Ashutosh Kumar 교수

Nowcasting of precipitation is a difficult spatiotemporal task because of the non-uniform characterization of meteorological structures over time. Recently, convolutional LSTM has been shown to be successful in solving various complex spatiotemporal based problems. In this research, we propose a novel precipitation nowcasting architecture 'Convcast' to predict various short-term precipitation events using satellite data. We train Convcast with ten consecutive NASA's IMERG precipitation data sets

Traffic flow estimation is required for road infrastructure management tasks such as road development planning, routing, and navigation. Determining traffic flow on a citywide scale is challenging because of the expensive costs and portability of current devices. Portable sensing devices such as drive cams and smartphones are an effective source of monitoring the road infrastructure environment because of their continuous interaction with the surrounding. However, the use of such devices to esti

Analysis of traffic fl ow pa rameters is ne cessary for Intelligent Transportation Systems (ITS) and autonomous driving research. Deep learning-based vehicle detection techniques have been widely used in reconstructing traffic fl ow parameters from video images. This research proposes a novel cross-sectional traffic fl ow es timation al gorithm to re construct tr affic volume from moving camera videos. We develop a vehicle detection dataset with more than one million annotations of vehicles with

Abstract. 3D point clouds acquired by laser scanning and other techniques are difficult to interpret because of their irregular structure. To make sense of this data and to allow for the derivation of useful information, a segmentation of the points in groups, units, or classes fit for the specific use case is required. In this paper, we present a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compare it with an implementation of the state-o

Analyzing vehicle movement trajectories is essential for understanding urban mobility and traffic flow patterns. Obtaining a reliable estimate of vehicle trajectory is challenging as it requires the vehicle to be observed and re-identified at different locations and times. Recently, a scalable citywide traffic flow estimation method has been proposed utilizing moving cameras on vehicle dashboards. This study extends the recently proposed method for traffic flow estimation by using cameras mounte

Estimating traffic flow is essential in planning road development, routing, navigation, autonomous driving, and other applications in road infrastructure management. Recently, cameras and portable devices mounted on moving vehicles have been proposed to estimate citywide traffic flow. Nevertheless, the efficacy of such algorithms has not been proven on a wide scale under varying environmental conditions. This paper modifies existing algorithms to estimate traffic flow from vehicle-mounted camera

Long tunnels are a necessary means of connectivity due to topological conditions across the world. In recent years, various technologies have been developed to support construction of tunnels and reduce the burden on construction workers. In continuation, mountain tunnel construction sites especially pose a major problem for continuous long conveyor belts to remove crushed rocks and rubbles out of tunnels during the process of mucking. Consequently, this process damages conveyor belts quite freq

Prediction of accurate wind speed is necessary for a variety of applications such as energy production, agriculture, climate modeling, and weather forecasting. Various satellites orbiting the earth measure the wind speed, which is particularly useful as they provide measurements of wind speed over large areas and in remote locations that might be difficult to measure using other methods. However, satellite-based wind speed measurements have relatively low spatial resolution compared to other met

Abstract Magneto encephalography (MEG) is a technique by which the activity of the cortical neurons can be measured with very good temporal and moderate spatial resolution. When using a MEG record, as a research or clinical tool, the investigator may face a problem of extracting the essential features of the neuromagnetic signals in the presence of artifacts. The amplitude of the disturbances may be higher than that of the brain signals, and the artifacts may resemble pathological signals in sha

<p>Analyzing vehicle movement trajectories is essential for understanding urban mobility and traffic flow patterns. Obtaining a reliable estimate of vehicle trajectory is challenging as it requires the vehicle to be observed and re-identified at different locations and times. Recently, a scalable citywide traffic flow estimation method has been proposed utilizing moving cameras on vehicle dashboards. As moving cameras constantly interact with their surroundings, they provide valuable infor

<p>Analyzing vehicle movement trajectories is essential for understanding urban mobility and traffic flow patterns. Obtaining a reliable estimate of vehicle trajectory is challenging as it requires the vehicle to be observed and re-identified at different locations and times. Recently, a scalable citywide traffic flow estimation method has been proposed utilizing moving cameras on vehicle dashboards. As moving cameras constantly interact with their surroundings, they provide valuable infor