This article presents an elementary change detection algorithm designed using a synchronous model of computation (MoC) aiming at efficient implementations on parallel architectures. The change detection method is based on a 2D-first-order autoregressive ([2D-AR(1)]) recursion that predicts one-lag changes over bitemporal signals, followed by a high-parallelized spatial filtering for neighborhood training, and an estimated quantile function to detect anomalies. The proposed method uses a model-based on the functional language paradigm and a well-defined MoC, potentially enabling energy and runtime optimizations with deterministic data parallelism over multicore, GPU, or FPGA architectures. Experimental results over the bitemporal CARABAS-II SAR UWB dataset are evaluated using the synchronous MoC implementation, achieving gains in detection and hardware performance compared to a closed-form and well-known complexity model over the generalized likelihood ratio test (GLRT). In addition, since the one-lag AR(1) is a Markov process, its extension for a Markov chain in multitemporal (n-lags) analysis is applicable, potentially improving the detection performance still subject to high-parallelized structures.
Distributed sensors based on phase-optical time-domain reflectometry (phase-OTDR) are suitable for aircraft health monitoring due to electromagnetic interference immunity, small dimensions, low weight and flexibility. These features allow the fiber embedment into aircraft structures in a nearly non-intrusive way to measure vibrations along its length. The capability of measuring vibrations on avionics structures is of interest for what concerns the study of material fatigue or the occurrence of undesirable phenomena like flutter. In this work, we employed the phase-OTDR technique to measure vibrations ranging from some dozens of Hz to kHz in two layers of composite material board with embedded polyimide coating 0.24 numerical aperture single-mode optical fiber.
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