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AbstracWA new form of signals will be adopted by GALILEO in its signals of E1 OS.MBOC is the important technology to produce the new signals. Firstly the definition of MBOC and the performance of its spectrum is introduced in this paper , then on the basis of exposition of the fundamental principle to the technology of MBOC, basing on the technology of Matlab/Simulink , a way to set up simulation model of MBOC in the environment of Matlab is presented�» the feasibility is inspected and verified in terms of theory, specific meaning of guidance will be given by this technology. The effectiveness of MBOC has been shown in the results of actual running.

A cross-impact hierarchy process (CHP), which develops an extended model of the analytic hierarchy process (AHP) that is linked to cross-impact analysis, is a useful decision making tool to deal with dependent technological alternatives. However, the intrinsic drawbacks (i.e., the weight concept being undefined and the priority vector derived form the eigenvalue method (EM) violating a condition of order preservation) still exist in the CHP. In view of this, a new MCDM approach based on cross-impact analysis is presented in this paper. Applied to a real example, the new approach is validated to be applicable.

To overcome the drawbacks that individual preference information for uncertainties is rarely considered in current complex alternative ranking methods usually resulting in unreasonable alternative rankings, an approach to rank complex alternatives based on behaviors is presented by means of prospect theory. The presented approach introduces a new analysis structure to decompose a complex problem into some simple components and facilitate judgments, based on which, a ranking procedure is proposed. This proposed procedure, which can reflect characteristics of complex system such as nonlinear and emergence, is performed upwards for alternatives one by one, and introduces individual preferences for gains, losses and probabilities on each element in the analysis structure. Consequently, the presented approach is able to rank complex alternatives correctly and reasonably. A numerical case proves its applicability.

Supplier selection has received extensive attention in supply chain management. However, selecting a proper supplier is not an easy decision because lots of evaluation criteria such as quality of products, cost efficiency, delivery dependability, information and customer service and so on should be considered. The Analytic network process (ANP) is a new decision method to deal with complex problems. However, selecting suppliers need consider not only benefits and potential opportunities, but also costs and potential risks. In view of this an ANP with benefits (B), opportunities (O), costs (C) and risks (R) is proposed to select a proper supplier. Finally, the method is validated by a real case of supplier selection to be effective.

To solve the problems of current portfolio selection methods of venture capital (VC) projects, resulting from the capital indivisibility and the scrutability of return of investment (ROI) of VC projects, two key concepts of project portfolio preference rate (TPR) and the variance of TPR (VTPR) are defined based on the project evaluation criteria (PEC) for VC projects, using the technical thought of mean-variance (MV) model. On the basis of TPR and VTPR, a model named VCP- DEA/AR, which is able to derive the Pareto-optimal project portfolios, is presented by way of the principle of data envelopment analysis (DEA) with assurance region (AR). Applied to an illustrative example, the VCP-DEA/AR model is proved to be scientific, reasonable, and well applicable to real-world portfolio-selection problem of VC.

The problem of comparison reference-point shifting (CRPS), which probably occurs in decision makers/experts' (DMEs') judgment matrices (JMs) of the analytic hierarchy process (AHP), is pointed out for the first time, and the CRPS influence on the estimation of decision-making priorities is analyzed. To solve the CRPS problem probably existing in the group AHP, an optimization model/approach of group priority estimation and its equivalent models of two-stage linear programming, aiming at reducing the CRPS influence as much as possible and at minimizing DMEs' subjective judgement deviations as well, are presented for the AHP group decision-making, by adopting the technical strategy of adjusting individual JMs based on the JM whose CRPS degree is the lowest. Applied to a simulation example, the presented approach is shown to perform better in the group priority estimation than the commonly-used prioritization methods for the group AHP.