Space-time visualizations of macroscopic or microscopic traffic variables is a qualitative tool used by traffic engineers to understand and analyze different aspects of road traffic dynamics. We present a deep learning method to learn the macroscopic
traffic speed dynamics from these space-time visualizations, and demonstrate its application in the framework of traffic state estimation. Compared to existing estimation approaches, our approach allows a finer estimation resolution, eliminates the dependence on the initial conditions, and is agnostic to external factors such as traffic demand, road inhomogeneities and driving behaviors. Our model respects causality in traffic dynamics, which improves the robustness of estimation. We present the high-resolution traffic speed fields estimated for several freeway sections using the data obtained from the Next Generation Simulation Program (NGSIM) and German Highway (HighD) datasets. We further demonstrate the quality and utility of the estimation by inferring vehicle trajectories from the estimated speed fields, and discuss the benefits of deep neural network models in approximating the traffic dynamics.
We propose a kinematic wave based Deep Convolutional Neural Network (Deep CNN) to estimate high resolution traffic speed dynamics from sparse probe vehicle trajectories. To that end, we introduce two key approaches that allow us to incorporate kinema
tic wave theory principles to improve the robustness of existing learning-based estimation methods. First, we use an anisotropic traffic-based kernel for the CNN. This kernel is designed to explicitly take forward and backward traffic wave propagation characteristics into account during reconstruction in the space-time domain. Second, we use simulated data for training the CNN. This implicitly imposes physical constraints on the patterns learned by the CNN, providing an alternate, unrestricted way to integrate complex traffic behaviors into learning models. We present the speed fields estimated using the anisotropic kernel and highlight its advantages over its isotropic counterpart in terms of predicting shockwave dynamics. Furthermore, we test the transferability of the trained model to real traffic by using two datasets: the Next Generation Simulation (NGSIM) program and the Highway Drone (HighD) dataset. Finally, we present an ensemble version of the CNN that allows us to handle multiple (and unknown) probe vehicle penetration rates. The results demonstrate that anisotropic kernels can reduce model complexity while improving the correctness of the estimation, and that simulation-based training is a viable alternative to model fitting using real-world data. This suggests that exploiting prior traffic knowledge adds value to learning-based estimation methods, and that there is great potential in exploring broader approaches to do so.
Infectious diseases are a significant threat to human society which was over sighted before the incidence of COVID-19, although according to the report of the World Health Organisation (WHO) about 4.2 million people die annually due to infectious dis
ease. Due to recent COVID-19 pandemic, more than 2 million people died during 2020 and 96.2 million people got affected by this devastating disease. Recent research shows that applying individual interactions and movements data could help managing the pandemic though modelling the spread of infectious diseases on social contact networks. Infectious disease spreading can be explained with the theories and methods of diffusion processes where a dynamic phenomena evolves on networked systems. In the modelling of diffusion process, it is assumed that contagious items spread out in the networked system through the inter-node interactions. This resembles spreading of infectious virus, e.g. spread of COVID-19, within a population through individual social interactions. The evolution behaviours of the diffusion process are strongly influenced by the characteristics of the underlying system and the mechanism of the diffusion process itself. Thus, spreading of infectious disease can be explained how people interact with each other and by the characteristics of the disease itself. This paper presenters the relevant theories and methodologies of diffusion process that can be used to model the spread of infectious diseases.
Geo-tagged tweets can potentially help with sensing the interaction of people with their surrounding environment. Based on this hypothesis, this paper makes use of geotagged tweets in order to ascertain various land uses with a broader goal to help w
ith urban/city planning. The proposed method utilises supervised learning to reveal spatial land use within cities with the help of Twitter activity signatures. Specifically, the technique involves using tweets from three cities of Australia namely Brisbane, Melbourne and Sydney. Analytical results are checked against the zoning data provided by respective city councils and a good match is observed between the predicted land use and existing land zoning by the city councils. We show that geo-tagged tweets contain features that can be useful for land use identification.
Efficient quantum state measurement is important for maximizing the extracted information from a quantum system. For multi-qubit quantum processors in particular, the development of a scalable architecture for rapid and high-fidelity readout remains
a critical unresolved problem. Here we propose reservoir computing as a resource-efficient solution to quantum measurement of superconducting multi-qubit systems. We consider a small network of Josephson parametric oscillators, which can be implemented with minimal device overhead and in the same platform as the measured quantum system. We theoretically analyze the operation of this Kerr network as a reservoir computer to classify stochastic time-dependent signals subject to quantum statistical features. We apply this reservoir computer to the task of multinomial classification of measurement trajectories from joint multi-qubit readout. For a two-qubit dispersive measurement under realistic conditions we demonstrate a classification fidelity reliably exceeding that of an optimal linear filter using only two to five reservoir nodes, while simultaneously requiring far less calibration data textendash{} as little as a single measurement per state. We understand this remarkable performance through an analysis of the network dynamics and develop an intuitive picture of reservoir processing generally. Finally, we demonstrate how to operate this device to perform two-qubit state tomography and continuous parity monitoring with equal effectiveness and ease of calibration. This reservoir processor avoids computationally intensive training common to other deep learning frameworks and can be implemented as an integrated cryogenic superconducting device for low-latency processing of quantum signals on the computational edge.
An eight element, compact Ultra Wideband-Multiple Input Multiple Output (UWB-MIMO) antenna capable of providing high data rates for future Fifth Generation (5G) terminal equipments along with the provision of necessary bandwidth for Third Generation
(3G) and Fourth Generation (4G) communications that accomplishes band rejection from 4.85 to 6.35 GHz by deploying a Inductor Capacitor (LC) stub on the ground plane is presented. The incorporated stub also provides flexibility to reject any selected band as well as bandwidth control. The orthogonal placement of the printed monopoles permits polarization diversity and provides high isolation. In the proposed eight element UWB-MIMO/diversity antenna, monopole pair 3-4 are 180o mirrored transform of monopole pair 1-2 which lie on the opposite corners of a planar 50 x 50 mm2 substrate. Four additional monopoles are then placed perpendicularly to the same board leading to a total size of 50 x 50 x 25 mm3 only. The simulated results are validated by comparing the measurements of a fabricated prototype. It was concluded that the design meets the target specifications over the entire bandwidth of 2 to 12 GHz with a reflection coefficient better than -10 dB (except the rejected band), isolation more than 17 dB, low envelope correlation, low gain variation, stable radiation pattern, and strong rejection of the signals in the Wireless Local Area Network (WLAN) band. Overall, compact and reduced complexity of the proposed eight element architecture, strengthens its practical viability for the diversity applications in future 5G terminal equipments amongst other MIMO antennas designs present in the literature.
While coherently-driven Kerr microcavities have rapidly matured as a platform for frequency comb formation, such microresonators generally possess weak Kerr coefficients; consequently, triggering comb generation requires millions of photons to be cir
culating inside the cavity. This suppresses the role of quantum fluctuations in the combs dynamics. In this paper, we realize a minimal version of coherently-driven Kerr-mediated microwave frequency combs in the circuit QED architecture, where the quantum vacuums fluctuations are the primary limitation on comb coherence. We achieve a comb phase coherence of up to 35~$mu$s, approaching the theoretical device quantum limit of 55~$mu$s, and vastly longer than the modes inherent lifetimes of 13~ns. The ability within cQED to engineer stronger nonlinearities than optical microresonators, together with operation at cryogenic temperatures, and excellent agreement of comb dynamics with quantum theory indicates a promising platform for the study of complex dynamics of quantum nonlinear systems
We demonstrate adiabatically tapered fibers terminating in sub-micron tips that are clad with a higher-index material for coupling to an on-chip waveguide. This cladding enables coupling to a high-index waveguide without losing light to the buried ox
ide. A technique to clad the tip of the tapered fiber with a higher-index polymer is introduced. Conventional tapered waveguides and forked tapered waveguide structures are investigated for coupling from the clad fiber to the on-chip waveguide. We find the forked waveguide facilitates alignment and packaging, while the conventional taper leads to higher bandwidth. The insertion loss from a fiber through a forked coupler to a sub-micron silicon nitride waveguide is 1.1 dB and the 3 dB-bandwidth is 90 nm. The coupling loss in the packaged device is 1.3 dB. With a fiber coupled to a conventional tapered waveguide, the loss is 1.4 dB with a 3 dB bandwidth extending beyond the range of the measurement apparatus, estimated to exceed 250 nm.
This letter considers two groups of source nodes. Each group transmits packets to its own designated destination node over single-hop links and via a cluster of relay nodes shared by both groups. In an effort to boost reliability without sacrificing
throughput, a scheme is proposed, whereby packets at the relay nodes are combined using two methods; packets delivered by different groups are mixed using non-orthogonal multiple access principles, while packets originating from the same group are mixed using random linear network coding. An analytical framework that characterizes the performance of the proposed scheme is developed, compared to simulation results and benchmarked against a counterpart scheme that is based on orthogonal multiple access.
We present a theoretical study of synchronization dynamics in incoherently pumped exciton-polariton condensates in coupled polariton traps. Our analysis is based on a coupled-mode theory for the generalized Gross-Pitaevskii equation, which employs an
expansion in non-Hermitian, pump-dependent modes appropriate for the pumped geometry. We find that polariton-polariton and reservoir-polariton interactions play competing roles and lead to qualitatively different synchronized phases of the coupled polariton modes as pumping power is increased. Crucially, these interactions can also act against each other to hinder synchronization. We map out a phase diagram and discuss the general characteristics of these phases using a generalized Adler equation.
