Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RF). We show that our CNN significantly outperforms the RF model reducing the error by almost half. Furthermore, for a fixed number of approximately 2,000 allowed false transient candidates per night we are able to reduce the miss-classified real transients by approximately 1/5. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope (LSST). We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS

Analysis of the Failure Tolerance of Linear Access Networks

In this paper, we study the disconnection of a moving vehicle from a linear access network composed by cheap WiFi Access Points in the context of the telecommuting in massive transportation systems. In concrete, we analyze the probability for a user to experience a disconnection longer than a threshold t∗, leading to a disruption of all on-going communications between the vehicle and the infrastructure network. We provide an approximation formula to estimate this probability for large networks. We then carry out a sensitivity analysis and supply a guide for operators when choosing the parameters of the networks. We focus on two scenarios: an intercity bus and an intercity train. Last, we show that such systems are viable, as they attain a very low probability of long disconnections with a very low maintenance cost

Component based approach using OMNeT++ for Train Communication Modeling

International audienceThis paper reports on our experience in using OMNeT++ to develop a network simulator focused on railway environments. Common design problems are analyzed, making emphasis on radio communication models. Scalability issues are raised when modeling the large topologies that are associated with railway communications. Our conclusions point out that model re-usability must be reinforced and that a component-based design must be adopted in order to build a tool for generating valuable performance results

Extending INET Framework for Directional and Asymmetrical Wireless Communications

International audienceThis paper reports our work on extending the Omnet INET Framework with a directional radio model, putting a special emphasis on the implementation of asymmetrical communications. We first analyze the original INET radio model, focusing on its design and components. Then we discuss the modifications that have been done to support directional communications. Our preliminary results show that the new model is flexible enough to allow the user to provide any antenna pattern shape, with only an additional reasonable computational cost

Generation of Realistic 802.11 Interferences in the Omnet++ INET Framework Based on Real Traffic Measurements

International audienceRealistic simulation of 802.11 traffic subject to high interference, for example in dense urban areas, is still an open issue. Many studies do not address the interference problem properly. In this paper, we present our preliminary work on a method to recreate interference traffic from real measurements. The method consists in capturing real traffic traces and generating interference patterns based on the recorded information. Furthermore, we assume that the coordinates of the sources of interference in the real scene are not known a priori. We introduce an extension to Omnet++ INET-Framework to replay the recreated interference in a transparent way into a simulation. We validate our proposed method by comparing it against the real measurements taken from the scene. Furthermore we present an evaluation of how the injected interference affects the simulated results on three arbitrary simulated scenarios

Network Provisioning for High Speed Vehicles Moving along Predictable Routes - Part 1: Spiderman Handover

This report presents our on-going work on a new system designed to provide a continuous network connectivity to communicating devices located on-board a vehicle moving at ”high speed” with a predictable trajectory such as trains, subways or buses. The devices on-board the vehicle form a sub-network called the ”in-motion network”. This system we propose is composed of two parts. The mobile part, called Spiderman Device (SD), installed on the roof of the vehicle, and the fixed part is composed of multiples access points, called Wireless Switch Access Points (WS APs), installed along the predictable route of the vehicle. To provide a continuous connectivity, we designed a new handover algorithm that relies on a two IEEE802.11 radio hardware placed in the SD device. This dual-radio architecture allows to minimize or even hide the handover effects, achieving a seamless continuous data-link connection at high speeds, up-to 150 Km/h and possibly more. The link between the SD and the WS AP forms a Layer 2 Ethernet Bridge, supporting any Layer 3 protocol between the infrastructure network and the in-motion network. This concept has been validated by simulations and is currently tested using a real prototype in order to assess the performances and practical feasibility of the system

Discovery of distant RR Lyrae stars in the Milky Way using DECam

We report the discovery of distant RR Lyrae stars, including the most distant known in the Milky Way, using data taken in the $g-$band with the Dark Energy Camera as part of the High cadence Transient Survey (HiTS; 2014 campaign). We detect a total of 173 RR Lyrae stars over a ~120 deg^2 area, including both known RR Lyrae and new detections. The heliocentric distances d_H of the full sample range from 9 to >200 kpc, with 18 of them beyond 90 kpc. We identify three sub-groups of RR Lyrae as members of known systems: the Sextans dwarf spheroidal galaxy, for which we report 46 new discoveries, and the ultra-faint dwarf galaxies Leo IV and Leo V. Following an MCMC methodology, we fit spherical and ellipsoidal profiles of the form rho(R) ~ R^n to the radial density distribution of RR Lyrae in the Galactic halo. The best fit corresponds to the spherical case, for which we obtain a simple power-law index of n=-4.17^{+0.18}_{-0.20}, consistent with recent studies made with samples covering shorter distances. The pulsational properties of the outermost RR Lyrae in the sample (d_H>90 kpc) differ from the ones in the halo population at closer distances. The distribution of the stars in a Period-Amplitude diagram suggest they belong to Oosterhoff-intermediate or Oosterhoff II groups, similar to what is found in the ultra-faint dwarf satellites around the Milky Way. The new distant stars discovered represent an important addition to the few existing tracers of the Milky Way potential in the outer halo.Comment: Accepted for publication in The Astrophysical Journa

Component based approach using OMNeT++ for Train Communication Modeling

International audienceThis paper reports on our experience in using OMNeT++ to develop a network simulator focused on railway environments. Common design problems are analyzed, making emphasis on radio communication models. Scalability issues are raised when modeling the large topologies that are associated with railway communications. Our conclusions point out that model re-usability must be reinforced and that a component-based design must be adopted in order to build a tool for generating valuable performance results