Electronics

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Many methods have been developed to derive respiration signals from electrocardiograms (ECGs). However, traditional methods have two main issues: (1) focusing on certain specific morphological characteristics and (2) not considering the nonlinear relationship between ECGs and respiration. In this paper, an improved ECG-derived respiration (EDR) based on empirical wavelet transform (EWT) and kernel principal component analysis (KPCA) is proposed. To tackle the first problem, EWT is introduced to decompose the ECG signal to extract the low-frequency part. To tackle the second issue, KPCA and preimaging are introduced to capture the nonlinear relationship between ECGs and respiration. The parameter selection of the radial basis function kernel in KPCA is also improved, ensuring accuracy and a reduction in computational cost. The correlation coefficient and amplitude square coherence coefficient are used as metrics to carry out quantitative and qualitative comparisons with three traditional EDR algorithms. The results show that the proposed method performs better than the traditional EDR algorithms in obtaining single-lead-EDR signals

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School of Electronics Engineering

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Computational Intelligence and Neuroscience

Improved ECG-Derived Respiration Using Empirical Wavelet Transform and Kernel Principal Component Analysis

In recent years, analysis of remote sensing imagery has become significant due to its good representation of data in region of interest (ROI) in the world. However, the noise-corrupted satellite image data may not be capable to recognize and analyze it accurately. It necessitates the usage of suitable image de-noising that aids in improving the clarity, sharpness of image for further processing like feature extraction and enhanced analysis of the image data. In this paper, an efficient hybrid bilateral-guided (HBG) filter has been proposed that can filter noise faithfully from the satellite digital image. The attractive feature of the HBG filter is its focus on preserving edges of the original image while removing noises, thereby supporting edge detection and other image processing in a better way. Weiner filtering, median filtering, bilateral filtering, guided filtering and HBG are implemented using MATLAB R2016a on Intel core i3 system. These filtering techniques are carried out on Gaussian noise, salt-and-pepper noise and speckle noise-corrupted satellite images. The performance measures used are mean square error (MSE) and peak signal-to-noise ratio (PSNR). PSNR of HBG is 83%, 76% and 81% more than that of conventional bilateral filter, and MSE of HBG is 49%, 50% and 49% lesser than that of conventional guided filter for 50% salt-and-pepper noise, speckle noise and Gaussian noise-corrupted image, respectively. It is also proved that PSNR of proposed HBG is 74, 71 and 69% more than that of existing hybrid median Wiener filter (HMW) and MSE of HBG is 85%, 95% and 84% lesser than that of hybrid median Wiener filter (HMW) for 50% Gaussian noise, salt-and-pepper noise and speckle noise-corrupted image, respectively. Thus, the simulation outcomes show that the HBG filtering is suitable for removal of all types of noise from satellite images

Lecture Notes in Electrical Engineering

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85118111870&amp;origin=resultslist">An Efficient Hybrid De-Noising Method for Remote Sensing Images</a>

This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems.The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications.The book is written as a reference that offers the latest technologies and research results to various industry problems.

Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems

Complex and Intelligent Systems

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Facial expression recognition (FER) requires better descriptors to represent the face patterns as the facial region changes due to the movement of the face muscles during an expression. In this paper, a method of concatenating spatial pyramid Zernike moments based shape features and Law's texture features is proposed to uniquely capture the macro and micro details of each facial expression. The proposed method employs multilayer perceptron and radial basis function feed forward artificial neural networks for recognizing the facial expressions. The suitability of the features in recognizing the expressions is explored across the datasets independent of the subjects or persons. The experiments conducted on JAFFE and KDEF datasets demonstrate that the concatenated feature vectors are capable of representing the facial expressions with better accuracy and least errors. The radial basis function based classifier delivers a performance with an average recognition accuracy of 95.86% and 88.87% on the JAFFE and KDEF datasets respectively for subject dependent FER. © 2013 IEEE.

IEEE Access

Shape and Texture Aware Facial Expression Recognition Using Spatial Pyramid Zernike Moments and Law's Textures Feature Set

A Remote Monitoring and Management Tool is a software that performs multiple operations like monitoring a group of computers and observing the behavior, installing software remotely, troubleshoot the computers from a single location. Nevertheless, remotely monitoring a network of more than 500 computers is a cumbersome task. This project aims at implementing a hassle-free solution, particularly for computer laboratories in a University by developing a GUI (Graphical User Interface) based tool with a Software-as-a-Service approach. This tool is built entirely out of Microsoft PowerShell (PoSH) right from scripting the functions of each feature to designing the tool’s GUI, while making use of many Networking Protocols and Standards wherever necessary. With PoSH being available as a cross-platform tool, this tool easily supports the implementation in both Windows and Linux operating systems. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

UD-RMM: A Remote Monitoring and Management Tool Using PowerShell Universal Dashboard for Universities

The applications of vehicles are gradually increasing day by day. Simultaneously, vehicle pollution is also drastically increased. Due to improper maintenance and monitoring, the performances of the vehicles have been decreased. The different types of system and methods are available to detect the vehicle performance and pollution control. This paper is presented and provides an idea about pollution and vehicle control based on Internet of Things (IoT). The sensors, communication network, micro controller and server have been used for vehicle performance and their pollution control. The different vehicle parameters are monitored through IoT based system. The pollution experimental trail is conducted and compared with and without internet of things. The experimental investigations and their comparison have been reported with and without IoT system. © 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research - 2019.

Automatic pollution sensing and control for vehicles using IoT technology

In recent years, most of the peoples are suffering from teeth issues. Increased cavities of the teeth are formed due to these issues. Cavities may lead to the fracturing of human teeth. Such partial dentures and affected teeth can cause patients to experience brutal problems. There is a risk to break teeth when having injuries which can lead to serious patient issues. Doctors tend to substitute original teeth with artificial teeth in order to reduce these issues. These artificial teeth are made up of chromium and zirconium materials in current innovations, which can cause high cost and time-taking processes for the manufacture of artificial teeth by using these types of materials. A new, revolutionary 3D printing technology is being used to prepare artificial teeth to reduce these factors. In this work, an attempt was made to create artificial teeth using Silica material via a 3D printing technique. The prototype model teeth model with silica material was manufactured and it's demonstrates to medical students and patients. Using modeling software (CATIA) and Analysis software (ANSYS) the necessary components were designed and the corresponding results were reported. Ó 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research - 2019. © 2021 Elsevier Ltd.

Materials Today: Proceedings

Analysis of silica based prosthetic human teeth using 3D printing machine for dental assistance

This paper presents a novel energy efficient logic called Charge Sharing Improved Pass Gate Adiabatic Logic (CSIPGL) operating using four phase power clock sources. The CSIPGL based circuit is capable of operating through a wider range of frequency from 100MHz to 1GHz. CSIPGL logic has been designed using UMC 90nm technology model files and are simulated using Cadence® Virtuoso EDA tools. Efficiency of CSIPGL circuit is validated by comparing it against CSSAL, SQAL, SyAL, adiabatic logic circuits based on single charge sharing transistor [14] and EE-SPFAL circuit designs. Power consumption of AND/NAND and XOR/XNOR sub modules used in the design of 4-bit Carry Lookahead Adder circuits (CLA) are compared. 4-bit CLA is taken as a benchmark circuit to validate the efficiency of the proposed CSCPAL circuit. © 2021 Institute of Physics Publishing. All rights reserved.

Journal of Physics: Conference Series

Design of energy efficient carry lookahead adder using novel CSIPGL adiabatic logic circuit

Research states that at least 2.2 billion people have a vision impairment or blindness all over the world. There are many reasons for blindness and few of them are leading causes such as cataract, macular degeneration due to age factor, glaucoma, diabetic retinopathy, corneal opacity, trachoma. In all of them, glaucoma is one of the main causes of blindness. Glaucoma is asymptomatic and non-reversible vision loss disease. This paper presents a method of early detection of glaucoma using deep Neural Network (NN) from the retinal images. In different retinal imaging modalities, fundus images are widely accepted. In deep learning, Convolution Neural Network (CNN) is used for feature extraction from the fundus image, and fully connected feed forward NN (FFNN) is used to find out the level of glaucoma. Typical image processing algorithms are used for feature extraction from fundus images and classified with FFNN. The accuracy is compared among different architectures. The TensorFlow software tool and python language are used for this research. © 2021 Institute of Physics Publishing. All rights reserved.