designed to enhance the semantic representation of minor defects through multi-path heterogeneous design and dynamic fusion. The Multi-path Gated Spatial Pyramid Pooling (MGSPPF) module significantly improves multi-scale defect modeling capability. Furthermore, a Location-Sensitive Adaptive Incentive Attention (LSAIAT) mechanism integrates depthwise separable convolutions with position-sensitive attention to improve the localization accuracy of low-contrast defects and enhances feature discriminability in low signal-to-noise ratio regions through stacked Adaptive Spatial Focusing Residual Blocks, thereby partially mitigating interference from complex backgrounds. Experimental results on wind turbine blade and high-voltage power line insulator defect datasets show that the WHTMLDet-s model achieves [email protected] of 88.6% and 95.9%, respectively, with a computational complexity of only 16.8 GFLOPs. Compared to the YOLOv13-s model, [email protected] increases by 6.2% and 0.5%, while GFLOPs decrease by 3.9. The results demonstrate that the proposed algorithm offers significant advantages in accuracy, robustness, and computational efficiency.

针对现有目标检测算法在风电叶片缺陷检测中的局限性，即特征表示不足、计算复杂度高、复杂背景下鲁棒性差等问题，本文提出了一种基于多路径学习的风电叶片缺陷检测算法（WHTMLDet），该算法利用通道分治策略和空间感知机制。该算法采用异质融合残差卷积（HFRConv）模块捕获不同接受域的局部到全局特征，有效减轻了传统下采样带来的信息丢失，同时降低了计算复杂度。设计了三源异构群卷积（THGConv）模块，通过多路径异构设计和动态融合，增强了微小缺陷的语义表示。多路径门控空间金字塔池（MGSPPF）模块显著提高了多尺度缺陷建模能力。此外，位置敏感自适应激励注意（LSAIAT）机制将深度可分卷积与位置敏感注意相结合，提高了低对比度缺陷的定位精度，并通过堆叠自适应空间聚焦残差块增强了低信噪比区域的特征可分辨性，从而部分缓解了复杂背景的干扰。在风力发电机叶片和高压电力线绝缘子缺陷数据集上的实验结果表明，whtmldt -s模型的[email protected]分别达到了88.6%和95.9%，计算复杂度仅为16.8 GFLOPs。与YOLOv13-s模型相比，[email protected]分别提高了6.2%和0.5%，而GFLOPs降低了3.9。结果表明，该算法在精度、鲁棒性和计算效率方面具有显著优势。

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引用次数: 0

Performance analysis and robust DOA estimation using acoustic vector sensor array under non-orthogonal deviation 非正交偏差下声矢量传感器阵列性能分析及鲁棒DOA估计

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.dsp.2025.105867

Weidong Wang , Tianyou Wang , Hui Li , Wentao Shi , Wasiq Ali

In this paper, the problem of direction of arrival (DOA) estimation under the non-orthogonal deviation (NOD) in an acoustic vector sensor array (AVSA) is systematically addressed. First, by incorporating NOD information into the ideal AVSA model, two AVSA models with NOD are established. Subsequently, closed-form expressions for DOA estimation bias, the Cramér-Rao lower bound (CRLB), and the root mean square error (RMSE) are analytically derived for scenarios where each AVS exhibits NOD to illustrate the degrading influence of NOD on DOA estimation accuracy. To mitigate the effect of NOD, an innovative optimal modification matrix construction (OMMC) method is proposed. The NOD range of each AVS is initially coarsely estimated using prior information from a known auxiliary source and the theoretical RMSE. Based on the estimated deviation range, an overcomplete redundant correction matrix is constructed, which is used to calibrate the measurement data of each AVS. The optimal correction matrix is selected by minimizing the deviation between the estimated and true DOAs, and a global correction matrix for the entire array is formed by extracting the optimal correction sub-matrix for each AVS, thereby enabling accurate array calibration. A comprehensive performance evaluation is conducted through extensive simulations, where the proposed OMMC method is demonstrated to significantly outperform existing techniques, especially in challenging environments with large NOD or limited snapshot.

本文系统地研究了声矢量传感器阵列（AVSA）在非正交偏差（NOD）条件下的到达方向估计问题。首先，将NOD信息引入理想AVSA模型，建立了两个带NOD的AVSA模型。随后，在每个AVS都显示NOD的情况下，解析导出了DOA估计偏差、cram - rao下限（CRLB）和均方根误差（RMSE）的封闭表达式，以说明NOD对DOA估计精度的退化影响。为了减轻NOD的影响，提出了一种创新的最优修正矩阵构造（OMMC）方法。每个AVS的NOD范围最初是使用已知辅助源的先验信息和理论RMSE粗略估计的。根据估计的偏差范围，构造过完备冗余校正矩阵，用于标定各AVS的测量数据。通过最小化估计doa与真实doa之间的偏差来选择最优校正矩阵，并通过提取每个AVS的最优校正子矩阵形成整个阵列的全局校正矩阵，从而实现精确的阵列校准。通过广泛的模拟进行了全面的性能评估，其中提出的OMMC方法被证明明显优于现有技术，特别是在具有大NOD或有限快照的挑战性环境中。

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引用次数: 0

Multi-component LFM signal representation method under impulsive noise: Principle, method and application 脉冲噪声下多分量LFM信号表示方法：原理、方法及应用

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.dsp.2025.105868

Weiwei Shang , Yong Guo , Lidong Yang

In the presence of impulse noise modeled by the α-stable distribution, conventional noise suppression methods inevitably introduce cross-terms when processing multi-component signal, leading to significant deviations in subsequent signal representation and parameter estimation. To effectively address this issue, this paper develops an impulsive noise suppression technique based on K-medoids cluster (KMC), and proposes two representation methods for multi-component linear frequency modulation (LFM) signal under impulse noise. Firstly, the reason for cross-terms introduction is analyzed from the mathematical perspective, and subsequently a KMC-based impulsive noise suppression technology is developed. Secondly, KMC-fractional Fourier transform (KMC-FRFT) and KMC-synchrosqueezing transform (KMC-SST) are proposed, enabling precise characterization of multi-component LFM signal in the fractional domain and time-frequency domain, respectively. Finally, KMC-FRFT is applied to the parameter estimation of multi-component LFM signal under impulsive noise. Simulation experiments demonstrate that, from fractional domain and time-frequency domain, KMC not only suppresses high-amplitude burst impulsive noise, but also completely resolves the cross-terms problem inherent in existing methods. On this basis, under impulsive noise, KMC-FRFT and KMC-SST effectively capture the fractional spectral characteristic and time-frequency distribution characteristic of multi-component LFM signal from complementary perspectives. For both simulated and measured impulsive noise, RMSE demonstrates that KMC-FRFT can accurately estimate the parameters of weak component signal when GSNR  ≥  6dB, addressing the issue of incorrect parameter estimation caused by the cross-terms interference.

在α-稳定分布建模的脉冲噪声存在的情况下，传统的噪声抑制方法在处理多分量信号时不可避免地引入交叉项，导致后续的信号表示和参数估计出现较大偏差。为了有效地解决这一问题，本文发展了一种基于k -媒质聚类（KMC）的脉冲噪声抑制技术，并提出了两种多分量线性调频（LFM）信号在脉冲噪声下的表示方法。首先从数学的角度分析了交叉项引入的原因，然后提出了一种基于kmc的脉冲噪声抑制技术。其次，提出了kmc -分数阶傅里叶变换（KMC-FRFT）和kmc -同步压缩变换（KMC-SST），分别在分数域和时频域对多分量LFM信号进行精确表征。最后，将KMC-FRFT应用于脉冲噪声下多分量LFM信号的参数估计。仿真实验表明，从分数域和时频域两方面来看，KMC不仅能够抑制高幅值突发脉冲噪声，而且完全解决了现有方法固有的交叉项问题。在此基础上，在脉冲噪声下，KMC-FRFT和KMC-SST从互补的角度有效捕获了多分量LFM信号的分数阶谱特征和时频分布特征。对于模拟和测量的脉冲噪声，RMSE均表明，当GSNR ≥ 6dB时，KMC-FRFT可以准确估计弱分量信号的参数，解决了交叉项干扰导致的参数估计错误的问题。

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引用次数: 0

Improved reinforcement learning-based joint decision-making of detection modes and transmit power for LPI radar 基于改进强化学习的LPI雷达探测模式与发射功率联合决策

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.dsp.2025.105872

Huilong Tang , Wei Wang , Zhiwei Pu , Jianlin Wei , Wang Zhang

Modern airborne radar reconnaissance systems employ range-divided multi-stage operations (e.g., passive detection, active detection, and active identification). However, traditional low probability of intercept (LPI) radar designs focus on optimizing performance for individual reconnaissance stages, resulting in suboptimal overall detection capability. Meanwhile, multi-stage operations yield excessive invalid and suboptimal actions, creating action space redundancy that deteriorates learning efficiency. This paper proposes a reinforcement learning (RL)-based joint decision-making method for enhanced detection performance, incorporating improved RL exploration mechanisms to accelerate learning. Firstly, adversarial strategies from each stage are integrated to construct a joint decision-making framework for detection modes and transmit power (JD-DMTP). Based on this framework, the RL elements are designed to enhance detection performance under LPI constraints. Secondly, we propose the trainable suboptimal action mask (TSAM), equipped with suboptimal action elimination criteria, to filter out both invalid and suboptimal actions, thereby improving learning efficiency. Finally, the experimental results validate the effectiveness of the JD-DMTP, showing 6.46×/4.04× higher hit value ratio and 1.52×/1.32× better successful decision-making rate (ideal/non-ideal environment) compared to the minimum-transmit-power baseline. The TSAM achieves comparable performance to the trainable action mask (TAM) baseline with only 25% of the required training iterations.

现代机载雷达侦察系统采用距离分割多阶段操作（例如，被动探测、主动探测和主动识别）。然而，传统的低截获概率（LPI）雷达设计侧重于优化单个侦察阶段的性能，导致整体探测能力不理想。同时，多阶段操作会产生过多无效和次优动作，造成动作空间冗余，降低学习效率。本文提出了一种基于强化学习（RL）的联合决策方法来提高检测性能，并结合改进的RL探索机制来加速学习。首先，整合各阶段的对抗策略，构建探测模式和发射功率联合决策框架（JD-DMTP）。基于该框架，设计了RL元素，以提高LPI约束下的检测性能。其次，我们提出了可训练次优动作掩模（TSAM），该掩模具有次优动作消除准则，可以过滤掉无效动作和次优动作，从而提高学习效率。最后，实验结果验证了JD-DMTP的有效性，与最小发射功率基线相比，在理想/非理想环境下，JD-DMTP的命中率提高了6.46×/4.04×，成功决策率提高了1.52×/1.32×。TSAM只需要25%的训练迭代就可以达到与可训练动作掩码（TAM）基线相当的性能。

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引用次数: 0

MLME -Net: A high-accuracy model for surgical instrument detection via multi-level MixEnhance network MLME -Net：通过多级MixEnhance网络进行手术器械检测的高精度模型

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.dsp.2025.105857

Haikun Chen , Shuwan Pan , Qin Ye , Yuanda Lin , Lixin Zheng

Accurate identification and tracking of surgical instruments are critical for computer-assisted minimally invasive surgery. To improve the detection accuracy of surgical instruments, we propose a Multi-Level MixEnhance Network (MLME-Net), whose core component is a novel Multi-branch Multi-Level MixEnhance (M²LME) module. The M²LME module employs a multi-level attention-guided architecture for weight redistribution, specifically designed to strengthen discriminative feature extractive capabilities for fine-grained through multi-level feature integration. To further enhance performance, MLME-Net integrates two critical components: the Multi-Order Gated Aggregation Block (MOGAB) for cross-complexity feature interaction through gating mechanisms, and the Coordinate Attention (CA) module for accurate instrument localization in complex surgical environments. Additionally, we address class imbalance among surgical instruments by introducing Adaptive Threshold Focal Loss (ATFL), which dynamically adjusts loss weights through an adaptive mechanism. Experimental results demonstrate that MLME-Net achieves a mean Average Precision at 50% IoU (mAP50) of 94.9% on the m2cai16-tool-locations dataset, outperforming the baseline by 1.1%. Notably, detection accuracy of the Grasper and Irrigator classes has improved by 3.3% and 2.6%, respectively.

准确识别和跟踪手术器械是计算机辅助微创手术的关键。为了提高手术器械的检测精度，我们提出了一种多层次混合增强网络（MLME-Net），其核心组件是一种新型的多分支多层次混合增强（M2LME）模块。M2LME模块采用多级注意引导架构进行权重再分配，通过多级特征集成，增强细粒度的判别特征提取能力。为了进一步提高性能，MLME-Net集成了两个关键组件：通过门控机制进行跨复杂性特征交互的多阶门控聚合块（MOGAB），以及在复杂手术环境中精确定位仪器的协调注意（CA）模块。此外，我们通过引入自适应阈值焦点损失（ATFL）来解决手术器械之间的类别不平衡，该功能通过自适应机制动态调整损失权重。实验结果表明，在m2cai16-tool-locations数据集上，MLME-Net在50% IoU （mAP50）下的平均精度为94.9%，比基线高1.1%。值得注意的是，“抓草者”和“灌溉者”的检测准确率分别提高了3.3%和2.6%。

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引用次数: 0

DEFusion: Dynamic parameter tuning for infrared-visible image fusion in day-night alternating environments 融合：昼夜交替环境下红外-可见光图像融合的动态参数调整

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.dsp.2025.105874

Yaochen Liu, Mingyue Han, Jianwei Fan

Infrared and visible image fusion aims to generate a fused image with rich texture detail information around the clock. However, existing fusion methods adopt fixed fusion to integrate features from different modalities, making them difficult to adapt to drastic illumination variations in day-night alternating scenes. To address this challenge, this paper proposes a dynamic parameter tuning for infrared and visible image fusion (DEFusion), which can flexibly adjust network parameters based on the differences in information of input images, thus effectively adapting to the complex characteristics of alternating day-night scenes. Specifically, DEFusion designs dynamic parameter tuning sub-networks that dynamically adjust the contribution of features from different modalities based on the feature information of the input image. Meanwhile, each layer of the network is equipped with an infrared and visible dual-information extraction module and a bidirectional cross-modal enhancement module. The former is responsible for preserving the unique features of unimodal images, while the latter achieves feature complementation and enhancement between modalities by performing bidirectional cross-modal interactions in parallel. In addition, the network introduces a dynamic selection algorithm, which adaptively adjusts the propagation weights of each module by sensing scene changes in real-time, so as to construct the optimal fusion path that fits the current day-night scene characteristics. On the public MSRS and TNO datasets, this method achieves maximum improvements of 59.9 % and 68.0 % in the Average Gradient (AG) metric, and 32.3 % and 37.4 % in the Spatial Frequency (SF) metric, respectively. Both qualitative and quantitative evaluations demonstrate that our model exhibits strong robustness in alternating day-night scenes.

红外图像与可见光图像融合的目的是全天候生成具有丰富纹理细节信息的融合图像。然而，现有的融合方法采用固定融合来整合不同模态的特征，难以适应昼夜交替场景中剧烈的光照变化。针对这一挑战，本文提出了一种红外与可见光图像融合（DEFusion）的动态参数调整方法，该方法可以根据输入图像信息的差异灵活调整网络参数，从而有效适应昼夜交替场景的复杂特征。具体来说，DEFusion设计了动态参数调整子网络，根据输入图像的特征信息动态调整来自不同模态的特征的贡献。同时，网络的每一层都配备了红外和可见光双信息提取模块和双向跨模态增强模块。前者负责保持单模态图像的独特特征，后者通过并行进行双向跨模态交互，实现模态之间的特征互补和增强。此外，该网络还引入了动态选择算法，通过实时感知场景变化，自适应调整各模块的传播权重，构建最优融合路径，以适应当前昼夜场景特征。在公开的MSRS和TNO数据集上，该方法在平均梯度（AG）度量上的最大改进率分别为59.9 %和68.0 %，在空间频率（SF）度量上的最大改进率分别为32.3 %和37.4 %。定性和定量评估表明，我们的模型在昼夜交替的场景中表现出很强的鲁棒性。

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引用次数: 0

Automatic detection of multiscale defects in selective laser melting prepared 3D lattice structures: A model with improved attention mechanism 选择性激光熔化制备三维晶格结构多尺度缺陷的自动检测：一种改进注意机制的模型

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.dsp.2025.105877

Yintang Wen , Shengli Xue , Yankai Feng , Yuyan Zhang

The 3D printing process is becoming more and more developed and lattice structure applications are more common, the products of the combination are seeing success in many fields. Selective Laser Melting (SLM) as an additive manufacturing technology, the defects in the process of generation have the characteristics of multiscale, high randomness, and multi-genre. This study presents a new model, combined with target detection to achieve highly accurate detection of lattice structure defects in 3D printing. Firstly, a new attention mechanism module, Transformer Bottleneck Attention Module (TBAM) is proposed to process the information by combining the multi-head self-attention mechanism and the channel attention module, which effectively solves the problem of difficult to recognize the multi-scale information of the defects of lattice structure. Secondly, the Custom Spatial-Channel Down-sampling (C-SCDown) module is proposed to improve the performance of processing defect location information and channel information through down sampling and residual linkage, and enhance the ability of the network to adaptively perform feature extraction on different image regions or channels. Finally, the Attentional Scale Sequence Fusion Head (ASF-Head) framework is introduced to improve the segmentation accuracy and segmentation speed of the model to enhance the detection performance. This paper names the model, TCA-YOLO, is named for its featured modules: TBAM, C-SCDown, and ASF-head. The present model realizes the detection of geometric distortion, geometric fracture and general types of defects, and achieves an average accuracy of 98.1% for 3D printed lattice structure defects, proving the effectiveness of the detection of the present model.

随着3D打印技术的日益发达和晶格结构的应用越来越普遍，其结合的产品在许多领域都取得了成功。选择性激光熔化（SLM）作为一种增材制造技术，其缺陷产生过程具有多尺度、高随机性和多类型的特点。本研究提出了一种新的模型，结合目标检测实现了3D打印中晶格结构缺陷的高精度检测。首先，提出了一种新的注意机制模块——变压器瓶颈注意模块（tham），将多头自注意机制与通道注意模块相结合，对缺陷信息进行处理，有效解决了晶格结构缺陷多尺度信息难以识别的问题；其次，提出自定义空间信道下采样（C-SCDown）模块，通过下采样和残差联动，提高网络对缺陷位置信息和信道信息的处理性能，增强网络对不同图像区域或信道的自适应特征提取能力。最后，引入注意尺度序列融合头（attention Scale Sequence Fusion Head, ASF-Head）框架，提高模型的分割精度和分割速度，提高检测性能。本文将该模型命名为TCA-YOLO，以其特征模块TBAM、C-SCDown和ASF-head命名。本模型实现了几何畸变、几何断裂和一般类型缺陷的检测，3D打印点阵结构缺陷的平均检测精度达到98.1%，证明了本模型检测的有效性。

{"title":"Automatic detection of multiscale defects in selective laser melting prepared 3D lattice structures: A model with improved attention mechanism","authors":"Yintang Wen , Shengli Xue , Yankai Feng , Yuyan Zhang","doi":"10.1016/j.dsp.2025.105877","DOIUrl":"10.1016/j.dsp.2025.105877","url":null,"abstract":"<div><div>The 3D printing process is becoming more and more developed and lattice structure applications are more common, the products of the combination are seeing success in many fields. Selective Laser Melting (SLM) as an additive manufacturing technology, the defects in the process of generation have the characteristics of multiscale, high randomness, and multi-genre. This study presents a new model, combined with target detection to achieve highly accurate detection of lattice structure defects in 3D printing. Firstly, a new attention mechanism module, Transformer Bottleneck Attention Module (TBAM) is proposed to process the information by combining the multi-head self-attention mechanism and the channel attention module, which effectively solves the problem of difficult to recognize the multi-scale information of the defects of lattice structure. Secondly, the Custom Spatial-Channel Down-sampling (C-SCDown) module is proposed to improve the performance of processing defect location information and channel information through down sampling and residual linkage, and enhance the ability of the network to adaptively perform feature extraction on different image regions or channels. Finally, the Attentional Scale Sequence Fusion Head (ASF-Head) framework is introduced to improve the segmentation accuracy and segmentation speed of the model to enhance the detection performance. This paper names the model, TCA-YOLO, is named for its featured modules: TBAM, C-SCDown, and ASF-head. The present model realizes the detection of geometric distortion, geometric fracture and general types of defects, and achieves an average accuracy of 98.1% for 3D printed lattice structure defects, proving the effectiveness of the detection of the present model.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"172 ","pages":"Article 105877"},"PeriodicalIF":3.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928502","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

Tunable polarization detection in nonzero-mean environment: Theoretical derivation and performance analysis 非零均值环境下的可调谐偏振检测：理论推导与性能分析

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.dsp.2025.105864

Haoqi Wu, Hongzhi Guo, Zhihang Wang, Zishu He

This paper addresses polarimetric adaptive detection of targets embedded in the nonzero-mean Gaussian environment with unknown mean vector (MV) and covariance matrix (CM). By adopting the generalized likelihood ratio test (GLRT) criterion, we derive two nonzero-mean polarimetric detectors, and then design a nonzero-mean tunable detector that includes the one-step and two-step GLRT tests. The proposed detectors are assessed from the aspects of the probability of detection (Pd) in both non-fluctuating and fluctuating target models and the probability of false alarm (Pfa). We derive the theoretical expressions for Pfa and Pd, which demonstrate that the proposed detectors achieve the constant false alarm rate (CFAR) property w.r.t. the MV and CM. In simulation experiments, we exploit the theoretical values and numerical simulation results to indicate the improvement of developed detectors in adaptive polarimetric detection. The results demonstrate the theoretical analyses on Pfa and Pd, verifying that the developed nonzero-mean polarimetric detectors achieve superior performance in Pd compared to the zero-mean counterparts. Further, it reveals that the proposed tunable detector can adjust the robustness or selectivity to mismatched signals.

本文研究了含有未知均值向量（MV）和协方差矩阵（CM）的非零均值高斯环境中嵌入目标的极化自适应检测。采用广义似然比检验（GLRT）准则，推导出两个非零均值极化检测器，并设计了包含一步和两步GLRT检验的非零均值可调检测器。从非波动和波动目标模型的检测概率（Pd）和虚警概率（Pfa）两方面对所提出的检测器进行了评估。我们推导了Pfa和Pd的理论表达式，证明了所提出的检测器在相对于MV和CM的情况下具有恒定虚警率（CFAR）的特性。在仿真实验中，我们利用理论值和数值模拟结果来说明所开发的探测器在自适应极化检测方面的改进。结果验证了对Pfa和Pd的理论分析，验证了所开发的非零平均极化检测器在Pd方面的性能优于零平均极化检测器。进一步表明，所提出的可调谐检测器可以调整对不匹配信号的鲁棒性或选择性。

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引用次数: 0