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Optimization of resource allocation in FDD massive MIMO systems

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Digital Communications and Networks 2024年第1期10卷 117-125页

作者： Jun Cai Chuan Yin Youwei Ding school of Artificial intelligence and information Technology Nanjing University of Chinese MedicineNanjing 210046China school of electronic and information Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina

The performance of massive MIMO systems relies heavily on the availability of Channel State information at the Transmitter(CSIT).A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CsI feedback *** fact,the downlink communication generally includes three stages,i.e.,pilot training,CsI feedback,and data *** three stages are mutually related and jointly determine the overall system ***,there exist few studies on the reduction of csIT acquisition overhead from the global point of *** this paper,we integrate the Minimum Mean Square Error(MMSE)channel estimation,Random Vector Quantization(RVQ)based limited feedback and Maximal Ratio Combining(MRC)precoding into a unified framework for investigating the resource allocation *** particular,we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio(SNR)based on the deterministic equivalence *** the three performance metrics(the spectral efficiency,energy efficiency,and total energy consumption)oriented problems are formulated *** practical system requirements,these three metrics can be collaboratively ***,we propose an optimization solver to derive the optimal partition of channel coherence *** results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

关键词： Massive MIMO FDD CSIT Resource allocation

CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

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Computers, Materials & Continua 2024年第8期80卷 2761-2783页

作者： Nannan Chong Fan Yang school of electronic and information Engineering Hebei University of TechnologyTianjin300401China school of information and intelligence Engineering Tianjin Renai CollegeTianjin301636China

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural ***,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and *** can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for *** address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of ***,a novel lightweight feature extraction and matching network based on attention mechanism is *** network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese *** extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur *** and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level *** ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic *** results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage *** compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense ***

关键词： Feature extraction and matching lightweighted network medical images endoscopic attention

Rethinking attribute localization for zero-shot learning

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Science China(information Sciences) 2024年第7期67卷 184-196页

作者： Shuhuang CHEN Shiming CHEN Guo-Sen XIE Xiangbo SHU Xinge YOU Xuelong LI school of electronic information and Communication Huazhong University of Science and Technology school of Computer Science and Engineering Nanjing University of Science and Technology school of Artificial intelligence Optics and Electronics Northwestern Polytechnical University

Recent advancements in attribute localization have showcased its potential in discovering the intrinsic semantic knowledge for visual feature representations, thereby facilitating significant visual-semantic interactions essential for zero-shot learning(ZSL). However, the majority of existing attribute localization methods heavily rely on classification constraints, resulting in accurate localization of only a few attributes while neglecting the rest important attributes associated with other classes. This limitation hinders the discovery of the intrinsic semantic relationships between attributes and visual features across all *** address this problem, we propose a novel attribute localization refinement(ALR) module designed to enhance the model's ability to accurately localize all attributes. Essentially, we enhance weak discriminant attributes by grouping them and introduce weighted attribute regression to standardize the mapping values of semantic attributes. This module can be flexibly combined with existing attribute localization *** experiments show that when combined with the ALR module, the localization errors in existing methods are corrected, and state-of-the-art classification performance is achieved.

关键词： zero-shot learning attention mechanism attribute localization image classification

SAM3D: zero-shot 3D object detection via the segment anything model

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Science China(information Sciences) 2024年第4期67卷 331-332页

作者： Dingyuan ZHANG Dingkang LIANG Hongcheng YANG Zhikang ZOU Xiaoqing YE Zhe LIU Xiang BAI school of Artificial intelligence and Automation Huazhong University of Science and Technology Baidu Inc. school of electronic information and Communication Huazhong University of Science and Technology

In the past few years, foundation models have thrived and succeeded in linguistic and visual tasks, showing astonishing zero-shot and few-shot capabilities. Their advances encourage researchers and industries to extend the boundaries of what artificial intelligence can do and have shown some fantastic products(e.g., ChatGPT [1]) with the potential to change the world.

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Modifying Lyapunov exponent of chaotic map by self-cascading

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Science China(Technological Sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of electronic and information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China school of Artificial intelligence Nanjing University of Information Science&TechnologyNanjing 210044China school of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

PAPS: Progressive Attention-Based Pan-sharpening

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 391-404页

作者： Yanan Jia Qiming Hu Renwei Dian Jiayi Ma Xiaojie Guo College of intelligence and Computing Tianjin UniversityTianjin 300350 China IEEE school of Robotics Hunan UniversityChangsha 410082China electronic information school Wuhan UniversityWuhan 430072China

Pan-sharpening aims to seek high-resolution multispectral(HRMS) images from paired multispectral images of low resolution(LRMS) and panchromatic(PAN) images, the key to which is how to maximally integrate spatial and spectral information from PAN and LRMS images. Following the principle of gradual advance, this paper designs a novel network that contains two main logical functions, i.e., detail enhancement and progressive fusion, to solve the problem. More specifically, the detail enhancement module attempts to produce enhanced MS results with the same spatial sizes as corresponding PAN images, which are of higher quality than directly up-sampling LRMS *** a better MS base(enhanced MS) and its PAN, we progressively extract information from the PAN and enhanced MS images, expecting to capture pivotal and complementary information of the two modalities for the purpose of constructing the desired HRMS. Extensive experiments together with ablation studies on widely-used datasets are provided to verify the efficacy of our design, and demonstrate its superiority over other state-of-the-art methods both quantitatively and qualitatively. Our code has been released at https://***/JiaYN1/PAPS.

关键词： High-resolution multispectral image image fusion pan-sharpening progressive enhancement

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 622-637页

作者： Han Xu Jiayi Ma Yixuan Yuan Hao Zhang Xin Tian Xiaojie Guo the electronic information school Wuhan UniversityWuhan 430072China IEEE the Department of electronic Engineering Chinese University of Hong KongHong Kong 999077China the College of intelligence and Computing Tianjin UniversityTianjin 300350China

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of *** enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is *** contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better *** self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and *** response,this paper proposes a self-supervised enhancement method,termed as *** can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised *** attenuation is estimated based on physical principles and local neighborhood *** removal and correction of noise and color shift removal are solely realized with noisy images and images with color ***,the comprehensive and fully self-supervised approach can achieve better adaptability and *** is applicable to various low light conditions,and can reproduce the original color of scenes in natural *** experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art *** code is available at https://***/hanna-xu/SLIE.

关键词： Color correction low-light image enhancement self-supervised learning.

Re-Distributing Facial Features for Engagement Prediction with ModernTCN

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Computers, Materials and Continua 2024年第1期81卷 369-391页

作者： Li X. Zhu W. Li Q. Hou C. Zhang Y. College of information and Artificial intelligence Nanchang Institute of Science and Technology Nanchang 330108 China school of Electrical and information Engineering Wuhan Institute of Technology Wuhan 430205 China school of electronic information Engineering Wuhan Donghu University Wuhan 430212 China

Automatically detecting learners’ engagement levels helps to develop more effective online teaching and assessment programs, allowing teachers to provide timely feedback and make personalized adjustments based on students’ needs to enhance teaching effectiveness. Traditional approaches mainly rely on single-frame multimodal facial spatial information, neglecting temporal emotional and behavioural features, with accuracy affected by significant pose variations. Additionally, convolutional padding can erode feature maps, affecting feature extraction’s representational capacity. To address these issues, we propose a hybrid neural network architecture, the redistributing facial features and temporal convolutional network (RefEIP). This network consists of three key components: first, utilizing the spatial attention mechanism large kernel attention (LKA) to automatically capture local patches and mitigate the effects of pose variations; second, employing the feature organization and weight distribution (FOWD) module to redistribute feature weights and eliminate the impact of white features and enhancing representation in facial feature maps. Finally, we analyse the temporal changes in video frames through the modern temporal convolutional network (ModernTCN) module to detect engagement levels. We constructed a near-infrared engagement video dataset (NEVD) to better validate the efficiency of the RefEIP network. Through extensive experiments and in-depth studies, we evaluated these methods on the NEVD and the Database for Affect in Situations of Elicitation (DAiSEE), achieving an accuracy of 90.8% on NEVD and 61.2% on DAiSEE in the four-class classification task, indicating significant advantages in addressing engagement video analysis problems. © 2024 The Authors. Published by Tech Science Press.

关键词： Engagement prediction re-distributing facial features spatiotemporal network temporal convolutional network

Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution

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Science China(information Sciences) 2024年第7期67卷 107-132页

作者： Bin CAO Jianwei ZHAO Xin LIU Yun LI State Key Laboratory of Reliability and intelligence of Electrical Equipment Hebei University of Technology school of Artificial intelligence Hebei University of Technology school of Economics and Management Hebei University of Technology Industrial Artificial intelligence Centre Shenzhen Institute for Advanced StudyUniversity of Electronic Science and Technology of China

Mobile telemedicine systems based on the next-generation communication will significantly enhance deep fusion of network automation and federated learning(FL), but data privacy is a paramount issue in sectors like healthcare. This work hence considers FL augments 5G-and-beyond networks by training deep learning(DL) models without the need to exchange raw data. The substantial communication loads imposed on by extensive parameters involved in DL models are managed through adaptive scheduling mechanisms effectively. To address the opaque nature of DL models and to improve the interpretability of FL models, we introduce a convolutional fuzzy rough neural network specifically designed for medical image processing. We also develop a multiobjective memetic evolutionary algorithm to streamline and optimize the neural network architectures. Our comprehensive FL framework integrates smart scheduling, interpretable fuzzy rough logic,and neuroevolution. This framework is shown to improve communication efficiency, increase interpretability of diagnosis with protected privacy, and generate low-complexity neural architectures.

关键词： 5G-and-beyond network interpretable federated learning mobile telemedicine system fuzzy rough set theory neuroevolution