Using global existing fiber networks for environmental sensing

We review recent advances in distributed fiber optic sensing (DFOS) and their applications. The scattering mechanisms in glass, which are exploited for reflectometry-based DFOS, are Rayleigh, Brillouin, and Raman scatterings. These are sensitive to either strain and/or temperature, allowing optical fiber cables to monitor their ambient environment in addition to their conventional role as a medium for telecommunications. Recently, DFOS leveraged technologies developed for telecommunications, such as coherent detection, digital signal processing, coding, and spatial/frequency diversity, to achieve improved performance in terms of measurand resolution, reach, spatial resolution, and bandwidth. We review the theory and architecture of commonly used DFOS methods. We provide recent experimental and field trial results where DFOS was used in wide-ranging applications, such as geohazard monitoring, seismic monitoring, traffic monitoring, and infrastructure health monitoring. Events of interest often have unique signatures either in the spatial, temporal, frequency, or wavenumber domains. Based on the temperature and strain raw data obtained from DFOS, downstream postprocessing allows the detection, classification, and localization of events. Combining DFOS with machine learning methods, it is possible to realize complete sensor systems that are compact, low cost, and can operate in harsh environments and difficult-to-access locations, facilitating increased public safety and smarter cities

Head pose estimation & TV Context: current technology

With the arrival of low-cost high quality cameras, implicit user behaviour tracking is easier and it becomes very interesting for viewer modelling and content personalization in a TV context. In this paper, we present a comparison between three common algorithms of automatic head direction extraction for a person watching TV in a realistic context. Those algorithms compute the different rotation angles of the head (pitch, roll, yaw) in a non-invasive and continuous way based on 2D and/or 3D features acquired with low cost cameras. These results are compared with a reference based on the Qualisys motion capture commercial system which is a robust marker-based tracking system. The performances of the different algorithms are compared function of different configurations. While our results show that full implicit behaviour tracking in real-life TV setups is still a challenge, with the arrival of next generation sensors (as the new Kinect one sensor), accurate TV personalization based on implicit behaviour is close to become a very interesting option

Demonstration of the detection of buckling effects in steel pipelines and beams by the distributed Brillouin sensor

We conducted the strain measurements to monitor steel pipe buckling for the first time using distributed Brillouin sensor system. Two specimens (steel pipeline and beam) were prepared by locally thinning the inner wall to provoke buckling. Fibre was laid along the external walls of the specimens. Strain gauges were glued in thinned wall area. An axial load was applied to the specimens and increased while compressive strain was measured by both Brillouin sensor and the strain gauges. The Brillouin sensor measurements showed the convolution of the all the strain distribution within the spatial resolution, the fitting Brillouin peak corresponds to the average strain over the spatial resolution, hence its reading can be smaller than the strain gauge reading depending on the location. With the Brillouin sensors, we observed compression in the whole specimens while elongation was detected in the neighbourhood of the thinned wall at onset of the buckling for the first time. The buckling was identified and localized with the Brillouin sensor measurement

Detection of buckling in steel pipeline and column by the distributed Brillouin sensor

We conducted a strain characterization experiment to monitor steel pipe and column buckling for the first time using a distributed Brillouin sensor system. Two specimens (steel pipe and column) were prepared by locally thinning the inner wall to initiate buckling. An axial load was applied to the specimens and increased while compressive strain was measured by both Brillouin sensor and strain gauges. With the Brillouin sensor, we observed compression on the whole specimens while elongation was detected in the neighborhood of the thinned wall at onset of the buckling. Both tension and compression are measured simultaneously from the same spectrum. This capability to extract both informations at the same time makes the Brillouin sensor a unique tool for structural health monitoring. The buckling was identified and localized thanks to this original approach

Pipeline buckling detection by the distributed Brillouin sensor

We conducted strain characterization experiment to monitor steel pipe buckling for the first time using distributed Brillouin sensor system. One specimen was prepared by locally thinning the inner wall to initiate buckling. An axial load was applied to the specimen and increased while compressive strain was measured by both Brillouin sensor and the strain gauges. With the Brillouin sensors, we observed compression in the whole specimen while elongation was detected in the neighbourhood of the thinned wall at onset of the buckling for the first time. The buckling was identified and localized with the Brillouin sensor measurement