Neural network based tomographic approach to detect earthquake-related ionospheric anomalies

A tomographic approach is used to investigate the fine structure of electron density in the ionosphere. In the present paper, the Residual Minimization Training Neural Network (RMTNN) method is selected as the ionospheric tomography with which to investigate the detailed structure that may be associated with earthquakes. The 2007 Southern Sumatra earthquake (<i>M</i> = 8.5) was selected because significant decreases in the Total Electron Content (TEC) have been confirmed by GPS and global ionosphere map (GIM) analyses. The results of the RMTNN approach are consistent with those of TEC approaches. With respect to the analyzed earthquake, we observed significant decreases at heights of 250–400 km, especially at 330 km. However, the height that yields the maximum electron density does not change. In the obtained structures, the regions of decrease are located on the southwest and southeast sides of the Integrated Electron Content (IEC) (altitudes in the range of 400–550 km) and on the southern side of the IEC (altitudes in the range of 250–400 km). The global tendency is that the decreased region expands to the east with increasing altitude and concentrates in the Southern hemisphere over the epicenter. These results indicate that the RMTNN method is applicable to the estimation of ionospheric electron density

Visualization based on grid computing for large-scale volume data

第10回IEEE広島支部学生シンポジウム(HISS), ポスター ; 開催場所:広島 ; 開催日:2008年11月21-23

Visualization based on grid computing for large-scale volume data

第10回IEEE広島支部学生シンポジウム(HISS), ポスター ; 開催場所:広島 ; 開催日:2008年11月21-23

Visualization Technique based on Grid Computing for Large-scale Volume-data

近年, 高解像度化の進むCTやMRIから出力されるボリュームデータを,高精度にかつ高速に可視化するため,本研究ではグリッドコンピューティングを用いてボリュームレンダリングを行う手法を提案する.医療施設や研究機関に多数導入されている計算機を計算資源とするグリッドコンピューティングを用いる.計算能力の不均一な環境下において,可視性に基づき動的にタスクの投入を行うための手法を提案し,シミュレーションによりその有用性を確認した.提案手法をインプリメントし,グリッドコンピューティングを用いて大規模ボリュームデータのレンダリングを行った.To visualize high-resolution volumedata acquired from a recent CT or MRI, we propose a method for rendering the large-scale volume data using a grid computing. We use existing computers with non-homogeneous computing tasks to agent machines based on the visibility of divided volume data in a grid computing environment. Simulation results demonstrate the usefulness of the propose method. A large scale volumedata is rendered using our grid computing system

Visualization Technique based on Grid Computing for Large-scale Volume-data

情報処理学会研究報告 グラフィクスとCAD研究会 2007.0

Volume rendering using grid computing for large-scale volume data

In this paper, we propose a volume rendering method using grid computing for large-scale volume data. Grid computing is attractive because medical institutions and research facilities often have a large number of idle computers. A large-scale volume data is divided into sub-volumes and the sub-volumes are rendered using grid computing. When using grid computing, different computers rarely have the same processor speeds. Thus the return order of results rarely matches the sending order. However order is vital when combining results to create a final image. Job-Scheduling is important in grid computing for volume rendering, so we use an obstacle-flag which changes priorities dynamically to manage sub-volume results. Obstacle-Flags manage visibility of each sub-volume when line of sight from the view point is obscured by other sub-volumes. The proposed Dynamic Job-Scheduling based on visibility substantially increases efficiency. Our Dynamic Job-Scheduling method was implemented on our university's campus grid and we conducted comparative experiments, which showed that the proposed method provides significant improvements in efficiency for large-scale volume rendering

Dual recognition of CENP-A nucleosomes is required for centromere assembly

CENP-C and CENP-N recognize distinct structural elements of CENP-A nucleosomes, providing a foundation for the assembly of other centromere and kinetochore components