camera calibration with one dimensional objects camera calibration has been studied extensively in computer vision and photogrammetry and the proposed techniques in the literature include those using 3d apparatus two or three planes orthogonal to each other or a plane undergoing a pure translation etc 2d objects planar patterns undergoing unknown motions and 0d features self calibration using unknown scene points yet the paper proposes a new calibration technique using 1d objects points aligned on a line thus filling the missing dimension in calibration in particular we show that camera calibration is not possible with free moving 1d objects but can be solved if one point is fixed a closed form solution is developed if six or more observations of such a 1d object are made for higher accuracy a nonlinear technique based on the maximum likelihood criterion is then used to refine the estimate singularities have also been studied besides the theoretical aspect the proposed technique is also important in practice especially when calibrating multiple cameras mounted apart from each other where the calibration objects are required to be visible simultaneously

adi scheme of a nonstandard fdtd method and its numerical properties an alternating direction implicit adi scheme for a nonstandard ns finite difference time domain fdtd method is proposed the ns fdtd method is derived by analyzing the dispersion relation two different groups of update coefficients are used to update the electric field and magnetic field respectively the dispersion property of the ns method is similar with that of the standard higher order method though it is only second order accurate in the space domain the total number of nonzero update coefficients for this method is fewer than that of the latter this small stencil property can be used to reduce computational burden in implicit formulation of this method the one step leapfrog adi scheme is adopted to examine this idea the detailed update equations of this adi scheme are derived the dispersion and stability properties are also analyzed and compared with the corresponding standard one the results show that the computational efficiency of the proposed method is higher than that of the standard one

an upwinding embedded boundary method for maxwell s equations in media with material interfaces 2d case we propose a new upwinding embedded boundary method to solve time dependent maxwell s equations in media with material interfaces a global second order finite difference method is obtained by combining central difference schemes away from the interfaces and upwinding technique with jump conditions near the interfaces the proposed finite difference method allows time step based on a uniform mesh independent of the locations and shapes of the interfaces moreover the scheme is simple to implement in multidimensional cases numerical tests of wave equations with various types of material interfaces and electromagnetic scattering of 2d cylinders confirm the stability uniform accuracy and ease of implementation of the method

temporal series analysis approach to spectra of complex networks the spacing of nearest levels of the spectrum of a complex network can be regarded as a time series joint use of the multifractal detrended fluctuation approach mf dfa and diffusion entropy de is employed to extract characteristics from this time series for the watts strogatz small world model there exists a critical point at rewiring probability p r 0 32 for a network generated in the range 0l p r l0 32 the correlation exponent is in the range of 1 0 char21 1 64 above this critical point all the networks behave similar to that at p r 1 for the erdos renyi model the time series behaves like fractional brownian motion noise at p text er 1 n for the growing random network grn model the values of the long range correlation exponent are in the range of 0 74 char21 0 83 for most of the grn networks the probability distribution function of a constructed time series obeys a gaussian form in the joint use of mf dfa and de the shuffling procedure in de is essential to obtain a reliable result

a study of causality structure and dynamics in industrial electricity consumption based on granger network abstract based on industrial electricity consumption we model industrial networks by granger causality method and mst minimum spanning tree and then further stick onto an industrial coupling mechanism from energy consumption perspective first we construct granger causality networks of five provinces in south china of gd gx gz hn and yn based on their industrial electricity consumption data and we demonstrate from a network topology perspective the distribution of weight of links of all industrial electricity consumption granger causality networks approximately follows power law distribution revealing a phenomenon that few industries may bring a tremendous influence on the rest moreover correlation analysis between weight and degree of a node shows that in most granger causality networks both span and strength of influence of a given industry will significantly increase further we analyze the relationship between the thresholds of granger causality significance and density of corresponding networks results show gd and hn could be classified into a group with relatively greater global differentiation in industries and unbalanced industrial development however gx gz and yn are grouped as second cluster with relatively balanced industrial development furthermore using chu liu edmonds mst algorithm we extract graphs of msts or maximal cliques from industrial electricity consumption granger causality networks and research on energy transmission structure feedback loop and bootstrap reliability by analyzing msts we find that only gd gx and yn can be extracted with mst graphs and capture the probable transmission routes of key nodes besides we illustrate all three mst graphs are involved with feedback loops structures with various characteristics gx has complete feed forward section feed back section and feedback loop section yn has only feed forward section and feedback loop section gd has multiple feedback loops section finally we conduct bootstrap reliability tests on msts results show branches of reliability of gd gx and yn are decentralized while those feedback loops sections are only concentrated on highly reliable branches as neither hn nor gz can be extracted with mst graphs we characterize them by maximal cliques it is demonstrated that both industrial systems of hn and gz are significantly grouped into two independent branches and those internal industries inside independent branches are highly correlated however from perspectives of whole south china the causal relationship is only converged on a few of industries for regions without mst graphs like hn and gz the reliability of links is also greater than that of gd gx and yn

dynamic analysis of traffic time series at different temporal scales a complex networks approach abstract the analysis of dynamics in traffic flow is an important step to achieve advanced traffic management and control in intelligent transportation system its complexity and periodicity are definitely two fundamental properties in traffic dynamics in this study we first measure the complexity of traffic flow data by lempel ziv algorithm at different temporal scales and the data are collected from loop detectors on freeway second to obtain more insight into the complexity and periodicity in traffic time series we then construct complex networks from traffic time series by considering each day as a cycle and each cycle as a single node the optimal threshold value of complex networks is estimated by the distribution of density and its derivative in addition the complex networks are subsequently analyzed in terms of some statistical properties such as average path length clustering coefficient density average degree and betweenness finally take 2 min aggregation data as example we use the correlation coefficient matrix adjacent matrix and closeness to exploit the periodicity of weekdays and weekends in traffic flow data the findings in this paper indicate that complex network is a practical tool for exploring dynamics in traffic time series

stability analysis and improvement of the conformal adi fdtd methods in order to model curved conducting boundaries with high accuracy conformal alternating direction implicit finite difference time domain adi fdtd methods have been developed with applications of dey mittra s and yu mittra s conformal techniques we show that the conformal adi fdtd methods developed as such lose the unconditional stability although it is expected with the adi schemes we present the theoretical analysis and numerical experiments that proves our findings we then present a modified conformal adi fdtd technique with better stability than those based on yu mittra s and dey mittra s conformal techniques

correlation networks from random walk time series stimulated by the growing interest in the applications of complex networks framework for time series analysis we devise a network model in which each of n nodes is associated with a random walk of length l connectivity between any two nodes is established when the pearson correlation coefficient pcc between the corresponding time series is greater than or equal to a threshold h resulting in similarity networks with interesting properties in particular these networks can have high average clustering coefficients and the small world property and their degree distribution can vary from scale free to quasiconstant depending on h a giant component of size n exists until a critical threshold h c is crossed at which point relatively rare walks begin to detach from it and remain isolated this model can be used as a first step for building a null hypothesis for networks constructed from time series

rapid and brief communication camera calibration with one dimensional objects moving under gravity camera calibration using one dimensional 1d rigid objects is arresting the attentions of researchers since the easy to construct geometrical structure of the apparatuses in this paper we extend the motion patterns applicable for calibration with the motion of 1d objects we show that a 1d object with three or more markers rotating around one marker which is moving in a plane provides constraint equations on camera intrinsic parameters a stick moving under gravity without other forces acting on performs such a motion simulated tests show the feasibility and numerical robustness of this method

finite difference computation of head related transfer function for human hearing modeling the head related transfer function hrtf is a key to many applications in spatial audio to understand and predict the effects of head geometry and the surrounding environment on the hrtf a three dimensional finite difference time domain model 3d fdtd has been developed to simulate acoustic wave interaction with a human head a perfectly matched layer pml is used to absorb outgoing waves at the truncated boundary of an unbounded medium an external source is utilized to reduce the computational domain size through the scattered field total field formulation this numerical model has been validated by analytical solutions for a spherical head model the 3d fdtd code is then used as a computational tool to predict the hrtf for various scenarios in particular a simplified spherical head model is compared to a realistic head model up to about 7 khz the hrtf is also computed for a realistic head model in the presence of a wall it is demonstrated that this 3d fdtd model can be a useful tool for spatial audio applications

the study of rmb exchange rate complex networks based on fluctuation mode abstract in the paper we research on the characteristics of rmb exchange rate time series fluctuation with methods of symbolization and coarse gaining first based on fluctuation features of rmb exchange rate we define the first type of fluctuation mode as one specific foreign currency against rmb in four days fluctuating situations and the second type as four different foreign currencies against rmb in one day s fluctuating situation with the transforming method we construct the unique currency and multi currency complex networks further through analyzing the topological features including out degree betweenness centrality and clustering coefficient of fluctuation mode complex networks we find that the out degree distribution of both types of fluctuation mode basically follows power law distributions with exponents between 1 and 2 the further analysis reveals that the out degree and the clustering coefficient generally obey the approximated negative correlation with this result we confirm previous observations showing that the rmb exchange rate exhibits a characteristic of long range memory finally we analyze the most probable transmission route of fluctuation modes and provide probability prediction matrix the transmission route for rmb exchange rate fluctuation modes exhibits the characteristics of partially closed loop repeat and reversibility which lays a solid foundation for predicting rmb exchange rate fluctuation patterns with large volume of data

a 3 d enlarged cell technique ect for the conformal fdtd method in this paper we present an enlarged cell technique ect to avoid the time step reduction encountered in the conformal finite difference time domain cfdtd method due to small irregular cells truncated by metallic boundaries we focus our efforts on the discussion of the accuracy and stability of the ect and its comparison with other conformal methods especially the one called the uniformly stable conformal usc method we also provide a simplified ect which is much easier to implement in the ect a stability criterion is first constructed to identify instable irregular cells i e those having so small an area to cause instability those instable cells are then enlarged into their adjacent cells to obtain a large stable area careful treatment is performed on the communication between the intruding and intruded cells in terms of electromotive force by keeping the total electromotive force conservative this technique is verified by several 3 d numerical experiments results show that the ect is second order accurate and numerically stable at the regular courant time step limit

complex network based time series analysis abstract recent works show that complex network theory may be a powerful tool in time series analysis we propose in this paper a reliable procedure for constructing complex networks from the correlation matrix of a time series an original stock time series the corresponding return series and its amplitude series are considered the degree distribution of the original series can be well fitted with a power law while that of the return series can be well fitted with a gaussian function the degree distribution of the amplitude series contains two asymmetric gaussian branches reconstruction of networks from time series is a common problem in diverse research the proposed strategy may be a reasonable solution to this problem

transmission of linear regression patterns between time series from relationship in time series to complex networks the linear regression parameters between two time series can be different under different lengths of observation period if we study the whole period by the sliding window of a short period the change of the linear regression parameters is a process of dynamic transmission over time we tackle fundamental research that presents a simple and efficient computational scheme a linear regression patterns transmission algorithm which transforms linear regression patterns into directed and weighted networks the linear regression patterns nodes are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window the transmissions between adjacent patterns are defined as edges and the weights of the edges are the frequency of the transmissions the major patterns the distance and the medium in the process of the transmission can be captured the statistical results of weighted out degree and betweenness centrality are mapped on timelines which shows the features of the distribution of the results many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective

scaling invariance in spectra of complex networks a diffusion factorial moment approach a new method called diffusion factorial moment is used to obtain scaling features embedded in the spectra of complex networks for an erdos renyi network with connecting probability p mathit er l1 n the scaling parameter is ensuremath delta 0 51 while for p mathit er ensuremath geqslant 1 n the scaling parameter deviates from it significantly for ws small world networks in the special region p r 0 05 0 2 typical scale invariance is found for growing random networks in the range of ensuremath theta 0 33 049 we have ensuremath delta 0 6 ifmmode pm else textpm fi 0 1 and the value of ensuremath delta oscillates around ensuremath delta 0 6 abruptly in the range of ensuremath theta 0 54 1 we have basically ensuremath delta g0 7 scale invariance is one of the common features of the three kinds of networks which can be employed as a global measurement of complex networks in a unified way

motif distributions in phase space networks for characterizing experimental two phase flow patterns with chaotic features the dynamics of two phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics we propose a method to characterize and distinguish patterns from inclined water oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology in particular we construct from measured time series phase space complex networks and then calculate the distribution of a set of distinct network motifs to gain insight we first test the approach using time series from classical chaotic systems and find a universal feature motif distributions from different chaotic systems are generally highly heterogeneous our main finding is that the distributions from experimental two phase flows tend to be heterogeneous as well suggesting the underlying chaotic nature of the flow patterns calculation of the maximal lyapunov exponent provides further support for this motif distributions can thus be a feasible tool to understand the dynamics of realistic two phase flow patterns

global renyi index of the distance matrix abstract in previous studies the heterogeneity of complex networks has been extensively studied in our study the heterogeneity of distance matrices is studied based on the renyi index of networks we define a new metric and name it global renyi index g r i and prove several properties in particular the g r i value of the distance matrix corresponding to the evenly distributed point set in the euclidean space is zero some model data were used to clarify the geometric meanings of g r i and then we studied the g r i value of financial data the results show that the g r i value in the real market changes drastically and is significantly different from the g r i value of the model generated data these results suggest that the proposed concept g r i is meaningful to the study distance matrix and provides a new perspective based on the network

collective chaos induced by structures of complex networks abstract mapping a complex network of n coupled identical oscillators to a quantum system the nearest neighbor level spacing nnls distribution is used to identify collective chaos in the corresponding classical dynamics on the complex network the classical dynamics on an erdos renyi network with the wiring probability p er 1 n is in the state of collective order while that on an erdos renyi network with p er 1 n in the state of collective chaos the dynamics on a ws small world complex network evolves from collective order to collective chaos rapidly in the region of the rewiring probability p r 0 0 0 1 and then keeps chaotic up to p r 1 0 the dynamics on a growing random network grn is in a special state deviates from order significantly in a way opposite to that on ws small world networks each network can be measured by a couple values of two parameters

visibility graph approach to exchange rate series abstract by means of a visibility graph we investigate six important exchange rate series it is found that the series convert into scale free and hierarchically structured networks the relationship between the scaling exponents of the degree distributions and the hurst exponents obeys the analytical prediction for fractal brownian motions the visibility graph can be used to obtain reliable values of hurst exponents of the series the characteristics are explained by using the multifractal structures of the series the exchange rate of euro to japanese yen is widely used to evaluate risk and to estimate trends in speculative investments interestingly the hierarchies of the visibility graphs for the exchange rate series of these two currencies are significantly weak compared with that of the other series

a new 3 d conformal pec fdtd scheme with user defined geometric precision and derived stability criterion a new conformal finite difference time domain cfdtd updating scheme for metallic surfaces nonaligned in the grid is presented in this paper in contrast to existing conformal models the new model can be formulated with the original yee fdtd update equation therefore the proposed scheme can be easily added in standard fdtd codes even if the codes are already parallelized or hardware accelerated in addition based on the commonly used conventional stability criterion a derivation of the stability is presented and based on the conformal geometric information a time step reduction formula is presented the time step reduction is used as a user defined parameter to tradeoff speed versus accuracy the achievable geometric precision is optimized to a given time step therefore even with the conventional time step no reduction the presented scheme profits from the conformal discretization to show the performance and the robustness of the proposed scheme canonical validations and two real world applications were investigated a broadband low profile circular antenna was successfully simulated showing the benefit of the conformal fdtd method compared to the conventional scheme furthermore a cad based mobile phone was conformally discretized and successfully simulated showing that the proposed scheme is highly suited for the simulation of advanced engineering problems

development of a novel fdtd 2 4 compatible conformal scheme for electromagnetic computations of complex curved pec objects we present a modified fourth order finite difference time domain fdtd 2 4 conformal scheme for accurately computing electromagnetic characteristics of some complex three dimensional 3d perfectly conducting pec objects it has higher accuracy and efficiency than those of conventional fdtd and fdtd 2 4 methods as coarse meshes are employed with staircase errors reduced effectively during its implementation two integration loops of the faraday s law are used for updating magnetic field components while the updating equations of electric field ones are the same as those in the normal fdtd method a rigorous analysis of its global stability based on the conventional high order fdtd stability criterion and the fourier method is also performed in order to obtain stable and accurate numerical results a user defined geometric precision technique which gives a criterion for determining the time step is employed for our computations it is numerically demonstrated that using our proposed fdtd 2 4 compatible conformal scheme high accuracy and low dispersion errors can be achieved for fast predicting radar cross sections as well as induced current distribution of some complex 3 d structures

scale free brain functional networks functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites analysis of the resulting networks in different tasks shows that a the distribution of functional connections and the probability of finding a link versus distance are both scale free b the characteristic path length is small and comparable with those of equivalent random networks and c the clustering coefficient is orders of magnitude larger than those of equivalent random networks all these properties typical of scale free small world networks reflect important functional information about brain states

scaling analysis of phase fluctuations in experimental three phase flows abstract the characterization of complex patterns arising from three phase e g oil gas water flows is an important problem with significant engineering and industrial applications based solely on measured conductance fluctuation signals from experimental three phase flows we propose a method to characterize and distinguish three commonly observed flow patterns using the phase characterization method we first calculate the instantaneous phase from the signals then through performing a scaling analysis detrended fluctuation analysis dfa we extract scaling behaviors associated with the phase fluctuations and find that the dfa scaling exponent is sensitive to the transition among different flow patterns which can be used to characterize nonlinear dynamics of the three phase flow from a novel perspective we investigate the three phase flow in terms of phase characterization and scaling analysis the results indicate that our method can provide new insights into the exploration of complex mechanism in flow pattern transition the effectiveness of the method is demonstrated and its broader applicability is articulated

uniform framework for the recurrence network analysis of chaotic time series we propose a general method for the construction and analysis of unweighted e recurrence networks from chaotic time series the selection of the critical threshold e c in our scheme is done empirically and we show that its value is closely linked to the embedding dimension m in fact we are able to identify a small critical range e numerically that is approximately the same for the random and several standard chaotic time series for a fixed m this provides us a uniform framework for the nonsubjective comparison of the statistical measures of the recurrence networks constructed from various chaotic attractors we explicitly show that the degree distribution of the recurrence network constructed by our scheme is characteristic to the structure of the attractor and display statistical scale invariance with respect to increase in the number of nodes n we also present two practical applications of the scheme detection of transition between two dynamical regimes in a time delayed system and identification of the dimensionality of the underlying system from real world data with a limited number of points through recurrence network measures the merits limitations and the potential applications of the proposed method are also highlighted

horizontal visibility graphs transformed from fractional brownian motions topological properties versus the hurst index abstract nonlinear time series analysis aims at understanding the dynamics of stochastic or chaotic processes in recent years quite a few methods have been proposed to transform a single time series to a complex network so that the dynamics of the process can be understood by investigating the topological properties of the network we study the topological properties of horizontal visibility graphs constructed from fractional brownian motions with different hurst indexes h 0 1 special attention has been paid to the impact of the hurst index on topological properties it is found that the clustering coefficient c decreases when h increases we also found that the mean length l of the shortest paths increases exponentially with h for fixed length n of the original time series in addition l increases linearly with respect to n when h is close to 1 and in a logarithmic form when h is close to 0 although the occurrence of different motifs changes with h the motif rank pattern remains unchanged for different h adopting the node covering box counting method the horizontal visibility graphs are found to be fractals and the fractal dimension d b decreases with h furthermore the pearson coefficients of the networks are positive and the degree degree correlations increase with degree which indicate that the horizontal visibility graphs are assortative with the increase of h the pearson coefficient decreases first and then increases in which the turning point is around h 0 6 the presence of both fractality and assortativity in the horizontal visibility graphs converted from fractional brownian motions is different from many cases where fractal networks are usually disassortative

statistical properties of visibility graph of energy dissipation rates in three dimensional fully developed turbulence abstract we study the statistical properties of complex networks constructed from time series of energy dissipation rates in three dimensional fully developed turbulence using the visibility algorithm the degree distribution is found to have a power law tail with the tail exponent 3 0 the exponential relation between the number of the boxes n b and the box size l b based on the edge covering box counting method illustrates that the network is not self similar which is also confirmed by the hub hub attraction according to the visibility algorithm in addition it is found that the skeleton of the visibility network exhibits excellent allometric scaling with the scaling exponent 1 163 0 005

locally conformal method for acoustic finite difference time domain modeling of rigid surfaces the finite difference time domain method is a simple but powerful numerical method for simulating full wave acoustic propagation and scattering however the method can demand a large amount of computational resources traditionally continuously curved boundaries are represented in a stair step fashion and thus accurately modeling scattering from a boundary will require a finer discretization than would otherwise be necessary for modeling propagation in a homogeneous medium however a fine discretization might not be practical due to limited computational resources a locally conformal technique is presented here for modeling acoustic scattering from continuously curved rigid boundaries this technique is low cost simple to implement and gives better results for the same grid discretization than the traditional stair step representation these improvements can be traded for a coarser discretization which reduces the computational burden the improved accuracy of this technique is demonstrated for a spherical scatterer

a novel fdtd time stepping scheme to calculate rcs of curved conducting objects using adaptively adjusted time steps the chief shortcoming of the conventional conformal finite difference time domain cfdtd method is a global time step reduction to ensure stability due to the small irregular cells truncated by the curved conducting geometry introducing the local time stepping into the cfdtd method is an efficient way and has been numerically proven in our previous work in this paper we further employ the concept of the equivalent material constants into the integral form of faraday s law to analyze the curved configuration with or without the coating hence we can theoretically derive the stability criterion of each irregular cell based on the same procedure for the courant friedrichs lewy cfl stability criterion therefore the local time step size of each small irregular cell can be chosen adaptively an adaptively adjusted time stepping procedure is presented and is theoretically proven to ensure numerical stability the radar cross section rcs results of various curved conducting objects with the coating are computed comparisons of accuracy and efficiency of our method with other established methods are performed

visibility graph analysis of economy policy uncertainty indices abstract uncertainty plays an important role in the global economy in this paper the economic policy uncertainty epu indices of the united states and china are selected as the proxy variable corresponding to the uncertainty of national economic policy by adopting the visibility graph algorithm the four economic policy uncertainty indices of the united states and china are mapped into complex networks and the topological properties of the corresponding networks are studied the hurst exponents of all the four indices are within 0 5 1 which implies that the economic policy uncertainty is persistent the degree distributions of the epu networks have power law tails and thus the networks are scale free the average clustering coefficients of the four epu networks are high and close to each other while these networks exhibit weak assortative mixing we also find that the epu network in united states based on daily data shows the small world feature since the average shortest path length increases logarithmically with the network size such that l n 0 626 ln n 0 405 our research highlights the possibility to study the epu from the view angle of complex networks

scaling and correlation in financial time series we discuss the results of three recent phenomenological studies focussed on understanding the distinctive statistical properties of nancial time series i the probability distribution of stock price uctuations stock price uctuations occur in all magnitudes in analogy to earthquakes from tiny uctuations to very drastic events such as the crash of 19 october 1987 sometimes referred to as black monday the distribution of price uctuations decays with a power law tail well outside the levy stable regime and describes uctuations that dier by as much as 8 orders of magnitude in addition this distribution preserves its functional form for uctuations on time scales that dier by 3 orders of magnitude from 1 min up to approximately 10 days ii correlations in nancial time series while price uctuations themselves have rapidly decaying correlations the magnitude of uctuations measured by either the absolute value or the square of the price uctuations has correlations that decay as a power law persisting for several months iii volatility and trading activity we quantify the relation between trading activity measured by the number of transactions nt and the price change gt for a given stock over a time interval t t t we nd that nt displays long range power law correlations in time which leads to the interpretation that the long range correlations previously found for jgtj are connected to those of nt c 2000 elsevier science b v all rights reserved

weighted similarity invariant linear algorithm for camera calibration with rotating 1 d objects in this paper a weighted similarity invariant linear algorithm for camera calibration with rotating 1 d objects is proposed first we propose a new estimation method for computing the relative depth of the free endpoint on the 1 d object and prove its robustness against noise compared with those used in previous literature the introduced estimator is invariant to image similarity transforms resulting in a similarity invariant linear calibration algorithm which is slightly more accurate than the well known normalized linear algorithm then we use the reciprocals of the standard deviations of the estimated relative depths from different images as the weights on the constraint equations of the similarity invariant linear calibration algorithm and propose a weighted similarity invariant linear calibration algorithm with higher accuracy experimental results on synthetic data as well as on real image data show the effectiveness of our proposed algorithm

in defense of the eight point algorithm the fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras and the eight point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches it has the advantage of simplicity of implementation the prevailing view is however that it is extremely susceptible to noise and hence virtually useless for most purposes this paper challenges that view by showing that by preceding the algorithm with a very simple normalization translation and scaling of the coordinates of the matched points results are obtained comparable with the best iterative algorithms this improved performance is justified by theory and verified by extensive experiments on real images

fractal analysis on human dynamics of library loans abstract in this paper the fractal characteristic of human behaviors is investigated from the perspective of time series constructed with the amount of library loans the values of the hurst exponent and length of non periodic cycle calculated through rescaled range analysis indicate that the time series of human behaviors and their sub series are fractal with self similarity and long range dependence then the time series are converted into complex networks by the visibility algorithm the topological properties of the networks such as scale free property and small world effect imply that there is a close relationship among the numbers of repetitious behaviors performed by people during certain periods of time our work implies that there is intrinsic regularity in the human collective repetitious behaviors the conclusions may be helpful to develop some new approaches to investigate the fractal feature and mechanism of human dynamics and provide some references for the management and forecast of human collective behaviors

geometric camera calibration using circular control points modern ccd cameras are usually capable of a spatial accuracy greater than 1 50 of the pixel size however such accuracy is not easily attained due to various error sources that can affect the image formation process current calibration methods typically assume that the observations are unbiased the only error is the zero mean independent and identically distributed random noise in the observed image coordinates and the camera model completely explains the mapping between the 3d coordinates and the image coordinates in general these conditions are not met causing the calibration results to be less accurate than expected in the paper a calibration procedure for precise 3d computer vision applications is described it introduces bias correction for circular control points and a nonrecursive method for reversing the distortion model the accuracy analysis is presented and the error sources that can reduce the theoretical accuracy are discussed the tests with synthetic images indicate improvements in the calibration results in limited error conditions in real images the suppression of external error sources becomes a prerequisite for successful calibration

statistical properties of world investment networks abstract we have performed a detailed investigation on the world investment networks constructed from the coordinated portfolio investment survey cpis data of the international monetary fund ranging from 2001 to 2006 the distributions of degrees and node strengths are scale free the weight distributions can be well modeled by the weibull distribution the maximum flow spanning trees of the world investment networks possess two universal allometric scaling relations independent of time and the investment type the topological scaling exponent is 1 17 0 02 and the flow scaling exponent is 1 03 0 01

a hybrid sim sem method for 3 d electromagnetic scattering problems a new method combining the spectral integral method and spectral element method sim sem is proposed to simulate 3 d electromagnetic scattering from inhomogeneous objects in this hybrid technique a special case of the finite element boundary integral fem bi combination the sem with the mixed order curl conforming vector gauss lobatto legendre gll basis functions are used to represent the interior electric field with high accuracy while the sim on a cuboid surface is used as an exact radiation boundary condition the toeplitz property of the sim matrix is utilized to reduce the memory and cpu time costs in an iterative solver by using the fast fourier transform algorithm unlike the traditional fem bi combination where the bi portion usually dominates the computational complexity the computational costs are much lower in the sim sem method numerical results verify the accuracy and capability of this method confirming that the sim sem method is a good alternative for solving scattering problems from inhomogeneous objects

the role of fluctuating modes of autocorrelation in crude oil prices abstract autocorrelation exists in the crude oil price due to price inertia the cobweb theorem model errors etc many researchers have studied the fluctuation of the crude oil price but few have focused on the autocorrelation fluctuation in crude oil prices exploring the fluctuating rules of autocorrelation can aid in understanding the fluctuating mechanism of crude oil prices to study the role of fluctuating modes of autocorrelation in crude oil prices which have time series characteristics this study selected international crude oil spot prices as sample data to employ the methods of statistical physics the fluctuating modes of autocorrelation were defined by the autocorrelation coefficient symbolization and a coarse graining process we set the modes as nodes and the transformation between modes as edges the fluctuating mode weight network of autocorrelation was then built thus the study of autocorrelation fluctuation was transformed to a network study then certain aspects such as the statistical properties the small world behavior and the transmission medium in the network could be analyzed using complex network theory and analytical methods the periodicity of the fluctuation was calculated using a spectral analysis method this study not only describes the fluctuation of the time series more precisely than other methods but also provides ideas for methods of studying the fluctuation of univariate autocorrelations

camera calibration with moving one dimensional objects in this paper we show that the rotating 1d calibrating object used in the literature is in essence equivalent to a familiar 2d planar calibration object in addition we also show that when the 1d object undergoes a planar motion rather than rotating around a fixed point such equivalence still holds but the traditional way fails to handle it experiments are carried out to verify the theoretical correctness and numerical robustness of our results

conformal fdtd methods to avoid time step reduction with and without cell enlargement during the last decades there have been considerable efforts to develop accurate and yet simple conformal methods for modelling curved boundaries within the finite difference time domain fdtd algorithm in an earlier publication we proposed the uniformly stable conformal usc approach as a general three dimensional extension of fdtd without the need to reduce the maximum stable time step the main idea of usc is the usage of virtually enlarged cells near to the boundary leading to an increased implementation effort in this paper we review the usc method and introduce a new simple and accurate conformal scheme which does not use such enlarged cells this simplified conformal sc scheme has the same number of operations and algorithmic logic as the standard staircase method and thus is easily realizable in existing fdtd codes like usc it leads to accurate results without time step reduction showing a nearly second order convergence in practice the method is verified and compared to other approaches by means of several numerical 2d and 3d examples

a study of hierarchical structure on south china industrial electricity consumption correlation abstract based on industrial electricity consumption data of five southern provinces of china from 2005 to 2013 we study the industrial correlation mechanism with mst minimal spanning tree and ht hierarchical tree models first we comparatively analyze the industrial electricity consumption correlation structure in pre crisis and after crisis period using mst model and bootstrap technique of statistical reliability test of links results exhibit that all industrial electricity consumption trees of five southern provinces of china in pre crisis and after crisis time are in formation of chain and the center periphery structure of those chain like trees is consistent with industrial specialization in classical industrial chain theory additionally the industrial structure of some provinces is reorganized and transferred in pre crisis and after crisis time further the comparative analysis with hierarchical tree and bootstrap technique demonstrates that as for both observations of gd and overall nf the industrial electricity consumption correlation is non significant clustered in pre crisis period whereas it turns significant clustered in after crisis time therefore we propose that in perspective of electricity consumption their industrial structures are directed to optimized organization and global correlation finally the analysis of distance of hts verifies that industrial reorganization and development may strengthen market integration coordination and correlation of industrial production except gz other four provinces have a shorter distance of industrial electricity consumption correlation in after crisis period revealing a better performance of regional specialization and integration

constraints on general motions for camera calibration with one dimensional objects this paper focuses on two problems in camera calibration with one dimensional 1d objects a to find out the general motion patterns well suited for solving the calibration problem and b to improve the robustness and accuracy of the method firstly a sufficient and necessary condition for the solvability of 1d calibration with general motions is proved then the special motion of tossing a 1d object is provided as an example to illustrate the correctness and feasibility of this condition after that some practical issues on obtaining the solution are inspected by avoiding singularities the precision and robustness of the method are improved the relative mean errors are reduced to less than 5 at the noise level of one pixel which surpasses the state of the art methods of the same category

signatures of chaotic and stochastic dynamics uncovered with recurrence networks an old and important problem in the field of nonlinear time series analysis entails the distinction between chaotic and stochastic dynamics recently recurrence networks have been proposed as a t

hurst exponent estimation of self affine time series using quantile graphs abstract in the context of dynamical systems time series analysis is frequently used to identify the underlying nature of a phenomenon of interest from a sequence of observations for signals with a self affine structure like fractional brownian motions fbm the hurst exponent h is one of the key parameters here the use of quantile graphs qgs for the estimation of h is proposed a qg is generated by mapping the quantiles of a time series into nodes of a graph h is then computed directly as the power law scaling exponent of the mean jump length performed by a random walker on the qg for different time differences between the time series data points the qg method for estimating the hurst exponent was applied to fbm with different h values comparison with the exact h values used to generate the motions showed an excellent agreement for a given time series length estimation error depends basically on the statistical framework used for determining the exponent of the power law model the qg method is numerically simple and has only one free parameter q the number of quantiles nodes with a simple modification it can be extended to the analysis of fractional gaussian noises

convergent cartesian grid methods for maxwell s equations in complex geometries a convergent second order cartesian grid finite difference scheme for the solution of maxwell s equations is presented the scheme employs a staggered grid in space and represents the physical location of the material and metallic boundaries correctly hence eliminating problems caused by staircasing and contrary to the popular yee scheme enforces the correct jump conditions on the field components across material interfaces a detailed analysis of the accuracy of the new embedding scheme is presented confirming its second order global accuracy furthermore the scheme is proven to be a bounded error scheme and thus convergent conditions for fully discrete stability is furthermore established this enables the derivation of bounds for fully discrete stability with cfl restrictions being almost identical to those of the much simpler yee scheme the analysis exposes that the effects of staircasing as well as a lack of properly enforced jump conditions on the field components have significant consequences for the global accuracy it is among other things shown that for cases in which a field component is discontinuous along a grid line as happens at general two and three dimensional material interfaces the yee scheme may exhibit local divergence and loss of global convergence to validate the analysis several one and two dimensional test cases are presented showing an improvement of typically 1 to 2 orders of accuracy at little or no additional computational cost over the yee scheme which in most cases exhibits first order accuracy c 2001 academic press

hierarchical structures in the gross domestic product per capita fluctuation in latin american countries abstract a description of relations between the yearly fluctuations of the gross domestic product gdp per capita of 19 latin american la countries is presented using either a linear pcc or a nonlinear correlation coefficient ncc various time windows have been examined to measure the weights giving rise to links used to built complex networks for which the countries are nodes the average overlap index aoi for la countries has been calculated for the two correlation coefficients showing that it is rather systematically higher than for the 25 eu countries the network structures are thereafter examined through a country elimination scheme based on the removal of the weakest links and raising the aoi threshold level the pcc and ncc schemes lead to different networks and clusters a principal component analysis is furthermore made for the two here above defined adjacency matrices resulting from the pcc and ncc methods to represent and analyze the country clustering structure and observe their coherence from such points of view it is concluded that both pcc and ncc schemes lead to similar clustering though have a different feeling for the sign of correlations moreover the 19 la countries seem to be less tied than the 25 eu

degree stability of a minimum spanning tree of price return and volatility we investigate the time series of the degree of minimum spanning trees msts obtained by using a correlation based clustering procedure which starts from i asset return and ii volatility time series the mst is obtained at different times by computing correlation among time series over a time window of fixed length t we find that the mst of asset return is characterized by stock degree values which are more stable in time than the ones obtained by analyzing a mst computed starting from volatility time series our analysis also shows that the degree of stocks has a very slow dynamics with a time scale of several years in both cases

scaling detection in time series diffusion entropy analysis the methods currently used to determine the scaling exponent of a complex dynamic process described by a time series are based on the numerical evaluation of variance this means that all of them can be safely applied only to the case where ordinary statistical properties hold true even if strange kinetics are involved we illustrate a method of statistical analysis based on the shannon entropy of the diffusion process generated by the time series called diffusion entropy analysis dea we adopt artificial gauss and levy time series as prototypes of ordinary and anomalous statistics respectively and we analyze them with the dea and four ordinary methods of analysis some of which are very popular we show that the dea determines the correct scaling exponent even when the statistical properties as well as the dynamic properties are anomalous the other four methods produce correct results in the gauss case but fail to detect the correct scaling in the case of levy statistics

a staggered upwind embedded boundary sueb method to eliminate the fdtd staircasing error in spite of its flexibility and second order accuracy in a homogeneous medium yee s finite difference time domain fdtd method suffers from serious degradation when treating material interfaces greatly reducing its accuracy in the presence of inhomogeneous media and perfect conductors indeed such so called staircasing approximation may lead to local zeroth order and global first order errors in this work an embedded fdtd scheme the staggered upwind embedded boundary sueb method is developed for the solution of oneand two dimensional maxwell s equations this simple embedded technique uses upwind conditions in the fdtd method to correctly represent the location and physical conditions of material and metallic boundaries hence eliminating problems caused by the staircasing approximation accuracy analysis has been made to show that the sueb method maintains a second order accuracy globally since the entire problem has been embedded into the simple staggered grid similar to that employed by the yee s scheme extra effort is only needed when treating the grid points close to the interfaces therefore little additional computational cost is needed over yee s scheme the sueb method has been validated by analytical solutions for plane wave normally incident to a planar boundary and for the tm wave propagation in the presence of a dielectric cylinder and a perfectly electrically conducting cylinder

analytical framework for recurrence network analysis of time series recurrence networks are a powerful nonlinear tool for time series analysis of complex dynamical systems while there are already many successful applications ranging from medicine to paleoclimatology a solid theoretical foundation of the method has still been missing so far here we interpret an ensuremath varepsilon recurrence network as a discrete subnetwork of a continuous graph with uncountably many vertices and edges corresponding to the system s attractor this step allows us to show that various statistical measures commonly used in complex network analysis can be seen as discrete estimators of newly defined continuous measures of certain complex geometric properties of the attractor on the scale given by ensuremath varepsilon in particular we introduce local measures such as the ensuremath varepsilon clustering coefficient mesoscopic measures such as ensuremath varepsilon motif density path based measures such as ensuremath varepsilon betweennesses and global measures such as ensuremath varepsilon efficiency this new analytical basis for the so far heuristically motivated network measures also provides an objective criterion for the choice of ensuremath varepsilon via a percolation threshold and it shows that estimation can be improved by so called node splitting invariant versions of the measures we finally illustrate the framework for a number of archetypical chaotic attractors such as those of the bernoulli and logistic maps periodic and two dimensional quasiperiodic motions and for hyperballs and hypercubes by deriving analytical expressions for the novel measures and comparing them with data from numerical experiments more generally the theoretical framework put forward in this work describes random geometric graphs and other networks with spatial constraints which appear frequently in disciplines ranging from biology to climate science

exploring dynamic property of traffic flow time series in multi states based on complex networks phase space reconstruction versus visibility graph abstract a new method based on complex network theory is proposed to analyze traffic flow time series in different states we use the data collected from loop detectors on freeway to establish traffic flow model and classify the flow into three states based on k means method we then introduced two widely used methods to convert time series into networks phase space reconstruction and visibility graph furthermore in phase space reconstruction we discuss how to determine delay time constant and embedding dimension and how to select optimal critical threshold in terms of cumulative degree distribution in the visibility graph we design a method to construct network from multi variables time series based on logical or finally we study and compare the statistic features of the networks converted from original traffic time series in three states based on phase space and visibility by using the degree distribution network structure correlation of the cluster coefficient to betweenness and degree degree correlation

a flexible new technique for camera calibration we propose a flexible technique to easily calibrate a camera it only requires the camera to observe a planar pattern shown at a few at least two different orientations either the camera or the planar pattern can be freely moved the motion need not be known radial lens distortion is modeled the proposed procedure consists of a closed form solution followed by a nonlinear refinement based on the maximum likelihood criterion both computer simulation and real data have been used to test the proposed technique and very good results have been obtained compared with classical techniques which use expensive equipment such as two or three orthogonal planes the proposed technique is easy to use and flexible it advances 3d computer vision one more step from laboratory environments to real world use

investigating existence of chaos in short and long term dynamics of moroccan exchange rates abstract this paper proposes a new methodology to investigate presence of chaos in exchange rate time series by combining wavelet transform and lyapunov exponent estimation in particular stationary wavelet transform swt is applied to exchange rate original time series for decomposition purpose as a result approximation and details coefficients are extracted they are used to represent long and short term dynamics of the original exchange rate time series then largest lyapunov exponent is estimated for each type of dynamics to check for presence of chaos our methodology is applied to several moroccan exchange rate time series the empirical results show that in general the hypothesis of chaotic structure is accepted for currency levels but it is rejected for currency returns on both long and short dynamics in addition long and short dynamics exhibit different chaotic patterns in some exchange rate time series our approach may be useful to understand chaotic behaviour in original exchange rate time series

application of random matrix theory to microarray data for discovering functional gene modules we show that spectral fluctuation of coexpression correlation matrices of yeast gene microarray profiles follows the description of the gaussian orthogonal ensemble goe of the random matrix theory rmt and removal of small values of the correlation coefficients results in a transition from the goe statistics to the poisson statistics of the rmt this transition is directly related to the structural change of the gene expression network from a global network to a network of isolated modules