: The new setup of the CODALEMA experiment installed at the Radio Observatory in Nançay, France, is described. It includes broadband active dipole antennas and an extended and upgraded particle detector array. The latter gives access to the air shower energy, allowing us to compute the efficiency of the radio array as a function of energy. We also observe a large asymmetry in counting rates between showers coming from the North and the South in spite of the symmetry of the detector. The observed asymmetry can be interpreted as a signature of the geomagnetic origin of the air shower radio emission. A simple linear dependence of the electric field with respect to v∧B is used which reproduces the angular dependencies of the number of radio events and their electric polarity. PACS:95.55. Jz; 95.85.Ry;

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After knee or ankle injury, Freeman has proposed a rehabilitation program consisting in a prolonged maintain of monopodal equilibrium on an unstable plateform. The efficacy of such programs, often debated, is evaluated in the present study by a quantification of equilibrium criteria and electromyographical activities along the rehabilitation program. Our aim is to detect all events in the four EMG signals of soleus, tibialis anterior, peroneus longus and vastus medialis muscles and then deduce the stability of the person. To achieve the detection in EMG, the signals are considered to be piecewise stationary, with no a priori knowledge of the parameters of the hypotheses on the process state to be detected. The detector is based on a combination of dynamic cumulative sum (DCS) and the detail coefficients obtained after the application of the Mallat's fast decomposition algorithm without reconstruction of the detail signals. The DCS detection algorithm is based on the recursive calculation of the local generalized likelihood ratios associated with a multi-scale decomposition using wavelet transform. Results show that there is a correlation between stability and the energy of EMG signals.

The aim of this article is to develop an automatic algorithm for the classification of non stationary signals. The application context is to classify uterine electromyogram (EMG) events to prevent the onset of preterm birth. The idea is to discriminate between the events by allocating them to the physiological classes: contractions, foetus motions, Alvarez or Long Duration Low Frequency waves. Our method is based on the Wavelet Packet (WP) decomposition and the choice of a best basis for classification purpose. Before classification, there is a need to detect events in the recorded signals. The discrimination criterion is based on the calculation of the ratio between intra-class variance and total variance (sum of the intra-class and inter-class variances), calculated directly from the coefficients of the selected WP. We evaluated the performance of the algorithm on real signals by using the classification methods Neural Networks (NN) and Support Vector Machines (SVM). Subband energies of the best selected WP are used as effective features. The determined best basis is applicable to a wide range of uterine EMG signals from large range of patients. In most cases, more than 85% of events are well classified whatever the term of gestation.

Facial landmark detection is a task of interest for facial dysmorphology, an important factor in the diagnosis of genetic conditions. In this paper, we propose a framework for feature points detection from 3D face images. The method is based on 3D Constrained Local Model (CLM) which learns both global variations in the 3D facial scan and local changes around every vertex landmark. Compared to state of the art methods our framework is distinguished by the following novel aspects: 1) It operates on facial surfaces, 2) It allows fusion of shape and color information on the mesh surface, 3) It introduces the use of LBP descriptors on the mesh. We showcase our landmarks detection framework on a set of scans including down syndrome and control cases. We also validate our method through a series of quantitative experiments conducted with the publicly available Bosphorus database.