Digital Signal Processing最新文献_第2页

Secrecy performance of D2D assisted cooperative uplink NOMA system with optimal power allocation strategy 采用最优功率分配策略的 D2D 辅助合作上行 NOMA 系统的保密性能

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-08 DOI: 10.1016/j.dsp.2024.104860

Kankanala Kavitha, Suseela Vappangi

This paper investigates the physical-layer security (PLS) aspects of the uplink cooperative non-orthogonal multiple access (NOMA) system in the context of two user scenario. More specifically, this work employs device-to-device (D2D) pair users (i.e.,

D_{1}

, and

D_{2}

) to further improve the spectral efficiency (SE) of the proposed cooperative uplink NOMA (CU-NOMA) system. One D2D user acts as a decode-and-forward relay, improving the performance of both the cell-edge user (CU) and another D2D user i.e., (

D_{2}

). We analyze the secrecy performance of CU and D2 under perfect and imperfect successive interference cancellation (SIC), and optimizes power allocation (PA) to boost the performance. The proposed CU-NOMA system is evaluated in terms of its performance metrics like ergodic secrecy capacity (ESC), ergodic secrecy sum capacity (ESSC), non-zero secrecy capacity (NSC), effective secrecy throughput (EST), and secrecy outage probability (SOP) under the presence of an external eavesdropper (Eav). In addition, this work derives the closed-form analytical expressions of ESC, NSC, EST, and SOP metrics for both CU and

D_{2}

under perfect SIC (pSIC) and imperfect (ipSIC) cases in order to characterize the secrecy performance of the proposed secure CU-NOMA network. Further, the outcomes of the simulations are shown as evidence for both the validation of the mathematical analysis and the performance of the method being suggested. The simulation result analysis of this work infers that the secrecy performance of CU-NOMA system with optimal PA exhibits superior performance than that of the fixed PA scheme.

本文研究了双用户场景下上行链路合作非正交多址接入（NOMA）系统的物理层安全性（PLS）问题。更具体地说，这项工作采用了设备对设备（D2D）对用户（即 D1 和 D2），以进一步提高拟议的合作上行链路非正交多址接入（CU-NOMA）系统的频谱效率（SE）。一个 D2D 用户充当解码和前向中继，提高了小区边缘用户（CU）和另一个 D2D 用户（D2）的性能。我们分析了完美和不完美连续干扰消除（SIC）条件下 CU 和 D2 的保密性能，并优化了功率分配（PA）以提高性能。在外部窃听者（Eav）存在的情况下，对所提出的 CU-NOMA 系统的性能指标进行了评估，如遍历保密容量（ESC）、遍历保密总和容量（ESSC）、非零保密容量（NSC）、有效保密吞吐量（EST）和保密中断概率（SOP）。此外，这项研究还推导出了完美 SIC（pSIC）和不完美 SIC（ipSIC）情况下 CU 和 D2 的 ESC、NSC、EST 和 SOP 指标的闭式解析表达式，以描述所提出的安全 CU-NOMA 网络的保密性能。此外，仿真结果还证明了数学分析的有效性和所建议方法的性能。这项工作的仿真结果分析推断出，采用最佳功率放大器的 CU-NOMA 系统的保密性能比固定功率放大器方案更优越。

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引用次数: 0

Group-based weighted nuclear norm minimization for Cauchy noise removal with TV regularization 基于组的加权核规范最小化，利用 TV 正则化去除考奇噪声

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-08 DOI: 10.1016/j.dsp.2024.104836

Wen Gao , Jianguang Zhu , Binbin Hao

Cauchy noise, as a kind of impulsive and non-Gaussian noise, has recently received a lot of attention in the image processing. In this paper, we combine group-based low rank regularization and total variation (TV) regularization to propose a new hybrid variational model for Cauchy noise removal. In order to solve the proposed model, we develop an efficient alternating minimization method by incorporating the Chambolle projection algorithm, the weighted nuclear norm minimization algorithm, and Newton method. Numerical experiments demonstrate that the proposed method is superior to the existing state-of-the-art methods in terms of visual quality and quantitative measures.

考奇噪声作为一种脉冲噪声和非高斯噪声，近年来在图像处理领域受到广泛关注。在本文中，我们将基于组的低秩正则化和总变异（TV）正则化结合起来，提出了一种新的用于去除考基噪声的混合变异模型。为了求解所提出的模型，我们结合 Chambolle 投影算法、加权核规范最小化算法和牛顿法，开发了一种高效的交替最小化方法。数值实验证明，就视觉质量和定量指标而言，所提出的方法优于现有的最先进方法。

引用次数: 0

Multi-scale transformer with conditioned prompt for image deraining 带条件提示的多尺度变压器，用于图像推导

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-08 DOI: 10.1016/j.dsp.2024.104847

Xianhao Wu , Hongming Chen , Xiang Chen , Guili Xu

Recently, vision Transformers have made significant advancements in image deraining due to their ability to model non-local information. However, most existing methods do not fully explore and utilize the multi-scale properties of rain streaks, which are crucial for achieving high-quality image reconstruction. To address this limitation, we propose an effective image deraining method called MSPformer, which is based on a multi-scale Transformer with conditioned prompt. Specifically, MSPformer consists of two parallel branches, i.e., a base network and a condition network. Motivated by the recent wave of prompt learning, our condition network employs soft prompts to encode diverse rain degradation information, which is then used to dynamically modulate the base network in the deraining process. Furthermore, we also develop a multi-scale feature prompt fusion method that enables representations learned at different scales to effectively communicate with each other. Extensive experiments demonstrate that the proposed framework performs favorably against the state-of-the-art approaches on both synthetic and real-world benchmarks.

最近，视觉变换器因其对非局部信息的建模能力，在图像派生方面取得了重大进展。然而，大多数现有方法并没有充分挖掘和利用雨滴条纹的多尺度特性，而这些特性对于实现高质量的图像重建至关重要。针对这一局限性，我们提出了一种有效的图像推导方法--MSPformer，它基于多尺度变换器和条件提示。具体来说，MSPformer 由两个并行分支组成，即基础网络和条件网络。在最近的提示学习浪潮的推动下，我们的条件网络采用软提示来编码不同的雨水降解信息，然后在降解过程中用于动态调节基础网络。此外，我们还开发了一种多尺度特征提示融合方法，使在不同尺度上学习到的表征能够有效地相互交流。广泛的实验证明，所提出的框架在合成和真实世界基准测试中的表现均优于最先进的方法。

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引用次数: 0

Causal softmax for out-of-distribution generalization 用于分布外概括的因果软最大值

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-07 DOI: 10.1016/j.dsp.2024.104861

Jing Luo, Wanqing Zhao, Jinye Peng

Most supervised learning algorithms follow the Empirical Risk Minimization (ERM) principle, which assumes that training and test data are independently and identically distributed (IID). However, when faced with out-of-distribution (OOD) data, these models may inadvertently learn spurious correlations introduced by confounding factors in the training data. This can result in suboptimal performance on the test data, ultimately compromising the model's practical reliability. In this paper, we propose a novel causal softmax algorithm to address this challenge. First, we introduce a method to define causal and non-causal features in image classification tasks. Then, by employing a causal feature discovery module, we analyze high-level semantic activations extracted by the feature extraction network to distinguish between causal and non-causal features. Subsequently, we penalize the weights associated with non-causal features in the classifier to mitigate their influence, enabling the classifier to establish associations solely based on causal features and labels. Extensive experiments on public datasets like NICO and ImageNet-9 demonstrate the superiority of our approach.

大多数监督学习算法都遵循经验风险最小化（ERM）原则，即假设训练和测试数据是独立且相同的分布（IID）。然而，当面对非分布（OOD）数据时，这些模型可能会无意中学习到训练数据中混杂因素引入的虚假相关性。这会导致测试数据的性能不理想，最终影响模型的实际可靠性。在本文中，我们提出了一种新颖的因果软最大算法来应对这一挑战。首先，我们介绍了在图像分类任务中定义因果特征和非因果特征的方法。然后，通过使用因果特征发现模块，我们分析了特征提取网络提取的高级语义激活，以区分因果和非因果特征。随后，我们对分类器中与非因果特征相关的权重进行惩罚，以减轻其影响，从而使分类器能够仅根据因果特征和标签建立关联。在 NICO 和 ImageNet-9 等公共数据集上的广泛实验证明了我们的方法的优越性。

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引用次数: 0

Learning rule in MFR pulse sequence for behavior mode prediction 用于行为模式预测的 MFR 脉冲序列学习规则

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-07 DOI: 10.1016/j.dsp.2024.104854

Kun Chi , Jun Hu , Liyan Wang , Jihong Shen

Radar behavior prediction is an important task in the field of electronic reconnaissance. For the extensive applied multi-function radar (MFR), which can flexibly transition between various work modes and make certain statistical rule of these radar behaviors exist in the signal sequence. Most of existing radar emission prediction methods are inapplicable to the non-cooperative scenario, since the labeled sequence samples are hard to obtain. To solve this challenge, an unsupervised framework is proposed for learning the behavior rule from the pulse sequence and predicting the radar mode in this paper. The framework includes three modules of sequence segmentation for mode switch boundaries detection, segment clustering for behavior mode recognition, and mode prediction for behavior rule extraction. The application of this framework can predict state and numerical values of next mode at the same time. Experimental results demonstrate that the proposed framework has a considerable prediction performance and shows good robustness under the non-ideal conditions.

雷达行为预测是电子侦察领域的一项重要任务。对于广泛应用的多功能雷达（MFR）来说，它可以在各种工作模式之间灵活转换，并使这些雷达行为在信号序列中存在一定的统计规律。由于标注序列样本难以获得，现有的雷达发射预测方法大多不适用于非合作场景。为解决这一难题，本文提出了一种从脉冲序列中学习行为规则并预测雷达模式的无监督框架。该框架包括三个模块：用于模式切换边界检测的序列分割、用于行为模式识别的序列聚类和用于行为规则提取的模式预测。应用该框架可同时预测下一模式的状态和数值。实验结果表明，所提出的框架具有相当高的预测性能，并在非理想条件下表现出良好的鲁棒性。

引用次数: 0

An enhanced domain generalization method for object detection based on text guided feature disentanglement 基于文本引导特征分解的增强型目标检测领域泛化方法

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-07 DOI: 10.1016/j.dsp.2024.104855

Meng Wang, Yudong Liu, Haipeng Liu

The application scenarios of object detection models are constantly changing, due to the alternation of day and night and weather changes. Detector often suffers from the scarcity of training sets on potential domains. Recently, this challenge known as domain shift has been relieved by single domain generalization (SDG). To further generalize towards multiple unseen domains, this paper proposes a detector that uses text semantic gaps to enhance scene diversity and utilizes feature disentangling to extract domain-invariant features from different scenes, thereby improving detection accuracy. Firstly, random semantic augmentation (RSA) is adopted leveraging the text modality to capture semantically generalized representations, thereby augmenting the diversity of domain related information. Second, by broadening the decision boundary between domain-invariant and domain-specific features, feature disentangling (FD) branches are applied to improve the detector's object-background differentiation. Additionally, a cross modality alignment (CMA) is performed by estimating the relevances between domain-specific features and textual domain prompts. Experimental results show the proposed detector has excellent performance among existing baselines on diverse weather conditions, such as rainy, foggy and night rainy, which also confirms the enhanced generalization ability on multiple unseen domains.

由于昼夜交替和天气变化，物体检测模型的应用场景不断变化。检测器经常会受到潜在领域训练集稀缺的困扰。最近，单域泛化（SDG）技术缓解了这一被称为 "域转移 "的挑战。为了进一步泛化到多个未见域，本文提出了一种检测器，利用文本语义间隙增强场景多样性，并利用特征分解从不同场景中提取域不变特征，从而提高检测精度。首先，采用随机语义增强（RSA）技术，利用文本模式捕捉语义泛化表征，从而增强领域相关信息的多样性。其次，通过拓宽领域不变特征和特定领域特征之间的决策边界，应用特征分离（FD）分支来提高检测器的物体-背景区分度。此外，通过估计特定领域特征与文本领域提示之间的相关性，还进行了跨模态对齐（CMA）。实验结果表明，在雨天、雾天和夜雨等不同天气条件下，所提出的检测器在现有基线中表现出色，这也证实了其在多个未见领域中增强的泛化能力。

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引用次数: 0

MCNN-CMCA: A multiscale convolutional neural networks with cross-modal channel attention for physiological signal-based mental state recognition MCNN-CMCA：基于生理信号的心理状态识别的跨模态通道关注多尺度卷积神经网络

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-07 DOI: 10.1016/j.dsp.2024.104856

Yayun Wei, Lei Cao, Yilin Dong, Tianyu Liu

Human mental state recognition (MSR) has significant implications for human-machine interactions. Although mental state recognition models based on single-modality signals, such as electroencephalogram (EEG) or peripheral physiological signals (PPS), have achieved encouraging progress, methods leveraging multimodal physiological signals still need to be explored. In this study, we present MCNN-CMCA, a generic model that employs multiscale convolutional neural networks (CNNs) with cross-modal channel attention to realize physiological signals-based MSR. Specifically, we first design an innovative cross-modal channel attention mechanism that adaptively adjusting the weights of each signal channel, effectively learning both intra-modality and inter-modality correlation and expanding the channel information to the depth dimension. Additionally, the study utilizes multiscale temporal CNNs for obtaining short-term and long-term time-frequency features across different modalities. Finally, the multimodal fusion module integrates the representations of all physiological signals and the classification layer implements sparse connections by setting the mask weights to 0. We evaluate the proposed method on the SEED-VIG, DEAP, and self-made datasets, achieving superior results compared to existing state-of-the-art methods. Furthermore, we conduct ablation studies to demonstrate the effectiveness of each component in the MCNN-CMCA and show the use of multimodal physiological signals outperforms single-modality signals.

人类精神状态识别（MSR）对人机交互具有重要影响。尽管基于脑电图（EEG）或外周生理信号（PPS）等单模态信号的心理状态识别模型已经取得了令人鼓舞的进展，但利用多模态生理信号的方法仍有待探索。在本研究中，我们提出了 MCNN-CMCA，这是一种采用跨模态通道关注的多尺度卷积神经网络（CNN）来实现基于生理信号的 MSR 的通用模型。具体来说，我们首先设计了一种创新的跨模态通道关注机制，它能自适应地调整每个信号通道的权重，有效地学习模态内和模态间的相关性，并将通道信息扩展到深度维度。此外，研究还利用多尺度时间 CNN 获取不同模态的短期和长期时间频率特性。最后，多模态融合模块整合了所有生理信号的表征，分类层通过将掩码权重设置为 0 来实现稀疏连接。我们在 SEED-VIG、DEAP 和自制数据集上评估了所提出的方法，结果优于现有的先进方法。此外，我们还进行了消融研究，以证明 MCNN-CMCA 中每个组件的有效性，并表明使用多模态生理信号的效果优于单模态信号。

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引用次数: 0

The short-term wind power prediction based on a multi-layer stacked model of BOCNN-BiGRU-SA 基于 BOCNN-BiGRU-SA 多层堆叠模型的短期风电预测

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-07 DOI: 10.1016/j.dsp.2024.104838

Wen Chen, Hongquan Huang, Xingke Ma, Xinhang Xu, Yi Guan, Guorui Wei, Lin Xiong, Chenglin Zhong, Dejie Chen, Zhonglin Wu

Wind power generation is influenced by various meteorological factors, exhibiting significant volatility and unpredictability. This variability presents considerable challenges for accurate wind power forecasting. In this study, we propose an innovative method for short-term wind power prediction that integrates a Bayesian-optimized Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Units (BiGRU), and a Self-Attention Mechanism (SA) within a multi-layer architecture. Initially, we preprocess features using Pearson correlation analysis and input them into the CNN to investigate complex nonlinear spatial relationships among multiple feature variables and the current load. Subsequently, the BiGRU captures long-term dependencies from both forward and backward time sequences. Finally, we implement the Self-Attention Mechanism to weigh the features and generate the predicted wind power. We optimize the model's numerous hyperparameters utilizing a Bayesian algorithm. Through comparative ablation experiments with varying time segment lengths on wind farm datasets from four regions, our method significantly outperforms 11 models, including Long Short-Term Memory (LSTM), and surpasses several state-of-the-art (SOTA) prediction models, such as iTransformer, PatchTST, Non-stationary Transformers, TSMixer, and DLinear. The highest coefficient of determination (R²) achieved was 0.981, with the Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) decreasing by 11.22 % to 62.04 % compared to other models. The results demonstrate the predictive accuracy and generalization performance of our proposed model.

风力发电受到各种气象因素的影响，表现出极大的不稳定性和不可预测性。这种可变性给风力发电的准确预测带来了巨大挑战。在本研究中，我们提出了一种用于短期风力预测的创新方法，该方法将贝叶斯优化的卷积神经网络（CNN）、双向门控递归单元（BiGRU）和自注意机制（SA）集成到一个多层架构中。首先，我们使用皮尔逊相关分析对特征进行预处理，然后将其输入 CNN，以研究多个特征变量与当前负载之间复杂的非线性空间关系。随后，BiGRU 从正向和反向时间序列中捕捉长期依赖关系。最后，我们采用自我关注机制来权衡特征并生成预测风力发电量。我们利用贝叶斯算法优化了模型的众多超参数。通过对四个地区的风电场数据集进行不同时间段长度的消融对比实验，我们的方法明显优于包括长短期记忆（LSTM）在内的 11 种模型，并超越了 iTransformer、PatchTST、Non-stationary Transformers、TSMixer 和 DLinear 等几种最先进的（SOTA）预测模型。与其他模型相比，对称平均绝对百分比误差 (SMAPE)、均方根误差 (RMSE) 和平均绝对误差 (MAE) 降低了 11.22 % 至 62.04 %。这些结果证明了我们提出的模型的预测准确性和泛化性能。

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引用次数: 0

MSL-CCRN: Multi-stage self-supervised learning based cross-modality contrastive representation network for infrared and visible image fusion MSL-CCRN：基于多级自监督学习的跨模态对比表示网络，用于红外和可见光图像融合

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-11-06 DOI: 10.1016/j.dsp.2024.104853

Zhilin Yan , Rencan Nie , Jinde Cao , Guangxu Xie , Zhengze Ding

Infrared and visible image fusion (IVIF) facing different information in two modal scenarios, the focus of research is to better extract different information. In this work, we propose a multi-stage self-supervised learning based cross-modality contrastive representation network for infrared and visible image fusion (MSL-CCRN). Firstly, considering that the scene differences between different modalities affect the fusion of cross-modal images, we propose a contrastive representation network (CRN). CRN enhances the interaction between the fused image and the source image, and significantly improves the similarity between the meaningful features in each modality and the fused image. Secondly, due to the lack of ground truth in IVIF, the quality of directly obtained fused image is seriously affected. We design a multi-stage fusion strategy to address the loss of important information in this process. Notably, our method is a self-supervised network. In fusion stage I, we reconstruct the initial fused image as the new view of fusion stage II. In fusion stage II, we use the fused image obtained in the previous stage to carry out three-view contrastive representation, thereby constraining the feature extraction of the source image. This makes the final fused image introduce more important information in the source image. Through a large number of qualitative, quantitative experiments and downstream object detection experiments, our propose method shows excellent performance compared with most advanced methods.

红外图像与可见光图像融合（IVIF）面临着两种模式下的不同信息，研究的重点是如何更好地提取不同的信息。在这项工作中，我们提出了一种基于多阶段自监督学习的红外与可见光图像融合的跨模态对比表示网络（MSL-CCRN）。首先，考虑到不同模态之间的场景差异会影响跨模态图像的融合，我们提出了一种对比性表示网络（CRN）。CRN 增强了融合图像与源图像之间的交互，并显著提高了各模态有意义特征与融合图像之间的相似性。其次，由于 IVIF 缺乏地面实况，直接获得的融合图像质量受到严重影响。我们设计了一种多阶段融合策略来解决这一过程中重要信息丢失的问题。值得注意的是，我们的方法是一种自监督网络。在融合阶段 I，我们重建初始融合图像作为融合阶段 II 的新视图。在融合阶段 II 中，我们使用前一阶段获得的融合图像进行三视图对比表示，从而约束源图像的特征提取。这使得最终的融合图像引入了源图像中更多的重要信息。通过大量的定性、定量实验和下游物体检测实验，我们提出的方法与大多数先进方法相比表现出了卓越的性能。

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引用次数: 0

Continuous discrete minimum error entropy Kalman filter in non-Gaussian noises system 非高斯噪声系统中的连续离散最小误差熵卡尔曼滤波器

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-31 DOI: 10.1016/j.dsp.2024.104846

Zhifa Liu , Ruide Zhang , Yujie Wang , Haowei Zhang , Gang Wang , Ying Zhang

This paper proposes continuous discrete linear Kalman filtering algorithm based on the minimum error entropy criterion under non-Gaussian noise environments. Traditional Kalman filters struggle in such environments due to their reliance on Gaussian assumptions. Our approach leverages stochastic differential equations to precisely model system dynamics and integrates the minimum error entropy criterion to capture higher-order statistical properties of non-Gaussian noise. Simulations confirm that the proposed algorithm significantly enhances estimation accuracy and robustness compared to conventional methods, demonstrating its effectiveness in handling complex, noisy environments.

本文提出了非高斯噪声环境下基于最小误差熵准则的连续离散线性卡尔曼滤波算法。传统的卡尔曼滤波器由于依赖于高斯假设，在这种环境下很难发挥作用。我们的方法利用随机微分方程对系统动态进行精确建模，并整合了最小误差熵准则，以捕捉非高斯噪声的高阶统计特性。模拟证实，与传统方法相比，所提出的算法大大提高了估计精度和鲁棒性，证明了它在处理复杂、高噪声环境方面的有效性。

引用次数: 0