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The theme of this paper is part of the Astronomy with a Neutrino Telescope and Abyss RESearch (ANTARES) international project, the object of which is to build a submarine neutrino telescope located near Toulon, France. In deep water, a neutrino can interact with a nucleus in a water molecule. The collision generates a luminous flash and an acoustic wave. The goal of this paper is to propose a system able to detect and estimate the initial direction of the particle. We first focus on the detector by comparing some detection methods and choose the one which best corresponds to our problem. Then, position and direction of the neutrino are estimated with a Gauss–Newton derived algorithm. This estimator is based on a wavefront propagation model and on the time detection information given by the telescope hydrophones. Performance of the system is then described. An antenna structure is then proposed and a global simulation finalizes this paper. The use of real underwater sea noise and the results of the simulation validate the work. [ABSTRACT FROM AUTHOR]

This paper presents an original algorithm that uses a new modeling approach of the interference constraints and uses a probabilistic taboo search algorithm to solve the frequency assignment problem in the field of broadcasting. The results obtained by our algorithm are compared to the operating solutions in the field of FM broadcasting in France and the best known results obtained by hybrid genetic algorithm [2] that uses a classical modeling of the interferences constraints. By analyzing the results obtained by our approach we can observe that we efficiently enhance the quality of the solutions. [ABSTRACT FROM AUTHOR]

The monitoring of land cover requires that stable land cover classes be distinguished from changes over time. Within this paper, a postclassification method is presented that provides land cover change information, based on a time series of land cover maps. The method applies a kernel filter to sequential land cover maps. Under some basic assumptions, it shows robustness against classification errors. Despite seasonality, land cover changes often occur at a low temporal frequency (e.g., maximum once every 5–10 years). If land cover maps are available more frequently, some of the information will become redundant (oversampling). The proposed method uses this redundancy for tolerating (nonsystematic) misclassifications. In order to demonstrate the benefits and limitations of the proposed method, analytical expressions have been derived. When compared to a simple postclassification comparison, one of the key strengths of the proposed approach is that it is able to improve both the overall and user's accuracy of change, while also maintaining the same level of producer's accuracy. As a case study, MODerate Resolution Imaging Spectroradiometer remote sensing data from 2006-2010 were classified into forest (F)/nonforest (NF) at pan-European scale. Promising results were obtained for detecting forest loss due to natural disasters. Quality was assessed using burnt area maps in southern Europe and a forest damage report after a windstorm in France. Results indicated a considerable reduction of change detection errors, confirming the theoretical results. [ABSTRACT FROM PUBLISHER]

The Large Hadron Collider at CERN offers unprecedented challenges to the design and construction of detectors and trigger/data acquisition systems. For ATLAS, a three level trigger system has been developed to extract interesting physics signatures with a 106 rate reduction. To accomplish this, components of physics analysis traditionally deferred to off-line physics analysis must be embedded within the on-line trigger system. For the Muon trigger, the specific off-line algorithms MOORE (Muon Object Oriented REconstruction) and MuId (Muon Identification) have been adopted so far for the on-line use, imposing an operation in a Bayesian-like environment where only specific hypotheses must be validated. After a short review of the ATLAS trigger, the paper shows the general strategy of the Muon Identification and Selection accessing the full event data, or being seeded from results derived at a previous stage of the trigger chain. [ABSTRACT FROM AUTHOR]

The Event Filter (EF) selection stage is a fundamental component of the ATLAS Trigger and Data Acquisition architecture. Its primary function is the reduction of data flow and rate to values acceptable by the mass storage operations and by the subsequent offline data reconstruction and analysis steps. The computing instrument of the EF is organized as a set of independent subfarms, each connected to one output of the Event Builder (EB) switch fabric. Each subfarm comprises a number of processors analyzing several complete events in parallel. This paper describes the design of the ATLAS EF system, its deployment in the 2004 ATLAS combined test beam together with some examples of integrating selection and monitoring algorithms. Since the processing algorithms are not explicitly designed for EF but are adapted from the offline ones, special emphasis is reserved to system reliability and data security, in particular for the case of failures in the processing algorithms. Other key design elements have been system modularity and scalability. The EF shall be able to follow technology evolution and should allow for using additional processing resources possibly remotely located [ABSTRACT FROM PUBLISHER]