Real-time stereo semi-global matching for video processing using previous incremental information

This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video sequences and uses previous information to reduce computation time and improve disparity image quality. It is based on the semi-global matching stereo algorithm but modified to reuse previous calculation information. Storing and reusing this information not only reduces computation time but improves accuracy in a cost filtering scheme. Some tests are presented to compare the computation time and results of the algorithm, which show that it can achieve better results in terms of quality and time than standard algorithms for some scenarios.

Sensitivity analysis of the DEM resolution and effective parameters of runoff yield in the SWAT model: a case study

This study investigates the impact of different digital elevation model (DEM) resolutions on the topological attributes and simulated runoff, as well as the sensitivity of runoff parameters in the Mahabad Dam watershed in Iran. The watershed and streamlines were delineated in ArcGIS, and the hydrologic analyses were performed using the Soil and Water Assessment Tool (SWAT). The sensitivity analysis on runoff parameters was performed, using the Sequential Uncertainties FItting Ver. 2 algorithm, in the SWAT Calibration and Uncertainty Procedures (SWAT-CUP) program. The results indicated that the sensitivity of runoff parameters, watershed surface area, and elevations changed under different DEM resolutions. As the distribution of slopes changed using different DEMs, surface parameters were most affected. Furthermore, higher amounts of runoff were generated when DEMs with finer resolutions were implemented. In comparison with the observed value of 8 m3/s at the watershed outlet, the 12.5 m DEM showed more realistic results (6.77 m3/s). Comparatively, the 12.5 m DEM generated 0.74% and 2.73% more runoff compared with the 30 and 90 m DEMs, respectively. The findings of this study indicate that in order to reduce computation time, researchers may use DEMs with coarser resolutions at the expense of minor decreases in accuracy.

Hybrid Scene Structuring for Accelerating 3D Radiative Transfer Simulations

Three-dimensional (3D) radiative transfer models are the most accurate remote sensing models. However, presently the application of 3D models to heterogeneous Earth scenes is a computationally intensive task. A common approach to reduce computation time is abstracting the landscape elements into simpler geometries (e.g., ellipsoid), which, however, may introduce biases. Here, a hybrid scene structuring approach is proposed to accelerate the radiative transfer simulations while keeping the scene as realistic as possible. In a first step, a 3D description of the Earth landscape with equal-sized voxels is optimized to keep only non-empty voxels (i.e., voxels that contain triangles) and managed using a bounding volume hierarchy (BVH). For any voxel that contains triangles, within-voxel BVHs are created to accelerate the ray–triangle intersection tests. The hybrid scheme is implemented in the Discrete Anisotropic Radiative Transfer (DART) model by integrating the Embree ray-tracing kernels developed at Intel. In this paper, the performance of the hybrid algorithm is compared with the original uniform grid approach implemented in DART for a 3D city scene and a forest scene. Results show that the removal of empty voxels can accelerate urban simulation by 1.4×~3.7×, and that the within-voxel BVH can accelerate forest simulations by up to 258.5×.

A DISTANCE BASED INCREMENTAL FILTER-WRAPPER ALGORITHM FOR FINDING REDUCT IN INCOMPLETE DECISION TABLES

Tolerance rough set model is an effective tool for attribute reduction in incomplete decision tables. In recent years, some incremental algorithms have been proposed to find reduct of dynamic incomplete decision tables in order to reduce computation time. However, they are classical filter algorithms, in which the classification accuracy of decision tables is computed after obtaining reduct. Therefore, the obtained reducts of these algorithms are not optimal on cardinality of reduct and classification accuracy. In this paper, we propose the incremental filter-wrapper algorithm IDS_IFW_AO to find one reduct of an incomplete desision table in case of adding multiple objects. The experimental results on some sample datasets show that the proposed filter-wrapper algorithm IDS_IFW_AO is more effective than the filter algorithm IARM-I [17] on classification accuracy and cardinality of reduct

Dose calculations in heterogeneous volumes with the GATE Monte Carlo software for radiological protection

Monte Carlo methods have become widespread in the field of radiation protection and in particular in medical physics where the use of voxelized volumes for the reconstruction of dosimetric quantities is increasing. Changing the resolution of a dose map can be useful to compare dosimetric results coming from voxelized volumes with different resolutions, or to reduce computation time. This can be done by superimposing a dosel grid with a different resolution than that of the voxelized volume. In this case, each dosel will cover several voxels, leading the Monte Carlo code to calculate the dose in heterogeneous volumes. Two algorithms are available in GATE to perform these calculations, the Volume-Weighting (V-W) and the Mass-Weighting (M-W) algorithms, the latter being the subject of this work. In a general way, the M-W algorithm tends to reconstruct a higher dose than that the V-W one. In dosels involving heavy and lightweight materials (air-skin, bone-tissue), the M-W reconstructed dose is better estimated than the V-W one (up to 10% better at the air-skin interface). Moreover, the statistical uncertainty of the M-W dose can be up to 80% lower than the V-W one at air-skin interfaces. These results show that the M-W algorithm is more suitable for radiological protection applications and must be preferentially used in GATE for dose calculations in heterogeneous volumes.

Parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors can be used to reduce computation time by using parallel computing. The parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive.