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Integration of 0.1 GHz to 40 GHz RF and microwave anechoic chamber and the intricacies
The aim of this paper is to highlight and elaborate the construction and establishment of a rectangular anechoic chamber (AC) of dimensions 7 m 4 m 3 m working from 0.1 GHz to 40 GHz. It is an informative checklist giving an insight on the reckoning of chamber dimensions and selection of appropriate absorbers as per the required specifications. It briefs the key features of validation of an anechoic chamber, namely, shielding effectiveness and reflectivity (quiet zone). It describes the intricacies of the integration of systems such as vector network analyzer (VNA), antenna mounting stands, three-axes motorized antenna rotation control circuitry, and customized software. The validation of the established chamber is accomplished for overall shielding effectiveness of ?80 dB and reflectivity of ?40 dB in one cubic meter area at the receiving antenna or the antenna under test (AUT) region far away from transmitter say, at 5.5 m separation. This paper covers the measurement results of three broadband horn antennas which can be used as reference antennas for characterization of other antennas in the chosen frequency range. The entire report will certainly be a guideline for any reader or aspirant who is interested in the development of a similar anechoic chamber and looking for complete intricacies. 2020, Electromagnetics Academy. All rights reserved. -
Economic growth and higher education in south asian countries: Evidence from econometrics
South Asian economies has witnessed very slow growth over the years and the gap has widened manifold between other nations of Asia particularly East Asian nations and South Asian nations. This paper examines co-integration between the economic growth and reach of higher education in South Asian nations explaining this disparity. The research employed an econometric panel co-integration investigation to analyse the long run relationship of higher education and economic growth among these nations. The research confirmed positive long run causality between the economic growth of the South Asian nations and gross enrolment ratio of higher education. So, if the South Asian nations continue with their existing pattern of paying less attention to higher education by allocating low share of investment on it, poor human capital formation would result in growing further economic disparity between developed and South Asian nations where rich nations would remain richer and poor nations would remain poor with the gap remaining unabridged. This research will serve as an aid to policy makers, educators and financers of South Asian nations to bridge the gap between high-and low-income nations. The focus on the quantum of spending on higher education by the government will help improve the reach of tertiary education and build economic prosperity in these nations. 2020, Sciedu Press. All rights reserved. -
Rapid Eye Movement (REM) Sleep Behavior Disorder and REM Sleep with Atonia in the Young
Background: Rapid eye movement (REM) sleep behavior disorder (RBD) and REM sleep without atonia (RWA) have assumed much clinical importance with long-term data showing progression into neurodegenerative conditions among older adults. However, much less is known about RBD and RWA in younger populations. This study aims at comparing clinical and polysomnographic (PSG) characteristics of young patients presenting with RBD, young patients with other neurological conditions, and normal age-matched subjects.Methods: A retrospective chart review was carried out for consecutive young patients (<25 years) presenting with clinical features of RBD; and data were compared to data from patients with epilepsy, attention deficit hyperactivity disorder (ADHD), and autism, as well as normal subjects who underwent PSG during a 2-year-period.Results: Twelve patients fulfilling RBD diagnostic criteria, 22 autism patients, 10 with ADHD, 30 with epilepsy, and 14 normal subjects were included. Eight patients with autism (30%), three with ADHD (30%), one with epilepsy (3.3%), and six patients who had presented with RBD like symptoms (50%) had abnormal movements and behaviors during REM sleep. Excessive transient muscle activity and/or sustained muscle activity during REM epochs was found in all patients who had presented with RBD, in 16/22 (72%) autistic patients, 6/10 (60%) ADHD patients compared to only 6/30 (20%) patients with epilepsy and in none of the normal subjects.Conclusion: We observed that a large percentage of young patients with autism and ADHD and some with epilepsy demonstrate loss of REM-associated atonia and some RBD-like behaviors on polysomnography similar to young patients presenting with RBD. 2019 The Canadian Journal of Neurological Sciences Inc. -
In vivo, in vitro and in silico screening of a potent Angiotensin Converting Enzyme (ACE) inhibitor from Trigonella foenum-graecum extract using Zebrafish as a model organism to reduce hypertension
The number of patients suffering from hypertension is on the rise worldwide and there is a need to explore natural products which can supplement current drugs to treat this disease. RAAS (Renin Angiotensin Aldosterone System) is one of the factors maintaining blood pressure. In the present investigation, we explored the potential of methanolic extract of fenugreek seeds in inhibiting Angiotensin Converting Enzyme (ACE), a key enzyme in the RAAS system, thereby reducing hypertension. In addition to in vivo studies conducted on zebrafish, in vitro and in silico studies were also performed to assess the inhibitory effect of the fenugreek extract on ACE. The bioactive components in Trigonella foenum-graecum revealed by GC-MS were further subjected to docking and binding studies with the receptor protein ACE. Of the various phytochemicals studied, arachidonic acid exhibited the maximum inhibitory effect on ACE. Thus, the present investigation was able to favorably screen a potent ACE inhibitor in Trigonella foenum-graecum extract which shows a potential to be used alone or supplemented with synthetic ACE inhibitors to treat high blood pressure. Further investigations are required to quantify the phytochemical for its inhibitory activity and also to understand the mechanism of inhibition of the enzyme. 2020 World Research Association. All rights reserved. -
Predicting and improvising the performance of rocket nozzle throat using machine learning algorithms
This paper is a study of one dimensional heat conduction with thermo physical properties like K, row, Cp of a material varying with temperature. The physical problem is characterized by a cylinder of infinite length and thickness L, imposed with a net heat flux at x= 0, with the other end being insulated. The temperatures at the insulate end are measured by placing thermocouples. As the temperatures at the other end are very high, it is not possible to measure temperatures by keeping thermocouples which will burn away. So the problem is initialized with known sensor values near insulated end. By proper predicting values by ARIMA Model, the temperature distribution in Rocket Nozzle throat system (RNT) is calculated. The outcome of the work is processed with Machine Learning algorithm like Genetic algorithm in identifying the optimal location of sensor position which helps in improvising the performance of RNT. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
An improved compocasting technique for uniformly dispersed multi-walled carbon nanotube in AA2219 Alloy Melt
Technology transfer for economic bulk production is the greatest challenge of the era. Production of high strength lightweight materials with nanocarbon reinforcement has attained its importance among the researchers. Property enhancement with multi-walled carbon nanotube (MWCNT) reinforcement is reported by all researchers. But effective utilization of its property remains a challenge even though it is the strongest material in the world. Achieving homogeneous dispersion especially in molten metal is a complex task. To address the same, a new approach was tried which could trigger de-bundling and make a uniform dispersion. Various metallurgical and mechanical characterizations were done. Grain refinement and the structure were studied with an optical microscope, MWCNT dispersion and structural damage was studied using field emission scanning microscope, Phase change and reactions during casting was done with XRD scan. The method remarkably facilitated 23.7% and 69.75% improvement in hardness and ultimate compressive strength respectively with the addition of MWCNT. Faculty of Mechanical Engineering, Belgrade. -
On certain topological indices of signed graphs
The first Zagreb index of a graph G is the sum of squares of the vertex degrees in a graph and the second Zagreb index of G is the sum of products of degrees of adjacent vertices in G. The imbalance of an edge in G is the numerical difference of degrees of its end vertices and the irregularity of G is the sum of imbalances of all its edges. In this paper, we extend the concepts of these topological indices for signed graphs and discuss the corresponding results on signed graphs. 2020 the author(s). -
An energy efficient authentication scheme based on hierarchical ibds and eibds in grid-based wireless sensor networks
Wireless sensor network is a peculiar kind of ad hoc network, consists of hundreds of tiny, resource constrained as sensor nodes. Clustering is a demanding task in such environment mainly due to the unique constraints such as energy efficiency and dynamic topology. In this paper, a novel energy efficient cluster-based routing algorithm is proposed on which hierarchical identity-based digital signature (IBDS) and enhanced-identity-based digital signature (EIBDS) scheme is concerning in grid-based wireless sensor networks. Firstly we form clusters using multi-parameters-based type-2 fuzzy logic algorithm. This paper proposes an improved ant colony optimisation algorithm, which optimises the energy consumption on data transfer in a WSN. Each node in a sensor network is authenticated using elliptic curve cryptography (ECC). After a set of simulation tests on NS-3 simulator, our proposed work achieves good performance for various metrics. Copyright 2020 Inderscience Enterprises Ltd. -
Secure visual cryptography scheme with meaningful shares
Visual cryptography is an outstanding design, which is also known as visual secret sharing. It used to encode a secret portrait into various pointless share images. Normally, item bossed on transparencies and decrypts as loading one or two or the entire share images by means of the human visual system. Suppose, if we encompass great sets of secret shares then the pointless shares are complicated to handle. In this paper, a meaningful secret sharing algorithm and a modified Signcryption algorithm is used to enhance the security of the Visual Cryptography encryption schemes. The foremost intend of the anticipated format is to extend consequential shares and similarly make sure the isolation on conveying the secret data. The anticipated process is executed in the functioning platform of MATLAB and the presentation results are investigated. 2020, Engg Journals Publications. All rights reserved. -
On the rainbow neighbourhood number of set-graphs
In this paper, we present results for the rainbow neighbourhood numbers of set-graphs. It is also shown that set-graphs are perfect graphs. The intuitive colouring dilemma in respect of the rainbow neighbourhood convention is clarified as well. Finally, the new notion of the maximax independence, maximum proper colouring of a graph and a new graph parameter called the i-max number of G are introduced as a new research direction. 2020 the author(s). -
Evaluation of machine learning algorithms for surface water delineation using landsat 8 images
Surface water detection and delineation is an important and necessary step in change detection studies on water bodies using multispectral images. Commonly used techniques for surface water delineation from multispectral images are single band density slicing, spectral index based, machine learning based classification and spectral un mixing based methods. This paper presents a comparative study of commonly used machine learning algorithms viz. ANN, SVM, Decision Tree, Random Forest and K-means clustering for their suitability and effectiveness when applied on Landsat 8 images for surface water detection and delineation. The algorithms are compared for their classification accuracy and execution time. While all the above mentioned algorithms exhibited their usefulness in water detection, Decision Tree and Random Forest algorithms were found be faster in both training phase and testing phase and also yielded better accuracy with fewer miss-classifications. Though K-means clustering with more than four clusters yielded results comparable to that of supervised classification methods, it requires appropriate post-processing to obtain the output image with only two clusters; corresponding to water pixels and non-water pixels. Pierson's correlation co-efficient and Structural similarity Index (SSIM) are computed to compare the correlation and similarity of the output images yielded by the algorithms being studied. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Geospatial crime analysis to determine crime density using kernel density estimation for the indian context
Crime is the most common social problem faced in a developing country. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. One such initiative, real-time accurate crime predictions can help reduce the occurrence of crime. In this paper, a crime analytics platform is developed, which processes newsfeed data analysis for different types of crimes and identify crime hotspots using Kernel Density Estimation method. This system enables criminologists to understand the hidden relationships between crime and geographical locations. Interactive visualization features are available that enable law enforcement agencies to predict crime. 2020 American Scientific Publishers. -
A comparative study on the adaptability of the different varieties of solanum lycopersicum L. (tomato) in salt stress condition
The objective of the present study was to study the levels of antioxidant and oxidant metabolites such as total protein, proline, peroxidase, lipid peroxidase, catalase,and anthocyanin and phenol contents in nine varieties of tomato plants (Cerasiforme (Cherry tomato), Indamrohini, Marglobe, Ns 538, Sacchariya, San Marzano 3, Suhyana, Tomato Oblate Yellow, Vanda) treated under various NaCl concentrations (50, 100, 200 and 250 Mm). Salinity is one of the significant abiotic stresses, which affects plant cell metabolism and reduces plant productivity. Plants tolerant to NaCl implement a series of adaptations to acclimate to salinity, including morphological, physiological and biochemical changes. Under saline conditions, plants have to activate different physiological and biochemical mechanisms in order to cope with the resulting stress. Such mechanisms inclu de changes in morphology, anatomy, water relations, photosynthesis, the hormonal profile, toxic ion distribution and biochemical adaptation (such as the antioxidative metabolism response). An updated discussion on salt-induced oxidative stress and its effect on the antioxidant machinery in both salt-tolerant and salt-sensitive plants is the major part of this study. The aim of the present study is to extend our understanding of how salinity may affect the physiological characteristics of plants. 2020 IJSTR. -
Friction and wear behaviour of copper reinforced acrylonitrile butadiene styrene based polymer composite developed by fused deposition modelling process
This paper focuses on the development of copper filled Acrylonitrile Butadiene Styrene (ABS) composites by fused deposition modelling (FDM) and to characterize its friction and wear behaviour. Twin screw extrusion technique was employed to extract copper-ABS composite filament. Three different materials were tested, i.e. pure ABS, ABS+2.5wt% Cu and ABS+5wt% Cu. Friction and wear characteristics of pure ABS and copper filled ABS composites were tested under various loads and sliding velocities. Addition of Copper powder has significantly improved the friction and wear properties of the developed composites. Further, it is also observed that friction and wear behaviour increased with increase in copper content in ABS. Worn out surfaces were subjected to scanning electron microscopy studies to analyse and identify the possible wear mechanisms involved. Faculty of Mechanical Engineering, Belgrade. -
A Signature-Based Mutual Authentication Protocol for Remote Health Monitoring
Remote health monitoring can offer a lot of advantage to all the players in healthcare industry and it can contribute to reduced healthcare expenses. Wireless medical sensor networks capable of accumulating and transferring vital parameters of patients play a crucial role in remote health monitoring. Security and privacy are major concerns preventing the patients from adopting this technology with an open mind. This paper presents a signature-based authentication protocol for remote health monitoring. The work also discusses an authentication protocol for the mutual authentication of users and medical server. The protocol does not require the server to maintain a password table. The proposed algorithms are resistant to various attacks such as replay attack, stolen verifier attack, and privileged insider attack. The work includes the informal and formal security analysis of the proposed protocols. Scyther tool is used for formal security analysis and the results show that the protocol is resistant to various common and automated attacks. 2019, Springer Nature Singapore Pte Ltd. -
Markov based genetic algorithm (M-GA): To mine frequent sub components from molecular structures
Processing the molecular compounds to identify the internal chemical structure is a challenging task in bio-chemical research. Popular approaches, mine the frequent subcomponents from the molecules with chemical and biological properties represented in the form of feature vector histogram. Though this helps to identify the absence or presence of mined feature, calculating the frequency of every frequent substructure involves sub graph isomorphism test which is an NP-Complete process. To overcome the above mentioned bottleneck we proposed Markov based Genetic algorithm (M-GA) in which the chemical descriptors were considered from two-dimensional representations of molecules that classify chemical compounds using mining significant substructure and generates the binary vector that generate pure active classes, singleton reactors, descriptor sets. This method scales down the process of mining substructures that are statistically significant from huge chemical databases. The results shows that the performance of proposed algorithm is improved compared to the existing algorithms. 2020, Research Trend. All rights reserved. -
Performance of pradhan mantri fasal bima yojana: Perception of farmers in rural bangalore
Crop insurance is an agricultural development program supporting the sustainability of farmers. PradhanMantriFasalBimaYojana crop insurance scheme was introduced to provide insurance cover, financial stability, innovative and modern methods of agricultural practice. The study primarily focuses on the reasons for enrollment, benefits, challenges and suggestions regarding the PradhanMantriFasalBimaYojana with respect to farmers of Rural Bengaluru. A qualitative thematic analysis using a primary study reveals PMFBY as a source of financial security and financial stability with reduced premium that increases the confidence level among the farmers. 2019 SERSC. -
Cuda implementation of non-local means algorithm for GPU processors
Non-Local Means algorithm (NLM) is a prominent image denoising algorithm. One of the major limitations of NLM algorithm and its variants is the time requirement. In this era of high performance computing, an efficient alternative to reduce the time complexity of any algorithm is its parallelization. In this paper, a parallelized version of basic NLM algorithm using CUDA architecture is proposed. The algorithm is developed on NVIDIA GeForce 940M GPU which follows Maxwell architecture with 3 SMs and 384 CUDA cores. Experiments are carried out using selected set of natural and medical images of various sizes. Our proposed parallelized version of NLM algorithm reduces the time requirement approximately by 50% in comparison to its basic version and also achieves comparable denoising performance in terms of PSNR, SSIM and FSIM evaluation metrics. The proposal is a model which can be customized for newer GPU architectures. 2020, Engg Journals Publications. All rights reserved. -
Random forest application on cognitive level classification of E-learning content
The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through E-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with random forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level. The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved.