A new noise-compensated estimation scheme for multichannel autoregressive signals from noisy observations

In many engineering applications concerning the recovery of signals from noisy observations, a common approach consists in adopting autoregressive (AR) models. This paper is concerned with not only the estimation of multichannel autoregressive (MAR) model parameters but also the recovery of signals. A new noise compensated parameter estimation scheme is introduced in this paper. It contains an advanced least square vector (ALSV) algorithm which not only keeps the advantage of blindly estimating the MAR parameters and the variance-covariance matrix of observation noises, but also aims at ensuring the variance-covariance matrix to be symmetric in each iterative procedure. Moreover, the estimation of variance-covariance matrix of input noise is proposed, and then we form an optimal filtering to recover the signals. In the numerical simulations, the estimation performance of the ALSV estimation algorithm significantly outperforms that of other existed methods. Moreover, the optimal filtering based on the ALSV algorithm leads to more accurate recovery of the true signals.     

A novel semi-blind-and-semi-reversible robust watermarking scheme for 3D polygonal models

We introduce a novel semi-blind-and-semi- reversible robust watermarking scheme for three-dimensional (3D) polygonal models. The proposed approach embeds watermarks in the significant features of 3D models in a spread-spectrum manner. This novel scheme is robust against a wide variety of attacks including rotation, translation, scaling, noise addition, smoothing, mesh simplifications, vertex reordering, cropping, and even pose deformation of meshes. To the best of our knowledge, the existing approaches including blind, semi-blind, and non-blind detection schemes cannot withstand the attack of pose editing, which is a very common routine in 3D animation. In addition, the watermarked models can be semi-reversed (i.e., the peak signal-to-noise ratio (PSNR) of the recovered models is greater than 90 dB in all experiments) in semi-blind detection scheme. Experimental results show that this novel approach has many significant advantages in terms of robustness and invisibility over other state-of-the-art approaches.     

A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image. The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.     

A framework for development and evaluation of a dynamic subchannel allocation scheme in an OFDMA system

This paper presents a framework for allocating radio resources to the Access Points (APs) introducing an Access Point Controller (APC). Radio resources can be either time slots or subchannels. The APC assigns subchannels to the APs using a dynamic subchannel allocation scheme. The developed framework evaluates the dynamic subchannel allocation scheme for a downlink multicellular Orthogonal Frequency Division Multiple Access (OFDMA) system. In the considered system, each AP and the associated Mobile Terminals (MTs) are not operating on a frequency channel with fixed bandwidth, rather the channel bandwidth for each AP is dynamically adapted according to the traffic load. The subchannels assignment procedure is based on quality estimations due to the interference measurements and the current traffic load. The traffic load estimation is realized with the measurement of the utilization of the assigned radio resources. The reuse partitioning for the radio resources is done by estimating mutual Signal to Interference Ratio (SIR) of the APs. The developed dynamic subchannel allocation ensures Quality of Service (QoS), better traffic adaptability, and higher spectrum efficiency with less computational complexity.

A fault detection scheme for linear discrete-time systems with an integrated online performance evaluation

This paper is concerned with the design of the fault detection systems, into which a residual generation, evaluation and threshold are integrated, for linear discrete time-varying processes over a finite horizon. In the proposed design scheme, the residual generation is realised in the context of H_∞ fault estimation with a prescribed attenuation level. This attenuation level is minimised by using the Krein-space linear estimation theory and, subsequently, an H_∞ fault estimator with the minimum attenuation level is designed in terms of the solution to a set of Riccati-like recursions. For the residual evaluation and decision making purpose, the false alarm rate and fault detection rate indicators are introduced in the norm-based framework, which is integrated into the decision making procedure. For the online computations of the false alarm rate and fault detection rate indicators, further estimates delivered by the H_∞ fault estimator are applied without additional (online) computations. By means of checking the change in the false alarm rate and fault detection rate indicators, a decision is then made. In this way, the fault detection performance can be significantly improved. Finally, one application example is exploited to demonstrate the application of the proposed integrated fault detection and performance evaluation schemes.     

A framework for the application of metaheuristics to?tasks-to-processors assignation problems

The optimization of the execution time of a parallel algorithm can be achieved through the use of an analytical cost model function representing the running time. Typically the cost function includes a set of parameters that model the behavior of the system and the algorithm. In order to reach an optimal execution, some of these parameters must be fitted according to the input problem and to the target architecture. An optimization problem can be stated where the modeled execution time for the algorithm is used to estimate the parameters. Due to the large number of variable parameters in the model, analytical minimization techniques are discarded. Exhaustive search techniques can be used to solve the optimization problem, but when the number of parameters or the size of the computational system increases, the method is impracticable due to time restrictions. The use of approximation methods to guide the search is also an alternative. However, the dependence on the algorithm modeled and the bad quality of the solutions as a result of the presence of many local optima values in the objective functions are also drawbacks to these techniques. The problem becomes particularly difficult in complex systems hosting a large number of heterogeneous processors solving non-trivial scientific applications. The use of metaheuristics allows for the development of valid approaches to solve general problems with a large number of parameters. A well-known advantage of metaheuristic methods is the ability to obtain high-quality solutions at low running times while maintaining generality. We propose combining the parameterized analytical cost model function and metaheuristic minimization methods, which contributes to a novel real alternative to minimize the parallel execution time in complex systems. The success of the proposed approach is shown with two different algorithmic schemes on parallel heterogeneous systems. Furthermore, the development of a general framework allows us to easily develop and experiment with different metaheuristics to adjust them to particular problems.     

Performance sensitivities for parameterized Markov systems

It is known that the performance potentials (or equivalently, perturbation realization factors) can be used as building blocks for performance sensitivities of Markov systems. In parameterized systerns, the changes in parameters may only affect some states, and the explicit transition probability matrix may not be known. In this paper, we use an example to show that we can use potentials to construct performance sensitivities m a more flexible way; only the potentials at the affected states need to be estimated, and the transition probability matrix need not be known. Policy iteration algorithms, which are simpler than the standard one, can be established.     

ER-TCP: an efficient TCP fault-tolerance scheme for cluster computing

This paper proposes a novel scheme, named ER-TCP, which transparently masks the failures happened on the server nodes of a cluster from clients at TCP connection granularity. In this scheme, TCP connections at the server side are actively and fully replicated to remain consistency so as to be transplanted over healthy parts during failure. A log mechanism is designed to cooperate with the replication to achieve small sacrifice on the performance of communication and makes the scheme scales beyond a few nodes, even when they have different processing capacities. We built a prototype system at a four-node cluster with ER-TCP, and conducted a series of experiments on that. The experimental result told us that ER-TCP has relatively small penalty on the communication performance, especially when it is used to synchronize multiple replicas. The results of real applications show that ER-TCP will incur small sacrifice on performance of web server at light load, and it can be used to distribute files very efficiently and reliably.

A gas-kinetic BGK scheme for gas–water flow

A new one-dimensional gas-kinetic BGK scheme for gas–water flow is developed with the inclusion of the stiffened equation of state for water. The mixture model is considered, where the gas and water inside a computational cell achieve the equilibrium state, with equal pressure, velocity and temperature, within a time step. The splitting method is adopted to calculate the flux of each component at a cell interface individually. The preliminary application of the present newly developed method in different types of shock tube problems, including gas–gas shock tube and gas–water shock tube problems, validates its good performance for gas–water flow.     

A Dual-Petrov-Galerkin Method for the Kawahara-Type Equations

An efficient and accurate numerical scheme is proposed, analyzed and implemented for the Kawahara and modified Kawahara equations which model many physical phenomena such as gravity-capillary waves and magneto-sound propagation in plasmas. The scheme consists of dual-Petrov-Galerkin method in space and Crank-Nicholson-leap-frog in time such that at each time step only a sparse banded linear system needs to be solved. Theoretical analysis and numerical results are presented to show that the proposed numerical is extremely accurate and efficient for Kawahara type equations and other fifth-order nonlinear equations. This work is partially supported by the National Science Council of the Republic of China under the grant NSC 94-2115-M-126-004 and 95-2115-M-126-003. This work is partially supported by NSF grant DMS-0610646.     

Boosting learning and inference in Markov logic through metaheuristics

Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for structure learning in ML, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. We show through real-world experiments that the algorithm improves accuracy and learning time over the state-of-the-art algorithms. On the other side MAP and conditional inference for ML are hard computational tasks. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) metaheuristic. The first algorithm performs MAP inference and we show through extensive experiments that it improves over the state-of-the-art algorithm in terms of solution quality and inference time. The second algorithm combines IRoTS steps with simulated annealing steps for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.     

A modified predictor-corrector scheme for the Klein–Gordon equation

A numerical method based on a three-time level finite-difference scheme has been proposed for the solution of the two forms of the Klein–Gordon equation. The method, which is analysed for local truncation error and stability, leads to the solution of a nonlinear system. To avoid solving it, a predictor-corrector scheme using as predictor a second-order explicit scheme is proposed. The procedure of the corrector is modified by considering, as known, the already evaluated corrected values instead of the predictor ones. This modified scheme is applied to problems possessing periodic, kinks and soliton waves. The accuracy as well as the long-time behaviour of the proposed scheme is discussed and comparisons with the relevant known in the bibliography schemes are given.     

A linear discrete scheme for the Ginzburg–Landau equation

This paper considers a 2D Ginzburg–Landau equation with a periodic initial-value condition. A fully discrete Galerkin–Fourier spectral approximation scheme, which is a linear scheme, is constructed and the dynamical behaviour of the discrete system is then analysed. First, the existence and convergence of global attractors of the discrete system are obtained by a priori estimates and the error estimates of the discrete solution without any restriction on the time step, and the convergence of the discrete scheme is then obtained. The numerical stability of the discrete scheme is proved.     

A high dynamic range rendering pipeline for interactive applications

Realistic images can be computed at interactive frame rates for Computer Graphics applications. Meanwhile, High Dynamic Range (HDR) rendering has a growing success in video games and virtual reality applications, as it improves the image quality and the player’s immersion feeling. In this paper, we propose a new method, based on a physical lighting model, to compute in real time a HDR illumination in virtual environments. Our method allows to re-use existing virtual environments as input, and computes HDR images in photometric units. Then, from these HDR images, displayable 8-bit images are rendered with a tone mapping operator and displayed on a standard display device. The HDR computation and the tone mapping are implemented in OpenSceneGraph with pixel shaders. The lighting model, together with a perceptual tone mapping, improves the perceptual realism of the rendered images at low cost. The method is illustrated with a practical application where the dynamic range of the virtual environment is a key rendering issue: night-time driving simulation.     

A hybrid metaheuristic case-based reasoning system for nurse rostering

In this paper we present a novel Case-Based Reasoning (CBR) system called CABAROST (CAsed-BAsed ROSTering) which was developed for personnel scheduling problems. CBR is used to capture and store examples of personnel manager behaviour which are then used to solve future problems. Previous examples of constraint violations in schedules and the repairs that were used to solve the violations are stored as cases. The sequence in which violations are repaired can have a great impact on schedule quality. A novel memetic algorithm is proposed which evolves good quality sequences of repairs generated by CABAROST. The algorithm was tested on instances of the real-world nurse rostering problem at the Queens Medical Centre NHS Trust in Nottingham.     

Image Fusion for Enhanced Visualization: A Variational Approach

We present a variational model to perform the fusion of an arbitrary number of images while preserving the salient information and enhancing the contrast for visualization. We propose to use the structure tensor to simultaneously describe the geometry of all the inputs. The basic idea is that the fused image should have a structure tensor which approximates the structure tensor obtained from the multiple inputs. At the same time, the fused image should appear ‘natural’ and ‘sharp’ to a human interpreter. We therefore propose to combine the geometry merging of the inputs with perceptual enhancement and intensity correction. This is performed through a minimization functional approach which implicitly takes into account a set of human vision characteristics.     

A space–time adaptation scheme for unsteady shallow water problems

We provide a space–time adaptation procedure for the approximation of the Shallow Water Equations (SWE). This approach relies on a recovery based estimator for the global discretization error, where the space and time contributions are kept separate. In particular we propose an ad hoc procedure for the recovery of the time derivative of the numerical solution and then we employ this reconstruction to define the error estimator in time. Concerning the space adaptation, we move from an anisotropic error estimator able to automatically identify the density, the shape and the orientation of the elements of the computational mesh. The proposed global error estimator turns out to share the good properties of each recovery based error estimator. The whole adaptive procedure is then combined with a suitable stabilized finite element SW solver. Finally the reliability of the coupled solution–adaptation procedure is successfully assessed on two unsteady test cases of interest for hydraulics applications.     

A Prolate-Element Method for Nonlinear PDEs on the Sphere

A p-type spectral-element method using prolate spheroidal wave functions (PSWFs) as basis functions, termed as the prolate-element method, is developed for solving partial differential equations (PDEs) on the sphere. The gridding on the sphere is based on a projection of the prolate-Gauss-Lobatto points by using the cube-sphere transform, which is free of singularity and leads to quasi-uniform grids. Various numerical results demonstrate that the proposed prolate-element method enjoys some remarkable advantages over the polynomial-based element method: (i) it can significantly relax the time step size constraint of an explicit time-marching scheme, and (ii) it can increase the accuracy and enhance the resolution.     

A recursive genetic framework for evolutionary decision-making in problems with high dynamism

Communication and coordination are the main cores for reaching a constructive agreement among multi-agent systems (MASs). Dividing the overall performance of MAS to individual agents may lead to group learning as opposed to individual learning, which is one of the weak points of MASs. This paper proposes a recursive genetic framework for solving problems with high dynamism. In this framework, a combination of genetic algorithm and multi-agent capabilities is utilised to accelerate team learning and accurate credit assignment. The argumentation feature is used to accomplish agent learning and the negotiation features of MASs are used to achieve a credit assignment. The proposed framework is quite general and its recursive hierarchical structure could be extended. We have dedicated one special controlling module for increasing convergence time. Due to the complexity of blackjack, we have applied it as a possible test bed to evaluate the system’s performance. The learning rate of agents is measured as well as their credit assignment. The analysis of the obtained results led us to believe that our robust framework with the proposed negotiation operator is a promising methodology to solve similar problems in other areas with high dynamism.     

A sixth-order finite difference WENO scheme for Hamilton–Jacobi equations

In this paper, a sixth-order finite difference weighted essentially non-oscillatory (WENO) scheme is developed to approximate the viscosity solution of the Hamilton–Jacobi equations. This new WENO scheme has the same spatial nodes as the classical fifth-order WENO scheme proposed by Jiang and Peng [Weighted ENO schemes for Hamilton–Jacobi equations, SIAM. J. Sci. Comput. 21 (2000), pp. 2126–2143] but can be as high as sixth-order accurate in smooth region while keeping sharp discontinuous transitions with no spurious oscillations near discontinuities. Extensive numerical experiments in one- and two-dimensional cases are carried out to illustrate the capability of the proposed scheme.