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Greedy Capon Beamformer 贪心卡彭波束成形器
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475351
Esa Ollila
We propose greedy Capon beamformer (GCB) for direction finding of narrow-band sources present in the array's viewing field. After defining the grid covering the location search space, the algorithm greedily builds the interference-plus-noise covariance matrix by identifying a high-power source on the grid using Capon's principle of maximizing the signal to interference plus noise ratio while enforcing unit gain towards the signal of interest. An estimate of the power of the detected source is derived by exploiting the unit power constraint, which subsequently allows to update the noise covariance matrix by simple rank-1 matrix addition composed of outerproduct of the selected steering matrix with itself scaled by the signal power estimate. Our numerical examples demonstrate effectiveness of the proposed GCB in direction finding where it performs favourably compared to the state-of-the-art algorithms under a broad variety of settings. Furthermore, GCB estimates of direction-of-arrivals (DOAs) are very fast to compute.
我们提出了贪婪卡彭波束成形器(GCB),用于阵列视场中窄波段信号源的方向搜索。在定义覆盖位置搜索空间的网格后,该算法利用卡彭原理,在网格上识别高功率信号源,从而贪婪地建立干扰加噪声协方差矩阵,该原理使信号与干扰加噪声比最大化,同时对感兴趣的信号强制执行单位增益。通过利用单位功率约束,可以得出检测到的信号源的功率估计值,然后通过简单的秩-1 矩阵加法更新噪声协方差矩阵,该矩阵由所选转向矩阵的外积和信号功率估计值缩放组成。我们的数值示例证明了所提出的 GCB 在测向中的有效性,在各种设置下,它的表现都优于最先进的算法。此外,GCB 对到达方向(DOA)的估计计算速度非常快。
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引用次数: 0
On the Strong Convexity of PnP Regularization Using Linear Denoisers 论使用线性去oisers 的 PnP 正则化的强凸性
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475913
Arghya Sinha;Kunal N. Chaudhury
In the Plug-and-Play (PnP) method, a denoiser is used as a regularizer within classical proximal algorithms for image reconstruction. It is known that a broad class of linear denoisers can be expressed as the proximal operator of a convex regularizer. Consequently, the associated PnP algorithm can be linked to a convex optimization problem $mathcal {P}$. For such a linear denoiser, we prove that $mathcal {P}$ exhibits strong convexity for linear inverse problems. Specifically, we show that the strong convexity of $mathcal {P}$ can be used to certify objective and iterative convergence of any PnP algorithm derived from classical proximal methods.
在即插即用(PnP)方法中,去噪器被用作图像重建经典近端算法中的正则化器。众所周知,一大类线性去噪器可以表示为凸正则的近似算子。因此,相关的 PnP 算法可以与凸优化问题 $mathcal {P}$ 联系起来。对于这种线性去噪器,我们证明 $mathcal {P}$ 对于线性逆问题具有强凸性。具体来说,我们证明了 $mathcal {P}$ 的强凸性可以用来证明任何从经典近似方法衍生的 PnP 算法的目标和迭代收敛性。
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引用次数: 0
Semantic Progressive Guidance Network for RGB-D Mirror Segmentation 用于 RGB-D 镜面分割的语义渐进引导网络
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475357
Chao Li;Wujie Zhou;Xi Zhou;Weiqing Yan
Existing salient target detection methods tend to use a single-mirror segmentation strategy, which ignores feature hierarchy information in the frequency domain and lacks fine-grained correspondence. To address these challenges, we propose a new semantic progressive guidance network (SPGNet). To mine sufficient effective information, we propose the wavelet bidirectional focusing (WBF) module to aggregate sub-band features through a bidirectional wavelet transform and fuse them with low-level features to deepen the detail mining. We also introduce the Gaussian fusion complementary (GFC) module, which adopts Gaussian filtering technology to optimize the feature space and then efficiently extracts the contour information through enhanced feature processing. In addition, we propose a global correlation bootstrapping (GCB) module that constructs region-to-pixel correlations from a global perspective to achieve fine-grained correspondence. The proposed model achieves competitive results on a benchmark dataset.
现有的突出目标检测方法倾向于使用单镜分割策略,这种方法忽略了频域中的特征层次信息,缺乏细粒度的对应关系。为了应对这些挑战,我们提出了一种新的语义渐进引导网络(SPGNet)。为了挖掘足够的有效信息,我们提出了小波双向聚焦(WBF)模块,通过双向小波变换聚合子带特征,并与低层次特征融合,以深化细节挖掘。我们还引入了高斯融合补充(GFC)模块,该模块采用高斯滤波技术优化特征空间,然后通过增强特征处理高效提取轮廓信息。此外,我们还提出了全局相关性引导(GCB)模块,从全局角度构建区域到像素的相关性,实现细粒度对应。所提出的模型在基准数据集上取得了具有竞争力的结果。
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引用次数: 0
Enhanced Dynamic Analysis for Malware Detection With Gradient Attack 利用梯度攻击加强恶意软件检测的动态分析
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475354
Pei Yan;Shunquan Tan;Miaohui Wang;Jiwu Huang
Malware detection is an effective way to prevent the intrusion of malware into computer systems, and the API-based dynamic analysis method can effectively detect obfuscated and packaged malware. However, existing methods still suffer from limited detection accuracy and weak generalization. To address this issue, this paper presents a gradient attack-based malware dynamic analysis method. Through exerting adversarial noise into the embedding layer, the malware detection model can learn more robust representations of API sequences during training, achieving broader coverage of sample representations. The strategy of normalizing attack noise and recovering attacked representation is designed, which controls the strength of the gradient attack within a reasonable range and prevents a negative impact on the model's detection performance. The proposed method can be applied to existing API-based malware detection models to enhance their detection performance, indicating the strong generality of the proposed method. Experimental results on two benchmark datasets (i.e., Aliyun and Catak) demonstrate the effectiveness of the proposed gradient attack method, which further improves the detection performance of the mainstream API-based models, with an average accuracy increase of 2.80% and 3.66% on these two datasets, respectively.
恶意软件检测是防止恶意软件入侵计算机系统的有效方法,而基于 API 的动态分析方法可以有效地检测出经过混淆和包装的恶意软件。然而,现有方法仍存在检测精度有限、泛化能力弱等问题。针对这一问题,本文提出了一种基于梯度攻击的恶意软件动态分析方法。通过在嵌入层中施加对抗噪声,恶意软件检测模型可以在训练过程中学习到更健壮的 API 序列表示,从而实现更广泛的样本表示覆盖。设计了攻击噪声归一化和恢复被攻击表示的策略,将梯度攻击的强度控制在合理范围内,避免了对模型检测性能的负面影响。所提出的方法可应用于现有的基于 API 的恶意软件检测模型,以提高其检测性能,这表明所提出的方法具有很强的通用性。在两个基准数据集(即阿里云和 Catak)上的实验结果证明了所提梯度攻击方法的有效性,它进一步提高了基于 API 的主流模型的检测性能,在这两个数据集上的平均准确率分别提高了 2.80% 和 3.66%。
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引用次数: 0
MVP: One-Shot Object Pose Estimation by Matching With Visible Points MVP:通过与可见点匹配进行单次物体姿态估计
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-03 DOI: 10.1109/LSP.2024.3472492
Wentao Cheng;Minxing Luo
We introduce a novel method for one-shot object pose estimation. Recent detector-free one-shot methods have achieved promising results for challenging low-textured objects. The features in a query image are directly matched with all features in an object point cloud reconstructed via Structure-from-Motion (SfM) techniques. Rejecting invisible 3D points, as well as associated features, is performed implicitly using a deep neural network that is trained specifically for feature matching. This tightly-coupled strategy is prone to preserve 3D points that are rarely visible from the query view. In contrast, we propose to prune such erroneous points using the explicit image-point relational graph, which is a lightweight by-product of the SfM reconstruction. By injecting the graph-based pruning into stacked feature transformers, our method is able to obtain high quality 2D-3D correspondences through matching with visible points in an early stage. The experiments demonstrate that our method outperforms state-of-the-art model-free one-shot methods with faster speed.
我们介绍了一种一次性物体姿态估计的新方法。最近的免检测器单次拍摄方法在处理具有挑战性的低纹理物体方面取得了可喜的成果。查询图像中的特征与通过运动结构(SfM)技术重建的物体点云中的所有特征直接匹配。剔除不可见的三维点以及相关特征,是通过专门为特征匹配而训练的深度神经网络隐式执行的。这种紧密耦合的策略容易保留从查询视图中很少可见的三维点。相比之下,我们建议使用显式图像-点关系图(SfM 重构的轻量级副产品)来修剪此类错误点。通过将基于图的修剪注入堆叠特征变换器,我们的方法能够在早期通过与可见点匹配获得高质量的 2D-3D 对应。实验证明,我们的方法以更快的速度超越了最先进的无模型单次方法。
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引用次数: 0
Oriented Object Detection Based on Adaptive Feature Learning and Enrichment 基于自适应特征学习和丰富的定向物体检测
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-03 DOI: 10.1109/LSP.2024.3472490
Pei Li;Zhongjie Zhu;Yongqiang Bai;Yuer Wang;Lei Zhang
Oriented object detection has broad utilization in many fields, including urban traffic monitoring, land utilization assessment, and environmental monitoring. However, current oriented object detecting methods are limited in leveraging multiscale information, failing to fully exploit the rich scale variation within images and resulting in suboptimal performance when detecting multiscale targets. Herein, an innovative method SH-Net is proposed based on adaptive feature learning and enrichment. First, an adaptive feature learning module (AFLM) is constructed to enhance the feature learning capability for multiscale objects. Second, a high-resolution feature pyramidal network (HRFPN) is constructed to enhance deep feature fusion for dense and small targets. Finally, a rotated proposal generation (RPG) module and rotated box refinement (RBR) module are proposed to generate and refine the bounding box for extracted oriented objects. The experimental results obtained on the DOTA dataset show that SH-Net can achieve a mAP of 82.67% and surpasses most state-of-the-art methods.
定向物体检测在城市交通监测、土地利用评估和环境监测等许多领域都有广泛应用。然而,目前的定向物体检测方法在利用多尺度信息方面存在局限性,无法充分利用图像内部丰富的尺度变化,导致在检测多尺度目标时性能不佳。在此,我们提出了一种基于自适应特征学习和丰富的创新方法 SH-Net。首先,构建一个自适应特征学习模块(AFLM),以增强多尺度目标的特征学习能力。其次,构建了一个高分辨率特征金字塔网络(HRFPN),以增强对密集和小型目标的深度特征融合。最后,提出了旋转提案生成(RPG)模块和旋转框细化(RBR)模块,用于生成和细化提取的定向物体的边界框。在 DOTA 数据集上获得的实验结果表明,SH-Net 的 mAP 高达 82.67%,超过了大多数最先进的方法。
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引用次数: 0
Multi-Scale Feature Fusion and Distribution Similarity Network for Few-Shot Automatic Modulation Classification 用于少镜头自动调制分类的多尺度特征融合与分布相似性网络
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-30 DOI: 10.1109/LSP.2024.3470762
Haoyue Tan;Zhenxi Zhang;Yu Li;Xiaoran Shi;Feng Zhou
Automatic modulation classification (AMC), as a key technology of cognitive radio, has become a focal point of research. However, most deep learning-based AMC methods require an extensive number of labeled signals to acquire a comprehensive understanding of modulation types, placing substantial pressure on signal acquisition and labeling. To solve this issue, we propose a few-shot AMC (FSAMC) method to facilitate rapid generalization and recognition with limited data, namely multi-scale feature fusion and distribution similarity network (MS2F-DS). Firstly, we design a multi-scale feature fusion (MS2F) model, which aims to extract features with varying fields of view and boost feature fusion, enabling the derivation of contextual information from the signal. Furthermore, we introduce a distribution similarity (DS) classifier to address the insufficient measurement of current similarity measurement functions by considering both micro and macro perspectives of vectors, further increasing intra-class compactness and inter-class separability. Finally, extensive experiments were conducted on 3-way 1, 3, and 5-shot FSAMC tasks using public datasets RML2016.10a and RML2016.10b, and the results demonstrated the effectiveness of our method.
自动调制分类(AMC)作为认知无线电的一项关键技术,已成为研究的焦点。然而,大多数基于深度学习的自动调制分类方法需要大量标记信号才能全面了解调制类型,这给信号采集和标记带来了巨大压力。为解决这一问题,我们提出了一种可在有限数据条件下实现快速泛化和识别的少量调制(FSAMC)方法,即多尺度特征融合和分布相似性网络(MS2F-DS)。首先,我们设计了一个多尺度特征融合(MS2F)模型,旨在提取不同视场的特征并促进特征融合,从而从信号中获取上下文信息。此外,我们还引入了分布相似性(DS)分类器,通过考虑向量的微观和宏观角度,解决当前相似性测量函数测量不足的问题,进一步提高类内紧凑性和类间可分性。最后,我们使用公开数据集 RML2016.10a 和 RML2016.10b 在 3 路 1、3 和 5 发 FSAMC 任务上进行了大量实验,结果证明了我们的方法的有效性。
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引用次数: 0
UWMamba: UnderWater Image Enhancement With State Space Model UWMamba:利用状态空间模型增强水下图像
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-30 DOI: 10.1109/LSP.2024.3470752
Guanhua An;Ao He;Yudong Wang;Jichang Guo
Recently, state space models (SSM) with efficient design, i.e., Mamba, have shown great potential in modeling long-range dependencies with linear complexity. However, the pure SSM-based model yields sub-optimal underwater enhancement performance due to insufficient local details. Given the superiority of convolution in local perception, we propose a hybrid network, named UWMamba, which combines SSM and convolution for underwater image enhancement. We introduce a conv mamba layer (CML) as the foundation layer to combine the visual state space block (VSSB) with convolution. The convolution is used to capture local detailed features, while the VSSB is employed to capture long-range global features, which complement each other. Furthermore, considering underwater images suffer from severe and uneven degradation of spatial regions and color channels, we propose a Mamba Attention Fusion Module (MAFM), which fuses VSSB with an attention mechanism for better perception of channels and spatial regions. Extensive experiments on real-world underwater image datasets demonstrate the promising performance of our method in both objective metrics and subjective comparisons.
最近,具有高效设计的状态空间模型(SSM),即 Mamba,在以线性复杂度模拟长程依赖关系方面显示出巨大潜力。然而,由于局部细节不足,基于 SSM 的纯模型无法获得最佳水下增强性能。鉴于卷积在局部感知方面的优越性,我们提出了一种混合网络,命名为 UWMamba,它结合了 SSM 和卷积,用于水下图像增强。我们引入了一个 conv mamba 层(CML)作为基础层,将视觉状态空间块(VSSB)与卷积结合起来。卷积用于捕捉局部细节特征,而视觉状态空间块用于捕捉远距离全局特征,两者相辅相成。此外,考虑到水下图像在空间区域和颜色通道方面存在严重且不均衡的退化,我们提出了一种 Mamba 注意力融合模块(MAFM),它将 VSSB 与注意力机制融合在一起,以便更好地感知通道和空间区域。在真实世界的水下图像数据集上进行的大量实验表明,我们的方法在客观指标和主观比较方面都有良好的表现。
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引用次数: 0
Pedestrian Intrusion Detection in Railway Station Based on Mirror Translation Attention and Feature Pooling Enhancement 基于镜像平移注意和特征集合增强的火车站行人入侵检测
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-30 DOI: 10.1109/LSP.2024.3471180
Zhufeng Jiang;Hui Wang;Guoliang Luo;Zizhu Fan;Lu Xu
Pedestrian intrusion detection is crucial to ensuring safe railway operation. Current pedestrian detection algorithms lack consideration for real-world railway scenarios, such as the reflective properties of screen doors and train windows, may mistakenly trigger pedestrian intrusion alerts. Scale variability and pedestrian overlap often lead to detection inaccuracy, making them inadequate for addressing the specific requirements of railway perimeter security. This letter introduces an innovative pedestrian detection algorithm that incorporates Mirror Translation Attention (MTA) and Feature Pooling Enhancement (FPE). MTA, including mirror flipping and offsetting the feature mapping, could significantly mitigate missed detection caused by reflective surfaces. Additionally, we introduce sparsity to the inputs of the self-attention, which significantly enhancing the model's inference speed. A multi-scale approach is adopted to accommodate the diversity in pedestrian sizes, while the FPE addresses occlusion issues across various scales. Compared to the advanced YOLOv8 model, the proposed method improves AP50 by 1.6% to 92.11% and reduces model parameters by 63.55% in our self-built railway pedestrian intrusion dataset.
行人入侵检测对于确保铁路安全运行至关重要。目前的行人检测算法缺乏对现实世界铁路场景的考虑,例如屏蔽门和列车窗户的反射特性,可能会错误地触发行人入侵警报。尺度变化和行人重叠经常导致检测不准确,使其无法满足铁路周边安全的特殊要求。这封信介绍了一种创新的行人检测算法,该算法结合了镜像平移注意(MTA)和特征集合增强(FPE)。MTA 包括镜像翻转和偏移特征映射,可显著减少反射表面造成的漏检。此外,我们还为自我注意的输入引入了稀疏性,从而大大提高了模型的推理速度。我们采用了多尺度方法来适应行人大小的多样性,而 FPE 则解决了不同尺度上的遮挡问题。在我们自建的铁路行人入侵数据集中,与先进的 YOLOv8 模型相比,所提出的方法将 AP50 提高了 1.6% 至 92.11%,并将模型参数减少了 63.55%。
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引用次数: 0
General Optimization Methods for YOLO Series Object Detection in Remote Sensing Images 遥感图像中 YOLO 系列物体检测的一般优化方法
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-27 DOI: 10.1109/LSP.2024.3469787
Guozheng Nan;Yue Zhao;Chengxing Lin;Qiaolin Ye
The You Only Look Once (YOLO) series of object detection algorithms has attracted considerable attention for its notable advantages in speed and accuracy, resulting in widespread applications in various real-world scenarios. However, achieving outstanding accuracy on remote sensing images with densely arranged small targets and complex backgrounds remains a challenging task. To address this issue, this letter proposes two easily integrated modules suitable for the YOLO architecture, namely global semantic information extraction (GSIE) and adaptive feature fusion (AFF). The GSIE module is designed to overcome the limitation of local information in traditional methods and facilitate global semantic information interaction by introducing multi-angle feature rotation to extend the receptive field. The AFF module effectively captures fine-grained features of objects by dynamically adjusting fusion weights, thereby reducing the loss of deep semantic information during feature transfer and fusion. The experimental results on the VEDAI and LEVIR remote sensing datasets demonstrate that when embedding these two modules into YOLO series algorithms that only use the small-scale detector, there is a significant improvement in performance while reducing computational complexity.
只看一次(YOLO)系列物体检测算法因其在速度和精度方面的显著优势而备受关注,并在现实世界的各种场景中得到了广泛应用。然而,在具有密集排列的小目标和复杂背景的遥感图像上实现出色的精度仍然是一项具有挑战性的任务。针对这一问题,本文提出了适合 YOLO 架构的两个易于集成的模块,即全局语义信息提取(GSIE)和自适应特征融合(AFF)。GSIE 模块旨在克服传统方法中局部信息的局限性,通过引入多角度特征旋转来扩展感受野,从而促进全局语义信息的交互。AFF 模块通过动态调整融合权重,有效捕捉物体的细粒度特征,从而减少特征转移和融合过程中深层语义信息的损失。在 VEDAI 和 LEVIR 遥感数据集上的实验结果表明,将这两个模块嵌入到只使用小尺度探测器的 YOLO 系列算法中,可以显著提高性能,同时降低计算复杂度。
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引用次数: 0
期刊
IEEE Signal Processing Letters
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