Particulate matter characterization by gray level co-occurrence matrix based support vector machines.
ABSTRACT An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-occurrence matrix (GLCM) of the image. This matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.
- SourceAvailable from: M. J. Abedin[Show abstract] [Hide abstract]
ABSTRACT: Present energy situation of the world is unsustainable due to unequal geographical distribution of natural wealth as well as environmental, geopolitical and economical concerns. Ever increasing drift of energy consumption due to growth of population, transportation and luxurious lifestyle has motivated researchers to carry out research on biofuels as a sustainable alternative fuel for diesel engine. Renewability, cost effectiveness and reduction of pollutants in exhaust gas emission are promoting biofuels as a suitable substitute of diesel fuel in near future. This paper reviews the suitability of feedstock and comparative performance and emission of palm, mustard, waste cooking oil (WCO) and Calophyllum inophyllum biofuels with respect to diesel fuel from various recent publications. Probable analysis of performance and emission of biofuel is also included in further discussion. Palm oil has versatile qualities in terms of productivity, oil yield and land utilization. But tremendous demand of edible oil is motivating the use of non-edible vegetable oils as biofuel feedstock. Mustard oil is a promising new biofuel especially regarding NOx reduction. WCO is one of the most economic sources of biofuel which efficiently helps in liquid waste management and prevents recycling of used oil, injurious to human health. C. inophyllum is completely non-edible and trans-esterified oil shows similar engine performance and emission characteristics like other biofuels. Limited data were published regarding mustard and C. inophyllum as their use as biofuel is still in primary state compared to palm or WCO. Therefore, in depth research needs to be carried out on these two oils to use them effectively as alternative fuels.Renewable and Sustainable Energy Reviews 11/2013; 27:664-682. · 5.63 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Due to the heterogeneity of metal distribution, it is challenging to identify the speciation, source and fate of metals in solid samples at micro scales. To overcome these challenges single particles of air pollution control residues were detected in situ by synchrotron microprobe after each step of chemical extraction and analyzed by multivariate statistical analysis. Results showed that Pb, Cu and Zn co-existed as acid soluble fractions during chemical extraction, regardless of their individual distribution as chlorides or oxides in the raw particles. Besides the forms of Fe2O3, MnO2 and FeCr2O4, Fe, Mn, Cr and Ni were closely associated with each other, mainly as reducible fractions. In addition, the two groups of metals had interrelations with the Si-containing insoluble matrix. The binding could not be directly detected by micro-X-ray diffraction (μ-XRD) and XRD, suggesting their partial existence as amorphous forms or in the solid solution. The combined method on single particles can effectively determine metallic multi-associations and various extraction behaviors that could not be identified by XRD, μ-XRD or X-ray absorption spectroscopy. The results are useful for further source identification and migration tracing of heavy metals.Journal of hazardous materials. 05/2014; 276C:241-252.
- [Show abstract] [Hide abstract]
ABSTRACT: Butterflies can be classified by their outer morphological qualities, genital characteristics that can be obtained using various chemical substances and methods which are carried out manually by preparing genital slides through some certain processes or molecular techniques which is a very expensive method. In this study, a new method which is based on artificial neural networks (ANN) and an image processing technique was used for identification of butterfly species as an alternative to conventional diagnostic methods. Five texture and three color features obtained from 140 butterfly images were used for identification of species. Texture features were obtained by using the average of gray level co-occurrence matrix (GLCM) with different angles and distances. The accuracy of the purposed butterfly classification method has reached 92.85 %. These findings suggested that the texture and color features can be useful for identification of butterfly species.The Visual Computer 01/2014; · 0.91 Impact Factor