Modeling discrete event scalable network systems

Scalability in simulation tools is one of the most important traits to measure performance of software. The reason is that today’s Internet is the main instance of a large-scale and highly complex system. Simulation of Internet-scale network systems has to be supported by any simulation tool. Despite this fact, many network simulators lacks support for building large models. In this work, in order to propose a new approach for scalability issue in network simulation tools, a network simulator is developed based on behavior of honeybees and high performance DEVS, modular and hierarchical system theoretic approach. A biologically-inspired discrete event modeling approach is described for studying networks’ scalability and performance traits. Since natural systems can offer important concepts for modeling network systems, key adaptive and emergent attributes of honeybees and their societal properties are incorporated into a set of simulation models that are developed using the discrete event system specification approach. Large-scale network models are simulated and evaluated to show the benefits of nature-inspired network models.     

An adaptive least squares channel estimation based on conjugate gradient approach for multipath fading CDMA systems

Multiple access interference (MAI) is the main factor affecting the performance of channel estimation techniques for code division multiple access (CDMA) systems. Although, several multi-user channel estimation algorithms have been proposed to mitigate MAI, these algorithms require high computational complexities. In this paper, we address the problem of iterative least squares (LS) mobile channel estimation at high channel efficiency that requires a short training sequence along with the spreading sequences. We employ an efficient iterative method based on conjugate gradient (CG) algorithm to reduce the computational complexity of the estimation method. Computer simulations illustrate that the proposed method performs almost identical to the exact LS estimate for reasonable training lengths.     

Supporting students’ participation in authentic proof activities in computer supported collaborative learning (CSCL) environments

In this paper, I review both mathematics education and CSCL literature and discuss how we can better take advantage of CSCL tools for developing mathematical proof skills. I introduce a model of proof in school mathematics that incorporates both empirical and deductive ways of knowing. I argue that two major forces have given rise to this conception of proving: a particular learning perspective promoted in reform documents and a genre of computer tools, namely dynamic geometry software, which affords this perspective of learning within the context of mathematical proof. Tracing the move from absolutism to fallibilism in the philosophy of mathematics, I highlight the vital role of community in the production of mathematical knowledge. This leads me to an examination of a certain CSCL tool whose design is guided by knowledge-building pedagogy. I argue that knowledge building is a suitable pedagogical approach for the proof model presented in this paper. Furthermore, I suggest software modifications that will better support learners’ participation in authentic proof tasks.     

Classification of abnormal plant operation using multiple process variable trends

This paper illustrates two strategies for the detection and classification of abnormal process operating conditions in which multiple process variable trends are available. The first strategy uses a hidden Markov model (HMM) for overall process classification while the second method uses a back-propagation neural network (BPNN) to determine the overall process classification. The methods are compared in terms of their ability to detect and correctly diagnose a variety of abnormal operating conditions for a non-isothermal CSTR simulation. For the case study problem, the BPNN method resulted in better classification accuracy with a moderate increase in training time compared with the HMM approach.     

Design and Testing of a Robotic System for mr Image-guided Interventions

The effective integration of robotics together with magnetic resonance (mr) technology is expected to facilitate the real-time guidance of various diagnostic and therapeutic interventions. Specially designed robotic manipulators are required for this purpose, the development of which is a challenging task given the strong magnetic fields and the space limitations that characterize the mr scanning environment. A prototype mr-compatible manipulator is presented, designed to operate inside cylindrical mr scanners. It was developed for the study of minimally invasive diagnostic and therapeutic interventions in the abdominal and thoracic area with real-time mr image guidance. Initial tests were performed inside a high-field clinical mr scanner and included mr-compatibility tests and phantom studies on image-guided targeting.     

A study on the yield stress of perlite-filled high-density polyethylenes

Four different types of high-density polyethylenes were blended with expanded perlite at different compositions. -aminopropyltriethoxysilane was applied to perlite (2 wt%) from ether and water solutions to enhance the interfacial adhesion between the polymer and the filler. It was shown that silane treatment advances the yield stress with improving dispersion and increasing the interfacial adhesion of the filler with the polymer matrix. The experimental results were then checked with the expression of Turcsanyiet al.'s.     

Equilibrium distributions of key components of spearmint oil in sub/supercritical carbon dioxide

Effects of temperature (at 35, 45 or 55°C) and pressure (10–110 atm) on the relative distribution coefficients of the twelve key components of spearmint oil (essential oil ofMentha cardiaca; Scotch spearmint) at equilibrium in dense CO₂ were investigated under conditions ranging from subcritical to supercritical regions. Effects of vapor pressure, molecular weight and polarity of the key components on their equilibrium distributions in sub/supercritical CO₂ are discussed. At 35°C, all key components of spearmint oil are equally soluble in dense CO₂ within the 12–102 atm pressure region. At 45 and 55°C, the key components are equally soluble for pressures greater than about 60 atm. However, around either 45°C/27 atm or 55°C/35 atm conditions, the relative distribution coefficients of all monoterpene hydrocarbons and of isomenthone (an oxygenated monoterpene) exhibit maxima, which are due to significantly higher vapor pressures of these components and significantly lower solvating power of the dense-gas solvent at these particular temperatures and pressures. Vapor-pressure effects, coupled with the decrease in solvating power, dominate the effects of polarity and molecular mass of the key components. Deterpenation of spearmint oil with dense CO₂ is possible around either 45°C/27 atm or 55°C/35 atm, where the monoterpene hydrocarbons tend to concentrate in the CO₂-rich phase.     

Groundwater legislation in Turkey: problems of conception and application

The uneven distribution of water resources, a growing population, urbanization and global climate change require new approaches for groundwater management in Turkey. “Safe yield” should yield to broader concepts such as “sustainability”. Groundwater management needs to consider future needs of the people and all ecosystems in accordance with basin development models. Groundwater law needs to be expanded beyond quantity to address quality concerns. A new institutional framework should be established and groundwater regulation should reflect new approaches and ideas, in particular to address problems of application.     

Identification of EMG signals using discriminant analysis and SVM classifier

The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. In this paper, a classification technique is proposed to classify signals required for a prosperous arm prosthesis control by using surface EMG signals. This work uses recorded EMG signals generated by biceps and triceps muscles for four different movements. Each signal has one single pattern and it is essential to separate and classify these patterns properly. Discriminant analysis and support vector machine (SVM) classifier have been used to classify four different arm movement signals. Prior to classification, proper feature vectors are derived from the signal. The feature vectors are generated by using mean absolute value (MAV). These feature vectors are provided as inputs to the identification/classification system. Discriminant analysis using five different approaches, classification accuracy rates achieved from very good (98%) to poor (96%) by using 10-fold cross validation. SVM classifier gives a very good average accuracy rate (99%) for four movements with the classification error rate 1%. Correct classification rates of the applied techniques are very high which can be used to classify EMG signals for prosperous arm prosthesis control studies.