Sharif Digital Repository

Purpose: Presence of photon attenuation severely challenges quantitative accuracy in single-photon emission computed tomography (SPECT) imaging. Subsequently, various attenuation correction methods have been developed to compensate for this degradation. The present study aims to implement an attenuation correction method and then to evaluate quantification accuracy of attenuation correction in small-animal SPECT imaging. Methods: Images were reconstructed using an iterative reconstruction method based on the maximum-likelihood expectation maximization (MLEM) algorithm including resolution recovery. This was implemented in our designed dedicated small-animal SPECT (HiReSPECT) system. For... 

A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics. The proposed framework provides a reliable approach to account for the variability of the inference results observed when using different data sets. The proposed formulation is compared with a state-of-the-art Bayesian approach using numerical and experimental examples. The results indicate that the hierarchical Bayesian framework provides a more realistic account of the uncertainties, whereas the non-hierarchical Bayesian approach severely... 

A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics. The proposed framework provides a reliable approach to account for the variability of the inference results observed when using different data sets. The proposed formulation is compared with a state-of-the-art Bayesian approach using numerical and experimental examples. The results indicate that the hierarchical Bayesian framework provides a more realistic account of the uncertainties, whereas the non-hierarchical Bayesian approach severely... 

In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconceptions in the Bayesian framework since it is robust with respect to the modeling assumptions and the observed data. Rather, this issue has deep roots in users’ inability to develop an appropriate class of probabilistic models. This paper bridges this significant gap, introducing a novel Bayesian hierarchical setting, which breaks time-history vibration responses into several segments so as to capture and identify the variability of inferred parameters over the... 

Background While several studies have investigated the traditional linear measures in patients with knee osteoarthritis, no study has yet reported the non-linear structure of postural sway in these patients. Methods We used two non-linear methods, recurrence quantification analysis (percent of determinism-%DET) and central tendency measure, to respectively investigate differences in the complexity and variability of sway dynamics between two groups of knee osteoarthritis patients (n = 27) and healthy controls (n = 27) under different conditions of postural and cognitive tasks. The experimental conditions included standing on (1) rigid surface with open eyes; (2) rigid surface with closed... 

This paper presents a methodology to extract a number of quantifier features to characterize volumetric behavior of trajectories in phase space. These features quantify expanding and contracting behaviors and complexity that can be used in nonlinear and chaotic signals classification or clustering problems. One of the features is directly extracted from the distance matrix and seven features are extracted from a matrix that is subsequently obtained from the distance matrix. To illustrate the proposed quantifiers, Mackey–Glass time series and Lorenz system were employed and feature evaluation was performed. It is shown that the proposed quantifier features are robust to different... 

The depth of anesthesia estimation has been of great interest in recent decades. In this paper, we present a new methodology to quantify the levels of consciousness. Our algorithm takes advantage of the fractal and self-similarity properties of the electroencephalogram (EEG) signal. We have studied the effect of anesthetic agents on the rate of the signal fluctuations. By translating these fluctuations with detrended fluctuation analysis (DFA) algorithm to fractal exponent, we could describe the dynamics of brain during anesthesia. We found the optimum fractal-scaling exponent by selecting the best domain of box sizes, which have meaningful changes with different depth of anesthesia.... 

Acknowledging the conditionality of model-based evidence facilitates the dialogue between model developers and model users, especially when models are used to guide decisions at the science-policy interface. In general, model users have limited access to verify the realism of a model, being only exposed to model plausibility and trustworthiness; instead, modellers have an an array of validation and verification techniques available. In the end, model credibility is what both developers and users aim for, also in the interest of shielding from the possible pitfall of over-interpreting the model results. To this end, in this contribution we discuss sensitivity auditing, an extension of... 

The purpose of this research was twofold; first, it focused on developing quantitative wastage models for rebar, concrete, brick, and cement, as major traditional bulk building materials, used in Tehran residential buildings. The primary results indicated that multiple linear regression was an apt tool to model the effects of the studied variables on materials wastage. In every developed wastage model, subtractive or accumulative effect of each studied variable was recognized by its coefficient value and sign. The developed models resulted in adjusted R2 values of 0.907, 0.875, 0.920, and 0.790, respectively, for rebar, cement, brick, and concrete waste. Cement, with average wastage of 8.57%... 

The purpose of this research was twofold; first, it focused on developing quantitative wastage models for rebar, concrete, brick, and cement, as major traditional bulk building materials, used in Tehran residential buildings. The primary results indicated that multiple linear regression was an apt tool to model the effects of the studied variables on materials wastage. In every developed wastage model, subtractive or accumulative effect of each studied variable was recognized by its coefficient value and sign. The developed models resulted in adjusted R2 values of 0.907, 0.875, 0.920, and 0.790, respectively, for rebar, cement, brick, and concrete waste. Cement, with average wastage of 8.57%... 

This paper presents a hierarchical Bayesian modeling framework for the uncertainty quantification in modal identification of linear dynamical systems using multiple vibration data sets. This novel framework integrates the state-of-the-art Bayesian formulations into a hierarchical setting aiming to capture both the identification precision and the variability prompted due to modeling errors. Such developments have been absent from the modal identification literature, sustained as a long-standing problem at the research spotlight. Central to this framework is a Gaussian hyper probability model, whose mean and covariance matrix are unknown, encapsulating the uncertainty of the modal parameters.... 

Central nervous system (CNS) uses vision, vestibular, and somatosensory information to maintain body stability. Research has shown that there is more lumbar proprioception error among low back pain (LBP) individuals as compared to healthy people. In this study, two groups of 20 healthy people and 20 non-specific low back pain (NSLBP) participants took part in this investigation. This investigation focused on somatosensory sensors and in order to alter proprioception, a vibrator (frequency of 70 Hz, amplitude of 0.5 mm) was placed on the soleus muscle area of each leg and two vibrators were placed bilaterally across the lower back muscles. Individuals, whose vision was occluded, were placed... 

The present study is set out to systematically investigate the combined impact of operational, geometrical, and model uncertainties on the hemodynamics and performance characteristics in the U.S. Food and Drug Administration (FDA) benchmark centrifugal blood pump. Non-intrusive Polynomial Chaos Expansion (NIPCE) has been utilized to propagate the uncertainty of 12 random input variables in the flow field and the performance characteristics of the blood pump at three working conditions. The global sensitivity of the Quantities of Interest (QoI) to the uncertain input parameters was measured through the Sobol’ indices. The Multiple Reference Frames (MRF) approach and the SST k−ω turbulence... 

This work develops a systematic uncertainty quantification framework to assess the reliability of prediction delivered by physics-based material models in the presence of incomplete measurement data and modeling error. The framework consists of global sensitivity analysis, Bayesian inference, and forward propagation of uncertainty through the computational model. The implementation of this framework on a new multiphase model of novel porous silica aerogel materials is demonstrated to predict the thermomechanical performances of a building envelope insulation component. The uncertainty analyses rely on sampling methods, including Markov-chain Monte Carlo and a mixed finite element solution of... 

Security is one of the important issues in developing and implementing software systems especially in highly critical applications. Quantification and measurement of security is one of the approaches adopted to achieve the desired degree of security. In this paper, Stochastic Activity Networks (SANs) are used to formally model the attacks on the system under investigation. To this end, the semi-Markov attack model is sketched. Having the semi-Markov model, Probability of Attack Success (PAS), Mean Time to First Breach (MTFB), and System Misuse Proportion (SMP) are measured according to the appropriate transformation of the model to a SAN model. As a case study, we have studied a high-level... 

A robust target recognition method proposed based on recurrence plot and recurrence quantification analysis (RQA) to generate robust features against noise, target velocity and aspect angle from micro-Doppler (m-D) signatures. The proposed method is tested on simulated data of three different targets using multiclass support vector machine (MSVM) and classification rate of about 95 % is achieved. Also, effect of noise and coherent processing time (CPT) on classification rate is investigated