Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

challenging aspects in removing the influence of environmental factors on modal parameter estimates

Abstract Modal based damage detection is a well-established procedure for Structural Health Monitoring (SHM) of civil structures. The development of robust algorithms for automated output-only modal parameter identification and tracking has renewed the interest towards modal based damage detection. However, the influence of environmental and operational variables on modal parameter estimates still represents a relevant shortcoming to their extensive use for SHM, because it can yield changes in the experimental estimates of the same order of magnitude of those induced by damage. As a consequence, there is the need to remove the effects of those factors in order to effectively detect damage. Different approaches can be adopted, some of which do not require measurements of the environmental and operational variable influencing the modal parameter estimates. Nevertheless, the effective removal of the environmental influence on modal parameters still remains a challenging aspect in SHM. In the present paper, different approaches for compensation of environmental effects are applied to a very large database of modal parameter estimates from a bridge in operational conditions. The objective of the paper is to investigate their performance under the concurrent influence of different environmental/operational variables (for instance, temperature and traffic) on modal parameter estimates. Static (effect on the estimate at time t depends only on the value of the variable at the same time instant) as well as dynamic (effect on the estimate at time t depends on the values of the variable at time t and also at previous time instants) methods are considered. The results of the study remark the relevance of identifying all sources of variability of the modal parameters in operational conditions