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Dynamic modeling and sensitivity analysis of atomic force microscope pushing force in nanoparticle manipulation on a rough substrate [electronic resource]
, Article Journal of Advanced Science, Engineering and Medicine ; 2013, Vol. 5, pp. 1-10 ; Mahboobi, Seyed Hanif ; Meghdari, Ali ; Sharif University of Technology
Abstract
An Atomic Force Microscope (AFM) is a capable tool to manipulate nanoparticles by exerting pushing force on the nanoparticles located on the substrate. In reality, the substrate cannot be considered as a smooth surface particularly at the nanoscale. Hence, the particle may encounter a step on the substrate during a manipulation. In this study, dynamics of the nanoparticle on a stepped substrate and critical pushing force in the manipulation are investigated. There are two possible dynamic modes that may happen in the manipulation on the stepped substrate. In one mode, the nanoparticle may slide on the step edge and then climb up to the step which is a desired mode. Another possible mode is...
A three-dimensional statistical volume element for histology informed micromechanical modeling of brain white matter
, Article Annals of Biomedical Engineering ; Volume 48, Issue 4 , 2020 , Pages 1337-1353 ; Farahmand, F ; Ahmadian, M. T ; Sharif University of Technology
Springer
2020
Abstract
This study presents a novel statistical volume element (SVE) for micromechanical modeling of the white matter structures, with histology-informed randomized distribution of axonal tracts within the extracellular matrix. The model was constructed based on the probability distribution functions obtained from the results of diffusion tensor imaging as well as the histological observations of scanning electron micrograph, at two structures of white matter susceptible to traumatic brain injury, i.e. corpus callosum and corona radiata. A simplistic representative volume element (RVE) with symmetrical arrangement of fully alligned axonal fibers was also created as a reference for comparison. A...
A novel procedure for micromechanical characterization of white matter constituents at various strain rates
, Article Scientia Iranica ; Volume 27, Issue 2 , 2021 , Pages 784-794 ; 10263098 (ISSN) ; Farahmand, F ; Ahmadian, M. T ; Sharif University of Technology
Sharif University of Technology
2021
Abstract
Optimal hyperplastic coeficients of the micromechanical constituents of the human brain stem were investigated. An evolutionary optimization algorithm was combined with a Finite Element (FE) model of a Representative Volume Element (RVE) to nd the optimal material properties of axon and Extra Cellular Matrix (ECM). The tension and compression test results of a previously published experiment were used for optimizing the material coeficients, and the shear experiment was used for the validation of the resulting constitutive model. The optimization algorithm was used to search for optimal shear moduli and ber sti ness of axon and ECM by tting the average stress in the axonal direction with the...
Effect of axonal fiber architecture on mechanical heterogeneity of the white matter—a statistical micromechanical model
, Article Computer Methods in Biomechanics and Biomedical Engineering ; 2021 ; 10255842 (ISSN) ; Farahmand, F ; Ahmadian, M. T ; Sharif University of Technology
Taylor and Francis Ltd
2021
Abstract
A diffusion tensor imaging (DTI) -based statistical micromechanical model was developed to study the effect of axonal fiber architecture on the inter- and intra-regional mechanical heterogeneity of the white matter. Three characteristic regions within the white matter, i.e., corpus callosum, brain stem, and corona radiata, were studied considering the previous observations of locations of diffuse axonal injury. The embedded element technique was used to create a fiber-reinforced model, where the fiber was characterized by a Holzapfel hyperelastic material model with variable dispersion of axonal orientations. A relationship between the fractional anisotropy and the dispersion parameter of...
Effect of axonal fiber architecture on mechanical heterogeneity of the white matter—a statistical micromechanical model
, Article Computer Methods in Biomechanics and Biomedical Engineering ; Volume 25, Issue 1 , 2022 , Pages 27-39 ; 10255842 (ISSN) ; Farahmand, F ; Ahmadian, M. T ; Sharif University of Technology
Taylor and Francis Ltd
2022
Abstract
A diffusion tensor imaging (DTI) -based statistical micromechanical model was developed to study the effect of axonal fiber architecture on the inter- and intra-regional mechanical heterogeneity of the white matter. Three characteristic regions within the white matter, i.e., corpus callosum, brain stem, and corona radiata, were studied considering the previous observations of locations of diffuse axonal injury. The embedded element technique was used to create a fiber-reinforced model, where the fiber was characterized by a Holzapfel hyperelastic material model with variable dispersion of axonal orientations. A relationship between the fractional anisotropy and the dispersion parameter of...
Classification of vascular function in upper limb using bilateral photoplethysmographic signals
, Article Physiological Measurement ; Volume 29, Issue 3 , 2008 , Pages 365-374 ; 09673334 (ISSN) ; Zahedi, E ; Jajai, H. M ; Sharif University of Technology
2008
Abstract
Bilateral PPG signals have been used for comparative study of two groups of healthy (free from any cardiovascular risk factors) and diabetic (as cardiovascular disease risk group) subjects in the age-matched range 40-50 years. The peripheral blood pulsations were recorded simultaneously from right and left index fingers for 90 s. Pulses have been modeled with the ARX440 model in the interval of 300 sample points with 100 sample points overlap between segments. Model parameters of three segments based on the highest fitness (higher than 80%) of modeled segments were retained for each subject. Subsequently, principal component analysis (PCA) was applied to the parameters of retained segments...
VoIP users’ Quality of Experience (QoE)Evaluation
, Ph.D. Dissertation Sharif University of Technology ; Jahangir, Amir Hossein (Supervisor)
Abstract
Quality of Experience (QoE) indicates the overall quality of one service such as Voice over IP (VoIP) from users' point of view by considering several systems, human, and contextual factors. QoE measurement and prediction are more challenging than Quality of Service (QoS) which is only related to network parameters. There exist various objective and subjective methods for QoE prediction. This research investigates various features affecting QoE by proposing a comprehensive subjective evaluation by employing a large number of users. We show that many unconsidered factors including speaker specifications and signal properties, such as signal-to-noise ratio (SNR), can affect QoE so that the SNR...
Toward a comprehensive subjective evaluation of VoIP users’ quality of experience (QoE): a case study on Persian language
, Article Multimedia Tools and Applications ; Volume 80, Issue 21-23 , 2021 , Pages 31783-31802 ; 13807501 (ISSN) ; Jahangir, A. H ; Hosseini, S. M ; Sharif University of Technology
Springer
2021
Abstract
Quality of Experience (QoE) measures the overall quality of a service from users’ point of view by considering several system, human, and contextual factors. There exist various objective and subjective methods for QoE prediction. Although the subjective approach is more expensive and challenging than the objective approach, QoE’s level can be more accurately determined by a subjective test. This paper investigates various features affecting QoE by proposing a comprehensive subjective evaluation. First, we show that many unconsidered factors can significantly affect QoE. We have generated voice samples featuring different values for novel factors related to the speaker, signal, and network....
Anisotropic finite element modelling of traumatic brain injury: A voxel-based approach
, Article Scientia Iranica ; Volume 28, Issue 3 B , 2021 , Pages 1271-1283 ; 10263098 (ISSN) ; Farahmand, F ; Ahmadian, M. T ; Masjoodi, S ; Sharif University of Technology
Sharif University of Technology
2021
Abstract
A computationally efficient 3D human head finite element model was constructed. The model includes the mesoscale geometrical details of the brain including the distinction between white and grey matter, sulci and gyri, the ventricular system, foramen magnum, and cerebrospinal fluid. The heterogeneity and anisotropy from diffusion tensor imaging data were incorporated by applying a one-to-one voxel-based correspondence between diffusion voxels and finite elements. The voxel resolution of the model was optimized to obtain a trade-off between reduced computational cost and higher geometrical details. Three sets of constitutive material properties were extracted from the literature to validate...
Modelling and Analysis of the Effect of Angular Velocity and Acceleration on Brain Strain Field in Traumatic Brain Injury
, M.Sc. Thesis Sharif University of Technology ; Ahmadiyan, Mohammad Tagh (Supervisor) ; Zohoor, Hassan (Supervisor)
Abstract
Traumatic brain injury (TBI) has long been known as one of the most anonymous reasons for death around the world. A presentation of a model of what happens in the process has been under study for many years; and yet it remains a question due to physiological, geometrical and computational complications. Although the modeling facilities for soft tissue modeling have improved, the precise CT-imaging of human head has revealed novel details of brain, skull and the interface (the meninges), a comprehensive FEM model of which is still being studied. This study aims to present an optimized model of human head after a comprehensive study of the previous models; which includes the brain, skull, and...
Modelling and analysis of the effect of angular velocity and acceleration on brain strain field in traumatic brain injury
, Article ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) ; Volume 3 A , 2013 ; 9780791856215 (ISBN) ; Ahmadian, M. T ; Barari, A ; Beidokhti, H. N ; Sharif University of Technology
2013
Abstract
Traumatic brain injury (TBI) has long been known as one of the most anonymous reasons for death around the world. A presentation of a model of what happens in the process has been under study for many years; and yet it remains a question due to physiological, geometrical and computational complications. Although the facilities for soft tissue modeling have improved and the precise CT-imaging of human head has revealed novel details of brain, skull and the interface (the meninges), a comprehensive FEM model of TBI is still being studied. This study aims to present an optimized model of human head including the brain, skull, and the meninges after a comprehensive study of the previous models....
Traumatic Brain Injury at Cellular Level by Using Multi-scale Modelling in Comparison with Clinical Data
, M.Sc. Thesis Sharif University of Technology ; Farahmand, Farzam (Supervisor) ; Ahmadian, Mohammad Taghi (Supervisor)
Abstract
This study aims to provide a multiscale model of traumatic brain injury including the three levels of macro, meso, and microscale information. In order to do this, a macroscale voxel-baed model of human head was constructed. The model was designed and generated to include mesoscale tissue information as well as a voxel-based approach to include voxel-based microscale data and to be coupled in a multiscale framework. Next, three different microscale models were constructed. The variations of fractional anisotropy within one standard deviation in the regions (including 60% to 70% of voxels) can change the stiffness of the tissue by up to the considerable amount of 40%. The microscale models...
Prediction of Rolling Element Bearings Degradation Trend Using Limited Data
, M.Sc. Thesis Sharif University of Technology ; Behzad, Mehdi (Supervisor) ; Arghand, Hesam Al-din (Co-Supervisor)
Abstract
Condition monitoring of machinery is of significant economic importance to mitigate production losses resulting from downtimes. Unforeseen failure of roller element bearings is the most common issue observed in industrial units. However, detecting and tracking the progression of these failures through machine vibration monitoring and predicting the deterioration of these rotating components are viable solutions. Numerous studies have focused on using laboratory accelerated life test data for fault detection and remaining useful life prediction of these components. While online monitoring of all equipment in the industry may not be feasible, and conditions in the field differ from laboratory...
Intelligent Fault Diagnosis using Multiple Sensor Data Fusion for Detecting Misalignment and Unbalance
, M.Sc. Thesis Sharif University of Technology ; Behzad, Mehdi (Supervisor) ; Arghand, Hesam Al-Din (Co-Supervisor)
Abstract
Intelligent predictive maintenance is recognized as a cornerstone of Industry 4.0, where intelligent software is employed for the early detection of faults and the prevention of unexpected failures. Recent research indicates that the integration of multi-sensor data for fault diagnosis of gearboxes and bearings, using artificial intelligence models, has been successful. However, conventional methods face several challenges. These include an over-reliance on the signal characteristics of a single sensor and the impracticality of applying intelligent learning methods, particularly deep learning, despite their high potential, due to the unavailability of sufficiently large and diverse...