3D Hair sketching for real-time dynamic & key frame animations

Physically based simulation of human hair is a well studied and well known problem. But the “pure” physically based representation of hair (and other animation elements) is not the only concern of the animators, who want to “control” the creation and animation phases of the content. This paper describes a sketch-based tool, with which a user can both create hair models with different styling parameters and produce animations of these created hair models using physically and key frame-based techniques. The model creation and animation production tasks are all performed with direct manipulation techniques in real-time.     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.     

General solution of one-dimensional,time dependent coupled heat flow systems

A new, powerful method of analysis, involving the combined use of finite integral transform and finite element techniques, is presented for the solution of time dependent heat flow systems composed of many one-dimensional elements connected through the nodes. This method leads to an eigenvalue problem which is not of the conventional Sturm-Liouville type. A procedure for the determination of the eigenvalues is described. The solution obtained is in the form of an infinite series and contains quasi-steady and transient terms. The general solution obtained can be applied in the mathematical modelling of many engineering applications such as the determination of the penetration of the daily temperature cycle into buildings, the analysis of heat transfer in array of extended surfaces in compact heat exchangers, and many others.     

Structural and magnetic characterization of norbornene-deuterated norbornene dicarboxylic acid diblock copolymers doped with iron oxide nanoparticles

A series of iron oxide doped norbornene (NOR)/deuterated norbornene dicarboxylic acid (NORCOOH) diblock copolymers were synthesized and characterized by X-ray photoelectron spectroscopy (XPS), small angle neutron scattering (SANS) and superconducting quantum interference device (SQUID) experiments. γ-Fe₂O₃ nanoparticles were synthesized within the microdomains of diblock copolymers with volume fractions of NOR/NORCOOH 0.64/0.36, 0.50/0.50 and 0.40/0.60. A spherical nanoparticle morphology was displayed in the polymer with 0.64/0.36 volume fraction. Polymers with 0.50/0.50 and 0.40/0.60 volume fractions exhibited interconnected metal oxide nanostructures. The observed changes in the shape and peak positions of the small-angle neutron scattering profiles of polymers after metal doping were related to the scattering from the metal oxide particles and to the possible deformed morphologies due to the strong interparticle interactions between metal particles, which may influence the polymer microphase separation. The combined scattering from both polymer domains and magnetic particles was depicted in SANS profiles of metal oxide doped polymers. γ-Fe₂O₃ containing block copolymers were superparamagnetic at room temperature. An increase in the blocking temperature (T_b) of interconnected nanoparticles was observed and was related to the interparticle interactions, which depends on the average distance (d) between particles and individual particle diameter (2R). The sample with volume fraction of 0.4/0.6 have the lowest d/(2R) ratio and exhibit the highest T_b at 115 K.     

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA–MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA–MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

Wıreless Communıcatıons Beyond 5 g: Teraherzwaves,Nano-Communıcatıons and the Internet of Bıo-Nano-Thıngs

Two promising technologies cosidered for the Beyond 5G networks are the terahertz and nano-technologies. Besides other possible application areas they hold the commitment to numerous new nano-scale solutions in the biomedical field. Nano-technology, as the name implies, examines the construction and design of nano-sized materials. These two interconnected emerging technologies have the potential to find application in quite many areas, one of the most importan being healthcare. This overview paper discusses the specifics of these technologies, their most important characteristics and introduces some of the trends for their application in the healthcare sector. In the first section terahertz frequency radio waves and their specific properties depending on the surrounding environment are discussed, followed by an introduction to nano-scale communications. Terahertz waves mandate the use of nano-scale antennas, which in turn brings us to the concept of nano-scale nodes. Nano-scale nodes are units that can perform the most basic functions of nano-machines and inter-nano-machine communications, which allow distributed nano-machines to perform more complex functions. Beyond 5G the development of these nano-communications is expected to lead to the emergence of new complex network systems. In the second part of this paper the paradigms of the Internet of Nano Things, molecular commnications and the Internet of Bio-Nano Things are discussed followed by details on their integration in healthcare related applications. The main goal of the article is to provide an introduction to these intriguing issues discussing advanced nano-technology enablers for Beyond 5G networks such as terahertz and molecular communications, nano-communications between nano-machines and the Internet of Bio-Nano-Things in light of health related applications.