(SoE) School of Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multi-channel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs. © 2001-2012 IEEE.

World University root node

Electrical Engineering

IEEE Sensors Journal

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

Mechanical Engineering

Lecture Notes in Mechanical Engineering

Performance Analysis of PCM-Integrated Greenhouse Dryer

Multistable laminates have attracted the attention of researchers for developing morphing structures, as they can stay in different stable configurations without any need for external forces to maintain any of those configurations. In this study, a novel application of a reconfigurable antenna made with rectangular composite laminates [90/0] in the shape of a star is analyzed using the Variational Asymptotic Method (VAM), which is free of ad-hoc assumptions and highly accurate, unlike semi-analytical theories present in the literature. The star shaped laminate is modeled as a plate using geometrically exact kinematics, and thermal analysis is done by applying uniform temperature to obtain its cool-down shape, which shows multistability. Snap-through forces are applied on the structure to change to the other stable configurations. Three parametric studies are performed to identify the variation of snap-through force. These studies are carried out by varying the aspect ratio, curing temperature, and thickness of the individual rectangular laminates to identify the lower and upper limits beyond which the laminate loses its multistability. All studies performed using VAM are validated against 3D FEA in ABAQUS are found to be in good agreement with each other although, VAM handled the 3D problem at the 2D level achieved by systematic dimensional reduction. The computational efficiency is found to increase by almost 70% compared to 3D FEA. © 2022 Taylor & Francis Group, LLC.

Mechanics Based Design of Structures and Machines

Snap-through analysis of multistable laminate using the variational asymptotic method

For a productive and healthy life, air quality plays an important role. This paper addresses the requirements to develop a system capable of providing real-time information, predictions, and alerts about the indoor environment using context-awareness. The proposed IoT system serves for data collection, pre-processing, defining rules, and forecasting the predicting states of the indoor environment by giving information to the end-user about the alerts and recommendations. A novel approach based on the indoor pollutants T, RH, CO2, PM 2.5, PM 10, and CO for the determination of the status of the environment is proposed. The pre-processing is used for filtering data using and extended Kalman filter. Further, the system uses an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters. The ANFIS model predictor considers the value of indoor pollutants to form a new index: State of indoor air (SIA). For analysis and forecasting of the new index SIA, the DTMC model is used. The collected and measured data is stored in the IoT cloud using the sensing setup, and sensed information is used to develop the SIA transfer matrix, generating return durations corresponding to each SIA and providing alerts based on the data to the end-user. The models are assessed using the expected and actual return durations. The most frequent interior ventilation states, according to the predictions, are poor and moderate. Only 0.08 percent of the time does the IAQ remain in a good state. Two-thirds of the time (66%), the indoor ventilation is severe (poor, very poor, or hazardous); 19% of the time it is very bad, and 15% of the time it is hazardous, suggesting and warning that there is a very high probability of unhealthy AQI in educational institutions in the Delhi-NCR region. Performance is measured by the comparison between actual and forecasted return periods, and the forecast error for our system is low, with an absolute forecast error of 3.47% on an average. © 2022 The Author(s)

Computer Science and Engineering

Intelligent Systems with Applications

Context-aware IoT-enabled framework to analyse and predict indoor air quality

Mental task categorization using single/limited channel(s) electroencephalogram (EEG) signals is crucial for designing portable brain–computer interface and neurofeedback systems. However, EEG signals are corrupted with ocular artifacts and muscle artifacts, which degrade the robustness of existing features resulting inaccurate categorization of mental tasks. Therefore, in this paper, we propose a real-time mental task categorization method using stationary wavelet transform (SWT)-based novel discriminant features and different classifiers. It consists of four major stages: EEG signal decomposition and reconstruction using SWT into subbands; computation of proposed relative subband energy features (i.e., total energy of all subbands(Et), relative energy of each subband k (Er(k)) and sum of relative energy pair ratio and difference); significant feature selection; classification using seven classifiers. The robustness of the proposed method is evaluated using two publicly available datasets and in-house dataset recorded using single-channel EEG headset. Performance evaluation results show that the proposed method with support vector machine classifier achieves the highest average subject-dependent accuracy of 98% for classification of mental/mental and baseline/mental tasks. Extensive comparative performance analysis depicts the superiority of the proposed method as compared to existing techniques in terms of accurate categorization of mental tasks, computational complexity, EEG processing length and robustness under artifact-contaminated EEG signals. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Circuits, Systems, and Signal Processing

Discriminatory Features Based on Wavelet Energy for Effective Analysis of Electroencephalogram During Mental Tasks

In today’s world, where we as humans are constantly aiming towards improving machines and giving them a brain of their own, it is about time that our traffic trends and parking systems become as efficient and automated too. The aim of this paper is to acknowledge the increasing traffic, unorganized parking, and the time wasted while searching for a parking spot in an urban high vehicle density area and make an efficient parking system to avoid the same. Our approach includes the live navigation through our android app, which solves the problem of searching for parking lots in the area. Secondly, an android application is used to show the slots available/occupied in a parking lot. Thirdly, nodes at the parking slot help in making the parking organized. Lastly, an OTP-driven convenient payment method is made for fast functioning of parking exit systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Smart Parking Management System in Smart City

Waste management has become one of the most important issues in our country. From densely populated cities to smaller rural communities, waste management systems keep our homes and communities free from unwanted clutter. The key to efficient waste management is to ensure useful segregation and disposal. The main problem in our country is the delay in the collection and disposal of waste. This is mainly due to the human intervention involved in the process of waste management. Smart Waste Management System deals with everyday problems of waste disposal by reducing this human intervention. In this paper we have implemented an efficient waste management system for the segregation and timely disposal of waste. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Lecture Notes in Networks and Systems

Smart Waste Management System in Smart City

The availability of high-resolution digital elevation models (DEM) and advancements in geospatial techniques enable a more accurate measure of slope position classes and classification of landforms using algorithms like topographic position index (TPI). In this study, TPI-based slope position classes are used for the identification of major relief features like ridges, and these are embedded with DEM-derived terrain slope to perform the topographic amplification-based classification of the North Eastern Region (NER) of India, according to the present European (Eurocode-8) and Italian seismic codes using GIS tools. They are classified into four topographic classes: T1, T2, T3, and T4. The relation of geological units, seismic site classification map, and land use land cover (LULC) classes with identified topographic classes for the study region are analyzed. The results reveal that ~ 25% of the study area is a potentially high amplification zone (T4 class; amplification factor of 1.4 as per Eurocode-8), primarily associated with dense and light vegetation type land cover. The Indian seismic recording station sites and the epicenters of past earthquake events in the region are classified based on the obtained topographic classification map. Approximately, 47% of earthquake events of large magnitudes and ~ 30% of strong motion network (SMN) station sites are in localities with potential topographic effects. The human settlements are predominantly found in places identified with no or minimal amplification zone (T1 class; amplification factor of 1). The presented topographic classification map of NER can assist the decision-makers and authorities to identify potentially high amplification areas considering seismic hazards and making choices for future infrastructural development. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Civil Engineering

Environmental Earth Sciences

Topographic classification of North Eastern Region of India using geospatial technique and following seismic code provisions

The present study focuses on investigating the bifurcation characteristics of a pitch–plunge aeroelastic system possessing coupled non-smooth nonlinearities, both in structural and aerodynamic fronts. To this end, a freeplay nonlinearity is considered in the stiffness of the pitch degree-of-freedom. The effects of dynamic stall arising due to large instantaneous angles-of-attackare incorporated using the semi-empirical Leishman–Beddoes aerodynamic model. A systematic response analysis is carried out to discern the bifurcation characteristics of the aeroelastic system considering the airspeed as the system parameter. At low airspeeds, a series of dynamical transitions, including aperiodic responses, occur predominantly due to the structural freeplay nonlinearity while the flow remains attached to the surface of the wing. However, beyond a critical value of airspeed, the system response is dominated by high amplitude pitch-dominated limit-cycle oscillations, which can be attributed to stall flutter. It is demonstrated that the freeplay gap plays a key role in combining the effects of structural and aerodynamic nonlinearities. At higher values of the freeplay gap, interesting discontinuity-induced bifurcation scenarios, such as grazing and boundary equilibrium bifurcations arise due to coupled nonlinear interactions, which can significantly impact the safety of the aeroelastic system. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Fulltext

Nonlinear Dynamics

Numerical investigation into discontinuity-induced bifurcations in an aeroelastic system with coupled non-smooth nonlinearities

In recent development, the High Electron Mobility Transistor (HEMT) technology was used for biosensor applications. In this contribution, an analytical model of MgZnO/ZnO MOSHEMT (Metal Oxide Semiconductor HEMT) was reported. For the first time, a nanogap cavity-based biosensor aimed at detecting various biomolecules, such as uricase, streptavidin, ChOx, and proteins, was designed. The developed analytical model was used to calculate the two-dimensional electron gas (2DEG) density, device capacitance, drain current (ID), output conductance (gd), and transconductance (gm) by using the dielectric modulation approach. MOSHEMTs used dielectric modulation, with the nanogap cavity considered under the gate region. The effective capacitance in the cavity region was obtained by using dielectric modulation. Hence, the nanogap cavity acts as a sensing area for the immobilisation of target biomolecules. The sensitivity of the device was analysed by observing the strong changes in drain current with the addition of different biomolecules in the nanogap cavity region. The proposed MgZnO/ZnO MOSHEMT biosensor exhibits a quick response to biomolecule changes since its drain-on-sensitivity (SIon) is comparatively higher than that of other semiconductor-based biosensors. The obtained analytical model results were verified by a comparison with other works available in the literature, and they showed good agreement with our results. © 2021 Elsevier Ltd