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An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this paper, we study a number of flood-risk related problems: given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, a rain distribution, and a volume of rain ψ, determine which portions of Σ are flooded. We give an overview of efficient algorithms for these problems as well as explore the efficacy and efficiency of these algorithms on real terrains. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative methodology to establish consensus among distributed information processing nodes (DIPNs) in the context of multi-target tracking (MTT) within environments characterized by resource constraints and communication uncertainties. Through the integration of the event-triggered (ET) strategy with a consensus-based algorithm, existing approaches foster consensus among DIPNs while simultaneously conserving communication resources. Nonetheless, a systematic investigation into the comprehensive analysis of data reliability from each node has not been conducted. Combining anomalous data resulting from communication uncertainties with other data on an equal footing leads to inaccurate results. To address this issue, we apply the multiple-model algorithm, assigning consensus weight to each DIPN based on the motion model distribution of the same target observed by different DIPN. Additionally, we introduce an auxiliary ET marker, considering the divergence in the motion model distribution between two consecutive moments of a certain target. This marker assists in determining whether local information must be transmitted to other DIPNs. The proposed approach yields more accurate and congruent output results from each DIPN in comparison to conventional methods, given the same triggering frequency. Numerical simulations demonstrate the efficacy of the suggested approaches in a distributed MTT scenario. • We apply the multiple-model generalized labeled multi-Bernoulli filter. • We combine event-triggered strategy with consensus approach to reduce communication. • We check data reliability from each node to improve multi-target tracking accuracy. • We suggest an additional event-triggered mark to locate the message for transmitting. [ABSTRACT FROM AUTHOR]

In this paper, the distributed constrained optimization problem over a directed graph is considered. We assume that the digraph has a row-stochastic adjacency matrix, which corresponds to the case when agents assign weights to the received information individually. We present an algorithm that converges to the optimal solution even with a time-varying stepsize which is not attenuated to zero. The choice of stepsizes is relatively easy. The usable type of stepsizes is added. The diminishing or constant stepsizes can be used in our algorithm. Equality constraints and set constraints are also considered in this paper. Convergence analysis of the proposed algorithm relies on a conversion theorem between column-stochastic and row-stochastic matrices. Finally, the results of the numerical simulation are provided to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive beamforming algorithm based on the frequency diverse array (FDA) array where the interference is located at the same angle (but different range) with the target. We take the cross subarray-based FDA with sinusoidal frequency offset (CSB sin-FDA) as the receiving array instead of the basic FDA. The sampling covariance matrix under insufficient snapshot can be corrected by the automatic diagonal loading method. On the basis of decomposing the mismatched steering vector error into a vertical component and a parallel one, this paper searches the vertical component of the error by the quadratic constraint method. The numerical simulation verifies that the beamformer based on the CSB sin-FDA can effectively hold the mainlobe at the target position when the snapshot is insufficient or the steering vector is mismatched. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new numerical simulation iterative algorithm to solve the split feasibility problem and the fixed point problem with demicontractive mappings. Our algorithm mainly involves primal-dual iterative, relaxed projection, inertial technique and self-adaptive step size. Under reasonable conditions, the strong convergence of our algorithm is established. Moreover, we provide some numerical simulation examples to demonstrate the efficiency of our iterative algorithm compared to existing algorithms in the other literature. [ABSTRACT FROM AUTHOR]

Over the last decades, the cooperative design of complex networked systems has received an increasing attention in real-world engineering practices. Traditionally, each node in the network is assumed to obtain the same signal. However, each agent often possesses different measurement due to the observability or configuration of the systems. To solve the stabilization problem in this case, we aim to establish a unified framework for the cooperative control of complex network with distributed observers. In detail, different from the traditional centralized design, this paper initiates a cooperative approach by only using the local information of the networked systems. In allusion to the three kinds of representative networks, this paper establishes some sufficient or necessary conditions for the existence of network parameters that guarantee the stabilization of the LTI plants. Moreover, some corresponding algorithms are developed to find the suitable parameters of networked observers. Last but not least, numerical simulations are also provided to verify the above theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents a COordinate Rotation Digital Computer (CORDIC)-based architecture for the computation of Nth root and proves its feasibility by hardware implementation. The proposed architecture performs the task of Nth root simply by shift-add operations and enables easy tradeoff between the speed (or precision) and the area. Technically, we divide the Nth root computation into three different subtasks, and map them onto three different classes of the CORDIC accordingly. To overcome the drawback of narrow convergence range of the CORDIC algorithm, we adopt several innovative methods to yield a much improved convergence range. Subsequently, in terms of convergence range and precision, a flexible architecture is developed. The architecture is validated using MATLAB with extensive vector matching. Finally, using a pipelined structure with fixed-point input data, we implement the example circuits of the proposed architecture with radicand ranging from zero to one million, and achieve an average mean of approximately 10−7 for the relative error. The design is modeled using Verilog HDL and synthesized under the TSMC 40-nm CMOS technology. The report shows a maximum frequency of 2.083 GHz with $197421.00~{\mu }\text{m}^{2}$ area. The area decreases to $169689.98~{\mu }\text{m}^{2}$ when the frequency lowers to 1.00 GHz. [ABSTRACT FROM AUTHOR]

In this paper, we construct two kinds of exponential SAV approach with relaxation (R-ESAV) for dissipative system. The constructed schemes are linear and unconditionally energy stable. They can guarantee the positive property of SAV without any assumption compared with R-SAV and R-GSAV approaches, preserve all the advantages of the ESAV approach and satisfy dissipation law with respect to a modified energy which is directly related to the original free energy. We also give the rigorous consistency estimates of the constructed schemes for the L 2 gradient flows. Moreover, the second version of R-ESAV approach is easy to construct high-order BDF k schemes. Especially for Navier–Stokes equations, we construct two kinds of novel schemes based on the R-ESAV method. Finally, ample numerical examples are presented to exhibit that the proposed approaches are accurate and effective. • R-ESAV schemes can improve the accuracy of the solution significantly compared with the original ESAV approach. • We construct two kinds of novel schemes based on the R-ESAV method for Navier–Stokes equations. • Only one linear system with constant coefficients at each time step needs to be solved. • The positive property of SAV without any assumption can be guaranteed. • Numerical simulations are tested to show that the modified energy equals to the original free energy at almost all times. [ABSTRACT FROM AUTHOR]

This paper presents a continuous fixed-time convergent control law driving the states of a stochastic super-twisting system at the origin for a fixed time. The stochastic super-twisting system includes both a stochastic white noise and an unbounded deterministic disturbance satisfying a Lipschitz condition. Sufficient conditions of the fixed-time convergence are obtained. Performance of the developed algorithm is verified with numerical simulations. [ABSTRACT FROM AUTHOR]

The finite-time attitude maneuvering control and vibration suppression for the flexible spacecraft with external disturbances, inertia uncertainties and input saturation are investigated in this paper. Firstly, the input shaping technique is applied to suppress most of the vibration caused by flexible appendages. Then, for the nominal attitude control system, a multivariable continuous sliding mode controller is applied to guarantee the finite-time convergence. Next, unlike the conventional discontinuous design of integral sliding mode, a continuous compensated controller with modified multivariable adaptive twisting algorithm is proposed to reject the lumped uncertainty, including disturbances, uncertainties, saturation and residual vibration. The controller can realize finite time convergence and the chattering suppression, while there's no need for the priori knowledge of the lumped uncertainty. A rigorous proof of the finite time stability of the closed-loop system is derived by the Lyapunov method. Finally, the efficiency of the proposed method is illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

A robust adaptive finite-time controller for satellite fast attitude maneuver is proposed in this paper. The finite-time sliding mode is proposed based on standard sliding mode, hence the terminal convergence rate could be largely improved, and the inherent robustness to some typical perturbations is maintained. A new method is proposed to deal with the singularity issue based on the property of Euler rotation. In order to deal with inertia matrix uncertainty, finite-time adaptive law for inertia matrix estimation variables is proposed. Considering that the variable estimation system has no direct feedback, an auxiliary state converging slower than system is designed to achieve finite-time stability. The overall global finite-time stability is proved by Lyapunov method and the performance of the controller is demonstrated by numerical simulation results. [ABSTRACT FROM AUTHOR]

To locate multiple sources through time-difference-of-arrival (TDOA) measurements, existing algorithms generally require the matching relationship between measurements and the corresponding sources. In this paper, we propose a new Bayesian learning method for cases where the matching relationship is not given and off-grid error is considered. To achieve this, first we propose a new basis generator, which casts the localization problem within the Bayesian learning scheme. Then, we modify the existing sparse Bayesian inference (SBI) approaches and explore the priors on fingerprinting weights, resulting in two intermediate algorithms. On these foundations, a subspace-based robust SBI (SRSBI) algorithm is proposed as the core of this paper. SRSBI is highlighted by its ability to work free from iteration when estimating off-grid targets. What' more, SRSBI offers considerable robustness against initial guesses of hyper-parameters. Numerical simulations demonstrate the superiority of SRSBI in terms of accuracy, robustness and speed, compared to the other reported ones. [ABSTRACT FROM AUTHOR]

Numerically asymmetric force field is examined in particle-oriented problems such as Quasicontinuum modeling and simulation. To configure the ground state of a large-scale physical system featured by numerically asymmetric force field, we propose a Hessian-free Newton–Raphson method where the Newton equation is solved using central-difference based BiCGstab algorithm (denoted as HFNR-BiCGstab-diff for simplicity). A detailed analytical and experimental investigation on the convergence performance of the HFNR-BiCGstab-diff algorithm is given in this paper. A pure HFNR-BiCGstab-diff algorithm may suffer from unreliable start-up, particularly in the case that the initial guess is far from the minimizer. As a remedy, a hybrid method that couples HFNR-BiCGstab-diff with preconditioned nonlinear conjugate gradient algorithm (PNCG) is developed to achieve optimal computational performance. The algorithms addressed in this paper have been implemented using POSIX-Thread C++. Their performance has been evaluated using three-dimensional Quasicontinuum simulation problems, which are featured by asymmetric force field and large dimensional sizes up to 122,808 degree-of-freedom, as benchmarks. The numerical experiment on IBM SP2 demonstrates that, compared to alternative unconstrained optimization methods such as preconditioned nonlinear conjugate gradient algorithm, the hybrid algorithm saves 20%–60% running time. [ABSTRACT FROM AUTHOR]

In this paper, we study the regularization l p -norm minimization problem to recover the sparse signals. We first prove that every global optimal solution to the regularization l p -norm minimization problem also solves the l 0 -norm minimization problem if the certain conditions are satisfied, and then generate a truncated approximated difference algorithm to recover the sparse signals. At last, we provide some numerical simulations to test the performance of the truncated approximated difference algorithm, and the numerical results show that the proposed algorithm performs effectively in recovering the sparse signals compared with some state-of-art methods. [ABSTRACT FROM AUTHOR]

Purpose Every student knows Newton’s iteration method from a textbook, which is widely used in numerical simulation, what few may know is that its ancient Chinese partner, Ying Buzu Shu, in about second century BC has much advantages over Newton’s method. The purpose of this paper is to introduce the ancient Chinese algorithm and its modifications for numerical simulation.Design/methodology/approach An example is given to show that the ancient Chinese algorithm is insensitive to initial guess, while a fast convergence rate is predicted.Findings Two new algorithms, which are suitable for numerical simulation, are introduced by absorbing the advantages of the Newton iteration method and the ancient Chinese algorithm.Research limitations/implications This paper focuses on a single algebraic equation; however, it is easy to extend the theory to algebraic systems.Practical implications The Newton iteration method can be updated in numerical simulation.Originality/value The ancient Chinese algorithm is elucidated to have modern applications in various numerical methods. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of identifiability of dynamical networks in the case where the vector of noises on the nodes does not have full rank. In the full-rank noise case, network identifiability is defined as the capability of uniquely identifying the transfer function matrices describing the network from informative data. This includes the noise model, which can be uniquely defined when the noise vector has full rank. When the noise vector has a singular spectrum, it admits an infinite number of different noise models and the definition of network identifiability must be adapted to demand that the correct noise spectrum be identified from informative data rather than a specific noise model. With this new definition, we show that a network with rank reduced noise is identifiable under the same conditions that apply to a network with full-rank noise. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to state estimation of multiple state-space models. Unlike the traditional methods (including the interacting multiple-model algorithm) that approximate a Gaussian mixture distribution with a single Gaussian distribution, the proposed method approximates the joint probability density functions of the state and model identity through Bayesian inference. It is shown that the proposed method reduces the approximation error considerably, and improves estimation accuracy without increasing computational cost. Analysis of its specific features as well as a potential extension is also presented. Numerical examples with a practically oriented simulation are employed to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the $H_2$ control of linear systems with multiple quantization channels. The quantization parameters of each channel are not required to be identical. The resultant mismatches are represented by polytopic uncertainties. A composite controller composed of linear and nonlinear parts is designed to meet the required $H_2$ performance and offset the quantization error. Resorting to a vertex separation technique and Finsler lemma instead of matrix inverse operations, new synthesis conditions for the desired linear part are derived in terms of linear matrix inequalities, which are further extended to treat systems with norm-bounded uncertainties. A comparison of conservativeness between the proposed methods and the existing ones is demonstrated by two numerical examples. [ABSTRACT FROM AUTHOR]

This paper presents a discrete-time distributed and noncooperative routing algorithm, which is proved, via Lyapunov arguments, to asymptotically converge to a specific equilibrium condition among the traffic flows over the network paths, known as Wardrop equilibrium. This convergence result improves the discrete-time algorithms in the literature, which achieve approximate convergence to the Wardrop equilibrium. Numerical simulations show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the problems of state space model identification of multirate processes with unknown time delay. The aim is to identify a multirate state space model to approximate the parameter-varying time-delay system. The identification problems are formulated under the framework of the expectation maximization algorithm. Through introducing two hidden variables, a new expectation maximization algorithm is derived to estimate the unknown model parameters and the time-delays simultaneously. The effectiveness of the proposed algorithm is validated by a simulation example. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel augmented complex-valued normalized subband adaptive filter (ACNSAF) algorithm is proposed for processing the noncircular complex-valued signals. Based on the augmented statistics, the proposed algorithm is derived by computing a constraint cost function. Due to contain all second-order statistical properties, the ACNSAF algorithm can process the circular and noncircular complex-valued signals simultaneously. Moreover, the stability and mean square steady-state analysis of the proposed algorithm is derived by using the energy conservation principle. Computer simulation experiments on complex-valued system identification, prediction and noise cancelling show that the proposed algorithm achieves the improved mean square deviation and prediction gain compared to the ACNLMS algorithm. And the simulation results are consistent with the analysis results. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we develop an optimal particle setup method for initial condition with Centroidal Voronoi Particle (CVP) dynamics, which combines the Centroidal Voronoi Tessellation (CVT) and Voronoi Particle (VP) concept. CVT optimizes the energy function in terms of compactness and consequently ensures the isotropy of particle distribution. The CVT configuration is computed by Lloyd's algorithm, which decreases the energy function monotonically. A physics-motivated model equation with tailored equation of state (EOS) is employed to relax the Voronoi particle system such that the convergent equilibrium matches the target configuration. The resulting particle distribution approximates the given analytical profiles of spatially adaptive density, smoothing-length and mass distribution with high interpolation accuracy. The level-set method is introduced to describe arbitrarily complex geometries. A set of Smoothed Particle Hydrodynamics (SPH) simulations is computed to demonstrate the performances of the proposed method. Without parameter tuning, good performance is obtained for presented benchmarks implying its promising potential. [ABSTRACT FROM AUTHOR]

Highlights • An accurate and a stable sharp interface IBM is presented for DNS of particle laden flows. • Torque computation on a particle is improved by exactly computing projected areas. • Current ghost-cell IBM does not show spurious oscillations in pressure force. • Present IBM is validated for relevant test cases of single and multi-particle systems. Abstract In this paper, an accurate and stable sharp interface immersed boundary method(IBM) is presented for the direct numerical simulation of particle laden flows. The current IBM method is based on the direct-forcing method by incorporating the ghost-cell approach implicitly. An important feature of this IBM is the sharp representation of the solid surface, contrary to other variants of IBM for freely moving particles in which the solid surface is diffuse. Moreover, a correction of the diameter is not necessary for obtaining accurate results. The current ghost-cell IBM is stable because spurious oscillations incurred due to discontinuity in the pressure and velocity field in moving particle simulations is avoided. An algorithm for accurate torque computation is developed. The proposed algorithm is verified by comparison to an analytical expression and is shown to give a substantial improvement over the existing method. Finally, the present IBM is validated for various test cases of single and multi-particle systems and is shown to be accurate and robust for a wide range of flow conditions. [ABSTRACT FROM AUTHOR]

Highlights • The robustness of our solver is proved for fully incompressible 2D–3D FSI problems. • A partitioned smoother to a monolithic GMRES is more robust than a partitioned solver. • An algorithm for the direct-to-steady-state FSI problem is proposed. • Our formulation solved with the MUMPS package shows a very good convergence rate. Abstract In this paper we study a monolithic Newton–Krylov solver with exact Jacobian for the solution of fully incompressible Fluid-Structure Interaction problems of either steady-state or time-dependent type. Unlike common approaches, the enforcement of the incompressibility conditions both for the fluid and for the solid parts is taken care of by using an inf-sup stable finite element pair, without stabilization terms. The Krylov solver is preconditioned using geometric multigrid with smoothers of Richardson type, in turn preconditioned by additive Schwarz algorithms. The separate solution of fluid or solid operators occurs only at the preconditioning stage of the smoother, thus guaranteeing at each level an accurate interface momentum balance. The definition of the subdomains in the Schwarz smoother is driven by the natural splitting between fluid and solid. For each part and level, the domain is subdivided into a number of minimally overlapping subdomains. Numerical investigations of two and three-dimensional benchmark tests with Newtonian fluids and nonlinear hyperelastic solids are carried out by reporting several performance indices, including condition number estimates. A robust performance of the proposed fully incompressible solver is observed, especially for the more challenging direct-to-steady-state problems. [ABSTRACT FROM AUTHOR]

Purpose The purpose of the paper is to propose some modifications to the SIMPLE (semi-implicit method for pressure-linked equations) algorithm. These modifications can ensure the numerical robustness and optimize computational efficiency. They remarkably promote the ability of the SIMPLE algorithm for incompressible DNS (direct numerical simulation) of multiscale problems, such as transitional flows and turbulent flows, by improving the properties of dispersion and dissipation.Design/methodology/approach The MDCD (minimized dispersion and controllable dissipation) scheme and MMIM (modified momentum interpolation method) are introduced. Six typical test cases are used to validate the modified algorithm, including the linear convective flow, lid-driven cavity flow, laminar boundary layer, Taylor vortex and DHIT (decaying homogenous isotropic turbulence). Particularly, a highly unsteady DNS of separated-flow transition in turbomachinery is precisely predicted by the modified algorithm.Findings The numerical examples show the distinct superiority of the modified algorithm in both internal flows and external flows. The advantages of the MDCD scheme and MMIM make the SIMPLE algorithm a promising method for DNS.Originality/value Some effective modifications to the SIMPLE algorithm are addressed. It is the first attempt to introduce the MDCD approach into the SIMPLE-type algorithms. The new algorithm is especially suitable for the incompressible DNS of convection-dominated flows. [ABSTRACT FROM AUTHOR]

Determining the frequencies of multiple resolvable exponentials is an important problem due to its application in diverse areas in science and engineering. In this paper, frequency estimation of two-dimensional (2-D) sinusoids is addressed. With the use of the periodogram in frequency domain, the required harmonics are first located coarsely. The characteristics of the 2-D spectrum is then analyzed, and the accurate estimates of the parameters are retrieved using an interpolation method iteratively. It is proved that at sufficiently high signal-to-noise ratio conditions, the harmonic estimates are asymptotically unbiased, and their variances are also analyzed. Furthermore, when only part of the data is observed, the proposed algorithm is tailored to get fast and accurate estimation results. Computer simulations are also included to compare the proposed approach with conventional 2-D harmonic retrieval schemes in terms of root mean square error performance and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, a novel method based on the entropy estimation of the observation space eigenvalues is proposed to estimate the number of independent sources impinging on a sensor array. In this method we do not need to know a priori information about the noise model and we can use it in any Gaussian or non-Gaussian model of observations and noise. Our analytical results show that the proposed algorithm is consistent and an approximation for probability of false alarm and an upper bound for probability of missed detection are derived analytically. The performance of the proposed algorithm is compared with the existing methods in the presence of Gaussian and non-Gaussian noise via the simulations. It is shown that this information theoretic method called EEE, has a better performance than those methods in the literature, especially in non-Gaussian noise environment. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of 35 dynamic memory allocation algorithms when used to service simulation programs as represented by 18 test cases. Algorithm performance was measured in terms of processing time, memory usage, and external memory fragmentation. Algorithms maintaining separate free space lists for each size of memory block used tended to perform quite well compared with other algorithms. Simple algorithms operating on memory ordered lists (without any free list) performed surprisingly well. Algorithms employing power-of-two block sizes had favorable processing requirements hut generally unfavorable memory usage. Algorithms employing LIFO, FIFO, or memory ordered free lists generally performed poorly compared with others. [ABSTRACT FROM AUTHOR]

Improvements to the exhaustive search method of best-match file searching have previously been achieved by doing a preprocessing step involving the calculation of distances from a reference point. This paper discusses the proper choice of reference points and extends the previous algorithm to use more than one reference point. It is shown that reference points should be located outside of data clusters. The results of computer simulations are presented which show that large improvements can be achieved by the proper choice and location of multiple reference points. [ABSTRACT FROM AUTHOR]

This paper presents a nonhomogeneous continuous super-twisting algorithm for systems of relative degree more than one. The conditions of finite-time convergence to the origin are obtained and the robustness of the designed algorithm is discussed. The paper concludes with numerical simulations illustrating performance of the designed algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a fast incompressible SPH (ISPH) solver implemented to run entirely on a graphics processing unit (GPU) capable of simulating several millions of particles in three dimensions on a single GPU. The ISPH algorithm is implemented by converting the highly optimised open-source weakly-compressible SPH (WCSPH) code DualSPHysics to run ISPH on the GPU, combining it with the open-source linear algebra library ViennaCL for fast solutions of the pressure Poisson equation (PPE). Several challenges are addressed with this research: constructing a PPE matrix every timestep on the GPU for moving particles, optimising the limited GPU memory, and exploiting fast matrix solvers. The ISPH pressure projection algorithm is implemented as 4 separate stages, each with a particle sweep, including an algorithm for the population of the PPE matrix suitable for the GPU, and mixed precision storage methods. An accurate and robust ISPH boundary condition ideal for parallel processing is also established by adapting an existing WCSPH boundary condition for ISPH. A variety of validation cases are presented: an impulsively started plate, incompressible flow around a moving square in a box, and dambreaks (2-D and 3-D) which demonstrate the accuracy, flexibility, and speed of the methodology. Fragmentation of the free surface is shown to influence the performance of matrix preconditioners and therefore the PPE matrix solution time. The Jacobi preconditioner demonstrates robustness and reliability in the presence of fragmented flows. For a dambreak simulation, GPU speed ups demonstrate up to 10–18 times and 1.1–4.5 times compared to single-threaded and 16-threaded CPU run times respectively. [ABSTRACT FROM AUTHOR]

Abstract Reversible data-hiding in an encrypted image (RDHEI) embeds additional data into the encrypted image content, in a manner such that the data-hiding operation does not affect the lossless recovery of the encrypted image content. In previous RDHEI methods, the encrypted images contain little redundant space; thus, these approaches may feature a low embedding rate and burden content owners with too many tasks. In contrast, in this paper, we propose a novel RDHEI by redundant space transfer (RST) scheme, which involves transferring redundant space from the original image to the encrypted image. Then, the encrypted image will necessarily contain redundant space. Thus, reversibly embedding data into this encrypted image becomes easy and efficient, and general reversible data-hiding (RDH) algorithms can be used. The proposed scheme has the advantages of a high embedding rate and requires few tasks of the content owner. The experimental results show that the performance of the proposed scheme outperforms other RDHEI algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the trace regression model with matrix covariates, where the parameter is a matrix of simultaneously low rank and row(column) sparse. To estimate the parameter, we formulate a convex optimization problem with the nuclear norm and group Lasso penalties, and propose an alternating direction method of multipliers (ADMM) algorithm. The asymptotic properties of the estimator are established. Simulation results confirm the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper considers the identification problems of a bilinear-in-parameter system with autoregressive moving average noise. The basic idea is to use the over-parameterization to transform a system into a linear regressive model, and to present a gradient based and a least squares based iterative algorithms for identifying the system parameters. The numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on two classes of coordinated tracking problems for networked robotic systems, including semi-global coordinated tracking (SCT) problem and global coordinated tracking (GCT) problem. Considering that the dynamics of the networked robotic system can be unattainable, several novel model-free controller-estimator algorithms are proposed to solve the SCT and GCT problems, as well as to reject the input disturbances contained in the system dynamics. By invoking nonsmooth analysis and Lyapunov argument, a number of novel criteria (including sufficient criteria, necessary and sufficient criteria) for semi-global and global asymptotic stability of the presented algorithms are established. Finally, simulation experiments are carried out to verify the theoretical results. [ABSTRACT FROM AUTHOR]

With the increasing popularity of online social networks, people today typically own several such accounts (e.g., Facebook, Twitter, and Flickr). In this scenario, it arises an interesting and challenging problem: how to identify the same person in different social networks, which is known as the network reconciliation problem. Prior work on this problem first assumes that the relationships between users are homogeneous, and then makes use of the local features (degree, common mapped neighbors) to achieve high precision. However, this assumption does not hold in reality since users usually have different tie strengths between each other. In this paper, we remodel the reconciliation problem by considering the users’ heterogeneous relationships and propose a unified framework called UniRank for incorporating the local features and global features together. Based on UniRank, we design an efficient two-stage network reconciliation algorithm. First, we design a global matching algorithm to explore more seeds with fast speed. Second, for each explored seed, we design a breadth-first strategy based local matching algorithm to match more seeds. Extensive simulations on real-world and synthetic social network datasets show that our algorithm significantly improves the state-of-art algorithm by up to 9 X in terms of F 1 score even under very rough conditions. [ABSTRACT FROM AUTHOR]

Purpose – The paper aims to focus on the memory-polynomial model (MPM) as special case of Volterra series, implemented in hardware. The behavior of the MPM is fully proved through a comparison with AM-AM and AM-PM measured data. The results show that this simulation technique is able to prove the effectiveness of the MPM implementation as behavioural model for high power radiofrequency amplifiers. The system is able to compensate perturbations caused by modern communication systems. Design/methodology/approach – The implementation uses Matlab-Simulink, and its digital signal processing (DSP) builder. The first stage allows developing the model in Matlab using the DSP builder blockset through the signal compiler block. Then, the design is downloaded to the DSP board. Findings – The paper demonstrates a proper behavior of the MPM as a truncation of the Volterra series, with respect to different inputs. This is a key point, because the series truncations allow first to implement this model in real time and second to obtain a correct precision, for instance when modeling amplification of digital signals in high frequency. Originality/value – The global system approach permits to easily develop, simulate, and validate a wireless system. The efficiency of a complete connected solution based on Agilent Technologies tools, combining simulations and measurements under true operating conditions, seems to be clearly demonstrated. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an adaptive algorithm is proposed for synchronization of chaotic systems with different orders. A modular adaptive control strategy is applied to make states of the slave system track those of the master, despite the unknown parameters. One of the most advantages of the modularity approach, which is applied for the first time in chaos synchronization, is its flexibility in choosing identification and control modules and designing them completely independently. In this paper, a modified recursive least square algorithm is used to identify the unknown parameters of the slave system, and the control module is designed by means of two different algorithms. First it is designed based on active control method, and then, in order to synchronize with a lower energy, we design an optimal controller. The two methods are applied on a practical case study, and the results are compared. Two different dimensional neuron models, the HR neuron model and the cable model of cylindrical cell, are considered as the master and slave systems, respectively. Simulation results confirm the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper presents an approach to aircraft target recognition using Inverse Synthetic Aperture Radar (ISAR) images. The goal of this work is to develop a robust algorithm to add Automated Target Recognition (ATR) capabilities to extract efficient feature vectors and ensure a comprehensive recognition process. In the first part on this paper, we used a database of ISAR images reconstructed from anechoic chamber simulations in order to extract efficient feature vectors. In the second part of this work, we proposed a recognition architecture to perform recognition tasks and provide a human operator with useful information for target recognition tasks. Several kinds of descriptor can be used to acquire information about target characteristics from radar signals and images. Indeed, a number of methods are currently used in automatic target recognition; notably based on ISAR imaging. However, target characteristic extraction from radar echoes remains a difficult task, and the methods are generally specific to either aircraft or ship recognition. In this work, we describe our approach to designing faster, more effective retrieval systems and a comprehensive architecture. Our approach uses global feature vectors for both ship and aircraft recognition. Firstly, the global feature vectors are described by two types of descriptor which have shown to be efficient in radar target recognition. The first type defines target shape obtained by watershed transformation, facilitating interpretation for the human operator. The second type of vector descriptor is based on so-called polar signatures, obtained using the polar mapping procedure. The latter are highly discriminative and thus significantly improve and facilitate target recognition, leading to greater precision and accuracy. Secondly, we describe the retrieval architecture suitable for the second type of vector descriptor, which checks invariance in relation to target rotation and scale. It is, in itself, more efficient and processing time at the retrieval step is reduced and controlled by the human operator. Finally, in order to validate our proposed feature vectors and architecture, the Support Vector Machine (SVM) classifier will be implemented; the results of which will be tested and presented in the last section of this paper. [Copyright &y& Elsevier]

Abstract: In this paper we describe the design of a control algorithm for MISO systems, which can be modelled as hybrid fuzzy models. Hybrid fuzzy models present a convenient approach to modelling nonlinear hybrid systems. We discuss the formulation of a hybrid fuzzy model, its structure and the identification procedure. This is followed by a derivation of the inverse model and its implementation in the control algorithm. The control scheme we are discussing splits the control algorithm in two parts: the feedforward part and the feedback part. In the paper, we deal with the feedforward part of the control algorithm, which is based on an inverse of a hybrid fuzzy model. Next, a batch-reactor process is introduced. The modelling of the batch reactor is tackled and the results of the simulation experiments using the proposed control algorithm are presented. The experiments involved controlling the temperature of a batch reactor using two on/off input valves and a continuous mixing valve. The main advantage of the proposed approach is that the feedforward part of the control algorithm can bring the system close to the desired adjusted feasible trajectory, which avoids the need for a very complex feedback part of the algorithm. Therefore, the control algorithm presents a low computational burden, particularly comparing to the standard model predictive control algorithms. These usually require a considerable computational effort, which often thwarts their implementation on real industrial systems. Nevertheless, we show that using the proposed control approach the hybrid fuzzy model framework presents a convenient option for modelling complex systems for control purposes in practice. [Copyright &y& Elsevier]

This paper presents a patented type-based group- delay equalization technique to compensate for in-band group- delay distortion typically existing in analog/RF filter circuits. The underlining principle is that for a given modulation scheme and pulse-shaping function, the modulated signal has a unique statistical distribution, and that when the modulated signal passes through a circuit, any in-band group-delay distortion in the circuit distorts its output statistical distribution. The technique employs an equalization filter to minimize the in-band group-delay distortion, with the filter coefficients derived from a measure of the distortion in the output statistical distribution using a weighted nonlinear least square algorithm. It requires simple analog and digital hardware and firmware to implement, and its implementation is inherently adaptive, capable of tracking and compensating for any variation in the group-delay distortion characteristic due to component aging and temperature variation. Computer simulations have been performed to show that an accurate equalization filter can be obtained to effectively compensate for the group-delay distortion, achieving significant performance improvements. [ABSTRACT FROM PUBLISHER]

Purpose - The purpose of this paper is to review the work of the artist Edward Ihnatowicz (1926-1988), describing his approach to his artistic practice and his major works (including sound-activated mobile, The Senster and The Bandit) and to examine how he achieved his aim of making his cybernetic sculptures move in an elegant way despite working with severely limited computational resources. Design/methodology/approach - The paper uses documentary research of the artist's private papers together with interviews with family and collaborators. Implementation of computer simulations of approaches to generating elegant motion. Findings - The paper presents a comprehensive description of Ihnatowicz's work. A simple algorithm for generating elegant motion. Originality/value - The paper is of historical value in documenting the work of an early cybernetic pioneer who had a unique approach to his artistic practice. The lessons learned from his installations are of relevance to current artists and designers who are interested in designing interactive environments. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we provide numerical evidence, via graphics generated with the help of computer simulations, that switching the control parameter of a dynamical system belonging to a class of fractional-order systems in a deterministic way, one obtains an attractor which belongs to the class of all admissible attractors of the considered system. For this purpose, while a multistep numerical method for fractional-order differential equations approximates the solution to the mathematical model, the control parameter is switched periodically every few integration steps. The switch is made inside of a considered set of admissible parameter values. Moreover, the synthesized attractor matches the attractor obtained with the control parameter replaced with the averaged switched parameter values. The results are verified in this paper on a representative system, the fractional-order Lü system. In this way we were able to extend the applicability of the algorithm presented in earlier papers using a numerical method for fractional differential equations. [Copyright &y& Elsevier]

Abstract: This paper proposes a passive fuzzy controller design methodology for nonlinear system with multiplicative noises. Applying the Itô''s formula and the sense of mean square, the sufficient conditions are developed to analyze the stability and to design the controller for stochastic nonlinear systems which are represented by the Takagi–Sugeno (T–S) fuzzy models. The sufficient conditions derived in this paper belong to the Linear Matrix Inequality (LMI) forms which can be solved by the convex optimal programming algorithm. Besides, the passivity theory is applied to discuss the effect of external disturbance on system. Finally, some numerical simulation examples are provided to demonstrate the applications of the proposed fuzzy controller design technique. [Copyright &y& Elsevier]

Abstract: In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method. [Copyright &y& Elsevier]

All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multi-agent systems. Under these assumptions, Olfati-Saber in a recent IEEE TRANSACTIONS ON AUTOMATIC CONTROL paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multi-agent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the Olfati-Saber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the Olfati-Saber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given. [ABSTRACT FROM AUTHOR]

Purpose - This paper aims to present the plan to develop the known algorithm for thermal and electromagnetic coupled problem calculation. This is used for a one-phase induction motor with locked rotor for nominal and lowered voltage excitation values. It also aims to prepare a calculating method for the average heat transfer coefficient for natural convection from the induction motor housing external face. Design/methodology/approach - The numerical investigations proposed are based on 3D finite element models for thermal and electromagnetic fields analysis and 3D volume element model for average heat transfer coefficient calculations. The thermal model is experimentally validated. Findings - The paper provides a numerical method to calculate average heat transfer coefficient for the induction motor housing external faces. This coefficient is shown as a temperature function. Temperature variations in the various parts of the induction motor with locked rotor are calculated. The calculation results are compared with the measurement results. Research limitations/implications - The average heat transfer coefficient is calculated for a limited range of temperature and for the natural convection case. Electromagnetic field analysis does not include losses in the motor core. These losses could be included in the thermal and electromagnetic fields coupled calculation problem as an additional heat source for the thermal field. Originality/value - The paper presents a 3D transient thermal field and electromagnetic field coupled problem and proposes a method for calculating the average heat transfer coefficient of natural convection from the housing external face of the induction motor with a locked rotor. [ABSTRACT FROM AUTHOR]

This paper addresses the design of generalized comb decimation filters, proposing some novel decimation schemes tailored to ΣΔ modulators. We present a mathematical framework to optimize the proposed decimation filters in such a way as to increase the ΣΔ quantization noise (QN) rejection around the so called folding bands, frequency intervals whose QN gets folded down to baseband because of the decimation process. Comparisons are given in terms of passband drop and selectivity with respect to classic comb filters with orders ranging from 3 to 6. As far as the practical implementation of the proposed filters is concerned, we present two different architectures, namely a recursive and a nonrecursive implementation, the latter of which constitutes the basis for realizing multiplier-less generalized comb filter (GCF) realizations. We propose a mathematical framework for evaluating the sensitivity of GCFs to the approximation of the multipliers embedded in the filter architectures. The considerations deduced from the sensitivity analysis, pave the way to an optimization algorithm useful for approximating the multipliers with power-of-2 coefficients. [ABSTRACT FROM AUTHOR]

Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satellite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph (DAG). Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process (DEC-POMDP). After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation. [ABSTRACT FROM AUTHOR]

This paper proposes a novel diffusion subband adaptive filtering algorithm for distributed networks. To achieve a fast convergence rate and small steady-state errors, a variable step size and a new combination method is developed. For the adaptation step, the upper bound of the mean-square deviation (MSD) of the algorithm is derived and the step size is adaptive by minimizing it in order to attain the fastest convergence rate on every iteration. Furthermore, for a combination step realized by a convex combination of the neighbor-node estimates, the proposed algorithm uses the MSD, which contains information on the reliability of the estimates, to determine combination coefficients. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of the convergence rate and the steady-state errors. [ABSTRACT FROM AUTHOR]