Sharif Digital Repository

The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which... 

The higher heating value (HHV) parameter of biomass is well known for its wide application in bioenergy industry and the economical study of energy resources. In the present study, the least squares support vector machine (LSSVM) strategy is used as a novel approach to estimate HHV of biomass as a function of volatile matters (VM), fixed carbon (FC), and ash content (ASH). A total number of 350 experimental data points have been extracted from previous works to train and test the proposed algorithm. In order to judge the proposed model, the statistical parameters such as R2, RMSE, and AARD are calculated as 0.92936, 4.2731%. Based on the calculated parameters, it can be concluded that the... 

Asphaltene precipitation is known as one of the challenging problems in petroleum industries which have significant effects on production such as formation damage and wellbore plugging. To solve this problem, calculation of precipitated asphaltene becomes highlighted so in the present study a novel approach is proposed based on ANFIS algorithm to estimate precipitated asphaltene in terms of dilution ration, carbon number of precipitants and temperature. The particle swarm optimization (PSO) method is applied to optimize ANFIS algorithm parameters. The proposed model was evaluated based on statistical parameters and the calculated R2, AARD and RMSE for the total data are 0.90309, 9.4908 and... 

Abstract A system designer needs to estimate the behavior of a system interconnection based on different patterns of switching which happen around an interconnect. Two different scenarios are supposed to estimate the effect of interconnect issues on system performance. First, based on a normalization technique for decreasing the number of a transfer function variables, a definitive environment for one interconnect is considered and an optimized look-up-table for the wire time delay is generated. Using some sampling methods, fast accessible look-up-tables are proposed for CAD tools in very simple and small one. A 4×4×4 table for the wire delay is introduced which results in very fast... 

The existing learning methods usually assume that training data and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these methods on the test data will be severely degraded. In this paper, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. The proposed method first finds a new representation for both the source and the target domain and then learns a prediction function for the classifier by optimizing an objective function which simultaneously tries to minimize the loss function on the source domain while also maximizes the consistency of manifold (which is based on... 

von Willebrand factor (VWF) is a naturally collapsed protein that participates in primary haemostasis and coagulation events. The clotting process is triggered by the adsorption and conformational changes of the plasma VWFs localized to the collagen fibres found near the site of injury. We develop coarse-grained models to simulate the adsorption dynamics of VWF flowing near the adhesive collagen fibres at different shear rates and investigate the effect of factors such as interaction and cooperativity of VWFs on the success of adsorption events. The adsorption probability of a flowing VWF confined to the receptor field is enhanced when it encounters an adhered VWF in proximity to the... 

The Hough transform is known to be an effective technique for target detection and track initiation in search radars. However, most papers have focused on the simplistic applications of this technique which consider a 2-D data space for the Hough transform. In this paper, a new method based on xthe Hough transform is introduced for detecting targets in a 3-D data space. The data space is constructed from returned surveillance radar signal using the range and bearing information of several successive scans. This information is mapped into a 3-D x-y-t Cartesian data space. Targets are modeled with four parameters in this data space. The proposed 3-D Hough detector is then used to detect the... 

We investigate the performance of wavelet shrinkage methods for the denoising of symmetric- α -stable (S αS) self-similar stochastic processes corrupted by additive white Gaussian noise (AWGN), where α is tied to the sparsity of the process. The wavelet transform is assumed to be orthonormal and the shrinkage function minimizes the mean-square approximation error (MMSE estimator). We derive the corresponding formula for the expected value of the averaged estimation error. We show that the predicted MMSE is a monotone function of a simple criterion that depends on the wavelet and the statistical parameters of the process. Using the calculus of variations, we then optimize this criterion to... 

In recent years, the city of Tehran, Iran's capital, has encountered numerous dust events so that the dust concentration of PM10 has reached even more than 800 μg m−3. This emphasizes the importance of the statistical study of dust in Tehran and the development of correlations for estimating dust concentration of PM10. In the present study, by evaluating the data measured during dust observations over the years 2013–2016 in Tehran, new statistical models are established for estimating PM10 concentration in terms of horizontal visibility and MODIS AOD. Firstly, simple nonlinear regression models between dust concentration of PM10 and horizontal visibility as well as MODIS AOD are developed.... 

The paper presents an effective algorithm to analyze MR-images in order to recognize Alzheimer's Disease (AD) which appeared in patient's brain. The features of interest are categorized in Features of the Spatial Domain (FSD's) and Features of the Frequency Domain (FFD's) which are based on the first four statistic moments of the wavelet transform. Extracted features have been classified by a multi-layer perceptron Artificial Neural Network (ANN). Before ANN, the number of features is reduced from 44 to 12 to optimize and eliminate any correlation between them. The contribution of this paper is to demonstrate that by using the wavelet transform number of features needed for AD diagnosis has... 

In this study, ZrO2 coatings with different thicknesses were grown by the electron beam evaporation technique. The crystalline structure was studied by XRD analysis which suggested the tetragonal and monoclinic phases for ZrO2 coatings. Additionally, the film thickness slightly enhanced the crystallinity. The surface morphology and fractal features were analyzed using Scanning Electron Microscopy (SEM). The surface statistical parameters and the fractal geometry were employed to analyze the impact of the coating thickness and homogeneity on the morphology of the films. The statistical processing and fractal dimension revealed variations in the morphology parameters due to the electron beam... 

This contribution focuses on the use of ladder particle swarm optimisation (LPSO) on modelling of oxadiazole- and triazolesubstituted naphthyridines as human immunodeficiency virus-1 integrase inhibitors. Artificial neural network (ANN) and Monte Carlo cross-validation techniques were combined with LPSO to develop a quantitative structure-activity relationship model. The techniques of LPSO, ANN and sample set partitioning based on joint x-y distances were applied as feature selection, mapping and model evaluation, respectively. The variables selected by LPSO were used as inputs of Bayesian regularisation ANN. The statistical parameters of correlation of deterministic, R2, and... 

Differential pulse polarography (DPP) was applied for the determination of the herbicide Tribenuron-methyl (TBM). This is a first study for various parameters affecting the reduction peak current were simultaneously optimized using experimental design and these results are different from other reports. The effect of factors such as voltage step, voltage step time, pulse amplitude, pulse time, sample pH, concentration of the supporting electrolyte and the mercury drop size were assessed by means of a (27-2) fractional factorial design. It was found that the effects and interactions of four out of seven factors were significant. Consequently, a central composite design (CCD) with four factors,... 

A novel hybrid algorithm based on a genetic algorithm and particle swarm optimization to design a fuzzy neural network, named self-organizing fuzzy neural network based on GA and PSO (SOFNNGAPSO), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. The proposed algorithm, as a new hybrid algorithm, consists of two phases. A tuning based on TS's fuzzy model is applied to identify the fuzzy structure, and also a fuzzy cluster validity index is utilized to determine the optimal number of clusters. To obtain a more precision model, GA and PSO are performed to conduct fine tuning for the obtained parameter set of the premise parts and consequent parts in the... 

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic... 

Dispersive liquid-liquid microextraction (DLLME) coupled with gas chromatography-mass spectrometry-selective ion monitoring (GC-MS-SIM) was applied to the determination of methyl tert-butyl ether (MTBE) in water samples. The effect of main parameters affecting the extraction efficiency was studied simultaneously. From selected parameters, volume of extraction solvent, volume of dispersive solvent, and salt concentration were optimized by means of experimental design. The statistical parameters of the derived model were R 2=0.9987 and F=17.83. The optimal conditions were 42.0μL for extraction solvent, 0.30mL for disperser solvent and 5% (w/v) for sodium chloride. The calibration linear range... 

The objective of the present work is development of joint approximate diagonalization of eigenmatrices (JADE) as a member of independent component analysis (ICA) family, for the analysis of gas chromatography-mass spectrometry (GC–MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC–MS) data to address incomplete separation problem occurred during the analysis of complex sample matrices. In this regard, simulated GC–MS and GC × GC–MS data sets with different number of components, different degree of overlap and noise were evaluated. In the case of simultaneous analysis of multiple samples, column-wise augmentation for GC–MS and column-wise super-augmentation...