Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects. The fast overlapped block-processing algorithm reduces the computational complexity of feature extraction. This paper also exploits Charlier polynomials and their moments along with support vector machine (SVM). The evaluation of the presented method is carried out using a well-known dataset, the McGill benchmark dataset. Besides, comparisons are performed with existing 3D object recognition methods. The results show that the proposed 3D object recognition approach achieves high recognition rates under different noisy environments. Furthermore, the results show that the presented method has the potential to mitigate noise distortion and outperforms existing methods in terms of computation time under noise-free and different noisy environments.
The present work involves studying the effect of electrolyte composition [@1= 0.5 wt.% NH4F / 5% H2O / 5% Glycerol (GLY)/ 90% Ethylene Glycol (EG)] and [ @2= 0.5 wt. % NH4F / 5% H2O / 95% Ethylene Glycol (EG)] on the structural and photoelectrochemical properties of titania nanotubes arrays (TNTAs). TNTAs substrates were successfully carried out via anodization technique and were carried out in 40 V for one hour in different electrolytes (@1, and @2). The properties of physicochemical of TNTAs were distinguished via an X-ray Diffractometer (XRD), Field Emission Scanning Electron Microscope (FESEM), an Energy Dispersive X-ray (EDX), and UV–visible diffuse reflectance. The photoelectrochemical response of TNTAs was evaluated
... Show MoreThe present work involves studying the effect of electrolyte composition [@1= 0.5 wt.% NH4F / 5% H2O / 5% Glycerol (GLY)/ 90% Ethylene Glycol (EG)] and [ @2= 0.5 wt. % NH4F / 5% H2O / 95% Ethylene Glycol (EG)] on the structural and photoelectrochemical properties of titania nanotubes arrays (TNTAs). TNTAs substrates were successfully carried out via anodization technique and were carried out in 40 V for one hour in different electrolytes (@1, and @2). The properties of physicochemical of TNTAs were distinguished via an X-ray Diffractometer (XRD), Field Emission Scanning Electron Microscope (FESEM), an Energy Dispersive X-ray (EDX), and UV–visible diffuse reflectance. T
... Show MoreIn this work, spinel ferrites (NiCoFe2O4) were prepared as thin films by dc reactive dual-magnetron co-sputtering technique. Effects of some operation parameters, such as inter-electrode distance, and preparation conditions such as mixing ratio of argon and oxygen in the gas mixture, on the structural and spectroscopic characteristics of the prepared samples were studied. For samples prepared at inter-electrode distance of 5 cm, only one functional group of OH- was observed in the FTIR spectra as all bands belonging to the metal-oxygen vibration were observed. Similarly, the XRD results showed that decreasing the pressure of oxygen in the gas mixture lead to grow more crystal planes in the samples prepare
... 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 More
In this work, a novel technique to obtain an accurate solutions to nonlinear form by multi-step combination with Laplace-variational approach (MSLVIM) is introduced. Compared with the traditional approach for variational it overcome all difficulties and enable to provide us more an accurate solutions with extended of the convergence region as well as covering to larger intervals which providing us a continuous representation of approximate analytic solution and it give more better information of the solution over the whole time interval. This technique is more easier for obtaining the general Lagrange multiplier with reduces the time and calculations. It converges rapidly to exact formula with simply computable terms wit
... Show More