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In emission tomography (ET), fast developing Bayesian reconstruction methods can incorporate anatomical information derived from co-registered scanning modalities, such as magnetic resonance (MR) and computed tomography (CT). We propose a Bayesian image reconstruction method for single photon emission computed tomography (SPECT), using a joint entropy (JE) similarity measure to embed MR anatomical...
This paper presents a novel methodology to perform consistent matching between visual features of a pair of images, particularly in the case of point-of-view changes between shots. Traditionally, such correspondences are determined by computing the similarity between descriptor vectors associated with each point which are obtained by invariant descriptors. Our methodology first obtains a coarse global...
This paper presents a theoretical and practical novel approach for computing the probability density function underlying a set of observations. The estimator we propose is an extension of the conventional Parzen Rosenblatt method that leads to a very specific interval-valued estimation of the density. Within this approach, we make use of the convenient representation of a set of usual (summative)...
Prepulse inhibition (PPI) refers to the reduction in startle reaction towards a startle-eliciting “pulse” stimulus when it is shortly preceded by a sub-threshold “prepulse” stimulus. PPI deficits have been seen in patients with schizophrenia and animal models of this mental disorder. The goal of this study was to provide an alternative method for the analysis of PPI data. The new method is expected...
Performance of communications systems receivers is generally estimated by the bit error rate (BER) which is computed using the Monte Carlo (MC) simulation (Bit Error Counting). In a previous paper, an alternative new method based on an estimation in an iterative and nonparametric way, of the probability density function (pdf) of the soft decision of the received bit, was suggested. It was shown that...
This paper studies and compares three nonlinear observers (Nonlinear Lyapunov Observer (NLO), Lipschitz Observer (LIO) and Partial Lipschitz Observer (PLIO)) applied to nonlinear model of the DC servo motor. The considered criteria of computations for white noise is the amplitude of the residual and the estimated shape of residual and error probability density functions (PDF) which is estimated by...
In this paper we present several information-theoretic similiarity measures for shape retrieval in combination with non-rigid registration processes. The challenging property of these measures is that they are bypass divergences, that is, do not require the estimation of the probability density function for each shape. After presenting the dissimilarities and proposing some new ones, we analyze their...
Alternate RF testing is a very promising candidate for replacing the costly standard specification-based approach. The defect filter in the alternate test flow is a crucial preparatory step for the overall success of alternate test. In this paper, we present a novel nonlinear defect filter based on an estimate of the joint probability density function of the alternate measurements. The construction...
In this paper, we improve the real-time object tracking algorithm of Yang [1] which uses a symmetric similarity function between spatially smoothed kernel-density estimates of the model and the target distributions. This spatial smoothed process applied on the centre points of the probability density functions increases not only computational complexity but also noise sensitivity. After reducing background...
In this article, a novel method to accurately estimate 3D surface of objects of interest is proposed. Each ray projected from 2D image plane to 3D space is modelled with the Gaussian kernel function. Then a mean shift algorithm with an annealing scheme is used to find maximums of the probability density function and recovers the 3D surface. Experimental results show that our method is more accurate...
Amyotrophic lateral sclerosis (ALS) is a type of neurological disease due to the degeneration of motor neurons. During the course of such a progressive disease, it would be difficult for ALS patients to regulate normal locomotion, so that the gait stability becomes perturbed. This paper presents a pilot statistical study on the gait cadence (or stride interval) in ALS, based on the statistical analysis...
Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic similarity. In this paper, we propose the use of semantic...
This paper studies the problem of the minimum mean squared error estimator for non-parametric nonlinear system identification. It is shown that for a wide class of nonlinear systems, the local linear estimator is a linear (in outputs) asymptotic minimum mean squared error estimator. The class of the systems allowed is characterized by a stability condition that is related to many well studied stability...
This appendix introduces the proofs of Property 1 and 2 related to the discretization scheme; and a new compact kernel that we use throughout our method.
This paper describes an improved semaphore with policies in Windows operating system. We introduce policies to help operating system kernel select next process (or thread) in the waiting list queue to satisfy. The paper present live policies: first in first out (FIFO), first in last out (FILO), highest priority first out (HPFO), lowest priority first out (LPFO) and random. We discuss the design and...
SVDD has been proved a powerful tool for outlier detection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a hyper-sphere for distinguishing outliers from normal data. Even if such a hyper-sphere can be identified, its performance is usually not good enough. This paper proposes an multi-sphere...
This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clustering, including normalized mutual information, rand index and F-measure. Different from previous MMC algorithms that always employ the error rate as the loss function, our formulation involves a multivariate loss function...
Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements, etc. With uncertainty, the value of each data item is represented by a probability distribution function (pdf). In this paper, we propose a novel naive Bayes classification...
Multi-task learning refers to the learning problem of performing inference by jointly considering multiple related tasks. There have already been many research efforts on supervised multi-task learning. However, collecting sufficient labeled data for each task is usually time consuming and expensive. In this paper, we consider the semi-supervised multitask learning (SSMTL) problem, where we are given...
The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures but are still computationally expensive. We propose a novel graph kernel based on the structural characteristics of graphs. The key is to represent node labels as binary arrays and characterize each node using logical operations...
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