Multi-Level Pre-Correlation RFI Flagging for Real-Time Implementation on UniBoard

Because of the denser active use of the spectrum, and because of radio telescopes higher sensitivity, radio frequency interference (RFI) mitigation has become a sensitive topic for current and future radio telescope designs. Even if quite sophisticated approaches have been proposed in the recent years, the majority of RFI mitigation operational procedures are based on post-correlation corrupted data flagging. Moreover, given the huge amount of data delivered by current and next generation radio telescopes, all these RFI detection procedures have to be at least automatic and, if possible, real-time. In this paper, the implementation of a real-time pre-correlation RFI detection and flagging procedure into generic high-performance computing platforms based on Field Programmable Gate Arrays (FPGA) is described, simulated and tested. One of these boards, UniBoard, developed under a Joint Research Activity in the RadioNet FP7 European programme is based on eight FPGAs interconnected by a high speed transceiver mesh. It provides up to ~4 TMACs with Altera Stratix IV FPGA and 160 Gbps data rate for the input data stream. Considering the high in-out data rate in the pre-correlation stages, only real-time and go-through detectors (i.e. no iterative processing) can be implemented. In this paper, a real-time and adaptive detection scheme is described. An ongoing case study has been set up with the Electronic Multi-Beam Radio Astronomy Concept (EMBRACE) radio telescope facility at Nan\c{c}ay Observatory. The objective is to evaluate the performances of this concept in term of hardware complexity, detection efficiency and additional RFI metadata rate cost. The UniBoard implementation scheme is described.Comment: 16 pages, 13 figure

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

SystemC Model Generation for Realistic Simulation of Networked Embedded Systems

Verification and design-space exploration of today's embedded systems require the simulation of heterogeneous aspects of the system, i.e., software, hardware, communications. This work shows the use of SystemC to simulate a model-driven specification of the behavior of a networked embedded system together with a complete network scenario consisting of the radio channel, the IEEE 802.15.4 protocol for wireless personal area networks and concurrent traffic sharing the medium. The paper describes the main issues addressed to generate SystemC modules from Matlab/Stateflow descriptions and to integrate them in a complete network scenario. Simulation results on a healthcare wireless sensor network show the validity of the approach

The VLT-FLAMES Tarantula Survey XXI. Stellar spin rates of O-type spectroscopic binaries

The initial distribution of spin rates of massive stars is a fingerprint of their elusive formation process. It also sets a key initial condition for stellar evolution and is thus an important ingredient in stellar population synthesis. So far, most studies have focused on single stars. Most O stars are however found in multiple systems. By establishing the spin-rate distribution of a sizeable sample of O-type spectroscopic binaries and by comparing the distributions of binary sub-populations with one another as well as with that of presumed single stars in the same region, we aim to constrain the initial spin distribution of O stars in binaries, and to identify signatures of the physical mechanisms that affect the evolution of the massive stars spin rates. We use ground-based optical spectroscopy obtained in the framework of the VLT-FLAMES Tarantula Survey (VFTS) to establish the projected equatorial rotational velocities (\vrot) for components of 114 spectroscopic binaries in 30 Doradus. The \vrot\ values are derived from the full-width at half-maximum (FWHM) of a set of spectral lines, using a FWHM vs. \vrot\ calibration that we derive based on previous line analysis methods applied to single O-type stars in the VFTS sample. The overall \vrot\ distribution of the primary stars resembles that of single O-type stars in the VFTS, featuring a low-velocity peak (at \vrot < 200 kms) and a shoulder at intermediate velocities (200 < \vrot < 300 kms). The distributions of binaries and single stars however differ in two ways. First, the main peak at \vrot \sim100 kms is broader and slightly shifted toward higher spin rates in the binary distribution compared to that of the presumed-single stars. Second, the \vrot distribution of primaries lacks a significant population of stars spinning faster than 300 kms while such a population is clearly present in the single star sample.Comment: 16 pages, 16 figures, paper accepted in Astronomy & Astrophysic

Characterisation of collaborative decision making processes

This paper deals with the collaborative decision making induced or facilitated by Information and Communication Technologies (ICTs) and their impact on decisional systems. After presenting the problematic, we analyse the collaborative decision making and define the concepts related to the conditions and forms of collaborative work. Then, we explain the mechanisms of collaborative decision making with the specifications and general conditions of collaboration using the modelling formalism of the GRAI method. Each specification associated to the reorganisation of the decisional system caused by the collaboration is set to the notion of decision-making centre. Finally, we apply this approach to the e-maintenance field, strongly penetrated by the ICTs, where collaborations are usual. We show that the identified specifications allow improving the definition and the management of collaboration in e-maintenance

Visual Spike-based Convolution Processing with a Cellular Automata Architecture

this paper presents a first approach for implementations which fuse the Address-Event-Representation (AER) processing with the Cellular Automata using FPGA and AER-tools. This new strategy applies spike-based convolution filters inspired by Cellular Automata for AER vision processing. Spike-based systems are neuro-inspired circuits implementations traditionally used for sensory systems or sensor signal processing. AER is a neuromorphic communication protocol for transferring asynchronous events between VLSI spike-based chips. These neuro-inspired implementations allow developing complex, multilayer, multichip neuromorphic systems and have been used to design sensor chips, such as retinas and cochlea, processing chips, e.g. filters, and learning chips. Furthermore, Cellular Automata is a bio-inspired processing model for problem solving. This approach divides the processing synchronous cells which change their states at the same time in order to get the solution.Ministerio de Educación y Ciencia TEC2006-11730-C03-02Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Junta de Andalucía P06-TIC-0141