This paper investigates ontology-based approaches for representing information semantics and in particular the World Wide Web. A general Manufacturing System Engineering (MSE) knowledge representation scheme, called an MSE Ontology Model, to facilitate communication and information exchange in inter-enterprise, multi-disciplinary engineering design teams has been developed and encoded in the st...

Performance of equalizers depends on the discrete time model of the input signal and noise. The use of higher sampling rate results in colored noise when the bandwidth of the noise-limiting prefilter is not sufficiently large. It is shown that the mean square error (MSE) performance of linear equalizers becomes sensitive to the decision delay when the input noise is colored, and by using the ap...

| A lower bound on the mean square error (MSE) of image reconstruction in emission tomography is obtained and used in the evaluation of diierent image approximations. One component of the lower bound on the MSE is the Cram er-Rao (CR) bound on the error variance. The CR bound for diierent image approximations is derived by knowing how the CR bound is modiied when one is interested in some funct...

We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the signal reconstruction mean squared error (MSE). To that end, we formulate the task at hand as the minimization of MSE subject to binary constraints, and approximate the resulting NP-hard problem via semidefinite program...

We discuss the problem of image restoration from observed images. Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance of the filters, the parameter values should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image ...

In this brief, we develop an least mean square (LMS) algorithm that converge in a statistical sense to the global minimum of the mean square error (MSE) objective function. This is accomplished by estimating the gradient as a smoothed version of the MSE. The smoothed MSE objective begins as a convex functional in the mean. The amount of dispersion or smoothing is reduced, such that over time it...

In this paper, a powerful open Multiple Instance Learning (MIL) framework is proposed. Such an open framework is powerful since different sub-methods can be plugged into the framework to generate different specific Multiple Instance Learning algorithms. In our proposed framework, the Multiple Instance Learning problem is first converted to an unconstrained optimization problem by the Minimum Sq...

An efficient sensor scheduling is proposed� which is an interesting issue by applying wireless sensor� network (WSN) for addressing to track a mobile target� under fading channel is proposed in this report, The� proposed efficient and simple algorithm is mainly for� assisting mobile sensors management that is deployed in� tracking a considerable moving target. The presented� novel method leads ...

This module motivates and introduces the minimum variance unbiased estimator (MVUE). This is the primary criterion in the classical (frequentist) approach to parameter estimation. We introduce the concepts of mean squared error (MSE), variance, bias, unbiased estimators, and the bias-variance decomposition of the MSE. The Minimum Variance Unbiased Estimator 1 In Search of a Useful Criterion In ...