Abhay Upadhyay - Academia.edu (original) (raw)

Papers by Abhay Upadhyay

Research paper thumbnail of A Review of Deep Reinforcement Learning for Traffic Signal Control

International Journal For Multidisciplinary Research, Jan 5, 2024

Research paper thumbnail of Automated Variational Nonlinear Chirp Mode Decomposition for Bearing Fault Diagnosis

IEEE Transactions on Industrial Informatics

Research paper thumbnail of Sleep Stage Classification Using DWT and Dispersion Entropy Applied on EEG Signals

Computer-aided Design and Diagnosis Methods for Biomedical Applications, 2021

Research paper thumbnail of New methods based on variational mode decomposition for speech signal analysis

Research paper thumbnail of Empirical wavelet transform based classification of surface electromyogram signals for hand movements

Research paper thumbnail of Automated diagnosis of muscle diseases from EMG signals using empirical mode decomposition based method

Biomedical Signal Processing and Control, 2022

Abstract Muscle activity decreases due to various conditions like age factors and muscle diseases... more Abstract Muscle activity decreases due to various conditions like age factors and muscle diseases namely, amyotrophic lateral sclerosis (ALS) and myopathy. Electromyogram (EMG) signals are regularly explored by specialists to analyze the irregularity of muscles. Manual investigation of EMG signals is a tedious task for medical practitioners. Therefore, this work proposes a new method for classifying the ALS, myopathy, and normal EMG signals. In the proposed method, the empirical mode decomposition (EMD) method is applied to decompose the EMG signals into intrinsic mode functions (IMFs). The suitable IMFs for feature selection are selected using the t-test based approach and used to compute the foot distances denoted as fp 1 and fp 2 by constructing the complex plane plot. The quadrilateral is drawn over a complex plot by considering fp 1 and fp 2 as a diagonal of it, followed by calculating the area (A) and circumference (CF) of the quadrilateral. These measures are utilized for separating the three classes of myopathy, ALS, and normal EMG signals. The proposed algorithm has been trained and validated using a feed forward neural network (FFNN), support vector machine (SVM), and decision tree. The algorithm, when tested with a FFNN, achieved the maximum classification accuracy, sensitivity, and specificity of 99.53%, 99.25% and 99.60%, respectively.

Research paper thumbnail of A Nonparametric Approach for Multicomponent AM–FM Signal Analysis

Circuits, Systems, and Signal Processing, 2020

Research paper thumbnail of Accurate tunable-Q wavelet transform based method for QRS complex detection

Computers & Electrical Engineering, 2019

Research paper thumbnail of Determination of instantaneous fundamental frequency of speech signals using variational mode decomposition

Computers & Electrical Engineering, 2017

Research paper thumbnail of Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals

Future Generation Computer Systems, 2019

Research paper thumbnail of Speech enhancement based on mEMD‐VMD method

Electronics Letters, 2017

Research paper thumbnail of Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Research paper thumbnail of Automatic sleep stages classification based on iterative filtering of electroencephalogram signals

Neural Computing and Applications, 2017

Research paper thumbnail of An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Applied Soft Computing, 2017

Graphical abstractDisplay Omitted HighlightsWe propose a new method for diagnosis of alcoholism u... more Graphical abstractDisplay Omitted HighlightsWe propose a new method for diagnosis of alcoholism using TQWT.New feature set based on correntropy derived from TQWT have been proposed.The effects of Q on classification performance have been evaluated.A novel Alcoholism Risk Index (ARI) is developed using 3 clinically significant features.Performance has been compared with existing methods. Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.

Research paper thumbnail of Detection of glottal closure instants from voiced speech signals using the Fourier-Bessel series expansion

2015 International Conference on Communications and Signal Processing (ICCSP), 2015

Glottal closure instants (GCIs) present in the voiced speech is the term used to define the insta... more Glottal closure instants (GCIs) present in the voiced speech is the term used to define the instants of significant excitation of the vocal tract system during the production of the speech. In this paper, a novel method for GCI detection via Fourier-Bessel (FB) series expansion of the voiced speech signal in low frequency range (LFR) is explored. GCIs in the proposed method are detected as the local minima of the fundamental frequency (F0) component obtained from the voiced speech signal in LFR. The proposed method has been studied for male and female speech signals from CMU-Arctic database. Experimental results are included in order to show the performance of the proposed method for GCI detection from speech signals.

Research paper thumbnail of Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition

Journal of the Franklin Institute, 2015

Research paper thumbnail of Integrating shape and Edge Histogram Descriptor with Stationary Wavelet Transform for Effective Content Based Image Retrieval

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], 2014

In this paper we propose a hybrid approach for Effective Content Based Image Retrieval based on t... more In this paper we propose a hybrid approach for Effective Content Based Image Retrieval based on texture and shape feature. Towards this, first Stationary Wavelet Transform (SWT) is applied on query image to extract horizontal, vertical and diagonal detail matrices. Stationary Wavelet Transform is used because of its translational invariant property. After this Edge Histogram Descriptor (EHD) is used to exploit the absolute location of edges in the image as well their global composition. To aid the retrieval process, five different shape measures have also been included. Finally Euclidean distance is used to retrieve the relevant results. Experimental results show that the combination of SWT and EHD techniques provides significant improvement over existing methods thereby increasing the retrieval efficiency.

Research paper thumbnail of Fusion of local and global features using Stationary Wavelet Transform for efficient Content Based Image Retrieval

2014 IEEE Students' Conference on Electrical, Electronics and Computer Science, 2014

In this paper we propose a hybrid approach for Content Based Image Retrieval that takes into acco... more In this paper we propose a hybrid approach for Content Based Image Retrieval that takes into account both global as well as local features of an image. Towards this, first Stationary Wavelet Transform is applied on query image to extract horizontal, vertical and diagonal detail matrices. Stationary Wavelet Transform is used because of its translational invariant property. After this global textural features are extracted using Gray level Co-occurrence Matrix for each of these sub-matrices. To aid the retrieval process, a local descriptor is also computed by splitting the image into sub-regions. Finally Euclidean distance is used to retrieve the relevant results. Experimental results show that the proposed approach provides significant improvement over existing methods.

Research paper thumbnail of A new method for determination of instantaneous pitch frequency from speech signals

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015

This paper presents a new method for instantaneous pitch frequency determination from speech sign... more This paper presents a new method for instantaneous pitch frequency determination from speech signals. The proposed method is based on the variational mode decomposition (VMD) and the Hilbert transform. The VMD is applied in iterative way with specific input parameters in order to determine the fundamental frequency component from the speech signals. The fundamental frequency component has been used for detection of voiced and non-voiced regions from speech signals. The instantaneous pitch frequency is computed using Hilbert transform of the fundamental frequency component corresponding to voiced regions of speech signals. The experimental results are shown on speech signals taken from Keele pitch extraction reference database. The experimental results obtained from the proposed method are compared with the other existing methods for determining pitch frequency from speech signals.

Research paper thumbnail of A Review of Deep Reinforcement Learning for Traffic Signal Control

International Journal For Multidisciplinary Research, Jan 5, 2024

Research paper thumbnail of Automated Variational Nonlinear Chirp Mode Decomposition for Bearing Fault Diagnosis

IEEE Transactions on Industrial Informatics

Research paper thumbnail of Sleep Stage Classification Using DWT and Dispersion Entropy Applied on EEG Signals

Computer-aided Design and Diagnosis Methods for Biomedical Applications, 2021

Research paper thumbnail of New methods based on variational mode decomposition for speech signal analysis

Research paper thumbnail of Empirical wavelet transform based classification of surface electromyogram signals for hand movements

Research paper thumbnail of Automated diagnosis of muscle diseases from EMG signals using empirical mode decomposition based method

Biomedical Signal Processing and Control, 2022

Abstract Muscle activity decreases due to various conditions like age factors and muscle diseases... more Abstract Muscle activity decreases due to various conditions like age factors and muscle diseases namely, amyotrophic lateral sclerosis (ALS) and myopathy. Electromyogram (EMG) signals are regularly explored by specialists to analyze the irregularity of muscles. Manual investigation of EMG signals is a tedious task for medical practitioners. Therefore, this work proposes a new method for classifying the ALS, myopathy, and normal EMG signals. In the proposed method, the empirical mode decomposition (EMD) method is applied to decompose the EMG signals into intrinsic mode functions (IMFs). The suitable IMFs for feature selection are selected using the t-test based approach and used to compute the foot distances denoted as fp 1 and fp 2 by constructing the complex plane plot. The quadrilateral is drawn over a complex plot by considering fp 1 and fp 2 as a diagonal of it, followed by calculating the area (A) and circumference (CF) of the quadrilateral. These measures are utilized for separating the three classes of myopathy, ALS, and normal EMG signals. The proposed algorithm has been trained and validated using a feed forward neural network (FFNN), support vector machine (SVM), and decision tree. The algorithm, when tested with a FFNN, achieved the maximum classification accuracy, sensitivity, and specificity of 99.53%, 99.25% and 99.60%, respectively.

Research paper thumbnail of A Nonparametric Approach for Multicomponent AM–FM Signal Analysis

Circuits, Systems, and Signal Processing, 2020

Research paper thumbnail of Accurate tunable-Q wavelet transform based method for QRS complex detection

Computers & Electrical Engineering, 2019

Research paper thumbnail of Determination of instantaneous fundamental frequency of speech signals using variational mode decomposition

Computers & Electrical Engineering, 2017

Research paper thumbnail of Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals

Future Generation Computer Systems, 2019

Research paper thumbnail of Speech enhancement based on mEMD‐VMD method

Electronics Letters, 2017

Research paper thumbnail of Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Research paper thumbnail of Automatic sleep stages classification based on iterative filtering of electroencephalogram signals

Neural Computing and Applications, 2017

Research paper thumbnail of An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Applied Soft Computing, 2017

Graphical abstractDisplay Omitted HighlightsWe propose a new method for diagnosis of alcoholism u... more Graphical abstractDisplay Omitted HighlightsWe propose a new method for diagnosis of alcoholism using TQWT.New feature set based on correntropy derived from TQWT have been proposed.The effects of Q on classification performance have been evaluated.A novel Alcoholism Risk Index (ARI) is developed using 3 clinically significant features.Performance has been compared with existing methods. Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.

Research paper thumbnail of Detection of glottal closure instants from voiced speech signals using the Fourier-Bessel series expansion

2015 International Conference on Communications and Signal Processing (ICCSP), 2015

Glottal closure instants (GCIs) present in the voiced speech is the term used to define the insta... more Glottal closure instants (GCIs) present in the voiced speech is the term used to define the instants of significant excitation of the vocal tract system during the production of the speech. In this paper, a novel method for GCI detection via Fourier-Bessel (FB) series expansion of the voiced speech signal in low frequency range (LFR) is explored. GCIs in the proposed method are detected as the local minima of the fundamental frequency (F0) component obtained from the voiced speech signal in LFR. The proposed method has been studied for male and female speech signals from CMU-Arctic database. Experimental results are included in order to show the performance of the proposed method for GCI detection from speech signals.

Research paper thumbnail of Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition

Journal of the Franklin Institute, 2015

Research paper thumbnail of Integrating shape and Edge Histogram Descriptor with Stationary Wavelet Transform for Effective Content Based Image Retrieval

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], 2014

In this paper we propose a hybrid approach for Effective Content Based Image Retrieval based on t... more In this paper we propose a hybrid approach for Effective Content Based Image Retrieval based on texture and shape feature. Towards this, first Stationary Wavelet Transform (SWT) is applied on query image to extract horizontal, vertical and diagonal detail matrices. Stationary Wavelet Transform is used because of its translational invariant property. After this Edge Histogram Descriptor (EHD) is used to exploit the absolute location of edges in the image as well their global composition. To aid the retrieval process, five different shape measures have also been included. Finally Euclidean distance is used to retrieve the relevant results. Experimental results show that the combination of SWT and EHD techniques provides significant improvement over existing methods thereby increasing the retrieval efficiency.

Research paper thumbnail of Fusion of local and global features using Stationary Wavelet Transform for efficient Content Based Image Retrieval

2014 IEEE Students' Conference on Electrical, Electronics and Computer Science, 2014

In this paper we propose a hybrid approach for Content Based Image Retrieval that takes into acco... more In this paper we propose a hybrid approach for Content Based Image Retrieval that takes into account both global as well as local features of an image. Towards this, first Stationary Wavelet Transform is applied on query image to extract horizontal, vertical and diagonal detail matrices. Stationary Wavelet Transform is used because of its translational invariant property. After this global textural features are extracted using Gray level Co-occurrence Matrix for each of these sub-matrices. To aid the retrieval process, a local descriptor is also computed by splitting the image into sub-regions. Finally Euclidean distance is used to retrieve the relevant results. Experimental results show that the proposed approach provides significant improvement over existing methods.

Research paper thumbnail of A new method for determination of instantaneous pitch frequency from speech signals

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015

This paper presents a new method for instantaneous pitch frequency determination from speech sign... more This paper presents a new method for instantaneous pitch frequency determination from speech signals. The proposed method is based on the variational mode decomposition (VMD) and the Hilbert transform. The VMD is applied in iterative way with specific input parameters in order to determine the fundamental frequency component from the speech signals. The fundamental frequency component has been used for detection of voiced and non-voiced regions from speech signals. The instantaneous pitch frequency is computed using Hilbert transform of the fundamental frequency component corresponding to voiced regions of speech signals. The experimental results are shown on speech signals taken from Keele pitch extraction reference database. The experimental results obtained from the proposed method are compared with the other existing methods for determining pitch frequency from speech signals.