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Analysis of Human Physiological Parameters Using Real-Time HRV Estimation from Acquired ECG Signals
The overall healthiness of the heart can be computed from Electrocardiogram. The healthiness of the heart depends on several lifestyle parameters, like as- stress, sleeping pattern, smoking habit etc. In this paper, an algorithm to determine Heart Rate Variability from the acquired ECG signal on a real-time basis is presented. Impacts of above-stated lifestyle parameters on cardiac health using Heart Rate Variability analysis are also computed. ECG signal gets contaminated with different sources of noises while acquisition. Multi-rate FIR Impulse Filter is used for de-noising of the acquired signal. Heart Rate Variability analysis and real-time plotting are done on de-noised output for accurate feature extraction. A simple robust hardware realizable algorithm was developed for analyzing obtained HRV to state different health conditions of the heart. 2019 IEEE. -
Optimizing the efficiency of solar flat plate collector with trapezoidal reflector
Solar flat plate collectors are the most vital parts of a solar heating system. The collector plate absorbs the energy from the sun and transforms this radiation into heat and then transmit this heat into a fluid, it can either water or air. This research paper proposes a new technology to enhance the performance of the solar flat plate collector. A trapezoidal solar reflector is connected with the flat plate collector to enhance the amount of sunlight which hits the collector plate surface. The trapezoidal reflector concentrates both the direct and diffused radiation of the sun towards the flat plate collector. To maximize the concentration of incident radiation the trapezoidal reflector was permitted to change its inclination with the direction of sunlight. A prototype of a solar water heating system with trapezoidal reflector was constructed and achieved the improvement of collector plate efficiency by around 12%-13%. Thus the current solar heating system has the best thermal performance compared to the existing systems. 2019 Author(s). -
Extractive Text Summarization Using Sentence Ranking
Automatic Text summarization is the technique to identify the most useful and necessary information in a text. It has two approaches 1)Abstractive text summarization and 2)Extractive text summarization. An extractive text summarization means an important information or sentence are extracted from the given text file or original document. In this paper, a novel statistical method to perform an extractive text summarization on single document is demonstrated. The method extraction of sentences, which gives the idea of the input text in a short form, is presented. Sentences are ranked by assigning weights and they are ranked based on their weights. Highly ranked sentences are extracted from the input document so it extracts important sentences which directs to a high-quality summary of the input document and store summary as audio. 2019 IEEE. -
Water Demand Prediction Using Support Vector Machine Regression
Water is a critical resource for sustainable economic and social development of a country. To maintain health hygiene, energy agricultural products, and the environment management water plays a key role. Water demand prediction is essential to analyze the requirement that indicate emergency state for water management decisions. This paper explores the water usage data for dairy plants to understand the spatial and temporal patterns for future water requirements, to optimize the water demand estimation. It uses concept of Machine learning algorithms to compare and achieve an effective and reliable system for water prediction. 2019 IEEE. -
Wide band cascade RF amplifier for 0.01GHz to 6 GHz application
This paper presents a design of wide band cascade RF amplifier for 0.01 GHz to 6 GHz application using Hybrid Microwave Integrated (MIC) Technology. Wideband amplifier provides ultra-flat gain response of 1 dB for 4 GHz bandwidth and 3 dB for 6 GHz bandwidth. A coplanar wave guide (CPWG) is fabricated using printed circuit board technology and used for RF transmission line topology to convey microwave frequency signals. The output power at 1 dB compression is 17 dBm while the high gain is 22 dBm. The return loss shows below minimum -10 dB for all frequency and amplifier have good linearity and stability. The proposed amplifier can be used for L, S, and C band applications. 2019 IEEE -
A Survey on 5G Standards, Specifications and Massive MIMO Testbed Including Transceiver Design Models Using QAM Modulation Schemes
Massive MIMO (Multiple Input Multiple Output)is the advanced technology in 5G architecture which improves mobile and data wireless system parameters in multiple folds. The basic idea of this technology is to include huge number of antennas in the base stations serving limited user equipment. This will enhance the parameters like spectral efficiency, data rate, wireless devices connectivity, energy or power efficiency and also, significant reduction in interference and error rates. The Third Generation Partnership Project (3GPP)consortium, International Mobile Telecommunication (IMT)and various partner telecom companies are on the way to develop unified architecture to meet the proposed 5G standards by the year 2020. Initial test beds and field-trials are already in process at various universities and telecom companies considering Long Term Evolution (LTE)releases features in the 5G architecture framework. However, the research is still an open issue on improving the parameters. This research paper provides a detailed overview on 5G standards, specifications and Field trials and test beds implemented by various universities and telecom industry utilizing Massive MIMO technology. This literature survey paper aims to enlighten the researchers working in the area of Massive MIMO to understand the test bed and field trials designs existing till date. This paper also motivates to complete experiments on Bit error rate (BER)estimation in various modulation schemes for single transmitter-receiver as well as in MIMO configuration. The reduction in BER is observed when MIMO models are used for transceiver design. The hardware utilization and simulation work of the field trials and testbed provide different existing techniques to develop a transceiver system which meets 5G standard. 2019 IEEE. -
Waveform Analysis and Feature Extraction from Speech Data of Dysarthric Persons
Speech recognition systems provide a natural way of interacting with computers and serve as an alternative to the more popular but less intuitive peripherals (input / output devices). Tools employing the techniques of Automatic Speech Recognition (ASR) can be extended to serve people with speech disabilities so that they can overcome the difficulties faced in their interaction with general public. An attempt is made here to achieve this goal by mapping the distorted speech signals of people with severe levels of dysarthria to that of a normal speech and/or less severe dysarthric speech. The analysis is carried out by comparing the speech waveforms of the people with and without communication disorders and then extracting the features from the audio files. The differences in time, duration, frequency and PSD are used to facilitate the mapping of unintelligible speech data to intelligible ones. When reasonable accuracy levels are achieved in this mapping, the normal voice can be used as the substitute / surrogate of the original distorted voice. 2019 IEEE. -
Feature Based Fuzzy Framework for Sentimental Analysis of Web Data
Social mass media has emerged as a projectile platform for the evolution of web data. The sentimental Analysis where the huge textual online reviews are analyzed to extract the actual sentiment or emotions hidden in the reviews. In this paper an effective approach for sentimental analysis of web data is proposed which deploys the fuzzy based machine learning algorithm to accomplish fine-level sentiment analysis of huge online opinions by assimilating the fuzzy linguistic hedges influence on opinion descriptors. The seven layered categories are designed that uses SentiWordNet which has three stages: Pre-processing phase, Feature Selection Phase and Fuzzy based Sentiment Analysis phase. Various machine learning algorithms like AdaBoost, (IBK) K-Nearest Neighbour, (NB) Nae Bayes and (SVM)/SMO Support Vector Machine are used for classification. Jsoup is implemented for gathering web opinions which are subjected to initial processing task later applied with stemming and tagging. This fuzzy based methodology is investigated for Mobile, Laptops dataset, also compared with state-of-the-art approaches which demonstrate upper indication of 94.37% accurateness through Kappa indicators showcasing lesser error rates. The investigational outcomes are tested on training data using Ten-Fold cross validation which concludes that this approach can be efficaciously used in Sentimental analysis as an aid for online decision. 2019 IEEE. -
Compact out-of-phase wideband substrate integrated waveguide based power divider loaded by slots for Ku and K band applications
In this paper a novel Substrate Integrated Waveguide (SIW) based single layer ground-loaded compact wideband out-of phase equal power divider is proposed . The wide-band and out-of-phase operation of the proposed power divider is obtained by creating defects in the ground plane with rectangular slots. The Defected Ground Structure (DGS) allows the power divider to exhibit a wide passband. The obtained passband is 11.5 GHz wide considering the return loss better than -10dB. Compactness in the proposed design is attributed to the dispersion characteristic of the slow-wave. The proposed design working in the passband from 14.5 GHz to 26 GHz is fabricated and tested. The size of the proposed design is 0.57 ?2g excluding feed lines. Here ?g is the guided wavelength at free space. The measured amplitude imbalance of (01) dB is obtained within the passband. The measured and simulated results are compared and found with in good agreement. 2019 IEEE. -
An Effective Time Series Analysis for Equity Market Prediction Using Deep Learning Model
A stock Exchange is a market where securities are traded. Every day, billions are traded at various stock exchanges across the world. In recent years prediction of movement of stock market is regarded as fascinating and has created a demand in financial market time series prediction. A precise forecasting of equity market is needed to provide higher returns for investors. Since there is high complexity in predicting stock market profits, developing models for it becomes difficult. The data mining and machine learning techniques has played an important role in Prediction of stock market movement. This study attempted to develop a deep learning model using Recurrent Neural Network for forecasting movement in the National Stock Exchange of India's benchmark broad based stock market index(NIFTY 50) for the Indian equity market. In this paper the NIFTY 50 index and INFYOSYS Ltd historical data from Yahoo finance companies has been selected for forecasting and analysis. 2019 IEEE. -
Estimation of Vehicle Distance Based on Feature Points Using Monocular Vision
In this digital era safety and security have the highest precedence, the advanced driver assistance system is the latest trend and where many challenges are open for researchers. Vehicle to vehicle distance estimation is one of the most important challenges to provide the security and safety alerts for the driver. In order to achieve this, image of the front vehicle is captured using the single camera under monocular vision to estimate the vehicle distance. Then three key steps are designed to estimate the vehicle distance: extracting and locating the key features of the vehicle, characteristic triangle is drawn between those features to calculate pixel area and develop the measuring formula to calculate the distance. For efficient feature extraction and localizing of the feature position, conventional AdaBoost algorithm is utilized to find the strong features for scalable samples. Distance measurement formulation is used to derive the correlation between the pixel area and distance by considering the different parameters from the prototype of pinhole camera, camera standardization and plotting of area. Formula is developed to estimate the optimum moving distance between vehicles to vehicle. After the experimental analysis, the accuracy rate is improved and time complexity satisfies the precision. 2019 IEEE. -
Deformation Diagnostic Methods for Transformer Winding through System Identification
Transformers play a critical role in the power system. Dynamics of the power system changes if the transformers are out of service for scheduled and unscheduled maintenance work under contingency situations. Faults, overloading, and mechanical abnormalities causes the incipient and critical damages to the transformer. The isolation of transformers leads to the voltage profile change, load curtailments, high compensation, economic loss, and many more problems. It is very important to know the problems occurred in the transformer parts to repair and restore it into the system to attain better stability, reliability, and economics. The transformer health monitoring system consisting of prediction, identification, and diagnostics in online as well as offline mode that will provide sufficient content to the managerial utility to take actions against the problem anticipated or occurred. The heuristic survey inks, the probability of damage in the transformer winding is more compared to the other parts. A novel method using system identification is proposed for the diagnosis of transformer winding. The location and extent of mechanical deformations can be ascertained along with specifically detecting radial and axial deformations in the transformer windings. A system identification approach in frequency and time domain were employed in the diagnostic algorithms for the sweep frequency response dataset. For both transfer function and state space model, a reference table called deformation information tableau has been synthesized for lumped parameter transformer model by varying series and shunt circuit elements systematically. The details of deformation are extracted from the tableau for actual frequency response data for a specified frequency range and winding type. The crosscorrelation of two-dimensional frequency response arrays, one being a signature array and other being deformation array, is used to represent relativity as a singleton. A toolbox is developed for the generation of heuristic deformation information tableau and to diagnose using the diagnostics algorithm developed. The proposed algorithms were verified and simulated for continuous disk type winding. 2019 IEEE. -
Detection of fake opinions on online products using decision tree and information gain
Online reviews are one of the major factors for the customers to purchase any product or to get service from many sources of information that can be used to determine the public opinion on the products. Fake reviews will be published intentionally to drive the web traffic towards the particular products. These fake reviewers mislead the customers to distract the purchasers mind. Reviewers behaviors are extracted based the semantical analysis of his review content for the purpose of identifying the review as fake or not. In this work the reviews are extracted from the web for a particular product, along with the reviews of several other information related to the reviewers also been extracted to identify the fake reviewers using decision tree classifier and Information Gain.Significance of the features on the decision is validated using information gain. Experiments are conducted on exhaustive set of reviews extracted from the web and demonstrated the efficacy of the proposed approach. 2019 IEEE -
Electrochemical behavior of cast and forged aluminum based in-situ metal matrix composites
The present work focuses on the electrochemical behaviour of Al6061 alloy and Al6061-TiB 2 in-situ metal matrix composites. Al6061-TiB 2 in-situ Composites were synthesized by a stir casting route at a temperature of 860C using potassium hexafluorotitanate (K 2 TiF 6 ) and potassium tetrafluoroborate (KBF 4 ) halide salts. Percentage of TiB 2 was kept at 0 wt% and 10wt%. The cast Al6061 alloy and Al6061-TiB 2 composites (0wt% &10wt %) were subjected to open die hot forging process at a temperature of 500C. Both cast and forged Al6061 alloy and its composites were subjected to micro-structural and electrochemical characterization. Corrosion behaviour of alloy and composites in both cast and forged conditions were evaluated using electrochemical impedance spectroscopy and the results were backed up by a potentiodynamic polarization test. Results indicate that addition of TiB 2 particles increases the corrosion rate and reduces the polarization resistance of aluminium alloy in both cast and forged condition owing to galvanic coupling between the reinforcements and base metal. Further, when compared with cast alloy and its composites, forged alloy and its composites exhibited poor corrosion resistance under identical test conditions. 2019 Author(s). -
Miniaturization of Microstrip Antenna with Enhanced Gain Using Defected Ground Structures
The rapid advancement and growth in the wireless technology demands miniaturized communication equipment's. Microstrip antennas attracted many researchers over the past decades because of its various features like small in size, light weight, low cost and conformability. These antennas can operate at high frequencies and multiple bands with high gain and larger bandwidths if suitably designed. This work presents a Rectangular Microstrip Antenna (RMSA)performance improvement using defected Ground Structures (DGS). The simulation results revealed that the creation of Complementary Split Ring Resonator (CSRR)and Phi as a defect in the ground of proposed antenna has improved its gain. Introduction of DGS improved the gain by 27% and reduced the size by approximately 3.35%. Proposed Rectangular Microstrip Antenna with Defected Ground (RMSA-DGS)exhibits gain of 3 dB at 2.4 GHz with S11 response of -30.44 dB. In addition to this the antenna also shows one more resonance at 4.66 GHz with S11 of -14.29 dB and gain of -1.24 dB. RMSA-DGS has an overall dimension of 37.2 47.23 mm2. 2019 IEEE. -
Synthesis of Yttria-Stabilized Zirconia Nano Powders for Plasma Sprayed Nano Coatings
Plasma sprayed Yttria Stabilized Zirconia (YSZ) coatings, with few microns sized microstructure/grain morphology has been well researched, reported and established as an industrial Thermal Barrier Coatings (TBC) material/system. However, nano structured YSZ coatings possess improved characteristics when compared with their micron sized counterparts. However, due to their nano sizes, light weight, and low density, plasma spray coating process of nano powders suffers from flowability issues due to lack of nano powder inertia/momentum, leading to poor deposition/uneven coating thickness. In this research work, nano structured YSZ coatings were synthesized by using an Atmospheric Spray Coating (APS) facility. Nano powders of YSZ were used as the starting materials to prepare micron sized plasma sprayable powders. 80?m thick NiCrAlY bond coat (commercial) and 200?m thick YSZ top coat with nano microstructure (lab synthesized) were built on steel substrates. The starting nano crystalline (YSZ) powders, measuring 30-70 nanometers (nm) were synthesized in the laboratory via chemical method (sol-gel) by employing zirconium oxy chloride hexa-hydrate and yttrium nitrate as precursors, citric acid as chelating agent and ethylene glycol for the diversification reaction followed by calcination @ 1000C. They were then re-constituted into micron sized (53-106 ?m) plasma sprayable powders by agglomerating with polyvinyl alcohol (PVA) binders. The nano crystallite morphology of powders and coatings were analyzed by Scanning Electron Microscope (SEM), chemical composition by Energy Dispersive spectroscopy (EDS) and crystal structural phase by X-ray diffraction (XRD). The influence of calcination temperature of 1150C on nano crystallite morphology was also studied. 2019 Elsevier Ltd. -
Synthesis of Value Added Refractories from Aluminium Dross and Zirconia Composites
Results of a developmental study on the potential to synthesize industrial grade refractories from aluminum dross with un-stabilized zirconia are reported. The merit of the developed product to perform as refractories suitable for use at or above 1000C was assessed by studying the thermo-physical behavior as per guidelines of ASTM and IS. Aluminum dross, an industrial waste (slag) is generated in several millions of tons in the production of Aluminum and is dumped into landfills, which releases poisonous gases like methane and ammonia upon contact with moisture present in the land and the heat generated by the earth, warranting stringent mitigation efforts. Rich in aluminum metal (?15%), ?-Al2O3 (7-15%), MgAl2O4 (10-15%) and AlN (20-30%), the general prime dross composition draws interest due to its abundance and presence of ?-Al2O3 and MgAl2O4, for the production of refractories with insulating, shock resistance and stability at high temperature (?1000C and above) characteristics. Nevertheless, presence of AlN, a good thermal conductor acts as a deterrent in the production of refractories. Aluminium dross (after leaching out AlN) was processed with un-stabilized zirconia (monoclinic ZrO2) to synthesize the refractory composites. Conventional process (calcination, ball milling, compaction and sintering (1550C/6 hrs)) was employed. Characterization involved thermal shock cycling (air quench at furnace ambient of 1000C and room temperature) to determine the number of shock cycles endured before failure. Structural phase analyses at various stages of processing were carried out by via XRD. Magnesia present in dross did not appear to stabilize either the tetragonal or cubic ZrO2. Microstructural and chemical composition studies were carried out via SEM and EDS. The favourable results confirm the viability of the process methodology. 2019 Elsevier Ltd. -
REMAP: Determination of the inner edge of the dust torus in AGN by measuring time delays
Active galactic nuclei (AGN) are high luminosity sources powered by accretion of matter onto super-massive black holes (SMBHs) located at the centres of galaxies. According to the Unification model of AGN, the SMBH is surrounded by a broad emission line region (BLR) and a dusty torus. It is difficult to study the extent of the dusty torus as the central region of AGN is not resolvable using any conventional imaging techniques available today. Though, current IR interferometric techniques could in principle resolve the torus in nearby AGN, it is very expensive and limited to few bright and nearby AGN. A more feasible alternative to the interferometric technique to find the extent of the dusty torus in AGN is the technique of reverberation mapping (RM). REMAP (REverberation Mapping of AGN Program) is a long term photometric monitoring program being carried out using the 2 m Himalayan Chandra Telescope (HCT) operated by the Indian Institute of Astrophysics, Bangalore, aimed at measuring the torus size in many AGN using the technique of RM. It involves accumulation of suitably long and well sampled light curves in the optical and near-infrared bands to measure the time delays between the light curves in different wavebands. These delays are used to determine the radius of the inner edge of the dust torus. REMAP was initiated in the year 2016 and since then about one hour of observing time once every five days (weather permitting) has been allocated at the HCT. Our initial sample carefully selected for this program consists of a total of 8 sources observable using the HCT. REMAP has resulted in the determination of the extent of the inner edge of the dusty torus in one AGN namely H0507+164. Data accumulation for the second source is completed and observations on the third source are going on. We will outline the motivation of this observational program, the observational strategy that is followed, the analysis procedures adopted for this work and the results obtained from this program till now. 2019 Societe Royale des Sciences de Liege. All rights reserved. -
Plasma Sprayed Refractory Coatings from Aluminium Dross
Refractory coatings on metals offer a unique blend of chemical inertness, stability and mechanical properties at temperatures higher than what the metal can normally withstand. However, a balance must be struck with many factors: Thickness, adhesion, performance, durability, economy and suitability for specific end use requirements. The present-day technology requires the coating to give effective service under extreme temperatures while being environmentally friendly and be easily available. One application of refractory coating is in steel industry-pipe linings. This research works highlights the potential to use aluminum dross, an industrial waste material to generate refractory coatings, comprised of Al2O3 and MgAl2O4 after suitable processing. Al dross is a byproduct of the Aluminium smelting process which can be recycled mechanically to separate the residual Aluminium metal from the Aluminium oxide. These are usually produced in tones every year and are found to be dumped in landfills and other empty spaces which generate toxic fumes like methane and other gases when reacted with moisture. The Aluminium dross used in this work was analyzed and found to comprise of its usual constituents such as metallic Al, MgAl2O4, Al2O3, AlN and other oxides and nitrides in minute quantities. Manual procedures were conducted to synthesize plasma spray-able dross which was further introduced to standard laboratory tests for the removal of undesirable constituents like AlN and other nitrides which led to the optimization of quality of powders. Atmospheric plasma spray (APS) coating methodology was used to deposit 250?m thick coatings of re-processed Al dross, involving the spraying of the processed powder onto a bond coated (NiCrAlY) steel substrate. The raw, reprocessed and the plasma sprayed coated Al dross were evaluated for their material characteristics by employing X-ray Diffractometry (XRD) for crystal structural phases, microstructure and chemical composition by employing sophisticated microscopy (SEM) technique and EDS associated with the SEM. The paper is presented keeping in in view the aptness of reprocessed Al dross, an industrial waste material to be utilized as refractories for use in engineering industries. 2019 Elsevier Ltd. -
Adhesion strength studies on zirconia based pyrochlore and functionally gradient thermal barrier coatings
Thermal Barrier Coating (TBC) plays a major role in the improvement of gas turbine and engine components in terms of their service life and performance. Generally, all coatings must possess certain primary properties to perform in the intended applications. However, regardless of applications, suitable adhesion strength is one major characteristic they must have to adequately protect the basic components on which they are applied upon. In TBCs, adhesion (or Bond) strength is a parameter that helps to illustrate the resistance of the ceramic top coat against spallation either from the bond coat (and component) or within the TBC layers itself. The performance of TBCs are reliant upon the adhesion between the coating and the metal substrate and also adhesion (or cohesion) between the bond coat and the overlying ceramic top coat layer. The de-bonding of the top coat layer or the inter-metallic bond coat layers are the main reasons of the failure of the overall TBC system. Some of the prominent problems associated with coatings applications are residual stresses, micro-cracks and pores etc. These and many other factors influence the adhesion of the coatings in addition to service environment conditions and pre coating substrate preparations such as substrate cleaning, grit blasting and very importantly plasma spray parameters. In the present work, results obtained from adhesion strength measurements carried out by following the ASTM C 633 standard test method, on various types of TBCs are being shared. Thermal barrier coatings (TBCs) were synthesized with NiCrAlY bond coat deposited on SS 304L substrate by using air plasma spray and different ceramic top coats (a) commercial 8%Yttria Stabilized Zirconia (8YSZ) (b) lab synthesized plasma spray powders of (i) Lanthanum Zirconate (La2Zr2O7) (ii) Lanthanum Ceria Zirconate (La2 (Zr0.7Ce0.3)2O7) and (iii) Lanthanum cerate (La2Ce2O7). The coating depositions were carried out in different configurations i.e. two layers, three layers and gradient layers (Functionally gradient materials). The evaluation of properties includes the studies of morphology of the strength (adhesive/cohesive failure mode) tested specimen as well. General conclusions drawn from the studies on several specimen in various configurations are that cohesive failures (between the ceramic top coat layers) is the predominant mechanisms followed by few adhesive failures in bond coat coat/ceramic interface. 2019 Elsevier Ltd. All rights reserved.