No abstract

The signal-to-noise ratio of measurements by electron holography could be considerably improved if longer exposure times were possible: increasing the number of electrons contributing to the hologram improves the counting statistics. However, instrumental instabilities causing drift in the hologram fringes and specimen position make acquisition times of above a few seconds counterproductive. The current approach is to acquire image stacks of holograms, with short exposure times, followed by numerical realignment through sophisticated post-processing. The associated data storage and manipulation make in-situ and tomography experiments extremely cumbersome. Here, we implement dynamic automation of electron holography experiments to overcome these problems. The real-time drift measurement and feedback control of the instrument allow single holograms to be acquired with exposure times of 30 min or more. Indeed, there are no longer any limitations from instrumental instabilities and only those imposed by the specimen itself. Furthermore, automation allows the implementation of sophisticated phase reconstruction techniques based on precise control of the experimental conditions. Smart acquisition of electron holograms preludes future computer-controlled electron microscopy capabilities.

Structural health and operation monitoring are of growing interest in the development of railway networks. Conventional systems of infrastructure monitoring already exist (e.g. axle counters, track circuits) but present some drawbacks. Alternative solutions are therefore studied and developed. In this field, optical fiber sensors, and more particularly fiber Bragg grating (FBG) sensors, are particularly relevant due to their immunity to electromagnetic fields and simple wavelength-division-multiplexing capability. Field trials conducted up to now have demonstrated that FBG sensors provide useful information about train composition, positioning, speed, acceleration and weigh-in-motion estimations. Nevertheless, for practical deployment, cost-effectiveness should be ensured, specifically at the interrogator side that has also to be fast (>1 kHz repetition rate), accurate (∼1 pm wavelength shift) and reliable. To reach this objective, we propose in this paper to associate a low cost and high-speed interrogator coupled with an adequate signal-processing algorithm to dynamically monitor cascaded wavelength-multiplexed FBGs and to accurately capture the parameters of interest for railway traffic monitoring. This method has been field-tested with a Redondo Optics Inc. interrogator based on the well-known edge-filter demodulation technique. To determine the train speed from the raw data, a dominant frequency analysis has been implemented. Using this original method, we show that we can retrieve the speed of the trains, even when the time history strain signature is strongly affected by the measurement noise. The results are assessed by complimentary data obtained from a spectrometer-based FBG interrogator.