May 6, 2024 · In this work, we present NN-based particle tracking and denoising algorithms to reconstruct the trajectory of a magnetic tracer in MPT. Our approach utilizes a ...

Nov 17, 2023 · The task-specific downstream classification module of DeepSPT trains and predicts directly on experimental data, which has been transformed to a combined ...

Jun 3, 2024 · ... data that contains only track properties used by a neural network. The skimmed data is used for model training. Trained models are stored in the ONNX format ...

Apr 10, 2024 · We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow ...

May 8, 2024 · Deep Neural Networks-Assisted Particle Tracking. Deep neural networks (DNNs), particularly the convolutional neural networks (CNNs), have become the popular ...

Nov 15, 2023 · Abstract. We present a deep-learning based tracking objects of interest in walking droplet and granular intruder experiments. In a typical walking droplet ...

Apr 15, 2024 · In addition we have developed Bayesian estimation methods for particle tracking as well as deep learning methods for temporal data association and particle ...

Dec 18, 2023 · A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future ... Deep-learning method for data association in particle tracking.

Sep 11, 2023 · Data association in multi-sensor-based tracking is a significant challenge, involving multiple objects and detections received at each time step, with varying ...

Dec 1, 2023 · These analyses indicate that u-track3D presents a tracking solution that is competitive to both conventional and deep-learning-based approaches. We then present ...