Spaceborne observations serve as input to models and retrieval algorithms of various atmospheric properties. Specifically, we seek 3D volumetric scattering tomography of clouds. Towards this, cloud-fields are to be imaged simultaneously from multiple directions. The CloudCT project aims to demonstrate this by a formation of ten nanosatellites. Based on this data, scattering tomography will seek the 3D volumetric distribution of cloud microphysical properties. We present constraints and fundamental considerations for spaceborne formation-based cloud tomography. We quantitatively compare visible polarized imagers, visible unpolarized imagers, and short-wave infra-red unpolarized imagers. Each possibility is considered using a large eddy simulation of clouds. We study tomographic quality in the presence of sensor and photon noise, calibration errors and stray light. We find that a polarized imager of a red waveband is preferable.

Nowadays, people are getting more and more concern on their own fitness conditions and it has become a digital healthy lifestyle movement. A basic activity tracker has given many data on the user's movement to fulfill their daily goal of calories burned. An advance tracker can also measure heart rate with accurate activity intense level. However, consider if these fitness devices can be used for clinical diagnosis by adding an ECG, then it can be a daily health monitoring or a health assisted device by connecting it to the internet via a smartphone. To use an ECG as a wearable device, the electrode positions has to fulfill the clinical placement. A new biomedical electrodes placement is proposed in this paper to meet the practicality of a fitness lifestyle device but has a medical ECG result for continuous heart monitoring in a form of a necklace. The device has a single lead ECG analog front end that is connected to an ARM-Cortex M4 microcontroller. It uses a 4 GB memory card, rechargeable battery, and a Bluetooth Low Energy 4.0 to communicate with an Android 4.3 smartphone. The test results were taken from a 32-year-old male subject with normal heart condition. The signal acquired from the electrode placement at the backside of the neck shows Lead I waveform with 10% from the normal position amplitude value. The R-wave of every heartbeat can be seen for heart rate calculation. Therefore, it is able to do a daily heart monitoring with a lifestyle device.

Since the advent of thermal imaging, devices with a high optical resolution that use detector arrays to capture the emitted radiance in the thermal infrared range of an entire scene simultaneously have developed as a standard in monitoring energy related aspects. They have had a huge impact on the building industry and in manufacturing, where they are commonly used to monitor proecces that require stable temperature conditions. As beneficial as contactless measurements are, the subsequent localization of points of interest in the environment is often difficult. To overcome this problem we propose a portable system that combines thermal imaging with Augmented Reality (AR). The idea of the approach is to project the gathered temperature information back into the scene to facilitate visual inspection.

Remote maintenance of industrial manipulators often is performed via telephone support. Recent approaches in the context of the 'Industry 4.0' consider internet technologies and Augmented Reality (AR) to enhance situation awareness between external experts and local service technicians. We present two AR-based case studies: First, a mobile AR architecture based on optical see through glasses is used for an on-site local repair task. Second, a remote architecture based on a portable tablet PC and a high precision tracking system is used to realize an off-site expert access. The to-be-serviced machine is visualized inside of a large area similar to a machinery hall and can be inspected by the experts walking around this virtual plant using the tablet and perspectively correct rendering to understand the production process and the operation context. Both methods have been evaluated in first user studies.

Data centers are increasingly becoming larger and dynamic due to virtualization. In order to leverage the performance modeling and prediction techniques, such as Palladio Component Model or Descartes Modeling Language, in such a dynamic environments, it is necessary to automate the model extraction. Building and maintaining such models manually is not feasible anymore due to their size and the level of details. This paper is focused on traffic models that are an essential part of network infrastructure. Our goal is to decompose real traffic dumps into models suitable for performance prediction using Descartes Network Infrastructure modeling approach. The main challenge was to efficiently encode an arbitrary signal in the form of simple traffic generators while maintaining the shape of the original signal. We show that a typical 15 minute long tcpdump trace can be compressed to 0.4-15% of its original size whereas the relative median of extraction error is close to 0% for the most of the 69 examined traces.

Moving tumors, especially in the vicinity of lungs, pose a challenging problem in radiotherapy as healthy tissue should not be irradiated. We developed a system to compensate tumor motion with the robotic treatment couch HexaPOD to improve treatment quality. The HexaPOD, carrying the patient, counteracts the tumor motion so that it is eliminated in the beams-eye-view of the linear accelerator. The focus of this work is on two control methods for the HexaPOD in order to realize reference tracking. The first method is a simple position control scheme to enable reference tracking. It is reformulated as second method to be adopted by a model predictive controller to better account for patient comfort and to maintain tracking accuracy. The performance of both methods is compared in experiments with real hardware using prerecorded patient trajectories and human volunteers whose breathing motion was compensated.

This paper presents a route scheduling algorithm for efficient snow shoveling of big international airports. The keynote of the project consists in using temporary formations of autonomous snowploughs. The size of the formations depends on the width of runway to shovel, which supposes splitting and merging of the teams during the cleaning process.The route scheduling method itself is based on the design of cleaning coalitions which are adapted for each specific task. The decisions about the optimal team composition and the optimal sequence of the tasks are taken by an heuristic approach that is combined with an exploration of the space of solutions on a limited horizon. Furthermore in the paper a formation driving approach is discussed. The method commands the robots to form a formation which optimally covers the roads surface and enables to push the snow by their shovels to the sides effectively. The scheduling method as well as the formation driving approach were verified by simulations and by hardware experiments.

An efficient snow cleaning system is essential for optimal airport traffic during the winter term. In this paper an autonomous approach based on formation driving of cooperative mobile vehicles is described. The method is composed of two independent elements. The route scheduling part designs a time optimal plan for all robots and the formation driving method on the other hand guides the ploughs in an appropriate way during the shoveling. The bridge connecting both modules is a path generator, which creates a feasible trajectory for the reference points of the formations. Optimality for the covering task is achieved by splitting and reuniting the robot groups depending on the varying size of the runways. The robustness of the algorithm is increased by the ability to adjust the formations shape reactively on obstacle detection. The proposed method was verified by simulations in the environment of the International Frankfurt Airport as well as by hardware experiments in a test field.

Future applications of mobile robot teams or robot teleoperation require highly dynamic network topologies. One promising approach is the use of relay nodes in wireless ad-hoc networks which require special routing protocols to provide a transparent communication network to the user. This work tests and compares four different existing ad-hoc routing protocol implementations with respect to aspects of mobile robot teleoperation. The reactive routing protocols ad-hoc on-demand distance vector (AODV) and dynamic source routing (DSR), the proactive optimized link state routing (OLSR) and B.A.T.M.A.N. are used in test scenarios to command a mobile robot via an ad-hoc network of several communication relay nodes. For all four ad-hoc routing protocols, the route reestablishing behavior is observed. In particular the packet loss and the duration of route reestablishing during test runs with real hardware in an outdoor environment are analyzed.

Humans and robots can combine their complementary capabilities in joint teams. This requires appropriate support systems and related user interfaces. This paper presents an approach, how to organize and present information to support a human supervisor in coordinating a small group of mobile robots or a human-robot team. The user interface enables the human to keep track about the current status of the different entities, the overall mission and all important information already gathered at the current stage. The described experiment evaluates the presented approach and different information presentation methods (messages vs. autonomous updates) are compared.

This paper is concerned with modeling and prediction of lung tumor motion for a new adaptive tumor tracking system in radiotherapy. Information of the tumor motion is used to control a robotic treatment couch, which moves in six degrees of freedom and maintains the tumor in a certain fixed spatial position. With this new technology it is possible to avoid gating during the radiotherapy as well as active breathing control, such that duration and cost of the therapy as well as trauma of the patient can be reduced. The tumor position is detected by an electronic portal imaging device (EPID) such that internal markers for the tumor tracking are no longer necessary. An infrared (IR) camera is introduced to detect abdominal motion, which provides a reference to the respiratory signal and can be used to predict the tumor position over a complete breathing period. This paper presents the effort of tumor motion modeling and prediction. Main challenges with the proposed method in this context are: lack of breathing control causes irregular respiration, low data rate of the EPID device delivers too few information about the tumor position. Two methods, namely an adaptive filtering and a nonlinear method based on Takens Theorem, have been investigated and compared with respect to diverse criteria including prediction accuracy, model complexity and computational effort. Performance analysis with clinical data has shown that both methods can well describe correlation between the EPID and the IR signals, while the adaptive filter possesses advantage with respect of computational effort.