The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Soil moisture is one of the key environmental variables in the Earth science. Data assimilation (DA) provides a way to effectively combine model simulations and observations, thus can yield superior soil moisture estimations. Among various DA methods, the particle filter (PF) is free from the constraints of linear models and Gaussian error distributions, thus receiving increasing attention in DA....
There are some common problems, such as low location accuracy and low sampling efficiency, existing in the present node localization algorithms that are based on Monte Carlo Localization (MCL) in mobile wireless sensor networks. To improve these issues, a Monte Carlo box localization algorithm based on RSSI(MCBBR) is proposed in this paper. In the algorithm, sampling box was constructed through RSSI...
Nonparametric density estimates may be used as a basis for construction of global random search algorithms. The distribution density reflects the main peculiarities of objective function behavior. Consequently, the density mode may be used as approximate argument value, providing objective function maximum.
Localization in Wireless Sensor Networks (WSNs) denotes the procedure of a single sensor node to determine its geographical position in space. As these nodes are limited in computational power, battery lifetime and communication range, there is the requirement for efficient localization algorithms which is an ongoing topic in research. Nearly all algorithms are based on the usage of seed nodes which...
We propose a Maximum Likelihood-based method that combines the Expectation Maximization algorithm with Sequential Monte Carlo methods to estimate fixed parameters and transition probabilities for a general class of nonlinear jump Markov systems in state space form. This method is an extension to a previous method originally proposed by T. B. Schön, A. Wills, and B. Ninness for identifying the parameters...
The Gaussian plume model is the core of most regulatory atmospheric dispersion models. The parameters of the model include the source characteristics (e.g. location, strength, size) and environmental parameters (wind speed, direction, atmospheric stability conditions). A sensor network is at disposal to measure the concentration of biological pathogen or chemical substance within the plume. This paper...
Stereo visual odometry (VO) is a common technique for estimating a camera's motion, features are tracked across frames and the pose change is subsequently inferred. This position estimation method can play a particularly important role in environments in which the global positioning system (GPS) is not available (e.g., Mars rovers). Recently, some authors have noticed a bias in VO position estimates...
Coded pulse compression waveforms with matched filtering suffer from range sidelobe interferences. In this paper, we consider the estimating technique with side lobe suppression capability known as the linear minimum mean square error (LMMSE) based reiterated filtering, i.e. the adaptive pulse compression (APC). We derive the theoretical amplitude probability density function (PDF) of the APC estimation...
A novel Sparse Grid-Based Likelihood Evaluation (SGLE) method is proposed for the first time to reduce the number of Likelihood Evaluations (LE) of the Particle Filter (PF) and to decrease the overall computational cost. We use a sparse grid to identify clusters of sample points that have similar estimated values of likelihood, and take one LE for each cluster to approximate the likelihood of each...
This paper proposes a Monte - Carlo based statistical simulation technique for estimating the error probability for different modulation schemes of a log-normal Raleigh fading channel in a digital wireless communication network. The generalised expressions which are found in the literature involve not only tedious mathematical analysis and lengthy computational time but also need to be re-derived...
Estimating extremely low SRAM failure-probabilities by conventional Monte Carlo (MC) approach requires hundreds-of-thousands simulations making it an impractical approach. To alleviate this problem, failure-probability estimation methods with a smaller number of simulations have recently been proposed, most notably variants of consecutive mean-shift based Importance Sampling (IS). In this method,...
Memory circuits have become important components in today's IC designs which demands extremely high integration density and reliability under process variations. The most challenging task is how to accurately estimate the extremely small failure probability of memory circuits where the circuit failure is a “rare event”. Classic importance sampling has been widely recognized to be inaccurate and unreliable...
Dynamics of biological processes is typically specified by a system of coupled biochemical stochastic reactions, whose reaction rates are the unknown parameters. The paper proposes a Bayesian algorithm for estimation of reaction rates of stochastic reactions networks. In the similar vein as the particle MCMC, the parameters (the rates) are estimated in a hierarchical manner: the particle filter is...
In this paper, a new integrated weight particle filter (IWPF) algorithm is proposed based on the combination of correlation particle estimation (CPE) weight and sequential importance re-sampling (SIR) weight. This method can reduce degeneracy phenomenon and re-sampling times of traditional particle filter. By choosing the typical nonlinear system model, the simulation results show that IWPF performs...
The traditional estimator ξp, n for the p-quantile ξp of a random variable X, given n observations from the distribution of X, is obtained by inverting the empirical cumulative distribution function (cdf) constructed from the obtained observations. The estimator ξp, n requires O(n) storage, and it is well known that the mean squared error of ξp, n (with respect to p) decays as O(n−1). In this article,...
In this paper, we study the problem of estimating the price of an American option and its price sensitivities via Monte Carlo simulation. Compared to estimating the option price which satisfies a backward recursion, estimating the price sensitivities is more challenging. With the readily-computable pathwise derivatives in a simulation run, we derive a backward recursion for the price sensitivities...
Current mining algorithms for attributed graphs exploit dependencies between attribute information and edge structure, referred to as homophily. However, techniques fail if this assumption does not hold for the full attribute space. In multivariate spaces, some attributes have high dependency with the graph structure while others do not show any dependency. Hence, it is important to select congruent...
As intermittent renewable energy penetrates electrical power grids more and more, assessing grid reliability is of increasing concern for grid operators. Monte Carlo simulation is a robust and popular technique to estimate indices for grid reliability, but the involved computational intensity may be too high for typical reliability analyses. We show that various reliability indices can be expressed...
The problem of estimating an autoregressive conditionally heteroscedastic (ARCH) model in the presence of missing data is investigated. A two-stage least squares estimator which is easy to calculate is proposed and its strong consistency and asymptotic normality are established. The behaviour of the estimator for finite samples is analyzed via Monte Carlo simulations, and is compared to a Yule-Walker...
This paper presents a safety estimation methodology for soft landing explorations on the Moon. In our application we use digital elevation models (DEM) of given lunar terrain extracted from on-board lidars. Currently most landing safety analysis methods are based on topographical analysis which needs quite a lot of computation. Here we adopt a Monte-Carlo simulation method which doesn't need to identify...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.