Web Based Remote Access Microcontroller Laboratory

This paper presents a web based remote access microcontroller laboratory. Because of accelerated development in electronics and computer technologies, microcontroller-based devices and appliances are found in all aspects of our daily life. Before the implementation of remote access microcontroller laboratory an experiment set is developed by teaching staff for training microcontrollers. Requirement of technical teaching and industrial applications are considered when experiment set is designed. Students can make the experiments by connecting to the experiment set which is connected to the computer that set as the web server. The students can program the microcontroller, can control digital and analog inputs and can observe experiment. Laboratory experiment web page can be accessed via www.elab.aku.edu.tr address.

Quantitative Determination of Trace Elements in Some Oriental Herb Products

The quantitative determination of several trace elements (Cr, As, Se, Cd, Hg, Pb) existing as inorganic impurities in some oriental herb-products such as Lingzhi Mushroom capsules, Philamin powder, etc using ICP-MS has been studied. Various instrumental parameters such as power, gas flow rate, sample depth, as well as the concentration of nitric acid and thick background due to high concentration of possible interferences on the determination of these above-mentioned elements was investigated and the optimum working conditions of the sample measurement on ICP-MS (Agilent-7500a) were reported. Appropriate isotope internal standards were also used to improve the accuracy of mercury determination. Optimal parameters for sampling digestion were also investigated. The recovery of analytical procedure was examined by using a Certified Reference Material (IAEA-CRM 359). The recommended procedure was then applied for the quantitative determination of Cr, As, Se, Cd, Hg, Pb in Lingzhi Mushroom capsule, and Philamine powder samples. The reproducibility of sample measurement (average value between 94 and 102%) and the uncertainty of analytical data (less than 20%) are acceptable.

The Influence of Surface Roughness of Drawbead on Non-Symmetry Deep Drawing Cold Rolled Steel Sheet

This study was aimed to explain the influence of surface roughness of the drawbead on non-symmetry deep drawing cold rolled steel sheet to improve the drawability of cold rolled steel sheet. The variables used in this study included semi-circle drawbead with 3 levels of surface roughness which are 6.127 mm Ra, 0.963 mm Ra and 0.152 mm Ra and cold rolled steel sheet according to 3 grades of the JIS standards which are SPCC, SPCE and SPCD with the thickness of 1.0 mm and the blankholder force which is 50% of the drawing force and the depth of 50 mm. According to the test results, when there was the increase in the surface roughness of drawbead, there would be the increase in deep drawing force, especially the SPCC cold rolled steel sheet. This is similar to the increase in the equivalent strain and the wall thickness distribution when the surface roughness of the drawbead increased. It could be concluded that the surface roughness of drawbead has an influence on deep drawing cold rolled steel sheet, especially the drawing force, the equivalent strain and the wall thickness distribution.

Complexity of Component-based Development of Embedded Systems

The paper discusses complexity of component-based development (CBD) of embedded systems. Although CBD has its merits, it must be augmented with methods to control the complexities that arise due to resource constraints, timeliness, and run-time deployment of components in embedded system development. Software component specification, system-level testing, and run-time reliability measurement are some ways to control the complexity.

Contribution of Vitaton (Β-Carotene) to the Rearing Factors Survival Rate and Visual Flesh Color of Rainbow Trout Fish in Comparison With Astaxanthin

In this study Vitaton (an organic supplement which contains fermentative β-carotene) and synthetic astaxanthin (CAROPHYLL® Pink) were evaluated as pro-growth factors in Rainbow trout diet. An 8 week feeding trial was conducted to determine the effects of Vitaton versus astaxanthin on rearing factors, survival rate and visual flesh color of Rainbow trout (Oncorhnchynchus mykiss) with initial weight of 196±5. Four practical diets were formulated to contain 50 and 80 (ppm) of β- carotene and astaxanthin and also a control diet was prepared without any pigment. Each diet was fed to triplicate groups of fish rearing in fresh water. Fish were fed twice daily. The water temperature fluctuated from 12 to 15 (C˚) and also dissolved oxygen content was between 7 to 7.5 (mg/lit) during the experimental period. At the end of the experiment, growth and food utilization parameters and survival rate were unaffected by dietary treatments (p>0.05). Also, there was no significant difference between carcass yield within treatments (p>0.05). No significant difference recognized between visual flesh color (SalmoFan score) of fish fed Vitaton-containing diets. On the contrary, feeding on diets containing 50 and 80 (ppm) of astaxanthin, increased SalmoFan score (flesh astaxanthin concentration) from

Improving Convergence of Parameter Tuning Process of the Additive Fuzzy System by New Learning Strategy

An additive fuzzy system comprising m rules with n inputs and p outputs in each rule has at least t m(2n + 2 p + 1) parameters needing to be tuned. The system consists of a large number of if-then fuzzy rules and takes a long time to tune its parameters especially in the case of a large amount of training data samples. In this paper, a new learning strategy is investigated to cope with this obstacle. Parameters that tend toward constant values at the learning process are initially fixed and they are not tuned till the end of the learning time. Experiments based on applications of the additive fuzzy system in function approximation demonstrate that the proposed approach reduces the learning time and hence improves convergence speed considerably.

Effects of Material Properties of Warhead Casing on Natural Fragmentation Performance of High Explosive (HE) Warhead

This research paper presents numerical studies of the characteristics of warhead fragmentation in terms of initial velocities, spray angles of fragments and fragment mass distribution of high explosive (HE) warhead. The behavior of warhead fragmentation depends on shape and size of warhead, thickness of casing, type of explosive, number and position of detonator, and etc. This paper focuses on the effects of material properties of warhead casing, i.e. failure strain, initial yield and ultimate strength on the characteristics of warhead fragmentation. It was found that initial yield and ultimate strength of casing has minimal effects on the initial velocities and spray angles of fragments. Moreover, a brittle warhead casing with low failure strain tends to produce higher number of fragments with less average fragment mass.

Hull Separation Optimization of Catamaran Unmanned Surface Vehicle Powered with Hydrogen Fuel Cell

This paper presents an optimization of the hull separation, i.e. transverse clearance. The main objective is to identify the feasible speed ranges and find the optimum transverse clearance considering the minimum wave-making resistance. The dimensions and the weight of hardware systems installed in the catamaran structured fuel cell powered USV (Unmanned Surface Vehicle) were considered as constraints. As the CAE (Computer Aided Engineering) platform FRIENDSHIP-Framework was used. The hull surface modeling, DoE (Design of Experiment), Tangent search optimization, tool integration and the process automation were performed by FRIENDSHIP-Framework. The hydrodynamic result was evaluated by XPAN the potential solver of SHIPFLOW.

Two Wheels Balancing Robot with Line Following Capability

This project focuses on the development of a line follower algorithm for a Two Wheels Balancing Robot. In this project, ATMEGA32 is chosen as the brain board controller to react towards the data received from Balance Processor Chip on the balance board to monitor the changes of the environment through two infra-red distance sensor to solve the inclination angle problem. Hence, the system will immediately restore to the set point (balance position) through the implementation of internal PID algorithms at the balance board. Application of infra-red light sensors with the PID control is vital, in order to develop a smooth line follower robot. As a result of combination between line follower program and internal self balancing algorithms, we are able to develop a dynamically stabilized balancing robot with line follower function.

Harvesting of Kinetic Energy of the Raindrops

This paper presents a methodology to harvest the kinetic energy of the raindrops using piezoelectric devices. In the study 1m×1m PVDF (Polyvinylidene fluoride) piezoelectric membrane, which is fixed by the four edges, is considered for the numerical simulation on deformation of the membrane due to the impact of the raindrops. Then according to the drop size of the rain, the simulation is performed classifying the rainfall types into three categories as light stratiform rain, moderate stratiform rain and heavy thundershower. The impact force of the raindrop is dependent on the terminal velocity of the raindrop, which is a function of raindrop diameter. The results were then analyzed to calculate the harvestable energy from the deformation of the piezoelectric membrane.

Optimum Shape and Design of Cooling Towers

The aim of the current study is to develop a numerical tool that is capable of achieving an optimum shape and design of hyperbolic cooling towers based on coupling a non-linear finite element model developed in-house and a genetic algorithm optimization technique. The objective function is set to be the minimum weight of the tower. The geometric modeling of the tower is represented by means of B-spline curves. The finite element method is applied to model the elastic buckling behaviour of a tower subjected to wind pressure and dead load. The study is divided into two main parts. The first part investigates the optimum shape of the tower corresponding to minimum weight assuming constant thickness. The study is extended in the second part by introducing the shell thickness as one of the design variables in order to achieve an optimum shape and design. Design, functionality and practicality constraints are applied.

Analysis of Message Authentication in Turbo Coded Halftoned Images using Exit Charts

Considering payload, reliability, security and operational lifetime as major constraints in transmission of images we put forward in this paper a steganographic technique implemented at the physical layer. We suggest transmission of Halftoned images (payload constraint) in wireless sensor networks to reduce the amount of transmitted data. For low power and interference limited applications Turbo codes provide suitable reliability. Ensuring security is one of the highest priorities in many sensor networks. The Turbo Code structure apart from providing forward error correction can be utilized to provide for encryption. We first consider the Halftoned image and then the method of embedding a block of data (called secret) in this Halftoned image during the turbo encoding process is presented. The small modifications required at the turbo decoder end to extract the embedded data are presented next. The implementation complexity and the degradation of the BER (bit error rate) in the Turbo based stego system are analyzed. Using some of the entropy based crypt analytic techniques we show that the strength of our Turbo based stego system approaches that found in the OTPs (one time pad).

Adaptation of Iterative Methods to Solve Fuzzy Mathematical Programming Problems

Based on the fuzzy set theory this work develops two adaptations of iterative methods that solve mathematical programming problems with uncertainties in the objective function and in the set of constraints. The first one uses the approach proposed by Zimmermann to fuzzy linear programming problems as a basis and the second one obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. We outline similarities between the two iterative methods studied. Selected examples from the literature are presented to validate the efficiency of the methods addressed.

A Robust Wheel Slip Controller for a Hybrid Braking System

A robust wheel slip controller for electric vehicles is introduced. The proposed wheel slip controller exploits the dynamics of electric traction drives and conventional hydraulic brakes for achieving maximum energy efficiency and driving safety. Due to the control of single wheel traction motors in combination with a hydraulic braking system, it can be shown, that energy recuperation and vehicle stability control can be realized simultaneously. The derivation of a sliding mode wheel slip controller accessing two drivetrain actuators is outlined and a comparison to a conventionally braked vehicle is shown by means of simulation.

Prediction of the Epileptic Events 'Epileptic Seizures' by Neural Networks and Expert Systems

Many studies have focused on the nonlinear analysis of electroencephalography (EEG) mainly for the characterization of epileptic brain states. It is assumed that at least two states of the epileptic brain are possible: the interictal state characterized by a normal apparently random, steady-state EEG ongoing activity; and the ictal state that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called in neurology, a seizure. The spatial and temporal dynamics of the epileptogenic process is still not clear completely especially the most challenging aspects of epileptology which is the anticipation of the seizure. Despite all the efforts we still don-t know how and when and why the seizure occurs. However actual studies bring strong evidence that the interictal-ictal state transition is not an abrupt phenomena. Findings also indicate that it is possible to detect a preseizure phase. Our approach is to use the neural network tool to detect interictal states and to predict from those states the upcoming seizure ( ictal state). Analysis of the EEG signal based on neural networks is used for the classification of EEG as either seizure or non-seizure. By applying prediction methods it will be possible to predict the upcoming seizure from non-seizure EEG. We will study the patients admitted to the epilepsy monitoring unit for the purpose of recording their seizures. Preictal, ictal, and post ictal EEG recordings are available on such patients for analysis The system will be induced by taking a body of samples then validate it using another. Distinct from the two first ones a third body of samples is taken to test the network for the achievement of optimum prediction. Several methods will be tried 'Backpropagation ANN' and 'RBF'.

Region Based Hidden Markov Random Field Model for Brain MR Image Segmentation

In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L1 model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms.

Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification

In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.

Enhancing the Quality of Learning by Using an Innovative Approach for Teaching Energy in Secondary Schools

This paper presents the results of the authors in designing, experimenting, assessing and transferring an innovative approach to energy education in secondary schools, aimed to enhance the quality of learning in terms of didactic curricula and pedagogic methods. The training is online delivered to youngsters via e-Books and portals specially designed for this purpose or by learning by doing via interactive games. An online educational methodology is available teachers.

Application of Fluorescent Pseudomonads Inoculant Formulations on Vigna mungo through Field Trial

Vermiculite was used to develop inorganic carrier-based formulations of fluorescent pseudomonad strains R62 and R81. The effect of bio-inoculation of fluorescent pseudomonad strains R62 and R81 (plant growth promoting and biocontrol agent) on growth responses of Vigna-mungo under field condition was enumerated. The combined bioinoculation of these two organisms in a formuation increased the pods yield by 300% in comparison to the control crop. There was also significant increment in the other plant growth responses such as dry root weight, dry shoot weight, shoot length and number of branches per plant.