Optical fiber links are customarily monitored by Optical Time Domain Reflectometer (OTDR), an optoelectronic instrument that measures the scattered or reflected light along the fiber and returns a signal, namely the OTDR trace. OTDR traces are typically analyzed by experts in laboratories or by hand-crafted algorithms running in embedded systems to localize critical events occurring along the fiber. In this work, we address the problem of automatically detecting optical events in OTDR traces through a deep learning model that can be deployed in embedded systems. In particular, we take inspiration from Faster R-CNN and present the first 1D object-detection neural network for OTDR traces. Thanks to an ad-hoc preprocessing pipeline for OTDR traces, we can also identify unknown events, namely events that are not represented in training data but that might indicate rare and unforeseen situations that need to be reported. The resulting network brings several advantages with respect to existing solutions, as these typically classify fixed-size windows of OTDR traces, thus are less accurate in the localization. Moreover, existing solutions do not report events that cannot be safely associated to any label in the training set. Our experiments, performed on real OTDR traces, show very promising performance, and can be directly executed on embedded OTDR devices.

In this paper we consider the restoration of images corrupted by both uniform motion blur and Poissonian noise. We formulate an image formation model that explicitly takes into account the length of the blur point-spread function and the noise level as functions of the exposure time. Further, we present an analysis of the achievable restoration performance by showing how the root mean squared error varies with respect to the exposure time. It turns out that the worst situations are represented by either too short or too long exposure times. In between there exists an optimal exposure time that maximizes the restoration performance, balancing the amount of blur and noise in the observation. We justify such result through a mathematical analysis of the signal-to-noise ratio in Fourier domain; this study is then validated by deblurring synthetic data as well as camera raw data.

Accurate and computationally light algorithms for estimating the State of Charge (SoC) of a batterys cells are crucial for effective battery management on embedded systems. In this letter, we propose an Adaptive Extended Kalman Filter (AEKF) for SoC estimation using a covariance adaptation technique based on maximum likelihood estimation -a novelty in this domain. Furthermore, we tune a key design parameter -the estimation window size -to obtain an optimal memory-performance trade-off, and experimentally demonstrate our solution achieves superior estimation accuracy with respect to existing alternative methods. Finally, we present a fully custom implementation of the AEKF for a general-purpose low-cost STM32 microcontroller, showing it can be deployed with minimal computational requirements adequate for real-world usage.

We present a novel calibration method for off-axis catadioptric cameras, i.e. standard perspective cameras placed in a generic position w.r.t. an axial-symmetric mirror of unknown shape. The proposed method estimates the intrinsic parameters of the natural perspective camera, the 3D shape of the mirror and its pose w.r.t. the camera. The peculiarity of our approach is that, unlike several other calibration methods, we do not require any cross section of the mirror to be visible in the image. Instead, we require that the catadioptric image contains at least the image of one generic space line. We then derive some constraints that, combined with the harmonic homology relating the apparent contours of the mirror, allow us to calibrate the off-axis camera. We provide experimental results both on synthetic and camera images that prove the validity of the technique.