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Mental Workload Estimation Using EEG
Mental workload contributes considerably to the outcome or the performance of any task. The concern of human workload increases during a human-machine collaboration task or in a multitasking environment. This paper presents a comparative study of machine learning algorithms used to estimate workload using Electroencephalography (EEG) data. An open-access EEG dataset acquired during a 'simultaneous capacity (SIMKAP) experiment' and 'no task' is used to create and validate models for binary classification of workload as present and absent respectively. The paper presents an implementation of various classification models that use EEG data to predict the workload. In this paper, implementation for KNN classifier (57.3%), Random Forest classifier (57.19%), MLP network classifier (58.2%), CNN+ LSTM network classifier (58.68%), and LSTM network classifier (61.08%) has been reported. The paper can be further extended to study operator workload in real-time using a brain-computer interface paradigm for any kind of task in a real-world application. The workload classification can be further used in human-machine tasks to decide task allocation between the system to achieve optimal performance in a complex critical system. 2020 IEEE. -
Rational suitability of low cost activated carbon in removing hexavalent chromium ions from wastewater by uninterrupted mode of adsorption
Heavy metals such as chromium, lead, arsenic and others are dense metals whose contamination of water may exterminate life on earth at the niche in industrial activities, foodstuffs or medicines and so on. Activated carbons are very helpful in removing heavy metal ions from aqueous solutions by adsorption, and have been investigated by many researchers so far. The practical relevance of activated carbon made from de oiled soya in the removal of hexavalent chromium ions through continuous adsorption mode is reported in this paper. A breakthrough plot was plotted in finding the effect of initial concentration and adsorbent bed height in the adsorption of hexavalent chromium through activated carbon of de oiled soya. The breakthrough time and saturation time increased as the concentration of the initial solution shot up from 40 mg/L to 60 mg/L. The saturation time was in an incremental mode when the thickness of the adsorbent bed height in a fixed bed was increased from 5cm to 7cm for 40 mg/L initial concentration of hexavalent chromium. The Adams-Bohart's model was found to fit perfectly the fixed bed column in the removal of hexavalent chromium from aqueous solutions. The fabricated adsorbent worked well in detoxifying hexavalent chromium metal ion contaminated wastewater. 2020 Published under licence by IOP Publishing Ltd. -
A Comparative Assessment of Cascaded Double Voltage Lift Boost Converter
In several power conversion applications, dc-dc boost converters with voltage boost techniques are extensively used in order to meet the growing power demand. The main drawback of conventional dc-dc boost converter is obtaining high DC voltages, when operated at high duty ratio which causes switching losses and decreases overall efficiency because of the switch being used to be in 'ON' state for long time and voltage stresses across switch increases. The main objective of proposed converter is to obtain high voltage without extreme duty ratio. When input voltage of 15V DC is given, 201.1V DC output voltage is attained at duty ratio of 0.4 by the cascaded double voltage lift boost converter. To validate the performance of proposed converter, simulation is carried out in LTspice XVII and a comparative assessment of proposed converter with other converters at different duty ratio are realized. 2020 IEEE. -
Study on Spray Dried Yttria Stabilized Zirconia Dental Implants
Medical implants are devices, tissues or supports that are positioned in a suitable manner on any defective part of the human body to facilitate its smooth functioning again. Known as 'prosthetics', they may be used to offer support to a specific organ or tissues, distribute medication, or observe the body condition. While many of the implants are made from skin, bone or other tissues removed from the body itself, the artificial ones are made from engineering materials which could be any of the compatible metals, plastics, ceramics or even composites. The high end technologically advanced implant material is expected to withstand severe barriers and compatibility issues when in contact with the human body. One such application is dental implants, where, the materials must possess superior mechanical properties, exhibit good hydro-chemical and low thermal degradation characteristics. They are also required to possess characteristics such as low friction, strong wear resistance, good wettability and biocompatibility, when placed in the mouth. The only materials that come close to meeting the needs are ceramics, limited by the associated high fracture rate. Stabilized zirconia (stabilized with yttria, ceria etc.) has provided potential solution. Among the two stabilizers, ceria stabilized zirconia may be a better alternative to yttria stabilized zirconia. Other alternatives are alumina, apatites: but their use are constrained based upon technological and cost considerations. Implant product is a highly demanding technology. Spray drying is a suitable process methodology to obtain free flowing powders with uniform morphology and chemical composition, essential for an implant production. This paper presents (i) results from spray drying 8% Y2O3-stabilized ZrO2 and (ii) a review of published literature pertaining to dental implant materials, the various processing methodologies, with special reference to stabilized zirconia and spray drying. Published under licence by IOP Publishing Ltd. -
Review on Emerging Internet of Things Technologies to Fight the COVID-19
The Internet of Things (IoT) has been gaining attention in various disciplines ranging from agriculture, health, industries and home automation. When a pandemic first breaks out early detection, isolating the infected, and tracing the contacts are the most important challenges. IoT protocols like Radio-frequency identification (RFID), Wireless Fidelity (WiFi), Global Positioning System (GPS) are gaining popularity for providing solutions to these challenges. IoT based applications in the health sector are benefitting COVID-19 (coronavirus disease of 2019) patients during this pandemic situation. This article explores and reviews the various Internet of Things enabled technologies and applications used in screening, contact tracing, and surveillance. IoT based telemedicine processes are very useful during the pandemic COVID-19. The purpose of this paper is to deliver an overall understanding of the existing and proposed technologies of IoT based solutions to make the situations better during COVID-19. 2020 IEEE. -
Transparent Data Encryption: Comparative Analysis and Performance Evaluation of Oracle Databases
This Transparent Data Encryption (TDE) can provide enormous benefits to the Relational Databases in the aspects of Data Security, Cryptographic Encryption, and Compliances. For every transaction, the stored data must be decrypted before applying the updates as well as should be encrypted before permanently storing back at the storage level. By adding this extra functionality to the database, the general thinking denotes that the Database (DB) going to hit some performance overhead at the CPU and storage level. However, The Oracle Corporation has adversely claimed that their latest Oracle DB version 19c TDE feature can provide significant improvement in the optimization of CPU and no overhead at the storage level for data processing. Impressively, it is true. the results of this paper prove too. Most interestingly the results also revealed about highly impacted components in the servers which are not yet disclosed in any of the previous research work. This paper completely concentrates on CPU, IO, and RAM performance analysis and identifying the bottlenecks along with possible solutions. 2020 IEEE. -
A Study and Analysis on Various Types of Agricultural Drones and its Applications
Drones are considered to be the greatest invention of mankind. Drones can be used in many areas widely. Drones can also be used in agriculture and it is called as unnamed aerial vehicles (UAV). In the traditional agriculture methods land vehicles are used to monitor various activities of the agriculture, this was consuming lot of human effort and time. Using drones in agriculture is more beneficial than using traditional methods for the activities. Usage of drones in agriculture provides a huge benefit in terms of economy and time due to their most astonishing features. In recent years many surveys have proved that drones can cover almost 10 to 15 times of the area which can be covered with traditional land based techniques. Drones can be controlled by computers according to their capacities, that is drones can be automated over some range of area, locating remote area, and even can be semi-automated. Drones can be efficiently used in agriculture for performing certain activities such as, studying weather conditions and variations, infection for the crops, land fertility and many more. Because of the efficiency of the drones they can be used in various activities of agriculture. In this paper, a detailed study has been made on various types of agricultural drones based on the feature, capacity, range as well as cost and the area of agriculture where they suit the most, and a statistical analysis about the usage of the drones in the field of agriculture. 2020 IEEE. -
Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification
Web page classification is an important task in various areas like web content filtering, contextual advertising and maintaining or expanding web directories etc. Machine Learning methods have been found to perform well to classify web pages, and ensemble models have been used to improve the results obtained from single classifiers. The Gradient Boosting and Extreme Boosting ensemble models are used in this work for binary classification. The dataset containing URLs of web pages have been collected manually. The comparison between the two boosting algorithms validated the improvement in accuracy and speed obtained through Extreme boosting. Extreme boosting has been found to be around ten times faster than Gradient boosting and also shows improvement in accuracy. The effect of three preprocessing techniques; lemmatization, stop words removal and regular expressions shows that these preprocessing techniques improves the accuracy of the results but not significantly. 2020 IEEE. -
Effect of phonon-substrate scattering on lattice thermal conductivity of monolayer MoS2
The effect of phonon-substrate scattering on lattice thermal conductivity (LTC) of supported MoS2 MLs is investigated over a wide temperature range (1 -
Design, analysis and fabrication of EV with level-1 autonomous vehicle capability
The fact to this day remains true and the same for over a hundred years the Automobile industry and vehicles, in general, have become the pivoting point in our day to day lives. We might as well call it a necessary evil. Although it is very true that they have made our lives more convenient when we speak in terms of transportation; the pollution that conventional IC engine vehicles produce hasn't done much to create a cleaner environment especially with Global warming on the rise as we speak. The simplest remedy would be is to replace IC engine vehicles with Electric one, EV. A Problem common to both conventional IC engine vehicles and EV's alike is the accidents occurring due to collision caused by human error on-road. While safety measures have greatly been taken in order to reduce the damage done to the driver and passengers in the event of a collision it would be far better to avoid the collision altogether. Thus having at least, a Level-1 Autonomous Vehicles capability where the system alerts the driver in the event of a crash or collision and deploy full braking capability. Thanks to increasing urbanization and the advent of modern technology the need of the hour of the 21st century has given rise to high demands for employment in the motorized transport sectors. The authors were successfully able to design, analyze and fabricate an EV with Level-1 Autonomous Vehicles capability. The successful implementation of this project will help in reducing not only pollution and accidents occurring on-road due to vehicle collision but also pave paths in alimenting Level-1 Autonomous Vehicles capability in EV's inexpensively. 2020 Author(s). -
Natural Language Processing on Diverse Data Layers through Microservice Architecture
With the rapid growth in Natural Language Processing (NLP), all types of industries find a need for analyzing a massive amount of data. Sentiment analysis is becoming a more exciting area for the businessmen and researchers in Text mining NLP. This process includes the calculation of various sentiments with the help of text mining. Supplementary to this, the world is connected through Information Technology and, businesses are moving toward the next step of the development to make their system more intelligent. Microservices have fulfilled the need for development platforms which help the developers to use various development tools (Languages and applications) efficiently. With the consideration of data analysis for business growth, data security becomes a major concern in front of developers. This paper gives a solution to keep the data secured by providing required access to data scientists without disturbing the base system software. This paper has discussed data storage and exchange policies of microservices through common JavaScript Object Notation (JSON) response which performs the sentiment analysis of customer's data fetched from various microservices through secured APIs. 2020 IEEE. -
Constraint Governed Association Rule Mining for Identification of Strong SNPs to Classify Autism Data
Autism is a heterogeneous neuro developmental disorder found among all age groups. Nowadays more patients are detected with autism but very less awareness is prevailing in the society related to it. This paved a way for many researchers to carry out serious study on autism and its characteristics. Studying behavior and characteristics of Autistic patients is very important for diagnosing the level of autism. Classifying the association of different characteristic in autistic patients at gene level using machine learning techniques can give an important insight to the doctors and the care takers of the patients. Research is being carried out to identify the genes responsible for autism. The changes in gene sequence may lead to different characteristics in different people. Thus genotypic research is found to reveal well defined insight about various characteristics in autistic patients and their associations with genes. Single Nucleotide Polymorphism (SNP) being high in features indicate human genome variability and is associated with identification of traits for many human diseases including autism. The main aim of the proposed work is to identify SNP sequences which are responsible for carrying the autistic traits. This paper explore the application of Constraint Governed Association Rule Mining (CGARM) technique on SNP data for dimensionality reduction and thereby selecting the strong predominant SNP features which are relevant enough to accomplish classification with high accuracy. The research work incorporates the application of CGARM and is carried out in two stages. In the first stage CGARM was used to choose significant SNP features resulting in dimensionality reduction. In the second stage classification was carried out by subjecting the selected features to Artificial Neural Network (ANN) algorithm. The main advantage of the proposed work is its ability to reduce the dimensions without compromising the quality i.e. using CGARM strong SNPs were selected by applying various constraints like Syntactical constraints, Semantical constraints and Dimensionality Constraints resulting in higher accuracy. The CGARM technique is applied on Autism data collected from National Center for Biotechnology Information (NCBI) repository. The data is divided into a set of 118 features, out of 118 features CGARM contributed in identifying 22 predominant SNPs. Further by applying forward selection method top 17 features were selected and were given as input to ANN. The 10 fold cross validation resulted in 76.9% accuracy which was found to be 50% more than that of original features. The proposed work contributed in reducing the dimension by 85% and provided 76.9% accuracy with the help of only 15% features. 2020 IEEE. -
Accuracy Enhancement of Portrait Segmentation by Ensembling Deep Learning Models
Portrait segmentation is widely used as a preprocessing step in multiple applications. The accuracy of portrait segmentation models indicates its reliability. In recent times, portrait segmentation using deep learning models have achieved significant success in performance and accuracy. However, these portrait segmentation models are limited to a single model. In this paper, we propose ensemble approach using multiple portrait segmentation models to improve the segmentation accuracy. The result of experiment shows that the proposed ensemble approach produces better accuracy than individual models. Accuracy of single models and proposed ensemble approach were compared with Intersection over Union (IoU) metric and false prediction rate to evaluate the accuracy performance. The result shows reduced false negative rate and false discovery rate, this reduction in false prediction has enabled ensemble approach to produce segmented images with optimized error and improved result of segmentation in portrait area of human body than individual portrait segmentation models 2020 IEEE. -
The effect of non-thermal argon plasma treatment on material properties and photo-catalytic behavior of TiO2 nanoparticles
In this paper, a brief study on the effect of non-thermal plasma generated with argon carrier on material properties and photo-catalytic reduction behavior of TiO2 is presented. Commercially available TiO2 nanoparticles (20 nm size) were subjected to Ar cold plasma at different time durations. Then the plasma treated materials were explored for chemical reduction of carbon dioxide (CO2) into methane (CH4) using sunlight as photo-irradiation source. The results show that the non-thermal plasma affects the material properties of TiO2 such as UV-visible absorption, XRD patterns and Raman scattering significantly and also the enhancement of CH4 yields in CO2photo-chemical reduction. 2020 American Institute of Physics Inc.. All rights reserved. -
A Study of Segmentation Techniques to Detect Leukaemia in Microscopic Blood Smear Images
In medical image processing, the segmentation of the image is considered to be a vital stage and is effectively used to extract the region of interest. Automated diagnosis of leukaemia is highly associated with the accurate segmentation of the cell nucleus. The purpose of this paper is to review and analyze literature related to some of the major segmentation techniques used in the field of Acute lymphoblastic leukaemia (ALL) detection. This paper presents an overview of segmentation methods along with the experimental results of six implemented methods and highlights some of the advantages and disadvantages of implemented segmentation techniques. 2020 IEEE. -
A miniaturized antenna array for direct air-to-ground communication of aircrafts
In this paper, a miniaturized, high directivity low-cost antenna array is presented. The uniqueness of the proposed array (PA) exists in the feed mechanism designed using Dolph-Chebyshev non-uniform excitations. Authors simulated the designed antenna array using ANSYS EM 18.2 (HFSS) software and characterization is carried out in a fully established anechoic chamber. The simulated array antenna is operating at 2.4 GHz with a gain of 8.12 dB and a reflection coefficient of -28.45 dB having a bandwidth of 110 MHz. On contrast with the traditional array (TA), PA exhibits enhanced resonance characteristics by maintaining the same radiation characteristics. The bandwidth is increased by 37.5%, maintaining the same gain of 8.12 dB. In contrast, there is a remarkable reduction in the size compared to the traditional corporate feed array antenna with non-uniform excitation. The overall size of the PA antenna is 242.5 mm 58.8 mm, which is 33.73% less compared to the TA. Published under licence by IOP Publishing Ltd. -
Employing Deep Learning in Intraday Stock Trading
Accurate stock price prediction is a significant benefit to the Stock investors. The future Stock value of any company is determined by Stock market prediction. A successful prediction of the stock's future price could result in a significant profit; Hence investors prefer a precise Stock price prediction. Although there are many different approaches to helps in forecasting stock prices, this paper will briefly look into the deep learning models and compare LSTM model and its variants. The key intention of this study is to propose a model that is best suitable and can be implemented to forecasting trend of stock prices. This paper focuses on binary classification problem, predicting the next-minute price movement of SPDR SP 500 index The testing experiments performed on the SPDR SP 500 index reveals that the variants of LSTM models, Slim LSTM1, slim LSTM2, and Slim LSTM3 with less parameters, provide better performance when compared to the Standard LSTM Model. 2020 IEEE. -
Low cost ANN based MPPT for the mismatched PV modules
Due to manufacturing dispersal, the photovoltaic (PV) panels of similar rating and manufacturer have distinctive characteristics in practical. As the maximum power point tracking (MPPT) becomes essential to optimally utilize the solar PV panel, distributed maximum power point tracking (DMPPT) is considered in this paper to follow the MPP of each panel. As the common MPP value is used in the existing DMPPT method to control all the panels, it fails to consider the uniqueness of each panel. By considering the uniqueness of each panel, the ANN based MPPT is implemented in this paper. As the ANN is trained using the actual characteristics of each panel based on the operating current, voltage and temperature, it is able to track the actual MPP. Due to the solar irradiance free MPPT, the costly pyranometer is not required in the actual PV system for MPPT. It reduces the cost of the system and also provides the interruption free tracking due to its independent nature on Voc and Isc values. Also, because of the looping free behaviour of the proposed algorithm, it is capable of following the MPP at rapidly varying condition. The proposed technique and the verified outcomes are discussed here in detail. Published under licence by IOP Publishing Ltd. -
Rotor Dynamics of TurbineGenerator Shaft with Dampers During Subsynchronous Resonance Generated by Series Capacitors
Purpose In this paper, an electromechanical approach to study the turbinegenerator shaft stability with and without dampers is made. The shaft is subjected to electrical disturbances created by series capacitors. The high power capacitors help the electric power system to improve the reactive power in high voltage transmission lines. Methods Finite element method is used to study the stability of the shaft under subsynchronous resonance when compared to the traditional methods like eigenvalue analysis, frequency scanning method and digital time simulation techniques. At the same time, it leads to subsynchronous resonance. Results Electromechanical stress in the rotating shaft arises when the resonance is created in the system. Maximum stress and strain of the shaft are calculated with other necessary parameters to prove the system instability. In order to maintain stability, dampers are installed at an optimum location. Conclusion Best location of installing damper is found using ANSYS 16.0 by modal analysis, harmonic and phase response analysis. The damper installed at the point reduces the stress caused by subsynchronous resonance and maintains the stability of the system. 2021, Springer Nature Singapore Pte Ltd. -
Experimental Approach and CFD Analysis on Flow Devices
This paper deals with the study of experimental approach and investigation by using computational fluid dynamics (CFD) on various flow devices. An orifice meter, venturimeter and a nozzle meter are the most common type of measuring devices used for rate of flow by creating the differences in velocity and pressure. Pressure drop is an important parameter occurring in these flow devices, which is due to restricted passage of flow, properties, diameter ratio, etc. The focus here is to calculate the coefficient of discharge and other flow parameters to analyze theoretically with the application of Bernoullis equation. The main objective of this paper is to analyze the variations across the sections of orifice meter, venturimeter and nozzle meter. Comparison of results by both experimental and computational methods was clearly understood, and also, the flow level was calibrated by calculating the coefficient of discharge in both the methods. 2021, Springer Nature Singapore Pte Ltd.