Ball-Nose Vehicle's Atmospheric Parameters Estimation Based on Airflow Velocity

2014 ◽  
Vol 696 ◽  
pp. 3-7 ◽  
Author(s):  
Li Lin Qian ◽  
Jian Wu Tao ◽  
Fei Yu ◽  
Hai Fa Dai
Keyword(s):  
Real Time ◽  
Compressible Flow ◽  
Good Accuracy ◽  
Angle Of Attack ◽  
Parameters Estimation ◽  
Flight Speed ◽  
Atmospheric Parameters ◽  
Airflow Velocity ◽  
Final Model ◽  
Time Performance

The airflow velocity over blunt sphere is used to calculate ball-nose vehicle's atmospheric parameters about angle of attack, angle of sideslip and flight speed. A final model satisfying compressible flow is developed, and the expressions for atmospheric parameters is derived from three strategically selected sensors' velocity. The expressions of atmospheric parameters are verified through Fluent stimulation. The result of stimulation demonstrates a good accuracy of angles and flight speed, and the system has a good real-time performance.

scholarly journals A 64-Line Lidar-Based Road Obstacle Sensing Algorithm for Intelligent Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Long Chen
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Good Accuracy ◽  
Intelligent Vehicles ◽  
Svm Classifier ◽  
Distance Threshold ◽  
The Road ◽  
Time Performance ◽  
Motion State ◽  
Time Requirements

Based on the 64-line lidar sensor, an object detection and classification algorithm with both effectiveness and real time is proposed. Firstly, a multifeature and multilayer lidar points map is used to separate the road, obstacle, and suspension object. Then, obstacle grids are clustered by a grid-clustering algorithm with dynamic distance threshold. After that, by combining the motion state information of two adjacent frames, the clustering results are corrected. Finally, the SVM classifier is used to classify obstacles with clustered object position and attitude features. The good accuracy and real-time performance of the algorithm are proved by experiments, and it can meet the real-time requirements of the intelligent vehicles.

A Smart Wiper System Based on Feedforward-Feedback Composite Controller

2012 ◽  
Vol 472-475 ◽  
pp. 1446-1450
Author(s):  
Xian Zhong Ren ◽  
Mei Fu Luo ◽  
Ya Nan Qiu
Keyword(s):  
Real Time ◽  
Software Design ◽  
Good Accuracy ◽  
Experimental Results ◽  
Time Performance ◽  
Key Factor ◽  
Feedback Algorithm

The velocity of the wiper arms is a key factor which can influence the clarity of the windshield and the comfort of the driver’s view. So how to control the velocity of the wiper arms is important. To solve the problem, a method is elaborated based on feedforward-feedback composite controller. It introduces the hardware and software design based on the feedforward-feedback algorithm, controlling the velocity of the wiper arms by adjusting the duty of PWM. The experimental results indicate that the smart wiper system is reliable, and has good accuracy and real-time performance.

Real-time Performance Monitoring of MPC Based on Covariance Index Prediction

2014 ◽  
Vol 39 (5) ◽  
pp. 658-663 ◽  
Author(s):  
Xue-Min TIAN ◽  
Ya-Jie SHI ◽  
Yu-Ping CAO
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Time Performance

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

scholarly journals Optimization of time and frequency fiber-optic links exploiting bi-directional amplifiers, based on real-time performance measurement

2021 ◽  
Vol 62 ◽  
pp. 102465
Author(s):  
Karol Salwik ◽  
Łukasz Śliwczyński ◽  
Przemysław Krehlik ◽  
Jacek Kołodziej
Keyword(s):  
Performance Measurement ◽  
Real Time ◽  
Fiber Optic ◽  
Time Performance

A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

2021 ◽  
pp. 1-13
Author(s):  
Andres Bell ◽  
Tomas Mantecon ◽  
Cesar Diaz ◽  
Carlos R. del-Blanco ◽  
Fernando Jaureguizar ◽  
...  
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Time Performance

scholarly journals Stochastic-Depth Ambient Occlusion

2021 ◽  
Vol 4 (1) ◽  
pp. 1-15
Author(s):  
Jop Vermeer ◽  
Leonardo Scandolo ◽  
Elmar Eisemann
Keyword(s):  
Real Time ◽  
High Performance ◽  
Depth Map ◽  
Visual Quality ◽  
Ambient Light ◽  
Missing Information ◽  
Time Performance ◽  
Ambient Occlusion ◽  
Depth Buffer ◽  
Screen Space

Ambient occlusion (AO) is a popular rendering technique that enhances depth perception and realism by darkening locations that are less exposed to ambient light (e.g., corners and creases). In real-time applications, screen-space variants, relying on the depth buffer, are used due to their high performance and good visual quality. However, these only take visible surfaces into account, resulting in inconsistencies, especially during motion. Stochastic-Depth Ambient Occlusion is a novel AO algorithm that accounts for occluded geometry by relying on a stochastic depth map, capturing multiple scene layers per pixel at random. Hereby, we efficiently gather missing information in order to improve upon the accuracy and spatial stability of conventional screen-space approximations, while maintaining real-time performance. Our approach integrates well into existing rendering pipelines and improves the robustness of many different AO techniques, including multi-view solutions.

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