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A self-adaptive AP Selection algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning

IEEE INTERNET OF THINGS JOURNAL 2021年第3期8卷 1406-1416页

作者： Zhang, Wei Yu, Kegen Wang, Weixi Li, Xiaoming Shenzhen Univ Res Inst Smart Cities Shenzhen 518060 Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China Shenzhen Univ Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China

With the widely deployed wireless access points (APs) and the worldwide popularization of smartphones, WiFi-based indoor positioning has attracted great attention to both industry and academia. Locating and tracking objects within an indoor environment plays an important role in Internet of Things application and service. However, it is a challenging problem to achieve high accuracy using WiFi positioning technique due to the high instability in received signal strength from AP. Thus, it is desirable to select APs by considering both signal strength and connection quality. In this article, an AP selection algorithm based on multiobjective optimization is proposed to improve indoor WiFi positioning accuracy. The self-adaptive AP selection algorithm can be easily applied to various real scenarios and the performance of the new method is considerably better than classical algorithms. Learning algorithm is exploited to obtain the optimal solution for the self-adaptive AP selection algorithm. Experiments are conducted and the proposed algorithm is compared with classical algorithms. The experimental results demonstrate that the performance of the self-adaptive AP selection algorithm is at least a few decimeters better than classical algorithms in terms of RMSE of position estimation. Meanwhile, the new method is robust to the random generation of initial particles and normalizing factor as their effect on the positional accuracy is less than 1 decimeter.

关键词： Wireless fidelity Internet of Things Optimization Calibration IP networks Fingerprint recognition Fixtures Access point (AP) selection indoor WiFi positioning Internet of Things (IoT) multiobjective optimization self-adaptive algorithm

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An efficient algorithm with double inertial steps for solving split common fixed point problems and an application to signal processing

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COMPUTATIONAL & APPLIED MATHEMATICS 2025年第1期44卷 1-17页

作者： Thong, Duong Viet Reich, Simeon Li, Xiao-Huan Tham, Pham Thi Hong Natl Econ Univ Fac Math Econ Hanoi Vietnam Technion Israel Inst Technol Dept Math IL-32000 Haifa Israel Shandong Univ Technol Sch Math & Stat Zibo 255000 Peoples R China

The split common fixed point problem is an optimization challenge that involves finding an element within one fixed point set such that when transformed by a bounded linear operator, it belongs to another fixed point set. This problem falls under the category of inverse problems in mathematics. We present a novel self-adaptive algorithm based on double inertial steps for solving the split common fixed point problem for demicontractive mappings. We also establish a weak convergence theorem for our method. Furthermore, we also present some numerical experiments illustrating the convergence behavior and the efficiency of our proposed algorithm.

关键词： Demicontractive operator Double inertial steps self-adaptive algorithm Signal processing Split common fixed point problem

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NOVEL STRONG CONVERGENCE THEOREMS FOR THE SPLIT COMMON FIXED POINT PROBLEM OF DEMICONTRACTIVE OPERATORS WITH AN APPLICATION TO SIGNAL PROCESSING

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第5期21卷 3258-3277页

作者： Viet, Thong duong Tien, Dung vu Ngan, Dang huy An, Nguyen thi Natl Econ Univ Fundamental Sci Fac Hanoi City Vietnam Vietnam Natl Univ Univ Sci Dept Math Hanoi Vietnam

The purpose of this paper is to introduce two self-adaptive algorithms using inertial effects and step sizes which do not depend on the norm of the bounded linear operator for solving the split common fixed point problem of demicontractive operators in real Hilbert spaces. Strong convergence results of the proposed algorithms are proved and analyzed under some control conditions. Furthermore, some numerical experiments illustrating the convergence behavior and the efficiency of our proposed algorithm are also given.

关键词： Split common fixed point demicontractive operator self-adaptive algorithm the signal processing

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self-adaptive relaxed CQ algorithms for solving split feasibility problem with multiple output sets

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JOURNAL OF APPLIED MATHEMATICS AND COMPUTING 2024年第2期70卷 1441-1469页

作者： Tong, Xiaolei Ling, Tong Shi, Luoyi Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China Tiangong Univ Sch Software Tianjin 300387 Peoples R China

In this paper, we propose two new self-adaptive relaxed CQ algorithms to solve the split feasibility problem with multiple output sets, which involve the computation of projections onto half-spaces instead of the computation onto the closed convex sets. Our proposed algorithms with selection technique reduce the computation of projections. And then, as a generalization, we construct two new algorithms to solve the variational inequalities over the solution set of split feasibility problem with multiple output sets. More importantly, strong convergence of all proposed algorithms is proved under suitable conditions. Finally, we conduct numerical experiments to show the efficiency and accuracy of our algorithms compared to some existing results.

关键词： Split feasibility problem Multiple output sets Strong convergence Relaxed CQ algorithm self-adaptive algorithm

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New inertial self-adaptive algorithms for the split common null-point problem: application to data classifications

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JOURNAL OF INEQUALITIES AND APPLICATIONS 2023年第1期2023卷 136-136页

作者： Promkam, Ratthaprom Sunthrayuth, Pongsakorn Kesornprom, Suparat Tanprayoon, Ekapak Rajamangala Univ Technol Thanyaburi RMUTT Fac Sci & Technol Dept Math & Comp Sci Pathum Thani 12110 Thailand Chiang Mai Univ Off Res Adm Chiang Mai 50200 Thailand

In this paper, we propose two inertial algorithms with a new self-adaptive step size for approximating a solution of the split common null-point problem in the framework of Banach spaces. The step sizes are adaptively updated over each iteration by a simple process without the prior knowledge of the operator norm of the bounded linear operator. Under suitable conditions, we prove the weak-convergence results for the proposed algorithms in p-uniformly convex and uniformly smooth Banach spaces. Finally, we give several numerical results in both finite- and infinite-dimensional spaces to illustrate the efficiency and advantage of the proposed methods over some existing methods. Also, data classifications of heart diseases and diabetes mellitus are presented as the applications of our methods.

关键词： p-uniformly convex Banach spaces Weak convergence Split common null-point problem Maximal monotone operator self-adaptive algorithm

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A data-dependent energy reduction algorithm for SAR ADC using self-adaptive window

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MICROELECTRONICS JOURNAL 2020年 100卷 104754-104754页

作者： Lu, Hao-wei Yu, Xiao-Peng Lu, Zheng-Hao Yeo, Kiat-Seng Chen, Jer-Ming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Soochow Univ Suzhou 215006 Peoples R China Singapore Univ Technol & Design 8 Somapah Rd Singapore 487372 Singapore

A new data-dependent energy reduction algorithm for successive approximation register (SAR) analog-to-digital convert (ADC) is presented in this paper. The proposed algorithm starts with a less significant bit (LSB) window with N-bit length, which is configurable depending on signal characteristics. By using less significant bit to more significant bit (L2M) successive extending (SE), the signal window is self-adaptive to cover the input signal within boundary. The proposed technique leads to less mean bit trials per sample, suggesting higher energy efficiency in many data-dependant ADC applications. Furthermore, this algorithm can be implemented based on conventional charge redistribution SAR ADC without any change in analog circuits. According to MATLAB simulation, the proposed technique is able to reduce mean bit trials effectively in biomedical signal detection applications. The simulation results show 37.9%, 32.3% and 18.2% less mean bit trials than using conventional SAR algorithm in processing electrocardiogram (ECG), electroencephalogram (EEG) and in electro-myography (EMG) signals respectively.

关键词： Successive approximation register(SAR) Analog-to-digital converter(ADC) Bit-trial saving Biomedical detection self-adaptive algorithm

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A new iterative method for solving the multiple-set split variational inequality problem in Hilbert spaces

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OPTIMIZATION 2023年第6期72卷 1549-1575页

作者： Nguyen Thi Thu Thuy Nguyen Trung Nghia Hanoi Univ Sci & Technol Sch Appl Math & Informat Hanoi Vietnam

This paper proposes a new self-adaptive algorithm for solving the multiple-set split variational inequality problem in Hilbert spaces. Our algorithm uses dynamic step-sizes, chosen based on information of the previous step. In comparison with the work by Censor et al. [Numer algorithms. 2012;59:301-323], the new algorithm gives strong convergence results and does not require information about the transformation operator's norm. Some applications of our main results regarding the solution of the multiple-set split feasibility problem and the split feasibility problem are presented and show that the iterative method converges strongly under weaker assumptions than the ones used recently by Xu [Inverse Probl. 2006;22:2021-2034] and by Buong [Numer algorithms. 2017;76:783-798]. Numerical experiments on finite-dimensional and infinite-dimensional spaces and an application to discrete optimal control problems are reported to demonstrate the advantages and efficiency of the proposed algorithms over some existing results.

关键词： Variational inequality problems split feasibility problems multiple-set self-adaptive algorithm discrete optimal control problems

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A generalized cyclic iterative method for solving variational inequalities over the solution set of a split common fixed point problem

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NUMERICAL algorithmS 2022年第1期91卷 1-17页

作者： Reich, Simeon Truong Minh Tuyen Technion Israel Inst Technol Dept Math IL-32000 Haifa Israel Thai Nguyen Univ Sci Dept Math & Informat Thai Nguyen 24000 Vietnam

We introduce a new generalized cyclic iterative method for finding solutions of variational inequalities over the solution set of a split common fixed point problem with multiple output sets in a real Hilbert space.

关键词： Hilbert space Metric projection Nonexpansive mapping self-adaptive algorithm Split common fixed point problem

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self-adaptive algorithms with inertial effects for solving the split problem of the demicontractive operators

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REVISTA DE LA REAL ACADEMIA DE CIENCIAS EXACTAS FISICAS Y NATURALES SERIE A-MATEMATICAS 2020年第1期114卷 40-40页

作者： Suparatulatorn, Raweerote Cholamjiak, Prasit Suantai, Suthep Chiang Mai Univ Fac Sci Dept Math Chiang Mai 50200 Thailand Univ Phayao Sch Sci Phayao 56000 Thailand Chiang Mai Univ Fac Sci Res Ctr Math & Appl Math Dept Math Chiang Mai 50200 Thailand

The purpose of this paper is to introduce two new versions of self-adaptive algorithms by using inertial effects for solving the split common fixed point problem of demicontractive operators in Hilbert spaces and prove its weak and strong convergences under some weakened assumptions. Furthermore, we provide numerical experiments to illustrate the convergence behavior and efficiency of our proposed algorithms.

关键词： Split common fixed point problem Demicontractive operator self-adaptive algorithm Inertial algorithm

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Two new self-adaptive algorithms for solving the split common null point problem with multiple output sets in Hilbert spaces

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JOURNAL OF FIXED POINT THEORY AND APPLICATIONS 2021年第2期23卷 16-16页

作者： Reich, Simeon Truong Minh Tuyen Technion Israel Inst Technol Dept Math IL-32000 Haifa Israel Thai Nguyen Univ Sci Dept Math & Informat Thai Nguyen Vietnam

To solve the split common null point problem with multiple output sets in Hilbert spaces, we introduce two new self-adaptive algorithms and prove strong convergence theorems for both of them.

关键词： Hilbert space metric projection nonexpansive mapping self-adaptive algorithm split common null point problem

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