Surfaces Reconstruction Via Inertial Sensors for Monitoring

International audienceThis document deals with the new capabilities of monitoring via the surface reconstruction of stuctures with sensors' arrays systems. Indeed, we will detail here our new demonstrator composed of a smart textile equipped with inertial sensors and a set of processings allowing to reconstruct the shape of the textile moving along time. We show here how this new tool can provide very useful information from the structures

Morphorider: Acquisition and Reconstruction of 3D Curves with Mobile Sensors

International audienceThis paper introduces a new method for real-time shape sensing. Using a single inertial measurement unit (IMU), our method enables to scan physical objects and to reconstruct digital 3D models. By moving the IMU along the surface, a network of local orientation data is acquired together with traveled distances and network topology. We then reconstruct a consistent network of curves and fit these curves by a globally smooth surface. To demonstrate the feasibility of our approach, we have constructed a mobile device called the Morphorider, which is equipped with a 3A3M-sensor node and an odometer for distance tracking

Morphorider: a new way for Structural Monitoring via the shape acquisition with a mobile device equipped with an inertial node of sensors

International audienceWe introduce a new kind of monitoring device, allowing the shape acquisition of a structure via a single mobile node of inertial sensors and an odometer. Previous approaches used devices placed along a network with fixed connectivity between the sensor nodes (lines, grid). When placed onto a shape, this sensor network provides local surface orientations along a curve network on the shape, but its absolute position in the world space is unknown. The new mobile device provides a novel way of structures monitoring: the shape can be scanned regularly, and following the shape or some specific parameters along time may afford the detection of early signs of failure. Here, we present a complete framework for 3D shape reconstruction. To compute the shape, our main insight is to formulate the reconstruction as a set of optimization problems. Using discrete representations, these optimization problems are resolved efficiently and at interactive time rates. We present two main contributions. First, we introduce a novel method for creating well-connected networks with cell-complex topology using only orientation and distance measurements and a set of user-defined constraints. Second, we address the problem of surfacing a closed 3D curve network with given surface normals. The normal input increases shape fidelity and allows to achieve globally smooth and visually pleasing shapes. The proposed framework was tested on experimental data sets acquired using our device. A quantitative evaluation was performed by computing the error of reconstruction for our own designed surfaces, thus with known ground truth. Even for complex shapes, the mean error remains around 1%

Monitoring of Bridges by Using Static and Dynamic Data from MEMS Accelerometers

International audienceStructural Health Monitoring methods may be divided into two major categories depending on the type of data used during the damage identification: static or dynamic. In this paper, it is shown that both analyses can be performed with the same instrumentation composed only of Micro Electro Mechanical System (MEMS) accelerometers. The latter has the capability to measure static and dynamic data. In very low frequency, accelerometers are used as inclination sensors to estimate static deflection. In higher frequency, accelerometers are used as vibration sensors to perform modal analysis. Both analyses are illustrated in the case of a real footbridge. Static deflections and modal flexibility-based deflections are compared in operational conditions, including pedestrian loads and temperature changes, and in artificially-introduced damage conditions. Very good agreements are obtained showing the relevance of the two approaches. Static and dynamic analyses could be used in a complementary way and provide additional information in order to reinforce the confidence and the accuracy of the damage identification. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG

Rugate filters obtained by a mechanical modulation technique

Communication to : International symposium on optical systems design, thin films for optical systems, 14-18 septembre 1992, Technische Universitat Berlin, New Physics Building, Berlin, FRGSIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : RM 1368 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Wearable Smart Sensing platform for environmental and health monitoring: the Convergence project

International audienceThe low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare applications in terms of autonomy for quasi-continuous collection of data for early-detection strategies. The system is compatible with different kinds of sensors, able to monitor not only health indicators of individual person (physical activity, core body temperature and biomarkers) but also the environment with chemical composition of the ambient air (NOx, COx, NHx particles) returning meaningful information on his/her exposure to dangerous (safety) or pollutant agents. In this article, we introduce the specifications and the design of the low-power sensing platform and the different sensors developed in the project, with a particular focus on pollutant sensing capabilities and specifically on NO2 sensor based on graphene and CO sensor based on polyaniline ink