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Structural image representation for image registration
, Article 2015 International Symposium on Artificial Intelligence and Signal Processing, AISP 2015, 3 March 2015 through 5 March 2015 ; March , 2015 , Pages 95-100 ; 9781479988174 (ISBN) ; Shirpour, M ; Manzuri, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2015
Abstract
Image registration is an important task in medical image processing. Assuming spatially stationary intensity relation among images, conventional area based algorithms such as CC (Correlation Coefficients) and MI (Mutual Information), show weaker results alongside spatially varying intensity distortion. In this research, a structural representation of images is introduced. It allows us to use simpler similarity metrics in multimodal images which are also robust against the mentioned distortion field. The efficiency of this presentation in non-rigid image registration in the presence of spatial varying distortion field is examined. Experimental results on synthetic and real-world data sets...
Blood pressure estimation using photoplethysmogram signal and its morphological features
, Article IEEE Sensors Journal ; Volume 20, Issue 8 , 2020 , Pages 4300-4310 ; Ahmadi, M. M ; Mohammadzade, H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
In this paper, we present a machine learning model to estimate the blood pressure (BP) of a person using only his photoplethysmogram (PPG) signal. We propose algorithms to better detect some critical points of the PPG signal, such as systolic and diastolic peaks, dicrotic notch and inflection point. These algorithms are applicable to different PPG signal morphologies and improve the precision of feature extraction. We show that the logarithm of dicrotic notch reflection index, the ratio of low-to high-frequency components of heart rate (HR) variability signal, and the product of HR multiplied by the modified Normalized Pulse Volume (mNPV) are the key features in accurately estimating the BP...
Properties of functional brain networks correlate frequency of psychogenic non-epileptic seizures
, Article Frontiers in Human Neuroscience ; Issue DEC , 2012 ; 16625161 (ISSN) ; Joudaki, A ; Jalili, M ; Rossetti, A. O ; Frackowiak, R. S ; Knyazeva, M. G ; Sharif University of Technology
Frontiers Media S. A
2012
Abstract
Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness metrics, respectively. Yet the number of PNES attacks per month correlated with a...
Application of artificial neural network for estimation of formation permeability in an iranian reservoir
, Article 1st International Petroleum Conference and Exhibition, Shiraz, 4 May 2009 through 6 May 2009 ; 2009 ; Masihi, M ; Fatholahi, S ; Sharif University of Technology
European Association of Geoscientists and Engineers, EAGE
2009
Abstract
The permeability is one of the most important reservoir parameters and its accurate prediction is necessary for reservoir management and enhancement. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs [1], these correlations cannot be modified accurately in carbonate reservoir for the wells which are not cored and there is no welltest data. Therefore estimation of these parameters is a challenge in reservoirs with no coring sample and welltest data. One of the most powerful tools to estimate permeability from well logs is Artificial Neural Network (ANN) whose advantages and disadvantages have been discussed by several authors [2]. In this...
Is there a reliable and invariant set of muscle synergy during isometric biaxial trunk exertion in the sagittal and transverse planes by healthy subjects?
, Article Journal of Biomechanics ; Volume 48, Issue 12 , Sep , 2015 , Pages 3234-3241 ; 00219290 (ISSN) ; Mousavi, S. J ; Hadizadeh, M ; Narimani, R ; Khalaf, K ; Campbell Kyureghyan, N ; Parnianpour, M ; Sharif University of Technology
Elsevier Ltd
2015
Abstract
It has been suggested that the central nervous system simplifies muscle control through basic units, called synergies. In this study, we have developed a novel target-matching protocol and used non-negative matrix factorization (NMF) technique to extract trunk muscle synergies and corresponding torque synergies. Isometric torque data at the L5/S1 level and electromyographic patterns of twelve abdominal and back muscles from twelve healthy participants (five females) were simultaneously recorded. Each participant performed a total number of 24 isometric target-matching tasks using 12 different angular directions and 2 levels of uniaxial and biaxial exertions. Within- and between-subject...
The effects of movement speed on kinematic variability and dynamic stability of the trunk in healthy individuals and low back pain patients
, Article Clinical Biomechanics ; Volume 30, Issue 7 , Aug , 2015 , Pages 682-688 ; 02680033 (ISSN) ; Sanjari, M. A ; Mokhtarinia, H. R ; Moeini Sedeh, S ; Khalaf, K ; Parnianpour, M ; Sharif University of Technology
Elsevier Ltd
2015
Abstract
Background: Comparison of the kinematic variability and dynamic stability of the trunk between healthy and low back pain patient groups can contribute to gaining valuable information about the movement patterns and neuromotor strategies involved in various movement tasks. Methods: Fourteen chronic low back pain patients with mild symptoms and twelve healthy male volunteers performed repeated trunk flexion-extension movements in the sagittal plane at three different speeds: 20 cycles/min, self-selected, and 40 cycles/min. Mean standard deviations, coefficient of variation and variance ratio as variability measures; maximum finite-time Lyapunov exponents and maximum Floquet multipliers as...
ECG-derived respiration estimation from single-lead ECG using gaussian process and phase space reconstruction methods
, Article Biomedical Signal Processing and Control ; Volume 45 , 2018 , Pages 80-90 ; 17468094 (ISSN) ; Shamsollahi, M. B ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
Respiratory activity influences electrocardiographic measurements (ECG) in various ways. Therefore, extraction of respiratory information from ECG, namely ECG-derived respiratory (EDR), can be used as a promising noninvasive method to monitor respiration activity. In this paper, an automatic EDR extraction system using single-lead ECG is proposed. Respiration effects on ECG are categorized into two different models: additive and multiplicative based models. After selection of a proper model for each subject using a proposed criterion, gaussian process (GP) and phase space reconstruction area (PSRArea) are introduced as new methods of EDR extraction for additive and multiplicative models,...
Developing group contribution models for the estimation of Atmospheric Lifetime and Minimum Ignition Energy
, Article Chemical Engineering Science ; Volume 226 , 2020 ; Jhamb, S ; Sharifzadeh, M ; Rashtchian, D ; Kontogeorgis, G. M ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
The Atmospheric Lifetime (ALT) of a compound represents the potential for the atmospheric accumulation of chemicals. Chemicals with a long lifetime are more resistant to natural decomposition and remain in the environment for a longer period. Minimum Ignition Energy (MIE) is one of the most important properties when evaluating hazardous chemicals. Despite the significance of these environment and safety-related properties, currently, there are no group contribution (GC) models that enable their predictive modeling. The present research aims at filling this gap. To this end, experimental data were collected from literature and the GC model parameters were estimated using the weighted...
Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes
, Article Flow Measurement and Instrumentation ; Volume 76 , 2020 ; Wood, D. A ; Mohamadian, N ; Rashidi, S ; Davoodi, S ; Soleimanian, A ; Kiani Shahvand, A ; Mehrad, M ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (QL) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble...
Multiple partial discharge sources separation using a method based on laplacian score and correlation coefficient techniques
, Article Electric Power Systems Research ; Volume 210 , 2022 ; 03787796 (ISSN) ; Vakilian, M ; Firuzi, K ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Partial discharge (PD) activity can be destructive to the transformer insulation, and ultimately may result in total breakdown of the insulation. Partial discharge sources identification in a power transformer enables the operator to evaluate the transformer insulation condition during its lifetime. In order to identify the PD source; in the case of presence of multiple sources; the first step is to capture the PD signals and to extract their specific features. In this contribution, the frequency domain analysis, the time domain analysis and the wavelet transform are employed for feature extraction purpose. In practice, there might be plenty of features, and in each scenario, only some of...
Prediction of CO2-oil molecular diffusion using adaptive neuro-fuzzy inference system and particle swarm optimization technique
, Article Fuel ; Volume 181 , 2016 , Pages 178-187 ; 00162361 (ISSN) ; Sahebi, H ; Ghiasi, M. M ; Mirjordavi, N ; Esmaeilzadeh, F ; Lee, M ; Bahadori, A ; Sharif University of Technology
Elsevier Ltd
Abstract
The quantification of carbon dioxide (CO2) dissolution in oil is crucial in predicting the potential and long-term behavior of CO2 in reservoir during secondary and tertiary oil recovery. Accurate predicting carbon dioxide molecular diffusion coefficient is a key parameter during carbon dioxide injection into oil reservoirs. In this study a new model based on adaptive neuro-fuzzy inference systems (ANFIS) is designed and developed for accurate prediction of carbon dioxide diffusivity in oils at elevated temperature and pressures. Particle Swarm Optimization (PSO) as population based stochastic search algorithms was applied to obtain the optimal ANFIS model parameters. Furthermore, a simple...
Adaptive neuro-fuzzy inference system approach in bandwidth and mutual coupling analyses of a novel UWB MIMO antenna with notch bands applicable for massive MIMOs
, Article AEU - International Journal of Electronics and Communications ; Volume 94 , 2018 , Pages 407-417 ; 14348411 (ISSN) ; Ghobadi, C ; Nourinia, J ; Samoodi, Y ; Najafi Mashhadi, S ; Sharif University of Technology
Elsevier GmbH
2018
Abstract
A novel UWB MIMO antenna with band-notched characteristic operating in the frequency range between 3.1 GHz and 10.6 GHz is presented. The designed MIMO antenna has two ports and the size of 32×14 mm2 which is fabricated on an FR-4 printed-circuit-board. The feeding system of the proposed antenna is a microstrip line. The two antennas are positioned face to face but laid reversely upon the substrate in order to have a good isolation, less cross polarization, high gain and good envelope correlation coefficient. S-parameters describe the input-output relationship between ports (or terminals) in a MIMO antenna. The effects of the strip feed line width on the variations in reflection coefficient...
Prediction of limiting activity coefficients for binary vapor-liquid equilibrium using neural networks
, Article Fluid Phase Equilibria ; Volume 433 , 2017 , Pages 174-183 ; 03783812 (ISSN) ; Bozorgmahry Boozarjomehry, R ; Sharif University of Technology
Elsevier B.V
2017
Abstract
The activity coefficient at infinite dilution is a representative of the limiting non-ideality of a solute in a mixture. Various methods for the prediction of infinite dilution activity coefficients (IDACs) have been developed. Artificial neural networks are powerful mapping tools for nonlinear function approximations. Accordingly, an artificial neural network model is proposed for the prediction of the IDACs of binary systems where the properties of the individual components are used as inputs to the network. The input parameters of the neural network are the mixture temperature, critical temperature, critical pressure, critical volume, molecular weight, dipole moment and the acentric...
Methylene blue removal using modified celery (Apium graveolens) as a low-cost biosorbent in batch mode: Kinetic, equilibrium, and thermodynamic studies
, Article Journal of Molecular Structure ; Volume 1173 , 2018 , Pages 541-551 ; 00222860 (ISSN) ; Bastani, D ; Shayesteh, H ; Sharif University of Technology
Elsevier B.V
2018
Abstract
Celery residue modified with H2SO4 was utilized as a low-cost adsorbent for elimination of methylene blue cationic dye from aqueous solution in batch adsorption process. The adsorbent was characterized by Fourier transform infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM). The efficacy of dye removal of the modified celery residue (MCR) was verifying by changing adsorbent dose, contact time, pH, initial dye concentration, and temperature. The isotherm models analysis shows that the experimental data can be better demonstrated by Freundlich isotherm model. In order to evaluate the best fit isotherm, three error analysis methods (χ2, ARE and MPSD) as well as correlation...
Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods
, Article Colloids and Surfaces A: Physicochemical and Engineering Aspects ; Volume 541 , 2018 , Pages 154-164 ; 09277757 (ISSN) ; Alhuyi Nazari, M ; Ghasempour, R ; Madah, H ; Shafii, M. B ; Ahmadi, M. A ; Sharif University of Technology
Elsevier B.V
2018
Abstract
Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The...
Prediction of the pressure drop for CuO/(Ethylene glycol-water) nanofluid flows in the car radiator by means of Artificial Neural Networks analysis integrated with genetic algorithm
, Article Physica A: Statistical Mechanics and its Applications ; Volume 546 , 2020 ; Ghazvini, M ; Maddah, H ; Kahani, M ; Pourfarhang, S ; Pourfarhang, A ; Zeinali Herisg, S ; Sharif University of Technology
Elsevier B.V
2020
Abstract
In this investigation, neural networks were used to predict pressure drop of CuO-based nanofluid in a car radiator. For this purpose, the neural network with the multilayer perceptron structure was used to formulate a model for estimating the pressure drop In this way, different concentrations of copper oxide-based nanofluid were prepared. The base fluid was the mixture of ethylene glycol and pure water (60:40 wt%) which usually used as the cooling fluid in automotive industries. The prepared nanofluid samples were used in a car radiator and the pressure drop of nanofluid flows in the system at different Reynolds were measured. The main purpose of this study was developing the optimized...
Interaction of lake-groundwater levels using cross-correlation analysis: A case study of Lake Urmia Basin, Iran
, Article Science of the Total Environment ; 2020 , Volume 729 ; Ataie Ashtiani, B ; Hosseini, S. M ; Simmons, C. T ; Sharif University of Technology
Elsevier B.V
2020
Abstract
Lake Urmia (LU) is the second largest hypersaline lake in the world. Lake Urmia's water level has dropped drastically from 1277.85 m to 1270.08 m a.s.l (equal to 7.77 m) during the last 20 years, equivalent to a loss of 70% of the lake area. The likelihood of lake-groundwater connection on the basin-scale is uncertain and understudied because of lack of basic data and precise information required for physically-based modeling. In this study, cross-correlation analysis is applied on a various time-frames of water level of the lake and groundwater levels (2001–2018) recorded in 797 observation wells across 17 adjacent aquifers. This provides insightful information on the lake-groundwater...
Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature
, Article Computers and Electronics in Agriculture ; Volume 124 , 2016 , Pages 150-160 ; 01681699 (ISSN) ; Habibi, J ; Mohammadi, K ; Shamshirband, S ; Al Razgan, O. S ; Sharif University of Technology
Elsevier B.V
Abstract
In this study, the self-adaptive evolutionary (SaE) agent is employed to structure the contributing elements to process the management of extreme learning machine (ELM) architecture based on a logical procedure. In fact, the SaE algorithm is utilized for possibility of enhancing the performance of the ELM to estimate daily soil temperature (ST) at 6 different depths of 5, 10, 20, 30, 50 and 100 cm. In the developed SaE-ELM model, the network hidden node parameters of the ELM are optimized using SaE algorithm. The precision of the SaE-ELM is then compared with the ELM model. Daily weather data sets including minimum, maximum and average air temperatures (Tmin, Tmax and Tavg), atmospheric...
In situ solid-phase microextraction and post on-fiber derivatization combined with gas chromatography-mass spectrometry for determination of phenol in occupational air
, Article Analytica Chimica Acta ; Volume 742 , 2012 , Pages 17-21 ; 00032670 (ISSN) ; Baghernejad, M ; Bagheri, H ; Sharif University of Technology
Elsevier
2012
Abstract
A method based on solid-phase microextraction (SPME) followed by on-fiber derivatization and gas chromatography-mass spectrometry detection (GC-MS) for determination of phenol in air was developed. Three different types of SPME fibers, polar and non-polar poly(dimethylsiloxane) (PDMS) and polyethylene glycol (PEG) were synthesized using sol-gel technology and their feasibility to the sampling of phenol were investigated. Different derivatization reagents for post on-fiber derivatization of phenol were studied. Important parameters influencing the extraction and derivatization process such as type of fiber coating, type and volume of derivatizing reagent, derivatization time and temperature,...
Immersed solvent microextraction and gas chromatography-mass spectrometric detection of s-triazine herbicides in aquatic media
, Article Analytica Chimica Acta ; Volume 537, Issue 1-2 , 2005 , Pages 81-87 ; 00032670 (ISSN) ; Khalilian, F ; Sharif University of Technology
Elsevier
2005
Abstract
An immersed solvent microextraction (SME) method was successfully developed for the trace enrichment of s-triazine herbicides from aquatic media. A microdrop of butyl acetate was applied as the extraction solvent. After extraction, the microdrop was introduced directly into a gas chromatography-mass spectrometry (GC-MS) injection port. Some important extraction parameters such as type of solvent, extraction time, stirring rate, and temperature were investigated and optimized. The highest possible microdrop volume of 3 μl, a sampling temperature of 60°C, and use of butyl acetate are major parameters to obtain high enrichment factors. The enrichment factor and linearity was studied by...