CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferrin...CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.展开更多
An asymmetric MOSFET-C band-pass filter （BPF） with on chip charge pump auto-tuning is presented. It is implemented in UMC （United Manufacturing Corporation） 0.18 μm CMOS process technology. The filter system with...An asymmetric MOSFET-C band-pass filter （BPF） with on chip charge pump auto-tuning is presented. It is implemented in UMC （United Manufacturing Corporation） 0.18 μm CMOS process technology. The filter system with auto-tuning uses a master-slave technique for continuous tuning in which the charge pump outputs 2.663 V, much higher than the power supply voltage, to improve the linearity of the filter. The main filter with third order low-pass and second order high-pass properties is an asymmetric band-pass filter with bandwidth of 2.730-5.340 MHz. The in-band third order harmonic input intercept point （ⅡP3） is 16.621 dBm, with 50Ω as the source impedance. The input referred noise is about 47.455 μVrms. The main filter dissipates 3.528 mW while the auto-tuning system dissipates 2.412 mW from a 1.8 V power supply. The filter with the auto-tuning system occupies 0.592 mm2 and it can be utilized in GPS （global positioning system） and Bluetooth systems.展开更多
文摘CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
文摘An asymmetric MOSFET-C band-pass filter （BPF） with on chip charge pump auto-tuning is presented. It is implemented in UMC （United Manufacturing Corporation） 0.18 μm CMOS process technology. The filter system with auto-tuning uses a master-slave technique for continuous tuning in which the charge pump outputs 2.663 V, much higher than the power supply voltage, to improve the linearity of the filter. The main filter with third order low-pass and second order high-pass properties is an asymmetric band-pass filter with bandwidth of 2.730-5.340 MHz. The in-band third order harmonic input intercept point （ⅡP3） is 16.621 dBm, with 50Ω as the source impedance. The input referred noise is about 47.455 μVrms. The main filter dissipates 3.528 mW while the auto-tuning system dissipates 2.412 mW from a 1.8 V power supply. The filter with the auto-tuning system occupies 0.592 mm2 and it can be utilized in GPS （global positioning system） and Bluetooth systems.
