Rishi Raj Sharma

Defence Institute of Advanced Technology | DIAT · Department of Electronics Engineering

As the role of drones progresses in all sectors of society, it may raise concerns in technical, security, and public safety aspects. While drones offer unprecedented capabilities for monitoring and data collection, they also pose inherent risks. Drones equipped with high-resolution cameras can intrude upon individuals’ privacy, capturing images and...

Surface electromyography (sEMG)-based hand gesture recognition (HGR) is crucial for pattern identification in today's society. Muscle fatigue presents a major challenge in sEMG signal analysis which affects the reliability of sEMG-based systems. This paper proposes a method for fatigue invariant HGR using sEMG signals. An onset-offset detection met...

The use of EEG for stress detection has driven the development of advanced algorithms to overcome the challenge of visually analyzing subtle changes in EEG patterns. This paper introduces an automated method for identifying induced stress from EEG signals using multivariate variational mode decomposition and machine learning (ML). Various time doma...

Coronary Artery Disease (CAD) is a key factor in several serious heart conditions such as ischemic heart disease, myocardial infarction, and heart failure. Detecting and treating CAD early is crucial for preventing further progression of the disease. Computer-aided techniques are needed to automate the characterization of CAD conditions. In this pa...

Surface electromyography (sEMG)-based automated grasp recognition (AGR) has emerged as a vital technology in the field of automatic control, human-machine interfaces, prosthetics, virtual reality, etc. Grasps recognition comes under the category of hand gesture recognition. However, due to its unique characteristics, traits and advanced application...

The amplitude envelope is a crucial parameter to analyse natural systems as it provides useful amplitude modulation (AM) based information. In many cases, power spectral entropy (PSE) of a non-stationary signal is not able to discriminate AM based information. This paper proposes amplitude envelope based spectral entropy (ASE) which quantifies AM r...

Glaucoma is an eye disease which has been one of the leading causes of loss of vision worldwide. It occurs when the fluid pressure in the optic nerve increases which causes the damage to the optic nerve. Detection of glaucoma is necessary for proper and timely treatment. In this paper, an auto-mated system is developed for glaucoma diagnosis in whi...

The time–frequency analysis is highly suited technique for non-stationary signal analysis which studies a signal in both time and frequency domains simultaneously. The combination of real time signals of two systems hold quadrature property and become complex in nature. In such cases, information is distinct in positive and negative frequency range...

A contact-free people walk identification has numerous applications in surveillance and suspicious activity detection to take the precautionary actions. This paper presents a millimeter-wave radar-based automated system for walk type identification in which the received complex radar signals are decomposed using flexible analytic wavelet transform....

One of the most crucial use of hands in daily life is grasping. Sometimes people with neuromuscular disorders become incapable of moving their hands. This paper proposes a grasp motor imagery identification approach based on multivariate fast iterative filtering (MFIF). The proposed methodology involves the selection of relevant electroencephalogra...

Surface electromyography (sEMG) is an important tool for pattern recognition in modern society. Electrode shift is a major challenge in sEMG based systems and affects the performance greatly. In this letter, a method is suggested for hand gesture recognition (HGR) using sEMG which is suitable for small angle electrode rotation scenario. Root- mean-...

Surface electromyography (sEMG)-based biometrics authentication has drawn a significant attention in recent years as a promising methodology. This technique takes advantage of the distinctive electrical activity patterns that muscles produce when performing various gestures. Individual differences can be observed during training for multiple users...

Seismicity offers valuable information on the internal activity of volcanoes and the interpretation of seismic signals is crucial for volcano monitoring. As the seismic signal is multi-component & non-stationary, the time-frequency analysis like Wigner-Ville distribution (WVD) has played a significant role, but the presence of cross-term limits the...

This paper demonstrate the combined approach of Blind Source Separation (BSS) and canonical correlation analysis (CCA) to detect the frequency component of Steady state Visual evoked Potential (SSVEP) based Brain computer Interface (BCI) system from non-invasive recorded electroencephalography (EEG) signal. Detection of SSVEP frequency component wi...

Hand gesture recognition (HGR) has a vital role to develop an intelligent interface for human-computer interaction. This letter proposed an automated system for HGR using three ultra-wideband radars. In this system, the received radar reflection is measured for each hand gesture action which is recorded with respect to time duration and distance of...

The Wigner-Ville distribution (WVD) is a widely used tool in the time-frequency analysis of non-stationary signals. However, the presence of false-terms in WVD for multicomponent signals can limit its applicability and interpretation. Various kernel and window-based smoothing methods have been used to remove false-terms from WVD, but they often com...

Epilepsy is a neurological disorder characterized by recurrent seizures which are caused by abnormal electrical activity in the brain. The electroencephalogram (EEG) is a commonly used method for detecting and analyzing seizures. Identifying subtle changes in the EEG waveform by visual inspection can be challenging. It has led to a significant doma...

Wigner-Ville distribution (WVD) has become one of the powerful tools in the time-frequency analysis of nonstationary signals. Still, the presence of cross-terms in WVD for multi-component signals limits its applicability and interpretation. Variational mode decomposition (VMD) has been applied to remove the cross-terms from WVD. However, it fails t...

With the expansion of machine learning and deep learning technology, facial expression recognition methods have become more accurate and precise. However, in a real-case scenario, the presence of facial attributes, weakly posed expressions and variation in viewpoint can significantly reduce the performance of those systems designed only for frontal...

A few characteristics of advanced commercial drones have attracted increasing attention in recent years. Because of their ability to carry payloads bypassing ground security, there is a greater possibility of small drones being exploited for illegitimate operations. The drone tracking and surveillance is critical to prevent security breaches like t...

The use of radar technology in the field of human activity recognition (HAR) has garnered considerable interest due to its notable benefits in terms of accuracy, resilience, and safeguarding of privacy. As the back-scattered radar returns are composed of multiple components & non-stationary in behavior, the time-frequency analysis like Wigner-Ville...

The variational non-linear chirp mode decomposition (VNCMD) requires initialization of number of modes (NMs) and instantaneous frequency (IF). This paper proposes an automated method for NM selection and IF initialization which works on the scale-space representation based automated boundary detection in magnitude spectrum (MS). The proposed automa...

Emotion is a significant parameter in daily life and is considered an important factor for human interactions. The human-machine interactions and their advanced stages like humanoid robots essentially require emotional investigation. This paper proposes a novel method for human emotion recognition using electroencephalogram (EEG) signals. We have c...

In recent years, automated seizure identification with electroencephalogram (EEG) signals has received considerable attention and appears to be an appropriate approach for diagnosis and treatment of the disease. This paper analyze the ability of Hjorth parameters for seizure detection using EEG signals. The tunable-Q wavelet transform (TQWT) is app...

The finger flexion movement prediction is a challenging problem of the Brain-computer interface. This chapter focuses on decoding the finger flexion movement using electrocorticogram (ECoG) signals. The variational mode decomposition (VMD) is applied to obtain the sub-components of each channel ECoG recording. Various correlation-based and other pa...

A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local bin...

In this chapter, the electroencephalogram (EEG) signals obtained from the polysomnography (PSG) recordings are analyzed using discrete (DWT) wavelet transform and dispersion entropy. The PSG recordings are taken from PhysioNet Sleep European Data Format (EDF) Database in this work. We investigate the performance of dispersion entropy (DEn) and one...

The Wigner-Ville distribution (WVD) is a signal processing approach to evaluate a high-resolution time-frequency representation (TFR) of a multicomponent signal. The WVD of a multi-component signal produces unwanted cross-terms in the TFR. The elimination of these cross-terms using various signal processing techniques is a challenging research prob...

The time-series forecasting makes a substantial contribution in timely decision making. In this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA) is applied to develop a forecasting model for non-stationary time series. The Phillips-Perron test (PPT) is us...

This paper presents an efficient methodol- ogy based on empirical wavelet transform (EWT) to remove cross-terms from the Wigner-Ville Distribution (WVD). An EWT based filter bank method is sug- gested to remove the cross-terms that occur due to nonlinearity in modulation. The mean-squared error based filter-bank bandwidth selection is done which is...

The Wigner–Ville distribution (WVD) gives a very high-resolution time–frequency distribution but diminishes due to the existence of cross-terms. The cross-terms suppression in WVD is crucial to get the actual energy distribution in time–frequency (TF) plane. This chapter proposes a method to remove both inter and intra cross-terms from TF distribut...

Time-frequency distributions (TFDs) are strong mechanisms for the analysis of non-stationary signal in time- frequency plane. This paper proposes a new method to over- come the problem of cross-terms in Wigner-Ville distribution (WVD) for linear signals. In this method Varitional Mode decomposition (VMD) is applied to obtain the narrow-band compone...

Electromyogram (EMG) signals are commonly used by doctors to diagnose abnormality of muscles. Manual analysis of EMG signals is a time-consuming and cumbersome task. Hence, this chapter aims to develop an automated method to detect abnormal EMG signals. First, authors have applied the improved eigenvalue decomposition of Hankel matrix and Hilbert t...

In this work, the time-frequency matrix based modified features are proposed. The proposed features are applied to detect the presence of coronary artery disease (CAD) using electrocardiogram (ECG) signals. These features are utilized to detect the presence of CAD using ECG signals. In the proposed work, ECG beats are subjected to the improved eige...

Coronary artery disease (CAD) is a condition where coronary arteries be-come narrow due to the deposition of plaque inside them. It may result to heart failure and heart attack which are life threatening conditions. Therefore, human life can be saved by detection of CAD at an early stage. Electrocardiogram (ECG) signals can be used to detect CAD. M...

The identification of neuromuscular abnormalities can be performed us-ing electromyogram (EMG) signals. In this paper, we have presented a method forthe analysis of amyotrophic lateral sclerosis (ALS) and normal EMG signals. Themotor unit action potentials (MUAPs) have been extracted from EMG signals. Theproposed method is based on improved eigenva...

The non-stationary characteristics present in electroencephalogram (EEG) signal requires a crucial analysis which can reveal a method for diagnosis of neurological abnormalities, especially epilepsy. This letter presents a new technique for automated classification of epileptic EEG signals based on iterative filtering (IF) of EEG signals. The super...

Congestive heart failure (CHF) is a cardiac abnormality in which heart is not able to pump sufficient blood to meet the requirement of all the parts of the body. This study aims to diagnose the CHF accurately using heart rate variability (HRV) signals. The HRV signals are non-stationary and nonlinear in nature. We have used eigenvalue decomposition...

The analysis of non-stationary signals using time-frequency representation (TFR) presents simultaneous information in time and frequency domain. Most of TFR methods are developed for real-valued signals. In several fields of science and technology, the study of unique information presented in the complex form of signals is required. Therefore, an e...

In this work, a novel data-driven methodology is proposed to reduce cross-terms in the Wigner-Ville distribution (WVD) using improved eigenvalue decomposition of the Hankel matrix (IEVDHM). The IEVDHM method decomposes a multi-component non-stationary (NS) signal into mono-component NS signals. After that, amplitude-based segmentation is applied to...

In this paper, a novel method is proposed for baseline wander (BW) and power line interference (PLI) removal from electrocardiogram (ECG) signals. The proposed methodology is based on the eigenvalue decomposition of the Hankel matrix. It has been observed that the endpoint eigenvalues of the Hankel matrix formed using noisy ECG signals are correlat...

Coronary artery disease (CAD) is a condition where coronary arteries become narrow due to the deposition of plaque inside them. It may result to heart failure and heart attack which are life threatening conditions. Therefore, human life can be saved by detection of CAD at an early stage. Electrocardiogram (ECG) signals can be used to detect CAD. Ma...

The identification of neuromuscular abnormalities can be performed using electromyogram (EMG) signals. In this paper, we have presented a method for the analysis of amyotrophic lateral sclerosis (ALS) and normal EMG signals. The motor unit action potentials (MUAPs) have been extracted from EMG signals. The proposed method is based on improved eigen...

Time-frequency representation (TFR) is useful for non-stationary signal analysis as it provides information about the time-varying frequency components. This paper proposes a novel TFR based on the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT). In the proposed method, first we decompose non-stationary signals...

Non-stationary signal analysis is an essential part for many engineering fields. Time-frequency analysis methods are commonly used methods for analysis of non-stationary signals. In this paper, a new domain for time-frequency analysis has been proposed which has been studied for the analysis of non-stationary signals. The proposed method combines t...

In Wireless Sensor Network, near by sink nodes face heavier traffic load because of many to one traffic pattern along with irregular data flow rate in multi-hop transmission scenario. Therefore energy depletion rate is very high at near by sink nodes that leads to creation of energy holes. Consequently, the life time of wireless sensor network is a...

High Efficiency Video Coding (HEVC/H.265) is the latest video coding standard proposed by the Joint Collaborative Team on Video Coding (JCT-VC). There are several parameters for HEVC/H.265 encoder that are used in video encoding. During video encoding single standard configuration file is used for all the videos that may not maintain the quality in...