The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

Virtual reality (VR) is regarded as a useful tool for teleoperation systems and provides operators with immersive visual feedback on the robot and the environment. However, without any haptic feedback or physical constructions, VR-based teleoperation systems normally suffer from poor maneuverability, and operational faults may be caused in some fine movements. In this article, we employ mixed reality (MR), which combines real and virtual worlds, to develop a novel teleoperation system. A new system design and control algorithms are proposed. For the system design, an MR interface is developed based on a virtual environment augmented with real-time data from the task space with the goal of enhancing the operator’s visual perception. To allow the operator to be freely decoupled from the control loop and offload the operator’s burden, a new interaction proxy is proposed to control the robot. For the control algorithms, two control modes are introduced to improve the long-distance movements and fine movements of the MR-based teleoperation system. In addition, a set of fuzzy-logic-based methods are proposed to regulate the orientation, position, velocity, and force of the robot to enhance the system’s maneuverability and address potential operational faults. A barrier Lyapunov function and a backstepping method are leveraged to design the control laws and simultaneously guarantee the system’s stability under state constraints. Experiments conducted using a six-degree-of-freedom robotic arm prove the feasibility of the system.

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

Probiotics are suggested to impact physiological and psychological stress responses by acting on the gut-brain axis. We investigated if a probiotic product containing Bifidobacterium longum R0175, Lactobacillus helveticus R0052 and Lactiplantibacillus plantarum R1012 affected stress processing in a double-blinded, randomised, placebo-controlled, crossover proof-of-concept study (NCT03615651). Twenty-two healthy subjects (24.2 ± 3.4 years, 6 men/16 women) underwent a probiotic and placebo intervention for 4 weeks each, separated by a 4-week washout period. Subjects were examined by functional magnetic resonance imaging while performing the Montreal Imaging Stress Task (MIST) as well as an autonomic nervous system function assessment during the Stroop task. Reduced activation in regions of the lateral orbital and ventral cingulate gyri was observed after probiotic intervention compared to placebo. Significantly increased functional connectivity was found between the upper limbic region and medioventral area. Interestingly, probiotic intervention seemed to predominantly affect the initial stress response. Salivary cortisol secretion during the task was not altered. Probiotic intervention did not affect cognitive performance and autonomic nervous system function during Stroop. The probiotic intervention was able to subtly alter brain activity and functional connectivity in regions known to regulate emotion and stress responses. These findings support the potential of probiotics as a non-pharmaceutical treatment modality for stress-related disorders.

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.