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Community detection is significative in the complex network. This paper focuses on community detection based on clustering algorithms. We tend to find out the central nodes of the communities by centrality algorithms. Firstly, we define the distance between nodes using similarity. Then, a new centrality measuring the local density of nodes is put forward. Combining the independence of the centrality, the nodes in the network can be divided into four classes. Leveraging the product of centrality and independence, the central nodes in the network are easily identified. We also find that we can distinguish bridge nodes from central nodes using centrality and independence. This research designs a community detection algorithm combining centrality and independence. Simulation results reveal that our centrality is more effective than existing centralities in measuring local density and identifying community centers. Compared with other community detection algorithms, results prove the effectiveness of our algorithm. This paper just shows one application of independence of the centrality. There may be more useful applications of it. [ABSTRACT FROM AUTHOR]

Image enhancement processing is a very important operation during image preprocessing. Compared with to enhancc the overall contrast level of image, enhancing the local contrast of image can improve the level of such contrast directly as well as the quality and effect of image enhancement. In this paper, the gray prediction model is applied to the process of enhancing image local contrast, so as to measure the change range of image local contrast and adaptively adjust the scale of enhancing image local contrast. The simulation results show that, in addition to enhancing the contrast of gray level on the edge of image, the proposed algorithm can inhibit roughened nonedge region and improve the quality of local enhancement processing, which create a more favorable condition for the further image edge detection. [ABSTRACT FROM AUTHOR]

Network-on-chip (NoC) mapping algorithms significantly affect NoC system performance in terms of communication cost and energy consumption. For a specific application represented by a task graph, this paper proposes an energy-efficient mapping algorithm that searches for the mapping decision with best communication locality and therefore lowest energy consumption. To this end, we formulate the concerned mapping problem as an optimization model, and propose an effective meta-heuristic algorithm to solve the formulated optimization model. During the mapping procedure, we employ a simulation-free, communication probability-based energy model to evaluate the quality of each candidate mapping. By iteratively updating the best explored mapping decision using a meta-heuristic search strategy, the mapping procedure can eventually identify an mapping decision with optimal energy efficiency in the search space. The proposed mapping algorithm has been verified on NoC systems of different sizes using a variety of benchmark applications. Simulation results demonstrate that the mapping decision produced by this algorithm achieves an up to 23% energy reduction compared with the traditional round-robin strategy. [ABSTRACT FROM AUTHOR]

In this paper, we present splitting schemes for the two-level Bloch model. After proposing two ways to split the Bloch equation, we show that it is possible in each case to generate exact numerical solutions of the obtained sub-equations. These exact solutions involve matrix exponentials which can be expensive to compute. Here, for 2 × 2 matrices we develop equivalent formulations which reduce the computational cost. These splitting schemes are nonstandard ones and conserve all the physical properties (Hermicity, positiveness and trace) of Bloch equations. In addition, they are explicit, making effective their implementation when coupled with the Maxwell’s equations. [ABSTRACT FROM AUTHOR]

The signal phase differences of Coriolis sensor and the mass flow are proportional. To improve the measurement accuracy of flow signal processing for Coriolis mass flowmeter, a novel method based on Hilbert Transform algorithm was proposed. The main method is as follows, two signals enhanced by LZ-filter with noise-canceling were considered as Hilbert Transform, then, the phase difference of the two filtered signals was calculated by a triangle characteristic of sin function. Synchronously, the instantaneous frequency was estimated by the structure of the analytical signal. Finally, the mass flow value was obtained by calculating the phase difference. The simulation experiments and Digital Signal Processing system (DSP) verification testing demonstrate that the new signal processing method, which is the LZ algorithm, has better characteristics of real-time and high-precision than the others. The experimental results show that the accuracy of phase difference measurement is 0.02% and the tracking accuracy of frequency is better than 0.07%. [ABSTRACT FROM AUTHOR]

In this paper we adopt a phenomenological approach in order to develop a suitable Cellular Automata (CA) model capable to satisfactory mimic city growth and urban sprawl. The use of CA in urban expansion modeling is well known since many years, but very rarely it has been related with a down-top approach which considers inhabitants' preferences as a driving tool to characterize the CA algorithm. In addition, we consider as a control mechanism of the cell conversion rate (i.e. the number of cells experiencing conversion into urban use) the logistic function. This function is tuned on the free space (cells) at disposal for the urban development. In this paper we present the basics of the model and we perform a simple simulation in the case of a generic geographic pattern. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to present computational methods for simulating some typical problems in the emerging computational geoscience field. Due to remarkable differences between engineering systems and Earth ones, existing computational methods, which are designed for solving engineering problems, cannot be directly used to solve geoscience problems without any modification. However, the fundamental philosophy of developing computational methods is applicable to the computational simulation of both geoscience and engineering problems. Because of their inherent approximation, computational methods must be verified before putting into application. After several computational methods and algorithms, which are developed for simulating some typical problems in the emerging computational geoscience field, are briefly introduced, a typical geoscience problem, known as the chemical dissolution-front instability problem in ore-forming systems of supercritical Zhao numbers, is selected to demonstrate how computational methods can be used to solve geoscience problems. [ABSTRACT FROM AUTHOR]

In this paper, we explore the perspectives for canonical simulations in the worldline formulation of a lattice field theory. Using the charged field in two dimensions as an example, we present the details of the canonical formulation based on worldlines and outline the algorithmic strategies for canonical worldline simulations. We discuss the steps for converting the data from the canonical approach to the grand canonical picture which we use for cross-checking our results. The canonical approach presented here can easily be generalized to other lattice field theories with a worldline representation. [ABSTRACT FROM AUTHOR]

How to identify influential nodes in complex networks continues to be an open issue. A number of centrality measures have been presented to address this problem. However, these studies focus only on a centrality measure and each centrality measure has its own shortcomings and limitations. To solve the above problems, in this paper, a novel method is proposed to identify influential nodes based on combining of the existing centrality measures. Because information flow spreads in different ways in different networks, in the specific network, the appropriate centrality measures should be selected to calculate the ranking of nodes. Then, an interval can be generated for the ranking of each node, which includes the upper limit and lower limit obtained from different centrality measures. Next, the final ranking of each node can be determined based on the median of the interval. In order to illustrate the effectiveness of the proposed method, four experiments are conducted to identify vital nodes simulations on four real networks, and the superiority of the method can be demonstrated by the results of comparison experiments. [ABSTRACT FROM AUTHOR]

In this paper, the problem of pinning impulsive synchronization for complex dynamical networks with directed or undirected but a strongly connected topology is investigated. To remedy this problem, we propose an efficient algorithm to find certain suitable nodes to be controlled via pinning and these selected nodes, in general, could be different at distinct impulsive time instants. The proposed algorithm guarantees the efficiency of the designed pinning impulsive strategy for the global exponential synchronization of state-coupled dynamical networks under an easily-verified condition. In other words, impulsive controllers and an efficient algorithm are designed to control a small fraction of the nodes, which successfully controls the whole dynamical network. Furthermore, we also estimate the upper bound of the number of pinning nodes, which is shown to be closely related to the impulsive intervals. The relationship implies that the required number of pinning nodes, which should be controlled for the successful control of the whole dynamical network, can be greatly reduced by reducing the impulses interval. Finally, simulations of scale-free and small-world networks are given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper discusses the exact simulation of the stock price process underlying the 3/2 model. Using a result derived by Craddock and Lennox using Lie Symmetry Analysis, we adapt the Broadie-Kaya algorithm for the simulation of affine processes to the 3/2 model. We also discuss variance reduction techniques and find that conditional Monte Carlo techniques combined with quasi-Monte Carlo point sets result in significant variance reductions. [ABSTRACT FROM AUTHOR]

The testing of RFID information services requires a test data set of business events comprising object, aggregation, quantity and transaction events. To generate business events, we need to address the performance issues in creating a large volume of event data. This paper proposes a new model for the tag life cycle and a fast generation algorithm for this model. We present the results of experiments with the generation algorithm, showing that it outperforms previous methods. [ABSTRACT FROM AUTHOR]

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems. [ABSTRACT FROM AUTHOR]

This paper proposes a new block encryption algorithm. The chaotic trajectories are computed by weighting. Then the result is used to mask the plaintext. Multiple blocks of plaintext are encrypted at the same time and this decreases the chaotic iterations. So the encryption speed is improved to some extent. The proposed algorithm is flexible. When the number of weights is increased, the number of the encrypted plaintext block at the same time is increased and the encryption speed is improved. The simulation result shows that the proposed algorithm has fast encryption speed and fine security. [ABSTRACT FROM AUTHOR]

Resistance spot welding is a major metal connecting method in vehicle and other domestic electronic domains. Among all the welding techniques, the spot welding inverter is an important direction at the present time. The high nonlinearity and strongly coupled multiple parameters in the resistance spot welding process challenge the classical control theory based on some specific conditions and ideal assumptions, which in real practice obstacle the high-quality welding. This paper put the fuzzy neural network into a constant-current-controlled spot welding inverter, where the welding current peak and its variation are adopted as the input parameters and the duty ratio of the switches is regarded as the output. Eventually a five-layer feed-forward network was constructed, back propagation (BP) algorithm was applied to revise the adjustable parameters in the network, and a mathematical model was established to obtain the training samples serving for the network. The ultimate precision could reach 1.75%, the relative control error is 2.28% with strong external disturbances, the overmodulation is 3.35%, and the total modulating period is seven switching period, which indicated that the proposed algorithm has good performance. [ABSTRACT FROM AUTHOR]

In this paper, a new blind restoration scheme for blurred noisy image is proposed. The blurred noisy image is assumed to be the output of a linear space-invariant (LSI) system with an unknown point spread function (PSF) contaminated by an additive noise. The scheme passes the blurred noisy image through a two-dimensional (2D) finite impulse response filter whose parameters are updated by the modified constant modulus algorithm (CMA) with averaging and variable step size adaptive filter. When convergence occurs, the output of the filter is an estimate of the unobserved true image. Simulation and real blurred noisy image results show that the presented algorithm is effective and robust. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient symbol detection algorithm for multiple-input multiple-output spatial multiplexing (MIMO-SM) systems and present its design and implementation results. By enhancing the error performance of the first detected symbol that causes error propagation, the proposed algorithm achieves a considerable performance gain compared with the conventional sorted QR decomposition (SQRD) based detection and the ordered successive detection (OSD) algorithms. The bit error rate (BER) performance of the proposed detection algorithm is evaluated by a simulation. In the case of a 16QAM MIMO-SM system with 4 transmit and 4 receive (4 × 4) antennas, at BER = 10-3 the proposed algorithm results in a gain improvement of about 2.5-13.5 dB over the previous algorithms. The proposed detection algorithm was designed in a hardware description language (HDL) and synthesized to gate-level circuits using 0.18 μm 1.8 V CMOS standard cell library. The results show that the proposed algorithm can be implemented without increasing the hardware costs significantly. [ABSTRACT FROM AUTHOR]

Network robot system (NRS) is a new concept that integrates physical autonomous robots, environmental sensors, and human-robot interactions through network-based cooperation. The aim of this paper is to provide a ubiquitous and cooperative service framework for NRS. We first present foundational concepts of semantic map and service definition for the framework. Then, in order to generate feasible service configurations to fulfill tasks, we propose service configuration and reconfiguration algorithms, which dynamically search the appropriate service configurations for different tasks. Additionally, we put forward a service reasoning and enabling process to tackle the service unavailable problems. A cost evaluation function for service configuration is also proposed to facilitate the selection of suitable configurations. We tested and evaluated the framework in both simulation system and physical environment. Specifically, by separately varying the parameter settings, system performance was measured in three aspects: the success rate of tasks, the average waiting time per task, and the average cost per task. The experiment results indicate that the versatile service framework provides self-adaptive capability and utilizes available resources efficiently under a range of different scenarios. [ABSTRACT FROM AUTHOR]

In this paper we propose a type of mean reverting model with jumps, where the mean reverting level changes according to a continuous time, finite state Markov chain. This model could be applied to the interest rate and energy markets. We apply filtering techniques and obtain finite dimensional filters for the unobservable state of the Markov chain based on observations of the mean reverting diffusion. Various auxiliary filters are developed that allow us to estimate the parameters of the Markov chain by the EM algorithm. A simulation study is done for a concrete example. [ABSTRACT FROM AUTHOR]

The popularity of online auctions and the associated frauds have led to many auction sites preferring English auction over other auction mechanisms. The ease of adopting multiple fake identities over the Internet nourishes shill bidding by fraudulent sellers in English auction. In this paper, we derive an equilibrium bidding strategy to counteract shill bidding in an online English auction. An algorithm based on this strategy is developed. We conduct experiments to evaluate the strategy in a simulated eBay like auction environment. Five popular bidding strategies are compared with the proposed one. In the simulation, bidders compete to buy a product in the presence of a shill. Each bidder is randomly assigned a bidding strategy. She draws her valuation from a uniform distribution. The experiments show hat the average expected utility of agents with proposed strategy is the highest when the auction continues for a longer duration. [ABSTRACT FROM AUTHOR]

As Dempster-Shafer theory spreads in different application fields, and as mass functions are involved in more and more complex systems, the need for algorithms randomly generating mass functions arises. Such algorithms can be used, for instance, to evaluate some statistical properties or to simulate the uncertainty in some systems (e.g., data base content, training sets). As such random generation is often perceived as secondary, most of the proposed algorithms use straightforward procedures whose sample statistical properties can be difficult to characterize. Thus, although such algorithms produce randomly generated mass functions, they do not always produce what could be expected from them (for example, uniform sampling in the set of all possible mass functions). In this paper, we briefly review some well-known algorithms, explaining why their statistical properties are hard to characterize. We then provide relatively simple algorithms and procedures to perform efficient random generation of mass functions whose sampling properties are controlled. [ABSTRACT FROM AUTHOR]

This paper defines an outlier and a measure that an outlier does not fit the theoretical model in the regression problems, studies the relationship between the theoretical model and the regression model in the regression problems, proposes and verifies an approximate theorem in which one-by-one deletes outlier and constructs SVR to approximate its theoretical model, draws an algorithm of detecting outliers in the SVR problems based on the approximate theorem, and analyzes the convergence and effectiveness of the algorithm in the theory. Next, it combines the step-by-step search algorithm were proposed by authors and the former detecting outlier algorithm to improve it, and brings forward an algorithm detecting outliers in SVR based on large-scale samples. Then, its analysis in theory shows the improved algorithm is convergent and effective too. Finally, its simulation results show that the detecting outlier algorithms are effective and robust, using samples produced by two test functions and in UCI dataset. [ABSTRACT FROM AUTHOR]

The Stochastic Alpha Beta Rho Stochastic Volatility (SABR-SV) model is widely used in the financial industry for the pricing of fixed income instruments. In this paper we develop a low-bias simulation scheme for the SABR-SV model, which deals efficiently with (undesired) possible negative values in the asset price process, the martingale property of the discrete scheme and the discretization bias of commonly used Euler discretization schemes. The proposed algorithm is based the analytic properties of the governing distribution. Experiments with realistic model parameters show that this scheme is robust for interest rate valuation. [ABSTRACT FROM AUTHOR]

Control of induction motor due to its nonlinear property is one of the most popular research topics recently. Vector control based algorithms which are used for proper functioning estimate the rotor speed and magnetic flux. In many applications, flux and speed are estimated without using sensors. Thus, in these cases, it is necessary to obtain speed and flux by measuring voltage and current. In this paper, a nonlinear observer for estimating motor parameters based on Lyapunov function is presented. Using the Lyapunov theory, inputs regulating system and control system are earned which improve system performance. Simulation and experimental results are included to validate the effectiveness of the proposed control scheme under possible uncertainties and different reference trajectories. These results show the advantages of the proposed observer in control of induction motor transient performances. The merits of the proposed control system are also indicated comparing with a traditional optimal control system. [ABSTRACT FROM AUTHOR]

A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system. [ABSTRACT FROM AUTHOR]

This paper presents a new method to decrease the error of real time numerical integration in an Inertial Navigation System (INS), which is widely used in aircrafts, spacecrafts, and field robotics. The well-known Runge-Kutta method is often used for real time numerical integration in INS algorithms. Here a combination of the composite Simpson's 1/3 and 3/8 rules is developed to effectively reduce the total integration error, named Subtractive Composite Simpson Method (SCSM). Also we propose a new method that efficiently reduces the error of the integrator block using the combination of a fourth-order Runge-Kutta (RK4) and SCSM. Simulation results show one order of magnitude decrease in position error after double integration of acceleration value. Extra accuracy gain using this new method makes it possible to choose low cost inertial sensors to realize a low cost INS. [ABSTRACT FROM AUTHOR]

In view of the support vectors of ε-SVR that are not distributed in the ε belt and only located on the outskirts of the ε belt, a novel algorithm to construct ε-SVR of a large-scale training sample set is proposed in this paper. It computes firstly the ε-SVR hyper-plane of a small training sample set and the distances d of all samples to the hyper-plane, then deletes the samples not in field ε ≤ d ≤ dmax and searches SVs gradually in the scope ε ≤ d ≤ dmax, and trains step-by-step the final ε-SVR. Finally, it analyzes the time complexity of the algorithm, and verifies its convergence in the theory and tests its efficiency by the simulation. [ABSTRACT FROM AUTHOR]

In this paper, the generalized projective synchronization of chaotic neural networks is investigated. Based on the modified nonlinear state observer algorithm and the pole placement technique, a synchronization scheme is designed. The generalized projective synchronization of different chaotic neural networks can be achieved by using the proposed method. Numerical simulations further demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a concurrent implementation of a background subtraction algorithm suitable for monochromatic video sequences. In the training period, a set of frames is used to determine an estimate of the scene background, as well as background noise. In the test period, each new frame of the video sequence is compared to the background model, and foreground objects are obtained taking into account the background noise estimate. Shadows and highlights are also detected and removed. Concurrent programming tools and techniques, such as multithreading, are used to implement a concurrent solution for this problem, and combined with other optimization tools (such as destination word accumulation for morphological operators and the use of lookup tables for shadow detection/removal) to achieve the highest frame rate possible. Experimental results comprise a comparison of the performance including progressively each optimization tool, and they show that the fully optimized algorithm is much faster than a naive sequential implementation. Also, experiments performed on two-way processor and on dual-core processor computers indicate that the proposed algorithm is suited for the recent multi-core technology. [ABSTRACT FROM AUTHOR]

One of the most important aspects in the design of multi-robot systems (MRS) is the allocation of tasks among the robots in a productive and efficient manner. This paper presents an empirical study on task allocation strategies in multirobot environment. In general, optimal solutions are found through an exhaustive search, but because there are n × m ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible with increased number of tasks. Task allocation methodologies for multirobot systems are developed by considering their capability in terms of time and space. The present work adopts a two-phase methodology to allocate tasks optimally amongst the candidate robots. The allocation cost of the robots is determined during the first phase and alternate algorithms are used in the second phase for optimizing the allocation. The work considers systems of practical sizes and the results obtained through this are helpful in recommending appropriate techniques to the users of MRS for increasing producibility and robot utilization. Three different approaches using Linear programming, Hungarian Algorithm and Knapsack Algorithm are presented and their results are analyzed for the suitability of the methods for an allocation problem. Simulation results are presented and compared for the benefit of the users. [ABSTRACT FROM AUTHOR]

In this paper, a type of new fractional order hyperchaotic Lorenz system is proposed. Based on the fractional calculus predictor-corrector algorithm, the fractional order hyperchaotic Lorenz system is investigated numerically, and the simulation results show that the lowest orders for hyperchaos in hyperchaotic Lorenz system is 3.884. According to the stability theory of fractional order system, an improved state-observer is designed, and the response system of generalized synchronization is obtained analytically, whose feasibility is proved theoretically. The synchronization method is adopted to realize the generalized synchronization of 3.884-order hyperchaotic Lorenz system, and the numerical simulation results verify the effectiveness. [ABSTRACT FROM AUTHOR]

This paper addresses the cryptanalysis of three chaos-based secure communication schemes recently proposed by Yu et al. [2007]. Attacks with adaptive observers designed by the dynamical minimization algorithm are assumed. Considering Kerckhoff's principle, i.e. assuming that the basic structure of a cryptosystem is known but some user-specified parameters remain in secret, it is demonstrated that the covered messages in all these schemes can be estimated and hence their securities are challenged. [ABSTRACT FROM AUTHOR]

The contact dynamics (CD) method is an efficient simulation technique of dense granular media where unilateral and frictional contact problems for a large number of rigid bodies have to be solved. In this paper, we present a modified version of the CD to generate homogeneous random packings of rigid grains. CD simulations are performed at constant external pressure, which allows the variation of the size of a periodically repeated cell. We follow the concept of the Andersen dynamics and show how it can be applied within the framework of the CD method. The main challenge here is to handle the interparticle interactions properly, which are based on constraint forces in CD. We implement the proposed algorithm, perform test simulations, and investigate the properties of the final packings. [ABSTRACT FROM AUTHOR]

This paper presents a semi-automatic approach for confounding-aware subgroup discovery: Confounding essentially disturbs the measured effect of an association between variables due to the influence of other parameters that were not considered. The proposed method is embedded into a general subgroup discovery approach, and provides the means for detecting potentially confounded subgroup patterns, other unconfounded relations, and/or patterns that are affected by effect-modification. Since there is no purely automatic test for confounding, the discovered relations are presented to the user in a semi-automatic approach. Furthermore, we utilize (causal) domain knowledge for improving the results of the algorithm, since confounding is itself a causal concept. The applicability and benefit of the presented technique is illustrated by real-world examples from a case-study in the medical domain. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive grid subdivision algorithm based on the Youngs' interface reconstruction algorithm to treat the advection of the mixed grids in the multi-material Eulerian method. The advantage of this algorithm is that it only needs to calculate the advection between pure grids, escaping the complexity encountered in the advection of mixed grids with two materials. In addition, this algorithm is a multi-dimensional advection algorithm, and it can decrease the error encountered in the direction-split advection algorithm. In order to verify the efficiency of the proposed adaptive grid subdivision algorithm in the actual physical field, it is added to the indigenously developed EXPLOSION-2D simulation software, and some numerical tests are performed to demonstrate the utility of this algorithm. The simulation results verify that the adaptive grid subdivision algorithm is simple and efficient. [ABSTRACT FROM AUTHOR]

In this paper, a high-order Recursive Quadratic (RQ) algorithm is introduced, and the features of this algorithm are thoroughly studied and illustrated. In addition, a robust RQ algorithm is also developed in the presence of general bounded noisy data to enhance model robustness. A numerical example is included to demonstrate the efficiency of RQ algorithms by comparing the results with both the projection algorithm and the conventional recursive least squares algorithm. Also some simulations are carried out to illustrate the effectiveness of the RQ and robust RQ algorithms. [ABSTRACT FROM AUTHOR]

The semantics of a language for reasoning about finite-dimensional quantum systems is presented. This language can express most important classes of assertions about quantum systems, including formulas for outputs of all combinational quantum circuits/algorithms. The main result of this paper is an algorithm for efficient translation of a formula of language into an equivalent formula in another decidable language ℝℂ, which is the language of reals and its complex extension. An important consequence is a descriptive characterization of quantum circuits that can be efficiently simulated classically. We illustrate this with examples of two classes of quantum circuits which are known to have efficient classical simulation. The algorithm for deciding the satisfiability of a general formula can be adapted for the simulation. [ABSTRACT FROM AUTHOR]

In this paper we present some new tearing techniques to systematically formulate the state equations in symbolic normal-form for linear and/or nonlinear time-invariant large-scale analog circuits. The excess elements of the first and of the second kind are unitarily treated in order to allow a symbolic representation of the circuit with a minimum number of state variables. A procedure to reduce the state equation number of each subcircuit is also presented. The reduced-order is based on an implicit integration algorithm and on the successive elimination of the selected state variables. Examples are given to illustrate the decomposition procedure, the assignment of the connection sources and the reduced-order technique. [ABSTRACT FROM AUTHOR]

A temporal flexible planning problem that involves contingent and requirement events can be formulated as a simple temporal network with uncertainty (STNU). An STNU is controllable when there is a strategy for executing the requirement events (or actions) in such a way that all the conditions involving contingent events can be satisfied in all situations. The most interesting and useful controllability property is dynamic controllability in which the remaining actions in an STNU can always be scheduled under all possible feasible durations of future contingent events when all the past contingent events are known. In this paper, we propose and study a novel problem of assigning bounds on the duration of each requirement link in order for the resulting STNU to be dynamically controllable and to minimize the total cost over the allowed durations of all requirement links. We first prove the NP hardness of the problem with a linear cost function. We then formulate the dynamic controllability of an STNU as the constraints in a nonlinear optimization problem. Finally, we present methods for reducing the number of constraints in order to make the problem tractable and to demonstrate the computational performance of our methods. [ABSTRACT FROM AUTHOR]

In this paper we discuss a noisy mean field model for the genetic toggle switch. We show that this model approximates very well the characteristics of the system, observed using the exact Gillespie stochastic simulation algorithm. Also, we show that the system can be made exponentially stable depending on reaction parameters. [ABSTRACT FROM AUTHOR]

In this paper, we first introduce a network of learning automata, which we call it as distributed learning automata and then propose some iterative algorithms for solving stochastic shortest path problem. These algorithms use distributed learning automata to find a policy that determines a path from a source node to a destination node with minimal expected cost (length). In these algorithms, at each stage distributed learning automata determines which edges to be sampled. This sampling method may result in decreasing unnecessary samples and hence decreasing the running time of algorithms. It is shown that the shortest path is found with a probability as close as to unity by proper choice of the parameters of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper develops the Bayesian model selection based on Bayes factor for a rich class of partially-observed micro-movement models of asset price. We focus on one recursive algorithm to calculate the Bayes factors, first deriving the system of SDEs for them and then applying the Markov chain approximation method to yield a recursive algorithm. We prove the consistency (or robustness) of the recursive algorithm. To illustrate the construction of such a recursive algorithm, we consider a model selection problem for two micro-movement models with and without stochastic volatility, and provide simulation and real-data examples to demonstrate the effectiveness of the Bayes factor in the model selection for this class of models. [ABSTRACT FROM AUTHOR]

New simulation approaches to evaluating path-dependent options without matrix inversion issues nor Euler bias are evaluated. They employ three main contributions: (1) stochastic approximation replaces regression in the LSM algorithm; (2) explicit weak solutions to stochastic differential equations are developed and applied to Heston model simulation; and (3) importance sampling expands these explicit solutions. The approach complements Heston [(1993) A closed-form solutions for options with stochastic volatility with applications to bond and currency options, Review of Financial Studies 6, 327-343] and Broadie & Kaya [(2006) Exact simulation of stochastic volatility and other affine jump diffusion processes, Operations Research 54 (2), 217-231] by handling the case of path-dependence in the option's execution strategy. Numeric comparison against standard Monte Carlo methods demonstrates up to two orders of magnitude speed improvement. The general ideas will extend beyond the important Heston setting. [ABSTRACT FROM AUTHOR]

An effective test algorithm and built-in self-test (BIST) with diagnostic support for embedded static random access memories (SRAM) is proposed. This work focuses on implementing the algorithm using the BIST with diagnostic support for a 64 × 8 bit embedded SRAM. The algorithm can locate and identify all the target faults in SRAM. The BIST with diagnostic support is realized by programming using very high speed integrated circuit hardware description language codes and proved very valuable for diagnosing the target faults. When analyzing experimental results, the fault dictionary is constructed from the simulated responses under the given test algorithm and fault models. The fault dictionary shows that the algorithm has a completely diagnostic ratio. [ABSTRACT FROM AUTHOR]

A novel measurement method of parallel-plate waveguide (PPW) system is introduced to detect the characteristics of left-handed metamaterials (LHMs). A sample of LHM composed of triangular open-loop resonator plus wire structures is proposed and measured by using this PPW system. The measurement results are in good agreement with the simulation results. Furthermore, the parametric retrieval algorithm, the calculated negative phase shift and the simulated subwavelength imaging result are given to validate the authenticity of PPW measurement system, which effectively demonstrates the measured transmission passband is indeed a left-handed passband. [ABSTRACT FROM AUTHOR]

A logistic equation with piecewise constant arguments is investigated. Firstly, the linear stability of the model is studied. It is found that there exists a Hopf bifurcation when the parameter passes a critical value. Then the explicit algorithm for determining the direction of the Hopf bifurcation and the stability of the bifurcating periodic solution is derived by using the normal form method and center manifold theorem. Finally, computer simulations are performed to illustrate the analytical results found. [ABSTRACT FROM AUTHOR]

An improved method is proposed based on compressed and Krylov-subspace iterative approaches to perform efficient static and transient simulations for large-scale power grid circuits. It is implemented with CG and BiCGStab algorithms and an excellent result has been obtained. Extensive experimental results on large-scale power grid circuits show that the present method is over 200 times faster than SPICE3 and around 10–20 times faster than ICCG method in transient simulations. Furthermore, the presented algorithm saves the memory usage over 95% of SPICE3 and 75% of ICCG method, respectively while the accuracy is not compromised. [ABSTRACT FROM AUTHOR]