Journal of King Saud University-Computer and Information Sciences最新文献

Visually meaningful image encryption for secure and authenticated data transmission using chaotic maps 利用混沌图对有视觉意义的图像进行加密，以实现安全的认证数据传输

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-14 DOI: 10.1016/j.jksuci.2024.102235

Deep Singh , Sandeep Kumar , Chaman Verma , Zoltán Illés , Neerendra Kumar

Image ciphering techniques usually transform a given plain image data into a cipher image data resembling noise, serving as an indicator of the presence of secret image data. However, the transmission of such noise-like images could draw attention, thereby attracting the attackers and may face several possible attacks. This paper presents an approach for generating a visually meaningful image encryption (VMIE) scheme that combines three layers of security protection: encryption, digital signature, and steganography. The present scheme is dedicated to achieving a balanced performance in robustness, security and operational efficiency. First, the original image is partially encrypted by using the RSA cryptosystem and modified Hénon map (MHM). In the second stage, a digital signature is generated for the partially encrypted image by employing a hash function and the RSA cryptosystem. The obtained digital signature is appended to the partially encrypted image produced after implementing the zigzag confusion in the above partially encrypted image. Further, to achieve better confusion and diffusion, the partially encrypted image containing a digital signature undergoes through the application of 3D Arnold cat map (

A R_{n o}

times), to produce the secret encrypted image

(S_{r 5})

. To ensure the security and robustness of the proposed technique against various classical attacks, the hash value obtained from the SHA-256 hash function and carrier images is utilized to generate the initial conditions

M h_{10}

and

M h_{20}

for modified Hénon map, and initial position

Z_{i p} = (z_{r o w}, z_{c o l})

for zigzag confusion. In the proposed algorithm, the digital signature is utilized for both purposes to verify the sender’s authenticity and to enhance the encryption quality. The carrier image undergoes lifting wavelet transformation, and its high-frequency components are utilized in the embedding process through a permuted pattern of MHM, resulting in a visually meaningful encrypted image. The proposed scheme achieves efficient visual encryption with minimal distortion and ensures lossless image quality upon decryption (infinite PSNR), balancing high level of security along with a good computational efficiency.

图像加密技术通常将给定的普通图像数据转换成类似噪声的加密图像数据，作为存在秘密图像数据的指示器。然而，传输这种类似噪声的图像会引起注意，从而吸引攻击者，并可能面临多种攻击。本文提出了一种生成视觉意义图像加密（VMIE）方案的方法，该方案结合了三层安全保护：加密、数字签名和隐写术。本方案致力于实现稳健性、安全性和运行效率的平衡。首先，使用 RSA 密码系统和修正的赫农图谱（MHM）对原始图像进行部分加密。第二阶段，使用哈希函数和 RSA 密码系统为部分加密的图像生成数字签名。在对上述部分加密图像进行之字形混淆后，将获得的数字签名附加到部分加密图像上。此外，为了达到更好的混淆和扩散效果，包含数字签名的部分加密图像还要经过三维阿诺德猫图的应用（ARno 次），以生成秘密加密图像（Sr5）。为确保所提技术的安全性和鲁棒性，以抵御各种经典攻击，利用 SHA-256 哈希函数和载波图像获得的哈希值生成修正 Hénon 映射的初始条件 Mh10 和 Mh20，以及之字形混淆的初始位置 Zip=（zrow,zcol）。在所提出的算法中，数字签名既可用于验证发送者的真实性，也可用于提高加密质量。载波图像经过提升小波变换，其高频分量通过 MHM 的包络模式被用于嵌入过程，从而得到视觉上有意义的加密图像。所提出的方案以最小的失真实现了高效的视觉加密，并确保了解密时的无损图像质量（PSNR 无穷大），同时兼顾了高水平的安全性和良好的计算效率。

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引用次数: 0

Leukocyte segmentation based on DenseREU-Net 基于 DenseREU-Net 的白细胞分割技术

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-12 DOI: 10.1016/j.jksuci.2024.102236

Jie Meng , Yingqi Lu , Wangjiao He , Xiangsuo Fan , Gechen Zhou , Hongjian Wei

The detection of white blood cells provides important information in cellular research regarding infections, inflammation, immune function, and blood pathologies. Effective segmentation of WBCs in blood microscopic images not only aids pathologists in making more accurate diagnoses and early detections but is also crucial for identifying the types of lesions. Due to significant differences among various types of pathological WBCs and the complexities associated with cellular imaging and staining techniques, accurately recognizing and segmenting these different types of WBCs remains challenging. To address these challenges, this paper proposes a WBC segmentation technique based on DenseREU-Net, which enhances feature exchange and reuse by employing Dense Blocks and residual units. Additionally, it introduces mixed pooling and skip multi-scale fusion modules to improve the recognition and segmentation accuracy of different types of pathological WBCs. This study was validated on two datasets provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). Experimental results indicate that on the multi-class dataset, DenseREU-Net achieves an average IoU of 73.05% and a Dice coefficient of 80.25%. For the binary classification blind sample dataset, the average IoU and Dice coefficient are 83.98% and 90.41%, respectively. In both datasets, the proposed model significantly outperforms other advanced medical image segmentation algorithms. Overall, DenseREU-Net effectively analyzes blood microscopic images and accurately recognizes and segments different types of WBCs, providing robust support for the diagnosis of blood-related diseases.

白细胞的检测为有关感染、炎症、免疫功能和血液病理的细胞研究提供了重要信息。有效分割血液显微图像中的白细胞不仅有助于病理学家做出更准确的诊断和早期检测，而且对确定病变类型也至关重要。由于各种类型的病理白细胞之间存在显著差异，而且细胞成像和染色技术非常复杂，因此准确识别和分割这些不同类型的白细胞仍然具有挑战性。为了应对这些挑战，本文提出了一种基于 DenseREU-Net 的白细胞分割技术，该技术通过使用密集块和残留单元来增强特征交换和重用。此外，它还引入了混合池和跳过多尺度融合模块，以提高不同类型病理白细胞的识别和分割精度。这项研究在 DML-LZWH（柳州市工人医院医学实验室）提供的两个数据集上进行了验证。实验结果表明，在多类数据集上，DenseREU-Net 的平均 IoU 为 73.05%，Dice 系数为 80.25%。在二元分类盲样本数据集上，平均 IoU 和 Dice 系数分别为 83.98% 和 90.41%。在这两个数据集中，所提出的模型明显优于其他先进的医学图像分割算法。总之，DenseREU-Net 能有效分析血液显微图像，准确识别和分割不同类型的白细胞，为血液相关疾病的诊断提供有力支持。

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引用次数: 0

Knowledge-embedded multi-layer collaborative adaptive fusion network: Addressing challenges in foggy conditions and complex imaging 知识嵌入式多层协作自适应融合网络：应对多雾条件和复杂成像的挑战

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-07 DOI: 10.1016/j.jksuci.2024.102230

Zhu Chen, Fan Li, Yueqin Diao, Wanlong Zhao, Puyin Fan

Infrared and visible image fusion aims at generating high-quality images that serve both human and machine visual perception under extreme imaging conditions. However, current fusion methods primarily rely on datasets comprising infrared and visible images captured under clear weather conditions. When applied to real-world scenarios, image fusion tasks inevitably encounter challenges posed by adverse weather conditions such as heavy fog, resulting in difficulties in obtaining effective information and inferior visual perception. To address these challenges, this paper proposes a Mean Teacher-based Self-supervised Image Restoration and multimodal Image Fusion joint learning network (SIRIFN), which enhances the robustness of the fusion network in adverse weather conditions by employing deep supervision from a guiding network to the learning network. Furthermore, to enhance the network’s information extraction and integration capabilities, our Multi-level Feature Collaborative adaptive Reconstruction Network (MFCRNet) is introduced, which adopts a multi-branch, multi-scale design, with differentiated processing strategies for different features. This approach preserves rich texture information while maintaining semantic consistency from the source images. Extensive experiments demonstrate that SIRIFN outperforms current state-of-the-art algorithms in both visual quality and quantitative evaluation. Specifically, the joint implementation of image restoration and multimodal fusion provides more effective information for visual tasks under extreme weather conditions, thereby facilitating downstream visual tasks.

红外和可见光图像融合旨在生成高质量的图像，以满足人类和机器在极端成像条件下的视觉感知。然而，目前的融合方法主要依赖于在晴朗天气条件下拍摄的红外和可见光图像数据集。当应用到实际场景时，图像融合任务不可避免地会遇到大雾等恶劣天气条件带来的挑战，导致难以获得有效信息和视觉感知能力下降。为了应对这些挑战，本文提出了一种基于平均值教师的自监督图像复原和多模态图像融合联合学习网络（SIRIFN），该网络通过从指导网络到学习网络的深度监督，增强了融合网络在恶劣天气条件下的鲁棒性。此外，为了增强网络的信息提取和整合能力，我们引入了多层次特征协作自适应重构网络（MFCRNet），该网络采用多分支、多尺度设计，针对不同特征采用不同的处理策略。这种方法既能保留丰富的纹理信息，又能保持源图像的语义一致性。大量实验证明，SIRIFN 在视觉质量和定量评估方面都优于目前最先进的算法。具体来说，图像复原和多模态融合的联合实施为极端天气条件下的视觉任务提供了更有效的信息，从而为下游视觉任务提供了便利。

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引用次数: 0

Feature-fused residual network for time series classification 用于时间序列分类的特征融合残差网络

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-06 DOI: 10.1016/j.jksuci.2024.102227

Yanxuan Wei , Mingsen Du , Teng Li , Xiangwei Zheng , Cun Ji

In various fields such as healthcare and transportation, accurately classifying time series data can provide important support for decision-making. To further improve the accuracy of time series classification, we propose a Feature-fused Residual Network based on Multi-scale Signed Recurrence Plot (MSRP-FFRN). This method transforms one-dimensional time series into two-dimensional images, representing the temporal correlation of time series in a two-dimensional space and revealing hidden details within the data. To enhance these details further, we extract multi-scale features by setting receptive fields of different sizes and using adaptive network depths, which improves image quality. To evaluate the performance of this method, we conducted experiments on 43 UCR datasets and compared it with nine state-of-the-art baseline methods. The experimental results show that MSRP-FFRN ranks first on critical difference diagram, achieving the highest accuracy on 18 datasets with an average accuracy of 89.9%, making it the best-performing method overall. Additionally, the effectiveness of this method is further validated through metrics such as Precision, Recall, and F1 score. Results from ablation experiments also highlight the efficacy of the improvements made by MSRP-FFRN.

在医疗保健和交通等多个领域，对时间序列数据进行准确分类可为决策提供重要支持。为了进一步提高时间序列分类的准确性，我们提出了基于多尺度符号递归图的特征融合残差网络（MSRP-FFRN）。该方法将一维时间序列转换为二维图像，在二维空间中表示时间序列的时间相关性，并揭示数据中隐藏的细节。为了进一步增强这些细节，我们通过设置不同大小的感受野和使用自适应网络深度来提取多尺度特征，从而提高图像质量。为了评估该方法的性能，我们在 43 个 UCR 数据集上进行了实验，并将其与九种最先进的基线方法进行了比较。实验结果表明，MSRP-FFRN 在临界差分图上排名第一，在 18 个数据集上达到了最高的准确率，平均准确率为 89.9%，是整体表现最好的方法。此外，精确度、召回率和 F1 分数等指标也进一步验证了该方法的有效性。消融实验的结果也凸显了 MSRP-FFRN 所做改进的功效。

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引用次数: 0

Low-light image enhancement: A comprehensive review on methods, datasets and evaluation metrics 弱光图像增强：方法、数据集和评估指标综合评述

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-05 DOI: 10.1016/j.jksuci.2024.102234

Zhan Jingchun , Goh Eg Su , Mohd Shahrizal Sunar

Enhancing low-light images in computer vision is a significant challenge that requires innovative methods to improve its robustness. Low-light image enhancement (LLIE) enhances the quality of images affected by poor lighting conditions by implementing various loss functions such as reconstruction, perceptual, smoothness, adversarial, and exposure. This review analyses and compares different methods, ranging from traditional to cutting-edge deep learning methods, showcasing the significant advancements in the field. Although similar reviews have been studied on LLIE, this paper not only updates the knowledge but also focuses on recent deep learning methods from various perspectives or interpretations. The methodology used in this paper compares different methods from the literature and identifies the potential research gaps. This paper highlights the recent advancements in the field by classifying them into three classes, demonstrated by the continuous enhancements in LLIE methods. These improved methods use different loss functions showing higher efficacy through metrics such as Peak Signal-to-Noise Ratio, Structural Similarity Index Measure, and Naturalness Image Quality Evaluator. The research emphasizes the significance of advanced deep learning techniques and comprehensively compares different LLIE methods on various benchmark image datasets. This research is a foundation for scientists to illustrate potential future research directions.

在计算机视觉中增强低照度图像是一项重大挑战，需要创新方法来提高其鲁棒性。低照度图像增强（LLIE）通过实施各种损失函数（如重建、感知、平滑度、对抗和曝光）来提高受低照度条件影响的图像质量。本综述分析并比较了从传统方法到前沿深度学习方法等不同方法，展示了该领域的重大进展。虽然类似的综述已对 LLIE 进行了研究，但本文不仅更新了相关知识，还从不同的角度或解释关注了最新的深度学习方法。本文采用的方法比较了文献中的不同方法，并找出了潜在的研究空白。本文重点介绍了该领域的最新进展，将其分为三类，并通过 LLIE 方法的不断改进加以展示。这些改进方法使用不同的损失函数，通过峰值信噪比、结构相似性指数测量和自然度图像质量评估器等指标显示出更高的功效。研究强调了先进深度学习技术的重要性，并在各种基准图像数据集上全面比较了不同的 LLIE 方法。这项研究为科学家说明未来潜在的研究方向奠定了基础。

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引用次数: 0

Binocular camera-based visual localization with optimized keypoint selection and multi-epipolar constraints 通过优化关键点选择和多极性约束进行基于双目摄像头的视觉定位

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-05 DOI: 10.1016/j.jksuci.2024.102228

Guanyuan Feng, Yu Liu, Weili Shi, Yu Miao

In recent years, visual localization has gained significant attention as a key technology for indoor navigation due to its outstanding accuracy and low deployment costs. However, it still encounters two primary challenges: the requirement for multiple database images to match the query image and the potential degradation of localization precision resulting from the keypoints clustering and mismatches. In this research, a novel visual localization framework based on a binocular camera is proposed to estimate the absolute positions of the query camera. The framework integrates three core methods: the multi-epipolar constraints-based localization (MELoc) method, the Optimal keypoint selection (OKS) method, and a robust measurement method. MELoc constructs multiple geometric constraints to enable absolute position estimation with only a single database image, while OKS and the robust measurement method further enhance localization accuracy by refining the precision of these geometric constraints. Experimental results demonstrate that the proposed system consistently outperforms existing visual localization systems across various scene scales, database sampling intervals, and lighting conditions

近年来，视觉定位因其出色的精度和较低的部署成本成为室内导航的一项关键技术，受到广泛关注。然而，它仍然面临两个主要挑战：一是需要多个数据库图像来匹配查询图像，二是关键点聚类和不匹配可能导致定位精度下降。本研究提出了一种基于双目摄像头的新型视觉定位框架，用于估算查询摄像头的绝对位置。该框架集成了三种核心方法：基于多极约束的定位（MELoc）方法、最优关键点选择（OKS）方法和稳健测量方法。MELoc 构建了多个几何约束条件，只需一张数据库图像即可实现绝对位置估算，而 OKS 和稳健测量方法则通过完善这些几何约束条件的精度来进一步提高定位精度。实验结果表明，在不同的场景尺度、数据库采样间隔和照明条件下，所提出的系统始终优于现有的视觉定位系统。

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引用次数: 0

Real-time semantic segmentation for autonomous driving: A review of CNNs, Transformers, and Beyond 用于自动驾驶的实时语义分割：CNN、变形器及其他技术综述

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-04 DOI: 10.1016/j.jksuci.2024.102226

Mohammed A.M. Elhassan , Changjun Zhou , Ali Khan , Amina Benabid , Abuzar B.M. Adam , Atif Mehmood , Naftaly Wambugu

Real-time semantic segmentation is a crucial component of autonomous driving systems, where accurate and efficient scene interpretation is essential to ensure both safety and operational reliability. This review provides an in-depth analysis of state-of-the-art approaches in real-time semantic segmentation, with a particular focus on Convolutional Neural Networks (CNNs), Transformers, and hybrid models. We systematically evaluate these methods and benchmark their performance in terms of frames per second (FPS), memory consumption, and CPU runtime. Our analysis encompasses a wide range of architectures, highlighting their novel features and the inherent trade-offs between accuracy and computational efficiency. Additionally, we identify emerging trends, and propose future directions to advance the field. This work aims to serve as a valuable resource for both researchers and practitioners in autonomous driving, providing a clear roadmap for future developments in real-time semantic segmentation. More resources and updates can be found at our GitHub repository: https://github.com/mohamedac29/Real-time-Semantic-Segmentation-Survey

实时语义分割是自动驾驶系统的重要组成部分，准确高效的场景解读对确保安全和运行可靠性至关重要。本综述深入分析了最先进的实时语义分割方法，尤其关注卷积神经网络（CNN）、变形器和混合模型。我们系统地评估了这些方法，并根据每秒帧数（FPS）、内存消耗和 CPU 运行时间对其性能进行了基准测试。我们的分析涵盖了各种架构，突出了它们的新特点以及准确性和计算效率之间的内在权衡。此外，我们还确定了新兴趋势，并提出了推动该领域发展的未来方向。这项工作旨在为自动驾驶领域的研究人员和从业人员提供宝贵的资源，为实时语义分割的未来发展提供清晰的路线图。更多资源和更新请访问我们的 GitHub 存储库：https://github.com/mohamedac29/Real-time-Semantic-Segmentation-Survey

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引用次数: 0

TFDNet: A triple focus diffusion network for object detection in urban congestion with accurate multi-scale feature fusion and real-time capability TFDNet：用于城市拥堵路段物体检测的三重聚焦扩散网络，具有精确的多尺度特征融合和实时能力

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102223

Caoyu Gu , Xiaodong Miao , Chaojie Zuo

Vehicle detection in congested urban scenes is essential for traffic control and safety management. However, the dense arrangement and occlusion of multi-scale vehicles in such environments present considerable challenges for detection systems. To tackle these challenges, this paper introduces a novel object detection method, dubbed the triple focus diffusion network (TFDNet). Firstly, the gradient convolution is introduced to construct the C2f-EIRM module, replacing the original C2f module, thereby enhancing the network’s capacity to extract edge information. Secondly, by leveraging the concept of the Asymptotic Feature Pyramid Network on the foundation of the Path Aggregation Network, the triple focus diffusion module structure is proposed to improve the network’s ability to fuse multi-scale features. Finally, the SPPF-ELA module employs an Efficient Local Attention mechanism to integrate multi-scale information, thereby significantly reducing the impact of background noise on detection accuracy. Experiments on the VisDrone 2021 dataset reveal that the average detection accuracy of the TFDNet algorithm reached 38.4%, which represents a 6.5% improvement over the original algorithm; similarly, its mAP50:90 performance has increased by 3.7%. Furthermore, on the UAVDT dataset, the TFDNet achieved a 3.3% enhancement in performance compared to the original algorithm. TFDNet, with a processing speed of 55.4 FPS, satisfies the real-time requirements for vehicle detection.

在拥堵的城市场景中进行车辆检测对于交通管制和安全管理至关重要。然而，在这种环境中，多尺度车辆的密集排列和遮挡给检测系统带来了相当大的挑战。为了应对这些挑战，本文介绍了一种新颖的物体检测方法，即三重聚焦扩散网络（TFDNet）。首先，引入梯度卷积来构建 C2f-EIRM 模块，取代原有的 C2f 模块，从而增强网络提取边缘信息的能力。其次，在路径聚合网络的基础上，利用渐近特征金字塔网络的概念，提出了三重焦点扩散模块结构，提高了网络融合多尺度特征的能力。最后，SPPF-ELA 模块采用高效局部关注机制来整合多尺度信息，从而显著降低背景噪声对检测精度的影响。在 VisDrone 2021 数据集上的实验表明，TFDNet 算法的平均检测准确率达到了 38.4%，比原始算法提高了 6.5%；同样，其 mAP50:90 性能也提高了 3.7%。此外，在 UAVDT 数据集上，TFDNet 的性能比原始算法提高了 3.3%。TFDNet 的处理速度为 55.4 FPS，满足了车辆检测的实时要求。

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引用次数: 0

DNE-YOLO: A method for apple fruit detection in Diverse Natural Environments DNE-YOLO：在多样化自然环境中检测苹果果实的方法

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102220

Haitao Wu , Xiaotian Mo , Sijian Wen , Kanglei Wu , Yu Ye , Yongmei Wang , Youhua Zhang

The apple industry, recognized as a pivotal sector in agriculture, increasingly emphasizes the mechanization and intelligent advancement of picking technology. This study innovatively applies a mist simulation algorithm to apple image generation, constructing a dataset of apple images under mixed sunny, cloudy, drizzling and foggy weather conditions called DNE-APPLE. It introduces a lightweight and efficient target detection network called DNE-YOLO. Building upon the YOLOv8 base model, DNE-YOLO incorporates the CBAM attention mechanism and CARAFE up-sampling operator to enhance the focus on apples. Additionally, it utilizes GSConv and the dynamic non-monotonic focusing mechanism loss function WIOU to reduce model parameters and decrease reliance on dataset quality. Extensive experimental results underscore the efficacy of the DNE-YOLO model, which achieves a detection accuracy (precision) of 90.7%, a recall of 88.9%, a mean accuracy (mAP50) of 94.3%, a computational complexity (GFLOPs) of 25.4G, and a parameter count of 10.46M across various environmentally diverse datasets. Compared to YOLOv8, it exhibits superior detection accuracy and robustness in sunny, drizzly, cloudy, and misty environments, making it especially suitable for practical applications such as apple picking for agricultural robots. The code for this model is open source at https://github.com/wuhaitao2178827/DNE-YOLO.

苹果产业作为农业中举足轻重的行业，越来越重视采摘技术的机械化和智能化。本研究创新性地将雾气模拟算法应用于苹果图像生成，构建了一个名为 DNE-APPLE 的晴天、多云、小雨和大雾混合天气条件下的苹果图像数据集。它引入了一种名为 DNE-YOLO 的轻量级高效目标检测网络。在 YOLOv8 基本模型的基础上，DNE-YOLO 加入了 CBAM 注意机制和 CARAFE 上采样算子，以加强对苹果的关注。此外，它还利用 GSConv 和动态非单调聚焦机制损失函数 WIOU 来减少模型参数，降低对数据集质量的依赖。广泛的实验结果证明了 DNE-YOLO 模型的有效性，它在各种不同环境的数据集上实现了 90.7% 的检测准确率（精确度）、88.9% 的召回率、94.3% 的平均准确率（mAP50）、25.4G 的计算复杂度（GFLOPs）和 10.46M 的参数数。与 YOLOv8 相比，它在晴天、小雨、多云和雾霾环境中都表现出了更高的检测精度和鲁棒性，因此特别适合农业机器人采摘苹果等实际应用。该模型的代码开源于 https://github.com/wuhaitao2178827/DNE-YOLO。

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引用次数: 0

Energy-efficient resource allocation for UAV-aided full-duplex OFDMA wireless powered IoT communication networks 无人机辅助全双工 OFDMA 无线供电物联网通信网络的高能效资源分配

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102225

Tong Wang

The rapid development of wireless-powered Internet of Things (IoT) networks, supported by multiple unmanned aerial vehicles (UAVs) and full-duplex technologies, has opened new avenues for simultaneous data transmission and energy harvesting. In this context, optimizing energy efficiency (EE) is crucial for ensuring sustainable and efficient network operation. This paper proposes a novel approach to EE optimization in multi-UAV-aided wireless-powered IoT networks, focusing on balancing the uplink data transmission rates and total system energy consumption within an orthogonal frequency-division multiple access (OFDMA) framework. This involves formulating the EE optimization problem as a Multi-Objective Optimization Problem (MOOP), consisting of the maximization of the uplink total rate and the minimization of the total system energy consumption, which is then transformed into a Single-Objective Optimization Problem (SOOP) using the Tchebycheff method. To address the non-convex nature of the resulting SOOP, characterized by combinatorial variables and coupled constraints, we developed an iterative algorithm that combines Block Coordinate Descent (BCD) with Successive Convex Approximation (SCA). This algorithm decouples the subcarrier assignment and power control subproblems, incorporates a penalty term to relax integer constraints, and alternates between solving each subproblem until convergence is reached. Simulation results demonstrate that our proposed method outperforms baseline approaches in key performance metrics, highlighting the practical applicability and robustness of our framework for enhancing the efficiency and sustainability of real-world UAV-assisted wireless networks. Our findings provide insights for future research on extending the proposed framework to scenarios involving dynamic UAV mobility, multi-hop communication, and enhanced energy management, thereby supporting the development of next-generation sustainable communication systems.

在多种无人飞行器（UAV）和全双工技术的支持下，无线供电的物联网（IoT）网络发展迅速，为同时进行数据传输和能量采集开辟了新的途径。在这种情况下，优化能源效率（EE）对于确保网络的可持续高效运行至关重要。本文提出了一种在多无人机辅助的无线供电物联网网络中优化能效的新方法，重点是在正交频分多址（OFDMA）框架内平衡上行数据传输速率和系统总能耗。这涉及将 EE 优化问题表述为多目标优化问题（MOOP），包括上行链路总速率最大化和系统总能耗最小化，然后使用 Tchebycheff 方法将其转化为单目标优化问题（SOOP）。为了解决以组合变量和耦合约束为特征的 SOOP 的非凸性质，我们开发了一种结合了块坐标下降 (BCD) 和连续凸逼近 (SCA) 的迭代算法。该算法将子载波分配和功率控制子问题分离开来，加入惩罚项以放松整数约束，并交替解决每个子问题，直至达到收敛。仿真结果表明，我们提出的方法在关键性能指标上优于基准方法，突出了我们的框架在提高现实世界无人机辅助无线网络的效率和可持续性方面的实际适用性和稳健性。我们的研究结果为未来研究提供了启示，有助于将所提出的框架扩展到涉及无人机动态移动性、多跳通信和增强能源管理的场景，从而支持下一代可持续通信系统的开发。

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