Transmission vocale via Internet utilisant les codeurs G.729 et G.729a

Différentes solutions ont été développées pour résoudre les problèmes de transmission vocale via Internet. Le premier groupe de solutions maximise le codage et le décodage de l'information. Le deuxième groupe de solutions s'attaque aux problèmes reliés au canal de communication. C'est avec les outils mathématiques que sont présentés les principes des codeurs de la grande famille des CELP ( Code Excited Linear Prediction ) et de la sous-classe des CS-ACELP ( Conjugate Structure Algebraic Code Excited Linear Prediction ) dont font partie le G.729 et le G.729a. Ces deux codeurs sont particulièrement bien adaptés à la transmission de la voix sur un réseau numérique grâce à leur résistance au bruit et aux pertes de paquets. Néanmoins, ces codeurs ajoutent au délai total du système. Lorsque le G.729 et G.729a sont intégrés à différentes plates-formes, leur potentiel est évalué pour un transfert de données en temps réel. Réussir ce test n'est toutefois pas suffisant pour parvenir à une conclusion définitive. La plate-forme doit être la plus universelle possible puisque le transfert efficace de la parole par Internet dépend aussi de son accessibilité."--Résumé abrégé par UM

Global mapping of river sediment bars

Recently, deep learning has been increasingly applied to global mapping of land‐use and land‐cover classes. However, very few studies have addressed the problem of separating lakes from rivers, and to our knowledge, none have addressed the issue of mapping fluvial sediment bars. We present the first global scale inventory of fluvial gravel bars. Our workflow is based on a state‐of‐the‐art fully convolutional neural network which is applied to Sentinel‐2 imagery at a resolution of 10 m. We use Google Earth Engine to access these data for a study site that covers 89% of the Earth's surface. We count 8.9 million gravel bars with an estimated area of 41 000 km2. Crucially, the workflow we present can be executed within a month of highly automated processing and thus allows for global scale, monthly, monitoring of gravel bars and associated rivers

Quantifying the Impact of Spatiotemporal Resolution on the Interpretation of Fluvial Geomorphic Feature Dynamics From Sentinel 2 Imagery: An Application on a Braided River Reach in Northern Italy

Machine learning algorithms applied on the publicly available Sentinel 2 images (S2) are opening the opportunity to automatically classify and monitor fluvial geomorphic feature (such as sediment bars or water channels) dynamics across scales. However, there are few analyses on the relative importance of S2 spatial versus temporal resolution in the context of geomorphic research. In a dynamic, braided reach of the Sesia River (Northern Italy), we thus analyzed how the inherent uncertainty associated with S2's spatial resolution (10 m pixel size) can impact the significance of the active channel (a combination of sediment and water) delineation, and how the S2's weekly temporal resolution can influence the interpretation of its evolutionary trajectory. A comparison with manually classified images at higher spatial resolutions (Planet: 3 m and orthophoto: 0.3 m) shows that the automatically classified water is ∼20% underestimated whereas sediments are ∼30% overestimated. These classification errors are smaller than the geomorphic changes detected in the 5 years analyzed, so the derived active channel trajectory can be considered robust. The comparison across resolutions also highlights that the yearly Planet‐ and S2‐derived active channel trajectory are analogous and they are both more effective in capturing the river geomorphic response after major flood events than the trajectory derived from sequential multiannual orthophotos. More analyses of this type, across different types of river could give insights on the transferability of the spatial uncertainty boundaries found as well as on the spatial and temporal resolution trade‐off needed for supporting different geomorphic analyses

The accuracy and reliability of traditional surface flow type mapping: Is it time for a new method of characterizing physical river habitat?

Surface flow types (SFTs) are advocated as ecologically relevant hydraulic units, often mapped visually from the bankside to characterize rapidly the physical habitat of rivers. SFT mapping is simple, non-invasive and cost-efficient. However, it is also qualitative, subjective and plagued by difficulties in recording accurately the spatial extent of SFT units. Quantitative validation of the underlying physical habitat parameters is often lacking and does not consistently differentiate between SFTs. Here, we investigate explicitly the accuracy, reliability and statistical separability of traditionally mapped SFTs as indicators of physical habitat, using independent, hydraulic and topographic data collected during three surveys of a c. 50 m reach of the River Arrow, Warwickshire, England. We also explore the potential of a novel remote sensing approach, comprising a small unmanned aerial system (sUAS) and structure-from-motion photogrammetry (SfM), as an alternative method of physical habitat characterization. Our key findings indicate that SFT mapping accuracy is highly variable, with overall mapping accuracy not exceeding 74%. Results from analysis of similarity tests found that strong differences did not exist between all SFT pairs. This leads us to question the suitability of SFTs for characterizing physical habitat for river science and management applications. In contrast, the sUAS–SfM approach provided high resolution, spatially continuous, spatially explicit, quantitative measurements of water depth and point cloud roughness at the microscale (spatial scales ≤1 m). Such data are acquired rapidly, inexpensively and provide new opportunities for examining the heterogeneity of physical habitat over a range of spatial and temporal scales. Whilst continued refinement of the sUAS–SfM approach is required, we propose that this method offers an opportunity to move away from broad, mesoscale classifications of physical habitat (spatial scales 10–100 m) and towards continuous, quantitative measurements of the continuum of hydraulic and geomorphic conditions, which actually exists at the microscale

Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry

Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process oriented investigations of flow hydraulics, sediment dynamics and in-stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through-water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. Whilst the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM-photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary-winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02m) for two different river systems over channel lengths of 50- 100m. Errors in submerged areas range from 0.016m to 0.089m, which can be reduced to between 0.008m and 0.053m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM-photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10m to a few hundred metres)

Automated grain size measurements from airborne remote sensing for long profile measurements of fluvial grain sizes

Recent research has demonstrated that image processing can be applied to derive surficial median grain size data automatically from high-resolution airborne digital imagery in fluvial environments. However, at the present time, automated grain size measurement is limited to the dry exposed bed areas of the channel. This paper shows that the application area of automated grain size mapping can be extended in order to include the shallow wetted areas of the channel. The paper then proceeds to illustrate how automated grain size measurement in both dry and shallow wetted areas can be used to measure grain sizes automatically for long river lengths. For the present study, this results in a median grain size profile covering an 80 km long river which is constructed from over three million automated grain size measurements

An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking

Monitoring rivers is vital to manage the invaluable ecosystem services they provide, and also to mitigate the risks they pose to property and life through flooding and drought. Due to the vast extent and dynamic nature of river systems, Earth Observation (EO) is one of the best ways to measure river characteristics. As a first step, EO-based river monitoring often requires extraction of accurate pixel-level water masks, but satellite images traditionally used for this purpose suffer from limited spatial and/or temporal resolution. We address this problem by applying a novel Convolutional Neural Network (CNN)-based model to automate water mask extraction from daily 3 m resolution PlanetScope satellite imagery. Notably, this approach overcomes radiometric issues that frequently present limitations when working with CubeSat data. We test our classification model on 36 rivers across 12 global terrestrial biomes (as proxies for the environmental and physical characteristics that lead to the variability in catchments around the globe). Using a relatively shallow CNN classification model, our approach produced a median F1 accuracy score of 0.93, suggesting that a compact and efficient CNN-based model can work as well as, if not better than, the very deep neural networks conventionally used in similar studies, whilst requiring less training data and computational power. We further show that our model, specialised to the task at hand, performs better than a state-of-the-art Fully Convolutional Neural Network (FCN) that struggles with the highly variable image quality from PlanetScope. Although classifying rivers that were narrower than 60 m, anastomosed or highly urbanised was slightly less successful than our other test images, we showed that fine tuning could circumvent these limitations to some degree. Indeed, fine tuning carried out on the Ottawa River, Canada, by including just 5 additional site-specific training images significantly improved classification accuracy (F1 increased from 0.81 to 0.90, p < 0.01). Overall, our results show that CNN-based classification applied to PlanetScope imagery is a viable tool for producing accurate, temporally dynamic river water masks, opening up possibilities for river monitoring investigations where high temporal variability data is essential

Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments

The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to other agricultural pollutants (e.g. nutrients) it is imperative that the end-user expectations of FIO models are appropriately managed. In response, this commentary highlights four over-arching questions associated with: (i) model purpose; (ii) modelling approach; (iii) data availability; and (iv) model application, that must be considered as part of good practice prior to the deployment of any modelling approach to predict FIO behaviour in catchment systems. A series of short and longer-term research priorities are proposed in response to these questions in order to promote better model deployment in the field of catchment microbial dynamics

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570