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A non-parametric probability density function (pdf) estimation technique is presented. The estimation consists in approximating the unknown pdf by a network of Gaussian Radial Basis Functions (GRBFs). Complexity analysis is introduced in order to select the optimal number of GRBFs. Results obtained on real data show the potentiality of this technique.
Spline is a continuous function piecewise-defined by polynomials and is widely used for interpolation and smoothing of observed data. In 1994, Heß and Schmidt proposed a positive quartic C2-spline interpolation for estimation of a non-negative and twice continuously differentiable function. In this paper, first we generalize the positive quartic C2-spline interpolation to the positive quartic C2-spline...
Purpose To study the outlier detection methods and supply practical tools for data mining. Methods A novel parametric outlier detection method called maximum minimum (MaxMin) was presented to solve the crucial outlier detection issue, i.e., the estimation of the distribution parameters of the target normal distribution 'contaminated' with the outliers. Through an iterative rejection sampling procedure,...
The paper deals with adaptive control of a system described by a mixture of normal regression models with external variables (ARX). A recently proposed method of quasi-Bayes recursive estimation of such models is complemented with optimal quadratic control design of such a mixture using certainty-equivalent combination. The design technology guarantees that an optimal stabilizing controller is found...
The paper describes an advanced methodology of automatic knowledge elicitation. It merges fragmental uncertain knowledge pieces into the prior distribution of unknown parameter of a probabilistic model of a dynamic system.
Probability Density Functions defined on IR+ can be successfully modeled with the help of the Mellin Transform : this rather underrated transform is well suited for such functions so that we propose the new definitions of "second kind" characteristic functions based on this transform. By this way, second kind moments and second kind cumulants can also be defined, so that multiplicative noise,...
In this paper, we propose a new error criteria for determining the optimal multi-channel model system. The error criteria is based on assuming that the probability density function of the resulted error signal is t-distributed with α degrees of freedom. A small weighting factor is assigned for large amplitude signal portion parts and large weighting factor is used for small amplitude signal portion...
In this contribution, we derive for the first time the closed-form expressions for the Cramér-Rao lower bounds (CRLBs) of the signal-to-noise ratio (SNR) estimates from turbo-coded square-QAM transmissions. By exploiting the structure of the Gray mapping, we are able to factorize the likelihood function thereby linearizing all the derivation steps for the FIM elements. The analytical CRLBs coincide...
An ongoing work, proposing a modified method to solve the nonlinear filtering problems is presented in this paper. The proposed method, which uses orthogonally transformed cubature quadrature points, is an extension of cubature quadrature Kalman filter (CQKF). The modified filtering method, developed here is regarded as transformed cubature quadrature Kalman filter (TCQKF). The computational load...
In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is...
Taxicab demand discovering is one of the most fundamental issues of taxicab services. Most of the regions in one city suffer the demand and supply disequilibrium problem. It causes the difficulty in scheduling taxicabs for taxicab companies. It will be solved by modeling the regional demand of taxicabs by using trajectory data. In this paper, we propose a method to model regional taxicab demand. Firstly,...
The informational energy (IE) can be interpreted as a measure of average certainty. In previous work, we have introduced a non-parametric asymptotically unbiased and consistent estimator of the IE. Our method was based on the kth nearest neighbor (kNN) method, and it can be applied to both continuous and discrete spaces, meaning that we can use it both in classification and regression algorithms....
Multi-path fading, environmental shadowing and channel interference always result in the significant temporal and spatial variations of Received Signal Strength (RSS), and eventually lead to the low accuracy in Wireless Local Area Networks (WLAN) fingerprint based indoor localization. Motivated by this, we focus on deriving out the positioning error bound which can be applied to characterize the theoretical...
Moment estimation is one of the most important tasks to appropriately characterize the performance variability of today's nanoscale integrated circuits. In this paper, we propose an efficient algorithm of multi-population moment estimation via Dirichlet Process (MPME-DP) for validation of analog and mixed-signal circuits with extremely small sample size. The key idea is to partition all populations...
In this paper, we shall describe a new iterative method of unsupervised multisensor image segmentation based on the evidence theory. We show that the modeling by means of evidence theory is well suited to the processing of redundant and complementary data as the satellite images. This theory turns out to be quite efficient in unsupervised multisensor image segmentation. The application of the evidence...
In decentralized detection, the sensors first make a local decision before transmitting it to the fusion center (FC). The optimal design of the sensors' decision rule as well as the fusion rule requires knowledge of the probability distributions of the sensors' observations. This information, however, may not be available prior to deployment. Moreover, these probability distributions may vary over...
This paper describes a method for score-level fusion in multi-cue two-class classification problems. Fusion based on the probability density function (PDF) of multiple scores given for each class is a promising approach because it guarantees optimality as long as the estimated PDFs are correct. Instead of lattice-type control points used in previous non-parametric density-based approaches, floating...
For the design of dependable and efficient wireless sensor networks it is essential to estimate the achievable packet reception rate (PRR) in the deployment environment. Making such estimation is not trivial as packet delivery success depends on the level of interference present in the deployment area. In this work we show that it is possible to obtain a meaningful representation of the expected interference...
Many challenging problems could be better solved by exploiting crowdsourcing platforms than traditional machine-based methods. However, data quality in crowdsourcing applications has become a crucial aspect since crowdsourcing workers may have different capabilities. In this paper, we propose a novel weighted aggregation rule (WAR) to improve the result accuracy in crowdsourcing systems. According...
Neural spike detection is an important step in understanding neurological activities. The spike firing rate which could be rapidly changing in the recording experiment would make noise estimation inaccurate thus compromises the spike detection performance. In this paper, we propose a new noise estimation method for neural spike detection. Different from the traditional methods that deal with all the...
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