The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examining the data they provide. This is the case of MEMS (Micro Electro-Mechanical Systems) components like accelerometers and gyroscopes, where tiny differences and imprecisions in the manufacturing process determine unique patterns in the data output. In this paper, we present the experimental evaluation of the identification of smartphones through their built-in MEMS components. In our study, three different phones of the same model are subject to repeatable movements (composing a repeatable scenario) using an high precision robotic arm. The measurements from MEMS for each repeatable scenario are collected and analyzed. The identification algorithm is based on the extraction of the statistical features of the collected data for each scenario. The features are used in a support vector machine (SVM) classifier to identify the smartphone. The results of the evaluation are presented for different combinations of features and Inertial Measurement Unit (IMU) outputs, which show that detection accuracy of higher than 90% is achievable.

The berthing of an ultra large ship is always a difficult issue and becomes yet more complex when vessels must be handled in restricted manoeuvring areas of limited depth, exposed to a forceful crosswind, or manoeuvring in a strong current, or all three. The final approaching manoeuvre and precise positioning is particularly demanding at container terminals where many STS cranes are located along the quay, seriously limiting margin for error in the process of mooring a ship, especially when the cranes are located nearby a bridge wing or at the very edge of the pier. In order to avoid collisions, the final manoeuvre (side-push) must be fully controlled; the ship’s orientation must be parallel with the quay while maintaining the minimum lateral approaching velocity without significantly shifting the vessel longitudinally. The mooring of a Ro-Ro vessel is occasionally even more challenging: a precise docking manoeuvre is normally executed without any towing assistance. In this paper low cost laser-based berthing and docking systems developed for the ports of Koper and Swinousce are presented and several berthing manoeuvres are analysed and compared with the most commonly used GNSS-based navigational aid system portable pilot units (PPU).

This paper presents experimental results obtained during live GNSS jamming tests performed in a road environment. Different jamming detection strategies are considered with specific focus on signal agnostic approaches where no stringent model assumptions are made for the detector design. A measurement station made of a commercial GPS high-sensitivity receiver and an SDR front-end was deployed and used to collect measurements in the presence of jamming. The tests were performed using three different jammers and considering the effects introduced by a vehicle moving at different speeds. The advantages and shortcomings of the different detection approaches are analyzed, and the most effective detection strategies are selected. Copyright © 2017 Institute of Navigation

This paper is intended to give an overview of the experiments to evaluate the cognitive load of the officer on watch (OOW) during a collision avoidance maneuver in a full-mission simulator. The main goal is to investigate the possibilities of recording the biometric parameters of an OOW during a simulated collision avoidance maneuver. Potentially dangerous navigation errors known as human erroneous action (HEA) are induced by excessive cognitive load. Despite modern navigational aids on the ship’s bridge, investigators of maritime incidents typically link the reason for incidents at sea with human factors, including high cognitive load. During the experimental tasks on the bridge, the biometric parameters of the OOW are recorded. Statistical tools are used to visualize the data and evaluate the cognitive load of the OOW. Biometric peaks of the OOW typically occur either during the collision avoidance maneuver or when the OOW has been exposed to disturbing factors that increase reaction time and cause potentially dangerous navigation. Assessing the cognitive load of OOWs in the simulator is challenging for several reasons: e.g., the environmental conditions of the simulator, the type of task to be simulated, and even the type of sensor used. After careful study of the available literature, an original experimental design using non-invasive biometric sensors is proposed.