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The huge size of online social networks (OSNs) makes it prohibitively expensive to precisely measure any properties which require the knowledge of the entire graph. To estimate the size of an OSN, i.e., the number of users an OSN has, this paper introduces two estimators using widely available OSN functionalities/services. The first estimator is a maximum likelihood estimator (MLE) based on uniform...
Time of Arrival (ToA) estimation is a cornerstone of many of the remote sensing applications including radar, sonar, and reflective seismology. The conventional Matched Filter Maximum Likelihood (MFML) ToA estimator suffers from rapid deterioration in the accuracy as Signal to Noise Ratio (SNR) falls below certain threshold value. In this paper we suggest an alternative method for ToA estimation based...
We present solutions to two problems that prevent the effective use of population-based algorithms in clustering problems. The first solution presents a new representation for arbitrary covariance matrices that allows independent updating of individual parameters while retaining the validity of the matrix. The second solution involves an optimization formulation for finding correspondences between...
This paper concerns a high-precision linear method for camera pose determination. The key idea is to replace the algebraic error in the classical linear method with the weighted algebraic error as an approximation of the geometric error. The proposed method provides a linear solution with accuracy closed to that of maximum likelihood (ML) estimation, but more efficient than ML estimation. Based on...
In order to improve tracking estimation accuracy of existing unscented Kalman particle filter (UPF), an improved particle filter algorithm based on iterative measurement update UKF is proposed. The algorithm uses maximum posteriori estimate of iterative unscented Kalman filter as the important density function of the particle filter and amends the state covariance using Levenberg-Marquardt method...
A new method for the dual estimation in state-space dynamic model was described, with a focus on sequential Bayesian learning about time-varying state and static parameters simultaneously. The new algorithm combines auxiliary particle filtering (APF) with particle swarm optimization (PSO) to achieve computational efficiency and stability. By properly choosing the fitness function of PSO, the algorithm...
In this paper, an OFDM-based high resolution, channel sounding (CS) method is proposed under T/2-spaced sampling. The proposed CS method consists of three iterative steps. In each iteration, it attempts to catch a new path with accurate fractional delay. Besides, it is equipped with EDC criterion for path order determination. The CFR-MSE performance can be greatly improved over the conventional LS...
We consider a simple statistical model of the image, in which the image is represented as a sum of two parts: one part is explained by an i.i.d. color Gaussian mixture and the other part by a (piecewise-) smooth gray scale shading function. The smoothness is ensured by a quadratic (Tikhonov) or total variation regularization. We derive an EM algorithm to estimate simultaneously the parameters of the...
We consider the problem of Maximum Likelihood (ML) estimation of clock parameters in a two-way timing exchange scenario where the random delays assume a Weibull distribution, which represents a more generalized model. The ML estimate of the clock offset for the case of exponential distribution was obtained earlier. Moreover, it was reported that when the fixed delay is known, MLE is not unique. We...
Low resolution faces are the main barrier to efficient face recognition and identification in several problems primarily surveillance systems. To mitigate this problem we proposes a novel learning based two-step approach by the use of Direct Locality Preserving Projections (DLPP), Maximum a posterior estimation (MAP) and Kernel Ridge Regression (KRR) for super-resolution of face images or in other...
In this paper we redefine and generalize the classic k-nearest neighbors (k-NN) voting rule in a Bayesian maximum-a-posteriori (MAP) framework. Therefore, annotated examples are used for estimating pointwise class probabilities in the feature space, thus giving rise to a new instance-based classification rule. Namely, we propose to "boost" the classic k-NN rule by inducing a strong classifier...
Sound Source Localization (SSL) based on microphone arrays has numerous applications, and has received significant research attention. Common to all published research is the observation that the accuracy of SSL degrades with reverberation. Indeed, early (strong) reflections can have amplitudes similar to the direct signal, and will often interfere with the estimation. In this paper, we show that...
According to the distribution function of the Inverse Weibull distribution, under some hypotheses, the definition of the environment factor was given out. In order to estimate the the Inverse Weibull distribution environment factor, the MLE and Bayes estimation methods were used. The Monte-Carlo simulation was taken out to compare the estimations. The result shows that the Bayes estimation method...
Under various rules of failure intensity of repairable systems, a generalized reliability growth model has been presented to assess the reliability of repairable system. For the estimation of the model parameters, MLE method has been used, both for time censored and failure censored data. For the test data of some system, the goodness-of-fit test and reliability growth assessment were carried out...
This paper proposes a novel symbol timing estimation technique for multiband orthogonal frequency division multiplexing (MB-OFDM) based ultra wideband system. The symbol timing is estimated in two steps namely, coarse synchronization and fine synchronization. The coarse synchronization is obtained using the correlation between adjacent symbols. A new algorithm is proposed using maximum likelihood...
Broadly speaking, IRT models can be divided into two families: unidimensional and multidimensional. Unidimensional models require a single trait (ability) dimension θ. Multidimensional IRT models model response data hypothesized to arise from multiple traits. However, because of the greatly increased complexity, the majority of IRT research and applications utilize a unidimensional model. With the...
In this work, maximum a posteriori (MAP) despeckling, implemented in the multiresolution domain defined by the undecimated discrete wavelet transform (UDWT), will carried out on very high resolution (VHR) SAR images and compared with earlier multiresolution approaches developed by the authors. The MAP solution in UDWT domain has been specialized to SAR imagery. Every UDWT subband is segmented into...
SAR tomography is the extension of conventional two dimensional SAR imaging principle to three dimensions. In order to improve the vertical resolution with respect to classical Fourier-based methods, high resolution approaches are used in this paper to perform SAR tomography. Both nonparametric spectral estimators, like beamforming and Capon and parametric ones, like MUSIC, maximum likelihood, are...
In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markovrandom field (MRF) was proposed for despeckling images that contains speckle. Image process is assumed to combine the random fields associated with the observed intensity process and the image texture process respectively. The objective measure for determining the optimal restoration of this “double compound...
From 1999 to 2009, the SeaWinds scatterometer has been used to detect and track large Antarctic icebergs on a daily basis. Here, we develop an automated estimation algorithm to supplement iceberg position reports with estimates of the iceberg's major axis length, minor axis length, and angle of orientation. A maximum-likelihood objective function that relates measured backscatter to model-based simulated...
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