A Local Implementation of the POD-Based Ensemble 4DVar with R-Localization

The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar) through Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The feasibility and effectiveness of the PODEn4DVar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations(OSSEs) in comparison with the original version of the PODEn4DVar with B-localization and that without localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.

Mechanistic-empirical pavement design guide(MEPDG):a bird's-eye view

Past editions of the American Association of State Highway and Transportation Officials （AASHTO） Guide for Design of Pavement Structures have served well for several decades; nevertheless, many serious limitations exist for their continued use as the nation＇s primary pavement design procedures. Researchers are now incorporating the latest advances in pavement design into the new Mechanistic-Empirical Pavement Design Guide （MEPDG）, developed under the National Cooperative Highway Research Program （NCHRP） 1-37A project and adopted and published by AASHTO. The MEPDG procedure offers several dramatic improvements over the current pavement design guide and presents a new paradigm in the way pavement design is performed. However, MEPDG is substantially more complex than the AASHTO Design Guide by considering the input parameters that influence pavement performance, including traffic, climate, pavement structure and material properties, and applying the principles of engineering mechanics to predict critical pavement responses. It requires significantly more input from the designer. Some of the required data are either not tracked previously or are stored in locations not familiar to designers, and many data sets need to be preprocessed for use in the MEPDG. As a result, tremendous research work has been conducted and still more challenges need to be tackled both in federal and state levels for the full implementation of MEPDG. This paper, for the first time, provides a comprehensive bird＇s eye view for the MEPDG procedure, including the evolvement of the design methodology, an overview of the design philosophy and its components, the research conducted during the development, improvement, and implementation phases, and the challenges remained and future developments directions. It is anticipated that the efforts in this paper aid in enhancing the mechanistic-empirical based pavement design for future continuous improvement to keep up with changes in trucking, materials, construction, design concepts, computers, and so on.

Low Complexity SOVA for Turbo Codes

Recently, trimming Soft-output Viterbi algorithm(T-SOVA) has been proposed to reduce the complexity of SOVA for Turbo codes. In its fi rst stage, a dynamic algorithm, lazy Viterbi algorithm, is used to indicate the minimal metric differences which brings obstacle on hardware implementation. This paper proposes a Viterbi algorithm(VA) based T-SOVA to facilitate hardware implementation. In the first stage of our scheme, a modified VA with regular structure is used to fi nd the maximum likelihood(ML) path and calculate the metric differences. Further, local sorting is introduced to trim the metric differences, which reduces the complexity of trimming operation. Simulation results and complexity analysis show that VA based T-SOVA performs as well as lazy VA based T-SOVA and is easier to be applied to hardware implementation.