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A new approach for optimal time-series segmentation
(Elsevier, 2020)
This paper proposes a new optimal approach, called OSTS, to improve the segmentation of time series. The proposed method is based on A* algorithm and it uses an improved version of the well-known Salotti method for obtaining ...
Optimal online time-series segmentation
(Springer, 2024)
When time series are processed, the difficulty increases with the size of the series. This fact is aggravated when time series are processed online, since their size increases indefinitely. Therefore, reducing their number ...
ReSLAM: Reusable SLAM with heterogeneous cameras
(Elsevier, 2024)
State-of-the-art SLAM methods are designed to work only with the type of camera employed to create the map, and little attention has been paid to the reusability of the maps created. In other words, the maps generated by ...
A new approach for optimal offline time-series segmentation with error bound guarantee
(Elsevier, 2021)
This paper proposes a new optimal approach, called OSFS, based on feasible space (FS) Liu et al. (2008)[, that minimizes the number of segments of the approximation and guarantees the error bound using the L∞-norm. On the ...
Assessing polygonal approximations: A new measurement and a comparative study
(Elsevier, 2023)
Two proposals related to the evaluation of polygonal approximations are presented in this document. First, a new measurement, called normalized compression ratio and adjustment error (NCA), to provide a fair evaluation of ...
Unsupervised generation of polygonal approximations based on the convex hull
(Elsevier, 2020)
The present paper proposes a new non-optimal but unsupervised algorithm, called ICT-RDP, for generation of polygonal approximations based on the convex hull. Firstly, the new algorithm takes into account the convex hull ...