Concepts of Hyperloop Wireless Communication at 1200 km/h: 5G, Wi-Fi, Propagation, Doppler and Handover

The new generation of capsules that circulate through vacuum tubes at speeds up to 1200 km/h, which is being developed, demands communication systems that can operate at these speeds with high capacity and quality of service. Currently, the two technologies available are the new generation of 802.11ax networks and 5G NR. Using these technologies at such high speeds in a confined environment requires a careful study and design of the configuration of the network and optimization of the physical interface. This paper describes the requirements for critical and business communications, proposing a WLAN and 5G network design based on the analysis of the propagation characteristics and constraints of vacuum tubes and using propagation measurements and simulations made in similar environments at frequencies of 2.5/5.7/24 GHz. These measurements and simulations show that propagation losses in this environment are low (4–5 dB/100 m), as a consequence of the guided propagation, so that the use of bands is preferred. Finally, considering the propagation constraints and requirements of a Hyperloop system, a complete wireless communication system is proposed using two networks with 802.11 and 5G technology.

Asymptotic method for constructing a model of adiabatic guided modes of smoothly irregular integrated optical waveguides

The paper considers a class of smoothly irregular integrated optical multilayer waveguides, whose properties determine the characteristic features of guided propagation of monochromatic polarized light. An asymptotic approach to the description of such electromagnetic radiation is proposed, in which the solutions of Maxwells equations are expressed in terms of the solutions of a system of four ordinary differential equations and two algebraic equations for six components of the electromagnetic field in the zero approximation. The gradient of the phase front of the adiabatic guided mode satisfies the eikonal equation with respect to the effective refractive index of the waveguide for the given mode.The multilayer structure of waveguides allows one more stage of reducing the model to a homogeneous system of linear algebraic equations, the nontrivial solvability condition of which specifies the relationship between the gradient of the radiation phase front and the gradients of interfaces between thin homogeneous layers.In the final part of the work, eigenvalue and eigenvector problems (differential and algebraic), describing adiabatic guided modes are formulated. The formulation of the problem of describing the single-mode propagation of adiabatic guided modes is also given, emphasizing the adiabatic nature of the described approximate solution of Maxwells equations.

A new Rayleigh-like wave in guided propagation of antiplane waves in couple stress materials

Motivated by the unexpected appearance of shear horizontal Rayleigh surface waves, we investigate the mechanics of antiplane wave reflection and propagation in couple stress (CS) elastic materials. Surface waves arise by mode conversion at a free surface, whereby bulk travelling waves trigger inhomogeneous modes. Indeed, Rayleigh waves are perturbations of the travelling mode and stem from its reflection at grazing incidence. As is well known, they correspond to the real zeros of the Rayleigh function. Interestingly, we show that the same generating mechanism sustains a new inhomogeneous wave, corresponding to a purely imaginary zero of the Rayleigh function. This wave emerges from ‘reflection’ of a bulk standing mode: This produces a new type of Rayleigh-like wave that travels away from , as opposed to along, the free surface, with a speed lower than that of bulk shear waves. Besides, a third complex zero of the Rayleigh function may exist, which represents waves attenuating/exploding both along and away from the surface. Since none of these zeros correspond to leaky waves, a new classification of the Rayleigh zeros is proposed. Furthermore, we extend to CS elasticity Mindlin’s boundary conditions, by which partial waves are identified, whose interference lends Rayleigh–Lamb guided waves. Finally, asymptotic analysis in the thin-plate limit provides equivalent one-dimensional models.

metPropagate: network-guided propagation of metabolomic information for prioritization of neurometabolic disease genes

ABSTRACTMany inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis before irreversible damage occurs is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, Whole Exome Sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is currently the primary method used to identify novel disease-causing variants; however, it can be challenging to identify the causal candidate gene given the large number of plausible variants. Using untargeted metabolomics (UM) to prioritize metabolically relevant candidate genes is a promising approach to diagnosing known or novel IEMs in a single patient. Here, we present a network-based bioinformatics approach, metPropagate, that uses UM data from a single patient and a group of controls to prioritize candidate genes. We validate metProp on 107 patients with diagnosed IEMs and 11 patients with novel IEMs diagnosed through the TIDE gene discovery project at BC Children’s Hospital. The metPropagate method ranks candidate genes by considering the network of interactions between them. This is done by using a graph smoothing algorithm called label propagation to quantify the metabolic disruption in genes’ local neighbourhood. metPropagate was able to prioritize the causative gene in the top 20th percentile of candidate genes for 91% of patients with known IEM disorders. For novel IEMs, metPropagate placed the causative gene in the top 20th percentile in 9/11 patients. Using metPropagate, the causative gene was ranked higher than Exomiser’s phenotype-based ranking in 6/11 patients. The results of this study indicate that for diagnostic and gene discovery purposes, network-based analysis of metabolomics data can lend support to WES gene-discovery methods by providing an additional mode of evidence to help identify causal genes.

Video Object Detection with Locally-Weighted Deformable Neighbors

Deep convolutional neural networks have achieved great success on various image recognition tasks. However, it is nontrivial to transfer the existing networks to video due to the fact that most of them are developed for static image. Frame-byframe processing is suboptimal because temporal information that is vital for video understanding is totally abandoned. Furthermore, frame-by-frame processing is slow and inefficient, which can hinder the practical usage. In this paper, we propose LWDN (Locally-Weighted Deformable Neighbors) for video object detection without utilizing time-consuming optical flow extraction networks. LWDN can latently align the high-level features between keyframes and keyframes or nonkeyframes. Inspired by (Zhu et al. 2017a) and (Hetang et al. 2017) who propose to aggregate features between keyframes and keyframes, we adopt brain-inspired memory mechanism to propagate and update the memory feature from keyframes to keyframes. We call this process Memory-Guided Propagation. With such a memory mechanism, the discriminative ability of features in keyframes and non-keyframes are both enhanced, which helps to improve the detection accuracy. Extensive experiments on VID dataset demonstrate that our method achieves superior performance in a speed and accuracy trade-off, i.e., 76.3% on the challenging VID dataset while maintaining 20fps in speed on Titan X GPU.

A novel deep learning scheme for morphology-based classification of mycobacterial infection in unstained macrophages

ObjectiveMycobacterium tuberculosis (Mtb) is an airborne, contagious bacterial pathogen that causes widespread infections in humans. Using Mycobacterium marinum (Mm), a surrogate model organism for Mtb research, the present study develops a deep learning-based scheme that can classify the Mm-infected and uninfected macrophages in tissue culture solely based on morphological changes.MethodsA novel weak-and semi-supervised learning method is developed to detect and extract the cells, firstly. Then, transfer learning and fine-tuning from the CNN is built to classify the infected and uninfected cells.ResultsThe performance is evaluated by accuracy (ACC), sensitivity (SENS) and specificity (SPEC) with 10-fold cross-validation. It demonstrates that the scheme can classify the infected cells accurately and efficiently at the early infection stage. At 2 hour post infection (hpi), we achieve the ACC of 0.923 ± 0.005, SENS of 0.938 ± 0.020, and SPEC of 0.905 ± 0.019, indicating that the scheme has detected significant morphological differences between the infected and uninfected macrophages, although these differences are hardly visible to naked eyes. Interestingly, the ACC at 12 and 24 hpi are 0.749 ± 0.010 and 0.824 ± 0.009, respectively, suggesting that the infection-induced morphological changes are dynamic throughout the infection. Finally, deconvolution with guided propagation maps the key morphological features contributing to the classification.SignificanceThis proof-of-concept study provides a novel venue to investigate bacterial pathogenesis in a macroscopic level and has a great promise in diagnosis of bacterial infections.