6,119 research outputs found
AN ANALYSIS OF PROJECTION ANGLE OF THE LONG JUMP
The purpose of this study was to evaluate the optimum projection angle and to determine the characteristics of optimal takeoff techniques. Subjects of this study included 12 male and 12 female elite long jumpers. Their performance was filmed at long jump event of the 8th China National Games. Mathematical methods, including regression analysis were used to determine the relationship of projection angle with projection velocity and projection distance, then to calculate the optimum projection angle. It was concluded that an increase in projection angle, at the expense of a normal loss of projection velocity, would benefit the projection distance according to the capability of Chinese elite long jumpers. It was also concluded that the takeoff leg should extend forward more before active landing in order to avoid extremely small segment angles of the shank
Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization
A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported
Sapiens Chain: A Blockchain-based Cybersecurity Framework
Recently, cybersecurity becomes more and more important due to the rapid
development of Internet. However, existing methods are in reality highly
sensitive to attacks and are far more vulnerable than expected, as they are
lack of trustable measures. In this paper, to address the aforementioned
problems, we propose a blockchain-based cybersecurity framework, termed as
Sapiens Chain, which can protect the privacy of the anonymous users and ensure
that the transactions are immutable by providing decentralized and trustable
services. Integrating semantic analysis, symbolic execution, and routing
learning methods into intelligent auditing, this framework can achieve good
accuracy for detecting hidden vulnerabilities. In addition, a revenue incentive
mechanism, which aims to donate participants, is built. The practical results
demonstrate the effectiveness of the proposed framework
GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction
This paper presents a 3D lidar SLAM system based on improved regionalized
Gaussian process (GP) map reconstruction to provide both low-drift state
estimation and mapping in real-time for robotics applications. We utilize
spatial GP regression to model the environment. This tool enables us to recover
surfaces including those in sparsely scanned areas and obtain uniform samples
with uncertainty. Those properties facilitate robust data association and map
updating in our scan-to-map registration scheme, especially when working with
sparse range data. Compared with previous GP-SLAM, this work overcomes the
prohibitive computational complexity of GP and redesigns the registration
strategy to meet the accuracy requirements in 3D scenarios. For large-scale
tasks, a two-thread framework is employed to suppress the drift further. Aerial
and ground-based experiments demonstrate that our method allows robust odometry
and precise mapping in real-time. It also outperforms the state-of-the-art
lidar SLAM systems in our tests with light-weight sensors.Comment: Accepted by IROS 202
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