24 research outputs found

    Un environnement pour la representation et le traitement de phenomenes acoustiques. Application a la caracterisation de bruits sous-marins

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    SIGLEINIST T 76831 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

    An Improved Vision-Based Indoor Positioning Method

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    International audienc

    Towards Designing a Li-Fi-Based Indoor Positioning and Navigation System in an IoT Context

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    International audiencePeople generally find themselves lost while visiting a new indoor location because they are unaware of the building's architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented

    Apprentissage profond pour l’aide au diagnostic du mélanome à partir d’exemple

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    One study reveals that 15404 new cases of cutaneous melanoma have been estimated in France in 2017. The 5-year survival rate of a person with advanced melanoma is much lower than 20%, which raises the need for diagnose it at an early stage. The purpose of this work is to build a supervised computer-aided diagnosis system for melanoma. The database used for the implementation includes 1356 images divided into 9 classes. Two approaches have been implemented : classical approach and deep learning approach. The classical approach combinestwo support vector machine classifiers (SVM) trained on features extracted from three extractors. This approach yielded an area under the receptor curve (AUC) of 0.88, a sensitivity (SE) of 89% and a specificity (SPEC) of 77%. The deep learning approach uses features extracted from two pre-trained models VGG16 and resnet50 to train two linear SVM. The scores from these two classifiers are combined using a logistic regression algorithm to obtain the classification. This approach yielded an AED-CCR of 0.88, SE of 78% and SPEC of 83%

    Towards a Hybrid Indoor Positioning and Navigation System

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    International audienceWe propose a Hybrid Indoor Positioning and Navigation System (HIPNS), based on Li-Fi (Light-Fidelity) localization and optical camera positioning analyses deployed in an indoor environment. The localization approach is based on the fuse of two positioning strategies where the camera-based part is responsible for localizing individuals and recovering their trajectories in zones with low coverage of Li-Fi LEDs. A third-party element is planned to operate in the event of loss of contact. Step detection technique and heading estimation are used in a smartphone indoor localization application between two referenced points. The main contribution of this research work focuses on the integration of heterogeneous data, algorithms, and methods from different spheres of application
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