Results 131 to 140 of about 42,098 (267)

Lidar‐Based Object Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure

open access: yesAdvanced Intelligent Systems, EarlyView.
This paper presents a lidar‐based sensor node design and a rule‐based state observer for edge‐based traffic participant tracking. Unlike other state‐of‐the‐art methods, this state observer enables real‐time, CPU‐only edge processing without relying on machine learning approaches.
Simon Schäfer   +2 more
wiley   +1 more source

Concentric Rheostat Decoupled 3D Force‐Sensing Module for Smart Table Tennis Training

open access: yesAdvanced Intelligent Systems, EarlyView.
A 3D‐printed sensor array intrinsically decouples normal and shear forces through a unique concentric structural design. By integrating piezoresistive, sliding area‐varying capacitive, and concentric rheostat mechanisms, the 12‐sensor module achieves high‐resolution 3D force mapping without complex algorithms.
Zhe Liu   +7 more
wiley   +1 more source

ParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems

open access: yesAdvanced Intelligent Systems, EarlyView.
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng   +4 more
wiley   +1 more source

Interpreting How Neural Networks Infer Scatterer Geometry from Echolocation Echoes

open access: yesAdvanced Intelligent Systems, EarlyView.
Neural networks enable echolocation‐based shape classification but remain difficult to interpret due to their black‐box nature. This work presents a feature‐importance metric to uncover the echo regions driving decisions in shape‐specialized networks.
Ganesh U. Patil   +2 more
wiley   +1 more source

Longitudinal Phenotypic Trajectories in GNAO1‐Related Disorders: Defining Disease Progression and Clinical Profiles

open access: yesAnnals of Neurology, EarlyView.
Objective Pathogenic variants in GNAO1 cause a spectrum of epilepsy, movement disorders, and developmental impairment. Clinical heterogeneity complicates prognosis and therapeutic development. We present the first longitudinal natural history study of GNAO1‐related disorders (GNAO1‐RD) to delineate phenotypic trajectories. Methods Sixty‐six individuals
Jana Domínguez‐Carral   +52 more
wiley   +1 more source

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