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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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Computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

Adaptive visibility graph neural network and it's application in modulation classification

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arXiv 2021年

作者： Xuan, Qi Qiu, Kunfeng Zhou, Jinchao Chen, Zhuangzhi Xu, Dongwei Zheng, Shilian Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Our digital world is full of time series and graphs which capture the various aspects of many complex systems. Traditionally, there are respective methods in processing these two different types of data, e.g., Recurrent Neural Network (RNN) and Graph Neural Network (GNN), while in recent years, time series could be mapped to graphs by using the techniques such as Visibility Graph (VG), so that researchers can use graph algorithms to mine the knowledge in time series. Such mapping methods establish a bridge between time series and graphs, and have high potential to facilitate the analysis of various real-world time series. However, the VG method and its variants are just based on fixed rules and thus lack of flexibility, largely limiting their application in reality. In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AVGNet, by utilizing GNN model DiffPool as the classifier. We then adopt AVGNet for radio signal modulation classification which is an important task in the field of wireless communication. The simulations validate that AVGNet outperforms a series of advanced deep learning methods, achieving the state-of-the-art performance in this task. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

Path Planning for Mars Rovers Using Constraint-Based Approach on Path Length and Turning Radius

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SSRN

SSRN 2023年

作者： Dai, Juan Li, Jianfeng Zhu, Shengtao Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China School of Automation Beijing Information Science & Technology University Beijing100192 China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing100101 China

An improved dynamic window approach is proposed for local path planning of the Mars rover based on its constraint characteristics and kinematic model. The dynamic window algorithm is commonly utilized in intelligent rover obstacle avoidance and unmanned driving due to its simple principle and easy implementation. The proposed approach can effectively satisfy the dynamic obstacle avoidance requirements of the Mars rover. However, the traditional dynamic window method may suffer from local motion dead zones and longplanned paths. To resolve the issue of dynamic obstacle avoidance for the Mars rover, a length limitation vector is proposed to alleviate the problem of motion dead zone and enable dynamic obstacle recognition. Moreover, a new dynamic window space is generated by incorporating a turning radius constraint to accommodate the Mars rover’s large turning radius. The evaluation function is optimized to achieve a safe, efficient, and smooth path plan for the Mars rover. © 2023, The Authors. All rights reserved.

关键词： Motion planning

Detecting and Corrupting Convolution-based Unlearnable Examples

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arXiv 2023年

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN

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arXiv 2023年

作者： Hu, Hongwen Ye, Miao Zhao, Chenwei Jiang, Qiuxiang Wang, Yong Qiu, Hongbing Deng, Xiaofang School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China School of Information and Communication Guilin University of Electronic Technology Guilin541004 China

Multicast communication technology is widely applied in wireless environments with a high device density. Traditional wireless network architectures have difficulty flexibly obtaining and maintaining global network state information and cannot quickly respond to network state changes, thus affecting the throughput, delay, and other QoS requirements of existing multicasting solutions. Therefore, this paper proposes a new multicast routing method based on multiagent deep reinforcement learning (MADRL-MR) in a software-defined wireless networking (SDWN) environment. First, SDWN technology is adopted to flexibly configure the network and obtain network state information in the form of traffic matrices representing global network links information, such as link bandwidth, delay, and packet loss rate. Second, the multicast routing problem is divided into multiple subproblems, which are solved through multiagent cooperation. To enable each agent to accurately understand the current network state and the status of multicast tree construction, the state space of each agent is designed based on the traffic and multicast tree status matrices, and the set of AP nodes in the network is used as the action space. A novel single-hop action strategy is designed, along with a reward function based on the four states that may occur during tree construction: progress, invalid, loop, and termination. Finally, a decentralized training approach is combined with transfer learning to enable each agent to quickly adapt to dynamic network changes and accelerate convergence. Simulation experiments show that MADRL-MR outperforms existing algorithms in terms of throughput, delay, packet loss rate, etc., and can establish more intelligent multicast routes. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Simple Full-Spectrum Correlated k-Distribution Model based on Multilayer Perceptron

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arXiv 2024年

作者： Wang, Xin Kuang, Yucheng Wang, Chaojun Di, Hongyuan He, Boshu School of Information and Communication Engineering Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing102206 China Beijing100176 China Institute of Combustion and Thermal Systems School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China

While neural networks have been successfully applied to the full-spectrum k-distribution (FSCK) method at a large range of thermodynamics with k-values predicted by a trained multilayer perceptron (MLP) model, the required a-values still need to be calculated on-the-fly, which theoretically degrades the FSCK method and may lead to errors. On the other hand, too complicated structure of the current MLP model inevitably slows down the calculation efficiency. Therefore, to compensate among accuracy, efficiency and storage, the simple MLP designed based on the nature of FSCK method are developed, i.e., the simple FSCK MLP (SFM) model, from which those correlated k-values and corresponding ka-values can be efficiently obtained. Several test cases have been carried out to compare the developed SFM model and other FSCK tools including look-up tables and traditional FSCK MLP (TFM) model. Results show that the SFM model can achieve excellent accuracy that is even better than look-up tables at a tiny computational cost that is far less than that of TFM model. Considering accuracy, efficiency and portability, the SFM model is not only an excellent tool for the prediction of spectral properties, but also provides a method to reduce the errors due to nonlinear effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Multilayer neural networks

An Intelligent SDWN Routing Algorithm Based on Network Situational Awareness and Deep Reinforcement Learning

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arXiv 2023年

作者： Li, Jinqiang Ye, Miao Huang, Linqiang Deng, Xiaofang Qiu, Hongbing Wang, Yong School of Information and Communication Technology Guilin University of Electronic Technology Guilin541000 China Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Engineering Technology Research Center of Cloud Security and Cloud Service Guilin University of Electronic Technology Guilin541004 China

Due to the highly dynamic changes in wireless network topologies, efficiently obtaining network status information and flexibly forwarding data to improve communication quality of service are important challenges. This article introduces an intelligent routing algorithm (DRL-PPONSA) based on proximal policy optimization deep reinforcement learning with network situational awareness under a software-defined wireless networking architecture. First, a specific data plane is designed for network topology construction and data forwarding. The control plane collects network traffic information, sends flow tables, and uses a GCN-GRU prediction mechanism to perceive future traffic change trends to achieve network situational awareness. Second, a DRL-based data forwarding mechanism is designed in the knowledge plane. The predicted network traffic matrix and topology information matrix are treated as the environment for DRL agents, while next-hop adjacent nodes are treated as executable actions. Accordingly, action selection strategies are designed for different network conditions to achieve more intelligent, flexible, and efficient routing control. The reward function is designed using network link information and various reward and penalty mechanisms. Additionally, importance sampling and gradient clipping techniques are employed during gradient updating to enhance convergence speed and stability. Experimental results show that DRL-PPONSA outperforms traditional routing methods in network throughput, delay, packet loss rate, and wireless node distance. Compared to value-function-based Dueling DQN routing, the convergence speed is significantly improved, and the convergence effect is more stable. Simultaneously, its consumption of hardware storage space is reduced, and efficient routing decisions can be made in real-time using the current network state information. The source code can be accessed at https://***/GuetYe/DRL-PPONSA. Copyright © 2023, The Authors. All rig

关键词： Reinforcement learning

Embodied Intelligence Empowering Customized Manufacturing: Architecture, Opportunities, and Challenges

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IEEE Access 2025年 13卷 92740-92755页

作者： Jinbiao Tan Jianhua Shi Ligang Wu Baotong Chen Hao Tang Chunhua Zhang Wujie Zhang Shiyong Wang Jiafu Wan School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China School of Mechanical and Electrical Engineering Shanxi Datong University Datong China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China School of Information and Communication Engineering Hainan University Haikou China

With the continued advancement of Artificial Intelligence (AI) technology in Customized Manufacturing (CM), the current intelligence model, which separates ‘perception’ from ‘execution,’ lacks adaptability and generalizability. Embodied Intelligence (EI), an emerging technology emphasizing real-time environmental interaction and feedback, is expected to enable integrated intelligent manufacturing systems characterized by ‘perception-cognition-execution-feedback,’ enhancing the performance and intelligence of multifactor systems like the Internet of Manufacturing Things (IoMT), equipment management, and resource scheduling. However, current AI systems in CM remain isolated and lack methods for environmental interaction with multi-source perception and feedback, hindering the development of autonomous, evolving intelligent systems. To address the environmental interaction bottlenecks among diverse production elements in CM, this paper proposes a Circular Embodied Intelligence Manufacturing (CEIM) architecture aimed at enabling the fusion of heterogeneous production-element information. The objective is to enhance environmental perception and establish autonomous system evolution mechanisms, thereby optimizing production decision-making. By integrating sensor data and AI model outputs, and leveraging the advanced reasoning capabilities of large language models (LLMs), CEIM facilitates the semantic fusion of multiple production elements—including IoMT, intelligent devices, and manufacturing resources—to enable the deployment of EI applications in CM. The implementation of CEIM is illustrated and validated through a case study on a customized gift box packaging platform. Finally, this paper discusses the opportunities and challenges of applying EI in manufacturing and aims to provide insights into the future development of EI-oriented manufacturing systems.

关键词： Manufacturing Production Artificial intelligence Computer architecture Decision making Data models Collaboration Robot sensing systems Optimization Job shop scheduling

Fuzz Testing for Rust Library Functions

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Fuzz Testing for Rust Library Functions

IEEE International Conference on Trust, security and Privacy in Computing and communications (TrustCom)

作者： Yongjian Guo Xi Xiao Yuanyi Lin Hao Li Xiangbo Wu Tao Zhou Shenzhen International Graduate School Tsinghua University Shenzhen China Science and Technology on Communication Networks Laboratory Shijiazhuang China Guangdong Provincial Key Laboratory of Cyber and Information Security Vulnerability Research Guangzhou China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Rust is an emerging programming language that has gained popularity in many academic and industrial projects due to its memory safety features. However, despite its safety advantages, there are still many serious bugs in existing Rust projects. The lack of suitable fuzz targets has been a major challenge in Rust fuzz testing, as existing fuzzing tools often rely on manual efforts to write test targets, which is time-consuming and cannot be directly applied to library function interfaces. While the previous work has made significant progress in generating fuzz targets for Rust, it still cannot effectively test special functions with generics and *** address this issue, this paper proposes an automated approach for generating fuzz targets for Rust library functions, called RVFuzz. RVFuzz addresses the challenges by performing static analysis through variable instantiation, ensuring that special functions in Rust, such as those with generics and polymorphism, are covered in the interface sequence analysis. It then traverses the library function dependency graph to generate a set of reasonable and non-redundant target function sequences for fuzz testing. Any compilation errors encountered during the process are used to refine the sequence generation. RVFuzz successfully generated fuzz targets for 10 popular Rust projects, achieving an impressive interface coverage rate of 92.3%.

关键词： Privacy Computer languages Sequences Computer bugs Static analysis Manuals Fuzzing