An Electrodynamics Solver for Moving Sources

An Electrodynamics solver for moving sources is introduced. The main challenges and formulation are highlighted. The solver enables the simulation of fields for sources undergoing arbitrary motion. Two examples of uniformly moving current sources are provided to correlate the numerical solver computations with theory, based on the solution of Maxwell's equations and the relativistic transformation of the electromagnetic fields

The geology and geochronology of Al Wahbah maar crater, Harrat Kishb, Saudi Arabia

Al Wahbah is a large (∼2.2 km diameter, ∼250 m deep) maar crater in the Harrat Kishb volcanic field in western Saudi Arabia. It cuts Proterozoic basement rocks and two Quaternary basanite lava flows, and is rimmed with an eroded tuff ring of debris from the phreatomagmatic explosion that generated the crater. A scoria cone on the northern wall of the crater was dissected by the explosion and exposes a dolerite plug that was intruded immediately prior to crater formation. The dolerite plug yields a <sup>40</sup>Ar/<sup>39</sup>Ar age of 1.147 ± 0.004 Ma. This is the best possible estimate of the time Al Wahbah crater formed. It is a few tens of thousand years younger than the age of the lower and upper basalt flows, 1.261 ± 0.021 Ma and 1.178 ± 0.007 Ma respectively. A dolerite dyke exposed within the basement in the wall of the crater is dated at 1.886 ± 0.008 Ma. This is the most precise age so far determined for the initiation of basaltic volcanism of Harrat Kishb, and confirms that it is significantly younger than the other post-rift volcanic provinces in the region. This study provides constrains the timing of humid climatic conditions in the region and suggests that the Quaternary basaltic volcanism that stretches the length of the western side of the Arabian peninsula may prove to be useful for establishing palaeoclimatic conditions

Action recognition using Kinematics Posture Feature on 3D skeleton joint locations

Action recognition is a very widely explored research area in computer vision and related fields. We propose Kinematics Posture Feature (KPF) extraction from 3D joint positions based on skeleton data for improving the performance of action recognition. In this approach, we consider the skeleton 3D joints as kinematics sensors. We propose Linear Joint Position Feature (LJPF) and Angular Joint Position Feature (AJPF) based on 3D linear joint positions and angles between bone segments. We then combine these two kinematics features for each video frame for each action to create the KPF feature sets. These feature sets encode the variation of motion in the temporal domain as if each body joint represents kinematics position and orientation sensors. In the next stage, we process the extracted KPF feature descriptor by using a low pass filter, and segment them by using sliding windows with optimized length. This concept resembles the approach of processing kinematics sensor data. From the segmented windows, we compute the Position-based Statistical Feature (PSF). These features consist of temporal domain statistical features (e.g., mean, standard deviation, variance, etc.). These statistical features encode the variation of postures (i.e., joint positions and angles) across the video frames. For performing classification, we explore Support Vector Machine (Linear), RNN, CNNRNN, and ConvRNN model. The proposed PSF feature sets demonstrate prominent performance in both statistical machine learning- and deep learning-based models. For evaluation, we explore five benchmark datasets namely UTKinect-Action3D, Kinect Activity Recognition Dataset (KARD), MSR 3D Action Pairs, Florence 3D, and Office Activity Dataset (OAD). To prevent overfitting, we consider the leave-one-subject-out framework as the experimental setup and perform 10-fold cross-validation. Our approach outperforms several existing methods in these benchmark datasets and achieves very promising classification performance

Maximum-likelihood estimation of specific differential phase and attenuation in rain

Precise estimation of propagation parameters in precipitation media is of interest to improve the performance of communications systems and in remote sensing applications. In this paper, we present maximum-likelihood estimators of specific attenuation and specific differential phase in rain. The model used for obtaining the cited estimators assumes coherent propagation, reflection symmetry of the medium, and Gaussian statistics of the scattering matrix measurements. No assumptions about the microphysical properties of the medium are needed. The performance of the estimators is evaluated through simulated data. Results show negligible estimators bias and variances close to Cramer–Rao bounds