Conducting polyaniline/ZnFe_(2)O_(4) nanocomposites are synthesized by using a simple and inexpensive one-step in-situ polymerization method in the presence of ZnFe2O4 nanoparticles.The structural,morphological and el...Conducting polyaniline/ZnFe_(2)O_(4) nanocomposites are synthesized by using a simple and inexpensive one-step in-situ polymerization method in the presence of ZnFe2O4 nanoparticles.The structural,morphological and electrical properties of the samples are characterized by x-ray diffraction,Fourier transform infrared spectra and scanning electron microscopy.These results reveal the formation of polyaniline/ZnFe2O4 nanocomposites.The morphology of these samples is studied by scanning electron microscopy.Further,the ac conductivity(σac)of these composites is investigated in the frequency range of 1 kHz–10 MHz.The presence of polarons and bipolarons are responsible for the frequency dependence of ac conductivity in these nanocomposites.The ac conductivity is found to be constant up to 1MHz and thereafter it increases steeply.The ac conductivity of 0.695 S・cm^(−1) at room temperature is observed as the maxima for the polyaniline with 40wt%of the ZnFe2O4 nanocomposite.展开更多
Arecanut disease identification is a challenging problem in the field of image processing.In this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut dise...Arecanut disease identification is a challenging problem in the field of image processing.In this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and rot-split.Due to the effect of the disease,there are chances of losing vital details in the images.To enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced images.The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding.Further,the features are extracted for foreground information for disease identification by exploring the ResNet architecture.The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images.Experimental results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.展开更多
文摘Conducting polyaniline/ZnFe_(2)O_(4) nanocomposites are synthesized by using a simple and inexpensive one-step in-situ polymerization method in the presence of ZnFe2O4 nanoparticles.The structural,morphological and electrical properties of the samples are characterized by x-ray diffraction,Fourier transform infrared spectra and scanning electron microscopy.These results reveal the formation of polyaniline/ZnFe2O4 nanocomposites.The morphology of these samples is studied by scanning electron microscopy.Further,the ac conductivity(σac)of these composites is investigated in the frequency range of 1 kHz–10 MHz.The presence of polarons and bipolarons are responsible for the frequency dependence of ac conductivity in these nanocomposites.The ac conductivity is found to be constant up to 1MHz and thereafter it increases steeply.The ac conductivity of 0.695 S・cm^(−1) at room temperature is observed as the maxima for the polyaniline with 40wt%of the ZnFe2O4 nanocomposite.
文摘Arecanut disease identification is a challenging problem in the field of image processing.In this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and rot-split.Due to the effect of the disease,there are chances of losing vital details in the images.To enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced images.The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding.Further,the features are extracted for foreground information for disease identification by exploring the ResNet architecture.The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images.Experimental results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.
