Asaf Varol

An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognition

This paper presents a comparative study of implementation of feature extraction and classification algorithms based on discrete wavelet decompositions and Adaptive Network Based Fuzzy Inference System (ANFIS) for digital modulation recognition. Here, in first stage, 20 different feature extraction methods are generated by separately using Daubechies, Biorthogonal, Coiflets, Symlets wavelet families. In second stage, the performance comparison of these feature extraction methods is performed by using a new Expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System (EDWANFIS). The digital modulated signals used in this experimental study are ASK8, FSK8, PSK8, QASK8. EDWANFIS structure consists of two parts. The first part is Discrete Wavelet Transform (DWT)-adaptive wavelet entropy and Adaptive Network Based Fuzzy Inference System for Automatic Digital Modulation Recognition (ADMR). The performance of this comparison system is evaluated by using total 800 digital modulated signals for each of these feature extraction methods. The performance comparison of these features extraction methods and the advantages and disadvantages of the methods are examined.

Increasing the efficiency of wind turbines

The importance of clean energy sources was realised rapidly after the negative effects of the pollution caused by generators on the environment became clear. Wind energy is a clean and renewable energy source whose applications exist worldwide. Some countries changed their national energy forward planning for renewable clean energy projects. In developed countries some researchers have worked on developing new kinds of wind turbines in order to produce a part of their power from renewable wind energy. The rotational speed of the wind blades can be increased using steering aerofoils surrounding the blades. A prototype of this wind turbine has been made in the laboratory; a ventilator whose flow rate is variable generates the ‘wind’. The blade profiles are designed using the theory of aerodynamics. The steering aerofoils are fixed surrounding the wind blades at an optimum distance. The number of the aerofoils and the angle of inclination (tilt) of the foils can be changed. In the experiment the ambient conditions are held constant. Because of the optimum adjustment of the distance and angle of the aerofoils the rotational speed of the blades can be increased by 32% on the experimental device. In this paper the theoretical and experimental results will be discussed. # 2001 Elsevier Science Ltd. All rights reserved.

An expert diagnosis system for classification of human parasite eggs based on multi-class SVM

In this paper, it is proposed a new methodology based on invariant moments and multi-class support vector machine (MCSVM) for classification of human parasite eggs in microscopic images. The MCSVM is one of the most used classifiers but it has not used for classification of human parasite eggs to date. This method composes four stages. These are pre-processing stage, feature extraction stage, classification stage, and testing stage. In pre-processing stage, the digital image processing methods, which are noise reduction, contrast enhancement, thresholding, and morphological and logical processes. In feature extraction stage, the invariant moments of pre-processed parasite images are calculated. Finally, in classification stage, the multi-class support vector machine (MCSVM) classifier is used for classification of features extracted feature extraction stage. We used MATLAB software for estimating the success classification rate of proposed approach in this study. For this aim, proposed approach was tested by using test data. At end of test, 97.70% overall success rates were obtained.

A ONE DIMENSIONAL MATHEMATICAL MODEL FOR URODYNAMICS

Millions of people in the world suffer from urinary incontinence and overactive bladder with the major causes for the symptoms being stress, urge, overflow and functional incontinence. For a more effective treatment of these ailments, a detailed understanding of the urinary flow dynamics is required. This challenging task is not easy to achieve due to the complexity of the problem and the lack of tools to study the underlying mechanisms of the urination process. Theoretical models can help find a better solution for the various disorders of the lower urinary tract, including urinary incontinence, through simulating the interaction between various components involved in the continence mechanism. Using a lumped parameter analysis, a one-dimensional, transient mathematical model was built to simulate a complete cycle of filling and voiding of the bladder. Both the voluntary and involuntary contraction of the bladder walls is modeled along with the transient response of both the internal and external sphincters which dynamically control the urination process. The model also includes the effects signals from the bladder outlet (urethral sphincter, pelvic floor muscles and fascia), the muscles involved in evacuation of the urinary bladder (detrusor muscle) as well as the abdominal wall musculature. The necessary geometrical parameters of the urodynamics model were obtained from the 3D visualization data based on the visible human project. Preliminary results show good agreement with the experimental results found in the literature. The current model could be used as a diagnostic tool for detecting incontinence and simulating possible scenarios for the circumstances leading to incontinence.Copyright

Social networks' role in online education

Since students of online education do not meet face to face in a classroom environment, students get engaged to the classes via third party content or learning management tools. It is evident that young people spend most of their time on Social Networking websites when they are connected to internet. These sites provide free of speech environment without any restrictions. Moreover, the research in this area points out that the students are being more social when they use one of these Social Networking websites. Based on this findings, one can say using Social Networking in online education may provide an active cyber classroom environment with positive learning outcomes. Therefore, this paper is focusing on the role of Social Networking websites affect on student betrothal in online education.

Android based mobile application development for web login authentication using fingerprint recognition feature

Many mobile device makers now incorporate biometric security features into their products. And, some device manufacturers now allow application developers to use these features via their software development kits (SDKs). In this study, we utilize fingerprint scanning and recognition technology, a popular biometric security feature, to develop a web login authentication mobile app. Our application uses the Samsung Galaxy S5 fingerprint recognition feature and International Mobile Equipment Identity (IMEI) number to generate single time passwords. Within a limited time frame, the secure passwords can be used to sign in/log in to online user accounts related to government, banking, education, etc. As the production of mobile devices with fingerprint recognition continues to increase, finger print user authentication apps, like the one we introduce in this study, will become a prevalent security measure.

An improved digital evidence acquisition model for the Internet of Things forensic I: A theoretical framework

Digital evidence plays a vital role in determining legal case admissibility in electronic- and cyber-oriented crimes. Considering the complicated level of the Internet of Things (IoT) technology, performing the needed forensic investigation will be definitely faced by a number of challenges and obstacles, especially in digital evidence acquisition and analysis phases. Based on the currently available network forensic methods and tools, the performance of IoT forensic will be producing a deteriorated digital evidence trail due to the sophisticated nature of IoT connectivity and data exchangeability via the “things”. In this paper, a revision of IoT digital evidence acquisition procedure is provided. In addition, an improved theoretical framework for IoT forensic model that copes with evidence acquisition issues is proposed and discussed.

The importance of web activities for computer forensics

Web activities are big data revealing the thoughts and intentions of users. Every file that is downloaded, and each searching word in the search engine are thought as reflections of the users tendencies. Web activities are used to analyze in traces of crime in all kinds of information systems such as computers and mobile phones which are electromagnetic optical devices. The use of smart systems or statistical inference models in the electronic evidence analysis will contribute to the development of digital forensic tools and information technology law. The evidence analysis stage will be faster and automatic. The aim is to extract web browsing data by starting from search engine browsing data with appropriate methods, and sort out the data pointing crime by applying various techniques.

Review of evidence analysis and reporting phases in digital forensics process

This paper reviews digital forensic phases and problems in evidence analysis phase and smart methods in this area. Among these phases, studies on the evidence analysis phase are examined. In the analysis of electronic evidences, use of smart methods and their development will contribute to information technology law and improvement of digital forensic devices. Effective evidence analysis both provides easiness for digital forensic experts and helps jurists tor accurate decisions. In this paper, digital forensic process and smart methods used in evidence analysis are examined. This literature survey discussed which new methods can be added to this process.

A research on estimation of emotion using EEG signals and brain computer interfaces

The brain produces weak electrical signals that can be measured from the skull. Electroencephalography (EEG) is a method that provides monitoring electrical activity of the brain with the electrical methods. Brain Computer Interface (BCI) is a system that converts the electrical signals produced by the brain to the signals that can be interpreted by a computer or an electronic system. Brain Computer Interface aims to produce the result on developed computer systems rather than creating a response in the body from the signals sent by the brain. Emotion is defined as a psychophysiological change in the mood of a person that emerges by interaction with biochemical and environmental effects. There are many methods on emotion recognition research. These include voice recognition, recognition with mimics, and so on. However, face expression or tone of voice does not always represent feelings. Skin conductivity, skin temperature changes, blood pressure, heartbeat are also used for emotion estimation. In addition, BCI research on emotional recognition is important in recent years and its still a new topic in research. In this paper, studies on emotion estimation using EEG signals and the brain computer interface studies in this subject have been examined and the lack of relevant literature on this subject are discussed.