Advancing ADAS Perception: A Sensor-Parameterized Implementation of the GM-PHD Filter.

Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice.

Analysis of modular taper fractures of the revision hip stem Prevision and comparison of the original and current taper design.

Aims: The risk of mechanical failure of modular revision hip stems is frequently mentioned in the literature, but little is currently known about the actual clinical failure rates of this type of prosthesis. The current retrospective long-term analysis examines the distal and modular failure patterns of the Prevision hip stem from 18 years of clinical use. A design improvement of the modular taper was introduced in 2008, and the data could also be used to compare the original and the current design of the modular connection.