the wssus pulse design problem in multicarrier transmission optimal link adaption to the scattering function of wide sense stationary uncorrelated scattering wssus mobile communication channels is still an unsolved problem despite its importance for next generation system design in multicarrier transmission such link adaption is performed by pulse shaping i e by properly adjusting the transmit and receive filters for example pulse shaped offset quadrature amplitude modulation oqam systems have recently been shown to have superior performance over standard cyclic prefix orthogonal frequency division multiplexing ofdm while operating at higher spectral efficiency in this paper we establish a general mathematical framework for joint transmitter and receiver pulse shape optimization for so called weyl heisenberg or gabor signaling with respect to the scattering function of the wssus channel in our framework the pulse shape optimization problem is translated to an optimization problem over trace class operators which in turn is related to fidelity optimization in quantum information processing by convexity relaxation the problem is shown to be equivalent to a convex constraint quasi convex maximization problem thereby revealing the nonconvex nature of the overall wssus pulse design problem we present several iterative algorithms for optimization providing applicable results even for large scale problem constellations we show that with transmitter side knowledge of the channel statistics a gain of 3 6 db in signal to interference and noise ratio sinr can be expected

orthogonal time frequency signaling over doubly dispersive channels this paper develops a general framework for communication over doubly dispersive fading channels via an orthogonal short time fourier stf basis the stf basis is generated from a prototype pulse via time frequency shifts in general the orthogonality between basis functions is destroyed at the receiver due to channel dispersion the starting point of this work is a pulse scale adaptation rule first proposed by kozek to minimize the interference between the basis functions we show that the average signal to interference and noise sinr ratio associated with different basis functions is identical and is maximized by the scale adaptation rule the results in this paper highlight the critical impact of the channel spread factor the product of multipath and doppler spreads on system performance smaller spread factors result in lesser interference such that a scale adapted stf basis serves as an approximate eigenbasis for the channel a highly effective iterative interference cancellation technique is proposed for mitigating the residual interference for larger spread factors the approximate eigendecomposition leads to an intuitively appealing block fading interpretation of the channel in terms of time frequency coherence subspaces the channel is highly correlated within each coherence subspace whereas it is approximately independent across different subspaces the block fading model also yields an approximate expression for the coherent channel capacity in terms of parallel flat fading channels the deviation of the capacity of doubly dispersive channels from that of flat fading channels is quantified by studying the moments of the channel eigenvalue distribution in particular the difference between the moments of doubly dispersive and flat fading channels is proportional to channel spread factor the results in this paper indicate that the proposed stf signaling framework is applicable for spread factors as large as 0 01

topology identification of weighted complex dynamical networks abstract recently various papers investigated the geometry features synchronization and control of complex network provided with certain topology while the exact topology of a network is sometimes unknown or uncertain using lyapunov theory we propose an adaptive feedback controlling method to identify the exact topology of a rather general weighted complex dynamical network model by receiving the network nodes evolution the topology of such kind of network with identical or different nodes or even with switching topology can be monitored experiments show that the methods presented in this paper are of high accuracy with good performance

noncoherent capacity of underspread fading channels we derive bounds on the noncoherent capacity of wide sense stationary uncorrelated scattering wssus channels that are selective both in time and frequency and are underspread i e the product of the channel s delay spread and doppler spread is small the underspread assumption is satisfied by virtually all wireless communication channels for input signals that are peak constrained in time and frequency we obtain upper and lower bounds on capacity that are explicit in the channel s scattering function are accurate for a large range of bandwidth and allow to coarsely identify the capacity optimal bandwidth as a function of the peak power and the channel s scattering function we also obtain a closed form expression for the first order taylor series expansion of capacity in the infinite bandwidth limit and show that our bounds are tight in the wideband regime for input signals that are peak constrained in time only and hence allowed to be peaky in frequency we provide upper and lower bounds on the infinite bandwidth capacity our lower bound is closely related to a result by viterbi 1967 we find cases where the bounds coincide and hence the infinite bandwidth capacity is characterized exactly the analysis in this paper is based on a discrete time discrete frequency approximation of wssus time and frequency selective channels this discretization takes the underspread property of the channel explicitly into account

on the spectral efficiency of noncoherent doubly selective block fading channels in this paper we consider noncoherent single antenna communication over doubly selective block fading channels with discrete block fading interval n in our noncoherent setup neither the transmitter nor the receiver know the channel fading coefficients though both know the channel statistics in particular we consider discrete time channels whose impulse response trajectories obey a complex exponential basis expansion model with uncorrelated coefficients and we show that such a model holds in the limit n for pulse shaped transmission reception over certain wide sense stationary uncorrelated scattering channels first we show that when the inputs are chosen from continuous distributions the channel s multiplexing gain i e capacity pre log factor equals max 0 1 n delay n dopp n for discrete delay spread n delay and discrete doppler spread n dopp next for the case of strictly doubly selective fading i e n dopp 1 and n delay 1 we establish that for cyclic prefixed affine pilot aided transmission pat schemes designed to minimize the mean squared error mse attained by pilot aided minimum mse channel estimation the pre log factor of the achievable rate is less than the channel s multiplexing gain we then provide guidelines for the design of pat schemes whose achievable rate pre log factor equals the channel s multiplexing gain and construct an example

computing two dimensional poincar maps for hyperchaotic dynamics poincar map pm is one of the felicitous discrete approximation of the continuous dynamics to compute pm the discrete relation s between the successive point of interactions of the trajectories on the suitable poincar section ps are found out these discrete relations act as an amanuensis of the nature of the continuous dynamics in this article we propose a computational scheme to find a hyperchaotic pm hpm from an equivalent three dimensional 3d subsystem of a 4d or higher hyperchaotic model for the experimental purpose a standard four dimensional 4d hyperchaotic lorenz stenflo system hlss and a five dimensional 5d hyperchaotic laser model hlm is considered equivalent 3d subsystem is obtained by comparing the movements of the trajectories of the original hyperchaotic systems with all of their 3d subsystems the quantitative measurement of this comparison is made promising by recurrence quantification analysis rqa various two dimensional 2d poincar mas are computed for several suitable poincar sections for both the systems but only some of them are hyperchaotic in nature the hyperchaotic behavior is verified by positive values of both one dimensional 1d lyapunov exponent le i and 2d lyapunov exponent le ii at the end similarity of the dynamics between the hyperchaotic systems and their 2d hyperchaotic poincar maps hpm has been established through mean recurrence time mrt statistics for both of 4d hlss and 5d hlm and the best approximated discrete dynamics for both the hyperchaotic systems are found out

double series representation of bounded signals series representations of the form f t approximately sigma sub n infinity sup infinity sigma sub k infinity sup infinity a sub n k nu t n sub e sup 2 pi kts for bounded signals f t are studied as are conditions on the unit function nu t such that coefficients a sub n k reveal the energy content of f t in the time interval n 1 2

analysis of limit cycle flutter of an airfoil in incompressible flow both experimental and theoretical analyses are perfomed to investigate several types of self excited oscillations of a two dimensional wing model with non linear pitching stiffiness free play non linearity receives especial attention in the low speed wind tunnel test of the wing model with free play in pitch an interesting case of double limit cycle flutter is found subsequent harmonic balance analyses ascertain not only these two sustained oscillations but also two other unstable limit cycles a digital simulation method is also used for flutter analysis the theoretical results agree well with the experimental data

general stability analysis of synchronized dynamics in coupled systems we consider the stability of synchronized states including equilibrium point periodic orbit or chaotic attractor in arbitrarily coupled dynamical systems maps or ordinary differential equations we develop a general approach based on the master stability function and gershgorin disk theory to yield constraints on the coupling strengths to ensure the stability of synchronized dynamics systems with specific coupling schemes are used as examples to illustrate our general method

synchronization based topology identification of weighted general complex dynamical networks with time varying coupling delay abstract many existing papers investigated the geometric features control and synchronization of complex dynamical networks provided with certain topology however the exact topology of a network is sometimes unknown or uncertain based on lasalle s invariance principle we propose an adaptive feedback technique to identify the exact topology of a weighted general complex dynamical network model with time varying coupling delay by receiving the network nodes evolution the topology of such a kind of network with identical or different nodes or even with varying topology can be monitored in comparison with previous methods time delay is taken into account in this simple analytical and systematic synchronization based technique particularly the weight configuration matrix is not necessarily symmetric or irreducible and the inner coupling matrix need not be symmetric illustrative simulations are provided to verify the correctness and effectiveness of the proposed scheme

eigenvalue estimates and mutual information for the linear time varying channel we consider linear time varying channels with additive white gaussian noise for a large class of such channels we derive rigorous estimates of the eigenvalues of the correlation matrix of the effective channel in terms of the sampled time varying transfer function and thus provide a theoretical justification for a relationship that has been frequently observed in the literature we then use this eigenvalue estimate to derive an estimate of the mutual information of the channel our approach is constructive and is based on a careful balance of the tradeoff between approximate operator diagonalization signal dimension loss and accuracy of eigenvalue estimates

hybrid adaptive and impulsive synchronization of uncertain complex networks with delays and general uncertain perturbations this paper is concerned with the problem of asymptotic synchronization for a class of uncertain complex networks with delays and general uncertain perturbations in order to cope with the bad effects generated by the uncertain perturbations a novel hybrid adaptive and impulsive controller is designed such that the complex network can be asymptotically synchronized onto an isolate chaotic system with uncertain perturbations all the perturbations can be different from each other on the basis of a new lemma squeezing rule and lyapunov krasovskii functionals several sufficient conditions guaranteeing the realization of the synchronization goal are derived it is shown that the designed hybrid controllers exhibit powerful robustness some existing results are improved and extended numerical simulations verify the effectiveness of the theoretical results and the robustness of the new controller

optimization based structure identification of dynamical networks abstract the topological structure of a dynamical network plays a pivotal part in its properties dynamics and control thus understanding and modeling the structure of a network will lead to a better knowledge of its evolutionary mechanisms and to a better cottoning on its dynamical and functional behaviors however in many practical situations the topological structure of a dynamical network is usually unknown or uncertain thus exploring the underlying topological structure of a dynamical network is of great value in recent years there has been a growing interest in structure identification of dynamical networks as a result various methods for identifying the network structure have been proposed however in most of the previous work few of them were discussed in the perspective of optimization in this paper an optimization algorithm based on the projected conjugate gradient method is proposed to identify a network structure it is straightforward and applicable to networks with or without observation noise furthermore the proposed algorithm is applicable to dynamical networks with partially observed component variables for each multidimensional node as well as small scale networks with time varying structures numerical experiments are conducted to illustrate the good performance and universality of the new algorithm

structure identification of uncertain general complex dynamical networks with time delay it is well known that many real world complex networks have various uncertain information such as unknown or uncertain topological structure and node dynamics the structure identification problem has theoretical and practical importance for uncertain complex dynamical networks at the same time time delay often appears in the state variables or coupling coefficients of various practical complex networks this paper initiates a novel approach for simultaneously identifying the topological structure and unknown parameters of uncertain general complex networks with time delay in particular this method is also effective for uncertain delayed complex dynamical networks with different node dynamics moreover the proposed method can be easily extended to monitor the on line evolution of network topological structure finally three representative examples are then given to verify the effectiveness of the proposed approach

synchronization based estimation of all parameters of chaotic systems from time series by a simple combination of adaptive scheme and linear feedback with the updated feedback strength for a large class of chaotic systems it is proved rigorously by using the invariance principle of differential equations that all unknown model parameters can be estimated dynamically this approach supplies a systematic and analytical procedure for estimating parameters from time series and it is simple to implement in practice in addition this method is quite robust against the effect of noise and able to respond rapidly to changes in operating parameters of the experimental system lorenz and r ossler hyperchaos systems are used to illustrate the validity of this technique

deflection of rectangular orthotropic plates under uniform load an experiment was performed to determine the deflected shape of symmetrically supported orthotropic plates under uniform load the experimental results were then compared with the mathematical model for shape and deflection a symmetric clamped simply supported boundary condition case was selected because it has a larger difference in peak to overall plate deflection compared with a plate that is simply supported on all sides

chaos synchronization of general complex dynamical networks recently it has been demonstrated that many large scale complex dynamical networks display a collective synchronization motion here we introduce a time varying complex dynamical network model and further investigate its synchronization phenomenon based on this new complex network model two network chaos synchronization theorems are proved we show that the chaos synchronization of a time varying complex network is determined by means of the inner coupled link matrix the eigenvalues and the corresponding eigenvectors of the coupled configuration matrix rather than the conventional eigenvalues of the coupled configuration matrix for a uniform network especially we do not assume that the coupled configuration matrix is symmetric and its off diagonal elements are nonnegative which in a way generalizes the related results existing in the literature

conditions of parameter identification from time series we study the problem of synchronization based parameters identification of dynamical systems from time series through theoretical analysis and numerical examples we show that some recent research reports on this issue are not perfect or even incorrect long time full rank and finite time full rank conditions of gram matrix are pointed out which are sufficient for parameters identification of dynamical systems the influence of additive noise on the proposed parameter identifier is also investigated the mean filter is used to suppress the estimation fluctuation caused by the noise

a time varying complex dynamical network model and its controlled synchronization criteria today complex networks have attracted increasing attention from various fields of science and engineering it has been demonstrated that many complex networks display various synchronization phenomena in this note we introduce a time varying complex dynamical network model we then further investigate its synchronization phenomenon and prove several network synchronization theorems especially we show that synchronization of such a time varying dynamical network is completely determined by the inner coupling matrix and by the eigenvalues and the corresponding eigenvectors of the coupling configuration matrix of the network

on the numerical computation of poincare maps abstract this note describes a method for finding simply and accurately the intersections of a numerically integrated trajectory with a surface of section

reconstruction of complex networks with delays and noise perturbation based on generalized outer synchronization this paper proposes an approach to identify the topological structure and unknown system parameters of a weighted complex dynamical network with delay and noise perturbation based on the barbalat type invariance principle for stochastic differential equations an effective adaptive feedback technique with an updated law is developed to realize generalized outer synchronization the unknown topological structure and parameters are identified simultaneously through the established technique the weight configuration matrix was found to be unnecessarily symmetric numerical examples are examined to illustrate the effectiveness of the analytical results

identifying influential nodes in weighted networks based on evidence theory the design of an effective ranking method to identify influential nodes is an important problem in the study of complex networks in this paper a new centrality measure is proposed based on the dempster shafer evidence theory the proposed measure trades off between the degree and strength of every node in a weighted network the influences of both the degree and the strength of each node are represented by basic probability assignment bpa the proposed centrality measure is determined by the combination of these bpas numerical examples are used to illustrate the effectiveness of the proposed method

identifying the topology of a coupled fitzhugh nagumo neurobiological network via a pinning mechanism topology identification of a network has received great interest for the reason that the study on many key properties of a network assumes a special known topology different from recent similar works in which the evolution of all the nodes in a complex network need to be received this brief presents a novel criterion to identify the topology of a coupled fitzhugh nagumo fhn neurobiological network by receiving the membrane potentials of only a fraction of the neurons meanwhile although incomplete information is received the evolution of all the neurons including membrane potentials and recovery variables are traced based on schur complement and lyapunov stability theory the exact weight configuration matrix can be estimated by a simple adaptive feedback control the effectiveness of the proposed approach is successfully verified by neural networks with fixed and switching topologies

the wavelet transform time frequency localization and signal analysis two different procedures for effecting a frequency analysis of a time dependent signal locally in time are studied the first procedure is the short time or windowed fourier transform the second is the wavelet transform in which high frequency components are studied with sharper time resolution than low frequency components the similarities and the differences between these two methods are discussed for both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time frequency density finally the notion of time frequency localization is made precise within this framework by two localization theorems

effects of time delayed position feedback on a van der pol duffing oscillator abstract the mechanism for the action of time delay in a non autonomous system is investigated in this paper the original mathematical model under consideration is a van der pol duffing oscillator with excitation a delayed system is obtained by adding both linear and nonlinear time delayed position feedbacks to the original system functional analysis is used to change the delayed system into a functional differential equation fde the time delay is taken as a variable parameter to investigate its effect on the dynamics of the system such as the stability and bifurcation of an equilibrium point phase locked periodic and phase shifting solution period doubling quasi periodic motion and chaos a periodic solution expressed in the closed form with the aid of the center manifold and averaging theorem is found to be in good agreement with that obtained by numerical simulation two routes to chaos are found namely period doubling bifurcation and torus breaking the results obtained in this paper show that time delay may be used as a simple but efficient switch to control motions of a system either from order motion to chaos or from chaotic motion to order for different applications

adaptive feedback control algorithm this paper is motivated by giving the detailed proofs and some interesting remarks on the results the author obtained in a series of papers phys rev lett 93 214101 2004 phys rev e 71 037203 2005 69 067201 2004 where an adaptive feedback algorithm was proposed to effectively stabilize and synchronize chaotic systems this note proves in detail the strictness of this algorithm from the viewpoint of mathematics and gives some interesting remarks for its potential applications to chaos control a synchronization in addition a significant comment on synchronization based parameter estimation is given which shows some techniques proposed in literature less strict and ineffective in some cases

computing accurate poincare maps we present a numerical method particularly suited for computing poincare maps for systems of ordinary differential equations the method is a generalization of a stopping procedure described by henon physica d 5 1982 412 and it applies to a wide family of systems

connection graph stability method for synchronized coupled chaotic systems this paper elucidates the relation between network dynamics and graph theory a new general method to determine global stability of total synchronization in networks with different topologies is proposed this method combines the lyapunov function approach with graph theoretical reasoning in this context the main step is to establish a bound on the total length of all paths passing through an edge on the network connection graph in particular the method is applied to the study of synchronization in rings of 2k nearest neighbor coupled oscillators a rigorous bound is given for the minimum coupling strength sufficient for global synchronization of all oscillators this bound is explicitly linked with the average path length of the coupling graph contrary to the master stability function approach developed by pecora and carroll the connection graph stability method leads to global stability of synchronization and it permits not only constant but also time dependent interaction coefficients in a companion paper blinking model and synchronization in small world networks with a time varying coupling see this issue this method is extended to the blinking model of small world networks where in addition to the fixed 2 k nearest neighbor interactions all the remaining links are rapidly switched on and off independently of each other

synchronization based approach for parameters identification in delayed chaotic neural networks abstract in this paper an adaptive procedure to the problem of synchronization and parameters identification for chaotic neural networks with time varying delay is introduced by combining the adaptive control and linear feedback with appropriate update law based on the invariance principle of functional differential equations all the connection weight matrices can be efficiently estimated according to a simple rigorous and systematic technique this approach is also able to track the changes in the operating parameters of the experimental neural networks rapidly the speed of synchronization and parameters estimation can be adjusted under the adaptive gain properly chosen in addition the method is simple to implement in practice and it is quite robust against the effect of slight noise in the given time series and the estimated value of a parameter fluctuates around the correct value

mean field theory for scale free random networks random networks with complex topology are common in nature describing systems as diverse as the world wide web or social and business networks recently it has been demonstrated that most large networks for which topological information is available display scale free features here we study the scaling properties of the recently introduced scale free model that can account for the observed power law distribution of the connectivities we develop a mean eld method to predict the growth dynamics of the individual vertices and use this to calculate analytically the connectivity distribution and the scaling exponents the mean eld method can be used to address the properties of two variants of the scale free model that do not display power law scaling c 1999 elsevier science b v all rights reserved