In recent years, the underwater image processing has significantly improved due to the technological development. The real challenge is that the poor contrast and noise because of absorption of light and scattering in...
In recent years, the underwater image processing has significantly improved due to the technological development. The real challenge is that the poor contrast and noise because of absorption of light and scattering in the oceanic environment inherently disturb these underwater images. Hybrid filters depending on fuzzy for de-noising underwater images is proposed and analyzed clearly. The proposed algorithm is compared with various existing de- noising algorithms such as Gaussian median filter, TMED and median filter. The PSNR and NMSE of each algorithm has been measured and analyzed, which compares the ability to de-noising.
The article deals with the problems of the mathematical construct of the process of acquiring knowledge and the trajectory of learning in web-oriented education. Private and general categories are expressed, the decom...
The article deals with the problems of the mathematical construct of the process of acquiring knowledge and the trajectory of learning in web-oriented education. Private and general categories are expressed, the decomposition method in relation to the formalization of the process of teaching the web technologies or through web systems is applied, the importance of the need to implement expert subsystems of activity analysis in a browser window during testing or interactive interaction is expressed and substantiated. The process of the activation formula for the acquisition of knowledge is expressed, the essential role of feedback and the dialogue system for supporting the choice of behaviour strategy in the interactive pool of web-based education is indicated on the example of a game developed for students of a higher educational institution.
Image splicing is done by duplicate part(s) of an image and pasted in a different image. This basic technique is very common in image forgery therefore it reduced the user confidence in the digital image. The need for...
Image splicing is done by duplicate part(s) of an image and pasted in a different image. This basic technique is very common in image forgery therefore it reduced the user confidence in the digital image. The need for a reliable and effective method to detect this type of counterfeiting has been increased. Splicing detection in colour image based on Deep Learning of Haar wavelet decomposed image is developed in this work. The colour image is converted to grayscale and decomposed into 1st level (LL1, LH1, HL1, and HH1). Then to detect splicing, we used the semantic segmentation of the decomposed image. Consider that the Convolutional Neural Network model was used to segment the decomposed LL1 image. The SegNet is applied to boost the semantic segmentation and utilize the classification in our proposed approach. The experimental work confirmed the efficiency of the suggested method where the forgery (splicing) detected in 89% of the tested images with a very high percentage of localization.
In non-destructive evaluation/testing (NDE/NDT) and structural health monitoring (SHM) applications, guided waves are commonly employed and widely studied. Wave behavior characterization and analysis can be vital in d...
In non-destructive evaluation/testing (NDE/NDT) and structural health monitoring (SHM) applications, guided waves are commonly employed and widely studied. Wave behavior characterization and analysis can be vital in determining the state of the structure under inspection. Effective analysis of guided waves, however, is encumbered by their intricate nature. This intricacy is further aggravated in structures with anisotropic characteristics. Moreover, the data acquisition process can be costly and time-consuming. Therefore, it is significant to achieve behavior prediction of guided waves from limited measurements. To make this possible, compressive sensing based methodologies and predictive models have been presented in the literature. Specifically in prior work, a two-dimensional sparse wavenumber analysis (2D-SWA) framework was introduced to model anisotropic wave propagation. In this paper, we present a similar framework whereby a sparser representation of guided waves can be obtained by incorporating information of the measurements in polar coordinates. We implement this method, which we refer to as polar sparse wavenumber analysis (PSWA), on a simulated wavefield propagating in a composite material and demonstrate how it is capable of accurately reconstructing the entire wavefield from a few spatial measurements.
This paper proposes directional weighed hybrid median based fuzzy filter for de-noising random valued impulse noise from digital images. The two-step process namely, noise detection through fuzzy noise detection proce...
This paper proposes directional weighed hybrid median based fuzzy filter for de-noising random valued impulse noise from digital images. The two-step process namely, noise detection through fuzzy noise detection process followed by directional weighed fuzzy hybrid median (DWFHM) filtering for de-noising random valued impulse noise is the proposed technique studied in this paper. The application of DWFHM filter to remove random valued impulse noise yields improved result over existing methods based on PSNR (peak signal to noise ratio) values and RMSD (root mean square deviation) values.
Biodiesel is an alternative fuel derived from plant oil, animal fat or used oil through esterification with alcohol where it can be used without modifying the engine of a diesel machine. Used cooking oil is categorize...
Biodiesel is an alternative fuel derived from plant oil, animal fat or used oil through esterification with alcohol where it can be used without modifying the engine of a diesel machine. Used cooking oil is categorized as a waste that pollutes the environment, which can be the cause of some diseases such as cancer, coronary heart disease, stroke, and also hypertention. Calcium oxide (CaO) is an alkali metal oxide that can be used as a heterogeneous catalyst in biodiesel synthesis, its raw materials can be sourced from waste material such as chicken bones. The conversion of calcium to CaO is conducted through thermal decomposition of calcium carbonate (CaCO3) from chicken bones heated at a high temperature. This study utilizes used cooking oil as the raw material and CaO as the catalyst. The purpose of this study was to determine the effect of the amount of catalyst in the biodiesel synthesis from used cooking oil and also the effect of temperature variation on chicken bone heating as the material of the catalyst. This study was carried out by transesterification and calcination procedures with temperature variations of 700, 750, 800, 850 and 900°C, as well as the variations in the amount of CaO as the catalyst of 2, 3, 4, 5, and 6 % wt. This study shows that the most favorable calcination temperature for the chicken bones is at 750°C and the percentage of CaO as the catalyst is 2%.
The idea of saving human lives using machine learning algorithms has pervasive impression. This study has targeted the most significant cause of death and human health deterioration. In this paper we have collected da...
The idea of saving human lives using machine learning algorithms has pervasive impression. This study has targeted the most significant cause of death and human health deterioration. In this paper we have collected data from different smokers having different smoking status. Several studies have tried to collect the factors but none of them have identified the factors that can lead to quitting this habit. We present a novel approach where machine learning algorithms are used for identification of the factors for smoking cessation. Due to their high classification rate, the used algorithms are Logistic Regression (LR), Decision Tree (J48) and Artificial Neural Network (ANN). Data set is generated by combining different type of sources, in order to identify factors that can classify the status of the smoker as smoker, former smoker and non-smoker. 33 Attributes were used with 10000 instances, class problem was divided in three classes Smoker, Non-Smoker and Former Smoker. The significance of an attribute obtained using the odds ratio, and factors were identified using classification with 10 fold cross validation combined with 1-1 and 1-against all method. The classification rate was highest for Logistic Regression-95.77%, for predicting the class value FSMKR, odds ratio for attribute L_STATUS and B_P_&_HYP have the highest value in predicting the class. Smokers who are living with their parents and not having blood pressure and Hypertension tend to show the cessation behavior. Attribute SMK_MORNING exhibits the pattern of smokers desire to smoke in morning which has the highest value for all three algorithms, while ANN shows only correctly classified instances. When ANN is combined with evaluator and search method results were obtained at 95.10% correctly classified instances.
DLA crystals are of excellent optical material for the NLO utility and having enhanced projection by the computational structural predictions for entitled crystalline specimen and due to crystal combination it has ant...
DLA crystals are of excellent optical material for the NLO utility and having enhanced projection by the computational structural predictions for entitled crystalline specimen and due to crystal combination it has anti-diabetic and anti-oxidant utility for the enrichment in bio applications by the concentration-inhibition values and by interactions of atoms by computational softwares.
Silicon-Germanium heterojunction bipolar transistors (SiGe HBTs) were irradiated with 60Co gamma radiations at different dose rates up to 10 Mrad of total dose. The DC electrical characteristics such as Gummel charact...
Silicon-Germanium heterojunction bipolar transistors (SiGe HBTs) were irradiated with 60Co gamma radiations at different dose rates up to 10 Mrad of total dose. The DC electrical characteristics such as Gummel characteristics, excess base current (ΔIB), current gain (hFE), neutral base recombination (NBR) and output characteristics (IC−VCE) were studied before and after irradiation. The important parameters such as IB and hFE were found to degrade after irradiation. Gamma irradiations at varying dose rates are found to produce drastically different degradation signatures at the different oxide interfaces. The degradation is more for SiGe HBTs irradiated at dose rate of 30 rad/s.
The purpose of this study is the development of the method of activity of ontology-based intelligent agent (OBIA) for evaluating the software requirements specifications (SRS). OBIA works on the basis of the developed...
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The purpose of this study is the development of the method of activity of ontology-based intelligent agent (OBIA) for evaluating the software requirements specifications (SRS). OBIA works on the basis of the developed method, and evaluates the sufficiency of information in the SRS for assessing the non-functional software features - provides the conclusion and the numerical evaluation of the level of sufficiency of information in the SRS for assessment of non-functional features.
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