Smart sensing and multisensorial data fusion for road obstacle detection and tracking

In this article, we present a multisensorial solution for road obstacle detection and tracking . This solution is based on a mixe d camera/3D sensor mounted on the front of an experimental vehicle . The multisensor is described . The calibration step enables the matching of the heterogeneous data . Two capabalities of the senso r have been developped : the controlled perception making possible the acquisition of depth data in an area defined in the intensit y image; the visual servoing carrying out the focusing of the laser beam on a moving target detected in the intensity image . These two capabalities allow a Feedback control on the acquisition mode of the sensor according to the environment . ' The perception strategy is based on the selection of the best sensor for a given goal . The obstacle detection is based on th e segmentation and interpretation of depth data which are well suited in this context . However, the rate of acquisition of these dat a is too slow in order to extract the kinematic state of the obstacle . So, the tracking process is based on the collaboration betwee n intensity image processing which ensures the tracking itself and a 3D process which returns the obstacle model size to search in th e image. This algorithm of heterogeneous data fusion, associated with a Kalman filtering, permits to compute the state of obstacles . This work fits into the european project PROMETHEUS . Experimental results have been validated in real situation on the Prola b vehicle .Dans cet article, nous présentons une solution multisensorielle temps réel pour la détection et le suivi d'obstacles sur route. Cette solution est basée sur l'utilisation d'un capteur mixte caméra vidéo/capteur de profondeur placé à l'avant d'un véhicule expérimental. Le capteur multisensoriel est décrit. Le calibrage permet l'alignement des données hétérogènes. Deux facultés du capteur sont développées : la perception dirigée permet l'acquisition d'une image de profondeur dans une zone définie dans l'image de luminance ; l'asservissement visuel réalise la focalisation du faisceau laser sur un point de l'image de luminance. De façon générale, ces facultés permettent un contrôle par rétroaction sur le mode d'acquisition du capteur en fonction de la situation dans laquelle se trouve le système de perception. La stratégie de perception est basée sur la sélection du capteur adéquat pour un objectif donné. La détection d'obstacle repose sur la segmentation et l'interprétation des données de profondeur qui sont d'une grande pertinence dans ce contexte. En revanche, la cadence d'acquisition de ces données n'est pas suffisante si l'on souhaite dériver les caractéristiques cinématiques des obstacles. En conséquence, le suivi des obstacles combine un traitement de l'image de luminance rapide avec un traitement de l'information 3D. Le premier permet de réactualiser la position de l'obstacle afin d'asservir le faisceau laser sur celui-ci et le second assure la connaissance de la taille du modèle de l'obstacle à chercher dans l'image. Cet algorithme de fusion de données hétérogènes accompagné d'un filtrage de Kalman permet d'inférer les caractéristiques cinématiques des obstacles dont la connaissance est indispensable pour aborder ceux-ci dans de bonnes conditions. Ces recherches sont menées dans le cadre du projet européen PROMETHEUS et sont validées en situation réelle à bord du véhicule expérimental Prolab

Road detection and vehicles tracking by vision for ACC

This article deals with a process designed first to extract the lane of vehicle by on-board monocular vision. This detection process is based upon a recursive updating of a statistical model of the lane obtained by a training phase. Once the lane has been located, a reconstruction algorithm computes the vehicle location on its lane and the 3D shape of the road. Thereafter, we are focus at the detection, location and tracking of front vehicles equipped with specific visual markers in order to achieve an accurate determination of the location and speed of these vehicles. Merging these various informations allows to point out the most dangerous obstacle. Each of these three processes is detailed significant examples are provide.Cet article présente, dans un premier temps, un procédé permettant de détecter la voie de circulation d'un véhicule par vision monoculaire embarquée. Ce processus de détection est basé sur une mise à jour récursive d'un modèle statistique de la voie obtenu par une phase d'apprentissage. Après avoir localisé la voie, un algorithme de reconstruction détermine la position du véhicule dans sa voie de circulation et le profil 3D de la route. Par la suite, nous nous intéressons à la détection, la localisation et surtout le suivi des véhicules situés à l'avant et équipés de marques visuelles afin de déterminer avec précision la position et la vitesse relative de ces véhicules. La combinaison de ces différentes informations permet de déterminer le véhicule le plus dangereux. La description détaillée de chacune des étapes de notre algorithme est suivie d'exemples significatifs

Theoretical and practical convergence of a self-adaptive penalty algorithm for constrained global optimization

This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solving nonsmooth and nonconvex constrained optimization problems. We prove that the general constrained optimization problem is equivalent to a bound constrained problem in the sense that they have the same global solutions. The global minimizer of the penalty function subject to a set of bound constraints may be obtained by a population-based meta-heuristic. Further, a hybrid self-adaptive penalty firefly algorithm, with a local intensification search, is designed, and its convergence analysis is established. The numerical experiments and a comparison with other penalty-based approaches show the effectiveness of the new self-adaptive penalty algorithm in solving constrained global optimization problems.The authors would like to thank the referees, the Associate Editor and the Editor-in-Chief for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Funda¸c˜ao para a Ciˆencia e Tecnologia within the projects UID/CEC/00319/2013 and UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

Transcranial direct current stimulation combined with upper limb functional training in children with spastic, hemiparetic cerebral palsy: study protocol for a randomized controlled trial

The aim of the proposed study is to perform a comparative analysis of functional training effects for the paretic upper limb with and without transcranial direct current stimulation over the primary motor cortex in children with spastic hemiparetic cerebral palsy

Epidemiology of intra-abdominal infection and sepsis in critically ill patients: “AbSeS”, a multinational observational cohort study and ESICM Trials Group Project

Purpose: To describe the epidemiology of intra-abdominal infection in an international cohort of ICU patients according to a new system that classifies cases according to setting of infection acquisition (community-acquired, early onset hospital-acquired, and late-onset hospital-acquired), anatomical disruption (absent or present with localized or diffuse peritonitis), and severity of disease expression (infection, sepsis, and septic shock). Methods: We performed a multicenter (n = 309), observational, epidemiological study including adult ICU patients diagnosed with intra-abdominal infection. Risk factors for mortality were assessed by logistic regression analysis. Results: The cohort included 2621 patients. Setting of infection acquisition was community-acquired in 31.6%, early onset hospital-acquired in 25%, and late-onset hospital-acquired in 43.4% of patients. Overall prevalence of antimicrobial resistance was 26.3% and difficult-to-treat resistant Gram-negative bacteria 4.3%, with great variation according to geographic region. No difference in prevalence of antimicrobial resistance was observed according to setting of infection acquisition. Overall mortality was 29.1%. Independent risk factors for mortality included late-onset hospital-acquired infection, diffuse peritonitis, sepsis, septic shock, older age, malnutrition, liver failure, congestive heart failure, antimicrobial resistance (either methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, extended-spectrum beta-lactamase-producing Gram-negative bacteria, or carbapenem-resistant Gram-negative bacteria) and source control failure evidenced by either the need for surgical revision or persistent inflammation. Conclusion: This multinational, heterogeneous cohort of ICU patients with intra-abdominal infection revealed that setting of infection acquisition, anatomical disruption, and severity of disease expression are disease-specific phenotypic characteristics associated with outcome, irrespective of the type of infection. Antimicrobial resistance is equally common in community-acquired as in hospital-acquired infection

Cofactorization on Graphics Processing Units

We show how the cofactorization step, a compute-intensive part of the relation collection phase of the number field sieve (NFS), can be farmed out to a graphics processing unit. Our implementation on a GTX 580 GPU, which is integrated with a state-of-the-art NFS implementation, can serve as a cryptanalytic co-processor for several Intel i7-3770K quad-core CPUs simultaneously. This allows those processors to focus on the memory-intensive sieving and results in more useful NFS-relations found in less time

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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