The automation of the agricultural sector is continuously evolving. Especially communication plays a key role for the future agriculture 4.0 transformation. The coordination of agricultural machinery and the increasing usage of robots demands reliable communication. However, in rural areas a sufficient coverage by mobile networks is often not guaranteed. This paper addresses this issue by development of a portable standalone 5G nomadic network with satellite connectivity, which is optimized for agricultural use cases. The approach includes the use of Real Time Kinematic (RTK) for exact positioning, internet backhauling, and a concept for automatic frequency allocation. The proposed nomadic network architecture has been built and tested in a real-world scenario on an agricultural yard in Germany. It was shown that such a network can empower the agricultural transformation in different ways.

Data glasses do carry promising potential for hands-free interaction, but also raise various concerns amongst their potential users. In order to gain insights into the nature of those concerns, we investigate how potential usage scenarios are perceived by device users and their peers. We present results of a two-step approach: a focus group discussion with 7 participants, and a user study with 38 participants. In particular, we look into differences between the usage of data glasses and more established devices such as smart phones. We provide quantitative measures for scenario-related social acceptability and point out factors that can influence user attitudes. Based on our quantitative and qualitative results, we derive design implications that might support the development of head-worn devices and applications with an improved social acceptability.

MOTIVATION: Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging. RESULTS: We present an improved version of EVpedia, a public database for EVs research. This community web portal contains a database of publications and vesicular components, identification of orthologous vesicular components, bioinformatic tools and a personalized function. EVpedia includes 6879 publications, 172 080 vesicular components from 263 high-throughput datasets, and has been accessed more than 65 000 times from more than 750 cities. In addition, about 350 members from 73 international research groups have participated in developing EVpedia. This free web-based database might serve as a useful resource to stimulate the emerging field of EV research. AVAILABILITY AND IMPLEMENTATION: The web site was implemented in PHP, Java, MySQL and Apache, and is freely available at http://evpedia.info.

Mobile location recognition by capturing images of the environment (visual localization) is a promising technique for indoor navigation in arbitrary surroundings. However, it has barely been investigated so far how the user interface (UI) can cope with the challenges of the vision-based localization technique, such as varying quality of the query images. We implemented a novel UI for visual localization, consisting of Virtual Reality (VR) and Augmented Reality (AR) views that actively communicate and ensure localization accuracy. If necessary, the system encourages the user to point the smartphone at distinctive regions to improve localization quality. We evaluated the UI in a experimental navigation task with a prototype, informed by initial evaluation results using design mockups. We found that VR can contribute to efficient and effective indoor navigation even at unreliable location and orientation accuracy. We discuss identified challenges and share lessons learned as recommendations for future work.

In this paper, we review the use of gameful design in the automotive domain. Outside of vehicles the automotive industry is mainly using gameful design for marketing and brand forming. For in-vehicle applications and for applications directly connected to real vehicles, the main usage scenarios of gameful design are navigation, eco-driving and driving safety. The objective of this review is to answer the following questions: (1) What elements of gameful design are currently used in the automotive industry? (2) What other automotive applications could be realized or enhanced by applying gameful design? (3) What are the challenges and limitations of gameful design in this domain especially for in-vehicle applications? The review concludes that the use of gameful design for in-vehicle applications seems to be promising. However, gamified applications related to the serious task of driving require thought-out rules and extensive testing in order to achieve the desired goal.

Self-reporting techniques, such as data logging or a diary, are frequently used in long-term studies, but prone to subjects' forgetfulness and other sources of inaccuracy. We conducted a six-week self-reporting study on smartphone usage in order to investigate the accuracy of self-reported information, and used logged data as ground truth to compare the subjects' reports against. Subjects never recorded more than 70% and, depending on the requested reporting interval, down to less than 40% of actual app usages. They significantly overestimated how long they used apps. While subjects forgot self-reports when no automatic reminders were sent, a high reporting frequency was perceived as uncomfortable and burdensome. Most significantly, self-reporting even changed the actual app usage of users and hence can lead to deceptive measures if a study relies on no other data sources.

State-of-the-art visual odometry algorithms achieve remarkable efficiency and accuracy. Under realistic conditions, however, tracking failures are inevitable and to continue tracking, a recovery strategy is required. In this paper, we propose a relocalization system that enables realtime, 6D pose recovery for wide baselines. Our approach targets specifically resource-constrained hardware such as mobile phones. By exploiting the properties of low-complexity binary feature descriptors, nearest-neighbor search is performed efficiently using Locality Sensitive Hashing. Our method does not require time-consuming offline training of hash tables and it can be applied to any visual odometry system. We provide a thorough evaluation of effectiveness, robustness and runtime on an indoor test sequence with available ground truth poses. We investigate the system parameterization and compare the relocalization performance for the three binary descriptors BRIEF, unscaled BRIEF and ORB. In contrast to previous work on mobile visual odometry, we are able to quickly recover from tracking failures within maps with thousands of 3D feature points.

A Multi-View Video (MVV) is a set of related videos that capture an interesting scene from different perspectives at overlapping times. The work at hand is concerned with the design of innovative user interfaces (UI) for viewing MVVs. As a first contribution four different MVV UIs are designed. While different in design, their common aim is to allow a pleasant viewing and perspective switching experience by reducing the cognitive effort associated with constructing a mental map of the scene. This is achieved by incorporating the spatial relationships of the available views in the UI elements. As a second contribution a quality model is developed and a methodical evaluation process is designed. This is used to evaluate and compare the UIs. In a third contribution we use principal component analysis (PCA) to reveal information about the perceptual quality space which helps validating our proposed quality model. Based on the findings, a series of conclusions for best design practices are provided.

The ongoing evolution of mobile phones to "pocket computers" generated a demand for more and more applications to be ported to the mobile phone. Because a full security assessment for a whole mobile operating system would be prohibitively costly, currently security critical applications can not be implemented. We address this challenge by introducing security zones to enable applications with high security demands like driving licenses, health insurance cards, or passports on mobile phones. This zone concept creates the need for visualization of the current zone and a way to switch between zones. In this paper we discuss several possible ways of achieving this.

User authentication on publicly exposed terminals with established mechanisms, such as typing the credentials on a virtual keyboard, can be insecure e.g. due to shoulder surfing or due to a hacked terminal. In addition, username and password entry can be time-consuming and thus improvable with relation to usability. As security and comfort are often competing with each other, novel authentication and authorization methods especially for public terminals are desirable. In this paper, we present an approach on a distributed authentication and authorization system, where the user can be easily identified and enabled to use a service with his smartphone. The smartphone (as personal and private device the user is always in control of) can provide a highly secure authentication token that is renewed and exchanged in the background without the user's participation. The claimed improvements were supported by a user survey with an implementation of a digital room management system as an example for a public display. The proposed authentication procedure would increase security and yet enable fast authentication within publicly exposed terminals.

We present a visual odometry system for indoor navigation with a focus on long-term robustness and consistency. As our work is targeting mobile phones, we employ monocular SLAM to jointly estimate a local map and the device's trajectory. We specifically address the problem of estimating the scale factor of both, the map and the trajectory. State-of-the-art solutions approach this problem with an Extended Kalman Filter (EKF), which estimates the scale by fusing inertial and visual data, but strongly relies on good initialization and takes time to converge. Each visual tracking failure introduces a new arbitrary scale factor, forcing the filter to re-converge. We propose a fast and robust method for scale initialization that exploits basic geometric properties of the learned local map. Using random projections, we efficiently compute geometric properties from the feature point cloud produced by the visual SLAM system. From these properties (e.g., corridor width or height) we estimate scale changes caused by tracking failures and update the EKF accordingly. As a result, previously achieved convergence is preserved despite re-initializations of the map. To minimize the time required to continue tracking after failure, we perform recovery and re-initialization in parallel. This increases the time available for recovery and hence the likelihood for success, thus allowing almost seamless tracking. Moreover, fewer re-initializations are necessary. We evaluate our approach using extensive and diverse indoor datasets. Results demonstrate that errors and convergence times for scale estimation are considerably reduced, thus ensuring consistent and accurate scale estimation. This enables long-term odometry despite of tracking failures which are inevitable in realistic scenarios.

Vision-based approaches for mobile indoor localization do not rely on the infrastructure and are therefore scalable and cheap. The particular requirements to a navigation user interface for a vision-based system, however, have not been investigated so far.

We present GymSkill, a personal trainer for ubiquitous monitoring and assessment of physical activity using standard fitness equipment. The system records and analyzes exercises using the sensors of a personal smartphone attached to the gym equipment. Novel fine-grained activity recognition techniques based on pyramidal Principal Component Breakdown Analysis (PCBA) provide a quantitative analysis of the quality of human movements. In addition to overall quality judgments, GymSkill identifies interesting portions of the recorded sensor data and provides suggestions for improving the individual performance, thereby extending existing work. The system was evaluated in a case study where 6 participants performed a variety of exercises on balance boards. GymSkill successfully assessed the quality of the exercises, in agreement with the professional judgment provided by a physician. User feedback suggests that GymSkill has the potential to serve as an effective tool for motivating and supporting lay people to overcome sedentary, unhealthy lifestyles. GymSkill is available in the Android Market as `VMI Fit'.

We introduce and define Cognitive Objects for human-robot interaction and human-computer interaction and disambiguate them against existing ‘Smart Objects’. Cognitive Objects are physical real-world objects used in manipulation tasks by humans and robots. As such, they incorporate self-awareness, reduce ambiguity and uncertainty in object recognition, and provide services to both humans and robots during their usage in real-world environments. We distinguish Cognitive Objects from other ‘Smart Objects’ and computationally enriched artifacts, outline their characteristics, and describe their potential impact on human-computer and human-robot interaction with real-world objects.

Advances in the fields of distributed computing, wireless sensor networks, ubiquitous computing and many more have facilitated the development of a plenitude of Intelligent Environments (IE). The development processes, underlying tools and methods have however not yet been in the focus of researchers. A significant amount of time has to be spent for building a novel ecology of distributed systems to form an Intelligent Environment to conduct novel research in. A sophisticated middleware can shift the focus from building to research. Yet, a middleware is only one of the many requirements for leveraging the development process of Intelligent Environments. In this paper, we present a complete tool chain to simplify research on Intelligent Environments and to ease their creation. This tool chain consists out of middleware, simulation, visualization and prototyping tools. We start by modeling the physical environment using a CAD software and visualization tool including physical properties. The tool chain is complemented by a middleware and a sophisticated simulation environment allowing e.g. to simulate mobile phone interactions triggering, and the deployment to the real-world events. For developers and researchers of Intelligent Environments, creation and maintenance of Intelligent Environments is significantly improved. Development is speeded up as algorithms, interaction can be already evaluated and tested in simulation before the deployment to the real world.

"There's An App For That" -- but how do we actually develop them? While smartphones and tablet PCs are getting more and more popular and their application scenarios are growing, we still develop them using only a standard integrated development environment. Although context-based services and apps do, next to network connectivity, require lots of sensor data, the tools for providing realistic sensor data during development are still immature.