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IEEE International Symposium on Biomedical Imaging

作者： Eugenia Mylona Dimitrios I. Zaridis Charalampos N. Kalantzopoulos Vasileios C. Pezoulas Nikolaos S. Tachos George K. Matsopoulos Daniele Regge Nikolaos Papanikolaou Manolis Tsiknakis Kostas Marias Dimitrios I. Fotiadis Biomedical Research Institute FORTH Ioannina Greece Unit of Medical Technology Intelligent Information Systems University of Ioannina Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece Institute of Computer Science FORTH Heraklion Greece Department of Radiology Candiolo Cancer Institute FPO-IRCCS Strada Provinciale Candiolo Italy Computational Clinical Imaging Group Champalimaud Foundation Portugal Computational Biomedicine Laboratory Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability.

关键词： Deep learning Support vector machines Analytical models Biological system modeling Feature extraction Transformers Lesions Prostate cancer Radiomics Principal component analysis

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A Subspace Sparsity Driven Knowledge Transfer Strategy for Dynamic Constrained Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Chen, Guoyu Guo, Yinan Li, Changhe Wang, Feng Gong, Dunwei Yuan, Liang Anhui University of Science and Technology School of Artificial Intelligence Hefei231131 China State Key Laboratory of Intelligent Coal Mining and Strata Control Beijing100013 China China University of Mining and Technology School of Mechanical and Electrical Engineering Beijing100083 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine Huainan232001 China Wuhan University School of Computer Science Wuhan430072 China Qingdao University of Science and Technology School of Information Science and Technology Qingdao266061 China Anhui University of Science and Technology School of Safety Science and Engineering Huainan232001 China

Dynamic constrained multiobjective optimization problems (DCMOPs) require algorithms to quickly track the feasible Pareto optima under dynamic environments. The existing dynamic constrained multiobjective evolutionary algorithms (DCMOEAs) normally focus on the convergence speed, but cannot well guarantee distribution. To address this issue, a subspace sparsity driven knowledge transfer strategy based DCMOEA is developed in this article, called SSDKT. First, reference points are introduced to partition objective space into multiple subspaces. Subsequently, the feasibility of each subspace is determined by the distribution of all historical feasible optimal solutions in it, and defined as the sparsity of subspace. A predictor based on the gated recurrent unit (GRU) network is further constructed to estimate the sparsity under the future environment. Once a new environment appears, a subspace transfer strategy is designed to generate an initial population. In each feasible subspace, the GRU-based prediction method is developed and competed with Kalman filter to generate the initial solution under the new environment. Based on the predicted solution of the nearest feasible neighbor, a potential initial individual in each infeasible subspace is produced by transferring the corresponding knowledge. The experimental results on various benchmarks verify that, compared with several state-of-the-art DCMOEAs, the proposed algorithm achieves the most competitive performance in solving DCMOPs. © 1997-2012 IEEE.

关键词： Constrained optimization

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Consensus control for Discrete-Time Stochastic Multiagent systems Under a Multiple Description Coding Mechanism

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IEEE systems Journal 2025年

作者： Wang, Licheng Wang, Zidong Qian, Wei Du, Shuxin Shanghai University of Electric Power College of Automation Engineering Shanghai 200090 China Brunel University London Department of Computer Science Uxbridge UB8 3PH United Kingdom Henan Polytechnic University School of Electrical Engineering and Automation Jiaozuo 454000 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou 313000 China

This article addresses the consensus control problem for a specific class of discrete-time stochastic multiagent systems (MASs). When transmitting the local measurement output to the local controller and neighboring agents, a multiple description coding (MDC) scheme is introduced to reduce the communication burden and enhance data transmission reliability in a resource-constrained environment. The MDC scheme encodes each signal into two descriptions, which are then transmitted through separate channels, and decoding schemes are employed to address the different characteristics of the arrival of the two descriptions, ensuring the boundedness of the decoding error. The proposed consensus control scheme uses the relative decoded measurement errors between local agents and their neighbors. The aim is to design an output-feedback control scheme that ensures the error dynamics of the controlled MAS reach exponentially mean-square boundedness. Sufficient conditions are established for the existence of the controllers through stochastic analysis techniques, and the desired controller gains are parameterized using the feasibility of certain matrix inequalities. The effectiveness of the proposed coding-decoding-based consensus control scheme is verified through a numerical simulation. © 2007-2012 IEEE.

关键词： Consensus control multiagent systems (MASs) multiple description coding (MDC) packet dropout stochastic systems

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Constrained temperature and relative humidity predictive control:Agricultural greenhouse case of study

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Information Processing in Agriculture 2024年第3期11卷 409-420页

作者： Hafsa Hamidane Samira EL Faiz Iliass Rkik Mohamed El Khayat Mohammed Guerbaoui Abdelali Ed-Dahhak Abdeslam Lachhab Computer Engineering and Intelligent Electrical Systems Laboratory ModelingMaterials and Control of Systems TeamHigher School of TechnologyMoulay Ismail University of MeknesMorocco

The importance of Model Predictive control(MPC)has significant applications in the agricultural industry,more specifically for greenhouse’s control ***,the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the *** methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed *** this paper,we introduce an application of Constrained Model Predictive control(CMPC)for a greenhouse temperature and relative *** this purpose,two Multi Input Single Output(MISO)systems,using Numerical Subspace State Space System Identification(N4SID)algorithm,are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation *** this sense,linear state space models were adopted in order to evaluate the robustness of the control *** the system is identified,the MPC technique is applied for the temperature and the humidity *** results show that the regulation of the temperature and the relative humidity under constraints was guaranteed,both parameters respect the ranges 15℃≤T_(int)≤30℃and 50%≤H_(int)≤70%*** the other hand,the control signals uf and uh applied to the fan and the heater,respect the hard constraints notion,the control signals for the fan and the heater did not exceed 0≤uf≤4.3 Volts and 0≤uh≤5 Volts,respectively,which proves the effectiveness of the MPC and the tracking ***,we show that with the proposed technique,using a new optimization toolbox,the computational complexity has been significantly *** greenhouse in question is devoted to Schefflera Arboricola cultivation.

关键词： Constrained model predictive control Greenhouse MISO systems Optimization Relative humidity Temperature

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Sequential fusion estimation for multisensor systems with non-Gaussian noises

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Science China(Information Sciences) 2020年第12期63卷 153-165页

作者： Liping YAN Chenying DI Q.M.Jonathan WU Yuanqing XIA Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Department of Electrical and Computer Engineering University of Windsor

The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.

关键词： state estimation sequential fusion non-Gaussian disturbance heavy-tailed noise multivariate t-distribution

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TEA-IHPCR: Temporal Augmented-Invariant Vehicle-Infrastructure Cooperative Heterogeneous Point Cloud Registration

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Zhang, Shiyan Zhang, Xu Zhang, Xinyu Chen, Yuxi Zhu, Dongwei Wu, Fan Jin, Dafeng Li, Jun Tsinghua University State Key Laboratory of Automotive Safety and Energy School Vehicles and Mobility Beijing China Tsinghua University Suzhou Automotive Research Institute Suzhou China Nanyang Technological University School of Electrical and Electronic Engineering Singapore Singapore Tsinghua University State Key Laboratory of Automotive Safety and Energy Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Forestry University School of Technology Beijing China Shanghai Polytechnic University School Faculty of Intelligent Manufacturing and Control Engineering Shanghai China Tsinghua University State Key Laboratory of Automotive Safety and Energy The School of Vehicle and Mobility Beijing China

In the field of autonomous driving, relying solely on environmental information captured by single vehicle's LiDAR often falls short of perception requirements in complex scenarios. Adopting a vehicle-infrastructure cooperation approach can effectively address information limitations. Therefore, the development of an algorithm that can register heterogeneous LiDAR point clouds is crucial. This paper proposes a vehicle-infrastructure cooperative heterogeneous point cloud registration network (TEA-IHPCR) based on temporal augmented information and rotation invariance. By capturing temporal augmented information between continuous frames of point clouds, data augmentation is performed on the point cloud data from both vehicle and infrastructure sides, mitigating issues of poor heterogeneous point cloud registration caused by perspective differences. The rotation invariance property can address the issue of aligning coordinate systems between the vehicle and infrastructure sides that have not been collaboratively calibrated. Simultaneously, the use of a local-level encoder capable of extracting point cloud features with rotation invariance, combined with a global-level attention mechanism that can learn cross-frame features, improves the model's performance in low-overlap rate point cloud registration. The experiments were conducted on a vehicle-infrastructure collaborative dataset while the results demonstrate the effectiveness of the proposed module in the network, as well as its superiority and efficiency compared to other networks. © 1963-2012 IEEE.

关键词： Spatio-temporal data

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