scholarly journals Spatiotemporal Texture Reconstruction for Dynamic Objects Using a Single RGB‐D Camera

2021 ◽  
Vol 40 (2) ◽  
pp. 523-535
Author(s):  
Hyomin Kim ◽  
Jungeon Kim ◽  
Hyeonseo Nam ◽  
Jaesik Park ◽  
Seungyong Lee
Keyword(s):  
Dynamic Objects ◽  
Texture Reconstruction

Remote diagnostics of vibrations of dynamic objects by Doppler microwave sensors

Metrologiya ◽  
2020 ◽  
pp. 25-42
Author(s):  
Dmitrii V. Khablov
Keyword(s):  
Intelligent Systems ◽  
Electromagnetic Waves ◽  
Continuous Monitoring ◽  
Phase Changes ◽  
Vibration Diagnostics ◽  
Remote Diagnostics ◽  
Active Vibration ◽  
Dynamic Objects ◽  
Microwave Sensors ◽  
Non Destructive

This paper describes a promising method for non-contact vibration diagnostics based on the use of Doppler microwave sensors. In this case, active irradiation of the object with electromagnetic waves and the allocation of phase changes using two-channel quadrature processing of the received reflected signal are used. The modes of further fine analysis of the resulting signal using spectral or wavelet transformations depending on the nature of the active vibration are considered. The advantages of this non-contact and remote vibration analysis method for the study of complex dynamic objects are described. The convenience of the method for machine learning and use in intelligent systems of non-destructive continuous monitoring of the state of these objects by vibration is noted.

A MATHEMATICAL TRUST MODEL TO ENSURE IN THE INFORMATION ENVIRONMENT

2020 ◽  
Author(s):  
Мадина Усенбай ◽  
Акмарал Иманбаева
Keyword(s):  
Trust Model ◽  
Time Parameter ◽  
Information Environment ◽  
User Privacy ◽  
Cloud Technology ◽  
Secret Information ◽  
Dynamic Objects

Конфиденциальность является одним из важных параметров для повышения безопасности в сети, цель которого - сохранить секретную информацию. Рассмотрена модель доверия, состоящая из текущих и прошлых оценок на основе репутации объекта в сети. В модели используется параметр времени для защиты конфиденциальности пользователя для статических и динамических объектов, например, в IoT или облачной технологии. Confidentiality is one of the important parameters for increasing security on the network, the coal of which is to keep secret information. A trust model consisting of current and past assessments based on the object reputation in the network is considered. The model uses a time parameter to protect user privacy for static and dynamic objects, for example, in IoT or cloud technology.

scholarly journals ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

2021 ◽  
Author(s):  
Jiahui Huang ◽  
Sheng Yang ◽  
Zishuo Zhao ◽  
Yu-Kun Lai ◽  
Shi-Min Hu
Keyword(s):  
Motion Estimation ◽  
Rigid Body ◽  
Iterative Scheme ◽  
Dynamic Environments ◽  
Rigid Bodies ◽  
Factor Graph ◽  
Agglomerative Clustering ◽  
Multiple Objects ◽  
Graph Optimization ◽  
Dynamic Objects

AbstractWe present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments. While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers, their dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions for landmarks extracted from the same rigid body for clustering, and to identify static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix from landmarks, and uses agglomerative clustering to distinguish rigid bodies. Using decoupled factor graph optimization to revise their shapes and trajectories, we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally. Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach, and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.

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