AIMC Topic: Motion

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Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction.

IEEE transactions on pattern analysis and machine intelligence
Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on ...

Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.

Medical physics
BACKGROUND: Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBC...

Topology, vorticity, and limit cycle in a stabilized Kuramoto-Sivashinsky equation.

Proceedings of the National Academy of Sciences of the United States of America
A noisy stabilized Kuramoto-Sivashinsky equation is analyzed by stochastic decomposition. For values of the control parameter for which periodic stationary patterns exist, the dynamics can be decomposed into diffusive and transverse parts which act o...

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence.

Sensors (Basel, Switzerland)
Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free loc...

Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.

Journal of applied clinical medical physics
BACKGROUND: In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural ...

Unsupervised graph-level representation learning with hierarchical contrasts.

Neural networks : the official journal of the International Neural Network Society
Unsupervised graph-level representation learning has recently shown great potential in a variety of domains, ranging from bioinformatics to social networks. Plenty of graph contrastive learning methods have been proposed to generate discriminative gr...

A Sampling-Based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning.

Sensors (Basel, Switzerland)
Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* i...

Soft dorsal/anal fins pairs for roll and yaw motion in robotic fish.

Bioinspiration & biomimetics
Fish has primarily served as a model for many bio-inspired underwater robots. However, most of the work on fish-inspired robots is focused on propulsion and turning in the horizontal plane. In this paper, we present our work on the 3D motion of bio-i...

Highly Integrated Multi-Material Fibers for Soft Robotics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Soft robots are envisioned as the next generation of safe biomedical devices in minimally invasive procedures. Yet, the difficulty of processing soft materials currently limits the size, aspect-ratio, manufacturing throughput, as well as, the design ...