Enhanced antenna-pattern measurements for Satellite systems

This paper describes an enhanced measurement technique for earth-station antennas in a satellite communication system. A new data-acquisition algorithm improves the dynamic range of the measurements by increasing the signal-to-noise ratio in the test setup. The resolution limitation of conventional techniques is eliminated without any modifications to the ground station setup. The proposed approach improves the conventional measurement techniques by utilizing pulse modulation of the test-signal amplitude and its synchronous detection. The noise floor in the pattern is reduced by biasing out the average noise power calculated during the OFF cycle of the pulse. In addition, a randomly fluctuating local mean in the receiver output is reduced. Experimental results show improvements of as much as 25 dB in the noise variance and over 11 dB in the dynamic range of the antenna pattern.     

Fabrication of co-PVDF/modacrylic/SiO2 nanofibrous membrane: Composite separator for safe and high performance lithium-ion batteries

Highly porous free-standing co-poly(vinylidene fluoride)/modacrylic/SiO₂ nanofibrous membrane was developed using electrically-assisted solution blow spinning method. The performance and the potential of the membrane as a lithium-ion battery separator were investigated. The addition of modacrylic enhanced the solution spinnability that resulted in defect-free membranes. Moreover, the presence of modacrylic enhanced the dimensional and thermal stabilities, while the addition of hydrophilic SiO₂ nanoparticle enhanced both mechanical property and ionic conductivity. Combustion test results illustrated that the presence of modacrylic provide flame retarding property over a set of different polymeric-based membranes. Electrochemical performance results showed that the developed membrane can increase the battery capacity compared with the commercial separator.     

A semantic backend for content management systems

The users of a content repository express the semantics they have in mind while defining the content items and their properties, and forming them into a particular hierarchy. However, this valuable semantics is not formally expressed, and hence cannot be used to discover meaningful relationships among the content items in an automated way. Although the need is apparent, there are several challenges in explicating this semantics in a fully automated way: first, it is difficult to distinguish between data and the metadata in the repository and secondly, not all the metadata defined, such as the file size or encoding type, contribute to the meaning. More importantly, for the developed solution to have practical value, it must address the constraints of the content management system (CMS) industry: CMS industry cannot change their repositories in production use and they need a generic solution not limited to a specific repository architecture. In this article, we address all these challenges through a set of tools developed which first semi-automatically explicate the content repository semantics to a knowledge-base and establish semantic bridges between this backend knowledge-base and the content repository. The repository content is dynamic; to be able to maintain the content repository semantics while new content is created, the changes in the repository semantics are reflected onto the knowledge-base through the semantic bridges. The tool set is complemented with a search engine that make use of the explicated semantics.     

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).     

Colon segmentation and colonic polyp detection using cellular neural networks and three-dimensional template matching

Abstract: In this study, an automatic three-dimensional computer-aided detection system for colonic polyps was developed. Computer-aided detection for computed tomography colonography aims at facilitating the detection of colonic polyps. First, the colon regions of whole computed tomography images were carefully segmented to reduce computational burden and prevent false positive detection. In this process, the colon regions were extracted by using a cellular neural network and then the regions of interest were determined. In order to improve the segmentation performance of the study, weights in the cellular neural network were calculated by three heuristic optimization techniques, namely genetic algorithm, differential evaluation and artificial immune system. Afterwards, a three-dimensional polyp template model was constructed to detect polyps on the segmented regions of interest. At the end of the template matching process, the volumes geometrically similar to the template were emhanced.     

Visualization of fibrous and thread-like data

Thread-like structures are becoming more common in modern volumetric data sets as our ability to image vascular and neural tissue at higher resolutions improves. The thread-like structures of neurons and micro-vessels pose a unique problem in visualization since they tend to be densely packed in small volumes of tissue. This makes it difficult for an observer to interpret useful patterns from the data or trace individual fibers. In this paper we describe several methods for dealing with large amounts of thread-like data, such as data sets collected using Knife-Edge Scanning Microscopy (KESM) and Serial Block-Face Scanning Electron Microscopy (SBF-SEM). These methods allow us to collect volumetric data from embedded samples of whole-brain tissue. The neuronal and microvascular data that we acquire consists of thin, branching structures extending over very large regions. Traditional visualization schemes are not sufficient to make sense of the large, dense, complex structures encountered. In this paper, we address three methods to allow a user to explore a fiber network effectively. We describe interactive techniques for rendering large sets of neurons using self-orienting surfaces implemented on the GPU. We also present techniques for rendering fiber networks in a way that provides useful information about flow and orientation. Third, a global illumination framework is used to create high-quality visualizations that emphasize the underlying fiber structure. Implementation details, performance, and advantages and disadvantages of each approach are discussed.     

Handling Updates and Crashes in VoD Systems

Though there have been several recent efforts to develop disk based video servers, these approaches have all ignored the topic of updates and disk server crashes. In this paper, we present a priority based model for building video servers that handle two classes of events: user events that could include enter, play, pause, rewind, fast-forward, exit, as well assystem events such as insert, delete, server-down, server-up that correspond to uploading new movie blocks onto the disk(s), eliminating existing blocks from the disk(s), and/or experiencing a disk server crash. We will present algorithms to handle such events. Our algorithms are provably correct, and computable in polynomial time. Furthermore, we guarantee that under certain reasonable conditions, continuing clients experience jitter free presentations. We further justify the efficiency of our techniques with a prototype implementation and experimental results.     

An approach to the calculation of multilayer magnetic shielding using artificial neural network

The electromagnetic interference has many undesired effects to the office equipments and the performance of technological devices. Therefore, electrical devices should have field protection mechanism against magnetic fields. The shielding mechanism prevents external magnetic field emitted from the device to the vicinity area. This work attempts to apply artificial neural network in order to estimate shielded magnetic field for multilayer shielding application. The multilayer magnetic shielding performances of cylindrical shaped ferromagnetic shields under extremely low frequency are investigated. For this purpose, three different ferromagnetic materials in cylindrical shape are chosen for magnetic shielding. The shielding performances of these materials are measured and a selected set of data is used to train and test an artificial neural network. The proposed neural network model achieves an intelligent decision for the shielded magnetic field level based on distance, unshielded magnetic field value, number of shield layer and skin-depth of the shield.     

Tensile and Springback Behavior of DP600 Advanced High Strength Steel at Warm Temperatures

 In recent years, the use of advanced high strength steels in automotive industry has been increased remarkably. From advanced high strength steels, dual-phase (DP) steels have gained a great attention due to a combination of high strength and good formability. However, high strength usually increases springback behavior of material which creates problem for the parts during the assembly. In this study, uniaxial tensile deformation and springback behaviors of DP600 advanced high strength steel were investigated at rolling (0o), diagonal (45o), and transverse (90o) directions in the temperature range from room (RT) to 300 oC. All tests were performed at 25 mm/min deformation speed. A V-shape die (60o) was used for springback measurements. Results indicate that the formability and springback of the material were decreased with increasing the temperatures. The material showed complex behaviors at different directions and temperatures.