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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Expediting In-Network Federated Learning by Voting-Based Con...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaoxin Su Yipeng Zhou Laizhong Cui Song Guo College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic.

关键词： Training Data privacy Quantization (signal) Federated learning Computational modeling Memory management Collaboration

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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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Chiral materials meet perovskite solar cells

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Green Carbon 2024年第4期2卷 405-406页

作者： Tie Liu Chongwen Li Ying Li Michael Saliba Hui-Ming Cheng Zaiwei Wang Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Electrical and Computer Engineering University of TorontoToronto M5S 1A4Canada Institute for Photovoltaics(IPV) University of StuttgartPfaffenwaldring 47Stuttgart D-70569Germany Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced TechnologyShenzhen 518055China

Perovskite solar cells(PSCs)have attracted significant interest in the photovoltaic field because of their remarkable efficiency,low cost,and straightforward fabrication process[1].However,the long-term stability of PSCs remains a *** major source of instability is the low mechanical reliability of heterointerfaces[2].Specifically,a thermal expansion coefficient mismatch between adjacent functional layers and inevitable temperature variations during day/night cycles can lead to interfacial sliding and void formation.

关键词： perovskite straight interfacial

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Image segmentation

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Pruning Dynamic Group Convolution with Static Substitute

Pruning Dynamic Group Convolution with Static Substitute

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Che, Jieyong Wang, Chong Chen, Weiwei Dai, Xinmiao Wang, Jun Wu, Jiafei Ningbo University Faculty of Electrical Engineering and Computer Science China School of Information and Control Engineering China University of Mining and Technology China SenseTime Research China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Deep learning networks are gradually deployed in edge applications, such as phones and cameras, which has a restriction of the computational resources. Thus, to improve the computational efficiency, numerous types of group convolution-based frameworks have been studied, including the dynamic group convolution (DGC). However, it is worth noting that sometimes the same channels are selected in different dynamic heads in DGC, which violates its original intention. It indicates the number of dynamic heads exceeds what is really needed. In this paper, a novel model by pruning dynamic group convolution with static substitute is proposed. Specifically, those channels pruned in the dynamic head can be reselected in the static head of the group convolution in the proposed model. In addition, a new polarization regularization is introduced to prune more useless channels with less accuracy loss. The experiment results on two image classification benchmarks (CIFAR-I00 and TinylmageNet-200) show promising performance. © 2022 IEEE.

关键词： Convolution

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NOVEL INSTANCE MINING WITH PSEUDO-MARGIN EVALUATION FOR FEW-SHOT OBJECT DETECTION 47

NOVEL INSTANCE MINING WITH PSEUDO-MARGIN EVALUATION FOR FEW-...

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2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022

作者： Liu, Weijie Wang, Chong Yu, Shenghao Tao, Chenchen Wang, Jun Wu, Jiafei Faculty of Electrical Engineering and Computer Science Ningbo University China School of Information and Control Engineering China University of Mining and Technology China SenseTime Research China

ISBN: (纸本)9781665405409

Few-shot object detection (FSOD) enables the detector to recognize novel objects only using limited training samples, which could greatly alleviate model's dependency on data. Most existing methods include two training stages, namely base training and fine-tuning. However, the unlabeled novel instances in the base set were untouched in previous works, which can be re-used to enhance the FSOD performance. Thus, a new instance mining model is proposed in this paper to excavate the novel samples from the base set. The detector is thus fine-tuned again by these additional free novel instances. Meanwhile, a novel pseudo-margin evaluation algorithm is designed to address the quality problem of pseudo-labels brought by those new novel instances. The experimental results on MS-COCO dataset show the effectiveness of the proposed model, which does not require any additional training samples or parameters. Our code is available at: https://***/liuweijie19980216/NimPme. © 2022 IEEE

关键词： Object detection

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Increased dependence on mycorrhizal fungi for nutrient acquisition under carbon limitation by tree girdling

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Plant Diversity 2025年第3期47卷 466-478页

作者： Chen, Jing Cao, Jingjing Guo, Binglin Han, Meixu Feng, Zhipei Tang, Jinqi Mo, Xiaohan Wang, Junjian Yang, Qingpei Pei, Yuxin Kuzyakov, Yakov Ding, Junxiang Makita, Naoki Yang, Xitian Zhang, Haiyang Zhao, Yong Kong, Deliang College of Forestry Henan Agricultural University Zhengzhou 450002 China College of Horticulture China Agricultural University Beijing 100193 China School of Urban Planning and Design Peking University Shenzhen Graduate School Peking University Shenzhen 518055 China State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Soil Science of Temperate Ecosystem University of Göttingen Göttingen 37077 Germany Peoples Friendship University of Russia (RUDN University) Moscow 117198 Russian Federation College of Ecology and Environment Zhengzhou University Zhengzhou 450001 China Faculty of Science Shinshu University 3-1-1 Asahi Nagano Matsumoto 390-8621 Japan Hawkesbury Institute for the Environment Western Sydney University Penrith 2751 NSW Australia College of Life Science Hebei University Baoding 071002 China

Nutrient acquisition through symbiotic ectomycorrhizal fungi is carbon (C) costly but fundamental for plant growth, community, and ecosystem functioning. Here, we examined the functions of roots and mycorrhiza with respect to nutrient uptake after artificially inducing C limitation-seven months after girdling of an ectomycorrhizal tree, Pinus taeda. Root physiological activity (measured as root nitrogen content and root exudation) declined after girdling and was accompanied with 110% and 340% increases in mycorrhizal colonization and extramatrical hyphal length, respectively. Fungi colonizing roots switched to a community characterized by higher C efficiency (lower C cost) of nutrient acquisition (CENA, the amount of nutrient acquisition per unit C cost) and lower network complexity, indicating a tradeoff between CENA and stability of the fungal community. Root transcriptome analysis suggested a shift in metabolic pathways from a tricarboxylic acid cycle decomposition of carbohydrate to lipid biosynthesis to maintain closer associations with mycorrhiza for nutrient cycling after the girdling. By integrating multi-level evidence, including root transcriptome, fungal composition, and network complexity data, we demonstrate an increased dependence on mycorrhiza for nutrient acquisition under the C limitation condition, which is likely due to a shift to fungal community with higher CENA at the cost of lower stability. © 2025 Kunming Institute of Botany, Chinese Academy of sciences

关键词： Carbon limitation Fungal network complexity Girdling effects Mycorrhizal and root strategies Plant–microbiome interaction Root transcriptome

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TSC-PCAC: Voxel Transformer and Sparse Convolution Based Point Cloud Attribute Compression for 3D Broadcasting

arXiv

引用

arXiv 2024年

作者： Guo, Zixi Zhang, Yun Zhu, Linwei Wang, Hanli Jiang, Gangyi School of Electronics and Communication Engineering Shenzhen Campus Sun Yat-Sen University Shenzhen518017 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Computer Science and Technology Tongji University Shanghai200092 China Faculty of Information and Science and Engineering Ningbo University Ningbo315211 China

Point cloud has been the mainstream representation for advanced 3D applications, such as virtual reality and augmented reality. However, the massive data amounts of point clouds is one of the most challenging issues for transmission and storage. In this paper, we propose an end-to-end voxel Transformer and Sparse Convolution based Point Cloud Attribute Compression (TSC-PCAC) for 3D broadcasting. Firstly, we present a framework of the TSC-PCAC, which includes Transformer and Sparse Convolutional Module (TSCM) based variational autoencoder and channel context module. Secondly, we propose a two-stage TSCM, where the first stage focuses on modeling local dependencies and feature representations of the point clouds, and the second stage captures global features through spatial and channel pooling encompassing larger receptive fields. This module effectively extracts global and local inter-point relevance to reduce informational redundancy. Thirdly, we design a TSCM based channel context module to exploit inter-channel correlations, which improves the predicted probability distribution of quantized latent representations and thus reduces the bitrate. Experimental results indicate that the proposed TSC-PCAC method achieves an average of 38.53%, 21.30%, and 11.19% bitrate reductions on datasets 8iVFB, Owlii, 8iVSLF, Volograms, and MVUB compared to the Sparse-PCAC, NF-PCAC, and GPCC v23 methods, respectively. The encoding/decoding time costs are reduced 97.68%/98.78% on average compared to the Sparse-PCAC. The source code and the trained TSC-PCAC models are available at https://***/igizuxo/TSC-PCAC. Copyright © 2024, The Authors. All rights reserved.

关键词： Augmented reality

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