Objective: To investigate the value of computer aided navigation system (CANS) in the treatment of post traumatic maxillofacial deformation.
Methods: Fifty-four patients (M = 37, F = 17) were included in the study, including 31 cases of zygomatic fracture, 7 cases of pure orbital fracture, 11 cases of temporal mandibular joint ankylosis, 1 case of foreign body and 4 cases of defect reconstruction with custom implant. Data acquisition was done through CT scan, and DICOM data was transferred into workstation. Computer assisted design, including osteotomy, reposition, fibula flap design, orbital implant construction was performed using Surgicase CMF and Brain Lab Iplan system. The virtual design was transferred to Brain Lab navigation system, and the osteotomy, reduction, location of bone graft and custom implant were guided by navigation. Postoperative CT scan was required 48 - 72 hours after surgery. Preoperative and postoperative CT images were superimposed automatically in BrainLab Iplan system, and compared both in 3D objects and 2D slices.
Results: All the cases achieved good results without serious complication. The error of important corresponding points in zygomatic fracture reduction, orbital reconstruction and defect reconstruction was 0.2 - 3.5 mm, 0.8 - 2.0 mm and 0.2 - 2.2 mm respectively.
Conclusions: Computer assisted design is of considerable value for the systematic and accurate planning for complicated post traumatic deformation. Virtual plan could be carried out accurately with the assistance of CANS.
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http://dx.doi.org/10.3760/cma.j.issn.1002-0098.2012.11.002 | 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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