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This paper focuses on an important and fundamental problem of connecting two points by a cable, subject to a tradeoff between cost and earthquake survivability. In particular, we address the problem of selecting a route for laying a cable under arbitrary topography, based on earthquake data. First, we derive a semi-supervised probability density estimation model for the likelihood of earthquake disaster...
This paper considers asymptotic perfect secrecy and asymptotic perfect estimation in distributed estimation for large sensor networks under threat of an eavesdropper, which has access to all sensor outputs. To measure secrecy, we compare the estimation performance at the fusion center and at eavesdropper in terms of their respective Fisher Information. We analyze the Fisher Information ratio between...
In this paper, a performance limit is derived for a distributed Bayesian parameter estimation problem in sensor networks where the prior probability density function of the parameter is known. The sensor observations are assumed conditionally independent and identically distributed given the parameter to be estimated, and the sensors employ independent and identical quantizers. The performance limit...
Extracting product aspects and their associated sentiments is one of the key tasks in sentiment analysis. Estimating the confidences of extracted aspects is important to ensure the performance. To tackle the issue, this paper proposes a two-step estimation method. Collocations of product features and opinion words are initially extracted through pattern bootstrapping. A criterion synthesizing two...
Though widely used in surveillance systems of human or fire detection, statistical color models suffer from long training time during parametric estimation. To solve this low-dimension huge-number density estimation problem, we propose a computationally efficient algorithm: weighted EM, which learns the parameters of finite mixture distribution from the histogram of training data. Thus by representing...
EM (expectation-maximization) algorithm is a classical method for parameter estimation of HMM (Hidden Markov model ). Concerning that EM algorithm is easily affected by initial parameter values, we proposed a mixture splitting algorithm based on decision boundary confusion (DBC) to describe more about boundary distribution. The algorithm mainly includes three aspects: firstly the number of incremented...
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