Estimation of local scale exponents for heartbeat time series based on DFA

The paper mainly introduces a method combining detrended fluctuation analysis (DFA) with moving fitting window to analyze the heartbeat time series in different pathologic states. Compared to traditional approaches, the estimation of the local scale exponent method shows more details of scale properties and provides a reliable analysis. We also quantify the effects of outliers, subseries length and gender on scale spectrum α(s). Comparing the results of the healthy subjects, patients with congestive heart failure (CHF), and patients with atrial fibrillation (AF) indicate clearly that at small scales, the exponents show great volatility and they all have their own scale pattern. In addition, the length of the series should be relatively longer to avoid properties losing. For the effect of gender, contrary to healthy results, the scale exponents of CHF male subjects are continuously greater than female. The spectrum of DFA scale exponents provides a new way to measure the heartbeat series and distinguishes healthy and pathologic groups.     

Multifractal characteristics of palmprint and its extracted algorithm

The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. In this paper, we propose a palmprint recognition method based on multifractal spectrum technology using statistical moment approaches. The multifractal spectrum of palmprint is calculated by developing an algorithm for extracting palmprint characteristics. The three parameters proposed as the distinguishing palmprint features include the width spread and maximum of multifractal spectrum, and a parameter which describes the asymmetry of the spectrum curve. The identification process can be divided into the following main steps: (1) capturing palmprint image, extracting and normalizing the subimages; (2) defining a coordinate system and calculating partition function; (3) estimating multifractal spectrum; (4) extracting the three parameters and, finally, (5) the feature matching and palmprint identification. The experimental results demonstrate the feasibility of the proposed method.     

Weighted multifractal analysis of financial time series

In this paper, we propose the weighted multifractal analysis based on variance-weighted partition function (WMA), to evaluate the fractals of nonlinear time series containing amplitude information. The reliability of the proposed WMA is supported by simulations on generated and real-world financial volatility data. Numerical simulations with synthesized data show that WMA is comparative to the classic partition function-based standard multifractal analysis (SMA) and the multifractal detrending moving average analysis. More importantly, empirical analyses of Chinese stock indices illustrate that WMA outperforms SMA in distinguishing Hang Seng Index form SSE Composite Index and SZSE Component Index qualitatively and quantitatively, showing the power of WMA in discriminating series with distinctive amplitudes.     

The multiscale large deviation spectrum based on higher moments for financial time series

Nonlinear Dynamics - In this letter, based on large deviations theory, we propose to use higher moments (e.g., skewness, kurtosis) to investigate the multifractal features of large deviations...     

Relative asynchronous index: a new measure for time series irreversibility

Nonlinear Dynamics - In this paper, we suggest a new measure for testing reversibility of time series which combines two different tools: the visibility algorithm and the inversion number. First,...     

高介电常数、低介电损耗的聚合物基复合材料*

考虑到高介电常数、低介电损耗聚合物基复合材料在电气、电子行业的广泛重要应用,本文对其研究进展进行了回顾。重点讨论了功能填料、聚合物基体及它们两者的相互作用等因素对聚合物基复合材料介电性能的影响规律,试图为建立高介电常数、低介电损耗的聚合物基复合材料的设计和制备原理理清思路。指出该领域在未来的主要发展方向是涉及制备结构、形貌、尺寸可控的新的功能填料,探索新型简单的复合工艺和界面控制技术,从理论上分析建立功能填料的结构和介电性能的关系模型等。     

Multifractal diffusion entropy analysis on stock volatility in financial markets

This paper introduces a generalized diffusion entropy analysis method to analyze long-range correlation then applies this method to stock volatility series. The method uses the techniques of the diffusion process and Rényi entropy to focus on the scaling behaviors of regular volatility and extreme volatility respectively in developed and emerging markets. It successfully distinguishes their differences where regular volatility exhibits long-range persistence while extreme volatility reveals anti-persistence.     

Modified DFA and DCCA approach for quantifying the multiscale correlation structure of financial markets

We use multiscale detrended fluctuation analysis (MSDFA) and multiscale detrended cross-correlation analysis (MSDCCA) to investigate auto-correlation (AC) and cross-correlation (CC) in the US and Chinese stock markets during 1997–2012. The results show that US and Chinese stock indices differ in terms of their multiscale AC structures. Stock indices in the same region also differ with regard to their multiscale AC structures. We analyze AC and CC behaviors among indices for the same region to determine similarity among six stock indices and divide them into four groups accordingly. We choose S&P500, NQCI, HSI, and the Shanghai Composite Index as representative samples for simplicity. MSDFA and MSDCCA results and average MSDFA spectra for local scaling exponents (LSEs) for individual series are presented. We find that the MSDCCA spectrum for LSE CC between two time series generally tends to be greater than the average MSDFA LSE spectrum for individual series. We obtain detailed multiscale structures and relations for CC between the four representatives. MSDFA and MSDCCA with secant rolling windows of different sizes are then applied to reanalyze the AC and CC. Vertical and horizontal comparisons of different window sizes are made. The MSDFA and MSDCCA results for the original window size are confirmed and some new interesting characteristics and conclusions regarding multiscale correlation structures are obtained.     

Tuning the phase evolution pathway of LiNi0.5Mn1.5O4 synthesis from binary intermediates to ternary intermediates with thermal regulating agent

Transition metal cation ordering is essential for controlling the electrochemical performance of cubic spinel LiNi_0.5Mn_1.5O₄(LNMO),which is conventionally adjusted by optimizing the high temperature sintering and annealing procedures.In this present work,multiple characterization techniques,including 6,7Li NMR,XRD and HRTEM,have been combined to trace the phase transformation and morphology evolution during synthesis.It has been illustrated that simultaneous formation of LiMn₂O₄(LMO)and LiNiO₂(LNO)binary oxides and their conversion into highly reactive LixNi³⁺_yMn_3.5+zO ternary intermediate is a thermal dynamically difficult but crucial step in the synthesis of LNMO ternary oxide.A new strategy of modifying the intermediates formation pathway from binary mode to ternary mode using thermal regulating agent has been adopted.LNMO synthesized with thermal regulating agent exhibits supreme rate capability,long-cycling performance(even at elevated temperature)and excellent capacity efficiency.At a high rate of 100 C,the assembled battery delivers a discharge capacity of 99 mAh g^-1.This study provides a way to control the formation pathway of complex oxides using thermal regulating agent.     

Complexity and information measures in planar characterization of chaos and noise

Nonlinear Dynamics - In this work, we present a comprehensive assessment of the Fisher information measure and statistical complexity measures based on Euclidean distance, Wootters distance and...