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Generally, the output control on numerical simulation is fundamentally dependent on true boundary conditions such as reliable load conditions, perfect supports, pure mechanical properties, etc. In the case of complicated numerical simulations, the preliminary works such as estimation of real-time load conditions, property determination are unavoidable ones for the attainment of an acceptable solution. In addition to that, advanced numerical simulations can solve complex problems. This work deals with the impact analysis on various Carbon, Glass and Kevlar fiber-based composites with the help of FSI analysis. Ansys Design Modeler is used for conceptual design construction and the various composites are generated in the ACP tool. Finally, the comparative Impact analyses are executed and thereby the suitable for material for impact application is selected.

The Unit Commitment (UC) problem is a combinatorial optimization problem in power system operation with the key focus on achieving optimum commitment schedule of the generators for forecasted demand and spinning reserve. The computational complexity to determine a solution for the UC problem grows exponentially with the number of generators and system constraints. In this paper, the UC problem is formulated as a mixed-integer optimization problem and solved using novel Adaptive Binary Salp Swarm Algorithm by considering minimum up/down time limits, prohibited operating zones, spinning reserve, valve-point effect, and ramp rate limits. The proposed algorithm is tested for efficiency on the standard 10-unit system, 26-unit RTS system, 54-unit IEEE 118-bus system, 20, 40, 60, 80, and 100-unit systems. Additionally, an Adaptive Multi-Objective Binary Salp Swarm Optimization Algorithm is proposed for resolving the bi-objective emission constrained UC problem and tested using a 10-unit system. The obtained results are analyzed for positive differences against other algorithms from the literature. The statistical analysis exhibits the efficiency of the proposed method for large scale real-time systems.

In this paper, a Four Dimensional Space Vector Modulation (4D-SVM) is implemented for five phase Voltage Source Inverter (VSI) to improve the performance of the inverter. The 4D-SVM control technique generates switching pulses for five phase VSI system. The reference vector for 4D-SVM is identified by tracking subcubes and prisms located in the complex 4D-cubic region. The 4D-SVM for five phase inverter includes 32 switching state vectors; all the switching states are non-redundant switching states, which are located in 32 vertices of the 4D-cube. The proposed five phase inverter with 4D-SVM reduces CMV and Total Harmonic Distortion (THD) and minimized neutral current value. The minimization of CMV leads to avoid the damage of the winding and increases the life time of the motor. So for in two level inverter, the CMV is reduced up to Vdc/3 times of input voltage; In this proposed system, made an attempt to reduce the CMV level below this reported value. The simulation and hardware results are verified using matlab simulink and Field Programmable Gate Array (FPGA) processor.

Purpose This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301. Design/methodology/approach In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function. Findings The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption. Originality/value The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

Wireless body area networks (WBAN) consist of various sensors to collect essential data signals to monitor blood pressure, heartbeat, blood sugar level, pulse, and body temperature. However, such dense sensor networks face serious issues due to interference which degrades the system throughput. Similarly, data security is another important factor to be considered in WBAN. Interference can be avoided by dividing the sensors into a group based on their operational characteristics and data security can be enhanced using suitable encryption modules. Recently, cloud-based WBAN has gained more attention, as concepts of cloud offer various advantages for efficient data management and security factors. Considering these issues in WBAN and the advantages of a cloud environment, this research work proposes an enhanced data security through Advanced Encryption Standard (AES) and efficient task flow scheduling using Genetic Algorithm (GA). The proposed model outperforms better in task scheduling which greatly reduces the interference and increases the system throughput. The proposed model is experimentally validated in terms of throughput, execution time, response time, and encryption time. Conventional models such as DES and 3DES are used to compare the outcomes of the proposed model to validate the superior performance, and the proposed model performs better than conventional models.

The power utility industry is virtually one of the major industries of every nation. Because each power network is so widely geographically distributed, the administration of a power system is faced with a number of operational challenges that are often hard to tackle, and computational approach has shown a way to optimize some of these problems, as shown by the increased attention that the research community has paid for it and by the number of studies that have been recently published. Given that a number of nonlinear mathematical functions need to be handled for the optimization of power system operational problems, we discuss here a novel algorithm based on an adaptive quasi-oppositional differential migrated biogeography-based optimization, with the aim to identify the optimal control variables for different objectives of optimal power flow problems, and it is expected that our work could motivate further exploration of this optimization algorithm in this field by the peers. In our work, we attempted to modify the mutation operator of a Differential Evolution algorithm with migration operator of a biogeography-based optimization (BBO) algorithm so as to improve the exploration ability of the resulting model. Furthermore, a quasi-oppositional based learning technique was evoked to increase the adaptability of the mutation operator of BBO, thereby enhancing its exploitation ability. Finally, the accuracy and robustness of the proposed algorithm were tested by applying on IEEE 30-bus and IEEE 118-bus systems and also results were compared with the results reported in the recent literature.

Forecasting involves the preparation of a statement concerning unknown events, which might occur in future. The main purpose of making forecast is to gain knowledge about uncertain events that are important to our present decisions. In this study, we discussed about Auto Regressive Integrated Moving Average (ARIMA) method. By using this method, we forecast the BSE SENSEX Closing point value and then compare the method with the help of RMSE measure.

The losses in the distribution networks due to the line resistance decrease the overall efficiency of the power distribution. Reducing the power losses and regulating the voltages within the limits are necessary to provide quality power to the consumers. The power loss can be minimized by optimum network reconfiguration and the placement of the capacitors. Considering independent network reconfiguration and placement of the capacitor is not effective during heavy loading conditions. This paper proposes a simultaneous network reconfiguration and capacitor placement in radial distribution network to minimize the real power losses, operating cost and to improve the bus voltages. The Johnson’s algorithm is used to find the minimal spanning tree during the network reconfiguration and an adaptive whale optimization algorithm is used as an optimization method. The proposed methodology is tested on standard IEEE 33-bus and 69-bus radial distribution systems. The effectiveness of the proposed method is validated by comparing the results with previous result reported in the literature in terms of cost saving and loss reduction.

The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.

A novel mechanism for the elimination of false selvedge in rapier looms was successfully designed and developed, which functions to grip the protruding weft ends after each pick insertion during the weaving process, with subsequent cutting and suction of the protruding ends. The task is otherwise, presently performed by the formation of dummy or false selvedges in the fabric, requiring additional yarn other than that required for the actual fabric. A significant amount of yarn is wasted to form false selvedges, as they are removed and disposed as hard waste post weaving, which consequently increases the cost of fabric and narrows the profit margins. The developed mechanism was successfully installed and commissioned on an indigenous rapier loom leading to a reduction in the amount of hard waste otherwise generated, without affecting the normal functioning of the loom and the weaving process. The amount of reduction in hard waste was estimated and the quality of fabric manufactured before and after the installation was tested and analyzed.

Power Routers offers many benefits to the power system, it helps in improving the existing transmission asset utilization. Security Constrained Optimal Power Flow (SCOPF) is becoming more important in the electrical power system especially in the present deregulated environment. This paper focuses on completely linearizing the complex non-linear SCOPF problem. The objective function is linearized using Piecewise linearization technique and the constraints are framed using linear sensitivity factors. A formal extension is made to the traditional SCOPF by including power router control in the post contingency time frame. DC power flow analysis is used to calculate the real power flow in the lines.Thus, in this paper, further minimization of cost is achieved by using Power Router control and it is compared with the conventional SCOPF.

Imaging modalities play a major role in early detection and diagnosis of various medical conditions related to the patient. Retinal image segmentation has been taken up for investigation in this research paper to efficiently detect the presence of eye disorder which could be indicators of major onset of conditions like hypertension, cataracts, diabetic retinopathy, age-related macular disorders, etc. A machine learning method for classification of given pixels in the search space into regions containing blood vessels and those that do not contain blood vessels is implemented using a three-stage neural classifier in this paper. Prior to classification, an optimization algorithm namely ant colony optimization derived from nature-inspired phenomena is used to provide an optimal feature vector set to set high standards for the neural network based classification approach. The novelty and merits of the paper lie in back tracing of the segmentation process in which optimization is done first on the preprocessed features followed by classification for segmented output on the optimized features. This results in elimination of redundant feature vectors which tend to occupy much memory as well increase the computational overhead on the process. The entire implemented system is automated by the machine learning process and tested on 30 samples, 15 each on DRIVE and STARE databases. Classification rates of nearly 98% on an average scenario have been achieved for segmentation and 96.5% for abnormality detection. The performances have been compared against Bayesian set models and standalone ANN models.

Among different manufacturing process of composite aircraft structures, pre-preg based manual layup is commonly used method. One of the major concerns during layup process is the accidental occurrence of inclusions like release film, pre-preg backup film etc., Presently, inclusions are detected through visual inspection by clean room inspector. As these inclusions are very thin, they may go unnoticed by the inspector. These inclusions can cause delamination or disbonds during the service life of an aircraft leading to the rejection of component resulting in loss of time and money. This raises requirement for a semi-automated/automated system to detect inclusion in a faster, efficient and reliable manner during layup process. This paper presents 3D laser scanner based methodology for detecting inclusions based on curvature analysis of point cloud data as thickness variation.

To handle bulk of power, the AC power transmission is not economical over long distance. High-voltage direct current (HVDC) transmission system is selected as the alternative not only in economic aspects but also in stability point of view. But the operation and control of HVDC links pose a challenge for the designers to choose the proper control strategy under various operating conditions. Traditionally, PI controllers are used for the rectifier current control of the HVDC system, but due to fixed proportional (P) and integral (I) gains, these controllers can perform well only over a limited operating range. However, in controlling a nonlinear plant such as the firing angle of the rectifier side in HVDC system, the model controls such as fuzzy logic controllers show better performance to the dynamic disturbances than traditional PI controllers. The CIGRE model as one of the conventional methods has been studied and improves the stability HVDC system.

Microgrid is an interconnection of renewable energy sources, loads, power electronic interfaces and energy storage devices. Renewable energy sources are intermittent in nature, and therefore, there is a need for energy storage devices to ensure continuous power supply to the load. Power electronic converters are required to interface the DC bus with energy storage devices. In this work, a bidirectional interleaved switched capacitor DC–DC converter is proposed as a power electronic interface for microgrids. The renewable energy source supplies energy to the load and charges the energy storage device when it is available. The stored energy in the energy storage device supplies energy to the load when the renewable energy source is not available. This is achieved by the proposed converter as it operates bidirectionally. The interleaving technique is carried out at the input side of the converter which reduces the input current ripples. The proposed converter has the following advantages: (i) reduction of ripple current, (ii) reduced voltage stress and (iii) extensive voltage gain and efficiency. It operates in both boost (step-up) and buck (step-down) mode. A 3 kW solar-powered microgrid with bidirectional interleaved switched capacitor DC–DC converter is designed and simulated in MATLAB. The simulation results are associated with the theoretical results obtained from MATLAB.

Among different manufacturing techniques available for composite aircraft structures, prepreg-based manual layup is widely used. During the fabrication process, the protective films of the prepregs or other materials used in the process could get inside as a foreign object between the layers. The present method of finding the inclusions during the prepreg layup is by visual inspection in the cleanroom. Carrying out visual inspection is challenging as the layup is usually carried out on large surfaces and reflective by nature. This paper proposes a 3D laser scanner-based approach for the detection of inclusion on flat and curved surfaces. Using the portable laser scanner, the surfaces of each layer are scanned and compared the resulting point clouds using with a reference layer data. Thicknesses between two surfaces are computed with Cloud to Cloud, Mesh to Cloud and Hausdorff distance to enhance the visibility of inclusions. It was found that this approach could enhance the visibility of inclusions over 50 micron and above. These enhanced features are used to train a multiview convolutional neural network to mark the inclusion regions, which can aid the inspector to identify the inclusion regions in a fast and efficient way.

In this modern era everyone is aware of comment analysis. For example, if a person wants to buy a product, the first thing will do is to see the review of that particular product, based on review they choosing the product. By this way the reviews made a huge impact on the products and on websites. There are number of websites increasing in the internet, which provides various facilities like shopping, trading, online betting etc. Some of them are good and some are questionable like whether the website is beneficial or not. Many of them are facing the issues when choosing a website, in order to overcome some of these fraud websites, we proposed a system that evaluates the websites based on the reviews or feedback provided by the customer. For this we collected the twitter dataset because on twitter platform various number of emotions are expressed on the products, user interface, online transactions etc. as well as we used different kinds of classifiers for classifying the text such are Naive-Bayes, Random forest, Decision tree, Support Vector Machine, K Nearest Neighbor's, Logistic Regression. In this paper we utilize the Logistic Regression classifier is used for classification of the text and for generating the sentiment of the word, which achieves the better accuracy (99.50%), recall and F1 score among the other classifiers used in this paper and naive-bayes has achieved the best high precision rather among the classifiers used. This paper emphasizes that the critiques expressed in the form of reviews are used to analyze the behavior is positive, negative or neutral and the system is used to rank satisfactory classifiers for sentiments.

A geometrical model for lockstitch seam 301 has been proposed based on elliptical profile to estimate the thread consumption. The realistic elliptical shape of a lockstitch 301 seam has been confir...

This paper examines the execution of a boost converter for a PV framework which was built utilizing four PV cell associated in arrangement to get input as 12 V and the yield voltage of this framework was obtained by employing different MPPT algorithms such as Perturb & Observe algorithm, Incremental Conductance algorithm and Fuzzy based algorithm and finally the yield obtained with these MPPT algorithms are compared to understand the performance of the PV cell system which is subjected to irradiation of 1000 W/m2 and temperature of $25^{\circ} \mathrm{C}$. The work was performed using matlab Simulink tool.

For some complex mixed-signal Integrated Circuits (ICs) testing for quality assurance is approaching the manufacturing costs. Mixed Signal IPs have Analog and Digital building blocks which are integrated to create a top-level IP block. Analog building blocks use variety of active and passive devices which will have different design rules to be followed to meet the manufacturing requirements. With lower process node, the number of design rules is increasing exponentially. These rules put lots of constraints on device placements. Lack of layout designs in the early phase of new process nodes inhibits the layout designers to validate all the design rule requirements and the violations are found later in the design cycle. Every time a new Process Design Kit is released with new design rules, the layout design teams have to validate the changes on live designs leading to lost design cycles. One of the solutions involved is to create layout test structures using different device types with all combinations available on a given process node and test structure to test integration guidelines given by SoC. These test structures will enable layout designers to qualify the design rules and then identify the valid placement and integration specifications. These can also be used to Quality Assurance (QA) of new Process Design Kit(PDK) before providing PDK to the design engineers. This will reduce the overall design cycle time by testing the design kits and provides guidelines to the layout designers for creating the layouts.

A significant part of policy-making involves matching policy goals with the ideas formulators hold about feasible and desirable mixes of policy means or tools. However, these plans have little reality until they have been ‘implemented’, or put into practice. The process of matching goals and means is part of policy formulation while putting these plans into practice is the subject of policy implementation. Instrument preferences or ‘implementation styles’ develop as a function of a variety of factors, including state capacity and familiarity of policy designers, decision makers, and administrators with specific kinds of tools. These are important determinants of the mix of policy tools found in any issue area or policy sector.

Low endurance is the predominant issue in the multi-rotor UAVs, which has been solved through various inventions such as advanced cum integrated propulsive systems and implementation of aerodynamic efficient designs. Similarly, this work deals with the extraction of energy through the components of UAV and its working principle to tackle energy insufficient issue, in which the methodologies are used for this extraction, is coupled engineering approaches. The fundamental platform of this work is a 20-inch two-bladed propeller, which can be suitable for both Rotary wing UAVs and Multi-rotor UAVs. Two primary material families are used in this comprehensive investigation, which is Glass Fiber Reinforced Polymer (GFRP) and Carbon Fiber Reinforced Polymer (CFRP). The subordinate material types used in this simulation are prepreg, wet, woven, and uni-directional for both the classifications. Because of the work nature of UAV’s propeller, the probability of non-linear in vibrations may happen in numerous manners. Therefore the study about vibration relayed energy harvesting is imposed on the short-listed propeller and thereby energy obtained through Piezoelectric layers. In this regard, the natural frequencies, aerodynamic pressure estimations on composite UAV’s propeller are estimated through advanced computational tools and thus the outcomes are coupled with an analytical energy estimator. At last, the energy extracted by the piezoelectric layer for different composite materials are determined, which guided the material optimization.

DDF is the most significant measure among different bunch execution procedures to assess the immaculateness of any group component. Ordinarily, best groups are assessing by processing the quantity of information focuses inside a bunch. At the point when this tally is comparable to the quantity of required information focuses then this group is viewed as great. The greatness of the bunch system is fundamental not exclusively to discover the information check inside a group yet in addition to inspect it by totalling the information focuses these are (I) present inside a group where it ought not be and the other way around and (ii) not grouped for example anomalies (OL). The principle usefulness of DDF is that all bunch focuses can be gathered in comparative groups without exceptions, the current paper features on how contrasted with DDF more effective Clusters can be shaped through the Modern DDF. Further, we assess the exhibition of some grouping calculations, K-Means. As of late we, fostered the Modified K-Means Algorithm and Hierarchical Algorithm by utilizing the Data Discrepancy Factor (DDF).

Vertical-axis wind turbine (VAWT) is evergreen and needful methodology in the energy conversion through the wind. Comparatively, VAWT is more fit in all environments because of its design methodology, which turned VAWT is more implementable one in real-time conditions. Because of this peak, unique characteristics of VAWT, this work desired to deal with the conceptual design and its optimization based on high power extraction. Various power-enhancing methodologies are submitted, in which the leading-edge modifications-based enhancement technique is implemented in the various formation. Two different power optimization techniques are imposed in the base model of VAWT, which are curved cut in the leading-edge and sharp-edge cut in the leading edge. The engineering approach used in this work for the entire comparative analysis is CFD. In this regard, the conceptual designs of all the VAWT models are designed in CATIA. A CFD tool, ANSYS Fluent is used as a solver for all the cases. Finally, the optimized model is selected based on the high power extraction rate from VAWT.

Unmanned aquatic vehicles (UAVs) are becoming the most important type of advanced vehicles that are capable of traveling through underwater. However, the design implemented was mostly the multi-rotor configuration which is not much efficient in terms of maneuvering achievability, lifetime, etc. Therefore, there arises the need for a UAV with a high lifetime, higher efficiency in design, secure flight, and low maintenance cost. In this paper, a unique UAV is proposed, which is capable of operating efficiently in underwater. The maneuverability of this proposed UAV can be greatly achieved by doing accurate research on the hydrodynamic effect. Therefore, this study aims to analyze the hydrodynamic behavior of the flow over a UAV, to get application domain details. The sketch of such a UAV is ultimately a challenging one since it implies significant propulsive and structural design trade-offs for mission in underwater. The challenging aspect of the design has been overcome by using advanced engineering applications like CFD simulations. ANSYS-FLUENT 17.2 software is used as a CFD solver, which is used for predicting the flow properties on the fish detecting environment. Also, the conceptual design of this entire UAV design is modeled through CATIA. Finally, the best UAV is optimized and that is implemented under CFD analysis in the application regions to get the hydrodynamic environmental conditions for deployment purpose.

Wind Energy is now becoming a widely used renewable source of energy for the restructured Power system operations around the world through Electric utilities. Unpredictability and instability of wind speed and wind power are the key problems with wind power generation. For solving the underlying problems, wind speed forecasting is essential. A lot of investigation has been going on over the last few years to predict wind speed with reduced prediction errors. This article introduces a new clustering approach based on a wind speed prediction based on the Adaptive-Neuro Fuzzy Inferencing Scheme (ANFIS). For the forecast, the original wind speed data for a month is used. The clustering is done with Fuzzy-C Means (FCM) algorithm. We like to specify that, we have taken user modified IEEE-30 Bus system for validation. The proposed FCM– ANFIS method proved to be better by comparing the Root Mean Square Error (RMSE) with the existing methods.

Propeller’s noise reduction is a prime technique in complicated application-oriented unmanned aerial vehicles (UAVs) because noise is the main targeted input in radar signature. Propeller vortices are vital in the noise generation, so reduction of vortices may chance to reduce the propeller noise. Thus, in this work suggested the various edge modifications on propeller for noise reduction with the help of Computational Fluid Dynamics (CFD) analysis. Totally, six different propellers are modeled by using CATIA, in which the base propeller dimensions are 4.5 inch in pitch and 5 inch in diameter. Primarily, the noise reduction techniques used in this works are located in the various cuts such as saw-tooth cut, curvy cut, aerofoil cut at both leading and trailing edges of a propeller. Acoustic prediction on rotor through CFD is more complicated, so advanced cum flexible simulation tool must be required for the attainment of an acceptable solution. Hence, ANSYS Fluent 16.2 is used for perfect prediction for the comparative acoustic analyses of UAV’s propeller with and without edge modifications, and thereby, the optimized model is suggested for complex UAVs.

Video content analysis and video classification are some of the very tricky problems among computer vision researchers. Especially in sports videos, shared common actions appear in different sports categories like jumping is a common action for both diving and high jump. These criteria need a tool or model to classify the video with an elevated rate of accuracy. Convolution neural network is an essential tool for deep learning. This is especially suited for image recognition and classification. Alexnet, googlenet and mobilenet are the examples for previously trained network. Feature extraction, classification, and transfer learning are the three primary functions of pre-trained networks. The main purpose of video classification in sports is to support sportspersons to find the video of their own interest in training and improve the performance. This paper analyzes the performance of the pre-trained network and the result is compared with a deep convolution network with customized layers on the same dataset. The experimental results are shown with graph and table format with the measure of accuracy and performance.

This paper contrasts IEEE 802.11 and 15.4e for IoT system. Such evolving protocols are for IoT-based applications. The legacy .11 has an open channel structure, Distributed Coordinating Function (DCF). By comparison, 15.4e has different medium access control modes. Among them, DSME suits for IoT. In this paper a distinction between these two norms is explored using an empirical model and is checked by means of simulations. Outcomes reveal when combined with IEEE 802.11 legacy DCF, the DSME shows major improvements in DSME performance.

Machine learning and data mining methods plays major role in biosciences. Now-a-days data mining methods are used to intelligently transform the information available into valuable knowledge. Gestational Diabetes Mellitus (GDM) is a kind of diabetes that occur in women during pregnancy. Some women develop high blood glucose levels during their gestation. Gestational Diabetes Mellitus if ignored and untreated can result in permanent medical problems to the baby in the future. This is identified as a serious open problem of research which calls for good prediction algorithms to predict the GDM at an earlier stage of gestation. Literature shows a wide range of machine learning algorithms employed for the prediction of GDM. This paper proposes novel prediction framework for gestational diabetes based on Logistic Regression. This results of the framework show promising results of better prediction at an early stage of gestation.

Retinal disorders are progressive in nature and remain passive for years together without causing any visual indication of disorder even to the subject themselves. Hence automated and intelligent methods of analysis of retinal scanned images are quite necessary to improve accuracy and detection time to aid in early detection and consequent treatment. This paper presents the findings of a vast literature survey done with respect to automated detection techniques by analyzing their underlying principles and obtained performance results. The entire survey has been done based on two main evaluation metrics namely detection accuracy and time of convergence. This is based on the underlying principle that migration from manual and conventional methods to automated systems is to improve the accuracy by overcoming the errors incurred in manual detection methods and at the same time to reduce the painstakingly long time required in the manual method of observation and detection. This paper proposes a computation complexity reduction mechanism in dehazing by utilizing the convolution properties of deep belief neural networks to train the data sets in the least possible time with improved image quality.

Efficient compression and reliable data transmission are the major areas in the information theory that governs various applications in mobile communication, Internets and modern digital technology. The exploitation of data patterns and redundancies offers better compression without loss of user information. The trade-off between the storage and the quality is the major requirement to apply the coding scheme in very large-scale integration. Alternatively, the reliable transmission in the presence of noise requires the prior addition of redundancy to the data. Polar codes are the practical codes suitable for channel coding and optimal performance achievement under lossy compression and complexity-based communication environment. This paper focuses on the novel architecture of successive cancellation (SC)-based polar decoding for an effective communication. The coefficient weight computation during the register update consumes a number of components in traditional SC decoding architectures. This paper proposes the semi-folded parallel successive cancellation (SFPSC) algorithm that modifies the coefficient weight computation process for register update resulting in a 4-folded polar decoding architecture to retrieve the information from the transmitted symbols with reduced resource utilization. The performance characteristics of SFPSC algorithm based on FPGA implementation are presented. The decoder architecture based on SFPSC algorithm achieves an efficient resource utilization. The reduction in a number of components efficiently reduces the time consumption. The comparative analysis between the SFPSC-based decoder with the existing SC schemes regarding the number of look up tables, FFs and memory assures the effectiveness of SFPSC in resource utilization.

The main objective of this research is to develop an automated constructability rating framework for different concrete formwork systems that are commonly used for the construction of reinforced concrete residential buildings. Initially, various constructability criteria (cost, time, quality, safety and environmental sustainability) that are analogous to the concrete formwork construction are rationally characterized through an intriguing data acquisition mechanism (a complete process involving the collection, recording and processing of data) known as constructability survey. Withal, an unified 3D Building Information Modeling (BIM) Model (i.e., 3D Structural BIM Model and 3D BIM Formwork Family or Module) is developed to providence CONSTaFORM, an automated constructability assessment framework for concrete formwork systems. The CONSTaFORM is a supplementary Add-in for Autodesk Revit developed by a process called API-fication, i.e., customizing Revit API to provide additional functionalities and hence enhancing the capabilities of existing framework invariably. The optimal constructability scores of various concrete formwork systems obtained from the constructability survey are initially fed into their respective 3D BIM formwork families as shared parameters, which are later used for the computation of the overall constructability rating of the formwork systems involved in the entire project, using BIM via CONSTaFORM Add-in. To reinforce the profundity and advocacy of CONSTaFORM Add-in, a suitable case study is reported.

In today’s digital processors the everlasting demand is the enhancement of knack in processors to handle the complexities resulted in the incorporation of some of the processor cores in a single chip. Then more the load on the processor is not fewer in the primary system. This load can be minimized by augmenting the central processor with the co-processors, which are made up to work with a particular type of functions like graphics, signal processing, numeric computation, etc. Hence, in which the multiplier and accumulator (MAC) unit is most leading co-processor and toil as the heart of the digital signal processors. Faster processes are of great significance in MAC unit. Ultimate digital signal processing algorithms depend significantly on multiply and accumulator (MAC) performance. Hence after a deep study and investigation, we found that the proficiency of Urdhva-tiryagbhyam Vedic multiplication algorithm found to be superior in comparison with other conventional multipliers. The rapidity of multiplication and addition governs the execution speed and performance of entire setup. Here we will design and analyze the performance of MAC unit, and thus, the delay and area parameters are optimized using various multipliers such as Array, Wallace, Vedic multipliers, etc. The proposed method is designed using Xilinx 12.3 and tested using the Virtex5-XC5VLX110T device.

In industries, the usage of wireless sensors has been increased tremendously due to various advantages like maintenance, convenient installation, and cable cost reduction. However, the transmission of signals over a wireless channel in a harsh industrial environment is vulnerable to interference of noise resulting in the introduction of errors. This leads to the corruption of data which may result in incorrect feedback that may lead to safety-related problems or economic loss. Thus, in order to reduce the errors, forward error correction codes (FEC) can be used to improve the reliability of the signal transmission. In this paper, the implementation of convolution coding technique in an industrial environment is presented, and the performance has been evaluated in MATLAB/Simulink by comparing bit error rate (BER) with signal-to-noise ratio (SNR).

This paper demonstrates the design and development of a novel system which comprises of a command boat and Unmanned Underwater Vehicle (UUV) with which, one is able to extend the range of operation of the Remotely Operated underwater Vehicle (ROV) which is limited by the length of tether. The UUV proposed in our system can be utilized along with features and functionality of existing designs, along with an added advantage of incorporating additional accessories depending upon the application. The system comprises of three components: a control unit, command boat and an UUV. The control unit is situated in any physical planar surface. The operator controls the command boat–UUV system wirelessly, which are linked using a tether. The UUV can assist in exploration, research, search and recovery etc. In the case of search and recovery, the command boat is guided to the required location, then the system identifies the retrievable using camera and appropriate sensors, then the UUV is deployed which is further guided towards the retrievable, and a pair of robotic arms with tangible gripper recovers the retrievable (weighing atmost 2kg). The UUV and the retrievable is docked to the command boat and the system is acquired, hence completing the operation.

A bike, otherwise called a motorbike or a bike, is the most well-known method of transportation because of its minimal effort. In any case, then again, this vehicle has been the most hazardous. The point of this task is to make a cap utilizing installed frameworks (explicitly, the Arduino microcontroller). It is hard to keep our loved ones refreshed nowadays. In the event that a bike rider is associated with an impact, find the head protector from far off. The vibration sensor is connected to the Arduino board in this venture, making the interaction much simpler. On the off chance that the rider has been in an accident, the vibration sensor detects it. If so, it imparts the sign sequentially to a GSM port utilizing Arduino and implanted C programming. The sensors are associated with the Arduino board’s info pins. On the off chance that the vibration esteem passes the boundary, a sign is shipped off the GSM port with the GPS position. Subsequently, the message is shipped off to their families and companions. This savvy cap has a cooling fan and glass cleaner.

The unit commitment (UC) is highly complex to solve the increasing integrations of wind farm due to intermittent wind power fluctuation in nature. This paper presents a hybrid methodology to solve the stochastic unit commitment (SUC) problem depending on binary mixed integer generator combination with renewable energy sources (RESs). In this combination, ON/OFF tasks of the generators are likewise included to satisfy the load requirement as for the system constraints. The proposed hybrid methodology is the consolidation of grey wolf optimization algorithm (GWOA) and artificial neural network (ANN), hence it is called the hybrid GWOANN (HGWOANN) technique. Here, the GWOA algorithm is used to optimizing the best combination of thermal generators depending on uncertain wind power, minimum operating cost and system constraints – that is, thermal generators limits, start-up cost, ramp-up time, ramp-down time, etc. ANN is utilized to capture the uncertain wind power events, therefore the system ensures maximal application of wind power. The combination of HGWOANN technique guarantees the prominent use of sustainable power sources to diminish the thermal generators unit operating cost. The proposed technique is implemented in MATLAB/Simulink site and the efficiency is assessed with different existing methods. The comparative analysis demonstrates that the proposed HGWOANN approach is proficient to solve unit commitment problems and wind integration. Here, the HGWOANN method is compared with existing techniques such as PSO, BPSO, IGSA to assess the overall performance using various metrics viz. RMSE, MAPE, MBE under 50 and 100 count of trials. In the proposed approach, the range of RMSE achieves 9.26%, MAPE achieves 0.95%, MBE achieves 1% in 50 count of trials. Moreover, in 100 count of trials, the range of RMSE achieves 7.38%, MAPE achieves 1.91%, MBE achieves 2.87%.

An important mode of failure of composite insulators using polymeric materials is tracking and erosion. Traditional laboratory tests that monitor leakage current alone are not adequate for predicting failure. Two second order sliding mode observers have been evaluated to determine if they can predict failure better than monitoring leakage current alone. The efficacy of the estimation algorithms was validated via experiments and computer simulations. It has been shown probably for the first time that the observers provide a more reliable indication of failure than provided by the commonly followed practice of monitoring leakage current.

Cognitive Radio Networks is the focused research area where optimal usage of resources takes place. The spectrum scarcity is cause for searching new techniques to serve resources to the users other than privileged (primary) which are called as Secondary users. By using interleaving paradigm, the secondary users also served the license bands without interrupting Primary users. While serving the Secondary users, the preemption of secondary users takes place because of high priority of Primary users and they have to leave the system if no buffer is provisioned for the interrupted secondary users. The focus of this research paper is to compare the impact of keeping a buffer to hold the interrupted Secondary users with the case of providing no buffer in the System. A comparative study reveals the necessity and role of the buffer in the System for the provision of better service to Secondary users. The main focus of this paper is the consideration of the system performance with and without the buffer mechanism for interrupted secondary users and this scenario is modeled as M/M/C/K analytical queuing model. The differential difference equations are derived for both the cases and based on these equations, performance metric average queue length and waiting time are calculated. Finally numerical illustrations are carried out for different values of primary, secondary user arrival rates, service rate, buffer size and number of channels. The analytical results are presented through tables and graphs were shown to draw the conclusions.