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FEC & BCH: Study and implementation on VHDL
Channel encoding and Forward Error Correction is a crucial element of any communication system. This paper gives a brief overview of the fundamentals, mechanism and importance of Forward Error Correction. The design and implementation of a (63,36,5) BCH Codec is also projected in the later sections. All simulations are made on MATLAB R2018b and the VHDL implementations have been carried out using Xilinx Vivado 2018.2. 2019 IEEE -
Process development to synthesize plasma sprayable powders from nano alumina ceramic powders
Nano sized (?100 nm) alumina powders were converted into micron sized (30-75 mm) plasma sprayable powders by employing synthetic polymers to agglomerate them. The agglomeration process was carried out (a) in a spray dryer and (b) through systematic manual granulation procedure. The importance of process parameters that govern the plasma spray powder synthesis and thereby the characteristics were being suitable for being plasma spray coated have been brought out in this research paper. A comparative study has been made between the two synthesis methods by testing the powders synthesized under different processing conditions for their flowability characteristics. Micro-structural features related with the shape morphology and powder grain sizes were studied by Scanning Electron Microscope and the elemental composition characterization was carried out by Energy Dispersive Spectroscopy. The most suitable plasma sprayable powders were further coated onto metal substrates by using an Atmospheric Plasma Spray coating unit. The plasma sprayable powders were developed with a goal to explore their potential for their applications as wear resistant nano coatings. 2019 Elsevier Ltd. All rights reserved. -
Effect of salt spray parameters on TiC reinforced aluminium based in-situ metal matrix composites
This paper aims attention at characteristics of corrosion of reinforced primary and secondary processed Al6061 based composites along TiC particles. Using potassium hexaflourotitanate (K2TiF6) and potassium tetrafluoroborate (KB4) halide salts, the synthesis of composites was done utilizing in-situ technique using stir casting route at temperature 850 Celsius. Open die forging was subjected upon in-situ composites of cast aluminium alloy at a temperature 500C. Both microstructure studies and salt spray test were subjected upon to forged and cast alloy 6061 and its in-situ composites. In accordance to ASTM B117 standard test procedure, salt spray test was conducted utilizing 5% NaCl test solution. The results impart that, the alloy forged, and respective in-situ composites exhibited enhanced corrosion resistance comparatively. 2019 Elsevier Ltd. All rights reserved. -
Impedance and electrochemical studies of rGO/Li-ion/PANI intercalated polymer electrolyte films for energy storage application
The present manuscript describes the synthesis of reduced graphene oxide (rGO) from coke by using modified Hummers method. The synthesized emeraldine poly aniline (PANI) polymer was used as a polymer host matrix. A series of polymer electrolyte films were prepared by varying concentration of rGO, PANI and Lithium carbonate. The synthesized PANI and rGO were soluble in common polar solvent. The structural, Nyquist and cyclic voltammetry studies of polymer electrolyte were investigated. The XRD and FTIR investigation confirms the formation of rGO and PANI in view of structural and chemical compositions respectively. The electrical property of polymer electrolyte was obtained by Nyquist plot which represents the perfect semicircular pattern. It confirms the charge transport mechanism with the decreased concentration of rGO in polymer electrolyte. The cyclic voltammetry performed at different scan rate on potential window ranged between-0.5 to 0.6 V represents the oxidation and reduction peaks. The overall results describe that the present electrolyte material can be a potential candidate for energy storage application.. 2019 Elsevier Ltd. -
A Novel Approach for Detection and Recognition of Traffic Signs for Automatic Driver Assistance System Under Cluttered Background
Traffic sign detection and recognition is a core phase of Driver Assistance and Monitoring System. This paper focuses on the development of an intelligent driver assistance system there by achieving road safty. In this paper a novel system is proposed to detect and classify traffic signs such as warning and compulsory signs even for occluded and angular tilt images using Support Vector Machines. Exhaustive experiments are performed in order to demonstrate the efficiency of proposed method. 2019, Springer Nature Singapore Pte Ltd. -
A review on feature selection algorithms
A large number of data are increasing in multiple fields such as social media, bioinformatics and health care. These data contain redundant, irrelevant or noisy data which causes high dimensionality. Feature selection is generally used in data mining to define the tools and techniques available for reducing inputs to a controllable size for processing and analysis. Feature selection is also used for dimension reduction, machine learning and other data mining applications. A survey of different feature selection methods are presented in this paper for obtaining relevant features. It also introduces feature selection algorithm called genetic algorithm for detection and diagnosis of biological problems. Genetic algorithm is mainly focused in the field of medicines which can be beneficial for physicians to solve complex problems. Finally, this paper concludes with various challenges and applications in feature selection. Springer Nature Singapore Pte Ltd 2019. -
Intelligence-Software Cost Estimation Model for Optimizing Project Management
With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling. 2019, Springer Nature Switzerland AG. -
Mechanical and abrasive wear behaviour of waste silk fiber reinforced epoxy biocomposites using taguchi method
The aim of this research article is to study the static mechanical properties and abrasive wear behavior of epoxy biocomposites reinforced with different weight percentage of waste silk fibers. The effect of parameters such as velocity (A), load (B), fiber loading (C) and abrading distance (D) on abrasive wear has been considered using Taguchi's L25 orthogonal array. The objective is to examine parameters which significantly affect the abrasive wear of biocomposites. The addition of silk fiber has resulted in improved flexural properties of the epoxy matrix. The results of ANOVA indicated that the parameter which played a significant role was abrading distance followed by fiber loading, load and sliding velocity. 2019 Trans Tech Publications Ltd, Switzerland. -
A review on ensembles-based approach to overcome class imbalance problem
Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms. Springer Nature Singapore Pte Ltd 2019. -
Enhancements to randomized web proxy caching algorithms using data mining classifier model
Web proxy caching system is an intermediary between the users and servers that tries to alleviate the loads on the servers by caching selective web pages, behaves as the proxy for the server, and services the requests that are made to the servers by the users. In this paper, the performance of a proxy system is measured by the number of hits at the proxy. The higher number of hits at the proxy server reflects the effectiveness of the proxy system. The number of hits is determined by the replacement policies chosen by the proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The outcomes of the paper are proactive strategies that augment the traditional replacement policies with data mining techniques. In this work, the performance of the randomized replacement policies such as LRU-C, LRU-S, HARM, and RRGVF are adapted by the data mining classifier based on the weight assignment policy. Experiments were conducted on various data sets. Hit ratio and byte hit ratio were chosen as parameters for performance. Springer Nature Singapore Pte Ltd. 2019. -
Effective Emoticon Based Framework for Sentimental Analysis of Web Data
The Explosive development in the social media domain has created a platform for mass generation of textual and emoticon based web data from micro blogging sites. Sentimental Analysis refers to analysis of sentiments or emotions from such heterogeneous reviews are the present urge of the market. Thus, an effective emoticon based framework is proposed which generates scores of both textual and emoticons into seven layered categories using SentiWordNet and weighs performance of various machine learning techniques like SVM/SMO, K-Nearest Neighbor (IBK), Multilayer Perception (MLP) and Naive Bayes (NB). Using Jsoup crawler input reviews are obtained and processed with initial pre-processing model for emoticons and text data followed by stemming and POS tagger. Projected framework is investigated on college and hospital dataset obtaining upper attainment level by Kappa statistic metrics having 98.4% correctness and lesses bug value. Proposed Framework showcases greater competence score with lesser FP Rate based on weighted average of correctness measures. The investigational outcomes are tested on training data with Ten-Fold cross validation. The outcome reveals that suggested emoticon based framework for the task of Sentimental analysis can be efficaciously applied in online decision job. 2019, Springer Nature Singapore Pte Ltd. -
Thermal Studies of Multiwalled Carbon Nanotube Reinforced with Silicone Elastomer Nanocomposites
This article studies the enhancement in the properties of silicon elastomer (SiR) reinforced by multiwalled carbon nanotube (MWCNT). Multiwalled carbon nanotube filled silicone rubber composites were prepared. The effects of loading levels of MWCNT on the thermal properties of silicone elastomer were investigated. SEM studies reveal the smooth distribution of MWCNT in silicon matrix. At higher concentration nanoparticles collapse together to form agglomerates. The high resolution transmission electron microscopy (HR-TEM) photographs shows excellent/homogeneous distribution of MWCNT in silicon matrix and agglomeration occurs at higher concentrations. Thermal properties of nanocomposites have been characterized using differential scanning calorimetry (DSC) and thermo-gravimetric analysis (TGA). The transition temperature appears at below -25C for MWCNT reinforced SiR nanocomposites. TGA thermogram, shows that temperature at 10%, 20%, 30%, and 50% weight loss for SiR nanocomposites is higher than as compared to unfilled SiR. The results indicate that the addition of MWCNT significantly enhanced the thermal stability of silicon elastomer. 2018 Elsevier Ltd. -
Extraction of features from video files using different image algebraic point operations
In the human-computer interaction (HCI) field, facial feature analysis and extraction are the most decisive stages which can lead to a robust and efficient classification system like facial expression recognition, emotion classification. In this paper, an approach to the problem of automatic facial feature extraction from different videos are presented using several image algebraic operations. These operations deal with pixel intensity values individually through some mathematical theory involved in image analysis and transformations. In this paper, 11 operations (point subtraction, point addition, point multiplication, point division, edge detecting, average neighborhood filtering, image stretching, log operation, exponential operation, inverse filtering, and image thresholding) are implemented and tested on the images (video frames) extracted from three different self-recorded videos named as video1, video2, video3. The videos are in .avi, .mp4 and .wmv format respectively. The work is tested on two types of data: grayscale and RGB (Red, Green, Blue). To assess the efficiency of each operation, three factors are considered: processing time, frames per second (FPS) and sharpness of edges of feature points based on image gradients. The implementation has been done in MATLAB R2017a. 2019 Association for Computing Machinery. -
Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision
This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. 2019, Springer Nature Singapore Pte Ltd. -
Discriminative Gait Features Based on Signal Properties of Silhouette Centroids
Among the biometric recognition systems, gait recognition plays an important role due to its attractive advantages over other biometric systems. One of the crucial tasks in gait recognition research is the extraction of discriminative features. In this paper, a novel and efficient discriminative feature vector using the signal characteristics of motion of centroids across video frames is proposed. These centroid based features are obtained from the upper and lower regions of the gait silhouette frames in a gait cycle. Since gait cycle contains the sequence of motion pattern and this pattern possesses uniqueness over individuals, extracting the centroid features can better represent the dynamic variations. These variations can be viewed as a signal and therefore the signal properties obtained from the centroid features contains more discriminant information of an individual. Experiments are carried out with CASIA gait dataset B and the proposed feature achieves 97.3% of accuracy using SVM classifier. 2019, Springer Nature Singapore Pte Ltd. -
Segmentation and Recognition of E. coli Bacteria Cell in Digital Microscopic Images Based on Enhanced Particle Filtering Framework
Image processing and pattern recognitions play an important role in biomedical image analysis. Using these techniques, one can aid biomedical experts to identify the microbial particles in electron microscopy images. So far, many algorithms and methods are proposed in the state-of-the-art literature. But still, the exact identification of region of interest in biomedical image is a research topic. In this paper, E. coli bacteria particle segmentation and classification is proposed. For the current research work, the hybrid algorithm is developed based on sequential importance sampling (SIS) framework, particle filtering, and Chan–Vese level set method. The proposed research work produces 95.50% of average classification accuracy. 2019, Springer Nature Singapore Pte Ltd. -
The Quantification of Human Facial Expression Using Trapezoidal Fuzzy Membership Function
Fuzzy Inference System is an interesting approach. Major benefit of the FIS is, it permits the natural narration in linguistic terms of tribulations that can be resolved rather than in requisites of associations between accurate arithmetical points. This helps, handling with the complicated systems in easy way, is the major motive why fuzzy system is broadly incorporated in practice. In the present research paper, an effective approach is proposed that quantifies the human facial expression using Mamdani implication based fuzzy logic system. The recent principle engages in retrieving arithmetical values from persons face and feed them to a fuzzy classifier. Fuzzification and Defuzzification process issues trapezoidal fuzzy membership function for input as well as output. The diverse characteristic of this method is its effortlessness and maximum correctness. Experimental outcome on Image dataset depicts excellent accomplishment of the proposed methodology. In this paper, a legitimate procedure proposed for quantification of human facial expression from the features of the face by means of Mamdani type fuzzy inference system, which is proficient to set up a convenient membership association involving the various dimensions of the happy expression. Values representing features of the face are fed to a Mamdani-type fuzzy classifier. This system recognizes three levels of same happy expression namely Normal, Bit Smiley and Loud Laugh. The total output expressions for this proposed scheme is three. Another discrete element of the proposed methodology is the membership method model of expression outcome which stands on various surveys and readings of psychology. Springer Nature Singapore Pte Ltd. 2019. -
A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan
Japan is a country that suffers a lot of earthquakes and disasters because it lies across four major tectonic plates. Subduction zones at the Japanese island curves are geologically complex and create various earthquakes from various sources. Earthquake prediction helps in evacuating areas, which are suspected and could save the lives of people. Artificial neural network is a computing model inspired by biological neurons, which learn from examples and can be able to do predictions. In this paper, we present an artificial neural network with PSO-BP model for the prediction of an earthquake in Japan. In PSO-BP model, particle swarm optimization method is used to optimize the input parameters of backpropagation neural network. Information regarding all major, minor and aftershock earthquake is taken into account for the input of backpropagation neural network. These parameters are taken from Japan seismic catalogue provided by USGS (United States Geological Survey) such as latitude, longitude, magnitude, depth, etc., of earthquake. 2019, Springer Nature Singapore Pte Ltd. -
Reliable monitoring security system to prevent MAC spoofing in ubiquitous wireless network
Ubiquitous computing is a new paradigm in the world of information technology. Security plays a vital role in such networking environments. However, there are various methods available to generate different Media Access Control (MAC) addresses for the same system, which enables an attacker to spoof into the network. MAC spoofing is one of the major concerns in such an environment where MAC address can be spoofed using a wide range of tools and methods. Different methods can be prioritized to get cache table and attributes of ARP spoofing while targeting the identification of the attack. The routing trace-based technique is the predominant method to analyse MAC spoofing. In this paper, a detailed survey has been done on different methods to detect and prevent such risks. Based on the survey, a new proposal of security architecture has been proposed. This architecture makes use of Monitoring System (MS) that generates frequent network traces into MS table, server data and MS cache which ensures that the MAC spoofing is identified and blocked from the same environment. 2019, Springer Nature Singapore Pte Ltd. -
Assessing Human Stress Through Smartphone Usage
Stress occurs in a human being when they are faced with exigent situations in life. Assessing stress has been always challenging. Smartphones have become a part of everyones day-to-day activity in the present time. Considering humansmartphone interaction, sensing of stress in an individual can be assessed as todays youth spends most of their time with smartphones. Taking this into consideration, a study is carried out in this paper on assessing stress of an individual based on their interaction with the smartphone. In this work, humansmartphone interaction features, like swipe, scroll, and text input, are examined. Text input is incorporated by disabling the autocorrection and spelling checker features of the keyboard. Moreover, sensor data is used by Google activity recognition API to analyze the physical activity of the individual to assess the stress level. 2019, Springer Nature Singapore Pte Ltd.