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Phase-based direct average strain estimation for elastography
Ara, Sharmin R.,Mohsin, Faisal,Alam, Farzana,Rupa, Sharmin Akhtar,Soo Yeol Lee,Hasan, Md Kamrul,Awwal, Rayhana IEEE 2013 and Frequency Control Vol.60 No.11
<P>In this paper, a phase-based direct average strain estimation method is developed. A mathematical model is presented to calculate axial strain directly from the phase of the zero-lag cross-correlation function between the windowed precompression and stretched post-compression analytic signals. Unlike phase-based conventional strain estimators, for which strain is computed from the displacement field, strain in this paper is computed in one step using the secant algorithm by exploiting the direct phase-strain relationship. To maintain strain continuity, instead of using the instantaneous phase of the interrogative window alone, an average phase function is defined using the phases of the neighboring windows with the assumption that the strain is essentially similar in a close physical proximity to the interrogative window. This method accounts for the effect of lateral shift but without requiring a prior estimate of the applied strain. Moreover, the strain can be computed both in the compression and relaxation phases of the applied pressure. The performance of the proposed strain estimator is analyzed in terms of the quality metrics elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe), and mean structural similarity (MSSIM), using a finite element modeling simulation phantom. The results reveal that the proposed method performs satisfactorily in terms of all the three indices for up to 2.5% applied strain. Comparative results using simulation and experimental phantom data, and in vivo breast data of benign and malignant masses also demonstrate that the strain image quality of our method is better than the other reported techniques.</P>
Relationship between Diversity and Productivity at Ratargul Fresh Water Swamp Forest in Bangladesh
Sharmin, Mahmuda,Dey, Sunanda,Chowdhury, Sangita Institute of Forest Science 2016 Journal of Forest Science Vol.32 No.3
One of the most concerned topics in ecology is the relationship between biodiversity and ecosystem functioning. However, there are few field studies, carried out in forests, although many studies have been done in controlled experiments in grasslands. In this paper, we describe the relationship pattern between three facets of diversity and productivity at Ratargul Fresh Water Swamp Forest (RFWSF) in Bangladesh, which is the only remaining fresh water swamp forest of the country. Sixty sample plots were selected from RFWSF and included six functional traits including leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), tree height, bark thickness and wood density. In analyzing TD, we used Shannon diversity and richness indices, functional diversity was measured by Rao's quadratic entropy (Rao 1982) and Faith's (1992) index was used for phylogenetic diversity (PD). It was found that, TD, FD and PD were positively related with productivity (basal area) due to resource use complementarity but surprisingly the best predictor of tree productivity was FD. The results contribute to the understanding the effects of biodiversity loss and it is essential for conservation decision-making and policy-making of Ratargul Fresh Water Swamp Forest.
Simultaneous feature selection and discretization based on mutual information
Sharmin, Sadia,Shoyaib, Mohammad,Ali, Amin Ahsan,Khan, Muhammad Asif Hossain,Chae, Oksam Elsevier 2019 Pattern recognition Vol.91 No.-
<P><B>Abstract</B></P> <P>Recently mutual information based feature selection criteria have gained popularity for their superior performances in different applications of pattern recognition and machine learning areas. However, these methods do not consider the correction while computing mutual information for finite samples. Again, finding appropriate discretization of features is often a necessary step prior to feature selection. However, existing researches rarely discuss both discretization and feature selection simultaneously. To solve these issues, Joint Bias corrected Mutual Information (JBMI) is firstly proposed in this paper for feature selection. Secondly, a framework namely modified discretization and feature selection based on mutual information is proposed that incorporates JBMI based feature selection and dynamic discretization, both of which use a <I>χ</I> <SUP>2</SUP> based searching method. Experimental results on thirty benchmark datasets show that in most of the cases, the proposed methods outperform the state-of-the-art methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We address discretization and feature selection jointly with a single criteria. </LI> <LI> The proposed discretization method is dynamic and independent of classification algorithms. </LI> <LI> The amount of errors introduced for Relevancy, Redundancy and Complementary Information are derived analytically. </LI> <LI> It is also analytically shown that Relevancy, Redundancy and Complementary follows χ<SUP>2</SUP>-distribution. </LI> <LI> A χ<SUP>2</SUP>-based search is introduced to select a small set of features and to discretize them with small number of intervals. </LI> </UL> </P>
( Sharmin Sultana Dipti ),( Uoo Chang Chung ),( Won Sub Chung ) 대한금속재료학회 ( 구 대한금속학회 ) 2007 METALS AND MATERIALS International Vol.13 No.5
Ni catalysts for direct methanol fuel cells (DMFCs) based on carbon nanotube and Vulcan XC-72 carbon black (VCB) were prepared and their catalytical activity was investigated for DMFCs. The deposition was used to prepare Ni/CNTs and Ni/VCB composites. The morphology of the nanocomposites was tested by XRD and TEM analyses. The XRD pattern clearly showed that the peaks of the Ni catalysts appeared separately for each catalyst, and the TEM analysis confirmed that the particle sizes of Ni were between 2 nm to 4 nm. The electrochemical analyses were carried out by cyclic voltammetry (CV) to find the catalytic activities of these two types of carbon-supported Ni catalysts.