266 Innovation, accumulation and assimilation: Three sources of productivity growth in ICT industries Original Research ArticleJournal of Policy Modeling, Volume 32, Issue 2, March-April 2010, Pages 268-285Carmen Lpez-Pueyo, Mara-Jess MancebnClose preview | Related articles | Related reference work articles Abstract | Figures/Tables | References AbstractThe purpose of this article is to explore the sources of labour-productivity growth in ICT (Information and Communication Technologies) sector in a set of developed countries. The appropriate technology theory extended with non-immediate spillovers is the theoretical framework used, while the decomposition analysis is carried out from a non-parametric approach. Obtained results point that high labour-productivity growth rates are mainly due to technical change and, to a lower extent, to capital intensification, while differences in speed of spillover assimilation has not been enough to shorten the existing distances to new frontiers (excluding USA). Policies that affect the incentives to invest in physical capital, as well as to create new knowledge and to favour the willingness to adapt to change are needed to foster labour-productivity growth in an industry that has a leading role for economic growth and social progress of nations in the 21st century.Article Outline1. Introduction2. Theoretical framework3. Empirical analysis 3.1. Sources of information and the sample3.2. Methodological issues 3.2.1. Non-parametric frontier approach and economic growth4. Discussion of results 4.1. Global frontiers in ICT-producing manufacturing industriesPurchase4.2. Decomposition of labour-productivity growth5. ConclusionsAcknowledgementsReferences267 Applying least squares support vector machines to the airframe wing-box structural design cost estimation Original Research ArticleExpert Systems with Applications, Volume 37, Issue 12, December 2010, Pages 8417-8423S. Deng, Tsung-Han YehClose preview | Related articles | Related reference work articles Abstract | Figures/Tables | References AbstractThis research used the least squares support vector machines (LS-SVM) method to estimate the project design cost of an airframe wing-box structure. We also compared the estimation performance using back-propagation neural networks (BPN) and statistical response surface methodology (RSM). The solution mechanism of the LS-SVM involved a simultaneous searched for the maximal margin as the target, taking into account the error calculated during training phase to determine the estimation problem models. Two case studies involving the wing-box structure was investigated the separate structural parts case and the mixed structural parts case. The test results verified the feasibility of using the LS-SVM as well as its ability to make accurate estimations.Article Outline1. Introduction2. Cost estimation methods 2.1. Statistical parametric cost estimation2.2. Neural networks2.3. Support vector machinesPurchase3. Least squares support vector machines (LS-SVM)4. Case study of airframe wing-box structural cost estimation 4.1. Case definition and assumption4.2. Estimating performance criteria4.3. LS-SVM modeling procedure4.4. Case 1: Separate structural parts4.5. Case 2: Mixed structural parts5. ConclusionsReferences268 Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System Original Research ArticleTransportation Research Part C: Emerging Technologies, In Press, Corrected Proof, Available online 9 February 2011Zhi Shen, Jacques Georgy, Michael J. Korenberg, Aboelmagd NoureldinClose preview | Related articles | Related reference work articles Abstract | Figures/Tables | References AbstractDue to their complementary characteristics, Global Positioning System (GPS) is integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low-cost. In this study, a low cost reduced inertial sensor system (RISS) is considered. It consists of a MEMS-grade gyroscope and the vehicle built-in odometer. The system works together with GPS to provide 2D navigation for land vehicles. With adequate accuracy, Kalman filter (KF) is the commonly used estimation technique to achieve the data fusion of GPS and inertial sensors in case of high-end IMUs. However, due to the Purchaseinherent error characteristics of MEMS grade devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be handled by KF and its linear error models. To overcome the problem, Fast Orthogonal Search (FOS), a nonlinear system identification technique, is suggested for modeling the higher order RISS errors. As a general-purpos