Spinel cubic ferrites have huge applications in me mory and high frequency devices.For the improvement of these modern devices,the magnetic coercivity,permeability,and dielectric properties of a ferrite are the import...Spinel cubic ferrites have huge applications in me mory and high frequency devices.For the improvement of these modern devices,the magnetic coercivity,permeability,and dielectric properties of a ferrite are the important issues.This article focuses on improving the magnetic coercivity,magnetic permeability,and dielectric properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ferrites,where x=0.00,0.06,and 0.10.The X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),field emission scanning electron microscopy(FESEM),energy dispersive X-ray(EDX),vibrating sample magnetometer(VSM),and an impedance analyzer were used to characterize the structural,magnetic,and dielectric properties of the samples.The XRD patterns indicate the formation of spinel cubic structure of the samples with a secondary peak(EuFeO_(3))for Eu doped samples.The densities and porosities of the samples follow an inverse trend,where the doped samples’lattice parameters are increased with the increment of rare earth Eu concentration.The FTIR analysis also proves the spinel cubic phase of the samples.The average grain size of the ferrites is obtained via FESEM images,and it is increased from 121 to 198 nm.VSM analysis confirms that doping of the Eu content also changes other hysteresis loop properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ferrites.Particularly,the coercivity of the Eu doped samples is greater than that of the mother alloy(x=0.00).The EDX study shows that there is no impurity contamination in the ferrites.The permeability and dielectric measurements show an improved quality factor of the Eu-doped samples with low magnetic and dielectric losses.Frequency dependent resistivity and impedance analysis also show the improved nature.From the observed properties of the samples,all the investigated ferrites might be strong candidates for potential applications in memory devices,magnetic sensors,and high frequency applications.展开更多
Introduction:The non-stationarity of electroencephalograms(EEG)has a substantial effect on the performance of classifiers in brain-computer interface(BCI)systems.To tackle this challenge,an adaptable version of the li...Introduction:The non-stationarity of electroencephalograms(EEG)has a substantial effect on the performance of classifiers in brain-computer interface(BCI)systems.To tackle this challenge,an adaptable version of the linear discriminant analysis(LDA)classifier was proposed.Accuracy is crucial when using BCIs for neurorestorative tasks or memory improvement.The accurate comprehension of brain responses facilitates more focused interventions,which may improve neurorestorative outcomes.BCIs equipped with adaptive classifers are useful for personalizing therapies to individual requirements and for improving neurorestorative processes.Notably,neurorestorative interventions that yield consistent,accurate,and reliable outcomes are more likely to inspire trust and elicit satisfaction in users.Methods:The proposed classifier continuously modified its parameters in accordance with EEG signals.The covariance matrix and mean values for each pair of classes were the updating parameters.The proposed classifier modifed the model parameters by prioritizing current signal data over older signal history.The proposed classifier was tested using a hybrid SSVEP+P300 BCI system.Results and conclusions:The proposed classifier had an estimated classification accuracy of 97.4%,and was more effective than pooled mean LDA and conventional multiclass LDA classifiers.Increased classification accuracy may increase the responsiveness of neurorestorative interventions and increase the usefulness of BCIs in neurorestoration.展开更多
基金supported by the Research and Innovation Centre,Khulna University(KURC ID-47/2020)。
文摘Spinel cubic ferrites have huge applications in me mory and high frequency devices.For the improvement of these modern devices,the magnetic coercivity,permeability,and dielectric properties of a ferrite are the important issues.This article focuses on improving the magnetic coercivity,magnetic permeability,and dielectric properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ferrites,where x=0.00,0.06,and 0.10.The X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),field emission scanning electron microscopy(FESEM),energy dispersive X-ray(EDX),vibrating sample magnetometer(VSM),and an impedance analyzer were used to characterize the structural,magnetic,and dielectric properties of the samples.The XRD patterns indicate the formation of spinel cubic structure of the samples with a secondary peak(EuFeO_(3))for Eu doped samples.The densities and porosities of the samples follow an inverse trend,where the doped samples’lattice parameters are increased with the increment of rare earth Eu concentration.The FTIR analysis also proves the spinel cubic phase of the samples.The average grain size of the ferrites is obtained via FESEM images,and it is increased from 121 to 198 nm.VSM analysis confirms that doping of the Eu content also changes other hysteresis loop properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ferrites.Particularly,the coercivity of the Eu doped samples is greater than that of the mother alloy(x=0.00).The EDX study shows that there is no impurity contamination in the ferrites.The permeability and dielectric measurements show an improved quality factor of the Eu-doped samples with low magnetic and dielectric losses.Frequency dependent resistivity and impedance analysis also show the improved nature.From the observed properties of the samples,all the investigated ferrites might be strong candidates for potential applications in memory devices,magnetic sensors,and high frequency applications.
文摘Introduction:The non-stationarity of electroencephalograms(EEG)has a substantial effect on the performance of classifiers in brain-computer interface(BCI)systems.To tackle this challenge,an adaptable version of the linear discriminant analysis(LDA)classifier was proposed.Accuracy is crucial when using BCIs for neurorestorative tasks or memory improvement.The accurate comprehension of brain responses facilitates more focused interventions,which may improve neurorestorative outcomes.BCIs equipped with adaptive classifers are useful for personalizing therapies to individual requirements and for improving neurorestorative processes.Notably,neurorestorative interventions that yield consistent,accurate,and reliable outcomes are more likely to inspire trust and elicit satisfaction in users.Methods:The proposed classifier continuously modified its parameters in accordance with EEG signals.The covariance matrix and mean values for each pair of classes were the updating parameters.The proposed classifier modifed the model parameters by prioritizing current signal data over older signal history.The proposed classifier was tested using a hybrid SSVEP+P300 BCI system.Results and conclusions:The proposed classifier had an estimated classification accuracy of 97.4%,and was more effective than pooled mean LDA and conventional multiclass LDA classifiers.Increased classification accuracy may increase the responsiveness of neurorestorative interventions and increase the usefulness of BCIs in neurorestoration.
