Self-Adaptive Architecture for Multi-sensor Embedded Vision System

International audienceArchitectural optimization for heterogeneous multi-sensor processing is a real technological challenge. Most of the vision systems involve only one single color sensor and they do not address the heterogeneous sensors challenge. However, more and more applications require other types of sensor in addition, such as infrared or low-light sensor, so that the vision system could face various luminosity conditions. These heterogeneous sensors could differ in the spectral band, the resolution or even the frame rate. Such sensor variety needs huge computing performance , but embedded systems have stringent area and power constraints. Reconfigurable architecture makes possible flexible computing while respecting the latter constraints. Many reconfigurable architectures for vision application have been proposed in the past. Yet, few of them propose a real dynamic adaptation capability to manage sensor heterogeneity. In this paper, a self-adaptive architecture is proposed to deal with heterogeneous sensors dynamically. This architecture supports on-the-fly sensor switch. Architecture of the system is self-adapted thanks to a system monitor and an adaptation controller. A stream header concept is used to convey sensor information to the self-adaptive architecture. The proposed architecture was implemented in Altera Cyclone V FPGA. In this implementation, adaptation of the architecture consists in Dynamic and Partial Reconfiguration of FPGA. The self-adaptive ability of the architecture has been proved with low resource overhead and an average global adaptation time of 75 ms

Auto-Adaptive Multi-Sensor Architecture

International audienceTo overcome luminosity problems, modern embedded vision systems often integrate technologically heterogeneous sensors. Also, it has to provide different functionalities such as photo or video mode, image improvement or data fusion, according to the user environment. Therefore, nowadays vision systems should be context-aware and adapt their performance parameters automatically. In this context, we propose a novel auto-adaptive architecture enabling on-the-fly and automatic frame rate and resolution adaptation by a frequency tuning method. This method also intends to reduce power consumption as an alternative to existing power gating method. Performance evaluation in a FPGA implementation demonstrates an inter-frame adaptation capability with a relative low area overhead. I. INTRODUCTION From decades, the ability of computer vision systems increases thanks to the multiplication of integrated sensors. Multi-sensor systems enable many high-level vision applications such as stereo vision, data fusion [1] or 3D stereo view [2]. Also smart camera networks take advantage of the multi-sensor concept for large-scale surveillance applications [3]. More and more vision systems involve several heterogeneous sensors such as color, infrared or intensified low-light sensor [4] to overcome the variable luminosity conditions or improve the application robustness. Frequently, the considered vision system accomplishes various tasks such as video streaming, photo capture or high level processing (i.e. face detection, object tracking, ...). Each one of these tasks imposes different performance computing ability to the hardware resources, according to the applicative context and used sensor. That is why, nowadays vision systems have to be context-aware and to possess the ability to adapt their performance according to the user environment [5]. Fig. 1 illustrates the differences between video and photo user mode parameters: latency, frame rate, resolution, image quality and power consumption. While a video mode needs a high frame rate and low latency, a photo mode rather expects a higher resolution and higher image quality. In this context, we expect the system architecture adapt itself on-the-fly to the required frame rate or resolution while minimizing the use-case transition time when the user mode changes. In addition, the frame rate and the resolution of the involved sensors are not supposed to be known in advance. Numerous adaptable architectures exist for high-performance image processing [6]–[8] and also even for energy aware heterogeneous vision systems [2], they do not enable such dynamic adaptation of the frame rate or the resolution. In this paper, we propose a novel pixel frequency tuning approach for heterogeneous multi-sensor vision systems. Th

Fast and efficient FPGA implementation of connected operators

International audienceThe Connected Component Tree (CCT)-based operators play a central role in the development of new algorithms related to image processing applications such as pattern recognition, video-surveillance or motion extraction. The CCT construction, being a time consuming task (about 80% of the application time), these applications remain far-off mobile embedded systems. This paper presents its efficient FPGA implementation suited for embedded systems. Three main contributions are discussed: an efficient data structure proposal adapted to representing the CCT in embedded systems, a memory organization suitable for FPGA implementation by using on-chip memory and a customizable hardware accelerator architecture for CCT-based applications

Quantification of Neural Network Uncertainties on the Hydrogeological Predictions by Probability Density Functions

International audienceThe risk of drought impacting the drinking water and agricultural production is worrying in the developed countries, especially in a changing climate context. To manage and prevent this phenomenon, real-time monitoring and predictive systems are emerging as the key solutions. In the field of artificial intelligence, neural networks are one of these predictive systems. This family of parameterized models is a composition of neuronal functions, which apply a non-linear transformation from their inputs to their outputs. These networks are able to learn a hydro(geo)logical system behaviour using a database composed of observed inputs (rainfall, evapotranspiration, etc.) and outputs (groundwater level, discharge, etc.), thanks to an algorithm minimizing a cost function between observed and simulated outputs. However, it remains difficult to assess the uncertainty generated by these models, possibly leading to misinterpretations by the end users. These uncertainties are mainly of three types. The first is related to the input data. Indeed, hydrosystems are surface elements whereas meteorological inputs are punctual elements. The interpolation error can, therefore, be significant because of the lack of knowledge between gauging stations. The second is the neural network model architecture itself. It is possible to deal with this source of uncertainty using regularization methods. Finally, the neural networks are submitted to uncertainties related to parameter initialization, before the training step. The initial parameters may have an important impact on the results. In this paper, we address the prediction of the Blavet groundwater level (Bretagne, France). In order to assess uncertainties, we will first focus on the parameters initialization of the model. Neuronal models are optimized using cross-validation and early stopping. Then, an ensemble model is realized, in which each member is the result of a unique set of parameters initialization. The purpose of the study is to define how many initializations are necessary to obtain a reasonable confidence interval for forecasts, with the smallest interval and the higher rate of observed points inside this interval. The best model will be determined using cross-validation scores thereby ensuring optimal robustness. We show that, in this case study, an ensemble model of 20 different initializations is sufficient to estimate uncertainty while preserving quality. In the second part, the resulting ensemble model will be used to estimate the global model uncertainty using probability density functions (pdf) applied to the distribution of groundwater level data and cross-validation scores of forecasts. It reveals that the groundwater level predictions are composed of two mixed distributions. Therefore, we will use the expectation-maximization algorithm (EM) to obtain parameters of mixed models. Mixed normal and mixed Gumbel laws, among five mixed distributions assessed, give the best groundwater distribution and are able to generate an abacus drawing uncertainty of mode

Memory System for a Dynamically Adaptable Pixel Stream Architecture

International audienceNowadays, embedded vision systems have to face new hard requirements involved by modern applications: realtime processing of high resolution images issued by multiple image sensors. Recently, a new adaptable ring-based interconnection network on chip has been proposed. Based on adaptive datapath, it allows handling of multiple parallel pixel streams. In this paper, we present a new hierarchical memory system proposed for this adaptable ring-based architecture. The design of its different levels is discussed and we show how the memory system adapts dynamically with respect to the datapath and data access management in the interconnection network. We also present the timing performance and area occupation measured on an FPGA prototype

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Adolescent Brain Development and the Risk for Alcohol and Other Drug Problems

Dynamic changes in neurochemistry, fiber architecture, and tissue composition occur in the adolescent brain. The course of these maturational processes is being charted with greater specificity, owing to advances in neuroimaging and indicate grey matter volume reductions and protracted development of white matter in regions known to support complex cognition and behavior. Though fronto-subcortical circuitry development is notable during adolescence, asynchronous maturation of prefrontal and limbic systems may render youth more vulnerable to risky behaviors such as substance use. Indeed, binge-pattern alcohol consumption and comorbid marijuana use are common among adolescents, and are associated with neural consequences. This review summarizes the unique characteristics of adolescent brain development, particularly aspects that predispose individuals to reward seeking and risky choices during this phase of life, and discusses the influence of substance use on neuromaturation. Together, findings in this arena underscore the importance of refined research and programming efforts in adolescent health and interventional needs

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Study of the uncertainties of neural models on hydrogeological forecasting. Application to watersheds of different typologies

Les crues et les sécheresses sont deux des risques majeurs en France et nécessitent une attention particulière. Dans ces conditions où le changement climatique engendre des phénomènes extrêmes de plus en plus fréquents, la modélisation de ces risques est désormais un élément incontournable pour la gestion de la ressource en eau.Actuellement, les débits ou hauteurs d’eau sont principalement anticipés à partir de modèles à base physique ou conceptuelle. Bien qu’efficaces et nécessaires, la calibration et la mise en œuvre de ces modèles nécessitent la réalisation d’études longues et coûteuses.Dans ce contexte, cette thèse, soutenue par l’IMT Mines Alès et conjointement financée par la société aQuasys et l’ANRT, a pour objectif de développer des modèles issus du paradigme systémique. Ceux-ci nécessitent uniquement des connaissances a priori basiques sur la caractérisation physique du bassin étudié, et qui peuvent être calibrés à partir des seules informations d’entrées et de sorties (pluies et débits/hauteurs).Les modèles les plus utilisés dans le monde environnemental sont les réseaux neuronaux, qui sont utilisés sur ce projet. Cette thèse cherche à répondre à trois objectifs principaux :1. Élaboration d’une méthode de conception de modèle adaptée aux différentes variables (débits/hauteur des eaux de surface) et à des bassins de types très différents : bassins versants ou bassins hydrogéologiques (hauteur des eaux souterraines)2. Évaluation des incertitudes liées à ces modèles en fonction des types de bassins visés3. Réduction de ces incertitudesPlusieurs bassins sont utilisés pour répondre à ces problématiques : la nappe du bassin du Blavet en Bretagne et le bassin de la nappe de la Craie de Champagne sud et Centre.Floods and droughts are the two main risks in France and require a special attention. In these conditions, where climate change generates increasingly frequent extreme phenomena, modeling these risks is an essential element for water resource management.Currently, discharges and water heights are mainly predicted from physical or conceptual based models. Although efficient and necessary, the calibration and implementation of these models require long and costly studies.Hydrogeological forecasting models often use data from incomplete or poorly dimensioned measurement networks. Moreover, the behavior of the study basins is in most cases difficult to understand. This difficulty is thus noted to estimate the uncertainties associated with hydrogeological modeling.In this context, this thesis, supported by IMT Mines Alès and financed by the company aQuasys and ANRT, aims at developing models based on the systemic paradigm. These models require only basic knowledge on the physical characterization of the studied basin, and can be calibrated from only input and output information (rainfall and discharge/height).The most widely used models in the environmental world are neural networks, which are used in this project. This thesis seeks to address three main goals:1. Development of a model design method adapted to different variables (surface water flows/height) and to very different types of basins: watersheds or hydrogeological basins (groundwater height)2. Evaluation of the uncertainties associated with these models in relation to the types of targeted basins3. Reducing of these uncertaintiesSeveral basins are used to address these issues: the Blavet basin in Brittany and the basin of the Southern and Central Champagne Chalk groundwater table