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

School of Electronics Engineering

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

World University root node

Chennai Campus

Fulltext

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

<figure class="table"><table><tbody><tr><td><figure class="image"><img src="https://www.iaras.org/iaras/images/oalogo2.jpg" alt="oalogo2"></figure>&nbsp;&nbsp;</td></tr><tr><td>AUTHOR(S):&nbsp;Kavya, Arivarasi A., Anand Kumar R., Jegatha Nambi Krishnan&nbsp;</td></tr><tr><td>TITLE<a href="https://www.iaras.org/iaras/filedownloads/ijce/2016/004-0017.pdf">Design &amp; Material Characterization for 3D Printing Sensor</a></td></tr><tr><td><figure class="image"><img src="https://www.iaras.org/iaras/images/pdf.gif" alt="pdf"></figure><a href="https://www.iaras.org/iaras/filedownloads/ijce/2016/004-0017.pdf">&nbsp;PDF</a></td></tr><tr><td>KEYWORDScopper, potassium gold cyanide, resistance measurement, nano porous gold</td></tr><tr><td>ABSTRACT3D printing requires constant innovation in materials for wider utilization. Sensor size impacts fabrication in a larger aspect and for printing the sensors effectively. Sensor performance measures include size, resolution, aspect ratio and response time. Speed has a tradeoff for resolution. The use of nano materials to develop electrochemical sensors has attracted growing interest in recent times. Enhanced chemical, physical, mechanical and electrical properties of nano materials coupled with the right technology make the sensing devices more efficient for key applications. The materials for 3D printed nano porous gold electrochemical sensor are analyzed and characterized. The nano porous gold film is fabricated by immersing copper in and chemically reacting with potassium gold cyanide solution at around 700C. The crude resistance value of copper prior to the chemical reaction is 0.94O. The resistance of the nanoporous gold film_x000D_ is 0.15O, 2 minutes after immersion in the potassium gold cyanide solution; the resistance does not change significantly after 6 hours of immersion. The porosity of the nano porous gold film is observed in a microscope to be 0.24% after 2 minutes of immersion. The porosity does not change with time during the process. The characterization of results is provided after experimentation.&nbsp;https://www.iaras.org/iaras/journals/computer-science-communications/caijce/design-material-characterization-for-3d-printing-sensor</td></tr></tbody></table></figure>

International Journal of Circuits and Electronics

Design and Material Characterization for 3D Printing Sensor

Nanoporous gold film can detect the presence of heavy metal ions in water. Nanoporous gold film based micro sensor is fabricated by layering copper on silicon substrate with an intermediate titanium layer acting as an adhesive. This film is formed through electroless plating by spraying potassium gold cyanide on the copper. It is proposed to fabricate this sensor using 3D printing, with nano resolution thickness of layering structure. 3D printing can address fabrication and packaging challenges. A variety of packaging materials and techniques are available. Ceramic packaging, plastic packaging and metal packaging techniques are prominent. 3D printed ceramic packaging with hermetic sealing is proposed for water quality sensors. A design of packaging prototype is presented and discussed. Micro-stereolithography, digital light processing techniques are suitable processes to prototype sensor contents and packaging model, which is 3D printing of the resin based cavity model. 3D Modeling of the sensor contents, packaging structures are modelled using Autodesk Maya3D2014, COMSOL software designs. The total electric energy through 3D modelling in COMSOL Multiphysics software is 25 joules. Electric potential (V) is estimated to be 2.0x10-18 volts. Digital light processing technique is proposed for this 3D printing of the resin based cavity model, FDM process for 3D printing working electrode and optomec lens process for the other electrodes involving metal 3D printing.&nbsp;https://www.iaras.org/iaras/journals/computer-science-communications/caijce/modelling-and-3d-printing-of-packaging-for-water-quality-sensor

Modelling and 3D printing of Packaging for Water Quality Sensor

We utilized laser irradiation as a potential technique in tuning the electrical performance of NiOx/SiO2 thin film transistors (TFTs). By optimizing the laser fluence and the number of laser pulses, the TFT performance was evaluated in terms of mobility, threshold voltage, on/off current ratio and subthreshold swing, all of which were derived from the transfer and output characteristics. The 500 laser pulses-irradiated NiOx/SiO2 TFT exhibited an enhanced mobility of 3 cm2/V-s from a value of 1.25 cm2/V-s for as-deposited NiOx/SiO2 TFT, subthreshold swing of 0.65 V/decade, on/off current ratio of 6.5 × 104 and threshold voltage of −12.2 V. The concentration of defect gap states as a result of light absorption processes explains the enhanced performance of laser-irradiated NiOx. Additionally, laser irradiation results in complex thermal and photo thermal changes, thus resulting in an enhanced electrical performance of the p-type NiOx/SiO2 TFT structure.

Condensed Matter

<a href="https://www.mdpi.com/2410-3896/6/2/21">Tuning the Electrical Parameters of p-NiOx-Based Thin Film Transistors (TFTs) by Pulsed Laser Irradiation</a>

Social distancing measures are important to reduce Covid spread. In order to break the chain of spread, social distancing is strictly followed as a norm. This paper demonstrates a system which is useful in monitoring public places like ATMs, malls and hospitals for any social distancing violations. With the help of this proposed system, it would be conveniently possible to monitor individuals whether they are maintaining the social distancing in the area under surveillance and also to alert the individuals as and when there is any violations from the predefined limits. The proposed deep learning technology based system can be installed for coverage within a certain limited distance. The algorithm can be implemented on the live images of CCTV cameras to perform the task. The simulated model uses deep learning algorithms with OpenCV library to estimate distance between the people in the frame, and a YOLO model trained on COCO dataset to identify people in the frame. The system has to be configured according to the location it is being installed at.

10.1109/ICCIKE51210.2021.9410745

A Social Distance Monitoring System to ensure Social Distancing in Public Areas

Complex and Intelligent Systems

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

In this paper, for a narrow band filter function, a two-dimensional photonic crystal with a hexagonal-shaped resonator has been proposed. The filter design consists of a 2D hexagonal lattice with silica rods (dielectric) placed in the air (substrate) with a lattice constant of 0.65 μm and rod radius of 0.25 μm produced on a wafer of size 10 μm * 13 μm. The filter is made up of a bus waveguide, dropping waveguide and a photonic crystal ring resonator (PCRR) by introducing defects in the crystalline edifice. The optimization is carried in the structure over the entire C band using the finite difference time domain (FDTD) method. And the performance of the designed structure can be calculated with the parameters like transmission efficiency and Q-factor. For the proposed edifice the transmission efficiency and Q-factor are 87 % and 389 at resonance wavelength 1556 nm, 80 % and 782 at resonance wavelength 1564 nm. The optimized filter provides a narrowband transmission suitable for DWDM applications to design multiplexers and de-multiplexers that obtains higher capacities in optical fiber for current as well as future demands. © 2020 Elsevier GmbH

Optik

Photonic crystal based narrowband optical filter: A brief analysis

Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms. © 2020 ISA