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Considering that some research and development (R&D) networks are becoming increasingly interdependent, risk not only propagates within one R&D network but also spreads to other R&D networks. This paper proposes a model of risk cascading propagation in order to explore the vulnerability of interdependent R&D networks. Firstly, we propose the generation algorithm of individual R&D networks based on the rules of preferential attachment and geographical proximity, and then build the interdependences between two R&D networks by identifying the input interdependence and shared interdependence. Secondly, we propose the model of risk propagation within one R&D network by defining the risk load and risk capacity of each enterprise, and building the rule of conditional triggering among risks. We then propose the four-stage model for describing the process of risk cascading propagation across interdependent R&D networks. Finally, we investigate the vulnerability of interdependent R&D networks to risk cascading propagation through numerical simulation. Simulation results show that the critical tolerance threshold β ∗ can effectively indicate the vulnerability of interdependent R&D networks to risk cascading propagation. When the value of the tolerance parameter β exceeds the critical tolerance threshold β ∗ , the risk cascading propagation can be entirely prevented in interdependent R&D networks. The vulnerability of interdependent R&D networks increases with the increasing heterogeneity degree of all the enterprises’ risk capacities. The interdependences between interdependent R&D networks can increase their vulnerability to risk cascading propagation. Random attack has the least impact on the vulnerability of interdependent R&D networks, whereas high-degree attack and high-capacity attack have the same impact on the vulnerability. [ABSTRACT FROM AUTHOR]

Abstract Evaluating the spreading capability of nodes in complex networks is highly significant for understanding the spreading behavior in complex networks. Recent studies showed that the degree centrality and the coreness centrality can effectively evaluate the spreading capability of nodes. However, specific network topologies significantly decrease the effectiveness of these two metrics. In this paper, we propose a new method called LS method. The LS method distinguishes the importance of the different edges of the node during the spreading process and figures out a new way to rank the spreading capability of nodes. Simulation experiments on real networks show that the proposed method is more efficient and more versatile than the degree centrality and the k-shell decomposition algorithms. Unlike other improved methods, the LS method does not need to compute any empirical parameter, which means the LS method is more efficient than these improved methods. [ABSTRACT FROM AUTHOR]

In this paper, A high-precision pose estimation method based on rotation parameters is proposed. By introducing the parameterization of rotation matrix, we first formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Then, based on Gröbner basis method, we solve the solution of polynomial equation directly, and then the attitude is calculated by back-substitution method. More importantly, one of the main advantages of our method is scalability, because the order of the polynomial equations we solve is independent of the number of points involved in the estimation. Finally, we compare the performance of our algorithm with the leading P n P method, both in simulations and experiments, and show that the proposed method achieves the accuracy of approaching the OP n P (optimal solution to the P n P problem). [ABSTRACT FROM AUTHOR]

Parameter estimation is important in signal modeling and signal filtering. This paper uses the filtering technique to study the parameter estimation problems of a class of bilinear systems, for which the input-output representation is derived by eliminating the state variables in the systems. According to the obtained identification model, a two-stage least squares based iterative algorithm and a filtering based least squares iterative algorithm are proposed for estimating the parameters of bilinear systems with colored noises by using the hierarchical identification principle and the data filtering technique. The least squares based iterative algorithm is given for comparison. The simulation results show that the proposed algorithms have good performance in estimating the parameters of bilinear systems. [ABSTRACT FROM AUTHOR]

Abstract: Motion is a common phenomenon in the real world so the consistence of virtual and real motion is one of hot focuses in augmented reality system. This paper mainly analyzes virtual and real motion blur consistence in augmented reality system, and mainly do improvement in two aspects, one is how to get the motion blur parameters, the other is how to render motion blur. From the available literature it can be seen that, most achieved methods to get motion blur parameters are based on machine vision. But this paper first acquires the motion trajectory by magnetic tracking, and then gets motion blur parameters. Considering the available literature on motion blur rendering methods, they mainly contain convolution and accumulator caching and so on. Due to rotation translation compound motion blur, this paper does rendering in advance based on improved mathematical model, and then render virtual object through looking up table in augmented reality system. Experimental results show that the new virtual and real motion blur consistence render algorithm has low time complexity and achieve the desired effect in simulation environment and real scenes. [Copyright &y& Elsevier]

Abstract: The topic of this paper is the estimation of modal parameters from nonstationary ambient vibration data by applying the random decrement algorithm. If the ambient excitation can be modeled as a nonstationary white noise in the form of a product model, then the nonstationary cross randomdec signatures of structural response evaluated at any fixed time instant are shown theoretically to be proportional to the nonstationary cross-correlation functions, which is of the same form as the free-vibration decay or impulse response of the original system. The practical problem of insufficient data samples available for evaluating nonstationary randomdec signatures can be approximately resolved by first extracting the amplitude-modulating function from the response and then transforming the nonstationary responses into stationary ones. However, the error involved in the approximate free-decay response would generally lead to a distortion in the modal identification. In the present paper, we also further propose that, if the ambient excitation can be modeled as a zero-mean nonstationary process, without any additional treatment of transforming the original nonstationary responses, the nonstationary cross randomdec signatures of structural response are shown in the same mathematical form as that of free vibration of a structure, from which modal parameters of the original system can thus be identified. Numerical simulations, including one example of using the practical excitation data, confirm the validity of the proposed method for identification of modal parameters from nonstationary ambient response data only. [Copyright &y& Elsevier]

Abstract: This paper investigates adaptive control design for nonlinear square MIMO systems. The control scheme is based on recurrent neural networks emulator and controller with decoupled adaptive rates. Networks'' parameters are updated according to an autonomous algorithm inspired from the Real Time Recurrent Learning (RTRL). The contributions of this paper are the determination of Lyapunov sufficient stability conditions for decoupled adaptive rates of the emulator and controller and the development of new adaptation strategies based on the tracking error dynamics and Lyapunov stability analysis to improve the closed loop performances. Efficiency of the proposed controller is illustrated with nonlinear system simulations. An application of the developed approaches to a hot-air blower is presented in order to validate simulations results. [Copyright &y& Elsevier]

Estimation/updating of Origin–Destination (OD) flows and other traffic state parameters is a classical, widely adopted procedure in transport engineering, both in off-line and in on-line contexts. Notwithstanding numerous approaches proposed in the literature, there is still room for considerable improvements, also leveraging the unprecedented opportunity offered by information and communication technologies and big data. A key issue relates to the unobservability of OD flows in real networks – except from closed highway systems – thus leading to inherent difficulties in measuring performance of OD flows estimation/updating methods and algorithms. Starting from these premises, the paper proposes a common evaluation and benchmarking framework, providing a synthetic test bed, which enables implementation and comparison of OD estimation/updating algorithms and methodologies under “standardized” conditions. The framework, implemented in a platform available to interested parties upon request, has been flexibly designed and allows comparing a variety of approaches under various settings and conditions. Specifically, the structure and the key features of the framework are presented, along with a detailed experimental design for the application of different dynamic OD flow estimation algorithms. By way of example, applications to both off-line/planning and on-line algorithms are presented, together with a demonstration of the extensibility of the presented framework to accommodate additional data sources. [ABSTRACT FROM AUTHOR]

In this paper, an efficient implementation of the Memory Improved Proportionate Affine Projection Algorithm (MIPAPA) is developed, for the adaptive identification of linear in parameters’ systems. The proposed algorithm offers an exact implementation of the MIPAPA scheme, while maintaining the computational burden at an affordable level. The performance of the proposed approach is illustrated by means of computer simulation, in the context of adaptive identification of sparse systems. Moreover, it is shown that the proposed algorithm can be applied for the efficient estimation of the communication channel in the transmission over optical fiber links using intensity modulated direct detection ON/OFF signaling. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to derive norm-constrained adaptive algorithms for sparse system identification. In contrast to other similar approaches found in the literature, the proposed approach is focused primarily on keeping the a posteriori error equal to zero (which is a characteristic of the normalized least-mean-square algorithm) while seeking to satisfy a norm constraint. To this end, the proposed algorithms look directly for a vector belonging to the intersection of a zero-error hyperplane and a hyperplane resulting from a relaxed norm constraint. This somewhat simpler strategy leads to effective sparsity-promoting adaptive algorithms that exhibit low computational complexity and use parameters that are easy to adjust. In this context, a general framework that allows obtaining adaptive algorithms using different norm functions is devised. From this framework, two norm-constrained algorithms based on the ℓ 1 and ℓ 0 norms are obtained. Moreover, enhanced versions of these algorithms are developed aiming to make them independent of user-defined norm-bound parameters. Numerical simulation results corroborate the effectiveness of the proposed framework as well as the very good performance of the obtained algorithms. [ABSTRACT FROM AUTHOR]

The greatest threat to the OFDM system is the loss of orthogonality between subcarriers due to the carrier frequency offset (CFO) which results in significant performance degradation. In addition to this last problem was the effect of propagation over multipath fading channel. Hence, both the channel and the CFO need to be estimated and corrected at the receiver for subsequent symbol detection. A new joint semi-blind CFO and channel estimation method for OFDM systems operating in multipath fading channels is investigated in this paper. The proposed algorithms are based on virtual subcarriers (VSC) in one hand and on a new design of pilot patterns in OFDM blocks in other hand. We propose to use Newton – Raphson algorithm for the estimation of CFO that has a faster convergence and a lower complexity compared to similar estimators that not employed this algorithm. Finally, we derive the Modified Cramer–Rao bounds (MCRB) for semi-blind CFO and channel estimation to evaluate the performance bounds of the proposed methods for commonly encountered scenarios. The simulation results are given to verify the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: Piezoelectric fuel injectors provide a means to reduce fuel consumption, noise and emissions in modern IC engines. This paper summarizes an effort resulting in within-a-cycle estimation of injected fuel flow rate for “rate shaped” injection profiles. A reduced order model is derived by simplifying a physically based, experimentally validated simulation model. A within-a-cycle FPGA-based estimation algorithm is derived from the model and experimentally demonstrated at a “loop time” of . [Copyright &y& Elsevier]

Abstract: According to the iterative identification technique and the hierarchical identification principle, this paper presents a two-stage gradient based and a least squares based iterative parameter estimation algorithms (i.e., the hierarchical gradient based iterative algorithm and the hierarchical least squares based iterative algorithm) for controlled autoregressive autoregressive moving average systems. The proposed two-stage least squares based iterative algorithm requires less computation compared with the least squares based iterative algorithm. The simulation results indicate that the two-stage least squares based iterative algorithm converges faster than the two-stage gradient based iterative algorithm. [Copyright &y& Elsevier]

Abstract: In Interferometric Fiber Optic Gyroscope (IFOG), the diminution of random noise and drift error is a critical task. These errors degrade the performance of IFOG. In this paper, a modified adaptive Kalman gain correction (AKFG) algorithm is proposed to denoise IFOG signal. The covariance matrix of innovation sequence is estimated using weighted average window method in which the weights are randomly generated in the range [0, 1]. Innovation based random weighted estimation (IRWE)-AKFG is applied to denoise the IFOG drift signal. The Kalman gain is adaptively updated using the covariance matrix of innovation sequence. The proposed algorithm is applied for denoising IFOG signal under static and dynamic environment. Allan variance method is used to analyze and quantify the stochastic errors in IFOG sensor. The performance of the proposed algorithm is compared with Conventional Kalman filter (CKF) and the simulation results reveal that the proposed algorithm is an efficient algorithm for denoising the IFOG signal. [Copyright &y& Elsevier]

Abstract: This paper presents a new regularized kernel-based approach for the estimation of the second order moments of stationary stochastic processes. The proposed estimator is defined by a Tikhonov-type variational problem. It contains few unknown parameters which can be estimated by cross validation solving a sequence of problems whose computational complexity scales linearly with the number of noisy moments (derived from the samples of the process). The correlation functions are assumed to be summable and the hypothesis space is a reproducing kernel Hilbert space induced by the recently introduced stable spline kernel. In this way, information on the decay to zero of the functions to be reconstructed is incorporated in the estimation process. An application to the identification of transfer functions in the case of white noise as input is also presented. Numerical simulations show that the proposed method compares favorably with respect to standard nonparametric estimation algorithms that exploit an oracle-type tuning of the parameters. [Copyright &y& Elsevier]

Abstract: Parameter estimation methods have wide applications in signal processing, communication and system identification. This paper derives an iterative least squares algorithm to estimate the parameters of output error systems and uses the partitioned matrix inversion lemma to implement the proposed algorithm in order to enhance computational efficiencies. The simulation results show that the proposed algorithm works well. [Copyright &y& Elsevier]

Abstract: In this paper, a modified particle swarm optimization algorithm (MPSO) is proposed and implemented for identification of the generalized Prandtl–Ishlinskii model for piezoelectric actuators. To prevent the particles from converging to the local optimum, an effective informed strategy is proposed to improve the quality of the equilibrium point and balance the exploration and exploitation of the algorithm. Also, a random mutation operator is introduced to produce potential good particles and help the swarm jump out from the local optimum. The algorithmic parameters of the proposed MPSO are optimized via simulation. Experiments are carried out to verify the effectiveness of the proposed approach on a piezoelectric actuated platform. The MPSO is compared with the other methods in terms of accuracy and efficiency. The results demonstrate that the MPSO is superior to its competitors for identification of the generalized Prandtl–Ishlinskii model. [Copyright &y& Elsevier]

Abstract: This paper designs a robust adaptive speed controller based on the certainty equivalence principle for a permanent magnet synchronous motor (PMSM). The proposed controller quickly adapts to the variation of model parameters and load torque, thus it does not need exact knowledge about system parameters. The proposed control system can guarantee good performance such as a fast transient response, a small steady-state error, and a good tracking capability. The proposed control algorithm is executed by using a TMS320F28335 floating point DSP. The simulation and experimental results are given to verify the effectiveness of the proposed method in the presence of the variation of system parameters and load torque. [Copyright &y& Elsevier]

Abstract: This paper presents a new controller to regulate a set of feature points on the image plane to desired positions by controlling motion of a robot manipulator in uncalibrated environments. This controller is designed to cope with both the unknown camera parameters and the unknown robot parameters. The controller employs the depth-independent image Jacobian matrix to map the image errors onto the joint space of the manipulator. Based on the fact that the unknown camera and robot parameters appear linearly in the closed-loop dynamics of the system if the depth-independent image Jacobian is used, an adaptive algorithm was developed to estimate the unknown parameters on-line. With a full consideration of dynamic responses of the robot manipulator, the convergence of the image errors on the image plane to zero was proved by the Lyapunov method. Simulation and experiments have been conducted to demonstrate good convergence of the trajectory errors under the control of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper puts forward a new scene based nonuniformity correction algorithm for IRFPA. This method adopts phase-correlation method for motion estimation and takes the sum of mean-square errors of the pixel brightness between several adjacent frames as the cost function when the brightness constancy assumption between two adjacent frames is satisfied. Nonuniformity correction parameters could be estimated by minimizing such cost function. In order to reduce calculation quantity, we can divide these images into several subblocks, and solve for the optimum solution of the cost function respectively in each subblock. From the analysis, it is shown that the optimum solution is of global uniqueness when all the elements in subblocks could satisfy the ergodicity condition. Then the estimated value of nonuniformity correction parameters could be deduced by minimizing the cost functions. The nonuniformity correction experiments for both infrared image sequence with simulated nonuniformity and infrared imagery with real nonuniformity show that the proposed algorithm could achieve a great correction effect by only analyzing a small number of frames. [Copyright &y& Elsevier]

Abstract: This paper focuses on identification problems for Hammerstein systems with saturation and dead-zone nonlinearities. An appropriate switching function is introduced to derive an identification model with fewer parameters and all the unknown parameters can be estimated by using an iterative method. A numerical simulation is carried out to show the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, we first propose two learning automata based decentralized dynamic guard channel algorithms for cellular mobile networks. These algorithms use learning automata to adjust the number of guard channels to be assigned to cells of network. Then, we introduce a new model for nonstationary environments under which the proposed algorithms work and study their steady state behavior when they use learning algorithm. It is also shown that a learning automaton operating under the proposed nonstationary environment equalizes its penalty strengths. Computer simulations have been conducted to show the effectiveness of the proposed algorithms. The simulation results show that the performances of the proposed algorithms are close to the performance of guard channel algorithm that knows all the traffic parameters. [Copyright &y& Elsevier]

Abstract: Parameter estimation of chaotic systems is an important issue and has attracted increasing interest from a variety of research fields. Recently, quantum-inspired evolutionary algorithms have been proposed and applied to some optimization problems. However, to the best of our knowledge, there is no published research work on quantum-inspired evolutionary algorithm (QEA) for estimating parameters of chaotic systems. In this paper, an effective hybrid quantum-inspired evolutionary algorithm with differential evolution (HQEDE) is proposed and applied to estimate the parameters of the Lorenz system. Numerical simulation and comparisons with other methods demonstrate the effectiveness and robustness of the proposed algorithm. In addition, the effects of the parameter settings on HQEDE are investigated. [Copyright &y& Elsevier]

Abstract: The joint angle–frequency estimation (JAFE) based on the multidimensional ESPRIT is recently developed high-resolution parameter estimation technique for determining the directions of arrival (DOAs) and center frequencies of a number of narrow-band sources in a band of interest impinging on the far field of a planar array. In this paper, we present a simple and efficient joint angle–frequency estimation method via the unitary transformation, named as Unitary-JAFE algorithm. In the final stage of Unitary-JAFE, the matrix required to eigendecomposition is a complex unsymmetric matrix. For the eigendecomposition of a complex unsymmetric matrix, we will develop an effective algorithm based on Frame method and Newton iteration method, referred as Frame–Newton algorithm. The proposed method offers a number of advantages over other recently proposed ESPRIT-based techniques by taking advantage of the temporal smoothing, spatial smoothing, and forward–backward averaging techniques. Firstly, except for the final eigenvalue decomposition of dimension equal to the number of sources, it is efficiently formulated in terms of real-valued computation throughout. Secondly, in the final stage of the proposed algorithm, the real and imaginary parts of the i th eigenvalue of a matrix are one-to-one related to the frequency and direction of arrival (DOA) of the i th source. That is, the pairing of the estimated frequency and DOA is automatically determined. Thirdly, the proposed method avoids the joint diagonalization processing, which reduces the computation complexity. Finally, simulation results are presented verifying the efficacy of the proposed algorithm. [Copyright &y& Elsevier]

• We derive the fused sparse diffusion algorithm to recover sparse parameter by enforcing sparsity of components themselves and similarity of adjacent components. • We provide the condition of convergence in the mean sense. • We study mean-square behavior and provide the expressions for calculating network steady-state MSD and steady-state individual weight error variance (IWEV). • Numerical simulations show that our proposed algorithm is able to achieve lower steady-state MSD than several other existing algorithms in both stationary and non-stationary environments. The problem of distributed estimation for sparse parameter has attracted much attention in recent years due to its wide applications. In some cases, the sparsity feature may also be found in the differences of successive components of the true parameter, i.e. the non-zero components may be assembled in one or more regions of the true parameter. In this paper, we propose the fused sparse diffusion LMS algorithm for recovering such parameter. The proposed algorithm relies on a sparse regularization term to enforce sparsity of components themselves, and fused sparse regularization term to enforce similarity of adjacent non-zero components. We then provide the condition of the proposed algorithm converging in the mean sense and study mean-square behaviors, which include mean square deviation (MSD) and individual weight error variance (IWEV). To further enhance estimation performance of the fused sparse diffusion algorithm, we provide an adaptive strategy in which the optimal regularization factors can be adjusted at each iteration. Finally, numerical simulations are conducted to show the superiority of our proposed algorithm over several other algorithms. [ABSTRACT FROM AUTHOR]