Aa Chanerley

Concerning Baseline Errors in the Form of Acceleration Transients When Recovering Displacements from Strong Motion Records Using the Undecimated Wavelet Transform

Abstract This paper discusses the progression of a novel algorithm that uses a wavelet‐transform approach. The transform is a generalization of the decimated, discrete wavelet transform (DWT) that is the undecimated DWT or stationary wavelet transform (SWT) also known as the undecimated a trous algorithm. It forms the basis for recovering displacements from acceleration time histories. The approach recovers a low‐frequency fling that is usually an almost sinusoidal or cosinusoidal pulse responsible for the big ground motions in strong motion events. The algorithm implements a well known and non‐linear, denoising scheme and is applied to the low‐frequency sub‐band and, in particular, succeeds in recovering the acceleration‐fling pulse. The progression is that in order to obtain estimates of displacements, the algorithmic baseline‐correction scheme can now locate an acceleration transient (i.e., a spike), which creates the DC shift in velocity and the linear trend in displacement, and is therefore the baseline error. Once this acceleration transient is corrected for or eliminated, double‐time reintegration recovers the velocity‐fling pulse and residual displacement. The paper infers that these acceleration transients may be due to ground rotation, embedded in the translational data. The scheme provides for easier integration once the low‐ and higher‐frequency accelerations are extracted. Online Material: Additional results for the Chi‐Chi TCU068 (1999) station, the New Zealand Darfield Station (2010), and the Olfus Earthquake (2008) in Iceland.

Obtaining Spectrum Matching Time Series Using a Reweighted Volterra Series Algorithm (RVSA)

In this paper, we introduce a novel algorithm for morphing any accelerogram into a spectrum matching one. First, the seed time series is re‐expressed as a discrete Volterra series. The first‐order Volterra kernel is estimated by a multilevel wavelet decomposition using the stationary wavelet transform. Second, the higher‐order Volterra kernels are estimated using a complete multinomial mixing of the first‐order kernel functions. Finally, the weighting of every term in this Volterra series is optimally adapted using a Levenberg–Marquardt algorithm such that the modified time series matches any target response spectrum. Comparisons are made using the SeismoMatch algorithm, and this reweighted Volterra series algorithm is demonstrated to be considerably more robust, matching the target spectrum more faithfully. This is achieved while qualitatively maintaining the original signal’s nonstationary statistics, such as general envelope, time location of large pulses, and variation of frequency content with time.

Unauthorised person recognition using gait biometry and information analysis: integration and transparency of security operations in a centralised intelligence environment

This paper shows how unique behavioural walking identification properties can be used to recognise unauthorised and suspicious persons when they enter a surveillance/recognition area. Specific gait properties will be examined in order to make clear the impact in the recognition accuracy. The system that is proposed comprise of various parts that operate either as stand alone capacities or as an integral part of a greater system. The two main entities of the system are the gait recognition and the information analysis module. The intelligent automated process in the information analysis enforces the public safety authorities to make simultaneous threat assessments based on complex search queries. The information and the functionalities of the system are delivered to the user through an integrated software solution that provides transparency for the majority of security operations. The integrated solution can be used for real time monitoring as well as for access control at sites of high risk.