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A Novel Direct Geometric Algorithm for Worst-Case Error Analysis of Six-Port Reflectometer and Its Guiding Significance in Application

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2024年第8期72卷 4701-4716页

作者： Feng, Penghao Huang, Binke Wang, Yanyang Yan, Sen Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China

In this article, a novel direct geometric algorithm for worst-case error analysis of six-port reflectometer is proposed. Due to the inevitable power measurement uncertainty, six-port reflectometer shows an inherent deviation that can be represented as an irregular area based on the classical geometrical theory. The previous worst-case error analysis geometric algorithm approximates the boundary of the irregular area with straight lines and utilizes several special points in comparison. Without approximation, points on the boundary of the irregular error distribution area can be directly obtained in the proposed algorithm. After traversing all points, the maximum errors of the reflection coefficient in magnitude and phase around the entire Smith Chart can be determined accurately. This algorithm is first verified by comparing state-of-the-art approximating algorithm in simulation. After plotting the error distributions of five classical six-port models, a novel common conclusion is obtained that the area that shows the minimum error in the entire Smith Chart can be found in geometry for an arbitrary six-port reflectometer. A practical six-port reflectometer system is set up, calibrated, and utilized for the algorithm verification. The consistent results show the availability and the fundamental guiding significance on specific six-port reflectometer applications design of the proposed algorithm.

关键词： Power measurement Reflection coefficient approximation algorithms Uncertainty Junctions Error analysis Measurement uncertainty Analytic geometry error analysis Monte Carlo method power measurement uncertainty six-port reflectometer

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Efficient Top-k DTW-Based Sensor Data Similarity Search Using Perceptually Important Points and Dual-Bound Filtering

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IEEE SENSORS JOURNAL 2024年第24期24卷 41231-41242页

作者： Zhang, Haowen Feng, Jinwang Li, Juan Yao, Qing Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China

The proliferation of Internet-of-Things (IoT) devices generates vast amounts of sensor data, making sensor data mining an increasingly important research topic in the IoT domain. The Top-k similarity search plays a crucial role in sensor data mining, forming the foundation for advanced techniques such as sensor data classification, clustering, and anomaly detection. The need for efficient algorithms to enhance search speed drives our research. This article introduces an efficient method named PIPDTW for Top-k similarity search of sensor data using dynamic time warping (DTW) distance, a prevalent but computationally intensive similarity measure in time-series data mining. To address the high computational complexity, PIPDTW first uses perceptually important points (PIPs) to transform lengthy sensor data into concise vector representations. We then introduce a novel distance measure, dynamic time warping based on index and value (DTWIV), based on PIP representations to quickly estimate DTW distances between the query q and all sensor data. Finally, during the online search, we apply two levels of filtering based on approximate ranking and the lower bound technique to efficiently prune dissimilar data, reducing the need for exact DTW distance computations and thereby achieving efficient querying. Experiments conducted on synthetic and real-world publicly available datasets, compared with relevant competitors, demonstrate that PIPDTW slightly sacrifices retrieval accuracy but significantly reduces runtime.

关键词： Sensors Data mining Filtering Accuracy Time series analysis approximation algorithms Trajectory Internet of Things Intelligent sensors Computational efficiency Dual-bound filtering dynamic time warping (DTW) perceptually important points (PIPs) sensor data Top-k similarity search

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Anytime Automatic Algorithm Selection for the Multi-Agent Path Finding Problem

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IEEE ACCESS 2024年 12卷 62177-62188页

作者： Zapata, Angelo Godoy, Julio Asin-Acha, Roberto Univ Concepcion Dept Comp Sci Concepcion 4070409 Chile Univ Tecn Federico Santa Maria Dept Informat Valparaiso 234000 Chile

In this study, we propose and develop a Machine Learning-based metasolver for the Multi-Agent Path Finding (MAPF) problem, with the aim of selecting the most suitable solver based on the specific characteristics of the problem and a user-provided time constraint. The approach aims to improve the performance of the best-performing solver on average and approximate the performance of a perfect selector. To achieve this, a comprehensive and diverse dataset was compiled, and state-of-the-art algorithms were selected and modified to efficiently handle the time constraint. Also, relevant features were identified, and a precise and robust Machine Learning model was constructed using the XGBoost algorithm. The model was evaluated and compared against other state-of-the-art methods. The results demonstrate that the proposed approach is effective and consistent, outperforming the Single Best Solver and approximating the performance of the Virtual Best Solver.

关键词： Algorithm design and analysis Multi-agent systems Path planning Time factors approximation algorithms Performance evaluation Algorithm selection multi-agent path finding machine learning

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Differentially Private Distributed Stochastic Optimization with Time-Varying Sample Sizes

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第9期69卷 6341-6348页

作者： Wang, Jimin Zhang, Ji-Feng Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Chinese Acad Sci Inst Syst Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Differentially private distributed stochastic optimization has become a hot topic due to the need for privacy protection in distributed stochastic optimization. In this article, two-time scale stochastic approximation-type algorithms for differentially private distributed stochastic optimization with time-varying sample sizes are proposed using gradient- and output-perturbation methods. For both gradient- and output-perturbation cases, the convergence of the algorithm and differential privacy with a finite cumulative privacy budget $\varepsilon$ for an infinite number of iterations are simultaneously established, which is substantially different from the existing works. By a time-varying sample size method, the privacy level is enhanced, and differential privacy with a finite cumulative privacy budget $\varepsilon$ for an infinite number of iterations is established. By properly choosing a Lyapunov function, the algorithm achieves almost sure and mean square convergence even when the added privacy noise has an increasing variance. Furthermore, we rigorously provide the mean square convergence rates of the algorithm and show how the added privacy noise affects the convergence rate of the algorithm. Finally, numerical examples, including distributed training on a benchmark machine learning dataset, are presented to demonstrate the efficiency and advantages of the algorithms.

关键词： Optimization Privacy Convergence Differential privacy Machine learning algorithms approximation algorithms Standards Convergence rate differential privacy distributed stochastic optimization privacy-preserving stochastic approximation

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Ego-Network Segmentation via (Weighted) Jaccard Median

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第9期36卷 4646-4663页

作者： Zhong, Haodi Loukides, Grigorios Conte, Alessio Pissis, Solon P. Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Kings Coll London London WC2R2LS England Univ Pisa I-6126 Pisa Italy CWI NL-1098XG Amsterdam Netherlands Vrije Univ NL-1081HV Amsterdam Netherlands

An ego-network is a graph representing the interactions of a node (ego) with its neighbors and the interactions among those neighbors. A sequence of ego-networks having the same ego can thus model the evolution of these interactions over time. We introduce the problem of segmenting a sequence of ego-networks into k segments. Each segment is represented by a summary network, and the goal is to minimize the total loss of representing k segments by k summaries. The main challenge is to construct a summary with minimum loss. To address it, we employ the Jaccard Median (JM) problem, for which, however, no effective and efficient algorithms are known. We develop several algorithms for JM: (I) an exact algorithm, based on Mixed Integer Linear Programming;(II) exact and approximation polynomial-time algorithms for minimizing an upper bound of the objective function of JM;and (III) efficient heuristics. We also study a generalization of the segmentation problem, in which there may be multiple edges between a pair of nodes in an ego-network, and develop a series of algorithms (exact algorithms and heuristics) for it, based on a more general problem than JM, called Weighted Jaccard Median (WJM). By building upon the above results, we design algorithms for segmenting a sequence of ego-networks. Extensive experiments show that our algorithms produce (near)-optimal solutions to JM or to WJM and that they substantially outperform state-of-the-art methods which can be employed for ego-network segmentation.

关键词： approximation algorithms Heuristic algorithms Social networking (online) Upper bound Mixed integer linear programming Linear programming Synthetic data Ego-network jaccard median segmentation

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Factorization of Matrices With Grades With Overcovering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 1641-1652页

作者： Belohlavek, Radim Trneckova, Marketa Palacky Univ Dept Comp Sci Olomouc 77900 Czech Republic

We present a novel algorithm for factorization of matrices with grades or, equivalently, for decomposition of fuzzy relations. The algorithm is inspired by a recent factorization method for Boolean matrices and develops two ideas in the setting of ordinal attributes. First, it uses formal concepts associated with the factorized matrix, or fuzzy relation, as essential components around which factors are built. Second, it steps back when computing new factors to check whether some computed factors may be eliminated or improved given the subsequently generated factors. The new algorithm thus uses convenient properties of formal concepts utilized by previous factorization algorithms. Still, unlike the previous algorithms, our algorithm allows for more general and therefore more precise factorizations due to a possible overcovering of the input data, which our new algorithm admits. We provide an experimental evaluation of the new algorithm and compare it to some existing algorithms for factorization of data with grades. The evaluation reveals that our new algorithm outperforms the current algorithms in terms of quality of factorization. We also present observations and improvements for factorization of Boolean matrices. We conclude with a discussion regarding open research topics.

关键词： Matrix decomposition approximation algorithms Lattices Limiting Iterative methods Indexes Hamming distances Decomposition of fuzzy relations factorization fuzzy concept lattice fuzzy logic fuzzy relation

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Distributed Optimization Methods for Multi-robot Systems: Part 2-A Survey

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IEEE ROBOTICS & AUTOMATION MAGAZINE 2024年第3期31卷 154-169页

作者： Shorinwa, Ola Halsted, Trevor Yu, Javier Schwager, Mac Stanford Univ Dept Mech Engn Stanford CA 94305 USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA

Although the field of distributed optimization is well developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited. This survey constitutes the second part of a two-part series on distributed optimization applied to multi-robot problems. In this article, we survey three main classes of distributed optimization algorithms-distributed first-order (DFO) methods, distributed sequential convex programming methods, and alternating direction method of multipliers (ADMM) methods-focusing on fully distributed methods that do not require coordination or computation by a central computer. We describe the fundamental structure of each category and note important variations around this structure, designed to address its associated drawbacks. Further, we provide practical implications of noteworthy assumptions made by distributed optimization algorithms, noting the classes of robotics problems suitable for these algorithms. Moreover, we identify important open research challenges in distributed optimization, specifically for robotics problem.

关键词： Optimization Surveys Robot kinematics Robot sensing systems Signal processing algorithms Heuristic algorithms approximation algorithms

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Joint User Association, Interference Cancellation, and Power Control for Multi-IRS Assisted UAV Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 13408-13423页

作者： Ning, Zhaolong Hu, Hao Wang, Xiaojie Wu, Qingqing Yuen, Chau Yu, F. Richard Zhang, Yan Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Univ Oslo Dept Informat N-0316 Oslo Norway

Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs, whereas it is extremely challenging for joint multi-IRS multi-user association in UAV communications with constrained reflecting resources and dynamic scenarios. To address the aforementioned challenges, we propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation to maximize system energy efficiency. We first propose an inverse soft-Q learning-based algorithm to optimize multi-IRS multi-user association. Then, successive convex approximation (SCA) and Dinkelbach-based algorithm are leveraged to optimize UAV trajectory followed by the optimization of SIC decoding order scheduling and power allocation. Finally, theoretical analysis and performance results show significant advantages of the designed algorithm in convergence rate and energy efficiency.

关键词： Autonomous aerial vehicles Wireless communication Heuristic algorithms Communication systems Resource management Interference cancellation approximation algorithms Intelligent reflecting surface UAV communications user association trajectory optimization inverse soft-Q learning

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Sea Surface Temperature Derived From FY-4A/AGRI

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 14237-14247页

作者： Yang, Chang Guan, Lei Ocean Univ China Coll Marine Technol Fac Informat Sci & Engn Qingdao 266100 Peoples R China Qingdao Marine Sci & Technol Ctr Lab Reg Oceanog & Numer Modeling Qingdao 266071 Peoples R China Ocean Univ China Sanya Oceanog Inst Key Lab Ocean Observat & Informat Hainan Prov SANYA Oceanog Lab Sanya Peoples R China

In this study, we develop an improved algorithm for retrieving sea surface temperature (SST) from the Advanced Geosynchronous Radiation Imager (AGRI) on the Fengyun-4A satellite (FY-4A). First, we use a multichannel nonlinear SST algorithm that combines data from the 3.7, 8.5, 10.7, and 12 mu m channels during the nighttime, while during the daytime it combines data from the three long-wavelength bands centered at 8.5, 10.7, and 12 mu m. Second, to minimize the impact of water vapor and obtain more accurate SST, we provide different retrieval coefficients obtained from the in situ SST and the observed brightness temperature for different latitude regions and different time periods using two-thirds of the matchups from January 2019 to December 2021. We validate the retrieved FY-4A/AGRI SST and operational FY-4A/AGRI SST by comparing them with in situ SST using one-third of the matchups from January 2019 to December 2021. Compared with the in situ data, the full-disk retrieved AGRI SST has the bias, median, standard deviation (STD), robust standard deviation (RSD), and root mean square error (RMSE) of 0.01, 0.03, 0.59, 0.52, and 0.59 K in the daytime, respectively. In nighttime, the bias, median, STD, RSD, and RMSE are 0.02, 0.05, 0.63, 0.55, and 0.63 K, respectively. Our analyses of the results further demonstrate that the improved algorithm significantly improves the accuracy compared with the operational AGRI SST, correcting the large bias in the temporal and spatial scales and effectively accounting for the effect of water vapor and satellite zenith angle.

关键词： Satellite broadcasting Brightness temperature Radiometry Orbits Clouds Atmospheric modeling approximation algorithms Advanced Geosynchronous Radiation Imager (AGRI) Fengyun-4A latitude segmentation multichannel algorithm retrieval coefficients sea surface temperature (SST)

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Online Learning algorithms for Context-Aware Video Caching in D2D Edge Networks

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第1期35卷 1-19页

作者： Xia, Qiufen Jiao, Zhiwei Xu, Zichuan Dalian Univ Technol Int Sch Informat Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China

With the emergence of various short video platforms such as TikTok and Instagram, coupled with the accelerated pace of people's lives, people are spending more time sharing and watching online videos than ever before, and they gradually turn their attention to short videos with short duration and novel content. Browsing and watching short videos by users with their energy-capacitated devices, such as phones and tablets, have become one of the main ways for users to entertain online. Timely response and high quality online video delivery are of the utmost importance to guarantee the quality of service (QoS) experienced by users. Caching videos at locations close to the video demanders can significantly improve the QoS by reducing the access delay of high quality videos. Together with an explosive growth of mobile devices and great demand for bandwidth, Device-to-Device (D2D) network is emerging as a promising technology to enable ultra-low latency communications, by allowing mobile devices to communicate with each other with or without the involvement of network infrastructures. Caching videos in D2D networks can further reduce video response delays of high quality videos, thereby improving the QoS experienced by users when watching short videos. However, how to cache the most appropriate videos at strategic mobile devices is crucial to satisfy the QoS requirements of users. Specifically, the QoS experienced by users depends on many intertwining factors from both users and the D2D network, such as videos characteristics, various users' demands for different videos, different communities of users, and energy levels of devices. Motivated by these facts, we investigate the video caching problems in a D2D network. The novelty of our study is to jointly consider the intertwining factors from both users and the D2D network when users access short videos. Specifically, we first formulate an optimization problem of video caching with the objective to minimize the average del

关键词： Device-to-device communication Mobile handsets Delays approximation algorithms Quality of service Uncertainty Prediction algorithms D2D edge network context-aware video caching approximation algorithm

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