Optically-acquired data, typically from digital image correlation, is increasingly being used in the area of structural dynamics, particularly modal testing and damage identification. One of the problems with such data is its extremely large size. Single images regularly extend to tens or even hundreds of thousands of data points and many thousands of images may be required for a vibration test. Such data must be stored and transmitted efficiently for later remote reconstruction and analysis, typically operational modal analysis. It is this requirement that is addressed in the research presented in this paper. This research builds upon previous work whereby digitised optical data was projected onto an orthogonal basis with coefficients (shape descriptors) of either greater or lesser significance; those deemed to be insignificant, according to a chosen threshold being removed. Data reduction by a combination of shapedescriptor decomposition and compressed-sensing is applied to an industrial printed circuit board and reconstructed for operational modal analysis by  1 optimisation.

The extraction of useful information and removal of redundant noise from data has become a major research topic in recent years. Data compression is necessary for all kinds of analysis, and the demand for efficient compression techniques has gained much attention. Digital image correlation is a camera-based measuring system, which has been widely applied in strain analysis because of the convenience of measuring displacement fields by simply selecting a region of interest. Currently, there is interest in applying such methods to engineering structures in dynamics. However, one of the major issues related to the integration of camera-based systems with dynamic measurement is the generation of huge amounts of data, typically extending to many thousands of data points, because of the requirements of high sampling rate, spatial resolution, and long duration of recording. In this paper a new algorithm is presented that addresses the need for efficiency in full-field data processing. By making use of the data itself and combining the concept of sparse representation with Gram-Schmidt orthogonalisation, the number of basis function used to represent the data can be reduced and a concise decomposition established. In both simulated and experimental cases, the compression ratios for data size and number of signals used in operational modal analysis are substantially diminished, thereby demonstrating the effectiveness of the proposed algorithm. A reduced number of new basis functions is determined for the representation of data under the condition that the reconstructed displacement map reproduces the raw measured data to within a chosen threshold on the coefficient of correlation.

The main goal of this paper is to identify last speckle images rapidly. Digital image processing techniques are employed to analyze the characteristics of laser speckle images and match them up to achieve laser speckle image identification. Besides the database is built to accelerate the identification process and further enhance its practicability. In terms of building the database, Gabor filter is utilized to enhance the extracted characteristics as well as to generate the feature vectors. The final step is adopting K-means clustering to build the classification model of feature vectors. The process of identifying laser speckle images is described as follows. Through experiments we observed that scale invariant feature transform (SIFT) can extract features of laser speckle images very well. However the drawback is that it took too much time to compute and match up those features, which is not suitable for fast laser speckle identification. Therefore the proposed method took enhance SIFT as backbone. Experimental results demonstrate that the retrieval performance of the proposed method is accurate when the database size contains 516 images.