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The real time probability density function (PDF) estimation of any environmental function from sensor network measurement is addressed. The sensor measurement data is modeled using Gaussian mixture PDFs and an algorithm is proposed to estimate the parameters by maximizing the log likelihood function of the sensor data. Here the real time probability density function (PDF) estimation of environmental...
Error-correcting output codes (ECOC) is an efficient method to solve the problem of multi-class classification, and how to get the accurate probability estimation via ECOC is also attractive for many applications. The characteristic of the probabilistic outputs via random ECOC is analyzed in this paper, and the proof is given for that the probability output based on random ECOC is the unbiased estimation...
Communication over Multiple-Input Multiple-Output (MIMO) fading channels is of much interest in the current telecommunication context. Estimation of very small outage probabilities in such channels is an important, yet time consuming task, owing to the lengthy run-time requirements of traditional Monte Carlo (MC) simulations. Hence, fast and accurate methods for estimating outage capacities are of...
In this paper, we consider the measurement allocation problem in a spatially correlated sensor field. Our goal is to determine the probability of each sensor's being measured based on its contribution to the estimation reliability; it is desirable that a sensor improving the estimation reliability is measured more frequently. We consider a correlation model reflecting transmission power limit, noise...
Most of constraint handling papers have focused on the selection of individuals by trade-off the feasible and infeasible regions. This paper studies the effect of two kinds of reproduction in constraint multiobjective optimization. It compares a probabilistic model-based multiobjective evolutionary algorithm to a genetic algorithm. They all use a min-max selection strategy as the main frame structure...
In this paper an algorithm suitable to interference detection for terminal embedding commercial GNSS chipsets is presented. The algorithm monitors the value of the C/N0 estimated by the receiver for multiple channels and a detection rule is set in order to obtain the desired performance in terms of false alarm and detection probabilities.
Generalized Lifting (GL) has been studied for lossy image compression in. It has been demonstrated that the method leads to a significant reduction of the wavelet coefficients energy and entropy. The definition of the GL relies on an estimation of the pdf of the pixel to encode conditioned to a surrounding context. The objective of this paper is to present an improved method for the estimation of...
Quantification is the name given to a novel machine learning task which deals with correctly estimating the number of elements of one class in a set of examples. The output of a quantifier is a real value, since training instances are the same as a classification problem, a natural approach is to train a classifier and to derive a quantifier from it. Some previous works have shown that just classifying...
By utilizing the must-link or cannot-link pair wise constraints in data, semi-supervised clustering improves the performance of unsupervised clustering significantly. A number of semi-supervised clustering algorithms have been proposed to consider such pair wise constraints. However, most of them assign a hard label to each data item and produce little information about the cluster itself. In this...
The present work deals with the reconstruction of the spectrum from the tristimulus values that is spectrally similar to the original function. It is known that in the estimation of reflectances from a small number of responses of colour sensors, some wavelengths occur with very high probability. Therefore the spectra of reflectances are sparse in nature. The recently proposed Compressed Sensing (CS)...
Given prior human judgments of the condition of an object it is possible to use these judgments to make a maximal likelihood estimate of what future human judgments of the condition of that object will be. However, if one has a reasonably large collection of similar objects and the prior human judgments of a number of judges regarding the condition of each object in the collection, then it is possible...
In this paper, we propose an approach to model the probability density function (PDF) of the QoS of a web service (QoWS) based on non-parametric statistical method. Mathematical formulas are designed to calculate the QoS distributions for service compositions (QoCS). Experiment has been done to show that the proposed QoWS distribution modeling approach is much more accurate than exiting methods. An...
In previous work we considered the problem of estimating target location parameters using an adaptive sensing paradigm, wherein one attempts to choose the most informative measurement from a set of possibilities characterized by a linear measurement matrix. Here we extend that work to target tracking. At each step in a discrete-time Bayesian filter, a measurement matrix is chosen to illuminate in...
In the work we consider the situation with exact classes and fuzzy information of object features. The classification error is presented for the two-class Bayes classifier. The results are received for the full probabilistic information. The new upper bound of the probability of an error is precise twice as much as the bound based on the information energy of fuzzy events.
A novel signal subspace algorithm based on test of hypothesis and masking properties of the human auditory system is proposed for microphone array speech enhancement. Different from conventional empirism dependent methods, first, for the optimal subspace selection, the paper determines the subspace dimension via solving a test of hypothesis. Then, according to the characters of the speech signal eigenvalues,...
Recently we proposed a cross-layer design to support video communication over error-prone mobile ad-hoc networks. The idea is to utilize routing messages and network parameters to estimate the corrupted frames, and to guide the reference frame selection at the video encoder to mitigate error propagation. In this paper, we focus on the frame corruption estimation method used in the design. We build...
We propose a real-time algorithm of search and path planning after a static or a moving target in a discrete probability space. The search is conducted by an autonomous mobile agent that is given an initial probability distribution of the target's location, and at each search step obtains information regarding target's location in the agent's local neighborhood. The suggested algorithm implements...
The paper deals with using so called singularity exponent in a classifier that is based on ordered distances of patterns to a given (classified) pattern. The approximation of probability distribution mapping function of the distribution of points from the viewpoint of distances from a given point in a form of a suitable power (exponent) of a distance is presented together with a way how to state it...
We propose a second-order-statistics-based approach to online multichannel noise tracking and reduction. We combine the multichannel speech presence probability (MC-SPP) that we proposed in with an alternative formulation of the minima-controlled recursive averaging (MCRA) technique that we generalize from the single- to the multichannel case. Then, we demonstrate the effectiveness of the proposed...
In this paper, a significant acceleration of estimating low-failure rate in a high-dimensional SRAM yield analysis is achieved using sequential importance sampling. The proposed method systematically, autonomously, and adaptively explores failure region of interest, whereas all previous works needed to resort to brute-force search. Elimination of brute-force search and adaptive trial distribution...
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