Seref Sagiroglu

Big data: A review

Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining richer and deeper insights and getting an advantage over the competition. For this reason, big data implementations need to be analyzed and executed as accurately as possible. This paper presents an overview of big datas content, scope, samples, methods, advantages and challenges and discusses privacy concern on it.

Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks

new method for calculating the resonant frequency of an equilateral triangular microstrip patch antenna, based on artificial neural networks, is presented. The back-propagation algorithm is used to train the networks. The theoretical resonant frequency results obtained by using this method are in very good agreement with the experimental results reported elsewhere.

Training multilayered perceptrons for pattern recognition: a comparative study of four training algorithms

This paper presents an overview of four algorithms used for training multilayered perceptron (MLP) neural networks and the results of applying those algorithms to teach different MLPs to recognise control chart patterns and classify wood veneer defects. The algorithms studied are Backpropagation (BP), Quickprop (QP), Delta-Bar-Delta (DBD) and Extended-Delta-Bar-Delta (EDBD). The results show that, overall, BP was the best algorithm for the two applications tested.

Artificial Neural Networks for the Resonant Resistance Calculation of Electrically Thin and Thick Rectangular Microstrip Antennas

A new method for calculating the resonant resistance of electrically thin and thick rectangular microstrip patch antennas, based on the artificial neural networks, is presented. The four learning algorithms, the backpropagation, the delta-bar-delta, the quick propagation, and the extended-delta-bar-delta, are used to train the networks. The theoretical resonant resistance results obtained by using this method are in very good agreement with the experimental results available in the literature.A new method for calculating the resonant resistance of electrically thin and thick rectangular microstrip patch antennas, based on the artificial neural networks, is presented. The four learning algorithms, the backpropagation, the delta-bar-delta, the quick propagation, and the extended-delta-bar-delta, are used to train the networks. The theoretical resonant resistance results obtained by using this method are in very good agreement with the experimental results available in the literature.

A novel web-based laboratory for DC motor experiments

This article introduces a novel web‐based DC motor laboratory, called NeTRe‐LAB, to support teaching electrical machines. The NeTRe‐LAB includes favorable animations to facilitate understanding characteristics of the DC motor, interactive DC motor models for instructive simulations, and web‐based experiments with online monitoring features. To illustrate the current operations, remote access to an experimental setup of a DC motor has been provided and the speed control of it has been realized over Internet. The evaluation results have shown that the NeTRe‐LAB presented in this study provides encouraging aspects, services, and support system for higher education.

The design and analysis of a 5-level cascaded voltage source inverter with low THD

Multilevel inverters have been important devices developed in recent years, owing to their capability to increase the voltage and power delivered to the load. Researches done based on basic inverter topologies show that, multilevel inverters have many advantages such as low power dissipation on power switches, low harmonic and low electromagnetic interference (EMI) outputs. A modified Sinusoidal Pulse Width Modulation (SPWM) modulator that reduces output harmonics is presented in this paper. The proposed modulation technique can be easily applied to any multilevel inverter topology carrying out the necessary calculations. The most common multilevel inverter topologies have been studied to define the best topology for SPWM modulation strategy. It is seen that cascaded H-bridges are the most convenient solution. The cascaded H-bridge cells have been constituted by IGBT semiconductors, and switched by the proposed 24-channel SPWM modulator to obtain 5-level output at the back-end of the 3-phase voltage source inverter (VSI). The designed H-bridge cells have a strong switching bandwidth up to 40 KHz, owing to its robustly designed modulator block. The proposed VSI in this paper also has a Total Harmonic Distortion ratio of output current (THDi) around at 0.1% without requiring any filtering circuit. The harmonic analysis of proposed design has been executed under several working conditions such as various switching frequencies and modulation indexes. The detailed comparisons have been performed to determine the best working conditions of VSI and presented in this paper.

Comparison of Neural Networks for Resonant Frequency Computation of Electrically Thin and Thick Rectangular Microstrip Antennas

Neural models for calculating the resonant frequency of electrically thin and thick rectangular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Six learning algorithms, backpropagation, delta-bar-delta, extended-delta-bar-delta, quick-propagation, directed random search and genetic algorithms, are used to train the multilayered perceptrons. The radial basis function network is trained according to its learning strategy. The reason for using six different learning algorithms and two different structures is to speed up the training time and to compare the performance of neural models for this specific application. The resonant frequency results obtained by using neural models are in very good agreement with the experimental results available in the literature. When the performances of neural models are compared with each other, the best results for training and test were obtained from the radial basis function network

A survey on security and privacy issues in big data

Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost every industry is trying to cope with this huge data. Big data phenomenon has begun to gain importance. However, it is not only very difficult to store big data and analyse them with traditional applications, but also it has challenging privacy and security problems. For this reason, this paper discusses the big data, its ecosystem, concerns on big data and presents comparative view of big data privacy and security approaches in literature in terms of infrastructure, application, and data. By grouping these applications an overall perspective of security and privacy issues in big data is suggested.

A Novel Integrated Web Based Learning System for Electrical Machines Education

This study introduces a novel web based learning tool for electrical machines education. The tool developed consists of the real time experiments remotely accessible via internet in addition to thereotical presentations with animations and simulations on mathematical models of the electrical machines. The system is based on server/client architecture. All operation steps are executed by the server. So, clients do not need any additional software rather than internet connection and an internet browser. A DC motor course is given as an example to illustrate the novel tool developed. The remotely located users can learn the therotical information about the DC motor by means of using interactive web pages. All thereotical presentations supported with favorable animations to facilitate the understanding. Also, users can use the simulation models over the web to observe the motors dynamic behaviours under the different parameters. Moreover, they can remotely access a DC motor experimental set via internet to perform real time experiments such as speed control and generator operation of DC motor. During the conducting real time experiments over the web, the experimental set can be monitored with a webcam. Experimental results show that the integrated web based learning system developed is user-friendly and can be used for electrical machines education successfully.

Keyloggers: Increasing threats to computer security and privacy

Keyloggers are powerful tools that can perform many task. Standard security measures for machine-to-machine interfaces do not protect computer systems from keylogger attacks. Human-to-machine interfaces must be considered to combat keylogger intrusions. The judicious use of keyloggers by employers and computer owners could, in some situations, improve security, privacy, and efficiency. But the possible positive effects must be balanced against the possible negative effects on employees, users, and children.