Searching for the multiple longest common subsequences (MLCS) has significant applications in the areas of bioinformatics, information processing, and data mining, and so forth, Although a few parallel MLCS algorithms have been proposed, the efficiency and effectiveness of the algorithms are not satisfactory with the increasing complexity and size of biologic data. To overcome the shortcomings of the existing MLCS algorithms, and considering that MapReduce parallel framework of cloud computing being a promising technology for cost-effective high performance parallel computing, a novel finite automaton (FA) based on cloud computing called FACC is proposed under MapReduce parallel framework, so as to exploit a more efficient and effective general parallel MLCS algorithm. FACC adopts the ideas of matched pairs and finite automaton by preprocessing sequences, constructing successor tables, and common subsequences finite automaton to search for MLCS. Simulation experiments on a set of benchmarks from both real DNA and amino acid sequences have been conducted and the results show that the proposed FACC algorithm outperforms the current leading parallel MLCS algorithm FAST-MLCS.
The long-term spatial-temporal deformation monitoring of densely distributed infrastructures near the lake area is of great significance to understand the urban health status and prevent the potential traffic safety problems. In this paper, the permanent scatterer interferometry (PSI) technology with TerraSAR-X imagery over the area around Dongting Lake was utilized to generate the long-term spatial-temporal deformation. Since the X-band SAR interferometric phases are highly influenced by the thermal dilation of the observed objects, and the deformation of large infrastructures are highly related to external temperature, a combined deformation model considering the thermal expansion and the seasonal environmental factors was proposed to model the temporal variations of the deformation. The time series deformation and the thermal dilation parameter over the area were obtained, and a comparative study with the traditional linear model was conducted. The Dongting Lake Bridge and the typical feature points distributed around the lake were analyzed in details. In order to compensate for the unavailability of external in situ measurements over the area, phase residuals and the subsidence generated through Differential Interferometric Synthetic Aperture Radar (D-InSAR) were utilized to verify the accuracy of the obtained deformation time series. Experiment results suggested that the proposed model is suitable and suggested for the selected study site. The root mean square error (RMSE) of the residual phase was estimated as 0.32 rad, and the RMSE compared with D-InSAR derived deformation was ±1.1 mm.
The infrastructures built on soft clay in the Dongting Lake area are more prone to settlement and instability due to its significant rheological properties. It is of great importance to conduct a long-term surface deformation monitoring over this area. The most commonly used Synthetic Aperture Radar Interferometry (InSAR) deformation models are based on combinations of one or several pure empirical mathematical functions without considering the physical and mechanical characteristics of the observed objects. In this work, we propose an improved deformation model based on the functional relationship between strain and time in the Maxwell rheological model. The rheological parameters of elastic modulus and viscosity are introduced into a traditional empirical seasonal model. The improved model is applied for the investigation of the spatial-temporal surface evolution over the Dongting Lake area with the Small Baseline Subset InSAR (SBAS-InSAR) technology and TerraSAR-X satellite imagery. With the proposed model, the rheological parameters and the time series deformation are estimated, with the maximum accumulated subsidence estimated as 38 mm. Through the analysis of the generated results, we find that the lower the viscosity and elastic modulus are, the higher the deformation is. Temporally, the overall deformation follows a generally subsiding trend with a seasonal recovery of 5 mm from October 2012 to November 2012 and 12 mm from January 2013 to February 2013. To compensate for the deficiency of the unavailability of external geodetic measurements over this area, three different accuracy indexes (residual phase, temporal coherence, and high-pass deformation) are utilized to evaluate the modeling accuracy. The results of the improved model are also compared to three traditional models (seasonal model, cubic polynomial model, and linear model). The comparison shows that the improved model is highly recommended in this area because of its better accuracy.
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