Maximum likelihood linear regression (MLLR) is an adaptation technique suitable for both speaker and environmental model-based adaptation. The models are adapted using a set of linear transformations, estimated in a maximum likelihood fashion from the available adaptation data. As these transformations can capture general relationships between the original model set and the current speaker, or ...

a method for solving the descriptor discrete-time linear system is focused. for easily, it is converted to a standard discrete-time linear system by the definition of a derivative state feedback. then partial eigenvalue assignment is used for obtaining state feedback and solving the standard system. in partial eigenvalue assignment, just a part of the open loop spectrum of the standard linear s...

Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for subsequent metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We have developed a unified probabilistic framework (named GRAMMy) by explicitly modeling read as...

in this paper, a new approach was presented for bus network design which took the effects of three out of four stages of the bus planning process into account. the presented model consisted of three majors steps 1- network design procedure (ndp), 2- frequency determination and assignment procedure (fdap), and 3- network evaluation procedure (nep). genetic algorithm (ga) was utilized to solve th...

the aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. in addition, we derive the corrected score test statistic for outliers detection ba...

In this paper, radar detection based on Monte Carlo sampling is studied. Two detectors based on Importance Sampling are presented. In these detectors, called Particle Detector, the approximated likelihood ratio is calculated by Monte Carlo sampling. In the first detector, the unknown parameters are first estimated and are substituted in the likelihood ratio (like &#10the GLRT method). In the s...

The locomotive assignment and the freight train scheduling are important problems in railway transportation. Freight cars are coupled to form a freight rake. The freight rake becomes a train when a locomotive is coupled to it. The locomotive assignment problem assigns locomotives to a set of freight rakes in a way that, with minimum locomotive deadheading time, rake coupling delay and locomotiv...

BACKGROUND The ability to connect breeding, stopover and wintering locations of populations of migratory birds greatly enhances our understanding of the phenomenon of migration and improves our chances of effectively conserving these species. Among Palearctic-Afrotropical migratory species, aerial insectivores like the house martin (Delichon urbicum) are sensitive to factors influencing the ava...

This paper considers an Extended Generalized Half Logistic distribution. We derive some properties of this distribution and then we discuss estimation of the distribution parameters by the methods of moments, maximum likelihood and the new method of minimum spacing distance estimator based on complete data. Also, maximum likelihood equations for estimating the parameters based on Type-I and Typ...

In this paper, radar detection based on Monte Carlo sampling is studied. Two detectors based on Importance Sampling are presented. In these detectors, called Particle Detector, the approximated likelihood ratio is calculated by Monte Carlo sampling. In the first detector, the unknown parameters are first estimated and are substituted in the likelihood ratio (like the GLRT method). In the sec...