(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

This paper presents a novel approach to transform the implicit current-voltage (I-V) expression of a double-diode model (DDM) of a photovoltaic (PV) module into an explicit expression. This is achieved by using the Taylor Series expansion of the exponential term in the I-V expression of a DDM PV module. The derived explicit expression is validated against the implicit expression for three different PV modules under different environmental conditions (DEC). The maximum power point (MPP) is estimated using the simple and computationally efficient Bisection method, using the derived explicit expression. The proposed method is then compared with other MPP estimation methods under DEC. The results obtained from the comparison indicate better accuracy of the proposed method. To further validate the results, the proposed method is compared with experimental data collected from KC200GT PV module. © 2020 IEEE.

Electrical Engineering

2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy, PESGRE 2020

MPP Estimation of a DDM PV Module Using Explicit I-V Expression

Communication using mediums like video and audio are essential for a lot of professions. In this paper, interaction with real time audio transmission is looked upon using the tools provided by the domains of IoT and Machine Learning. The transport layer protocols - TCP and UDP are examined for audio transmission quality. Further, different Recurrent Neural Networks (RNN) models are examined for their efficiency in predicting music in case of loss of packets during transmission. These predictions fill in the gaps and help prevent any lag in a real time performance/jam where the loss of musical notes can be a hindrance. © 2020 IEEE.

Computer Science and Engineering

2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020

Networked Remote Music Collaboration using Machine Learning

This study focuses on numerically investigating the response dynamics of a pitch–plunge airfoil with structural nonlinearity under dynamic stall conditions. The aeroelastic responses are investigated for both deterministic and randomly time varying flow conditions. To that end, a pitch–plunge airfoil under dynamic stall condition is considered and the nonlinear aerodynamic loads are computed using a Leishman–Beddoes formulation. It is shown that the presence of structural nonlinearities can give rise to a variety of dynamical responses in the pre-flutter regime. Next, a response analysis under the presence of a randomly fluctuating wind is carried out. It is demonstrated that the route to flutter occurs via a regime of pre-flutter oscillations called intermittency. Finally, the manifestation of these stochastic responses is characterized by invoking stochastic bifurcation concepts. The route to flutter via intermittency is presented in terms of topological changes occurring in the joint-probability density function of the state variables. © 2019 Elsevier Ltd

Mechanical Engineering

Journal of Fluids and Structures

Response analysis of a pitch–plunge airfoil with structural and aerodynamic nonlinearities subjected to randomly fluctuating flows

Distributed generator (DG) size and site are an important aspect while deciding DG capacity introduction in distribution system. The paper proposes the methodology of successive optimization for placement of multiple DG units in primary distribution system. The placement is optimized for system loss minimization. The paper investigates the effect of multiple DG units placement of same type as well as the effect of a mix of DG types is also investigated. © Springer Nature Singapore Pte Ltd 2020.

World University root node

Lecture Notes in Electrical Engineering

Successive optimization using analytical method for multiple dg placement in primary distribution system

Extreme learning machine (ELM) is a single layer feedforward neural network algorithm used for classification problems due to its accuracy and speed. It provides a robust learning algorithm, free of local minima, suitable for high-speed computation along with fast learning speed. In this paper, ELM algorithm implementation on hardware and software is discussed. A low-cost hardware implementation of 16-bit H-matrix generation on FPGA is discussed in the paper. Hardware implementation is carried out on Nexys-4 board using MATLAB and hardware description language (HDL). Generation of H-matrix is carried out using two activation functions, piecewise log-sigmoid and piecewise tan-sigmoid. This paper aims at optimizing the hardware implementation of ELM algorithm by minimizing the utilized resources of the FPGA. Finally, the ELM algorithm accuracy and hardware utilization for both activation functions are compared. © Springer Nature Singapore Pte Ltd 2020.

Fpga implementation of random feature mapping in elm algorithm for binary classification

Continuous scaling of CMOS technology has led to reliability issues and process variability that affect the circuit performance of the SRAM cell. The dynamic behavior of SRAM cells are characterized by critical read-stability (Tread) and critical write-ability (Twrite) while the Static Noise Margins (SNMs) are deduced by the static metrics that are the key performance metrics. The work in this paper demonstrates the cumulative impact of process variability and Negative Bias Temperature Instability (NBTI) degradation on the dynamic metrics of the SRAM cell under varied temperature conditions. Degradation due to NBTI is incorporated by considering different activity factors (α) for the dynamic metrics. Time-zero or process variability is performed for fresh-case, symmetric and asymmetric degradation by Monte Carlo run simulations using foundry models in addition to examining the effect of correlation with their corresponding static metrics. © Springer Nature Singapore Pte Ltd 2020.

Analyzing the impact of nbti and process variability on dynamic sram metrics under temperature variations

Research in the realm of accident prediction and analysis is mostly confined to the study of road accidents caused by motor vehicle collisions where the event of vehicle fall from high altitude is mostly ignored. An IoT system has been introduced in this work, which uses a budget smartphone and off-the-shelf sensors to accurately detect and notify vehicle fall event. The proposed system is low cost, reliable and can be easily retrofitted to any type of vehicle. It uses the vehicle speed, absolute linear acceleration and altitude to build an SVM classifier based model to detect the fall event. The proposed IoT system is found to be accurate with a MAPE of 1.8\%. © Springer Nature Singapore Pte Ltd. 2020.

Communications in Computer and Information Science

Modeling Vehicle Fall Detection Event Using Internet of Things

Traffic collisions are synonymous with road accidents worldwide. Falling of vehicles from heights is a significant cause of injury, property damage and death every year. This work addresses the research gap in the study of occurrence, classification and reporting of the vehicle falls. It presents an IoT system, which can detect and classify the occurrence of a vehicle fall event with the help of inbuilt sensors of a contemporary smartphone. The proposed system uses vehicular speed, linear acceleration and altitude values of smartphone and connected sensors to build a k-Nearest Neighbor (k-NN) based fall occurrence and classification model to report the occurrence and severity of a vehicle fall-off. The performance of the introduced model is evaluated with the help of metrics such as precision, recall and F1 score. © 2020 IEEE.

Vehicle Fall Severity Modeling using IoT and K-Nearest Neighbor Algorithm

Determination of Indoor Air Quality (IAQ) requires measurement and analysis of all the indoor air parameters. Often, the contribution of some parameters is substantial while that of others is meagre. This work aims to develop an IoT system for monitoring the concentrations of indoor air pollutants. Grey Relational Analysis (GRA) is then used to evaluate all the measured indoor air pollutants and select the ones which influence IAQ the most. The selected parameters are then used to build IAQ forecast model using Artificial Neural Networks (ANN). The accuracy of the ANN based forecast model is measured using mean absolute percentage error (MAPE) and coefficient of determination. © 2020 IEEE.

An IoT-based System to Evaluate Indoor Air Pollutants Using Grey Relational Analysis

The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets. The CNN architectures, and recently datasets also, are categorized as deep, shallow, wide, etc. This paper tries to formalize these terms along with answering the following questions. (i) What is the impact of deeper/shallow architectures on the performance of the CNN w.r.t. FC layers?, (ii) How the deeper/wider datasets influence the performance of CNN w.r.t. FC layers?, and (iii) Which kind of architecture (deeper/shallower) is better suitable for which kind of (deeper/wider) datasets. To address these findings, we have performed experiments with four CNN architectures having different depths. The experiments are conducted by varying the number of FC layers. We used four widely used datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CRCHistoPhenotypes to justify our findings in the context of image classification problem. The source code of this work is available at https://github.com/shabbeersh/Impact-of-FC-layers. © 2019