Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Surrogate modeling based cognitive decision engine for optimization of WLAN performance(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Plets, D.; Chemmangat, K.; Deschrijver, D.; Mehari, M.; Ulaganathan, S.; Pakparvar, M.; Dhaene, T.; Hoebeke, J.; Moerman, I.; Tanghe, E.Due to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes a novel experimentally verified surrogate model based cognitive decision engine which aims at performance optimization of IEEE 802.11 links. The surrogate model takes the current state and configuration of the network as input and makes a prediction of the QoS parameter that would assist the decision engine to steer the network towards the optimal configuration. The decision engine was applied in two realistic interference scenarios where in both cases, utilization of the cognitive decision engine significantly outperformed the case where the decision engine was not deployed. © 2016, Springer Science+Business Media New York.Item A novel pre-processing procedure for enhanced feature extraction and characterization of electromyogram signals(Elsevier Ltd, 2018) Powar, O.S.; Chemmangat, K.; Figarado, S.In the analysis of electromyogram signals, the challenge lies in the suppression of noise associated with the measurement and signal conditioning. The main aim of this paper is to present a novel pre-processing step, namely Minimum Entropy Deconvolution Adjusted (MEDA), to enhance the signal for feature extraction resulting in better characterization of different upper limb motions. MEDA method is based on finding the set of filter coefficients that recover the output signal with maximum value of kurtosis while minimizing the low kurtosis noise components. The proposed method has been validated on surface electromyogram dataset collected from seven subjects performing eight classes of hand movements (wrist flexion, wrist radial deviation, hand close, tripod, wrist extension, wrist ulnar deviation, cylindrical and key grip) with only two pairs of electrodes recorded from flexor carpi radialis and extensor carpi radialis on the forearm. The performance of the MEDA has been compared across four classifiers namely J-48, k-nearest neighbours (KNN), Naives Bayes and Linear Discriminant Analysis (LDA) attaining the classification accuracy of 85.3 ± 4%, 85.67 ± 5%, 76 ± 6% and 91.2 ± 2% respectively. Practical results demonstrate the feasibility of the approach with mean percentage increase in classification accuracy of 20.5%, without significant increase in computational time across seven subjects demonstrating the significance of MEDA in classification. © 2018 Elsevier LtdItem Dynamic time warping for reducing the effect of force variation on myoelectric control of hand prostheses(Elsevier Ltd, 2019) Powar, O.S.; Chemmangat, K.Research in pattern recognition (PR) for myoelectric control of the upper limb prostheses has been extensive. However, there has been limited attention to the factors that influence the clinical translation of this technology. A relevant factor of influence in clinical performance of EMG PR-based control of prostheses is the variation in muscle activation level, which modifies the EMG patterns even when the amputee attempts the same movement. To decrease the effect of muscle activation level variations on EMG PR, this work proposes to use dynamic time warping (DTW) and is validated on two databases. The first database, which has data from ten intact-limbed subjects, was used to test the baseline performance of DTW, resulting in an average classification accuracy of more than 90%. The second database comprised data from nine upper limb amputees recorded at three levels of force for six hand grips. The results showed that DTW trained at a single force level achieved an average classification accuracy of 60 ± 9%, 70 ± 8%, and 60 ± 7% at the low, medium and high force levels respectively across all amputee subjects. The proposed scheme with DTW achieved a significant 10% improvement in classification accuracy when trained at a low force level when compared to the traditional time-dependent power spectrum descriptors (TD-PSD) method. © 2019 Elsevier LtdItem Reducing the effect of wrist variation on pattern recognition of Myoelectric Hand Prostheses Control through Dynamic Time Warping(Elsevier Ltd, 2020) Powar, O.S.; Chemmangat, K.For upper limb prostheses, research carried out earlier mainly focused on increasing the classification accuracy of the hand movements; but there exist a little work done on factors affecting it in real-time control such as wrist variation. Amputees with functional wrist use their prostheses in multiple wrist positions. Since the Electromyography (EMG) data is taken while the subject is performing the motion in different wrist position, it can degrade the performance of the Pattern Recognition (PR) system. In this work, a wrist independent PR scheme has been developed. In this regard, Dynamic Time Warping (DTW) is used to overcome the effects due to wrist variation. The performance of the DTW scheme as a PR system is validated using two training methods; with classification accuracy as a performance measure on data taken from the database of ten intact subjects for six hand motions carried out at three different wrist orientations. On the database, an average classification accuracy of about 93.3% was obtained while trained using EMG data from all possible wrist positions. The effectiveness of the method is demonstrated in terms of classification accuracy and processing time when compared with the Time-domain power spectral descriptors (TD-PSD) method which outperformed other methods in the literature for reducing the impact of wrist variation on EMG based PR. The results show that the DTW can be a computationally cheap and accurate PR system for real-time hand movement classification. © 2019 Elsevier LtdItem Design and performance comparison of interconnection and damping assignment passivity-based control for vibration suppression in active suspension systems(SAGE Publications Inc., 2021) Sistla, P.; Figarado, S.; Chemmangat, K.; Manjarekar, N.S.; Kallu Valappil, G.This study presents the design of interconnection and damping assignment passivity-based control for active suspension systems. It is well known that interconnection and damping assignment passivity-based control’s design methodology is based on the physical properties of the system where the kinetic and potential energy profiles are shaped, and asymptotic stability is achieved by damping injection. Based on the choice of control variables, special cases of the control law are derived, and tuning of the control law with the physical meaning of the variables is demonstrated along with their simulation results. The proposed control law is experimentally validated on a scaled model of a quarter-car active suspension system with different road profiles, varying load conditions, and noise and delay in the sensor measurements and actuator respectively. The results are compared with that of an uncontrolled system with linear quadratic regulator and sliding mode control. © The Author(s) 2020.Item Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition(Elsevier Ltd, 2022) Chandrasekar, A.; Shekar, D.D.; Hiremath, A.C.; Chemmangat, K.The electrocardiogram is a widely used measurement for individual heart conditions, and much effort has been put into automatic arrhythmia diagnosis using machine learning. However, the classification performance is hampered by the use of less representative data in conjunction with traditional machine learning models. This paper proposes a novel algorithm for pre-processing raw Electrocardiogram signals via Gaussian Assisted Signal Smoothing. In this method, the ECG signal is modeled as a low pass component and a weighted sum of Gaussians. The Gaussians are used to model the peak characteristics of the signal, effectively preserving its structure and morphology while eliminating the noise, which is evident by the enhanced peak signal-to-noise ratio of the GASS signal. The R peaks obtained from the Pan Tompkins algorithm are used to extract the heartbeats from the filtered signal using a windowing technique. A cascaded combination of a Convolutional Neural Network and a Quadratic Support Vector Machine is then used to classify the heartbeats. The CNN model has 131,661 parameters, making it much lighter than previously reported works. The MIT-BIH Arrhythmia Database was used for our experiments. Across eleven classes, our results reveal that the model has an accuracy of 97.63% and an average F1 score of 0.9263. In contrast, previous works have primarily focused on a one vs. all or a five-class classification. From a signal processing standpoint, the proposed method offers a promising solution for Signal Filtering and Arrhythmia Classification. © 2021 Elsevier LtdItem Application of Fractal Analysis based Feature Extractor for Channel Reduction of Silent Speech Interface Using Facial Electromyography(Intelligent Network and Systems Society, 2023) Abdullah, A.; Powar, O.S.; Chemmangat, K.Surface electromyography (sEMG) based silent speech interface (SSI) is an actively investigated topic among the broad area of human computer interaction studies which is currently dominated by acoustic sound based speech recognition research. This research is an attempt to help people who have an impaired vocal system if they are having no issues with their facial muscle functions. The basic idea is to reduce the total number of sEMG electrodes that has to be affixed on the face thereby reducing the invasiveness of the silent speech recognition module. This is achieved by incorporating a new detrended fluctuation analysis (DFA) based feature along with the already existing features associated with electromyographic signals. DFA is used for the first time in literature in the area of surface electromyography based silent speech recognition. The main idea is to incorporate the DFA feature along with the state-of-the-art features to improve the performance of a sEMG based SSI model so that an efficient channel reduced model can be realised. Different channel combinations were tried to analyse the impact of each channel in word recognition accuracy and the optimal channel combination was identified. As a result of this research work, a reduced channel setup with 5 electrodes was proposed in place of the conventional 7 channel data acquisition setup. This was achieved while maintaining an accuracy of 83.88 % and 92.92 % using the decision tree (DT) model and K-nearest neighbours (KNN) model respectively © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.Item Design and implementation of passivity-based controller for active suspension system using port-Hamiltonian observer(SAGE Publications Ltd, 2023) Sistla, P.; Chemmangat, K.; Figarado, S.The objective of this study is to design and implement an observer for quarter-car active suspension system in Port-Hamiltonian form. A novel state observer is designed for active suspension system modelled in port-Hamiltonian form to estimate the states in presence of road disturbances. The observer is designed considering suspension deflection alone as the output, which is an easily measurable output. Performance of the proposed observer is evaluated experimentally with road disturbance input mimicking a sudden bump and a continuously varying road input, and proven to be effective in minimising the error dynamics in presence of bounded unmodelled disturbances. To prove the effectiveness of the state-estimator, an Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) designed using the desired physical properties of the closed-loop system is implemented using the observer states. Experimental results of the controller implemented using the designed state observer show good improvement in the ride comfort, ride stability and suspension stroke of the active suspension system, which proves the effectiveness of the proposed port-Hamiltonian observer in terms of minimising the error dynamics. © IMechE 2023.Item Automatic selection of IMFs to denoise the sEMG signals using EMD(Elsevier Ltd, 2023) Koppolu, P.K.; Chemmangat, K.Surface Electromyography (sEMG) signals are muscle activation signals, which has applications in muscle diagnosis, rehabilitation, prosthetics, and speech etc. However, they are known to be affected by noises such as Power Line Interference (PLI), motion artifacts etc. Currently, Empirical Mode Decomposition (EMD) and its modifications such as Ensemble EMD (EEMD), and Complementary EEMD (CEEMD) are used to decompose EMG into a series of Intrinsic Mode Functions (IMFs). The denoised EMG can be obtained from the selected IMFs. Statistical methods are used to select the signal dominant IMFs to reconstruct the denoised signal. In this work, a novel procedure is proposed to automatically separate noisy IMFs from the original sEMG signal. For this purpose, Permutation Entropy (PE) is employed in EEMD sifting process called Partly EEMD (PEEMD), to separate the noisy IMFs from the original sEMG signal according to the preset PE threshold. PEEMD decomposes the original signal into various modes according to a preset PE threshold and the denoised signal is reconstructed from resultant IMFs. The PEEMD denoising procedure is applied on the experimental sEMG data collected from eight subjects, that include six various upper limb movement classes. The proposed denoising procedure achieved an improved denoising performance in comparison with EMD, EEMD, and CEEMD. An alternate measure called Sample Entropy (SE) is also used in place of PE, for the automated sifting process as a comparison. Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), and Reconstruction Error (RE) parameters are used to evaluate the denoising performance. The results, averaged across eight subjects, demonstrate that the proposed denoising procedure outperforms the state-of-the-art EMD techniques in terms of these performance measures on the experimentally collected sEMG data samples. © 2023 Elsevier LtdItem A Search for Suitable Mother Wavelet in Discrete Wavelet Transform Based Analysis of Acoustic Emission Partial Discharge Signals(University of Kragujevac Faculty of Technical Sciences in Cacak, 2024) Vippala, S.R.; Punekar, G.S.; Chemmangat, K.; Tangella, B.Signal processing helps monitor the condition of power equipment. Partial discharge (PD) signals used in condition-based maintenance give crucial information in the diagnosis of degradation of insulation. The acoustic emission technique (AET) is one of the most widely used techniques in PD signal analysis due to its inherent advantages. Analyzing acoustic emission partial discharge (AEPD) signals in the wavelet-domain provides critical insights into the location and type of the sources of PD. Selection of the most suitable mother wavelet in applying discrete wavelet transform (DWT) on AEPD signals is important as it will directly impact the outcome. For this selection, 36 wavelets belonging to the Daubechies, Symlets, Coiflets, and Bi-orthogonal families are investigated. For this purpose, five experimentally collected AEPD test signals are used. The selection is based on the “accuracy of wavelet decomposition results” in this work, probably for the first time. One mother wavelet from each family is individually shortlisted for all three performances, namely (a) reconstruction, (b) denoising, and (c) compression, by computing and comparing their commonly used metrics. Further, based on percentage energy criteria, the most suitable mother wavelets are identified as coif3, coif4, and coif5, respectively, for the three performances. © 2024, University of Kragujevac Faculty of Technical Sciences in Cacak. All rights reserved.