Coherent Radio Emission from a Twisted Magnetosphere after a Magnetar-quake

Magnetars are a class of highly magnetized, slowly rotating neutron stars, only a small fraction of which exhibit radio emission. We propose that the coherent radio curvature emission is generated by net charge fluctuations from a twist-current-carrying bundle (the j-bundle) in the scenario of magnetar-quake. Two-photon pair production is triggered, which requires a threshold voltage not too much higher than 109 V in the current-carrying bundle, and which can be regarded as the open field lines of a magnetar. Continued untwisting of the magnetosphere maintains change fluctuations, and hence coherent radio emission, in the progressively shrinking j-bundle, which lasts for years until the radio beam is too small to be detected. The modeled peak flux of radio emission and the flat spectrum are generally consistent with the observations. We show that this time-dependent, conal-beam, radiative model can interpret the variable radio pulsation behaviors and the evolution of the X-ray hot spot of the radio-transient magnetar XTE J1810−197 and the high-B pulsar/anomalous X-ray pulsar PSR J1622−4950. Radio emission with luminosity of and high-frequency oscillations are expected to be detected for a magnetar after an X-ray outburst. Differences of radio emission between magnetars and ordinary pulsars are discussed

The effect of piles and their loading on nearby retaining walls – an artificial neural network approach

The assessment of stability of retaining walls that were constructed to prevent soil instability and collapse under the loads on nearby piles could be a sophisticated task. In urban areas, buildings or infrastructure are sometimes built relatively close to each other. Often pile foundations or groups of piles are used as the primary supporting systems and, inevitably, existing nearby retaining walls would be affected by these structures. The maximum wall deformation of these retaining walls was selected as a key factor to be determined to assess the retaining wall stability. In order to investigate the effect of loaded piles on the retaining wall, a set of parameters were selected such as the pile length, diameter and its location from the retaining wall. Considering all these parameters could lead to a large number of scenarios in order to establish the sensitivity of the system with respect to each variable. To reduce the required number of models needed to be analyzed, an Artificial Neural Network (ANN) was developed based on a representative dataset of base parameters. Similar to our brain, once the input (parameters) and output (maximum displacement and its location) baseline are given, the ANN is able to simulate and train by itself to provide a credible prediction of any corresponding scenario. Using the trained ANN model, for future designs engineers can predict a retaining wall maximum deformation and location under different geometrical scenarios, and as well to enhance or improve the serviceability of the entire pile-wall system

Root-MUSIC Based Angle Estimation for MIMO Radar with Unknown Mutual Coupling

Direction of arrival (DOA) estimation problem for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC) is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived

Efficient Near Maximum-Likelihood Efficient Near Maximum-Likelihood Reliability-Based Decoding for Short LDPC Codes

In this paper, we propose an efficient decoding algorithm for short low-density parity check (LDPC) codes by carefully combining the belief propagation (BP) decoding and order statistic decoding (OSD) algorithms. Specifically, a modified BP (mBP) algorithm is applied for a certain number of iterations prior to OSD to enhance the reliability of the received message, where an offset parameter is utilized in mBP to control the weight of the extrinsic information in message passing. By carefully selecting the offset parameter and the number of mBP iterations, the number of errors in the most reliable positions (MRPs) in OSD can be reduced, thereby significantly improving the overall decoding performance of error rate and complexity. Simulation results show that the proposed algorithm can approach the maximum-likelihood decoding (MLD) for short LDPC codes with only a slight increase in complexity compared to BP and a significant decrease compared to OSD. Specifically, the order-(m-1) decoding of the proposed algorithm can achieve the performance of the order-m OSD

Approximating actual flows in physical infrastructure networks : the case of the Yangtze River Delta high-speed railway network

Previous empirical research on urban networks has used data on infrastructure networks to guesstimate actual inter-city flows. However, with the exception of recent research on airline networks in the context of the world city literature, relatively limited attention has been paid to the degree to which the outline of these infrastructure networks reflects the actual flows they undergird. This study presents a method to improve our estimation of urban interaction in and through infrastructure networks by focusing on the example of passenger railways, which is arguably a key potential data source in research on urban networks in metropolitan regions. We first review common biases when using infrastructure networks to approximate actual inter-city flows, after which we present an alternative approach that draws on research on operational train scheduling. This research has shown that 'dwell time' at train stations reflects the length of the alighting and boarding process, and we use this insight to estimate actual interaction through the application of a bimodal network projection function. We apply our method to the high-speed railway (HSR) network within the Yangtze River Delta (YRD) region, discuss the difference between our modelled network and the original network, and evaluate its validity through a systemic comparison with a benchmark dataset of actual passenger flows