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International Conference on computer Research and Development (ICCRD)

作者： Hui Zhou Xiaofeng Wang YuJia Xie Yongxiang Cao Yu Tang Yongchun Huo Huan Wen Xiongbo Zhao Chaoran Li Beijing Aerospace Automatic Control Institute National key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China Computer Science and Engineering Beihang University Beijing China National Superior College for Engineers Beihang University Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-the-art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-to-end computation time for

关键词： YOLO Rockets Power demand Convolution Multicore processing Reviews Scalability Accelerator architectures Real-time systems Field programmable gate arrays

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A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Rehman, Khalil Ur Jianqiang, Li Yasin, Anaa Bilal, Anas Basheer, Shakila Ullah, Inam Jabbar, Muhammad Kashif Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Beijing University of Technology College of Software Engineering Beijing100124 China Hainan Normal University College of Information Science and Technology Hainan Haikou571158 China Princess Nourah Bint Abdulrahman University Department of Information Systems College of Computer and Information Science Riyadh11671 Saudi Arabia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity. To address these challenges, we propose a novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, to improve accuracy and efficiency in AD detection. Our approach mitigates issues related to texture fixation, background boundaries, and deep neural network limitations, enhancing the robustness of AD classification in mammograms. Experimental results demonstrate that the proposed model achieves state-of-the-art performance, outperforming eight existing deep learning models. On the PINUM dataset, it attains 0.97 sensitivity, 0.92 F1-score, 0.93 precision, 0.94 specificity, and 0.96 accuracy. On the DDSM dataset, it records 0.93 sensitivity, 0.91 F1-score, 0.94 precision, 0.92 specificity, and 0.95 accuracy. These results highlight the potential of our method for computer-aided breast cancer diagnosis, particularly in low-resource settings where access to high-end imaging technology is limited. By enabling more accurate and timely AD detection, our approach could significantly improve breast cancer screening and early intervention worldwide. © 2013 IEEE.

关键词： Deep neural networks

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Current Challenges and Advancements of Aerial Thermography for Outdoor Structural Health Monitoring: A Review

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IEEE Sensors Journal 2025年

作者： Ma, Yue Zeng, Zhi Luo, Zipei Tao, Ning Deng, Lei Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Chongqing Normal University College of Computer and Information Science Chongqing401331 China Capital Normal University Department of Physics Key Lab of Terahertz Optoelectronics of the Ministry of Education Beijing100048 China

In the rapid development of outdoor structural health monitoring, infrared thermal imaging and unmanned aerial vehicles (UAVs) have emerged as transformative tools, significantly enhancing maintenance efficiency in expansive outdoor structures. This review highlights the advancements in UAV-assisted thermography, focusing on its impact on the monitoring capabilities of outdoor engineering structures. It introduces UAV-assisted thermographic methods tailored for outdoor environments, explorees current challenges influencing their efficacy, and thoroughly examines their integration with machine learning techniques, demonstrating their applications in identifying structural flaws and deformations. Furthermore, the review emphasizes the transformative potential of 3D thermographic modelling to localize and characterize defects effectively. It contributes actionable insights to optimize UAV-assisted thermographic practices. The findings underscore the importance of integrating UAVs and thermography to overcome existing limitations in outdoor structural health monitoring inspections, setting the stage for a paradigm shift in large-scale outdoor structural condition assessments. © 2001-2012 IEEE.

关键词： Thermography (imaging)

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

arXiv

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arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative Systems

arXiv

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arXiv 2025年

作者： de Berardinis, Jacopo Porcaro, Lorenzo Meroño-Peñuela, Albert Cangelosi, Angelo Buckley, Tess Department of Computer Science University of Liverpool United Kingdom Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Informatics King’s College London United Kingdom Department of Computer Science University of Manchester United Kingdom Association of AI Ethicists

Generative AI is radically changing the creative arts, by fundamentally transforming the way we create and interact with cultural artefacts. While offering unprecedented opportunities for artistic expression and commercialisation, this technology also raises ethical, societal, and legal concerns. Key among these are the potential displacement of human creativity, copyright infringement stemming from vast training datasets, and the lack of transparency, explainability, and fairness mechanisms. As generative systems become pervasive in this domain, responsible design is crucial. Whilst previous work has tackled isolated aspects of generative systems (e.g., transparency, evaluation, data), we take a comprehensive approach, grounding these efforts within the Ethics Guidelines for Trustworthy Artificial Intelligence produced by the High-Level Expert Group on AI appointed by the European Commission – a framework for designing responsible AI systems across seven macro requirements. Focusing on generative music AI, we illustrate how these requirements can be contextualised for the field, addressing trustworthiness across multiple dimensions and integrating insights from the existing literature. We further propose a roadmap for operationalising these contextualised requirements, emphasising interdisciplinary collaboration and stakeholder engagement. Our work provides a foundation for designing and evaluating responsible music generation systems, calling for collaboration among AI experts, ethicists, legal scholars, and artists. This manuscript is accompanied by a website: https://***/raim-framework/. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Non-contact all-optic OCT–PAM imaging with shared detection light

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Applied Optics 2025年第13期64卷 3431-3437页

作者： Ning Ding Ben Xiang Huiwen Jiang Jinyun Yue Yao Yu Jian Liu Yuqian Zhao Jingmin Luan Yanqiu Yang Yi Wang Zhenhe Ma College of Information Science and Engineering Northeastern University Shenyang 110819 China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004 China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 China

We proposed a non-contact all-optic OCT–PAM dual-modal system based on a single detection light source. A homodyne low-coherence interferometer was adopted to detect the vibration induced by photoacoustic excitation. The optical coherence tomography (OCT) and photoacoustic microscopy (PAM) shared the same broadband light source for signal detection, thus simplifying the dual-modal system and making it compact. Using the proposed system, we performed absorption-scattering imaging of human hairs, measured the femoral artery and vein flow rates in mice, and imaged the auricle blood vessels in mice in vivo . The combination of OCT and PAM can provide complementary biological tissue information and offer a richer reference for the diagnosis of diseases.

关键词： Biomedical imaging Fabry Perot interferometers Imaging systems Imaging techniques Optical imaging Tissue imaging

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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GrasOpen: Biometric Authentication via Reach-and-Grasp for Smart Door Access Using Smartwatch

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IEEE Internet of Things Journal 2025年

作者： Ding, Lei Shi, Jiale Wu, Yuan Hu, Xinrong Bi, Hongliang Chen, Yanjiao Wuhan Textile University School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information 430200 China China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang University College of Electrical Engineering Hangzhou310027 China

In the domain of smart devices, biometric identity authentication has become a leading and crucial technology, mainly because of its improved security and user convenience. Traditional methods frequently depend on complex activities, facial recognition, or passwords, which can be cumbersome and error-prone. This paper presents GrasOpen, an innovative biometric authentication system tailored for door-opening scenarios, utilizing the natural reach-and-grasp motion linked to door handles. The system employs smartwatches with accelerometers and gyroscopes to track and analyze arm movements, ensuring a seamless and intuitive user experience. GrasOpen tackles key challenges by leveraging the unique characteristics of the reach-and-grasp motion, eliminating the necessity for users to remember complex motions, and avoiding redundant actions like waiting for facial recognition. Specifically, GrasOpen initially proposes a lightweight model to discern door-opening actions from daily activities. Then, to realize the robust authentication system, GrasOpen integrates a complementary filter (CF) method to capture diversity-dependent features in door-opening actions. These features are subsequently processed through a modified ConvBoost model for precise user authentication. Experimental results reveal an impressive accuracy of 98.78% for activity recognition and 98.65% for identity authentication, highlighting GrasOpen’s excellence in function and performance. The system’s security and robustness are validated across diverse authentication environments. © 2014 IEEE.

关键词： Biometrics

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