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Vector Fields with Stable Shadowable Property on Closed Surfaces

Acta Mathematica Sinica,English Series 2010年第8期26卷 1449-1456页

作者： Sheng Zhi ZHU School of Science Beijing Jiaotong University Beijing 100044 P. R. China

We prove that the set of vector fields satisfying the C1 stable shadowable property on closed surfaces is characterized as the set of Morse-Smale vector fields. Hence, the vector fields satisfying shadowing property o... 详细信息

关键词： Pseudo orbit shadowing stable shadowable

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A kind of symbolic dynamical systems describing non-chaotic maps

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Chinese Science Bulletin 1995年第13期40卷 1063-1067页

作者：麦结华 Department of Mathematics Guangxi Uniwsrsity Nanning 530004 China

1 A kind of non-chaotic dynamical systems on the symbolic space Let A0=A1=A2=…={0,1},and X=multiply from i=0 to ∞ Ai. For every integer k≥2, define metrics dkand dk’ on X by dk（a,b）=max{k-i·|ai-bi|: i=0, 1, 2,…}, dk’（a, b）=sum from i=0 to ∞ k-i·|ai-bi| forany a=（a0, a0, a2,…） and any b=（b0, b1, b2,…）∈X. It is easy to see that all dk anddk’ induce the same topological structure. Thus we now may only consider d≡d2. Thespace （X, d） is usually called a symbolic space. For simplicity, write X for （X, d）. It

关键词： symbolic dynamical system chaos almost periodic map pseudo-orbit shadowing property map of interval homeomorphism of plane.

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Distance Measurement Model Based on RSSI in WSN

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Wireless Sensor Network 2010年第8期2卷 606-611页

作者： Jiuqiang Xu Wei Liu Fenggao Lang Yuanyuan Zhang Chenglong Wang 不详

The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM.

关键词： WSN Dynamic Variance Distance Measurement RSSI Log-Normal shadowing Model

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<i>N</i>-Expansive Property for Flows

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Journal of Applied Mathematics and Physics 2019年第2期7卷 410-417页

作者： Le Huy Tien Le Duc Nhien Department of Mathematics Mechanics and Informatics Vietnam National University at Hanoi Hanoi Vietnam

In this paper, we discuss the dynamics of n-expansive homeomorphisms with the shadowing property defined on compact metric spaces in continuous case. For every n∈N, we exhibit an n-expansive homeomorphism but not (n-1)-expansive. Furthermore, that flow has the shadowing property and admits an infinite number of chain-recurrent classes.

关键词： Expansive Flow N-Expansive shadowing

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Second-Order Kalman Filtering Application to Fading Channels Supported by Real Data

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Journal of Signal and Information Processing 2016年第2期7卷 61-74页

作者： Azra Kapetanovic Redhwan Mawari Mohamed A. Zohdy Department of Electrical and Computer Engineering Oakland University Rochester MI USA

The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering as an effective alternative. In this paper, linear second-order state space Kalman Filtering is further investigated and tested for applicability. This is important to optimize estimates of received power signals to improve control of handoffs. Simulation models were used extensively in the initial stage of this research to validate the proposed theory. Recently, we managed to further confirm validation of the concept through experiments supported by data from real scenarios. Our results have shown that the linear second-order state space Kalman Filter (KF) can be more accurate in predicting local shadow power profiles than the first-order Kalman Filter, even in channels with imposed non-Gaussian measurement noise.

关键词： Kalman Filtering Rayleigh Gaussian Multipath shadowing Power Estimation

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