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The finite element model updating technique presented in this study improves aforementioned approaches to detect the structural damages and develops classical damage detection methods including changes in natural frequencies, mode shapes and physical properties of structures.
The ultimate goal of research in our Cardiovascular Molecular Imaging Laboratory is to develop novel imaging approaches to detect the molecular pathobiology of the vessel wall in vivo.
In this Supplement, we have focused on approaches to detect the presence of miRNAs and their impact on genomes, and we explore the roles they play in regulating biological functions.
You can still use traditional approaches to detect the presence or absence of mediation, but if you try to estimate direct and indirect effects your estimates will be biased.
Since the statistical analysis of MAI in MIMO-CDMA systems is quite involved, especially when these systems are fading, existing works in the literature, such as successive interference cancellation (SIC) or parallel interference cancellation (PIC), employ suboptimal approaches to detect the subscriber without involving the need for MAI statistics.
So, to alleviate these complexities, researchers in the past either used some strong assumptions, for example Gaussian assumption for interference in MIMO system [17], or suboptimal approaches to detect the subscriber without involving the need for MAI statistics such as in SIC [18] and PIC [19].
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This paper presents a novel approach to detect the milling chatter based on energy entropy.
In present paper, a new approach to detect the damage based on the auto correlation function is proposed.
We report a pattern recognition approach to detect the atomic structure in high-resolution transmission electron microscopy images of graphene.
To combine the quasi-Muskingum model with F-test and AICc facilitates an efficient approach to detect the change and infer the threshold river stage.
Hence, developing a fully-automatic approach to detect the abnormal structures from brain images is considered to be a challenging issue in medical image analysis.
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