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This work presents a methodology for automated
damage-sensitive feature extraction and anomaly
detection under multivariate operational variability
for in-flight assessment of wings. The
method uses a passive excitation approach, i. e.
without the need for artificial actuation. The
modal system properties (natural frequencies and
damping ratios) are used as damage-sensitive
features. Special emphasis is placed on the use
of Fiber Bragg Grating (FBG) sensing technology
and the consideration of Operational and
Environmental Variability (OEV). Measurements
from a wind tunnel investigation with a composite
cantilever equipped with FBG and piezoelectric
sensors are used to successfully detect an impact
damage. In addition, the feasibility of damage
localisation and severity estimation is evaluated
based on the coupling found between damageand
OEV-induced feature changes.
The Android operating system powers the majority of the world’s mobile devices and has been becoming increasingly important in day-to-day digital forensics. Therefore, technicians and analysts are in need of reliable methods for extracting and analyzing memory images from live Android systems. This paper takes different existing, extraction methods and derives a universal, reproducible, reliably documented method for both extraction and analysis. In addition the VOLIX II front-end for the Volatility Framework is extended with additional functionality to make the analysis of Android memory images easier for technically non-adept users.