Fuzzy logic predictive method for indoor environment parametric dataset

An environment becomes polluted and contaminated because of excessive construction which taking places in several urban area and this affects the overall air quality index. Indoor air quality index seems much poor than outdoor air quality index because contaminated gases trapped within indoor environment. This research implements the fuzzy logic method to predict ambient temperature of indoor office environment. The temperature data collected previously used as default point of reference for predicting the temperature of the next day. Temperature prediction is necessary to maintain good health from contiguous disease. The research prototype uses the IEEE 802.14.5 wireless device to send temperature data to remote base station. The standard deviation and mean show the correlation between all the collected data. The temperature data is harvested from three different zones in an office for five consecutive days to predict the temperature. As a result, fuzzy logic predicts expectedly for a small dataset but alternative approach needed for a larger dataset, preferably machine learning will be a good choice in cloud service for predicting indoor environment

A Study of shape-based point descriptor for building recognition

Le problème de la reconnaissance de bâtiments dans un contexte d'images acquises par un appareil photo standard est devenu de plus en plus important. Ceci nécessite un système efficace pour la recherche, la navigation ainsi que le traitement d'images. Cette efficacité peut être obtenue en utilisant des concepts sémantiques tels que bâtiment pour une classification automatique des objets. En conséquence, nous proposons une nouvelle méthode pour la reconnaissance de bâtiments basée sur une approche non segmentée en utilisant des informations dérivées à partir des points de contour. Cette approche est réalisée en deux étapes, la première étape comprend l'extraction de l'image primitive de bas niveau, i.e la forme du bâtiment en utilisant un détecteur de contour tandis que la deuxième étape comprend l'extraction de l'information de haut niveau, i.e spatiale. Nous proposons une étape d'optimisation du détecteur de contours nommée "Adaptive Optimal Neighbourhood". Pour la représentation de l'information spatiale, deux nouvelles méthodes sont proposées, i.e le "Dominant Structure Orientation Histogram" et le "Spatial Neighbouring Pattern". Les résultats expérimentaux montrent que notre méthode est efficace et rapide grâce à sa représentation compacte et compréhensive. Une étude comparative, utilisant une base de données de bâtiments personnelle, montre que notre méthode obtient un résultat compétitif si on la compare aux autres méthodes proposées dans la littérature et basées sur les pixels de contour et les informations spatiales.In this thesis project, the author has carried out a study involving low-level features and middle-level features that can be used for man-made object recognition. This study focuses on a very specific type of man-made structure, i.e buildings, taken from image capturing devices from ground-level. The author aims to introduce a novel method for a multi-purpose building retrieval system that can support both high power PC-based systems and a limited processing power system such as mobile devices. The method has to be simple but fast, and efficient algorythms need to be designed and implemented for building recognition, resulting in an acceptable recognition rate. In order to archieve the above objectives, the author has proposed a method based on the integration of low-level and middle-level features. We propose two novel methods for representing spatial information called Dominant Structure Orientation Histogram (DSOH) and Spatial Neighbouring Patterns (SNP) as well as an adaptation of Fuzzy Spatial Descriptior (FSD). For evaluating the retrieval performance of the proposed method, a building retrieval system is proposed and implemented in a computer using Matlab ®. Besides using a personal building database for comparing the performance of our system with existing systems, we also included the ZuBud image database for retrieval evaluation. The results obtained during simulation show that our proposed method is capable of competing with existing building retreival system albeit our very simple and straightforward approach.LA ROCHELLE-BU (173002101) / SudocSudocFranceF

Accès à des bases de données spatiales dans un environnement client-serveur (application à un système de navigation embarqué)

LA ROCHELLE-BU (173002101) / SudocSudocFranceF

Visualisation dynamique d'informations géographiques pour un utilisateur mobile

In this thesis, we study the principles for managing and indexing spatial data located on a client in a client-server architecture, as well as downloading data on the client by anticipating the next move of the user. The system has been designed for assisting the user when moving and getting around in an urban area. The client downloads parts of the digital maps while he moves. In order not to download spatial objects at every move, they are kept in the client's cache memory. For the purpose of speeding up the search for spatial objects in the cache memory, we propose to create an index on the client. In order to decide which type of index to use, we have studied several indexing techniques. This study has allowed us to compare their performance and see how relevant these techniques were for the development and use of our system. The choice of indexing mechanisms has led to the definition of a model for assessing the cost of using them when processing spatial queries and updating them as well in the framework of our system. This theoretical study has confirmed the interest of using an index on the client. In order to reduce the cost of updating the index on the client, we have studied the incremental transfer of the index to the client. The application of techniques for releasing the cache has been studied for avoiding the saturation of the cache when travelling on long runs. The objective of moves anticipation is to adapt data loading to the user's moves. The query area on the client is distorted (the expanse being unchanged) depending on the direction of the user's moves. Then, the query area may be reduced in order to reduce the data loading time. We propose various strategies for determining when to send queries to the server, with each move or at the estimated best time.On the client, the visualization system is a Java application running on a PDA equipped with a GPS that gives the position of the user and a cellular phone allowing to connect to a distant server.LA ROCHELLE-BU (173002101) / SudocSudocFranceF

Modélisation des relations spatiales (prise en compte des aspects topologiques et directionnels)

LA ROCHELLE-BU (173002101) / SudocSudocFranceF

La construction d'un système d'information géographique (principes et algorithmes du système Savane)

LA ROCHELLE-BU (173002101) / SudocSudocFranceF

Constructing decision tree with incomplete values for planting materials selection

Dans les cas de traitement d'information incomplète à l'aide d'arbres à décision, la qualité de l'affectation des valeurs dépend toujours du travail de classification. Dans certains cas, on ne pourra pas se contenter de méthodes générales qui tiennent peu compte de l'existant et il sera nécessaire d'affecter des valeurs vraisemblables. Afin de traiter ce problème d'affectation de valeurs manquantes à des attributs, nous proposons de généraliser les algorithmes de décision avec des modèles plus simples et plus compréhensibles, de manière à faciliter et optimiser le travail de l'expert humain. Notre proposition consiste à partitionner les données en nous basant sur l'information stockée et sur l'absence de certaines valeurs, mais également sur l'information globale afin d'améliorer aussi les performances de traitement. L'apport de ce travail consiste en de nouveaux algorihmes, ainsi que des analyses pour la classification de matériaux de plantation. Nous donnons des résultats d'expérimentation sur des données réelles, qui sont susceptibles d'améliorer de manière significative le travail de sélection des graines de palmier à huile.A missing value in incomplete information always inherent the accuracy of classification tasks when a decision tree is used to classify unseen cases. There will be cases where plausible values are required to retain towards more principled and less intrusive. In order to handle the attribute with missing values, the researcher generalizes decision algorithms that provide simpler and more understandable models to optimally fulfill human expert requirement and constraint. Our objective is to partition data by taking full advantage of the information with the presence of missing values ; but with supporting global information to achieve better performance. The contributions of this study are newly developed algorithms and analyses for planting material classification. The researcher reports the empirical results that may provide high returnin planting material breeders in oil palm industry through effective policies design and decision making.LA ROCHELLE-BU (173002101) / SudocSudocFranceF

MASIR: A Multi-agent System for Real-Time Information Retrieval from Microblogs During Unexpected Events

International audienceMicroblogs have proved their potential to attract people from all over the world to express voluntarily what is happening around them during unexpected events. However, retrieving relevant information from the huge amount of data shared in real time in these microblogs remain complex. This paper proposes a new system named MASIR for real-time information retrieval from microblogs during unexpected events. MASIR is based on a decentralized and collaborative multi-agent approach analyzing the profiles of users interested in a given event in order to detect the most prominent ones that have to be tracked in real time. Real time monitoring of these users enables a direct access to valuable fresh information. Our experiments shows that MASIR simplifies the real-time detection and tracking of the most prominent users by exploring both the old and fresh information shared during the event and outperforms the standard centrality measures by using a time-sensitive ranking model