Qualification: 
Ph.D, M.Tech, B-Tech
ea_gopalakrishnan@cb.amrita.edu

Dr. E. A. Gopalakrishnan currently serves as Assistant Professor (SG) at Amrita Center for Computational Engineering and Networking (CEN), Coimbatore Campus.

Gopalakrishnan finished his B-Tech in Mechanical Engineering from University of Calicut. He obtained his Masters from Amrita Vishwa Vidyapeetham in Engineering Design and started working in Amrita as an Assistant Professor. Later, he proceeded to IIT Madras to pursue his Ph. D under the guidance of Prof. R. I. Sujith in the Department of Aerospace Engineering.

His research interests include complex systems, combustion instabilities, nonlinear dynamics and stochastic systems. Currently he is working on developing precursors to impending catastrophic transitions in complex systems. Apart from his academic pursuits, he is interested in understanding Indian culture and Indian philosophy. His areas of interest in Indology are Indian philosophy, scientific achievements in ancient India and the relevance of Indian scriptures in the modern context.

Publications

Publication Type: Journal Article

Year of Publication Title

2020

Jyothish Lal G., Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Glottal Activity Detection from the Speech Signal Using Multifractal Analysis”, Circuits, Systems, and Signal Processing, vol. 39, no. 4, pp. 2118 - 2150, 2020.[Abstract]


This work proposes a novel method for the detection of glottal activity regions from the speech signal. Glottal activity detection refers to the problem of discriminating voiced and unvoiced segments of the speech signal. This is a fundamental step in the work flow of many speech processing applications. Much of the existing approaches for voiced/unvoiced detection are based on linear measures though the speech is produced from an underlying nonlinear process. The present work solves the problem from a nonlinear perspective, using the framework of multifractal analysis. The fractal property of the speech signal during the production of voiced and unvoiced sounds is sought to obtain the characterization of glottal activity. The characterization is done by computing the Hurst exponent from the evaluation of the scaling property of fluctuations present in the speech signal. Experimental analysis shows that Hurst exponent varies consistently with respect to the dynamics of glottal activity. The performance of the proposed method has been evaluated on the CMU-arctic, Keele and KED-Timit databases with simultaneous electroglottogram signals. Experimental results show that the average detection accuracy or error rate of the proposed method is comparable to the best performing algorithm on clean speech signals. Besides, evaluation of the robustness of the proposed method to noise degradation shows comparable results with other methods for signal-to-noise ratio greater than 10 dB and 20 dB, respectively, for white noise and babble noise.

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2019

V. R. Unni, Dr. E. A. Gopalakrishnan, Syamkumar, K. S., Sujith, R. I., Surovyatkina, E., and Kurths, J., “Interplay Between Random Fluctuations and Rate Dependent Phenomena at Slow Passage to Limit-cycle Oscillations in a Bistable Thermoacoustic System”, Chaos, vol. 29, no. 3, p. 031102, 2019.[Abstract]


We study the impact of noise on the rate dependent transitions in a noisy bistable oscillator using a thermoacoustic system as an example. As the parameter-the heater power-is increased in a quasi-steady manner, beyond a critical value, the thermoacoustic system undergoes a subcritical Hopf bifurcation and exhibits periodic oscillations. We observe that the transition to this oscillatory state is often delayed when the control parameter is varied as a function of time. However, the presence of inherent noise in the system introduces high variability in the characteristics of this critical transition. As a result, if the value of the system variable-the acoustic pressure-approaches the noise floor before the system crosses the unstable manifold, the effect of rate on the critical transition becomes irrelevant in determining the transition characteristics, and the system undergoes a noise-induced tipping to limit-cycle oscillations. The presence of noise-induced tipping makes it difficult to identify the stability regimes in such systems by using stability maps for the corresponding deterministic system.

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2018

H. M, Dr. E. A. Gopalakrishnan, Vijay Krishna Menon, and Dr. Soman K. P., “NSE Stock Market Prediction Using Deep-Learning Models”, Procedia Computer Science, vol. 132, pp. 1351 - 1362, 2018.[Abstract]


The neural network, one of the intelligent data mining technique that has been used by researchers in various areas for the past 10 years. Prediction and analysis of stock market data have got an important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). In this paper, we are using four types of deep learning architectures i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available. Here we are using day-wise closing price of two different stock markets, National Stock Exchange (NSE) of India and New York Stock Exchange (NYSE). The network was trained with the stock price of a single company from NSE and predicted for five different companies from both NSE and NYSE. It has been observed that CNN is outperforming the other models. The network was able to predict for NYSE even though it was trained with NSE data. This was possible because both the stock markets share some common inner dynamics. The results obtained were com- pared with ARIMA model and it has been observed that the neural networks are outperforming the existing linear model (ARIMA).

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2018

Jyothish Lal G., Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Epoch Estimation from Emotional Speech Signals Using Variational Mode Decomposition”, Circuits, Systems, and Signal Processing, vol. 37, pp. 3245–3274, 2018.[Abstract]


This paper presents a novel approach for the estimation of epochs from the emotional speech signal. Epochs are the locations of significant excitation in the vocal tract during the production of voiced sound by the vibration of vocal folds. The estimation of epoch locations is essential for deriving instantaneous pitch contours for accurate emotion analysis. Many well-known algorithms for epoch extraction are found to show degraded performance due to the varying nature of excitation characteristics in the emotional speech signal. The proposed approach exploits the effectiveness of a new adaptive time series decomposition technique called variational mode decomposition (VMD) for the estimation of epochs. The VMD algorithm is applied on the emotional speech signal for decomposition of the signal into various sub-signals. Analysis of these signals shows that the VMD algorithm captures the center frequency close to the fundamental frequency defined for each glottal cycle of emotional speech utterance through its modes. This center frequency characteristic of the corresponding mode signal helps in the accurate estimation of epoch locations from the emotional speech signal. The performance evaluation of the proposed method is carried out on six different emotions taken from the German emotional speech database with simultaneous electroglottographic signals. Experimental results on clean emotive speech signals show that the proposed method provides identification rate and accuracy comparable to that of the best performing algorithm. Besides, the proposed method provides better reliability in epoch estimation from emotive speech signals degraded by the presence of noise.

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2018

Jyothish Lal G., Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Accurate Estimation of Glottal Closure Instants and Glottal Opening Instants from Electroglottographic Signal Using Variational Mode Decomposition”, Circuits, Systems, and Signal Processing, vol. 37, pp. 810–830, 2018.[Abstract]


The objective of the proposed work is to accurately estimate the glottal closure instants (GCIs) and glottal opening instant (GOIs) from electroglottographic (EGG) signals. This work also addresses the issues with existing EGG-based GCI/GOI detection methods. GCIs are the instants at which excitation to the vocal tract is maximum and GOIs, on the other hand, have minimum excitation compared to GCIs. Both these instants occur instantaneously with a fundamental frequency defined for each glottal cycle in a given EGG signal. Accurate detection of these instants from the EGG signal is essential for the performance evaluation of GCIs and GOIs estimated from the speech signal directly. This work proposes a new method for accurate detection of GCIs and GOIs from the EGG signal using variational mode decomposition (VMD) algorithm. The EGG signal has been decomposed into sub-signals using the VMD algorithm. It is shown that VMD captures the center frequency close to the fundamental frequency of the EGG signal through one of its modes. This property of the corresponding mode helps to estimate GCIs and GOIs from the same. Besides, instantaneous pitch frequency is estimated from the obtained GCIs. The proposed method has been evaluated on the CMU-arctic database for GCI/GOI estimation and the Keele pitch extraction reference database for instantaneous pitch frequency estimation. The effectiveness of the proposed method is confirmed by comparison with state-of-the-art methods. Experimental results show that the proposed method has better accuracy and identification rate compared to state-of-the-art methods.

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2017

V. Godavarthi, Unni, V. R., Dr. E. A. Gopalakrishnan, and Sujith, R. I., “Recurrence networks to study dynamical transitions in a turbulent combustor”, Chaos, vol. 27, 2017.[Abstract]


Thermoacoustic instability and lean blowout are the major challenges faced when a gas turbine combustor is operated under fuel lean conditions. The dynamics of thermoacoustic system is the result of complex nonlinear interactions between the subsystems-turbulent reactive flow and the acoustic field of the combustor. In order to study the transitions between the dynamical regimes in such a complex system, the time series corresponding to one of the dynamic variables is transformed to an ɛ-recurrence network. The topology of the recurrence network resembles the structure of the attractor representing the dynamics of the system. The transitions in the thermoacoustic system are then captured as the variation in the topological characteristics of the network. We show the presence of power law degree distribution in the recurrence networks constructed from time series acquired during the occurrence of combustion noise and during the low amplitude aperiodic oscillations prior to lean blowout. We also show the absence of power law degree distribution in the recurrence networks constructed from time series acquired during the occurrence of thermoacoustic instability and during the occurrence of intermittency. We demonstrate that the measures derived from recurrence network can be used as tools to capture the transitions in the turbulent combustor and also as early warning measures for predicting impending thermoacoustic instability and blowout. © 2017 Author(s).

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2017

J. Tony, Subarna, S., Syamkumar, K. S., Sudha, G., Akshay, S., Dr. E. A. Gopalakrishnan, Surovyatkina, E., and Sujith, R. I., “Experimental investigation on preconditioned rate induced tipping in a thermoacoustic system”, Scientific Reports, vol. 7, 2017.[Abstract]


Many systems found in nature are susceptible to tipping, where they can shift from one stable dynamical state to another. This shift in dynamics can be unfavorable in systems found in various fields ranging from ecology to finance. Hence, it is important to identify the factors that can lead to tipping in a physical system. Tipping can mainly be brought about by a change in parameter or due to the influence of external fluctuations. Further, the rate at which the parameter is varied also determines the final state that the system attains. Here, we show preconditioned rate induced tipping in experiments and in a theoretical model of a thermoacoustic system. We provide a specific initial condition (preconditioning) and vary the parameter at a rate higher than a critical rate to observe tipping. We find that the critical rate is a function of the initial condition. Our study is highly relevant because the parameters that dictate the asymptotic behavior of many physical systems are temporally dynamic. © 2017 The Author(s).

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PDF iconexperimental -investigation-on-preconditioned-rate-induced-tipping-in-a-thermoacoustic-system.pdf

2016

Dr. E. A. Gopalakrishnan, Yogita, S., Tony, J., Dutta, P., and Sujith, R. I., “Early warning signals for critical transitions in a thermoacoustic system”, Scientific Reports Nature, In Press, 2016.[Abstract]


Dynamical systems can undergo critical transitions where the system suddenly shifts from one stable state to another at a critical threshold called the tipping point. The decrease in recovery rate to equilibrium (critical slowing down) as the system approaches the tipping point can be used to identify the proximity to a critical transition. Several measures have been adopted to provide early indications of critical transitions that happen in a variety of complex systems. In this study, we use early warning indicators to predict subcritical Hopf bifurcation occurring in a thermoacoustic system by analyzing the observables from experiments and from a theoretical model. We find that the early warning measures perform as robust indicators in the presence and absence of external noise. Thus, we illustrate the applicability of these indicators in an engineering system depicting critical transitions. More »»

2016

Dr. E. A. Gopalakrishnan, Tony, J., Sreelekha, E., and Sujith, R. I., “Stochastic Bifurcations In A Prototypical Thermoacoustic System”, Phys. Rev. E, vol. 94, no. 2, 2016.[Abstract]


We study the influence of noise in a prototypical thermoacoustic system, which represents a nonlinear self-excited bistable oscillator. We analyze the time series of unsteady pressure obtained from a horizontal Rijke tube and a mathematical model to identify the effect of noise. We report the occurrence of stochastic bifurcations in a thermoacoustic system by tracking the changes in the stationary amplitude distribution. We observe a complete suppression of a bistable zone in the presence of high intensity noise. We find that the complete suppression of the bistable zone corresponds to the nonexistence of phenomenological (P) bifurcations. This is a study in thermoacoustics to identify the parameter regimes pertinent to P bifurcation using the stationary amplitude distribution obtained by solving the Fokker-Planck equation. More »»

2015

J. Tony, Dr. E. A. Gopalakrishnan, Sreelekha, E., and Sujith, R. I., “Detecting Deterministic Nature Of Pressure Measurements From A Turbulent Combustor”, Physical Review E, vol. 92, no. 6, 2015.[Abstract]


Identifying nonlinear structures in a time series, acquired from real-world systems, is essential to characterize the dynamics of the system under study. A single time series alone might be available in most experimental situations. In addition to this, conventional techniques such as power spectral analysis might not be sufficient to characterize a time series if it is acquired from a complex system such as a thermoacoustic system. In this study, we analyze the unsteady pressure signal acquired from a turbulent combustor with bluff-body and swirler as flame holding devices. The fractal features in the unsteady pressure signal are identified using the singularity spectrum. Further, we employ surrogate methods, with translational error and permutation entropy as discriminating statistics, to test for determinism visible in the observed time series. In addition to this, permutation spectrum test could prove to be a robust technique to characterize the dynamical nature of the pressure time series acquired from experiments. Further, measures such as correlation dimension and correlation entropy are adopted to qualitatively detect noise contamination in the pressure measurements acquired during the state of combustion noise. These ensemble of measures is necessary to identify the features of a time series acquired from a system as complex as a turbulent combustor. Using these measures, we show that the pressure fluctuations during combustion noise has the features of a high-dimensional chaotic data contaminated with white and colored noise. More »»

2015

Dr. E. A. Gopalakrishnan and Sujith, R. I., “Effect of external noise on the hysteresis characteristics of a thermoacoustic system”, Journal of Fluid Mechanics, vol. 776, pp. 334-353, 2015.[Abstract]


We present the effect of noise on the hysteresis characteristics of a prototypical thermoacoustic system, a horizontal Rijke tube. As we increase the noise intensity, we find that the width of the hysteresis zone decreases. However, we find that the rate of decrease in hysteresis width is constant for all the mass flow rates considered in the present study. We also show that the subcritical transition observed in the absence of noise is no longer discernible once the intensity of noise is above a threshold value and the transition appears to be continuous. We compare our experimental observations with the results obtained from a numerical model perturbed with additive Gaussian white noise and we find a qualitative agreement between the experimental and the numerical results. More »»

2014

Dr. E. A. Gopalakrishnan and Sujith, R. I., “Influence of System Parameters on the Hysteresis Characteristics of a Horizontal Rijke Tube”, International Journal of Spray and Combustion Dynamics , vol. 6, pp. 293-316, 2014.[Abstract]


The influence of system parameters such as heater power, heater location and mass flow rate on the hysteresis characteristics of a horizontal Rijke tube is presented in this paper. It is observed that a hysteresis zone is present for all the mass flow rates considered in the present study. A power law relation is established between the non-dimensional hysteresis width and the Strouhal number, defined as the ratio between convective time scale and acoustic time scale. The transition to instability in a horizontal Rijke tube is found to be subcritical in all the experiments performed in this study. When heater location is chosen as the control parameter, period-2 oscillations are found for specific values of mass flow rate and heater power More »»

Publication Type: Conference Paper

Year of Publication Title

2019

N. A. Unnithan, Dr. E. A. Gopalakrishnan, Menon, V. Krishna, and Soman, K. P., “A Data-Driven Model Approach for DayWise Stock Prediction”, in Emerging Research in Electronics, Computer Science and Technology, Singapore, 2019, vol. 545, pp. 149-158.[Abstract]


Economy of a country is closely related to stock market. By analysing stock market performance, we can evaluate whether a country's economic growth is increasing or decreasing. Even though country's economic growth can be understood by predicting stock market, it is highly unpredictable. We used dynamic mode decomposition which is a spatio-temporal, equation-free, data-driven algorithm for stock market prediction Schmid (J Fluid Mech 656:5–28, [13]) by considering stock markets as a dynamical system. How the system evolves and prediction of future state is done using DMD by decomposing a spatio-temporal system to modes having predetermined temporal behaviour. We used this property of DMD to predict stock market behaviour. In Kuttichira et al. (2017 international conference on advances in computing, communications and informatics (ICACCI), IEEE, pp 55–60, [7]) DMD was used to predict Indian stock market for minutewise data. We used daywise data as our timescale. Time series data of stock price of companies listed in National Stock Exchange were used as data. Sampled daywise stock price of companies across sector was used to predict the stock price for next few days. Predicted prices were compared with original prices and mean absolute percentage error was used to calculate the deviation for every companies. We analysed the stock price prediction for both intra- and intersector companies. We used dynamic mode decomposition to predict the stock price using historical data. We also did fine tuning of sampling windows to find out the best parameters for our data set.

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2019

K. S. Suchithra and Dr. E. A. Gopalakrishnan, “Rate Dependent Transitions in Power Systems”, in Proceedings of the Conference on the Industrial and Commercial Use of Energy, ICUE, 2019, vol. 2018-October.[Abstract]


Bifurcations are the sudden qualitative transitions occurring in dynamical systems due to infinitesimal changes in the control parameters. These abrupt qualitative transitions are crucial in deciding the stable operating regime in the case of a power system. However, in the actual power system, the control parameters such as the electrical power demand, the inertia of the system, damping of the system etc. are found to vary with respect to time. In this paper, bifurcations in an electrical power system for the quasi-static and rate dependent variation of the control parameters are investigated. The canonical power system is represented by assuming a single machine connected to an infinite bus (SMIB). The mathematical modeling of the canonical system is carried out by a second order swing equation model of the generator. We observe a delay in the point of transition for rate-dependent variation of the control parameter, mechanical power, P m . We also investigate the influence of noise and sensitivity to initial conditions in rate-dependent variations of the control parameter. Our study is highly relevant as the stability regimes for the quasi-static and rate dependent variations of control parameters are different. © 2018 Asian Institute of Technology.

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2019

Sajith Variyar V. V., Dr. E. A. Gopalakrishnan, Sowmya V., and Dr. Soman K. P., “A complex network approach for plant growth analysis using images”, in Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, Melmaruvathur; India, 2019.[Abstract]


The process of plant growth monitoring and analysis changed its perspective from the way it was. The recent farming practices demand vision based sensors for monitoring and analyses of the plant growth characteristics from images acquired by satellites and Unmanned Aerial Vehicles (UAV's). The advanced plant phenotyping systems are equipped with digital cameras to report the plant growth on a daily basis. The time determined images from plant monitoring system require a better computational representation to understand and study the plant life cycle, plant to plant interaction and correlations between plants with-in the community. This paper presents a new and yet simple approach towards plant growth analysis and its correlations in community by applying the theory of complex network on visible images from a plant phenotyping system. The method is highly promising in the area of precision agriculture when we have large area to monitor. © 2019 IEEE.

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2017

R. Mohan, Dr. E. A. Gopalakrishnan, and K. P. Soman, “Classification of states of bi-stable oscillator using deep learning”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.[Abstract]


Study of critical transitions and early warning measures are of great importance for dealing with any complex system. Manually selected statistical features with handpicked parameters have been used in a wide variety of fields for this purpose. We envision the use of deep learning architectures like simple feed forward networks (FFN), convolutional neural networks (CNN) and long short-term memory networks (LSTM) to predict these critical transitions from raw time-series data obtained from complex systems with minimal human interference in parameter choosing. As a first step towards this goal, in this study we use the above mentioned deep learning architectures to classify the states of a modified Van der Pol oscillator. We observe that the deep learning architectures produce good classification results and show promise as a tool for detection of critical transitions from raw time-series data.

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2017

D. P. Kuttichira, Dr. E. A. Gopalakrishnan, Menon, V. K., and K. P. Soman, “Stock price prediction using dynamic mode decomposition”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.[Abstract]


Stock price prediction is a challenging problem as the market is quite unpredictable. We propose a method for price prediction using Dynamic Mode Decomposition assuming stock market as a dynamic system. DMD is an equation free, data-driven, spatio-temporal algorithm which decomposes a system to modes that have predetermined temporal behaviour associated with them. These modes help us determine how the system evolves and the future state of the system can be predicted. We have used these modes for the predictive assessment of the stock market. We worked with the time series data of the companies listed in National Stock Exchange. The granularity of time was minute. We have sampled a few companies across sectors listed in National Stock Exchange and used the minute-wise stock price to predict their price in next few minutes. The obtained price prediction results were compared with actual stock prices. We used Mean Absolute Percentage Error to calculate the deviation of predicted price from actual price for each company. Price prediction for each company was made in three different ways. In the first, we sampled companies belonging to the same sector to predict the future price. In the latter, we considered sampled companies from all sectors for prediction. In the first and second method, the sampling as well as the prediction window size were fixed. In the third method the sampling of companies was done from all sectors considered. The sampling window was kept fixed, but predictions were made until it crossed a threshold error. Prediction was found to be more accurate when samples were taken from all the sectors, than from a single sector. When sampling window alone was fixed; the predictions could be made for longer period for certain instances of sampling.

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2017

Dr. E. A. Gopalakrishnan, Kumar, A., Verma, M. K., and Sujith, R. I., “A first order phase transition model for Rijke oscillations”, in Accepted for presentation in 24th International Congress on Sound & Vibration, London, 2017.

2017

S. Selvin, Vinayakumar, R., Dr. E. A. Gopalakrishnan, Menon, V. K., and Dr. Soman K. P., “Stock Price Prediction using LSTM, RNN and CNN-sliding Window Model”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.

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2017

Rahul K Pathinarupothi, Vinaykumar R, Ekanath Srihari Rangan, Dr. E. A. Gopalakrishnan, and Dr. Soman K. P., “Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning”, in IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida, 2017, pp. 293-296.[Abstract]


Automated sleep apnea detection and severity identification has largely focused on multivariate sensor data in the past two decades. Clinically too, sleep apnea is identified using a combination of markers including blood oxygen saturation, respiration rate etc. More recently, scientists have begun to investigate the use of instantaneous heart rates for detection and severity measurement of sleep apnea. However, the best-known techniques that use heart rate and its derivatives have been able to achieve less than 85% accuracy in classifying minute-to-minute apnea data. In our research reported in this paper, we apply a deep learning technique called LSTM-RNN (long short-term memory recurrent neural network) for identification of sleep apnea and its severity based only on instantaneous heart rates. We have tested this model on multiple sleep apnea datasets and obtained perfect accuracy. Furthermore, we have also tested its robustness on an arrhythmia dataset (that is highly probable in mimicking sleep apnea heart rate variability) and found that the model is highly accurate in distinguishing between the two.

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2015

S. Jaensch, Merk, M., Dr. E. A. Gopalakrishnan, Bomberg, S., Emmert, T., Sujith, R. I., and Polifke, W., “Hybrid CFD/ low order modeling of thermoacoustic limit cycles”, in Sonderforschungsbereich/Transregio 40 – Summer Program Report 2015, 2015.[Abstract]


This paper proposes and compares two nonlinear time-domain models of self-excited thermoacoustic instabilities of laminar premixed flames. We resolve the flame and its immediate vicinity with a CFD simulation. Simultaneously, the acoustic field is modeled with a low-order model that is coupled to the CFD over the inlet boundary condition. The first model is based on a fully compressible CFD solver. Here, the low-order model describes the plenum of the combustor and is coupled via the characteristic wave amplitudes using the newly developed Characteristic Based State-Space Boundary Conditions. This reduces the computational costs and allows to change the plenum length of the combustor without changing the computational grid. The second model resolves the flame with an incompressible CFD solver. In order to include the thermoacoustic feedback this model is coupled on-line with an acoustic network model over the global heat release rate and an acoustic reference velocity according to the Rankine-Hugoniot equations. A bifurcation analyses using the plenum length as bifurcation parameter is conducted. Both models exhibit complex nonlinear oscillations. A comparison in terms of a root mean square (RMS), dominant frequency, power spectrum and phase portraits show that both models are in good agreement.

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2007

G. R. Sabareesh, Dr. E. A. Gopalakrishnan, Ajithkumar, R., and Gowda, B. H. L., “Interference effects on flow induced oscillations of rectangular cylinders”, in 12th International Conference on Wind Engineering and Industrial Aerodynamics, Cairns, Australia, 2007.

Publication Type: Conference Proceedings

Year of Publication Title

2016

Dr. E. A. Gopalakrishnan, Sharma, Y., John, T., Dutta, P. Sharathy, and Sujith, R. I., “Early warning measures for tipping points in a thermoacoustic system”, Conference on Nonlinear Systems & Dynamics, December 16-18, IISER Kolkata. 2016.

2016

S. Jaensch, Merk, M., Dr. E. A. Gopalakrishnan, Bomberga, S., Emmert, T., Sujith, R. I., and Polifke, W., “Hybrid CFD/ low-order modeling of nonlinear thermoacoustic oscillations”, 36th Combustion Symposium. 2016.[Abstract]


This paper proposes and compares two nonlinear time-domain models of self-excited thermoacoustic oscillations of laminar premixed flames. Both models are hybrid formulations, where the flame and its immediate vicinity are resolved with reactive flow simulation, while the acoustic field is modeled with a low-order model that is coupled to the reactive flow simulation. Firstly, a flame model based on the fully compressible Navier-Stokes equations is investigated. In this case the flame simulation is coupled to the low-order model via the characteristic wave amplitudes at the inlet boundary. Secondly, the flame is resolved with a low Mach number reactive flow simulation. In order to include two-way thermoacoustic feedback, this flame model is coupled with an acoustic network model via the global heat release rate and the fluctuation of the axial velocity at a reference position upstream of the flame. A bifurcation analysis using the plenum length as bifurcation parameter is conducted. Both models exhibit complex nonlinear oscillations and are in good agreement with each other. Therefore, we conclude that the coupling of a linear acoustic model and a nonlinear flame model via reference velocity and global heat release rate is sufficient to accurately capture thermoacoustic oscillations of the configuration investigated. This implies that the most important nonlinearities can be attributed to hydrodynamic effects and flame kinematics. Furthermore, the study corroborates that premixed flames respond predominantly to fluctuations of the upstream flow velocity. More »»

2015

J. Tony, Dr. E. A. Gopalakrishnan, Sreelekha, E., and Sujith, R., “Hurst exponent and translation error as discriminating measures to identify a chaotic nature of an experimental time series”, Bifurcations and Instabilities in Fluid Dynamics, July 15-17, 2015, Paris, France. 2015.[Abstract]


Identifying the existence of nonlinear structures in a time series acquired from real world systems, is necessary to distinguish chaos from correlated noise. Measures that detect temporal correlations in a time series might be insufficient to extract deterministic features from an experimental data that is contaminated with noise. Here, we employ surrogate methods to analyze experimental data obtained from an engineering system, a turbulent combustor, with Hurst exponent and translational error as discriminating measures. We conclude from the analysis that the noise level in the data could be sufficiently large to suppress the nonlinearities in the time series. Thus, the null hypothesis that the data is generated from a stochastic process cannot be rejected with sufficient confidence on a statistical basis. However, a high dimensional Mackey-Glass system also shows similar features in the presence of additive noise. Thus, we make a conjuncture that the experimental time series acquired during the stable operation in the turbulent combustor is generated from a high dimensional chaotic system contaminated with noise.

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2015

Dr. E. A. Gopalakrishnan, Tony, J., Sreelekha, E., and Sujith, R. I., “Hurst exponent and translation error as discriminating measures to identify the chaotic nature of an experimental time series”, Conference on Nonlinear Systems and Dynamics, Mar. 13-15, 2015, Mohali, India. 2015.[Abstract]


Identifying the existence of nonlinear structures in a time series acquired from real world systems, is necessary to distinguish chaos from correlated noise. Measures that detect temporal correlations in a time series might be insufficient to extract deterministic features from an experimental data that is contaminated with noise. Here, we employ surrogate methods to analyze experimental data obtained from an engineering system, a turbulent combustor, with Hurst exponent and translational error as discriminating measures. We conclude from the analysis that the noise level in the data could be sufficiently large to suppress the nonlinearities in the time series. Thus, the null hypothesis that the data is generated from a stochastic process cannot be rejected with sufficient confidence on a statistical basis. However, a high dimensional Mackey-Glass system also shows similar features in the presence of additive noise. Thus, we make a conjuncture that the experimental time series acquired during the stable operation in the turbulent combustor is generated from a high dimensional chaotic system contaminated with noise.

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2014

Dr. E. A. Gopalakrishnan and Sujith, R. I., “Influence of external noise on the nature of transition of a thermoacoustic system”, Dynamic Days Asia Pacific-08, July 21-24, 2014, Chennai, India. Chennai, India, 2014.

2014

Dr. E. A. Gopalakrishnan and Sujith, R. I., “Noise induced transition in a horizontal Rijke tube”, 10th European Fluid Mechanics Conference, Sep. 14-18, 2014, Copenhagen, Denmark. 2014.

2013

Dr. E. A. Gopalakrishnan and Sujith, R. I., “Influence of system parameters and external noise on hysteresis characteristics of a horizontal Rijke tube.”, n3l - Int’l Summer School and Workshop on Non-Normal and Nonlinear Effects in Aero- and Thermoacoustics, June 18-21, 2013, Munich, Germany. Munich, Germany, 2013.[Abstract]


The influence of system parameters such as heater power and heater location on the hysteresis characteristics of a horizontal Rijke tube is studied in this paper. It is observed that the hysteresis zone is present for all the mass flow rates considered in the present study. The nature of transition to instability in a horizontal Rijke tube is found to be subcritical, in the range that we tested. A decrease in instability amplitude along with a reduction in the width of the hysteresis zone is observed in the presence of external noise. Period-2 oscillations are found when heater location is chosen as the control parameter More »»