High-capacity quantum key distribution using Chebyshev-map values corresponding to Lucas numbers coding

We propose an approach that achieves high-capacity quantum key distribution using Chebyshev-map values corresponding to Lucas numbers coding. In particular, we encode a key with the Chebyshev-map values corresponding to Lucas numbers and then use k-Chebyshev maps to achieve consecutive and flexible key expansion and apply the pre-shared classical information between Alice and Bob and fountain codes for privacy amplification to solve the security of the exchange of classical information via the classical channel. Consequently, our high-capacity protocol does not have the limitations imposed by orbital angular momentum and down-conversion bandwidths, and it meets the requirements for longer distances and lower error rates simultaneously.     

Batch sequencing and cooperation

Game theoretic analysis of sequencing situations has been restricted to manufacturing systems which consist of machines that can process only one job at a time. However, in many manufacturing systems, operations are carried out by batch machines which can simultaneously process multiple jobs. This paper aims to extend the game theoretical approach to the cost allocation problems arising from sequencing situations on systems that consist of batch machines. To analyze the allocation problem at hand, it focusses on the existence of core elements, convexity, and the Shapley value.     

To watch from distance: an interactive film model based on Brechtian film theory

With the emergence of new media, interactive film projects have mainly struggled to resolve the contradiction between dramatic structures and interaction. Dramatic film presents identification with the main character, where the viewer is constantly oppressed by the narrative, and therefore lost in illusion. In this context, when we bring on the scene interaction, the drama apparently starts to lose its power.

In this article, a new interactive film model based on Brechtian film theory is proposed. This model presents a new way of spatiotemporal construction where different audiovisual combinations can be viewed successively, and this way the viewer can actively construct his/her own story. Theoretical framework of the Brechtian interactive film model is supported by an interactive film application, named Academia. The main feature of the model is that, while interaction is very simple, the continuity of the narrative is preserved and the film requiring an intellectual level of interpretation.     

Fuzzy multiattribute evaluation of R&D projects using a real options valuation model

R&D project selection decision is very important in two ways. First, in many organizations, R&D budget represents huge investment. Project selection decisions could be thought with the strategic objectives and plans of the firm. Second, R&D projects' organizational returns are multidimensional in nature and risky in terms of projected outcome. Real options approach helps to calculate this risky side of the selection process. This paper considers that multidimensional side of the R&D project selection process. Another consideration is the vagueness in the evaluation process. The fuzzy analytic hierarchy process, which takes monetary (fuzzy real option value) and nonmonetary (capability, success probability, trends, etc.) criteria into account, is used to make this selection among alternative R&D projects. A real case study is given to illustrate the application of the proposed approach. © 2008 Wiley Periodicals, Inc.     

Extraction of voltage harmonics using multi-layer perceptron neural network

This paper presents a harmonic extraction algorithm using artificial neural networks for Dynamic Voltage Restorers (DVRs). The suggested algorithm employs a feed forward Multi Layer Perceptron (MLP) Neural Network with error back propagation learning to effectively track and extract the 3rd and 5th voltage harmonics. For this purpose, two different MLP neural network structures are constructed and their performances compared. The effects of hidden layer, supervisors and learning rate are also presented. The proposed MLP Neural Network algorithm is trained and tested in MATLAB program environment. The results show that MLP neural network enable to extract each harmonic effectively.     

A Polynomial Time Solution to Minimum Forwarding Set Problem in Wireless Networks under Unit Disk Coverage Model

Network-wide broadcast (simply broadcast) is a frequently used operation in wireless ad hoc networks (WANETs). One promising practical approach for energy-efficient broadcast is to use localized algorithms to minimize the number of nodes involved in the propagation of the broadcast messages. In this context, the minimum forwarding set problem (MFSP) (also known as multipoint relay (MPR) problem) has received a considerable attention in the research community. Even though the general form of the problem is shown to be NP-complete, the complexity of the problem has not been known under the practical application context of ad hoc networks. In this paper, we present a polynomial time algorithm to solve the MFSP for wireless network under unit disk coverage model. We prove the existence of some geometrical properties for the problem and then propose a polynomial time algorithm to build an optimal solution based on these properties. To the best of our knowledge, our algorithm is the first polynomial time solution to the MFSP under the unit disk coverage model. We believe that the work presented in this paper will have an impact on the design and development of new algorithms for several wireless network applications including energy-efficient multicast, broadcast, and topology control protocols for WANETs and sensor networks.     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.