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Improving the lifetime of Wireless Sensor Networks (WSNs) is directly related with the energy efficiency of computation and communication operations in the sensor nodes. By employing the concepts of Compressive Sensing (CS) theory it is possible to reconstruct a signal with a certain number of random linear measurements, which is much less than the number of measurements necessary in conventional...
Binary (Binomial) Logistic Regression is a statistical model that can be used for classification. Concerning the targeted outcome, if the variance of observations is higher than the variance of expectations, because of overdispersion the success rate of the method in classification goes down. This overdispersion is thought as arising from the unobserved heterogen samples in the data set. In Composite...
In this paper, we present an infinite mixture model to partition short time series data. Components of this mixture model are piecewise linear sequences. The model is constructed using Chinese restaurant process and the posterior distribution over the sample assignments are calculated using collapsed Gibbs sampling. A piecewise linear sequence is represented by fewer parameters than its observations...
Tensor factorization is a frequently used modelling tool in problems involving large amounts of n-way data. Probabilistic Latent Tensor Factorization framework provides a probabilistic approach to solve the tensor factorization problem. The iterative algorithms use generalized tensor multiplication operations involving large amounts of arithmetic operations with similar structures. This work shows...
In this paper, statistical modeling of clutter data measured by a noncoherent S-band marine radar mounted on a fixed position is presented. Characterization is done by finding the best fitted density function to the clutter over eight candidate distribution. Real-time parameter estimation of the predetermined distribution and automatic threshold detection for Constant False Alarm Rate (CFAR) is provided.
We propose a finite mixture model for clustering of the spatial data patterns. The model is based on the spatial distances between the data locations in such a way that both the distances of the points to the cluster centers and the distances of a given point to its neighbors within a defined window are involved in the model. Nevertheless, we take into consideration the background noise as well in...
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