In this work, we propose a novel, fast, and memory-efficient unsupervised statistical method, combined with an unsupervised deep learning (DL) model, for de-snowing 3D LiDAR point clouds in a fully ...
Exploratory Data Analysis of Heart Disease Prediction using Machine Learning Techniques-RS Algorithm
Abstract: Heart disease has become very common nowadays. Machine learning-based heart disease prediction has significant potential in clinical applications, enhancing early diagnosis and treatment.
Abstract: LiDAR point cloud data is essential in autonomous driving, robotic navigation, and 3D modeling. However, noise caused by sensor errors and environmental factors degrades data quality and ...
Abstract: The demand for 3D scanning of workpiece geometries in automated assembly within workshops is increasingly critical, playing a vital role in the process. Point cloud registration, as an ...
Abstract: Changes in lake water levels are closely related to climate change and can also reflect information about local human activities. Therefore, obtaining high temporal resolution time series of ...
Abstract: Semantic segmentation of point clouds is an essential task for understanding the environment in autonomous driving and robotics. Recent range-based works achieve real-time efficiency, while ...
Abstract: With the increasing demand of high-precision data acquisition card in application, noise suppression in the process of signal acquisition becomes very important. The existence of noise will ...
Abstract: The flush air data sensing (FADS) system resolves air data state issues through redundant measurements of surface pressure distributions on the vehicle, with its fault-tolerant algorithm ...
Abstract: In this paper, we develop an online optimization algorithm for solving a class of nonconvex optimization problems with a linearly varying optimal point. The global convergence of the ...
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