SPOT-GPR: a freeware tool for target detection and localizationin GPR data developed within the COST action TU1208

SPOT-GPR (release 1.0) is a new freeware tool implementing an innovative Sub-Array Processing method, for the analysis of Ground-Penetrating Radar (GPR) data with the main purposes of detecting and localizing targets. The software is implemented in Matlab, it has a graphical user interface and a short manual. This work is the outcome of a series of three Short-Term Scientific Missions (STSMs) funded by European COoperation in Science and Technology (COST) and carried out in the framework of the COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar” (www.GPRadar.eu). The input of the software is a GPR radargram (B-scan). The radargram is partitioned in subradargrams, composed of a few traces (A-scans) each. The multi-frequency information enclosed in each trace is exploited and a set of dominant Directions of Arrival (DoA) of the electromagnetic field is calculated for each sub-radargram. The estimated angles are triangulated, obtaining a pattern of crossings that are condensed around target locations. Such pattern is filtered, in order to remove a noisy background of unwanted crossings, and is then processed by applying a statistical procedure. Finally, the targets are detected and their positions are predicted. For DoA estimation, the MUltiple SIgnal Classification (MUSIC) algorithm is employed, in combination with the matched filter technique. To the best of our knowledge, this is the first time the matched filter technique is used for the processing of GPR data. The software has been tested on GPR synthetic radargrams, calculated by using the finite-difference timedomain simulator gprMax, with very good results

Computer Vision Based Structural Identification Framework for Bridge Health Mornitoring

The objective of this dissertation is to develop a comprehensive Structural Identification (St-Id) framework with damage for bridge type structures by using cameras and computer vision technologies. The traditional St-Id frameworks rely on using conventional sensors. In this study, the collected input and output data employed in the St-Id system are acquired by series of vision-based measurements. The following novelties are proposed, developed and demonstrated in this project: a) vehicle load (input) modeling using computer vision, b) bridge response (output) using full non-contact approach using video/image processing, c) image-based structural identification using input-output measurements and new damage indicators. The input (loading) data due vehicles such as vehicle weights and vehicle locations on the bridges, are estimated by employing computer vision algorithms (detection, classification, and localization of objects) based on the video images of vehicles. Meanwhile, the output data as structural displacements are also obtained by defining and tracking image key-points of measurement locations. Subsequently, the input and output data sets are analyzed to construct novel types of damage indicators, named Unit Influence Surface (UIS). Finally, the new damage detection and localization framework is introduced that does not require a network of sensors, but much less number of sensors. The main research significance is the first time development of algorithms that transform the measured video images into a form that is highly damage-sensitive/change-sensitive for bridge assessment within the context of Structural Identification with input and output characterization. The study exploits the unique attributes of computer vision systems, where the signal is continuous in space. This requires new adaptations and transformations that can handle computer vision data/signals for structural engineering applications. This research will significantly advance current sensor-based structural health monitoring with computer-vision techniques, leading to practical applications for damage detection of complex structures with a novel approach. By using computer vision algorithms and cameras as special sensors for structural health monitoring, this study proposes an advance approach in bridge monitoring through which certain type of data that could not be collected by conventional sensors such as vehicle loads and location, can be obtained practically and accurately

Gearing up for action: attentive tracking dynamically tunes sensory and motor oscillations in the alpha and beta band

Allocation of attention during goal-directed behavior entails simultaneous processing of relevant and attenuation of irrelevant information. How the brain delegates such processes when confronted with dynamic (biological motion) stimuli and harnesses relevant sensory information for sculpting prospective responses remains unclear. We analyzed neuromagnetic signals that were recorded while participants attentively tracked an actor’s pointing movement that ended at the location where subsequently the response-cue indicated the required response. We found the observers’ spatial allocation of attention to be dynamically reflected in lateralized parieto-occipital alpha (8-12Hz) activity and to have a lasting influence on motor preparation. Specifically, beta (16-25Hz) power modulation reflected observers’ tendency to selectively prepare for a spatially compatible response even before knowing the required one. We discuss the observed frequency-specific and temporally evolving neural activity within a framework of integrated visuomotor processing and point towards possible implications about the mechanisms involved in action observation