Statistical Characterization of Bare Soil Surface Microrelief

Because the soil surface occurs at the boundary between the atmosphere and the pedosphere, it plays an important role for geomorphologic processes. Roughness of soil surface is a key parameter to understand soil properties and physical processes related to substrate movement, water infiltration or runoff, and soil erosion. It has been noted by many authors that most of the soil surface and water interaction processes have characteristic lengths in millimeter scales. Soil irregularities at small scale, such as aggregates, clods and interrill depressions, influence water outflow and infiltration rate. They undergo rapid changes caused by farming imple‐ ments, followed by a slow evolution due to rainfall events. Another objective of soil surface roughness study is investigating the effects of different tillage implements on soil physical properties (friability, compaction, fragmentation and water content) to obtain an optimal crop emergence. Seedbed preparation focuses on the creation of fine aggregates and the size distribution of aggregates and clods produced by tillage operations is frequently measured. Active microwave remote sensing allows potential monitoring of soil surface roughness or moisture retrieving at field scale using space-based Synthetic Aperture Radars (SAR) with high spatial resolution (metric or decametric). The scattering of microwaves depends on several surface characteristics as well as on imagery configuration. The SAR signal is very sensitive to soil surface irregularities and structures (clod arrangement, furrows) and moisture content in the first few centimeters of soil (depending on the radar wavelength). In order to link the remote sensing observations to scattering physical models as well as for modelling purpose, key features of the soil microtopography should be characterized. However, this characteri‐ zation is not fully understood and some dispersion of roughness parameters can be observed in the same field according to the methodology used. It seems also, that when describing surface roughness as a whole, some information related to structured elements of the micro‐ topography is lost

Estimation de la latence V des potentiels évoqués auditifs précoces (PEAP) à partir d'un a priori physiologique et clinique.Application à la recherche objective de seuil

- La mesure automatique de latence des potentiels évoqués s'apparente à un problème de détection-estimation. Nous proposons une approche de reconnaissance des formes supervisée. Nous analysons l'ensemble des potentiels recueillis pour une oreille, en tenant compte de propriétés physiologiques et optimisons l'estimation de latence par régression non linéaire selon les moindres carrés. Nous obtenons une précision moyenne inférieure à 0.1 ms. Nous envisageons ensuite l'application à la recherche objective de seuil

Classification of technical pitfalls in objective universal hearing screening by otoacoustic emissions, using an ARMA model of the stimulus waveform and bootstrap cross-validation

International audienceTransient-evoked otoacoustic emissions (TEOAE) are widely used for objective hearing screening in neonates. Their main shortcoming is their sensitivity to adverse conditions for sound transmission through the middle-ear, to and from the cochlea. We study here whether a close examination of the stimulus waveform (SW) recorded in the ear canal in the course of a screening test can pinpoint the most frequent middle-ear dysfunctions, thus allowing screeners to avoid misclassifying the corresponding babies as deaf for lack of TEOAE.Three groups of SWs were defined in infants (6–36 months of age) according to middle-ear impairment as assessed by independent testing procedures, and analyzed in the frequency domain where their properties are more readily interpreted than in the time domain. Synthetic SW parameters were extracted with the help of an autoregressive and moving average (ARMA) model, then classified using a maximum likelihood criterion and a bootstrap cross-validation.The best classification performance was 79% with a lower limit (with 90% confidence) of 60%, showing the results’ consistency. We therefore suggest that new parameters and methodology based upon a more thorough analysis of SWs can improve the efficiency of TEOAE-based tests by helping the most frequent technical pitfalls to be identified

Modeling clod evolution under rainfall according to clod size.

International audienceDeterming soil spatial variability is a key point in soil sciences either for soil preparation in precision agriculture, or because of influence on overland flow and erosion. Soil Surface Roughness (SSR) represents the undulation of the surface at small scale, due to the presence of small elevations and depressions. It results from tillage operations and changes over time due to weathering. SSR can be related to clod-size distribution. So, many research has been conducted on monitoring the size and number of clods using photogrammetry method. Nowadays, it is possible to acquire high resolution Digital Elevation Models (DEMs). This study seeks to model the evolution of clod size under rainfall impact with modeling and data processing tools.Seedbed-like soil surface was made in the laboratory by filling a tray with loose soil of silt loam and setting upon pre-sieved clods. It was eroded by controlled laboratory rainfalls. A millimeter DEM was recorded at each stage of the surface with laser-scanner. Wavelet-based clod segmentation leaded to measurement of the volume of individual clods. Clod subsets were formed according to clod size. Normalized mean volume decrease was modelled by exponential function.Greater clods showed swelling (volume increase) and erosion (volume decrease), with cumulated rainfall. These two phenomena being size dependent. Amplitude and slope parameters of the exponential decrease of clod volume could be modelled as a function of mean volume of the clod subset at initial stage. Results obtained with this surface strengthen those previously obtained with less data and basic segmentation. A power law is confirmed for amplitude parameter and a sigmoïd function is highlighted for slope parameter.Modelling and data processing tools are efficient to differentiate and estimate the dynamics of clods depending on their size. Usually, clod size distribution is addressed with statistics of clod diameters obtained by real or numerical sieving. Working on 2.5 D DEMs gives also an acces to the vertical dimension of clods, which is included in their volume. This technique completes the usual roughness description and is promissing for use in precision agriculture or numerical surface generation

Recherche des mottes de terre par analyse multirésolution

International audienceNous présentons une méthode d'identification des mottes de terre sur un modèle numérique de terrain (MNT) 3D d'une surface de sol agricole nu. Une décomposition multirésolution de la surface est effectuée par transformée en ondelettes. Les maxima locaux sont ensuite extraits sur les différentes approximations de la surface et validés par un test. Enfin, les résultats obtenus à chaque niveau sont fusionnés. Cette méthode a été appliquée à des surfaces agricoles de semis et de labour et validée par comparaison avec une méthode manuelle d'identification des mottes mise en oeuvre par un expert en science du sol. Les bas niveaux de décomposition sont tributaires de la résolution d'acquisition et du bruit de mesure du MNT. Ainsi, certaines frontières de mottes peuvent être délicates à estimer. La méthode de détection par analyse multirésolution semble adaptée aux surfaces de sol correctement décrites par 2 ou 3 niveaux d'approximation comme les semis

Soil surface roughness modelling with the bidirectional autocorrelation function

International audienceSurface roughness is a major part of soil surface condition. It results from tillage operations and weathering. Surface roughness parameterisation is still a scientific lock and the object of many studies. An efficient parametrisation of soil surface roughness by modelling the bidirectional autocorrelation function estimated from 2.5D digital elevation models of soil surfaces is introduced. It not only provides geostatistical parameters that can be related to other soil surface characteristics, but let us emphasise that it reproduces the autocorrelation function with very good accuracy. The autocorrelation function is often modelled by a function of three parameters, the height variance, a single correlation length, and a roughness exponent. We added two parameters in order to take into account the anisotropy of soil surfaces and to align the coordinate system in the direction of the maximum correlation length. We propose the way to estimate roughness parameters and show its robustness for soil surfaces using laboratory tests with repeated rainfall events. One soil surface evolves from isotropy to anisotropy, and the other undergoes a reduction of initial anisotropy. The improvement brought by a second correlation length is thus highlighted. Under rainfall impact, the variation of the correlation lengths is more marked than that of the usual roughness parameter that is the root mean squared of the heights. Both parameters are complementary, capturing horizontal or vertical variation respectively. The evolution of the roughness exponent showed a slight increasing trend, which can be related to surface smoothing

Baeps Averaging Analysis Using Autoregressive Modelling

International audienceThe present paper introduces a new perspective on the classical ensemble averaging which can be useful to analyse the Brainstem Auditory Evoked Potentials (BAEPs). The analysis of the dynamics, related to the BAEP, is performed directly after its acquisition from the electroencephalogram (EEG). Methods. The method primarily consists of dynamically modelling the averaged potential, obtained during the acquisition mode. Each averaging of signal at a given instant is considered as an autoregressive (AR) process. Results. It has been shown that the predicting error power of AR modelling can be useful to provide an efficient tool to analyse the BAEPs. It has also been shown that the method is capable of taking the non-stationarities of both the BAEP and the EEG into account. Conclusion. In order to validate our approach, the proposed technique has been implemented for both simulated and real signals. This approach can also be employed in the context of estimating other evoked potentials and shows rich promise for potential clinical applications in future

A new approach for roughness analysis of soil surfaces

International audienceWe propose a new method for roughness analysis of soil surfaces based on a multiscale approach. Tilled surfaces are decomposed into a large scale oriented structure due to the tillage practice and a non-orderly spatial distribution of clods. The multiresolution approximations of the soil surface allow to characterize these two components. The approximation at the level 4 enhances the large scale oriented structure due to the tillage practice. The spatial distribution of the size of the clods is modeled after the detection of the clods by a dedicated algorithm

UTILISATION DU DOSSIER MEDICAL COMME A PRIORI D'OPTIMISATION DU TRAITEMENT DES POTENTIELS EVOQUES AUDITIFS PRECOCES

PARIS12-CRETEIL BU Multidisc. (940282102) / SudocSudocFranceF

Wavelet-based detection of clods on a soil surface

International audienceOne of the aims of the tillage operation is to produce a specific range of clod sizes, suitable for plant emergence. Due to its cloddy structure, a tilled soil surface has its own roughness, which is connected also with soil water content and erosion phenomena. The comprehension and modeling of surface runoff and erosion require that the micro-topography of the soil surface is well estimated. Therefore, the present paper focuses on the soil surface analysis and characterization. An original method consisting in detecting the individual clods or large aggregates on a 3D digital elevation model (DEM) of the soil surface is introduced. A multiresolution decomposition of the surface is performed by wavelet transform. Then a supervised local maxima extraction is performed on the different sub surfaces and a last process makes the validation of the extractions and the merging of the different scales. The method of detection was evaluated with the help of a soil scientist on a controlled surface made in the laboratory as well as on real seedbed and ploughed surfaces, made by tillage operations in an agricultural field. The identifications of the clods are in good agreement, with an overall sensitivity of 84% and a specificity of 94%. The false positive or false negative detections may have several causes. Some very nearby clods may have been smoothed together in the approximation process. Other clods may be embedded into another peace of the surface relief such as another bigger clod or a part of the furrow. At last, the low levels of decomposition are dependent on the resolution and the measurement noise of the DEM. Therefore, some borders of clods may be difficult to determine. The wavelet-based detection method seems to be suitable for soil surfaces described by 2 or 3 levels of approximation such as seedbeds