This paper was accepted for publication in the journal Optics and Lasers in Engineering and the definitive published version is available at http://dx.doi.org/10.1016/j.optlaseng.2016.10.023.In 1964, just a few years after the invention of the laser, a fluid velocity measurement based on the frequency shift of scattered light was made and the laser Doppler technique was born. This\ud comprehensive review paper charts advances in the development and applications of laser Doppler vibrometry (LDV) since those first pioneering experiments. Consideration is first given to the challenges that continue to be posed by laser speckle. Scanning LDV is introduced and its significant influence in the field of experimental modal analysis described. Applications in structural health\ud monitoring and MEMS serve to demonstrate LDV’s applicability on structures of all sizes. Rotor vibrations and hearing are explored as examples of the classic applications. Applications in acoustics\ud recognise the versatility of LDV as demonstrated by visualisation of sound fields. The paper concludes with thoughts on future developments, using examples of new multi-component and multichannel instruments

A new noniterative frequency domain parameter estimation technique is proposed. It is based on a weighted total least squares approach, starting from multiple input multiple output frequency response functions. One of the specific advantages of the technique lies in the very stable identification of the system poles as a function of the specified system order, leading to easy-to-interpret stabilization diagrams. This implies a potential for automating the method and to apply it to “difficult” estimation cases. Several real-life case studies are discussed, one related to holographic modal analysis in the medium frequency range, one to the modal testing of a fully trimmed vehicle.

The detection of oil spills in water is a frequently researched area, but most of the research has been based on very large patches of crude oil on offshore areas. We present a novel framework for detecting oil spills inside a port environment, while using unmanned areal vehicles (UAV) and a thermal infrared (IR) camera. This framework is split into a training part and an operational part. In the training part, we present a process for automatically annotating RGB images and matching them with the IR images in order to create a dataset. The infrared imaging camera is crucial to be able to detect oil spills during nighttime. This dataset is then used to train on a convolutional neural network (CNN). Seven different CNN segmentation architectures and eight different feature extractors are tested in order to find the best suited combination for this task. In the operational part, we propose a method to have a real-time, onboard UAV oil spill detection using the pre-trained network and a low power interference device. A controlled experiment in the port of Antwerp showed that we are able to achieve an accuracy of 89% while only using the IR camera.