In this paper, a method of fusing ground-based laser range image and CCD images for the reconstruction of textured 3D urban object is proposed. An acquisition system is developed to capture laser range image and CCD images simultaneously from the same platform. A registration method is developed using both laser range and CCD images in a coarse-to-fine process. Laser range images are registered with an assumption on sensor's setup, which aims at robustly detecting an initial configuration between the sensor's coordinate system of two views. CCD images are matched to refine the accuracy of the initial transformation, which might be degraded by improper sensor setup, unreliable feature extraction, or limited by low spatial resolution of laser range image. Textured 3D model is generated using planar faces for vertical walls and triangular cells for ground surface, trees and bushes. Through an outdoor experiment of reconstructing a building using six views of laser range and CCD images, it is demonstrated that textured 3D model of urban objects can be generated in an automated manner.

In this study, a Coanda-drone with length, width, and height of 121.6, 121.6, and 191[mm] was designed, and its total mass was 1166.7[g]. Using four propulsion devices, it could produce a maximum of 5428[g] thrust. Its structure is very different from conventional drones because in this study it combines the design of the jet engine of a jet fixed-wing drone with the fuselage structure layout of a rotary-wing drone. The advantage of jet drone's high propulsion is kept so that it can output greater thrust under the same variation of PWM waveform output. In this study, the propulsion device performs high-speed jetting, and the airflow around the propulsion device will also be jetted downward along the direction of the airflow.

As big data attracts attention in a variety of fields, research on data exploration for analyzing large-scale scientific data has gained popularity. To support exploratory analysis of scientific data, effective summarization and visualization of the target data as well as seamless cooperation with modern data management systems are in demand. In this paper, we focus on the exploration-based analysis of scientific array data, and define a spatial V-Optimal histogram to summarize it based on the notion of histograms in the database research area. We propose histogram construction approaches based on a general hierarchical partitioning as well as a more specific one, the l-grid partitioning, for effective and efficient data visualization in scientific data analysis. In addition, we implement the proposed algorithms on the state-of-the-art array DBMS, which is appropriate to process and manage scientific data. Experiments are conducted using massive evacuation simulation data in tsunami disasters, real taxi data as well as synthetic data, to verify the effectiveness and efficiency of our methods.