Non-linear neoclassical model for poloidal asymmetries in tokamak pedestals: diamagnetic and radial effects included. (arXiv:1911.06365v1 [physics.plasm-ph])

Stronger impurity density in-out poloidal asymmetries than predicted by the most comprehensive neoclassical models have been measured in several tokamaks around the world during the last decade, calling into question the reduction of turbulence by sheared radial electric fields in H-mode tokamak pedestals. However, these pioneering theories neglect the impurity diamagnetic drift, or fail to…

Electrodynamic friction of a charged particle passing a conducting plate. (arXiv:1911.06369v1 [physics.class-ph])

The classical electromagnetic friction of a charged particle moving with prescribed constant velocity parallel to a planar imperfectly conducting surface is reinvestigated. As a concrete example, the Drude model is used to describe the conductor. The transverse electric and transverse magnetic contributions have very different character both in the low velocity (nonrelativistic) and high velocity…

MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring. (arXiv:1911.06364v1 [eess.SP])

In the multimodal traffic monitoring, we gather traffic statistics for distinct transportation modes, such as pedestrians, cars and bicycles, in order to analyze and improve people’s daily mobility in terms of safety and convenience. On account of its robustness to bad light and adverse weather conditions, and inherent speed measurement ability, the radar sensor is…

Automotive Radar Interference Mitigation Using Adaptive Noise Canceller. (arXiv:1911.06372v1 [eess.SP])

Interference among frequency modulated continues wave automotive radars can either increase the noise floor, which occurs in the most cases, or generate a ghost target in rare situations. To address the increment of noise floor due to interference, we proposed a low calculation cost method using adaptive noise canceller to increase the signal-to-interference ratio. In…

Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior. (arXiv:1911.06379v1 [stat.ML])

In this paper we address the problem of solving ill-posed inverse problems in imaging where the prior is a neural generative model. Specifically we consider the decoupled case where the prior is trained once and can be reused for many different log-concave degradation models without retraining. Whereas previous MAP-based approaches to this problem lead to…

Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling. (arXiv:1911.06393v1 [cs.LG])

Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. However, efficiently modelling long-term dependencies in these sequences is still challenging. Although the receptive field of these models grows exponentially with the number of layers, computing the…

Contrast Phase Classification with a Generative Adversarial Network. (arXiv:1911.06395v1 [eess.IV])

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy. A key challenge for image processing with contrast enhanced CT is that phase discrepancies are latent in different tissues due to contrast protocols, vascular dynamics, and metabolism…

Synthetic Event Time Series Health Data Generation. (arXiv:1911.06411v1 [cs.LG])

Synthetic medical data which preserves privacy while maintaining utility can be used as an alternative to real medical data, which has privacy costs and resource constraints associated with it. At present, most models focus on generating cross-sectional health data which is not necessarily representative of real data. In reality, medical data is longitudinal in nature,…