Gaussian Frequency Research Articles

An ADPLL-Based GFSK Modulator with Two-Point Modulation for IoT Applications.

To establish ubiquitous and energy-efficient wireless sensor networks (WSNs), short-range Internet of Things (IoT) devices require Bluetooth low energy (BLE) technology, which functions at 2.4 GHz. This study presents a novel approach as follows: a fully integrated all-digital phase-locked loop (ADPLL)-based Gaussian frequency shift keying (GFSK) modulator incorporating two-point modulation (TPM). The modulator aims to enhance the efficiency of BLE communication in these networks. The design includes a time-to-digital converter (TDC) with the following three key features to improve linearity and time resolution: fast settling time, low dropout regulators (LDOs) that adapt to process, voltage, and temperature (PVT) variations, and interpolation assisted by an analog-to-digital converter (ADC). It features a digital controlled oscillator (DCO) with two key enhancements as follows: ΔΣ modulator dithering and hierarchical capacitive banks, which expand the frequency tuning range and improve linearity, and an integrated, fast-converging least-mean-square (LMS) algorithm for DCO gain calibration, which ensures compliance with BLE 5.0 stable modulation index (SMI) requirements. Implemented in a 28 nm CMOS process, occupying an active area of 0.33 mm2, the modulator demonstrates a wide frequency tuning range of from 2.21 to 2.58 GHz, in-band phase noise of -102.1 dBc/Hz, and FSK error of 1.42% while consuming 1.6 mW.

Stochastic electrical stimulation of the thoracic or cervical regions with surface electrodes facilitates swallow in rats.

Aspiration pneumonia, a leading cause of mortality, poses an urgent challenge in contemporary society. Neuromuscular electrical stimulation (NMES) has been commonly used in dysphagia rehabilitation. However, given that NMES at motor threshold targets only specific muscles, it carries a potential risk of further compromising functions related to swallowing, respiration, and airway protection. Considering that the swallow motor pattern is orchestrated by the entire swallow pattern generator (the neural mechanism governing a sequence of swallow actions), a rehabilitation approach that centrally facilitates the entire circuit through sensory nerve stimulation is desirable. In this context, we propose a novel stimulation method using surface electrodes placed on the back to promote swallowing. The efficacy of the proposed method in promoting swallowing was evaluated by electrically stimulating sensory nerves in the back or neck. Probabilistic stimulus was applied to either the back or neck of male and female rats. Swallows were evoked by an oral water stimulus, and electromyographic (EMG) activity of the mylohyoid, thyroarytenoid, and thyropharyngeus muscles served as the primary outcome measure. Gaussian frequency stimulation applied to the skin surface of the thoracic back elicited significant increases in EMG amplitude of all three swallow-related muscles. Neck stimulation elicited a significant increase in EMG amplitude of the thyroarytenoid during swallow, but not the mylohyoid or thyropharyngeus muscles. While the targeted thoracic spinal segments T9-T10 have been investigated for enhancing respiration, the promotion of swallowing through back stimulation has not been previously studied. Furthermore, this study introduces a new probabilistic stimulus based on Gaussian distribution. Probabilistic stimuli have been reported to excel in nerve stimulation in previous research. The results demonstrate that back stimulation effectively facilitated swallow more than neck stimulation and suggest potential applications for swallowing rehabilitation.

Back Stimulation with Gaussian Frequency Biphasic Burst Stimulation for Swallowing Motor Activation

Aspiration of food containing bacteria or saliva into the trachea, leading to bacterial lung infection, is termed aspiration pneumonia. In the United States, aspiration pneumonia results in over 57,000 deaths annually, constituting 2.3% of the total mortality. Similarly, according to data from 2021 in Japan, more than 49,000 individuals succumbed to aspiration pneumonia, accounting for approximately 3.4% of the total mortality, making it the sixth leading cause of death in Japan. Notably, the risk of mortality due to aspiration pneumonia can be mitigated through early detection and prevention of swallowing disorders. Consequently, this research focuses on rehabilitation strategies for swallowing disorders. Swallowing is a coordinated motor function, orchestrated by a program involving multiple muscles under the directives of the Swallow Central Pattern Generator (CPG) located in the medulla. Thus, activation of the Swallowing CPG is pivotal for effective swallowing rehabilitation. To this end, we propose a novel method to activate the Swallow CPG by electrical stimulating of the back. The back has been recognized as a site that activates the expiratory phase of respiration, where the swallow reflex is often triggered. Moreover, given the adjacency of the Swallowing CPG and the Respiratory CPG, we postulate that stimulating the back to activate the expiratory phase can lead to enhanced activation of swallowing movements. Methods: Surface electrodes were placed on the back (T9~T10) and neck of adult rats. Initially, burst stimuli of two polarities were randomly generated at frequencies of either 30 Hz or 300 Hz in accordance with a Gaussian distribution at one of the two sites. The electric stimuli were applied for 120 seconds, three times at each location. To evaluate the impact of stimulus frequency and location on swallowing movements, we compared four groups (n=4 in each group). Following stimulation at one site, identical waveforms of stimulation were administered to both the back and neck for 120 seconds, three times. Swallowing was induced by water injection into the mouth after each stimulation. Electromyography (EMG) of three swallow-related muscles (mylohyoid, thyroarytenoid, and thyropharyngeus) was recorded and their amplitudes compared, both before stimulation (control) and after six consecutive stimulations. Results: It was observed that stimulation of both the back and neck resulted in larger maximum muscle potentials in all measured cases. Furthermore, the lower frequency of 30 Hz yielded larger muscle potentials than 300 Hz. Statistical analysis revealed that back stimulation led to early activation of the thyropharyngeus muscle compared to neck stimulation. Conclusion: The discovery that burst electrical stimulation with Gaussian frequency applied to the back activates swallow muscle groups suggests a potential novel swallow therapy option. This work was supported by NIH grants HL 111215, HL 103415 and OT20D001983, the Craig H. Neilsen Foundation Pilot Research Grant 546714, Kentucky Spinal Cord and Head Injury Research Trust, the Commonwealth of Kentucky Challenge for Excellence, and Grant-in-Aid for JSPS Fellows Number JP23KJ0966. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

CSF-GTNet: A Novel Multi-Dimensional Feature Fusion Network Based on Convnext-GeLU- BiLSTM for EEG-Signals-Enabled Fatigue Driving Detection.

Electroencephalography (EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features. More importantly, most current work only treats deep learning models as classifiers. They ignored the features of different subjects learned by the model. Aiming at the above problems, this paper proposes a novel multi-dimensional feature fusion network, CSF-GTNet, based on time and space-frequency domains for fatigue detection. Specifically, it comprises Gaussian Time Domain Network (GTNet) and Pure Convolutional Spatial Frequency Domain Network (CSFNet). The experimental results show that the proposed method effectively distinguishes between alert and fatigue states. The accuracy rates are 85.16% and 81.48% on the self-made and SEED-VIG datasets, respectively, which are higher than the state-of-the-art methods. Moreover, we analyze the contribution of each brain region for fatigue detection through the brain topology map. In addition, we explore the changing trend of each frequency band and the significance between different subjects in the alert state and fatigue state through the heat map. Our research can provide new ideas in brain fatigue research and play a specific role in promoting the development of this field.

Comparative study of 2FSK and GFSK modulation and demodulation techniques

In the digital communication era, efficient data transmission is paramount, spanning applications from mobile communication to satellite systems. Digital modulation, the process of converting digital data into a suitable format for transmission, plays a pivotal role in ensuring reliability and efficiency. This paper delves into the world of digital modulation, specifically focusing on Frequency Shift Keying (FSK) techniques. FSK encodes digital information using different carrier frequencies, making it adaptable and robust, particularly in noisy channels. This paper explores two variants: 2-level Frequency Shift Keying (2FSK) and Gaussian Frequency Shift Keying (GFSK). 2FSKs simplicity and noise tolerance make it ideal for applications like wireless LANs and radio broadcasting. However, it may not be the best choice for high-data-rate applications due to limited bandwidth efficiency. GFSK employs Gaussian filtering to reduce spectral width, enhancing bandwidth efficiency and reducing inter-symbol interference. It finds extensive use in applications like Bluetooth. This paper explores the modulation and demodulation processes for both techniques, emphasizing GFSKs spectral efficiency. Future trends encompass emerging technologies like signal processing and machine learning, reshaping telecommunications and IoT. In conclusion, understanding 2FSK and GFSK modulations nuances aids engineers and researchers. Future endeavors should focus on optimizing GFSKs bandwidth efficiency and bolstering demodulation techniques.

Performance analysis of GFSK modulation over AWGN channel

This paper analyses the performance of Gaussian Frequency Shift Keying (GFSK) modulation in Additive White Gaussian Noise (AWGN) channels. Using MATLAB 2022b for simulations, the study explores GFSK modulation principles, the role of Gaussian low-pass filters, the characteristics reflected by Power Spectral Density (PSD), and the influence of Bit Time-Bandwidth Product (BT) on Bit Error Rate (BER). It has been observed that the Gaussian filter restricts sidelobe amplitudes and enhances spectral efficiency. Through varying BT values, it is observed that higher BT values correlate with lower BER. Additionally, the study successfully reconstructs original baseband signals through sampling decisions, confirming GFSK modulation effectiveness.

AUTOMATIC DETECTION AND RECOGNITION OF DIGITAL MODULATED SIGNALS USING A NEURAL NETWORK

Issues on automatic detection and recognition of digital modulated signals are becoming increasingly relevant in modern communication systems. These processes play a key role in ensuring reliable and efficient communication in a wide range of areas, from mobile communication to broadcasting and satellite systems. The importance of these processes continues to grow with the increase in the amount of data transmitted through these systems and with the development of new communication technologies. Accurate detection and classification of signals allow communication systems to correctly interpret the transmitted information, which, in turn, ensures the reliability and efficiency of data transmission. This is especially important in conditions when signals are subject to various types of interference and distortions. Therefore, the development and improvement of the methods for automatic detection and recognition are becoming increasingly important for ensuring high-quality communication. The present article presents a neural network-based system capable of automatically detecting signals with a single carrier frequency in the range up to 6 GHz and classifying four types of digitally modulated signals: BPSK, QPSK, 16-QAM, and 64-QAM. The system is capable of operating in the presence of additive white Gaussian noise, phase and frequency offset in the received signal. The algorithms for detection and recognition of signals, developed in the LabVIEW software environment are presented. The architecture and hyperparameters of the neural network allowing quick and efficient training of the network are described. These algorithms and training methods can be applied and adapted for other systems and applications, making them very useful for a wide range of tasks. The system was tested both on simulated signals and in a real communication channel. The results obtained confirm the effectiveness of the proposed methods and emphasise their potential for further application and research.

Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks

Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity, improving coverage area. However, simultaneous periodic beacon transmissions among nearby GWs lead to interference and collisions. In this study, the impact of such intra-network interference is analyzed to determine the maximum number of GWs that can coexist. The paper presents a new collision model that considers the combined effects of the Medium Access Control (MAC) and Physical (PHY) layers. The model takes into account the partial overlap durations and relative power of all colliding events. It also illustrates the relationship between the collisions and the resulting packet loss rates. A performance evaluation is presented using a combination of analytical and simulation methods, with the former validating the simulation results. The system models are developed from experimental data obtained from field measurements. Numerical results are provided with Gaussian Frequency Shift Keying (GFSK) modulation. This paper provides guidance on selecting GFSK modulation parameters for low bit-rate and narrow-bandwidth IoT applications. The analysis and simulation results show that larger beacon intervals and frequency hopping help in reducing beacon loss rates, at the cost of larger beacon acquisition latency. On the flip side, the gateway discovery latency reduces with increasing GW density, thanks to an abundance of beacons.

Cellular shortening and calcium dynamics are improved by noisy stimulus in a model of cardiomyopathy

Noise is present in cell biology. The capability of cells to respond to noisy environment have become essential. This study aimed to investigate whether noise can enhance the contractile response and Ca2+ handling in cardiomyocytes from a cardiomyopathy model. Experiments were conducted in an experimental setup with Gaussian white noise, frequency, and amplitude control to stimulate myocytes. Cell shortening, maximal shortening velocity, time to peak shortening, and time to half relaxation variables were recorded to cell shortening. Ca2+ transient amplitude and raise rate variables were registered to measure Ca2+ transients. Our results for cell shortening, Ca2+ transient amplitude, and raise rate suggest that cell response improve when myocytes are noise stimulated. Also, cell shortening, maximal shortening velocity, Ca2+ transient amplitude, and raise improves in control cells. Altogether, these findings suggest novel characteristics in how cells improve their response in a noisy environment.

Cellular automata to understand the prograding limit of deltaic tidal flat

Deltaic tidal flat is an important ecosystem that supports the livelihoods of millions of people globally. Due to the complexity and stochastic nature of tidal flat development, it is a challenging task to predict the tidal flat prograding limit accurately. In this study, we fill out this gap from the perspective of numerical simulation by focusing on the tidal flat extension limit of Nanhui Shoal (NHS), the largest marginal shoal of the Changjiang Estuary. Our results suggest that the tidal flat of NHS extended 29.16 m/y seaward in 1989–2002, had a rapid progradation of 158.08 m/y in 2002–2012 and a retreat rate of 26.88 m/y in 2012–2019, while the fluvial sediment supply decreased 56.78% from 1989–2002 to 2003–2012 and another 20.68% from 2003–2012 to 2013–2019. During the 1989–2002 period, the advancing rate of the −5 m isobaths followed a Gaussian frequency distribution, in agreement with simulation results of cellular automata. We suggest that in the absence of human perturbations, as the sediment input to NHS declines to the threshold of 0.01 × 108 t/y, the seaward advance of tidal flats may significantly slow down. However, human interferences generated large uncertainties on the development tendency of the NHS.

Data augmentation for speech separation

Deep learning models have advanced the state of the art of monaural speech separation. However, the performance of a separation model considerably decreases when tested on unseen speakers and noisy conditions. Separation models trained with data augmentation generalize better to unseen conditions. In this paper, we conduct a comprehensive survey of data augmentation techniques and apply them to improve the generalization of time-domain speech separation models. The augmentation techniques include seven source-preserving approaches (Gaussian noise, Gain, Time masking, frequency masking, Short noise, Time stretch, and Pitch shift) and three non-source preserving approaches (Dynamix mixing, Mixup, and Cutmix). After hyperparameter search for each augmentation method, we test the generalization of the augmented model by cross-corpus testing on three datasets (LibriMix, TIMIT, and VCTK), and identify the best augmentation combination that enhances generalization. Experimental results indicate that a combination of several non-source preserving strategies (CutMix, Mixup, and Dynamic mixing) resulted in the best generalization performance. Finally, the augmentation combinations also improved the performance of the speech separation model even when fewer training data are available.

Identification of ring current proton precipitation driven by scattering of electromagnetic ion cyclotron waves

Among the most intense emissions in the Earth's magnetosphere, electromagnetic ion cyclotron (EMIC) waves are regarded as a critical candidate contributing to the precipitation losses of ring current protons, which however lacks direct multi-point observations to establish the underlying physical connection. Based upon a robust conjunction between the satellite pair of Van Allen Probe B and NOAA-19, we perform a detailed analysis to capture simultaneous enhancements of EMIC waves and ring current proton precipitation. By assuming that the ring current proton precipitation is mainly caused by EMIC wave scattering, we establish a physical model between the wave-driven proton diffusion and the ratio of precipitated-to-trapped proton count rates, which is subsequently applied to infer the intensity of EMIC waves required to cause the observed proton precipitation. Our simulations indicate that the model results of EMIC wave intensity, obtained using either the observed or empirical Gaussian wave frequency spectrum, are consistent with the wave observations, within a factor of 1.5. Our study therefore strongly supports the dominant contribution of EMIC waves to the ring current proton precipitation, and offers a valuable means to construct the global profile of EMIC wave intensity using low-altitude NOAA POES proton measurements, which generally have a broad L-shell coverage and high time resolution in favor of near-real-time conversion of the global EMIC wave distribution.

A Wavenumber–Frequency Spectrum Model for Sheared Convective Atmospheric Boundary Layer Flows

Abstract Parameterizing turbulence in the atmospheric boundary layer as a function of space and time is essential for weather and climate models. Here, we explore a model for wavenumber–frequency spectra based on a linear random advection approach to characterize sheared convective atmospheric boundary layer flows. Building on previous works, we obtain the wavenumber–frequency spectrum as a product of the wavenumber spectrum and a Gaussian frequency distribution, whose mean and variance are given by the mean advection and random sweeping velocities, respectively. The applicability of the model is tested with direct numerical simulation data in the mixed layer and the entrainment zone for the streamwise and vertical velocity components and buoyancy. To obtain a fully analytical model, we propose using a von Kármán wavenumber spectrum parameterized by the characteristic variances and integral length scales. These parameters are height dependent and vary considerably with the relative balance of buoyancy and shear forces. The introduced analytical model relies on fitting parameters obtained from numerical data in the relevant range of scales. The comparison of the von Kármán–based spectra for velocity and buoyancy to simulation results shows that the main features of the measured spectra are captured by the model.

GMSK Demodulation Combining 1D‐CNN and Bi‐LSTM Network Over Strong Solar Wind Turbulence Channel

AbstractTo solve the problem of bit error rate (BER) performance degradation over strong solar wind turbulence channel, this paper addresses to Gaussian minimum frequency shift keying (GMSK) demodulation using machine learning. First, by analyzing the scintillation characteristics of the telemetry signal caused by solar wind turbulence during the solar superior conjunction, the K distribution channel model is innovatively established over strong solar wind turbulence channel. Then, the approximate probability density function of the K distribution of the established channel is studied by using Laguerre orthogonal polynomial for convenience. Additionally, the BER performance of GMSK over strong solar wind turbulence channel is analyzed. Second, using a one‐dimensional convolutional neural network (1D‐CNN) and bidirectional long short‐term memory network (Bi‐LSTM), two demodulators for GMSK are proposed. By integrating the 1D‐CNN and LSTM to extract local features and process the dynamic information of GMSK signals, we propose a novel GMSK demodulator over strong solar wind turbulence channel with combining 1D‐CNN and Bi‐LSTM. The simulation results show that, even if the solar wind turbulence has serious effects on the signal at a low signal‐to‐noise ratio, the proposed combining neural network demodulation can obtain better BER performance than the classical Viterbi algorithm, demodulation using only a single 1D‐CNN or Bi‐LSTM. The proposed GMSK demodulator is more suitable for the deep space exploration of the solar system.

Radar Moving Target Detection Method Based on SET2 and AlexNet

Aiming at the nonstationary characteristics of echo signal for a high-speed maneuvering target, a signal feature extraction method is proposed by combining the time-frequency analysis and convolution neural network, and then the automatic detection of radar moving target in a noisy environment is realized. Firstly, the echo signal is modelled as a more accurate Gaussian modulation-linear frequency modulation (GM-LFM) signal and converted into the time-frequency image by a second-order synchroextracting transform (SET2). Then, ridge extraction is applied to extract the maximum energy ridge from the time-frequency distribution, and the data set is constructed by the maximum energy ridge. Finally, the data set is input into AlexNet for training, and the deep-level features of echo signal are extracted to realize the automatic moving targets detection. Simulation results show that SET2 and RE can effectively enhance the time-frequency characteristics of echo signal under the noisy environment, and the detection accuracy and noise robustness of the proposed method are better than that of SET1 and smooth pseudo-Wigner–Ville distribution (SPWVD).

A frequency-limited waviness and curvature measurement algorithm for composite fibre trackings

Given a set of irregularly sampled 3D polygonal curves representing composite fibres within a micro-computed tomography volume, a new approach based on the Frenet–Serret formulas is proposed to measure the point curvature and waviness along a polyline even when its oscillations are not coplanar. However, a direct computation of the measures would lead to ill-formed results depending on variant externalities across acquisitions such as noise, sampling, resolution, fractality, etc. Consequentially, we also propose a decoupling mechanism employing a low-pass Gaussian frequency filter to gradually discard features smaller than a certain user-specified σ wavelength referenced in actual space units. This proposal has been tested, characterized and visualized using both real and synthetic datasets contemplating complex waveform features to assess the filter selectivity and convergence across varying sampling frequencies (i.e. polyline resolution). The C++ VTK implementation, alongside an extra amount of supplementary materials encompassing the execution results and synthetic datasets is provided.

Experimental snapshot verification of non-Markovianity by quantum probing of convex coefficients

We apply the recently proposed quantum probing protocols with an unknown system-probe coupling to probe the convex coefficients in mixtures of commuting states. By using two reference states instead of one as originally suggested, we are able to probe both lower and upper bounds for the convex coefficient. We perform extensive analysis for the roles of the parameters characterizing the double peaked Gaussian frequency spectrum in the Markovian-to-non-Markovian transition of the polarization dynamics of a single photon. We apply the probing of the convex coefficient to the transition-inducing frequency parameter and show that the non-Markovianity of the polarization dynamics can be confirmed with a single snapshot measurement of the polarization qubit performed at unknown time and even with unknown coupling. We also show how the protocol can identify Markovian and non-Markovian time intervals in the dynamics. The results are validated with single photon experiments.

Autoencoder-Based Signal Modulation and Demodulation Methods for Sonobuoy Signal Transmission and Reception.

Sonobuoy is a disposable device that collects underwater acoustic information and is designed to transmit signals collected in a particular area to nearby aircraft or ships and sink to the seabed upon completion of its mission. In a conventional sonobuoy signal transmission and reception system, collected signals are modulated and transmitted using techniques such as frequency division modulation or Gaussian frequency shift keying. They are received and demodulated by an aircraft or a ship. However, this method has the disadvantage of a large amount of information being transmitted and low security due to relatively simple modulation and demodulation methods. Therefore, in this paper, we propose a method that uses an autoencoder to encode a transmission signal into a low-dimensional latent vector to transmit the latent vector to an aircraft or vessel. The method also uses an autoencoder to decode the received latent vector to improve signal security and to reduce the amount of transmission information by approximately a factor of a hundred compared to the conventional method. In addition, a denoising autoencoder, which reduces ambient noises in the reconstructed outputs while maintaining the merit of the proposed autoencoder, is also proposed. To evaluate the performance of the proposed autoencoders, we simulated a bistatic active and a passive sonobuoy environments. As a result of analyzing the sample spectrograms of the reconstructed outputs and mean square errors between original and reconstructed signals, we confirmed that the original signal could be restored from a low-dimensional latent vector by using the proposed autoencoder within approximately 4% errors. Furthermore, we verified that the proposed denoising autoencoder reduces ambient noise successfully by comparing spectrograms and by measuring the overall signal-to-noise ratio and the log-spectral distance of noisy input and reconstructed output signals.

The study of the dynamics of the order parameter of coupled oscillators in the Ott-Antonsen scheme for generic frequency distributions.

The Ott-Antonsen ansatz shows that, for certain classes of distribution of the natural frequencies in systems of N globally coupled Kuramoto oscillators, the dynamics of the order parameter, in the limit N → ∞, evolves, under suitable initial conditions, in a manifold of low dimension. This is not possible when the frequency distribution, continued in the complex plane, has an essential singularity at infinity; this is the case, for example, of a Gaussian distribution. In this work, we propose a simple approximation scheme that allows one to extend also to this case the representation of the dynamics of the order parameter in a low dimensional manifold. Using the Gaussian frequency distribution as a working example, we compare the dynamical evolution of the order parameter of the system of oscillators, obtained by the numerical integration of the N equations of motion, with the analogous dynamics in the low dimensional manifold obtained with the application of the approximation scheme. The results confirm the validity of the approximation. The method could be employed for general frequency distributions, allowing the determination of the corresponding phase diagram of the oscillator system.

Synchronization in the Kuramoto model in presence of stochastic resetting.

What happens when the paradigmatic Kuramoto model involving interacting oscillators of distributed natural frequencies and showing spontaneous collective synchronization in the stationary state is subject to random and repeated interruptions of its dynamics with a reset to the initial condition? While resetting to a synchronized state, it may happen between two successive resets that the system desynchronizes, which depends on the duration of the random time interval between the two resets. Here, we unveil how such a protocol of stochastic resetting dramatically modifies the phase diagram of the bare model, allowing, in particular, for the emergence of a synchronized phase even in parameter regimes for which the bare model does not support such a phase. Our results are based on an exact analysis invoking the celebrated Ott-Antonsen ansatz for the case of the Lorentzian distribution of natural frequencies and numerical results for Gaussian frequency distribution. Our work provides a simple protocol to induce global synchrony in the system through stochastic resetting.