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Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos.
The production and assembly of customized products increases the demand for flexible automation systems. One approach is to remove the safety fences that separate human and industrial robot to combine their skills. This collaboration possesses a certain risk for the human co-worker, leading to numerous safety concepts to protect him. The human needs to be monitored and tracked by a safety system using different sensors. The proposed system consists of a RGBD camera for surveillance of the common working area, an array of optical distance sensors to compensate shadowing effects of the RGBD camera and a laser range finder to detect the co-worker when approaching the work cell. The software for collision detection, path planning, robot control and predicting the behaviour of the co-worker is based on the Robot Operating System (ROS). A first prototype of the work cell shows that with advanced algorithms from the field of mobile robotics a very flexible safety concept can be realized: the robot not simply stops its movement when detecting a collision, but plans and executes an alternative path around the obstacle.
When exploring glacier ice it is often necessary to take samples or implement sensors at a certain depth underneath the glacier surface. One way of doing this is by using heated melting probes. In their common form these devices experience a straight one-dimensional downwards motion and can be modeled by standard close-contact melting theory. A recently developed melting probe however, the IceMole, achieves maneuverability by simultaneously applying a surface temperature gradient to induce a change in melting direction and controlling the effective contact-force by means of an ice screw to stabilize its change in attitude. A modeling framework for forced curvilinear melting does not exist so far and will be the content of this paper. At first, we will extend the existing theory for quasi-stationary close-contact melting to curved trajectories. We do this by introducing a rotational mode. This additional unknown in the system implies yet the need for another model closure. Within this new framework we will focus on the effect of a variable contact-force as well as different surface temperature profiles. In order to solve for melting velocity and curvature of the melting path we present both an inverse solution strategy for the analytical model, and a more general finite element framework implemented into the open source software package ELMER. Model results are discussed and compared to experimental data conducted in laboratory tests.
Ground or aerial robots equipped with advanced sensing technologies, such as three-dimensional laser scanners and advanced mapping algorithms, are deemed useful as a supporting technology for first responders. A great deal of excellent research in the field exists, but practical applications at real disaster sites are scarce. Many projects concentrate on equipping robots with advanced capabilities, such as autonomous exploration or object manipulation. In spite of this, realistic application areas for such robots are limited to teleoperated reconnaissance or search. In this paper, we investigate how well state-of-the-art and off-the-shelf components and algorithms are suited for reconnaissance in current disaster-relief scenarios. The basic idea is to make use of some of the most common sensors and deploy some widely used algorithms in a disaster situation, and to evaluate how well the components work for these scenarios. We acquired the sensor data from two field experiments, one from a disaster-relief operation in a motorway tunnel, and one from a mapping experiment in a partly closed down motorway tunnel. Based on these data, which we make publicly available, we evaluate state-of-the-art and off-the-shelf mapping approaches. In our analysis, we integrate opinions and replies from first responders as well as from some algorithm developers on the usefulness of the data and the limitations of the deployed approaches, respectively. We discuss the lessons we learned during the two missions. These lessons are interesting for the community working in similar areas of urban search and rescue, particularly reconnaissance and search.