Sharif Digital Repository

Numerical approximation based on different forms of the governing partial differential equation can lead to significantly different results for two-phase flow in porous media. Selecting the proper primary variables is a critical step in efficiently modeling the highly nonlinear problem of multiphase subsurface flow. A comparison of various forms of numerical approximations for two-phase flow equations is performed in this work. Three forms of equations including the pressure-based, mixed pressure-saturation and modified pressure-saturation are examined. Each of these three highly nonlinear formulations is approximated using finite difference method and is linearized using both Picard and... 

In this paper, we exploit a fuzzy controller on a flexible bevel-tip needle to manipulate the needle's base in order to steer its tip in a preset obstacle-free and target-tracking path. Although the needle tends to follow a curvature path, spinning the needle with an extremely high rotational velocity makes it symmetric with respect to the tissue to follow a straight path. The fuzzy controller determines an appropriate spinning to generate the planned trajectory and, the closed-loop system tries to match the needle body with that trajectory. The swine's brain tissue model, extracted from an in-vitro experimental setup, is a non-homogenous, uncertain and fast-updatable network to model real... 

in this paper we present an integrated classifier that is used in mammogram MR-image for classification of breast cancers and abnormalities using a Multi-stage classifier, the method developed here first classifies mammograms into normal and abnormal and then for abnormal cases determines that if the case cancer is benign or malignant and also determine the type of breast cancer. In this paper there are two main topics that must be considered. First one is selection of good features and second is designing a good structure for classifier. In this study, the features are a combination of some features that are extracted from Spatial Grey Level Dependency matrix and some statistical descriptor... 

In this study, we have proposed a novel approach to investigate the features of four subbands of 2-D wavelet transform in magnetic resonance images (MRIs) for normal and abnormal brains which defected by Multiple Sclerosis (MS). Concurrently, another method extracts different kinds of features in spatial domain. Totally, 116 features have been extracted. Before applying the algorithm, we have to use a registration method because of variety in size of brain images. All extracted features have been passed over the Principal Component Analysis (PCA) and have been pushed to an Artificial Neural Network (ANN) that is a feed-forward type. According to changing in position of defected parts of... 

In many medical applications, it is necessary to precisely control a robot to achieve exact positioning. In such cases, some components of robot which had been considered rigid should be modeled with visco-elastic elements to present more exact model of robot, and hence controllers designed under the assumption of rigidity may not accurately control them, especially when time-delay and disturbances have been appeared in the closed-loop system. In this study, we present a new control approach to force such robots to have rigid and exact motions, while they have visco-elastic components. Time-delay in the feedback path defects transient state response, while disturbances change the steady... 

In this paper we will address two major problems in mammogram analysis for breast cancer in MR-images. The first is classification between normal and abnormal cases and then, discrimination between benign and malignant in cancerous cases. Our proposed method extracts textural and statistical descriptive features that are fed to a learning engine based on the use of Support Vector Machine learning framework to categorize them. The obtained results show excellent accuracy in both classification problems, that proves the appropriate combination of our features and selecting powerful classifier i.e. Support Vector Machine leads us to a brilliant outcome  

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... 

Effects of the amount and mode of input energy to an active multi-stage solar still were investigated in this work. To control the input energy, an electrical heater controlled by a PLC was utilized to simulate the energy absorbed by solar collectors. The study of the amount of input energy indicated that the freshwater production was a quadratic function of the collector over basin area (CBA) ratio. It was also found that stages 1 to 4 produced about 36%, 26%, 20% and 18% of the overall yield, respectively. Moreover, the effect of employing a thermal energy storage (TES) on the system performance was studied by comparing the mode of feeding energy according to the daily solar radiation... 

Nanocomposite thin film of N-doped ZnO-CuO was deposited on glass substrate using ultrasonic spray pyrolysis method. Zinc acetate, copper acetate and ammonium acetate with different molar ratios were dissolved in water and used as precursors. A crumpled-shape morphology consisting of nanometer-sized particles was observed with FESEM. The elemental composition was evaluated with EDX analysis and the presence of nitrogen, oxygen, copper and zinc was confirmed. XRD analysis revealed the presence of ZnO and CuO crystalline phases with crystallite size of about 18 nm. The prepared nanocomposites exhibited high photocatalytic activity in degradation of methyl orange under UV light irradiation. ©... 

In this paper, effects of general anesthesia on the electroencephalogram (EEC) has been modeled with an enhanced physiological mean field theory of electrocortical activity. Enhancement is done by inserting two intrinsic ion channels (IKNa and IAR) in Liley's mean field model. In addition to excitatory and inhibitory synapses, intrinsic ion channels can generate or manipulate the brain rhythms. IKNa and IAR can produce slow brain rhythms (delta band frequency) in deep levels of anesthesia. We represent the activities of each mentioned ion channels by cascading a nonlinear function and a first order low pass filter. Linearized and numerical solutions of the modified model show that the power... 

One of the most important operating parameters which affects the performance and efficiency of active solar stills is brine depth. In all of the previous experimental or theoretical studies, effects of water depth were investigated during only the first 24-hour period (or even shorter periods) of the operation of active solar stills. In other words, only the first day was taken into account. However, the production of an active solar still depends on several parameters such as brine temperature at sunrise (initial temperature), which are all affected by the depth variation after the first day of operation. However, the present research experimentally investigates the long-term effects of... 

Interactions between P-selectin, expressed on activated endothelium, and its counterpart P-selectin glycoprotein ligand-1 (PSGL-1), expressed on leukocytes, play a pivotal role in adhesive events that recruit circulating leukocytes toward inflamed or injured tissues. Atomistic understanding of the association and dissociation of these bonds under blood flow is necessary to define the underlying mechanism. In this study, steered molecular dynamics (SMD) simulations were applied to investigate the conformational changes of P-LE/SGP-3 construct (an effective binding unit of the P-selectin/PSGL-1 complex) under stretching with constant velocity. In the present simulations, a self-built force... 

The electrooculogram (EOG) artifact is one of the main contaminators of electroencephalographic recording (EEG). EOG can make serious problems in results and interpretations of EEG processing. Rejecting contaminated EEG segments result in an unacceptable data loss. Many methods were proposed to correct EOG artifact mainly based on regression and blind source separation (BSS). In this study, we proposed an automatic correction method based on maximum likelihood estimation. The proposed method was applied to our simulated data (real artifact free EEG plus controlled EOG) and results show that this method gives superior performance to Schlögl and SOBI methods  

Sleep spindle is the hallmark of second stage of sleep in human being, which is defined as a rhythmic sequence with waxing and waning waves, whose frequency is approximately between 8 to 14 Hz, and its time duration is between 0.5 to 2 seconds. Bump modeling is a method for extracting regions with higher amounts of energy in a related time-frequency map. The bump model of the sleep spindle consists of a group of high energy bumps concentrating in approximately 8 to 14 Hz frequency band. In this study, it will be shown that the power of bumps of EEG can be used in automated detection of sleep spindle. The presented method sensitivity is 99.41% which shows high correctly detection rate, and...