Computer-assisted navigation systems for hip resurfacing arthroplasty are designed to minimize the chance of implant malposition. However, there is little evidence computer navigation is useful in the presence of anatomical deformity. We therefore determined the accuracy of an image-free resurfacing hip arthroplasty navigation system in the presence of a pistol grip deformity of the head and femoral neck junction and of a slipped upper femoral epiphysis deformity. We constructed an artificial phantom leg from machined aluminum with a simulated hip and knee. The frontal and lateral plane implant-shaft angles for the guide wire of the femoral component reamer were calculated with the computer navigation system and with an electronic caliper combined with micro-CT. There was a consistent disagreement between the navigation system and our measurement system in both the frontal plane and lateral plane with the pistol grip deformity. We found close agreement only for the frontal plane angle calculation in the presence of the slipped upper femoral epiphysis deformity, but calculation of femoral head size was inaccurate. The use of image-free navigation for the positioning of the femoral component appears questionable in these settings.
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http://dx.doi.org/10.1007/s11999-009-0850-6 | DOI Listing |
Sensors (Basel)
January 2025
University of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, Croatia.
The possibilities of the Ambient Assisted Living (AAL)/Enhanced Living Environments (ELE) concept in the environment of a smart home were investigated to improve accessibility and improve the quality of life of a person with disabilities. This paper focuses on the concept of predictive information for a person with disabilities in a smart home environment concept where artificial intelligence (AI) and machine learning (ML) systems use data on the user's preferences, habits, and possible incident situations. A conceptual mathematical model is proposed, the purpose of which is to provide predictive user information from defined data sets.
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January 2025
Laboratory of Intelligent Control, Rocket Force University of Engineering, Xi'an 710025, China.
Experts and scholars from various nations have proposed studying low Earth orbit (LEO) satellite signals as the space-based signals of opportunity (SOPs) for navigation and positioning. This method serves as a robust alternative in environments where global navigation satellite systems (GNSS) are unavailable or compromised, providing users with high-precision, anti-interference, secure, and dependable backup navigation solutions. The rapid evolution of LEO communication constellations has spurred the development of SOPs positioning technology using LEO satellites.
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January 2025
Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference.
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December 2024
College of Aerospace and Engineering, National University of Defense Technology, Changsha 410073, China.
Due to a short flight time, the dual-axis rotational inertial navigation system carried by some launch vehicles or missiles is often only used for self-calibration and self-alignment. It is generally in the strap-down state rather than the rotation modulation state during flight. This wastes the precision potential of the navigation system.
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December 2024
Electronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, Spain.
The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies.
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