To improve the computational efficiency, low-cost and computationally inexpensive surrogate models based on radial basis functions (RBF) are used as approximate for the high fidelity finite element (FE) analyzes.
We decided to use Microsoft Kinect [ 27] based on low-cost and computationally inexpensive gesture and voice recognition capabilities which proved to be robust enough to be utilized in many different engineering applications [ 28– 30].
The control and estimation algorithms are designed to operate with low-cost sensors, be computationally efficient, robust and easily adaptable from flight test data.
More specifically, we explore the feasibility of estimating the mesoscale strain fields in hexagonal close packed (HCP) α-titanium polycrystals during HCF loading conditions using the computationally low-cost MKS approach, and subsequently estimating the slip system activities via decoupled numerical integration of the relevant crystal plasticity (CP) constitutive relations.
To mitigate these problems, in this paper, we intend to develop a computationally low-cost general purpose feature descriptor that can perform well in diversified applications.
We have confined our sparse disparity map estimation on image edges because (1) the block matching-based disparity estimation, irrespective of the similarity metric used, can best be done on non-flat image patches and (2) HVS is most sensitive to edges; hence, a computationally low-cost approach should be prioritized on edges.
In this study, the average eigenvalue criterion was applied because it is an objective, computationally low-cost method which has shown good results according to Valle et al. [ 13].
In recent years GPU computing has gained wide acceptance as a simple low-cost solution for speeding up computationally expensive processing in many scientific and engineering applications.
We are developing and testing a simple, low-cost UVE system using a single-element imaging transducer that is simpler and less computationally demanding than array-based systems.
Previous work in the literature showed that the computationally less expensive CF can be robustly applied to attitude estimation using low-cost sensors and achieve performance that is comparable to that of a full EKF.
