Ce^（3＋） substituted Cu-spinel nanoferrites CuCe_xFe_（2-x）O_4（x=0.00, 0.02, 0.04, 0.06, 0.08 and 0.10） were synthesized via sol-gel self-combustion hybrid route. Single phase spinel ferrite of Cu nanoferrites we...Ce^（3＋） substituted Cu-spinel nanoferrites CuCe_xFe_（2-x）O_4（x=0.00, 0.02, 0.04, 0.06, 0.08 and 0.10） were synthesized via sol-gel self-combustion hybrid route. Single phase spinel ferrite of Cu nanoferrites were examined using X-ray diffraction（XRD） analysis whereas the multiphase structure was observed as Ce contents increased from x=0.06. Field emission scanning electron microscopy（FESEM）, Thermogravimetric and differential thermal analysis（TGA and DTA） and Fourier transform infrared spectroscopy（FTIR） were used to find out the morphology phase and metal stretching vibrations of Ce^（3＋） substituted nanocrystalline ferrites. The crystallite size was increased and found in the range of 25-91 nm. The agglomerations in Cu ferrite samples increase as the Ce^（3＋） concentration increases. The magnetic properties such as remanence, saturation magnetization, coercivity, Bohr magneton and magnetocrystalline anisotropy constant（K） were determined using M-H loops recorded from a vibrating sample magnetometer（VSM）. Saturation magnetization, remanence and coercivity are increased as the Ce^（3＋）contents increase in Cu nanocrystalline samples. Moreover, law of approach to saturation（LoA） was used to calculate the maximum value of saturation for Ce-doped Cu nanoferrites. The soft magnetic behaviour of the Cu nanoferrite is observed as compared to the samples substituted with the increased Ce contents in Cu nanocrystalline ferrite. Bohr magneton and magnetocrystalline anisotropy are found to increase with the substitution of rare earth Ce^（3＋） contents in Cu spinel nanocrystalline ferrite. Cedoped Cu nanocrystalline ferrites with excellent properties may be suitable for potential applications in sensing, security, switching, core, multilayer chip inductor, biomedical and microwave absorption applications.展开更多
The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion.The goal is to extract more spatial and spectral information from the resulting fused image t...The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion.The goal is to extract more spatial and spectral information from the resulting fused image than from the component images.The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images.This study provides a novel picture fusion technique that employs L0 smoothening Filter,Non-subsampled Contour let Transform(NSCT)and Sparse Representation(SR)followed by the Max absolute rule(MAR).The fusion approach is as follows:first,the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing filter.Then comes the fusion process,which uses an approach that combines NSCT and SR to fuse low frequency components.Similarly,the Max-absolute fusion rule is used to merge high frequency components.Finally,the final image is obtained through the disintegration of fused low and high frequency data.In terms of correlation coefficient,Entropy,spatial frequency,and fusion mutual information,our method outperforms other methods in terms of image quality enhancement and visual evaluation.展开更多
Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new meth...Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.展开更多
文摘Ce^（3＋） substituted Cu-spinel nanoferrites CuCe_xFe_（2-x）O_4（x=0.00, 0.02, 0.04, 0.06, 0.08 and 0.10） were synthesized via sol-gel self-combustion hybrid route. Single phase spinel ferrite of Cu nanoferrites were examined using X-ray diffraction（XRD） analysis whereas the multiphase structure was observed as Ce contents increased from x=0.06. Field emission scanning electron microscopy（FESEM）, Thermogravimetric and differential thermal analysis（TGA and DTA） and Fourier transform infrared spectroscopy（FTIR） were used to find out the morphology phase and metal stretching vibrations of Ce^（3＋） substituted nanocrystalline ferrites. The crystallite size was increased and found in the range of 25-91 nm. The agglomerations in Cu ferrite samples increase as the Ce^（3＋） concentration increases. The magnetic properties such as remanence, saturation magnetization, coercivity, Bohr magneton and magnetocrystalline anisotropy constant（K） were determined using M-H loops recorded from a vibrating sample magnetometer（VSM）. Saturation magnetization, remanence and coercivity are increased as the Ce^（3＋）contents increase in Cu nanocrystalline samples. Moreover, law of approach to saturation（LoA） was used to calculate the maximum value of saturation for Ce-doped Cu nanoferrites. The soft magnetic behaviour of the Cu nanoferrite is observed as compared to the samples substituted with the increased Ce contents in Cu nanocrystalline ferrite. Bohr magneton and magnetocrystalline anisotropy are found to increase with the substitution of rare earth Ce^（3＋） contents in Cu spinel nanocrystalline ferrite. Cedoped Cu nanocrystalline ferrites with excellent properties may be suitable for potential applications in sensing, security, switching, core, multilayer chip inductor, biomedical and microwave absorption applications.
文摘The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion.The goal is to extract more spatial and spectral information from the resulting fused image than from the component images.The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images.This study provides a novel picture fusion technique that employs L0 smoothening Filter,Non-subsampled Contour let Transform(NSCT)and Sparse Representation(SR)followed by the Max absolute rule(MAR).The fusion approach is as follows:first,the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing filter.Then comes the fusion process,which uses an approach that combines NSCT and SR to fuse low frequency components.Similarly,the Max-absolute fusion rule is used to merge high frequency components.Finally,the final image is obtained through the disintegration of fused low and high frequency data.In terms of correlation coefficient,Entropy,spatial frequency,and fusion mutual information,our method outperforms other methods in terms of image quality enhancement and visual evaluation.
基金Supported by the Ministerial Level Research Foundation(404040401)
文摘Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.
