Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor the removal of brain sections can be addressed in the subsequent steps, resulting in an unfixed mistake during further analysis. Therefore, accurate skull stripping is necessary for neuroimaging diagnostic systems. This paper proposes a system based on deep learning and Image processing, an innovative method for converting a pre-trained model into another type of pre-trainer using pre-processing operations and the CLAHE filter as a critical phase. The global IBSR data set was used as a test and training set. For the system's efficacy, work was performed based on the principle of three dimensions and three sections of MR images and two-dimensional images, and the results were 99.9% accurate.
New, simple and sensitive batch and reverse FIA spectrophotometric methods for the determination of doxycycline hyclate in pure form and in pharmaceutical preparations were proposed. These methods based on oxidative coupling reaction between doxycycline hyclate and 3-methylbenzothiazolinone-2-hydrazone hydrochloride (MBTH) in the presence ammonium ceric sulfate in acidic medium, to form green water-soluble dye that is stable and has a maximum absorbance at 626 nm. A calibration graph shows that a Beer's law is obeyed over the concentration range of 1-80 and 0.5-110 ?g.mL-1 of DCH for the batch and rFIA respectively with detection limit of 0.325 ?g.mL-1 of DCH for r-FIA methods. All different chemicals and physical experimental paramete
... Show MoreIntroduction: Carrier-based gutta-percha is an effective method of root canal obturation creating a 3-dimensional filling; however, retrieval of the plastic carrier is relatively difficult, particularly with smaller sizes. The purpose of this study was to develop composite carriers consisting of polyethylene (PE), hydroxyapatite (HA), and strontium oxide (SrO) for carrier-based root canal obturation. Methods: Composite fibers of HA, PE, and SrO were fabricated in the shape of a carrier for delivering gutta-percha (GP) using a melt-extrusion process. The fibers were characterized using infrared spectroscopy and the thermal properties determined using differential scanning calorimetry. The elastic modulus and tensile strength tests were dete
... Show MoreThe digital image with the wavelet tools is increasing nowadays with MATLAB library, by using this method based on invariant moments which are a set of seven moments can be derived from the second and third moments , which can be calculated after converting the image from colored map to gray scale , rescale the image to (512 * 512 ) pixel , dividing the image in to four equal pieces (256 * 256 ) for each piece , then for gray scale image ( 512 * 512 ) and the four pieces (256 * 256 ) calculate wavelet with moment and invariant moment, then store the result with the author ,owner for this image to build data base for the original image to decide the authority of these images by u
... Show MoreIn this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal
... Show MoreWith growing global demand for hydrocarbons and decreasing conventional reserves, the gas industry is shifting its focus in the direction of unconventional reservoirs. Tight gas reservoirs have typically been deemed uneconomical due to their low permeability which is understood to be below 0.1mD, requiring advanced drilling techniques and stimulation to enhance hydrocarbons. However, the first step in determining the economic viability of the reservoir is to see how much gas is initially in place. Numerical simulation has been regarded across the industry as the most accurate form of gas estimation, however, is extremely costly and time consuming. The aim of this study is to provide a framework for a simple analytical method to esti
... Show MoreIn this paper, a miniaturized 2 × 2 electro-optic plasmonic Mach– Zehnder switch (MZS) based on metal–polymer–silicon hybrid waveguide is presented. Adiabatic tapers are designed to couple the light between the plasmonic phase shifter, implemented in each of the MZS arms, and the 3-dB input/output directional couplers. For 6 µm-long hybrid plasmonic waveguide supported by JRD1 polymer (r33= 390 pm/V), a π-phase shift voltage of 2 V is obtained. The switch is designed for 1550 nm operation wavelength using COMSOL software and characterizes by 2.3 dB insertion loss, 9.9 fJ/bit power consumption, and 640 GHz operation bandwidth
The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.
... Show MoreThis paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH2) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue
... Show MoreThis paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul
... Show More