The aim of this paper is to discuss an Enhanced Robust Decentralized Probabilistic Network Management using probabilistic and in network techniques. Methods: Management of distributed networks becomes difficult accounted by its ever-increasing size, complexity and pervasiveness; which makes it difficult to model accounting for its dependencies. In particular, we introduce an algorithm for peer-to-peer metric propagation, which combines the results of partial fusion from the locally made probabilistic models consistently, which help to mitigate overheads incurred in dynamic distributed systems and related redundant information gathering and processing.

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With the increase in the number of smart phone users worldwide there comes a high demand for smart phone based location systems. Within the project “Indoor positioning using inertial measurement units” an android application has been developed wherein the position of the smart phone user with respect to the floor environment is determined. This has been achieved using the smart phone sensors step detector and rotation vector which act as inbuilt inertial measurement unit (IMU) in smart phones. The algorithm used is “Pedestrian Dead Reckoning” (PDR).

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Green networking is the practice of using energy efficient networking technologies and minimizing resource use by networking devices whenever and wherever possible. The main goal is to control the power consumption of devices, and provide best way to distribute traffic load among them. The current study envisages to dwell on the concept of packet processing engines which are functionally utilizing the concept of parallel processing of input traffic within the hardware and depending upon the maximum capacity of the system. The system focuses on energy-aware devices able to reduce their energy consumption by adapting their performance according to the network requirements. Green networking technologies like Adaptive Rate (AR) and Low Power Idle (LPI) are implemented. The concept addresses logically the streamlining of processes using interventions both at hardware and software level resulting in a reduced consumption of energy in networking devices.

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Peoples meeting with road accident is one of the major issues in our nation. On the off chance that a few people groups met with street mishap, they need to save promptly to spare their lives. Be that as it may, more often than not, in view of some situation like deferring to advise to crisis responders and postponing taking the casualty to the doctor's facility and so on will prompts neglecting to spare their lives. To conquer these issues we proposed a technique called “An effective way of detecting and notifying mechanism through Android Application”. In this system we implemented a two way techniques of immediate rescue source in one android application at a spot. One way is Auto accident detection method through GPM and GPS, another way is through registering the victim details by third person in a spot. In GSM and GPS Auto accident detection method, the person can view the nearby hospital while traveling on vehicle through our installed Android app, unfortunately if the person met with an accident an automatic alert message will send to the nearby hospital through this app. Another way is registration method, in this method, suppose a victim person doesn’t have this app in their mobile, a third person in their mobile using this application can register the victim details and their vehicle details at the spot. Once he/she registered at a spot, an alert message along with registered details will send immediately to the nearby hospital through GSM network, and the rescuer from the hospital can attend the victims quickly and can save their lives. Therefore this technique will be more useful to their peoples who met with an accident and save their precious life.

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Wireless Sensor Network (WSN) is becoming a very important area of research in today’s world and contributes a lot in the field of technology. Reducing energy consumption and improving the network lifetime is the key factor to be considered. In this paper, we propose a new protocol namely Minimizing Energy Consumption Using Unequal Clustering (MECUC). MECUC attempts to improve energy efficiency using duty cycling and unequal clustering concepts. The inclusion of duty cycling in MECUC is expected to provide a better balancing of energy amongst the sensor nodes. Selection of relay CH in MECUC is based on (i) distance to BS (ii) residual energy of the nodes and (iii) node proximity. Performances of MECUC are compared with the performances of the existing protocol EADUC and the obtained results show that operation of DTUCFA is found to be better than EADUC in terms of delay, energy consumption, packet loss ratio and packet received ratio.

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Important research efforts and attention have been received by Full Duplex 5G mobile wireless network from both the academia and industry. This kind of wireless network which provides 5G mobile network for full duplex are predictable to provide self-interference migration framework for a wide range of services that applications and users with a very different requirement for the enhancement of performance and energy savings for mobile, devices with battery powered and video content delivery with different strategies and provides different delay bounded QOS (Quality Of Services). In this research work, a self-interference migration framework for overcoming the challenges in 5G mobile network and also delay bounded QOS. The cancellation of self-interference offers the potential to complement and denser heterogeneous network sustain the evolution of 5G mobile networks which can be utilized by wireless communication system in multiple ways by including increased capacity of network, reliability, decreased path loss, packet loss. Self-interference has an impact on over all 5G network.

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With the huge production of Wi-Fi-enabled devices, people expect to be able to use them everywhere. Wi-Fi routers generally resort to simply sharing the password. Security and liability issues set obstacles to Wi-Fi networks. However, existing authentication methodologies breach user's location privacy. To this end, we present a Wi-Fi Authentication using Visual Cryptography, called Wi-Fi AVC, and the related devices for network discovery and authentication. Our prototype implementation uses Wi-Fi enabled device requests to connect the legitimate neighboring devices. Adopting Wi-Fi AVC cannot only enhance the uses of secure authentication, ultimately providing research directions for constructing advanced ubiquitous society.

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Indoor Positioning System (IPS) is one of the emerging technologies, such that locating the user’s position in indoor environment using GPS has many obstacles by the indoor objects such as walls, pillars, glass doors etc. To overcome these obstacles indoor positioning system is used. This paper is about positioning the smart phone user inside the building in real time using smart phone sensors. The smart phone sensors used for the experiment were Magnetometer and pedometer, using these sensors the smart phone user can track his/her position inside the building. This can be achieved by calculating the magnetic field strength from the pillar that present inside the building The variation in the magnetic field is noted and it varies from each pillar in the building and this variation in the magnetic field helps the user to know his/her position inside the building as they walk through in the building.

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Cloud computing provides numerous resources to end users. Users can access these resources through internet. Load balancing is very important technique. Max-Min and Min-Min algorithms are proposed for load balancing in cloud computing. These algorithms are implemented based on study of Cloud Resource Aware Scheduling Algorithm (CRASA). Advantages of both the algorithms are used by CRASA to overcome their drawbacks. The drawbacks of these algorithms are in Min-Min the larger tasks have to starve for VM and in Max-Min smaller tasks have to starve for VM allocation. CRASA is achieved by estimating the execution time and finish time of all the tasks on each resource that is available and then alternatively applies Max-Min and Min-Min algorithms. In this paper, the discussion is about CRASA on scheduling tasks that are independent for demonstrating the applicability of CRASA so as to achieve schedules that have lower make span.

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Mobile browsers use client side efficiency measures such as larger cache storage and fewer plugins. However, the impact on data security of such measured is an understudied area. This paper reveals a method to scrutinize the security of lightweight browsers. Using this opposing model, we disclose previously unknown vulnerabilities in the following popular light browsers, namely: UC Browser, Dolphin, CM Browser, and Samsung Stock Browser, which allows an attacker to obtain unauthorized access to the user’s private data. The latter include browser history, email content, and bank account details. The main issue with the storage remains the fact that all the cache is stored in the browser’s folder in the external memory card of the android device. With the way permissions work in android, this cache folder can easily be accessed by the other apps with the same permissions. Therefore, it is advisable that all the sensitive browser cache be stored in the internal memory while large files such as video clips and images can be stored in the external memory if memory space is a constraint. It is usually advisable to wipe the cache stored by the applications, but in this case the utility of the browser will cease. Thus, to make the cache more secure we propose to encrypt the data using a Java crypto and implement the Advanced Encryption Standard or AES. This encryption standard is a symmetric block cipher and most hardware and software vendors use this standard for protecting the sensitive data.

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Analytics often involves studying past historical data to research potential trends. Weather condition is the state of atmosphere at a given time in terms of weather variables like rainfall, cloud conditions, temperature, etc., The existing models use data mining techniques to predict the rainfall. The main disadvantage of these systems is that it doesn’t provide an estimate of the predicted rainfall. The system calculates average of values and understand the state of atmosphere, which doesn’t yield estimate results. This paper represents a mathematical method called Linear Regression to predict the rainfall in various districts in southern states of India. The Linear Regression method is modified in order to obtain the most optimum error percentage by iterating and adding some percentage of error to the input values. This method provides an estimate of rainfall using different atmospheric parameters like average temperature and cloud cover to predict the rainfall. The linear regression is applied on the set of data and the coefficients are used to predict the rainfall based on the corresponding values of the parameters. The main advantage of this model is that this model estimates the rainfall based on the previous correlation between the different atmospheric parameters. Thus, an estimate value of what the rainfall could be at a given time period and place can be found easily.

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High power consumption is one of the major problem for the cloud service providers. Also high power consumption results in maximum amount of carbon-di-oxide emission and this leads to environment pollution. Hence an efficient algorithm is used to optimize the cloud resources in Data Centers (DC). The main objective of this paper is to minimize the total power consumption of DC. Resource consolidation algorithm was used to maximize the resource utilization with minimum cost. Based on our experimental results our proposed algorithm reduces the power consumption, improves the resource utilization and also to reduce the user’s cost. Minimum power consumption leads to minimum carbon-di-oxide emission which leads to attain environment sustainability.

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Information security is one of the crucial issues in data transmission through Cloud Environment. Cloud environment are extremely exposed to security attacks and consign a great challenge today. This paper overviews looks at and advise scientists about the most recent created IDPSs and caution administration techniques by executing an entire scientific classification and surveying conceivable answers for identify and forestall interruptions in distributed computing frameworks. The study of these examination works is limited in view of the gave scientific classification which was portrayed in the paper. Distributed computing is just as of late embracing around the world; in this manner, there are not very many handy and exploratory interruption location frameworks prevail in this present reality. Notwithstanding every one of the fringes and restrictions of the momentum cutting edge, this exploration study depends on what criteria and necessities ought to an IDPS fulfill to be sent on distributed computing situations and which strategies or methods can meet these requests.

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Emotion recognition through physiological recording is an emerging field of research with many promising results. This work is involved in the construction of a device used to identify basic human emotions indexed by Electrodermal Activity (EDA) in real time; using non-invasive sensors in contact with the skin. The device measures changes in Skin Conductance Level (SCL) caused due to stimulating signals from brain which results from sympathetic neural activity using Ag/AgCl electrodes placed on the ventral side of the distal forearm to evaluate the emotions of the user outside the constrained laboratory environment without interrupting the normal daily routine. The device consists of an embedded system for EDA signal acquisition and a wireless communication module to send processed EDA signals to a remote system. A vibrator attached to the device is used to provide user feedback.

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This paper aims at applying the techniques of deep learning and study the behavior of its effective score comparing with traditional approaches like supervised learning and proposed to come up with a revised algorithm with application towards hand written character recognition using the principles of deep learning architecture and analyze its performance with the conjunction of benchmarking machine learning dataset like MNIST. Hand written character recognition is achieved using the deep learning model namely Deep Belief Network which is trained using a simple Restricted Boltzmann Machine (simple RBM) and three layers of Restricted Boltzmann Machine (stacked RBM). The performance of our model shows 92% accuracy. This shows that it outperforms the traditional supervised learning methods. This method can be extended to efficient text extraction in complex images.

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Face Recognition is the process of identifying the face from digital image and video. Face Recognition in video is challenging and long standing problem. Face Recognition in video is going to become most research area of Biometric, pattern recognition and computer vision. In this paper we propose the hybrid feature of Principal component analysis and Linear Discriminate analysis to recognize the face in video. This Face recognition system is more useful and it provides high accuracy and robustness with less computational time. Also the use of hybrid algorithm yields better result in comparison with individual PCA or LDA uses in face recognition.

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Encompasses the urgent need for health monitoring of patients. Human vital parameters such as Electrocardiography (ECG), Blood Pressure (BP), Oxygen Saturation - Sp02,Pulse Rate (bpm), and Body Temperature have been taken to predict any critical human health from Patient records of hospitals and health organization research documentation explains the parametric variation associated with the symptoms and by considering these variations the diseases can be identified to acknowledge the human body conditions and this has further been characterized into three major categories, Normal human health condition, and Maximum and Minimum level. A stochastic model has been developed to assess these variations using R Programming wherein the linear regression gives the accuracy of the values about the parameters. Using MS-SQL database is created. Further two matrices have been constructed, one having symptoms along with their associated diseases and the other having disease with the approved drugs that are available. These data values have then been fetched into the ASP.NET to create a web page for coming in handy to the patients in emergency situations Orin cases where home healthcare is being done for the patient. The users can select the symptom from the webpage and on submission, the disease with which the patient is suffering will be displayed and this further directs the drugs that can be provided to the patient.

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An Automatic Voltage Regulator (AVR) system maintains a constant terminal voltage irrespective of the load. The output of an AVR system without a proper controller has undesirable time domain specifications when subjected to a particular input. A Proportional-Integral-Derivative (PID) controller cascaded with the AVR system enables a stable control method resulting in optimum time domain specifications. PID controller is one of the most popularly used control algorithm in industrial problems. The controller must be tuned to obtain the best possible values for the three gains namely proportional (P), integral (I), and derivative (D) in order to achieve the desired performance by meeting the design requirements. Automatic tuning of PID controller is one of the feasible option for real time application of AVR systems. In general, nature inspired evolutionary algorithms are employed for optimal tuning of the PID controller for the given system. In this paper, Differential Evolution (DE) algorithm which is one the evolutionary algorithms is used for the optimal tuning of PID controller. The choice of the fitness function plays a crucial role in the tuning process. Different fitness functions are used for optimal tuning of PID controller and the obtained results are compared and presented for the given AVR system in this paper.

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CPU scheduling is an integral part of any operating system and defines the basic functionality of an operating system. A scheduling algorithm is in-tended to execute user and system requests with the highest efficiency possible. The algorithm is responsible for analyzing the processes, choosing and dispatching the most appropriate process for execution. Two of the most commonly used scheduling algorithms are the Round Robin (RR) algorithm and the Priority Scheduling algorithm. However, they both have their own pros and cons with respect to the qualities of service provided by a good scheduling algorithm. A new scheduling algorithm DFRRS (Dynamically Factored Round Robin Scheduling) has been developed to improve the performance of Round Robin Scheduler by incorporating the features of Priority Scheduling and SJF algorithm. A comparative analysis of Turnaround and Waiting Time is shown with the help of Bar Graphs (Histograms).

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Cloud computing is outsourcing of computing resources over the Internet where we can be connected to remote locations and can use the services over the Internet at another location to store our important information. The cloud service requirements provide access to advanced software applications. In cloud computing, the network of remote servers is used to process data. Workflow scheduling is one important issues in cloud. Scheduling of workflows is an NP complete problem. For NP complete problems, traditional scheduling algorithms do not provide optimal solution in polynomial time. In this paper a hybrid workflow scheduling algorithm is discussed for IaaS cloud environment. For simulation of the algorithm, WorkFlowSim simulator, an extension of CloudSim simulator, has been used. We have used varying types of workflows and it has been observed that the hybrid algorithm gives better result than the traditional PSO algorithm.

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Tissue engineering scientists believe that the next generation of functional tissue and artificial organ replacements truly need the use of advanced bioreactor system. Bioreactor system, in which the culture conditions can be adjusted and studied, will support the development of tissues with optimal mechanical, chemical, and biological stimuli for a given application. Although there have been various types of bioreactors designed and tested for several implantable biomaterials and tissue engineering applications, the development of a complete artificial organ remains a dream. This review addresses recent advances and future challenges in designing and using advanced bioreactor system to support the mass production of vascularized engineering tissues and artificial organ. The potential application of micro-electromechanical system (MEMS) bioreactor technology for future advancement in tissue engineering is also highlighted.

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This paper shows the calibration process of base flow separation methods. Six (6) base flow separation methods were used for this study. The main input for this research was discharge data from 54 watersheds in East Java. Firstly, each method is calibrated using daily discharge data for each year (annually) to separate base flow. Then, optimal parameter values are obtained by averaging the annual values. Calibration process produces optimal parameters value for each watershed. Furthermore, validation is performed using optimal parameter values from watershed having complete discharge data to other watersheds. The average RMSE values range for all methods are: 0.30 to 0.38 for calibration process, and 0.27 to 0.36 for validation process. It appears that the parameters values from calibrated watersheds are transferable to validation watersheds on the same boundary of UPT.

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This experiment aims to investigate the electrical conductivity and the resonance frequency of thin films. The materials of thin film made from 7059 corning glass substrate, lithium acetate, tantalum pentoxide, gallium oxide and also using 2-methoxyethanol as solvent. The growth of thin film is done by using the tool of spin coater at 7059 corning glass substrate with dimension 1?1 cm2 and annealed in furnace for eight hours with a temperature of 550oC. Thin films also have been prepared by Metal Organic Chemical Vapor Deposition (MOCVD) technique to create the aluminum contacts at 7059 corning glass substrate and the thin film layer with dimension 2?2 mm2. The thin film is measured by using LCR meter to found the data of conductance and phase in range frequency at 50Hz - 5MHz. The results of electrical conductivity curve shows in range 10-8 - 10-6 and LiTaO3 with doping (10%) Ga2O3 shows increasing the number of resonance frequency. Based on these results, it can be concluded that the thin films classified into semiconductor material and 10% Ga2O3-doped LiTaO3 generates the increasing number of resonance because of vibration from Ga2O3 ion.

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Partial discharge in transformer insulation can be detected by the product produced during the PD event. Electromagnetic signal is one kind of the product that produces by the PD event. By using appropriate sensor, the electromagnetic signal can be captured thus detect the PD event in the transformer insulation. The method capturing the electromagnetic signals to detect PD event in transformer has advantage compare to other PD detection methods. The advantage mainly due the electromagnetic signal prone to the disturbance noise around the transformer. To be able to capture the electromagnetic signals, a sensor with capability to detect the electromagnetic signals is needed. The capability of a sensor can be tested using a TEM cell. In this paper discussed the design of TEM cell (Transverse Electromagnetic cell) which able to test a sensor such the sensor which use to detect the PD event in transformer. The TEM cell is an open cell type and construct using alumina as the material. The TEM cell has length of 1200 mm and height 105 mm. The TEM cell shown has a good capability to test the sensor which designed to detect the PD in transformer.

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This paper focuses on developing the role of people participation through solid waste banks (SWB) and 3R waste treatment facilities (TPS 3R) for mitigating global warming in Padang City. Current municipal solid waste (MSW) management and 3 improved scenarios were simulated for the next 20 years to calculate the impact on global warming. Greenhouse gases (GHG) inventory of waste treatment activities was carried out using LCA methodology. Meanwhile, methane emission from solid waste decomposition at landfill was calculated using IPCC software. Current MSW management practices show the achievement of waste recycling rate was only 2.178 % of total waste generation in 2015. Simulation results also show that implementing the current practice will release GHG emissions of 123.54 Gg CO2eq in 2035. Improved scenario #3 suggests to increase the number of SWB, TPS 3R, integrated waste treatment facilities (TPST) and to install methane gas recovery. This improvement increases the recycling rate to around 34 % and reduces GHG emissions by around 57 %.

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Power inverter is a kind of power electronic converter that used to convert a dc input voltage to an Ac output voltage. In solar power plant application, the PV inverter converts the Dc voltage from Solar PV panel, which is usually stored in the battery, into an ac output voltage to serve the load of household appliances, such as lighting, television, mobile charger, even a washing machine and water pump. Therefore, a reliable inverter that can produce a good output voltage is necessary. The main purpose of this paper is to design and develop a dynamic evolution control (DEC) for a PV Inverter in solar power plant application. The analysis and design of the DEC control technique are provided. The performance of the PV inverter controller is verified through MATLAB Simulink. To validate the simulation results, an experimental prototype of PV inverter is developed. The controller of the PV inverter system was implemented based on dynamic evolution control. The performance of the proposed dynamic evolution control is tested through simulation and experiment.

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Oil palm empty fruit bunches (OPEFB) is one of the solid waste which can be used as raw material for pulp. The objective of this research was to determine the effect of acetic acid: formic acid ratio on the characteristics of OPEFB pulp. The result showed that the increasing of the amount of formic acid can reduce levels of cellulose, hemicellulose, lignin and pulp yield. The best results was achieved on the treatment ratio of acetic acid and formic acid = 85:15 which produced 73, 75% cellulose, 7.78% hemicellulose, 1.61% lignin, and 32.57% yield.

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The microstructures of as-cast and heat-treated Co-Cr-Mo with addition of C, Si and Mn have been investigated with a focus on phase and dissolution of precipitates. The heat treatment temperatures and heating periods employed ranged from 1448 to 1548 K and 0 to 43.2 ks, respectively. The precipitates observed in the as-cast and heat-treated alloys were carbides (M23C6, eta-phase, and pi-phase) and an intermetallic compound of chi-phase. Chi-phase was detected in the sample with lowest carbon content of this study, 0.15 wt%. The addition of Si seemed to increase the heating time for complete precipitate dissolution because of the effects of Si on the promotion of pi-phase formation at high temperatures and the increased carbon activity in the Co matrix. After polarization test in simulated body fluid to the single phase-contained specimens, it showed that chi-phase drastically decreased the corrosion resistance of the alloy. The alloys with chi-phase and M23C6 precipitates have lower corrosion resistance than those with pi-phase.

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This study aims to investigate dielectric constant and impedance characteristic of thin films made from LiTaO3 without (0%) and with doping (10%) Ga2O3. The solubility of LiTaO3 for the purpose of this study is regulated at 1 M by using 2-methoxyethanol [(CH3OCH2CH2OH)] as solvent. The growth of thin films on 7059 corning glass substrate was processed by Chemical Solution Deposition (CSD) method. By using a spin coater device on a speed of 3000 rpm for 30 seconds, the substrate is then annealed in furnace at a temperature of 550oC for eight hours. To create aluminum contacts, the thin films were then further processed by involving Metal Organic Chemical Vapor Deposition (MOCVD) technique. The final thin films were measured by LCR meter to provide certain data such as: inductance, capacitance and resistance values in range frequency from 50 Hz to 5 MHz. The study concludes that the film thickness (made from LiTaO3 without (0%) and with doping (10%) Ga2O3) as separator material between two aluminum contacts that embedded on 7059 corning glass substrate was affected not only by dielectric constant but also by magnitude of impedance that contributes to providing information about ionic phenomenon.

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The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the sub domain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation.

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We consider passive polarimetric near infra-red imaging systems that measure the three first elements of the Stokes vector and deduce from them the degree of linear polarization and the angle of polarization in near infra-red spectrum for analyzing plant leaves. By using the variance of the angel of polarization from each sample leaf, we identify the curl of the leaf surface and compare it to other sample leaves. The identification will be useful for the next research and application in leaves classification, especially in plant diseases and its level which can be detected by its leaves.

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The effect of samarium content on magnetic interaction intensity of cobalt samarium alloys in the form of thin films deposited on silicon (100) substrates has been studied. These films were fabricated using Dc magnetron sputtering technique. It was found that the intensity of magnetic interaction between grains in the films was reduced as samarium content increased. It was also showed that the coercivity of the films increases and reaches a maximum value at around 19 - 22 atomic % samarium, followed by a decrease with further increase in samarium concentration. The hysteresis loop squareness of the samples decreases as samarium content increases. Moreover, the degree of crystalline of the films decreases as samarium concentration is increased. Thus the increase of coercivity of the films in this range is also discussed.

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As an alternative to full Gaussianity, multivariate normal-Poisson model has been recently introduced. The model is composed by a univariate Poisson variable, and the remaining random variables given the Poisson one are real independent Gaussian variables with the same variance equal to the Poisson component. Under the statistical aspect of the generalized variance of normal-Poisson model, the parameter of the unobserved Poisson variable is estimated through a standardized generalized variance of the observations from the normal components. The proposed estimation is successfully evaluated through simulation study.

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This paper proposes a modified Carson method for modeling 20 kV overhead medium voltage twisted insulated cables (MVTIC) commonly found in the distribution systems in Lampung Province, Indonesia. Different cable sizes are considered in this work. Results are then compared to those obtained from OpenDSS and ETAP (with SPLN library). The sequence impedances obtained from the method are then utilized in the developed three-phase power flow software to analyze a real medium voltage distribution feeder consisting 119 buses and serving rural area.

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The concept of phasor measurement and its application in power system monitoring and control are introduced in this paper. The example of application of PMU in a Campus Wide Area Monitoring System (WAMS) is then elaborated. With the availability of synchro phasor data measurement, it makes possible to establish the power system monitoring by directly employing spectral analysis of power or phase angle response signals using Fourier Transforms or Short Time Fourier Transform, Prony or Wavelet analysis technique or any other combination among those methods. Moreover, using the wide area signal provided by PMU, power system stability control also gain benefit through the wide area control technique where the system provide a better response toward the inter-area oscillation compared to the conventional damping controller. Finally, at the end of this work, the application of phasor measurement technology in the small signal stability monitoring and the wide area control based on phasor measurement data are discussed.

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Polylacticacid (PLA) is used extensively for the design of drug delivery systems for peptides and vaccines, for the manufacture of medical devices and wound dressings, as well as for fabricating scaffolds in tissue engineering. Moreover, the polymer can be formulated with a variety of desirable physical properties and degradation rates, making it extremely versatile. PLA is traditionally manufactured in a three-phase process: (1) fermentation by various strains of Lactobacillus to produce lactic acid; (2) recovery of lactic acid from the fermentation broth, and (3) polymerization of the lactic acid. In this study, in order to achieve the low cost production, using the inedible waste from cassava waste as the substrate, lactic acid fermentation was conducted. Afterward, the lactic acid in the fermentation broth was separated by ion exchange resin. The model solutions were hydrolyzed to convert oligomers to monomer. The results of this research show that in batch adsorption of lactic acid (HLa) solution, the resin WA30 has the highest value of adsorbed concentration solution compared to the resins of Amberlite IRA A 400, SA 10A, WK 10 and PK 228. The amount of lactic acid exchanged decreases with increasing temperature. The maximum resin capability of lactic acid on WA 30 resin is much higher than the theoretical value which is separately measure with HCL method. Calculated from Langmuir equation; it was found that the adsorbed capacity for model was 128.8 mg/g-resin while for fermentation broth of 100 mg/g-resin.

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This paper investigates the slaking behavior of several kinds of crushed mudstone and its mechanical consequences using a comprehensive set of experimental data obtained through accelerated slaking tests and newly developed one-dimensional compression slaking tests. These data confirm that slaking in crushed mudstone is accompanied by a variation in the particle size distribution during wetting and drying cycles, and a variation in grading results in an irreversible change in mechanical characteristics, such as the reference packing density. Significant compression is also found to occur without any change in effective confining stress. The results of XRD (X-ray diffraction) analysis and scanning electron microscopy (SEM) are also used to elucidate the effects of mineralogy and particle texture on the slaking characteristics of crushed mudstone. Finally, it is also mentioned that constitutive modelling can take slaking into consideration by describing the evolution of an appropriate grading index due to slaking, and then linking this to reference packing density.

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In this paper, pricing schemes were set up on wireless internet of multi service network to the improved models as Internet service providers (ISPs) require new pricing schemes to maximize revenue and provide high quality of service to end users. The model was formed by improving the original model together with the model of multi- service network by setting the base price (a) and premium quality (ß) as variables and constants. The models are solved by the program Lingo 11.0 to get the optimal solution. The results show that the improved models yield maximum revenue for ISP.ISP’ maximum income is obtained by applying the improved model by setting up a variable a and ß as constant as well as by increasing the cost of all the changes in QoS and QoS value.

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Unmanned Aerial Vehicles (UAV) is being suggested for critical applications like border surveillance, difficult mapping and atmosphere studies. Especially in military application, it saves money and lives of soldiers compared to manned military aircraft. Major problems in the forest border region are human – animal interaction and animals poaching. With the help of the complete surveillance, these problems can able to solve. Nowadays the surveillance around the forest has been carried out by forest officers. Monitoring the forest through officers is not complete surveillance, due to the natural factors of human beings may be the error will occur in the monitoring. To avoid this problem, the monitoring of the forest region is to be covered by UAV. Animal activities and large forest coverage area is difficult task to handle in forest monitoring, so a normal UAV is not suitable for this process, hence Hexacopter is a better solution for this monitoring because it’s a have unique characters like high operational speed, more stability, etc. The proposed Hexacopter would be capable of the vertical take-off and landing feature like a rotary wing aircraft and also the six propellers provides the more stability and high manuvering capability during the monitoring process. This Hexacopter payload is about 1kg and total dimension is of 500mm x 500mm, the main purpose of this hexacopter is to provide the wealthy surveillance in the forest and gives an alert about the animal’s exact position and takes necessary action. The estimated design of hexacopter has been modeled by CATIA. With the help of the MATLAB 10.0 the image processing has been carried out in order to find the animal detection, animal hunting and animal counting. The emergency system will be activating in account of error animal matching percentage output from image processing.

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Ransomware is an emerging cyber threat having a clear intention of money making in the form of ransom. This timeits not a virus or a root-kit but its a whole bunch of malware targeting innocent users for financial gain. The main motive behind such attacks is to directly or indirectly drain out the money from the victims. Ransomware is one of the popular malware today which turns out to be the most advanced of its class. More than 60% of the Ransomware gets into the system through drive-by downloads. The format of the drive-by download has changed drastically in the last few years. What was disorganized has now evolved as a sophisticated source of distributing Ransomware. We are now in the era where drive-by downloads are mostly controlled by Exploit Kits aka EK. Depending upon the vulnerabilities, the control panel of the Exploit Kit will choose a malware for you. In this paper, we have analysed the droppers, Cerber and Crypt XXX Ransomware which was distributed by Rig Ek during the year 2016-17. We have also intercepted and dissected around 10 different variants of Rig EK communication and reverse engineered its working methodology. At the end, we have proposed a framework for the detection of malicious communication and prevention of the user data, from being encrypted by using a combination of Software Defined Networking and Certificate Authority Checker aka CAC respectively.

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Modeling of Permanent Magnet synchronous generator (PMSG) based wind energy conversion system is described throughout this paper. The wind turbine is connected across the load via super-lift boost converter and PMSG generator. Super-lift boost converter enhances the voltage gain and output voltage in geometric progression. The ultimate aim of this paper is to extort utmost power commencing from the air stream with high efficiency. To achieve maximum power, a conventional perturbs and observes MPPT because of its simple and robust control, which permits the PMSG to operate at optimal power. The proposed model can be simulated by MATLAB software, and simulated results are given to illustrate the validity of the proposed system with the conventional wind power system.

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Software Defined Networking (SDN) offers programmability at multiple layers of the network and flexibility in deriving abstractions, traffic engineering, security and orchestration services. This emerging paradigm of open network operating systems, network virtualization and SDN initiates new perspectives in network, but also presents new security challenges in the modern networks. In this paper, we investigate some of the major problems in securing the SDN architecture such as: detection of Side channel attacks, targeted control plane and data plane attacks, ensuring security policies as defined by the applications and chain of trust across all the SDN elements such as network switches, controllers, middle-boxes, end-point hosts, applications. The new features from SDN paradigm may help solve many of the current hard security problems, as these advanced functions are added; we need to ensure security in the design and operations. This paper contributes to a detailed analysis of the vulnerabilities in the SDN architecture and a discussion of the state of the art in countermeasures for securing modern network operating systems. We also propose a comprehensive approach for operational security monitoring and defending the SDN stack. This novel Multi-Plane correlated attack detection framework for SDN Operational Security (MP-SNOS) has a suite of real-time analytics engines that: analyses multiple data planes, traffic flows and patterns, network and protocols indicators, violations, attack signatures in control, data and application layers. Our multi plane cooperative design bases decisions on multiple data points, observations and indicators rather than an isolated event or data point at one layer or plane. The proposed design for a secure SDN architecture and open network operating systems is highly portable; extensible that may not require special hardware or any major software modifications to elements in a network environment. This secure SDN framework can be seamlessly deployed/integrated in the modern networks as an advanced real-time monitoring, operational security and defense System for securing modern Cloud, Software-Defined Data Center, SD-WAN, SDX, IoT, Smart Cities, Connected Health, Wireless and Vehicular networks.

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Compromises on data integrity and confidentiality have exposed the vulnerability of security architectures of traditional Linux-based operating systems against malicious attacks. Minimized functionality and increased complexity restrict the effectiveness of traditional approaches such as sandboxing in handling attacks. We proposed architecture based on restricted user privileges and authorization to secure the Linux operating system. We developed a Secure Daemon to authorize the system calls. All the system calls invoked by user processes are redirected to secure daemon using a dynamic dispatch mechanism (wrapper functions) implemented on top of the existing libraries. Our approach ensures that critical system resources are protected in the event of an attack. Since the major elements of the proposed system operate at the user level, it is portable across all Linux distributions.

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In the enormous stream of network traffic, there is no way to identify which packet is benign and which is an anomaly packet. Hence, we intend to develop a new network intrusion detection model using apache-spark to improve the performance and to detect the intrusions while handling the colossal stream of network traffic in IDS. The model can detect known intrusion effectively using real-time analytics and hence identify unknown data schema compared with traditional IDS. The objective of the model addresses the following capabilities: Deep Packet Inspection (DPI) by inspecting the network traffic and examining the properties that describe the intrusion characteristics. Collaborating the vulnerability assessment with human intervention, using C.45 decision tree algorithm, optimizes pattern matching to boost detection rate. The clustered hosts are grouped based on their number of visits in an unique IP. The intrusion classifiers are developed by investigating each IP groups which reflects different properties used for prediction. The prediction model is built over Amrita Big Data Apache-Spark framework as a sequence of workflows. The above workflow is implemented in Amrita Big Data Framework (ABDF) to improve the detection time and performance, the model output provides effective results in detecting DOS attacks and port scanning attacks.

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The security of cloud data is at risk as it is been stored by third party providers. Data tampering and unauthentic access of sensitive data by insiders and outsiders are the persisting security concerns of a cloud environment. Despite of all these security drawbacks, cloud computing finds place in various businesses and health care industries. The level of security requirement needed while adopting cloud computing into health care is much higher. In this paper, we address the authentication and integrity issues in cloud focusing the health care domain. We have proposed three major techniques: Data coloring based watermarking, Fog computing and User behavior profiling that resolves the above-mentioned issues. In the end, we provide the experimental results based on our analysis.

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The threat of malwares have been existing since the 1988-89, previously people were not connected to the internet the way they are now, the number of incidents of malware were very few as compared to what we see today. Ransomware is a form of malware which has existed since the very beginning but it rose to its glory in recent years. This paper talks about the importance of education and need of awareness among internet users against the recent threats that ransomware poses. The paper is aimed to understand the Ransomwares journey over the past decade. Static analysis is done on to understand the similarity and dissimilarity among ransomwares. Crypto-currency’s contribution in ransomware and anonymity while transaction strengthens ransomwares dominance. Furthermore the paper gives an insight on why brute force is not the solution. The results obtained from our analysis helps to distinguish the different workings structures of ransomware and also new findings on the communication that ransomware establishes gives us sufficient data that can be useful to come-up with a better defense mechanism against ransomware and its spread.

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