The paper analyzes the use of spaceborne spinning tethers for a reusable system to transfer payloads with a mass up to 4000 kg from low orbits to geostationary. The study indicates that a two-stage system is lighter than a single-stage tethered system with present day tether materials. A first stage in low orbit and a second stage in medium Earth orbit provide the required velocity increments for injecting the payload into geotransfer orbit with the final orbit circularization provided by the satellite kick motor.The orbits of the stages are resonant in order to provide periodic encounters and are optimized with the goal of reducing the overall system mass. The close-approach dynamics between the second stage and the payload released from the first stage is simulated to demonstrate the salient features of the rendezvous process. A total of 10 transfers over two years of operation without refueling is adopted for computing the propellant needed to reboost the stages. A preliminary analysis leads to the conclusion that a two-stage tethered system is more competitive, on a mass basis, than a chemical upper stage after two transfers.
Virtual reality applications are emerging into various regions of research and entertainment. Although visual and acoustic capabilities are already quite impressive, a wide range of users still criticizes the user interface. Frequently complex and very sensitive input devices are being used, although simple gestures would be preferred. While gesture recognition systems are quite common, see Nintendo's Wii mote, a CAVE has further challenges, as the person can be located in any random position and the gestures are not being performed related to a common fixpoint. Applying an infrared tracking system it is possible to reliably locate the hand and compute 3D trajectories. These are then further analyzed with a Long Short-Term Memory approach, which is able to model sequences of variable length with a higher reliability than HMMs.
In this paper we present a system for detecting unusual events in smart home environments. A primary application of this is to prolong independent living for elderly people at their homes. We show how to effectively combine information from multiple heterogeneous sensors which are typically present in a smart home scenario. Data fusion is done in a 3D voxel occupancy grid. Graph Cuts are used to accurately reconstruct people in the scene. Additionally we present a joint multi object Viterbi tracking framework, which allows tracking of all people, and simultaneously detecting critical events such as fallen persons.
Accurate 3D tracking of facial feature points from one monocular video sequence is appealing for many applications in human-machine interaction. In this work facial feature points are tracked with a Kanade-Lucas-Tomasi (KLT) feature tracker and the tracking results are linked with a 3D Active Shape Model (ASM). Thus, the efficient Gauss-Newton method is not solving for the shift of each facial feature point separately but for the 3D position, rotation and the 3D ASM parameters which are the same for all feature points. Thereby, not only the facial feature points are tracked more robustly but also the 3D position and the 3D ASM parameters can be extracted. The Jacobian matrix for the Gauss-Newton optimization is split via chain rule and the computations per frame are further reduced. The algorithm is evaluated on the basis of three handlabeled video sequences and it outperforms the KLT feature tracker. The results are also comparable to two other tracking algorithms presented recently, whereas the method proposed in this work is computationally less intensive.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.