Using a Cramer-Rao analysis, we study the theoretical performances of a time and spatially resolved fDOT imaging system for jointly estimating the position and the concentration of a point-wide fluorescent volume in a diffusive sample.
Finally, let us note that the fDOT systems considered in this paper are simple examples chosen to illustrate the potentiality of a Cramer-Rao analysis as a performance index for a given setup and a given task.
In section 5, we provide the Cramer-Rao bound analysis.
In this paper three methods for solving such equations are discussed: method for outer interval solution of systems of linear equations depending linearly on interval parameters, fuzzy finite element method proposed by Rama Rao and sensitivity analysis method.
However, a rigorous evaluation of bug localization effectiveness should consider the appropriate project version for every bug report under analysis (Rao and Kak 2013a), i.e., the version where the bug actually occurred.
To explore how underlying socio-cultural variables might have influenced the perception of ecosystem services, we applied a multivariate model of redundancy analysis (Rao [1964]) that showed the relationship between perception values and the socio-cultural attributes of respondents.
Eminent Indian plant scientist C. Kameswara Rao offered this analysis in a Wall Street Journal op-ed: Farmers "lose 50-70% of their annual marketable eggplant yield to two insects Leucinodes orbonalis and Helicoverpa armigera which cause severe shoot and fruit damage.
For Cramer-Rao lower bound analysis, there are no obvious differences between those techniques.
Cramér-Rao lower bound analysis indicates how a lower bound of the root mean square error depends on the synchronization error for the MB and the MB-SW difference, respectively.
The Cramer-Rao bound (CRB) analysis provides a perfect rationale for this task.
To foster the process of effectively identifying source code that is relevant to a particular bug report, a number of techniques have been developed using information retrieval (IR) models such as Latent Dirichlet Allocation (LDA) (Lukins et al. 2010), Latent Semantic Analysis (LSA) (Rao and Kak 2011), and Vector Space Model (VSM) (Zhou et al. 2012).
