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PublicationGenerative Machine Learning for Resource-Aware 5G and IoT SystemsExtrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system-allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible-e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.
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PublicationModular and scalable automation for field robots. Lighthouse project "cognitive agriculture"( 2020)Agricultural technology is under pressure due to unsolved farm successions, labour shortage and climate protection goals. Precision farming and smart farming are expected to have a high impact on sustainability on a long time scale. The Fraunhofer lighthouse project ""Cognitive Agriculture"" (COGNAC) is working on increasing the efficiency and sustainability of agricultural processes by developing a living digital ecosystem called Agriculture Data Space. A short introduction to parts of the ecosystem such as the ADS-enabling platform and the automated charging field robots are presented.
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PublicationSorting of black plastics using statistical pattern recognition on terahertz frequency domain data( 2016)The sorting of used plastics is an ever-growing market field which is further pushed by new EU regulations in, e.g., car recycling. Modern recycling techniques require pure or almost pure fractions of polymers. These pure fractions can be generated from waste using modern sorting technologies based on specific mechanical, electrical and chemical material properties such as density, conductivity and melting point. The thermal recycling of plastics is no longer seasonable. More modern recycling techniques require pure fractions containing only a single variety of polymer. A large portion of the plastic waste contains black or multilayer materials that are not sortable with todays' sorting technologies. To overcome this challenge, three Fraunhofer institutes are working together to develop a new type of sorting system. As a first step, we have developed a frequency domain line-scan camera working in the terahertz range with frequencies below 300 GHz. Since the entropy in terahertz signals below 300 GHz is not as high as needed for simple classification, more complex statistical pattern recognition methods are needed. The application of those methods to the problem of sorting black plastics as the second step in this joint project is presented in this paper. These methods have to be integrated into a real sorting system, which is the third part of our joint project. The modular approach gives the ability to integrate our sensors and algorithms into existing sorting systems.
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PublicationDynamic configuration of distributed systems for disaster management( 2015)In natural and man-made disasters, it is a necessity for rescue teams to get a quick overview of the situation in place. Robot-supported sensor networks are increasingly used to accelerate surveillance and search operations in complex situations. An appropriate robust system architecture has to support dynamical changes in connectivity as well as in number and type of robots and sensors in action. The proposed solution for a dynamic configuration of a distributed system with heterogeneous sensors and robots for disaster management is based on the Robot Operating System (ROS). The configuration uses an active Information Module with access to the descriptions of the characteristics and capabilities of all relevant system components. The modular descriptions are based on XML standard. Every component has at least one description file with capabilities of the component and their relevant technical characteristics. Descriptions of complex components containing sub-components are hierarchically with references to descriptions of sub parts. Between the system components direct communication links can be established to make the distributed system more robust. External systems may also get information about available capabilities from the Information Module and request needed services directly from the components. The main task of this work is to introduce a dynamic but robust system architecture for controling complex heterogeneous sensor systems to support rescue forces in disaster relive.
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PublicationSituation responsive networking of mobile robots for disaster management( 2014)If a natural disaster like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams have to get a quick overview of the situation in order to identify possible locations of victims, which need to be rescued, and dangerous locations, hich need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous enviroments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is the situation responsive networking of various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the objectives and first research results of the ongoing joint research project SENEKA.
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PublicationSENEKA - sensor network with mobile robots for disaster management( 2012)Developed societies have a high level of preparedness for natural or man-made disasters. But such incidents cannot be completely prevented, and when an incident like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams need to be employed. In such situations it is a necessity for rescue teams to get a quick overview of the situation in order to identify possible locations of victims that need to be rescued and dangerous locations that need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous environments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is to network the various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the goals and first research results of the ongoing joint research project SENEKA.
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PublicationCamInSens - demonstration of a distributed smart camera system for in-situ threat detection( 2012)The CamInSens system is a next-generation selforganizing video surveillance system that combines research being done in the fields of person-tracking, trajectory analysis, visual analytics, and self-organizing system management algorithms. Its purpose is the online threat detection by analysing anomalies in persons trajectories. Therefore, robust multicamera multi-person tracking is combined with a flexible analysis module, which uses online learning classification algorithms as well as user-generated filters to process the persons trajectories in the surveillance space.
