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Artykuły w czasopismach na temat "Capteurs de smartphones"
Delabre, Ulysse, Nicolas Bruni, Nicolas-Alexandre Goy i Antoine Girot. "La smartphonique au service de la photonique". Photoniques, nr 115 (8.08.2022): 19–22. http://dx.doi.org/10.1051/photon/202211519.
Pełny tekst źródłaBoelle, Pierre-Yves, Rodolphe Thiébaut i Dominique Costagliola. "Données massives, vous avez dit données massives ?" Questions de santé publique, nr 30 (wrzesień 2015): 1–4. http://dx.doi.org/10.1051/qsp/2015030.
Pełny tekst źródłaRosset, Christian. "La valeur ajoutée de la digitalisation: être plusinformé, connecté et agile". Schweizerische Zeitschrift fur Forstwesen 172, nr 4 (30.06.2021): 198–204. http://dx.doi.org/10.3188/szf.2021.0198.
Pełny tekst źródłaJavelot, H., S. Garcia, L. Weiner i G. Bertschy. "Le projet PSYCHE, un système de monitoring multiparamétrique : présentation du concept et évaluation de l’acceptabilité". European Psychiatry 28, S2 (listopad 2013): 31. http://dx.doi.org/10.1016/j.eurpsy.2013.09.076.
Pełny tekst źródłaDjamba, Kalema Josue, i Baraka Ntawanga Irene. "INTEGRATION D'UNE APPLICATION MOBILE AU SYSTEME DE REGULATION DU NIVEAU D'EAU D'UN RESERVOIR". British Journal of Multidisciplinary and Advanced Studies 5, nr 1 (8.01.2024): 8–22. http://dx.doi.org/10.37745/bjmas.2022.0388.
Pełny tekst źródłaBensamoun, D. "Place des nouvelles technologies dans les stratégies de dépistage et d’évaluation des troubles thymiques et cognitifs". European Psychiatry 30, S2 (listopad 2015): S49. http://dx.doi.org/10.1016/j.eurpsy.2015.09.137.
Pełny tekst źródłaLemos, André. "Pervasive Computer Games and Processes of Spatialization: Informational Territories and Mobile Technologies". Canadian Journal of Communication 36, nr 2 (4.08.2011). http://dx.doi.org/10.22230/cjc.2011v36n2a2187.
Pełny tekst źródłaRozprawy doktorskie na temat "Capteurs de smartphones"
Krieg, Jean-gabriel. "Localisation indoor à l'aide des capteurs d'un smartphone". Thesis, Toulouse, INPT, 2017. http://www.theses.fr/2017INPT0009/document.
Pełny tekst źródłaIndoor environments present opportunities for a rich set of location-aware services in the information and communications technology (ICT) area. Therefore, accurately localizing a user indoors has become a key enabling technology. This thesis addresses the issue of tracking a user equipped with an off-the-shelf smartphone by exploiting its embedded motion sensors. Leveraging key characteristics of human locomotion, we propose a complete, infrastructure-free indoor navigation solution, allowing a user to navigate any unknown building with meter-level accuracy. Finally, extending our understanding of locomotion to outdoors areas where users are inside vehicles, we design and implement a smartphone application for smart on-street parking
Nguyen, Van Khang. "Détection et agrégation d'anomalies dans les données issues des capteurs placés dans des smartphones". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL021/document.
Pełny tekst źródłaMobile and wireless networks have developed enormously over the recent years. Far from being restricted to industrialized countries, these networks which require a limited fixed infrastructure, have also imposed in emerging countries and developing countries. Indeed, with a relatively low structural investment as compared to that required for the implementation of a wired network, these networks enable operators to offer a wide coverage of the territory with a network access cost (price of devices and communications) quite acceptable to users. Also, it is not surprising that today, in most countries, the number of wireless phones is much higher than landlines. This large number of terminals scattered across the planet is an invaluable reservoir of information that only a tiny fraction is exploited today. Indeed, by combining the mobile position and movement speed, it becomes possible to infer the quality of roads or road traffic. On another level, incorporating a thermometer and / or hygrometer in each terminal, which would involve a ridiculous large-scale unit cost, these terminals could serve as a relay for more reliable local weather. In this context, the objective of this thesis is to study and analyze the opportunities offered by the use of data from mobile devices to offer original solutions for the treatment of these big data, emphasizing on optimizations (fusion, aggregation, etc.) that can be performed as an intermediate when transferred to center(s) for storage and processing, and possibly identify data which are not available now on these terminals but could have a strong impact in the coming years. A prototype including a typical sample application will validate the different approaches
Nguyen, Van Khang. "Détection et agrégation d'anomalies dans les données issues des capteurs placés dans des smartphones". Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL021.
Pełny tekst źródłaMobile and wireless networks have developed enormously over the recent years. Far from being restricted to industrialized countries, these networks which require a limited fixed infrastructure, have also imposed in emerging countries and developing countries. Indeed, with a relatively low structural investment as compared to that required for the implementation of a wired network, these networks enable operators to offer a wide coverage of the territory with a network access cost (price of devices and communications) quite acceptable to users. Also, it is not surprising that today, in most countries, the number of wireless phones is much higher than landlines. This large number of terminals scattered across the planet is an invaluable reservoir of information that only a tiny fraction is exploited today. Indeed, by combining the mobile position and movement speed, it becomes possible to infer the quality of roads or road traffic. On another level, incorporating a thermometer and / or hygrometer in each terminal, which would involve a ridiculous large-scale unit cost, these terminals could serve as a relay for more reliable local weather. In this context, the objective of this thesis is to study and analyze the opportunities offered by the use of data from mobile devices to offer original solutions for the treatment of these big data, emphasizing on optimizations (fusion, aggregation, etc.) that can be performed as an intermediate when transferred to center(s) for storage and processing, and possibly identify data which are not available now on these terminals but could have a strong impact in the coming years. A prototype including a typical sample application will validate the different approaches
Ta, Viet-Cuong. "Smartphone-based indoor positioning using Wi-Fi, inertial sensors and Bluetooth". Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM092/document.
Pełny tekst źródłaWith the popularity of smartphones and tablets in daily life, the task of finding user’s position through their phone gains much attention from both the research and industry communities. Technologies integrated in smartphones such as GPS, Wi-Fi, Bluetooth and camera are all capable for building a positioning system. Among those technologies, GPS has approaches have become a standard and achieved much success for the outdoor environment. Meanwhile, Wi-Fi, inertial sensors and Bluetooth are more preferred for positioning task in indoor environment.For smartphone positioning, Wi-Fi fingerprinting based approaches are well established within the field. Generally speaking, the approaches attempt to learn the mapping function from Wi-Fi signal characteristics to the real world position. They usually require a good amount of data for finding a good mapping. When the available training data is limited, the fingerprinting-based approach has high errors and becomes less stable. In our works, we want to explore different approaches of Wi-Fi fingerprinting methods for dealing with a lacking in training data. Based on the performance of the individual approaches, several ensemble strategies are proposed to improve the overall positioning performance. All the proposed methods are tested against a published dataset, which is used as the competition data of the IPIN 2016 Conference with offsite track (track 3).Besides the positioning system based on Wi-Fi technology, the smartphone’s inertial sensors are also useful for the tracking task. The three types of sensors, which are accelerate, gyroscope and magnetic, can be employed to create a Step-And-Heading (SHS) system. Several methods are tested in our approaches. The number of steps and user’s moving distance are calculated from the accelerometer data. The user’s heading is calculated from the three types of data with three methods, including rotation matrix, Complimentary Filter and Madgwick Filter. It is reasonable to combine SHS outputs with the outputs from Wi-Fi due to both technologies are present in the smartphone. Two combination approaches are tested. The first approach is to use directly the Wi-Fi outputs as pivot points for fixing the SHS tracking part. In the second approach, we rely on the Wi-Fi signal to build an observation model, which is then integrated into the particle filter approximation step. The combining paths have a significant improvement from the SHS tracking only and the Wi-Fi only. Although, SHS tracking with Wi-Fi fingerprinting improvement achieves promising results, it has a number of limitations such as requiring additional sensors calibration efforts and restriction on smartphone handling positions.In the context of multiple users, Bluetooth technology on the smartphone could provide the approximated distance between users. The relative distance is calculated from the Bluetooth inquiry process. It is then used to improve the output from Wi-Fi positioning models. We study two different combination methods. The first method aims to build an error function which is possible to model the noise in the Wi-Fi output and Bluetooth approximated distance for each specific time interval. It ignores the temporal relationship between successive Wi-Fi outputs. Position adjustments are then computed by minimizing the error function. The second method considers the temporal relationship and the movement constraint when the user moves around the area. The tracking step are carried out by using particle filter. The observation model of the particle filter are a combination between the Wi-Fi data and Bluetooth data. Both approaches are tested against real data, which include up to four different users moving in an office environment. While the first approach is only applicable in some specific scenarios, the second approach has a significant improvement from the position output based on Wi-Fi fingerprinting model only
Palacino, Julian. "Outils de spatialisation sonore pour terminaux mobiles : microphone 3D pour une utilisation nomade". Thesis, Le Mans, 2014. http://www.theses.fr/2014LEMA1007/document.
Pełny tekst źródłaMobile technologies (such as smartphones and tablets) are now common devices of the consumer market. In this PhD we want to use those technologies as the way to introduce tools of sound spatialization into the mass market. Today the size and the number of traducers used to pick-up and to render a spatial sound scene are the main factors which limit the portability of those devices. As a first step, a listening test, based on a spatial audio recording of an opera, let us to evaluate the 3D audio technologies available today for headphone rendering. The results of this test show that, using the appropriate binaural decoding, it is possible to achieve a good binaural rendering using only the four sensors of the Soundfield microphone.Then, the steps of the development of a 3D sound pick-up system are described. Several configurations are evaluated and compared. The device, composed of 3 cardioid microphones, was developed following an approach inspired by the sound source localization and by the concept of the "object format encoding". Using the microphone signals and an adapted post-processing it is possible to determine the directions of the sources and to extract a sound signal which is representative of the sound scene. In this way, it is possible to completely describe the sound scene and to compress the audio information.This method offer the advantage of being cross platform compatible. In fact, the sound scene encoded with this method can be rendered over any reproduction system.A second method to extract the spatial information is proposed. It uses the real in situ characteristics of the microphone array to perform the sound scene analysis.Some propositions are made to complement the 3D audio chain allowing to render the result of the sound scene encoding over a binaural system or any king of speaker array using all capabilities of the mobile devices
Montoya, David. "Une base de connaissance personnelle intégrant les données d'un utilisateur et une chronologie de ses activités". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLN009/document.
Pełny tekst źródłaTypical Internet users today have their data scattered over several devices, applications, and services. Managing and controlling one's data is increasingly difficult. In this thesis, we adopt the viewpoint that the user should be given the means to gather and integrate her data, under her full control. In that direction, we designed a system that integrates and enriches the data of a user from multiple heterogeneous sources of personal information into an RDF knowledge base. The system is open-source and implements a novel, extensible framework that facilitates the integration of new data sources and the development of new modules for deriving knowledge. We first show how user activity can be inferred from smartphone sensor data. We introduce a time-based clustering algorithm to extract stay points from location history data. Using data from additional mobile phone sensors, geographic information from OpenStreetMap, and public transportation schedules, we introduce a transportation mode recognition algorithm to derive the different modes and routes taken by the user when traveling. The algorithm derives the itinerary followed by the user by finding the most likely sequence in a linear-chain conditional random field whose feature functions are based on the output of a neural network. We also show how the system can integrate information from the user's email messages, calendars, address books, social network services, and location history into a coherent whole. To do so, it uses entity resolution to find the set of avatars used by each real-world contact and performs spatiotemporal alignment to connect each stay point with the event it corresponds to in the user's calendar. Finally, we show that such a system can also be used for multi-device and multi-system synchronization and allow knowledge to be pushed to the sources. We present extensive experiments
Amadou, Kountché Djibrilla. "Localisation dans les bâtiments des personnes handicapées et classification automatique de données par fourmis artificielles". Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4021/document.
Pełny tekst źródłaThe concept of « smart » invades more and more our daily life. A typical example is the smartphone, which becames by years an essential device. Soon, it’s the city, the car and the home which will become « smart ». The intelligence is manifested by the ability for the environment to interact and to take decisons in its relationships with users and other environments. This needs information on state changes occurred on both sides. Sensor networks allow to collect these data, to apply on them some pre-processings and to transmit them. Sensor network, towards some of their caracteristics are closed to Swarm Intelligence in the sense that small entities with reduced capababilities can cooperate automatically, in unattended, decentralised and distributed manner in order to accomplish complex tasks. These bio-inspired methods have served as basis for the resolution of many problems, mostly optimization and this insipired us to apply them on problems met in Ambient Assisted Living and on the data clustering problem. AAL is a sub-field of context-aware services, and its goals are to facilitate the everyday life of elderly and disable people. These systems determine the context and then propose different kind of services. We have used two important elements of the context : the position and the disabilty. Although positioning has very good precision outdoor, it faces many challenges in indoor environments due to the electromagnetic wave propagation in harsh conditions, the cost of systems, interoperabilty, etc. Our works have been involved in positioning disabled people in indoor environment by using wireless sensor network for determining the caracteristics of the electromagnetic wave (signal strenght, time, angle) for estimating the position by geometric methods (triangulation, lateration), fingerprinting methods (k-nearest neighbours), baysiens filters (Kalman filter). The application is to offer AAL services like navigation. Therefore we extend the definition of sensor node to take into account any device, in the environment, capable of emiting and receiving a signal. Also, we have studied the possibility of using Pachycondylla Apicalis for data clustering and for indoor localization by casting this last problem as data clustering problem. Finally we have proposed a system based on a middleware architecture
Dalstein, Olivier. "Nanoporous thin films structured by top-down & bottom-up approaches : towards smartphone-compatible optical sensors". Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066739.
Pełny tekst źródłaMulti-scale structuration of functional materials at nano- and micro- levels is an active scientific field driven by the tremendous potential of miniaturized devices in microelectronics, optics (light harvesting, photonics), sensing (selective sensors) or microfluidics (lab-on-a-chip). Diverse micro-nanofabrication techniques are exploited for device fabrication. On one hand, Top-Down techniques are developed to fabricate complex micro- and nano- structures from bulk materials; this approach relies on lithography which offers a wide flexibility on the final object architecture but suffers from low-throughput that hinders its use for large-scale production. On the other hand, Bottom-Up techniques based on the assembly of molecular building blocks are suited for the large-scale fabrication of nanostructured materials but are limited to simple architectures. The fruitful combination of both approaches is thus a vast field of investigation with promising technological outcomes.The scope of this thesis is to combine Bottom-Up and Top-Down approaches to obtain hierarchical architectures with original chemical characteristics and optical properties. In practical terms, the deposition by Chemical Liquid Deposition (dip-coating) of nanoporous inorganic or organic-inorganic (hybrid) films structured by self-assembly and the subsequent patterning by either lithographic or evaporation-driven patterning will be presented. The resulting multi-scale structures possess periodic micro- or submicro- organization and engineered nanopores (<100 nm) and are used as optical sensing devices for the detection of Volatile Organic Compounds (VOC). In the pursuit of simplicity, the compatibility of these sensors with Smartphone technology is emphasized; the final goal is to fabricate low-cost sensors with pronounced chemical selectivity that produce an optical signal directly readable by Smartphone cameras
Daou, Andrea. "Real-time Indoor Localization with Embedded Computer Vision and Deep Learning". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR002.
Pełny tekst źródłaThe need to determine the location of individuals or objects in indoor environments has become an essential requirement. The Global Navigation Satellite System, a predominant outdoor localization solution, encounters limitations when applied indoors due to signal reflections and attenuation caused by obstacles. To address this, various indoor localization solutions have been explored. Wireless-based indoor localization methods exploit wireless signals to determine a device's indoor location. However, signal interference, often caused by physical obstructions, reflections, and competing devices, can lead to inaccuracies in location estimation. Additionally, these methods require access points deployment, incurring associated costs and maintenance efforts. An alternative approach is dead reckoning, which estimates a user's movement using a device's inertial sensors. However, this method faces challenges related to sensor accuracy, user characteristics, and temporal drift. Other indoor localization techniques exploit magnetic fields generated by the Earth and metal structures. These techniques depend on the used devices and sensors as well as the user's surroundings.The goal of this thesis is to provide an indoor localization system designed for professionals, such as firefighters, police officers, and lone workers, who require precise and robust positioning solutions in challenging indoor environments. In this thesis, we propose a vision-based indoor localization system that leverages recent advances in computer vision to determine the location of a person within indoor spaces. We develop a room-level indoor localization system based on Deep Learning (DL) and built-in smartphone sensors combining visual information with smartphone magnetic heading. To achieve localization, the user captures an image of the indoor surroundings using a smartphone, equipped with a camera, an accelerometer, and a magnetometer. The captured image is then processed using our proposed multiple direction-driven Convolutional Neural Networks to accurately predict the specific indoor room. The proposed system requires minimal infrastructure and provides accurate localization. In addition, we highlight the importance of ongoing maintenance of the vision-based indoor localization system. This system necessitates regular maintenance to adapt to changing indoor environments, particularly when new rooms have to be integrated into the existing localization framework. Class-Incremental Learning (Class-IL) is a computer vision approach that allows deep neural networks to incorporate new classes over time without forgetting the knowledge previously learned. In the context of vision-based indoor localization, this concept must be applied to accommodate new rooms. The selection of representative samples is essential to control memory limits, avoid forgetting, and retain knowledge from previous classes. We develop a coherence-based sample selection method for Class-IL, bringing forward the advantages of the coherence measure to a DL framework. The relevance of the methodology and algorithmic contributions of this thesis is rigorously tested and validated through comprehensive experimentation and evaluations on real datasets
Angladon, Vincent. "Room layout estimation on mobile devices". Phd thesis, Toulouse, INPT, 2018. http://oatao.univ-toulouse.fr/20745/1/ANGLADON_Vincent.pdf.
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