This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-04-24 Creation time: 17-17-27 --- Number of references 6 inproceedings 2016-henze-cloudcom-trinics 2016 12 12 366-370 trinics https://www.comsys.rwth-aachen.de/fileadmin/papers/2016/2016-henze-cloudcom-trinics.pdf Online IEEE Proceedings of the 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Luxembourg, Luxembourg en 978-1-5090-1445-3 10.1109/CloudCom.2016.0064 1 MartinHenze DanielKerpen JensHiller MichaelEggert DavidHellmanns ErikMühmer OussamaRenuli HenningMaier ChristianStüble RogerHäußling KlausWehrle inproceedings 2016-ahmed-sensys-poster-incremental 2016 11 14 1--2 We propose incremental checkpointing techniques enabling transiently powered devices to retain computational state across multiple activation cycles. As opposed to the existing approaches, which checkpoint complete program state, the proposed techniques keep track of modified RAM locations to incrementally update the retained state in secondary memory, significantly reducing checkpointing overhead both in terms of time and energy. /fileadmin/misc/2016/2016-ahmed-sensys-poster-incremental.pdf http://dl.acm.org/citation.cfm?id=2996701 2016-11-20 http://sensys.acm.org/2016/ Online ACM Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems (SenSys 2016), Stanford, CA, USA Stanford, CA, USA Sensys '16 November 14-16, 2016 en 978-1-4503-4263-6/16/11 http://dx.doi.org/10.1145/2994551.2996701 1 SaadAhmed HassanKhan Junaid HaroonSiddiqui Jó AgilaBitsch Link Muhammad HamadAlizai inproceedings DombrowskiSRDS16 2016 9 27 307--316 fault tolerance;formal verification;protocols;wireless channels;EchoRing protocol;fault-tolerant methods;formal model-based verification;model-checking assisted protocol;probabilistic model checking;reliability constraints;safety-critical industrial applications;salient features;token loss;token-based system;ultrareliable low-latency wireless networks;unprecedented latency;wireless networking community;wireless protocols;wireless token-passing systems;Automata;Model checking;Payloads;Probabilistic logic;Protocols;Reliability;Wireless communication;Model checking;Probabilistic timed automata;Token passing;Wireless Industrial Networks;tool-assisted protocol design;validation cps,hodrian http://ieeexplore.ieee.org/document/7794360/ Proc. of IEEE 35th Symposium on Reliable Distributed Systems IEEE Budapest, Hungary IEEE 35th Symposium on Reliable Distributed Systems (SRDS) 10.1109/SRDS.2016.048 1 ChristianDombrowski SebastianJunges Joost-PieterKatoen JamesGross inproceedings 2016-ackermann-healthcom-eeg-emotion 2016 9 14 159--164 Automatic emotion recognition is an interdisciplinary research field which deals with the algorithmic detection of human affect, e.g. anger or sadness, from a variety of sources, such as speech or facial gestures. Apart from the obvious usage for industry applications in human-robot interaction, acquiring the emotional state of a person automatically also is of great potential for the health domain, especially in psychology and psychiatry. Here, evaluation of human emotion is often done using oral feedback or questionnaires during doctor-patient sessions. However, this can be perceived as intrusive by the patient. Furthermore, the evaluation can only be done in a non-continuous manner, e.g. once a week during therapy sessions. In contrast, using automatic emotion detection, the affect state of a person can be evaluated in a continuous non-intrusive manner, for example to detect early on-sets of depression. An additional benefit of automatic emotion recognition is the objectivity of such an approach, which is not influenced by the perception of the patient and the doctor. To reach the goal of objectivity, it is important, that the source of the emotion is not easily manipulable, e.g. as in the speech modality. To circumvent this caveat, novel approaches in emotion detection research the potential of using physiological measures, such as galvanic skin sensors or pulse meters. In this paper we outline a way of detecting emotion from brain waves, i.e., EEG data. While EEG allows for a continuous, real-time automatic emotion recognition, it furthermore has the charm of measuring the affect close to the point of emergence: the brain. Using EEG data for emotion detection is nevertheless a challenging task: Which features, EEG channel locations and frequency bands are best suited for is an issue of ongoing research. In this paper we evaluate the use of state of the art feature extraction, feature selection and classification algorithms for EEG emotion classification using data from the de facto standard dataset, DEAP. Moreover, we present results that help choose methods to enhance classification performance while simultaneously reducing computational complexity. /fileadmin/papers/2016/2016-ackermann-healthcom-emorec.pdf http://ieeehealthcom2016.com/ Online IEEE 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) Munich, Germany 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) September 14-17, 2016 en 978-1-5090-3370-6 1 PascalAckermann ChristianKohlschein Jó AgilaBitsch Link KlausWehrle SabinaJeschke article 2016-fgcs-henze-iotprivacy Future Generation Computer Systems 2016 3 56 701-718 ipacs https://www.comsys.rwth-aachen.de/fileadmin/papers/2016/2016-henze-fgcs-iotprivacy.pdf Online Elsevier en 0167-739X 10.1016/j.future.2015.09.016 1 MartinHenze LarsHermerschmidt DanielKerpen RogerHäußling BernhardRumpe KlausWehrle article 2016-kunz-tomacs-horizon ACM Transactions on Modeling and Computer Simulation (TOMACS) 2016 1 26 2 12:1--12:26 The technical evolution of wireless communication technology and the need for accurately modeling these increasingly complex systems causes a steady growth in the complexity of simulation models. At the same time, multi-core systems have become the de facto standard hardware platform. Unfortunately, wireless systems pose a particular challenge for parallel execution due to a tight coupling of network entities in space and time. Moreover, model developers are often domain experts with no in-depth understanding of parallel and distributed simulation. In combination, both aspects severely limit the performance and the efficiency of existing parallelization techniques. We address these challenges by presenting parallel expanded event simulation, a novel modeling paradigm that extends discrete events with durations which span a period in simulated time. The resulting expanded events form the basis for a conservative synchronization scheme that considers overlapping expanded events eligible for parallel processing. We furthermore put these concepts into practice by implementing Horizon, a parallel expanded event simulation framework specifically tailored to the characteristics of multi-core systems. Our evaluation shows that Horizon achieves considerable speedups in synthetic as well as real-world simulation models and considerably outperforms the current state-of-the-art in distributed simulation. Parallel discrete event simulation, Multi-core Systems, Wireless Systems, Simulation Modeling Paradigm, Conservative Synchronization horizon ACM en 10.1145/2832909 1 GeorgKunz MirkoStoffers OlafLandsiedel KlausWehrle JamesGross