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A recently proposed non-parameteric maximum likelihood (NPML) channel estimator shows superior performance to the least square (LS) estimator in presence of non-Gaussian noise. The derivation of the NPML estimator assumed perfect knowledge of the channel order, which, however, does not comply with most applications. In this paper, first we study the effects of the inaccurate order assumption on the...
Wireless location has now gained considerable attention. One of the main problems facing accurate location in wireless communication systems is non-line-of-sight (NLOS) propagation. There are parametric and non-parametric methods to cope with NLOS errors. Compared with the parametric method, the non-parametric method can provide a unified solution with an optimal performance for different channel...
Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. A fundamental difficulty when comparing two...
The problem of spectrum sensing in multi-frequency cognitive radio systems is addressed. We show that as the sensed bandwidth increases, the primary user detection is governed by a low signal-to-noise ratio (low-SNR) regime. By means of low-SNR approximations, we show that the optimal generalized likelihood ratio test (GLRT) only depends on the second order statistics of the observations and on a...
The most annoying artifacts in image deconvolution are ringing and amplified noise. These artifacts can be reduced significantly by regularization using the Maximum a Posteriori (MAP) method that exploits not only the likelihood but also the image prior in image deconvolution. Although ringing and noise can be reduced significantly with strong regularization, image details are also reduced, so the...
Motion blur due to camera shake is an annoying yet common problem in low-light photography. In this paper, we propose a novel method to recover a sharp image from a pair of motion blurred and flash images, consecutively captured using a hand-held camera. We first introduce a robust flash gradient constraint by exploiting the correlation between a sharp image and its corresponding flash image. Then...
We present a robust probabilistic method to classify targets based on their tracks. As is customary in supervised learning problems, it is assumed that example tracks from various classes are available to train a classifier. We present an optimal but computationally intensive sequential solution, and show that a computationally feasible naive Bayes approximation works better than ignoring sequential...
The problem of robust estimation of the complex amplitudes of sinusoidal signals using multiple sensors, in an unknown heavy-tailed, spatially and temporally i.i.d. noise environement is considered. A semiparametric approach for this case is presented, where non-parametric estimation of the noise density is succeeded by maximum likelihood estimation incorporating the estimated density. The suggested...
In this paper, an unsupervised image segmentation algorithm is proposed, which combines spatial constraints with a kernel fuzzy c-means (KFCM) clustering algorithm. Conventional KFCM clustering segmentation algorithm does not incorporate the spatial context information of image, which makes it sensitive to the noise and intensity variations. In order to overcome the shortcomings, the contents of image...
Credit risk analysis is not only an important research topic in finance, but also of interest in everyday life. Unfortunately, the non-linear nature of the widely accepted Black-Scholes option price model, which sits at the very heart of the structural credit risk model, causes great difficulty when inferring the latent asset value sequence from observed data. The main contribution of this paper is...
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