基于45nm SOI CMOS工艺,设计了一款两级流水线级联型逐次逼近ADC(Pipeline-SAR ADC).摒弃了传统流水线结构中大功耗级间运算放大器,采用过零比较器和受控电流源完成级间余量放大功能,极大地减小了ADC的功耗.分析了子ADC中比较器失调对AD...基于45nm SOI CMOS工艺,设计了一款两级流水线级联型逐次逼近ADC(Pipeline-SAR ADC).摒弃了传统流水线结构中大功耗级间运算放大器,采用过零比较器和受控电流源完成级间余量放大功能,极大地减小了ADC的功耗.分析了子ADC中比较器失调对ADC精度的影响,提出了一种具有失调校准的动态比较器,满足了高精度、高速度的要求.此外,在设计逐次逼近结构时,采用共模切换、上极板采样和全定制控制逻辑等技术进一步降低了系统功耗.仿真结果显示,ADC在125 MS/s、奈圭斯特输入频率下,实现了60.46dB的信噪失真比和77.33dB的无杂散动态范围,有效位数为9.75bit,系统总功耗只有1mW.ADC的FoM值仅为9.29fJ/step,较其他工作有很大的提升.展开更多
Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes ...Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which classifies the feature vector as Covid-19 or non-Covid 19.Moreover,the DNFN is trained by devised JHB0 approach,which is introduced by combining HBA and Jaya algorithm.展开更多
文摘基于45nm SOI CMOS工艺,设计了一款两级流水线级联型逐次逼近ADC(Pipeline-SAR ADC).摒弃了传统流水线结构中大功耗级间运算放大器,采用过零比较器和受控电流源完成级间余量放大功能,极大地减小了ADC的功耗.分析了子ADC中比较器失调对ADC精度的影响,提出了一种具有失调校准的动态比较器,满足了高精度、高速度的要求.此外,在设计逐次逼近结构时,采用共模切换、上极板采样和全定制控制逻辑等技术进一步降低了系统功耗.仿真结果显示,ADC在125 MS/s、奈圭斯特输入频率下,实现了60.46dB的信噪失真比和77.33dB的无杂散动态范围,有效位数为9.75bit,系统总功耗只有1mW.ADC的FoM值仅为9.29fJ/step,较其他工作有很大的提升.
文摘Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which classifies the feature vector as Covid-19 or non-Covid 19.Moreover,the DNFN is trained by devised JHB0 approach,which is introduced by combining HBA and Jaya algorithm.
