Ring Oscillator for 60 Meter Bandwidth

The 60-meter band range is tremendously useful in telecommunication,military and governmental applications.The I.T.U.(International Telecommunication Union)required isolationism to former radio frequency services because the various frequency bands are extremely overloaded.The allocation of new frequency bands are a lengthy procedure as well as time taking.As a result,the researchers use bidirectional,amateur radio frequency communication for 60-meter band,usually the frequency slot of 5250-5450 KHz,although the entire band is not essentially obtainable for all countries.For transmission and reception of these frequencies,a local oscillator is used in the mixer unit to generate the local signal for mixing the input and reference signals.For this function different type of oscillators are used.In this paper,a three-stage ring oscillator is designed with 1 V supply.Ring oscillators(RO)is the base to explore like to identifying,specify with modelling resources in the disparity in behaviour of the circuit in terms of industrialized design and layout parameters.This type of oscillators are free from noise as inductor is not used to the circuit as in LC oscillator,Heartly oscillator,Colpitt and tuned oscillators.The present approach of circuit designing,the scaling of CMOS(Complementary Metal Oxide Semiconductor)transistor will moderate,the procedure variability.In the forthcoming article,a ring oscillator with fixed capacitor(1 pF)and with variable capacitors(1 to 100 pF)is analysed.The frequency analysis with different capacitor is performed.The total delay of 3-stage oscillator is 4.82 ns with 5.2 MHz oscillation frequency.The overall Power dissipation of the circuit is 1.852μWat 1 V supply.The simulation analysis is performed on 45 nm CMOS technology with both transistor width are 278 and 420 nm.

EEG signal artefact removal using flower pollination fractional calculus optimisation

Purpose-The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition.Initially,pre-processing is done on EEG signal for quality improvement.Then,by using wavelet transform(WT)feature extraction is done.The artefacts present in the EEG are removed using deep convLSTM.This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.Design/methodology/approach-Nowadays’EEG signals play vital role in the field of neurophysiologic research.Brain activities of human can be analysed by using EEG signals.These signals are frequently affected by noise during acquisition and other external disturbances,which lead to degrade the signal quality.Denoising of EEG signals is necessary for the effective usage of signals in any application.This paper proposes a new technique named as flower pollination fractional calculus optimisation(FPFCO)algorithm for the removal of artefacts fromEEGsignal through deep learning scheme.FPFCOalgorithmis the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM.The existed FPO algorithm is used for solution update through global and local pollinations.In this case,the fractional calculus(FC)method attempts to include the past solution by including the second order derivative.As a result,the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization(FPO)method.Initially,5 EEGsignals are contaminated by artefacts such asEMG,EOG,EEGand randomnoise.These contaminatedEEG signals are pre-processed to remove baseline and power line noises.Further,feature extraction is done by using WTand extracted features are applied to deep convLSTM,which is trained by proposed fractional calculus based flower pollination optimisation algorithm.FPFCO is used for the effective removal of artefacts from EEG signal.The proposed technique is compared with existing techniques in terms of SNR and MSE.Findings-The proposed technique is compared with existing techniques in terms of SNR,RMSE and MSE.Originality/value-100%.