Fusion of Information and Analytics: A Discussion on Potential Methods to Cope with Uncertainty in Complex Environments (Big Data and IoT)

International audienceInformation overload and complexity are core problems to most organizations of today. The advances in networking capabilities have created the conditions of complexity by enabling richer, real-time interactions between and among individuals, objects, systems and organizations. Fusion of Information and Analytics Technologies (FIAT) are key enablers for the design of current and future decision support systems to support prognosis, diagnosis, and prescriptive tasks in such complex environments. Hundreds of methods and technologies exist, and several books have been dedicated to either analytics or information fusion so far. However, very few have discussed the methodological aspects and the need of integrating frameworks for these techniques coming from multiple disciplines. This paper presents a discussion of potential integrating frameworks as well as the development of a computational model to evolve FIAT-based systems capable of meeting the challenges of complex environments such as in Big Data and Internet of Things (IoT)

Semantic Remote Sensing Scenes Interpretation and Change Interpretation

A fundamental objective of remote sensing imagery is to spread out the knowledge about our environment and to facilitate the interpretation of different phenomena affecting the Earth’s surface. The main goal of this chapter is to understand and interpret possible changes in order to define subsequently strategies and adequate decision-making for a better soil management and protection. Consequently, the semantic interpretation of remote sensing data, which consists of extracting useful information from image date for attaching semantics to the observed phenomenon, allows easy understanding and interpretation of such occurring changes. However, performing change interpretation task is not only based on the perceptual information derived from data but also based on additional knowledge sources such as a prior and contextual. This knowledge needs to be encoded in an appropriate way for being used as a guide in the interpretation process. On the other hand, interpretation may take place at several levels of complexity from the simple recognition of objects on the analyzed scene to the inference of site conditions and to change interpretation. For each level, information elements such as data, information and knowledge need to be represented and characterized. This chapter highlights the importance of ontologies exploiting for encoding the domain knowledge and for using it as a guide in the semantic scene interpretation task

Contribution of automatic classification of sonar images for long term registration

This issue handles the ability of using segmentation results of sidescan sonar images for long term registration. This study take a part of AUV (Autonomous Underwater Vehicle) navigation problems, particularly to correct the drift of navigation sensors. Principle of images formation with this type of engines and the main properties are first reminded. Some images are shown, which allows us to understand difficulties of this type of data to realise AUV positioning. Then, we decide that segmentation results of sidescan images provide us landscapes on which registration will be able. Segmentation is then explained. It is supervised type on five classes, rocks, ripples, sand, mud and shadow. Using Gabor filters provides classifying parameters and classification is realised by the nearest neighbour. This is made step by step, refining step by step the segmentation. In order to know which landscapes may be extracted to enable the positioning, a statement is then realised on the obtained results. The registration methodology is then quickly explained and several results are commented. This allows us to realise a final conclusion on the ability to use results of segmentation of sonar images to make registration and to give possibilities and limits of this type of positioning system.Cet article aborde la possibilité d’utiliser les résultats de segmentation d’images issues d’un sonar latéral pour effectuer un recalage à long terme. Ce travail s’inscrit dans les problématiques liées à la navigation des AUV (Autonomous Underwater Vehicle). Ceux-ci, navigant généralement à l’estime, subissent une dérive qu’il est nécessaire de régulièrement contrecarrer. Le principe de formation des images à l’aide d’un tel engin est alors rappelé, ainsi que ses propriétés principales. Quelques exemples d’images sont également montrés, ce qui nous permet d’appréhender les difficultés liées à l’utilisation de telles données pour effectuer le positionnement. Nous décidons alors que les résultats de segmentation des images sonar fourniront les amers sur lesquels on pourra se recaler. La segmentation des images est alors expliquée. Elle est de type supervisé sur cinq classes, roches, rides, sable, vase et ombre. Le filtrage par ondelettes de Gabor fournit les paramètres classifiants et la classification est réalisée par le plus proche voisin. Celle-ci est alors effectuée pas à pas, en affinant peu à peu la segmentation alors obtenue. Un état des lieux est ensuite réalisé sur les résultats obtenus, permettant de savoir quels amers peuvent être extraits pour permettre le positionnement. La méthode de recalage est ensuite rapidement expliquée, et plusieurs résultats obtenus sont commentés en détail. Cela permet alors d’effectuer un bilan final sur la possibilité d’utiliser les résultats de segmentation d’images sonar pour effectuer un recalage et indiquer les possibilités et les limitations de l’utilisation d’un tel système de positionnement

A New Hybrid Possibilistic-Probabilistic Decision-Making Scheme for Classification

Uncertainty is at the heart of decision-making processes in most real-world applications. Uncertainty can be broadly categorized into two types: aleatory and epistemic. Aleatory uncertainty describes the variability in the physical system where sensors provide information (hard) of a probabilistic type. Epistemic uncertainty appears when the information is incomplete or vague such as judgments or human expert appreciations in linguistic form. Linguistic information (soft) typically introduces a possibilistic type of uncertainty. This paper is concerned with the problem of classification where the available information, concerning the observed features, may be of a probabilistic nature for some features, and of a possibilistic nature for some others. In this configuration, most encountered studies transform one of the two information types into the other form, and then apply either classical Bayesian-based or possibilistic-based decision-making criteria. In this paper, a new hybrid decision-making scheme is proposed for classification when hard and soft information sources are present. A new Possibilistic Maximum Likelihood (PML) criterion is introduced to improve classification rates compared to a classical approach using only information from hard sources. The proposed PML allows to jointly exploit both probabilistic and possibilistic sources within the same probabilistic decision-making framework, without imposing to convert the possibilistic sources into probabilistic ones, and vice versa

Apport de la modulation par evasion de frequence et du codage convolutif dans les milieux selectifs

INIST T 74672 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Telemedicine in Perspective: Trends and Challenges

International audienceAfter four generations of varied applications, telemedicine now finds itself at a crossroads. Validated concepts have integrated the available technology to handle the basic system components, as well as data and information processing, in multiple manners. Medical data exchange has been tested as a result. Among the multiple technology evolutions that could be identified as significant trends we have selected four wireless broadband, non-invasive sensors, emerging multimedia standards, and open source software which are likely to have an impact on the current telemedicine progression, at the functional and economic levels. What follows is a description of each technology's main characteristics

Amélioration de la classification automatique des fonds marins par la fusion multicapteurs acoustiques

Les systèmes automatiques de classification des fonds marins existent déjà depuis plusieurs années. Dans la grande majorité des cas (et pour tous les systèmes commerciaux), il s'agit de méthodes de classification mono-capteur. Les sonars se déclinent en plusieurs types de matériels dont les principaux sont: le sondeur vertical, le sonar latéral, et le sondeur multifaisceaux. Le principal objectif de cette thèse est d exploiter les complémentarités des capteurs par la fusion, afin d améliorer le résultat de classification. Ceci peut permettre d étendre le nombre de classes mais surtout d améliorer la fiabilité des décisions. Dans un premier temps, nous décrivons les différents capteurs utilisés, ainsi que les méthodes d'extraction des informations associées.Les informations extraites des capteurs peuvent représenter une quantité importante de données, qui sont en grande partie redondantes ou inutiles pour l'application souhaitée. Nous comparons différentes approches pour la sélection des informations les plus pertinentes pour l'application visée. Nous présentons ensuite, deux approches de fusion permettant de combiner les informations extraites des différentes sources. La première méthode est simple : il s agit de concaténer toutes les informations obtenues par les différentes sources d information, puis d appliquer un algorithme de classification. La deuxième méthode consiste à effectuer une classification séparée, puis de fusionner les décisions par la théorie de Demspter-Shafer. Deux applications de fusion bi-capteurs sont présentées: la fusion du sonar latéral et du sondeur vertical, puis la fusion du sonar latéral et de la bathymétrie.Many sea-related activities (underwater cable laying, dredging works, development of nautical charts and fishing maps, etc.) require information about the seabed. Acoustic sensors make it possible to gather data, which automatically analysed. Several acoustic sensors are available. The most commonly used for classification are the single beam vertical echo sounder (RoxAnn), the sidescan sonar. The aim of this study is to improve seabed classification by using several sensors together. A distinctive characteristic of this study is the frequency of the acoustic sources (455 kHz), which is noticeably higher than most of the existing systems. The study is divided into two parts: feature extraction from each sensor, and fusion of information from all sources. Three sources are used for classification : sidescan sonar images ; echo from a vertical single beam sounder, bathymetry. Two methods are compared for fusing information from sidescan sonar and bathymetry. Firstly, fusion is obtained by forming a vector with features from different sources. Because of the high dimensionality of the feature space, a feature reduction method is applied before classification. Several dimensionality reduction techniques have been tested and compared (discriminant analysis, projection pursuit, principal component analysis, etc.). The second method tested is the theory of evidence, which allows the introduction of knowledge about sensors to improve the fusion process. In both cases, improvements in classification performance were obtained.RENNES1-BU Sciences Philo (352382102) / SudocBREST-Télécom Bretagne (290192306) / SudocSudocFranceF

Information Processing for Remote Sensing

International audienceThis book provides the most comprehensive study of information processing techniques and issues in remote sensing. Topics covered include image and signal processing, pattern recognition and feature extraction for remote sensing, neural networks and wavelet transforms in remote sensing, remote sensing of ocean and coastal environment, SAR image filtering and segmentation, knowledge-based systems, software and hardware issues, data compression, change detection, etc. Emphasis is placed on environmental issues of remote sensing.With 58 color illustrations

Les réseaux de neurones artificiels et leurs applications en imagerie et en vision par ordinateur

International audienceLes réseaux de neurones désignent habituellement des réseaux neuromimétiques résultat de l’interconnexion d’un ensemble de neurones artificiels. Un neurone artificiel est un modèle simplifié du neurone biologique. L’objectif est de permettre la modélisation de certaines fonctions du cerveau, comme la mémorisation associative, l’apprentissage par l’exemple, etc. Cet ouvrage a pour objet de présenter cette thématique aux élèves ingénieurs. Le champs des applications en vision et en imagerie est considéré afin d’en illustrer les différents concepts. La particularité et l’originalité de cet ouvrage réside dans le fait qu’il est l’aboutissement d’une coopération en matière d’enseignement entre deux écoles d’ingénieurs : l’École Nationale Supérieure des Télécommunications de Bretagne, à Brest, et l’École de technologie supérieur, à Montréal. Ces deux écoles ont mise en commun leurs approches théoriques et pratiques de l’enseignement des réseaux de neurones pour un ouvrage didactique