Sharif Digital Repository

This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For... 

A modified solvent microextraction with back extraction method (SME/BE) combined with high performance liquid chromatography and fluorescence detection (HPLC-FD) was developed for the determination of citalopram in human plasma. Extraction process was performed in a home-made total glass vial without using a teflon ring, usually employed in SME/BE. Citalopram was first extracted from 0.5 mL of plasma, modified with sodium hydroxide, into hexane. Back extraction step was then performed into 5.2 μL of 45 mM ammonium formate solution (pH 4) using a GC microsyringe. The extract was subsequently transferred into a liner-like vial and then injected into the HPLC system. An enrichment factor of 150... 

Image based registration of rigid objects has been frequently addressed in the literature to obtain an object's motion parameters. In this paper, a new approach of joint segmentation-rigid registration, within the variational framework of the phase field approximation of the Mumford-Shah's functional, is proposed. The defined functional consists of two Mumford-Shah equations, extracting the discontinuity set of the reference and target images due to a rigid spatial transformation. Multiscale minimization of the proposed functional after finite element discretization provided a sub-pixel, robust algorithm for edge extraction as well as edge based rigid registration. The implementation... 

Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for...