Article | 05-June-2013
For estimating the missing data of wireless sensor networks, an estimation algorithm called HD method, which can make use of sensoring space-time correlation of the data, was proposed based on mathematical Hermite and DESM statistical models. The algorithm not only can adaptively adjust the time and space weights, but also can accurately estimate the missing or unavailable data. The experimental results show that the algorithm has good stability and relatively high estimation accuracy.
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 1032–1053
Sampling Methods | 22-July-2018
O. Olawale Awe,
A. Adedayo Adepoju
Statistics in Transition New Series, Volume 19 , ISSUE 2, 239–258
Article | 01-April-2018
International Journal of Advanced Network, Monitoring and Controls, Volume 1 , ISSUE 1, –
Article | 10-April-2018
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 20–26
Article | 01-September-2015
estimation algorithm is used to optimize the selection of path parameters, which avoid the blindness iterative process and consider the kinematics constraints such as the curvature, torsion and climbing angle. The multiple UAVs’ three dimensional path planning in dynamic environment are tested. Simulation results prove the validity and practicability of the algorithm.
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1641–1666
Research Article | 13-December-2017
estimation error of the proposed LAE+VFF estimation algorithm is bounded.
A. J. Alimin,
Y. M. Sam
International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 3, 754–770
Article | 06-July-2017
The paper presents the method of hierarchical Bayes (HB) estimation under small area models with spatially correlated random effects and a spatial structure implied by the Simultaneous Autoregressive (SAR) process. The idea was to improve the spatial EBLUP by incorporating the HB approach into the estimation algorithm. The computation procedure applied in the paper uses the concept of sampling from a posterior distribution under generalized linear mixed models implemented in WinBUGS software
Statistics in Transition New Series, Volume 17 , ISSUE 3, 365–390