This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-04-24 Creation time: 10-23-10 --- Number of references 8 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 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 2015-cheng-piap-jmu JMIR Mhealth Uhealth 2016 7 20 4 3 e88 piap http://mhealth.jmir.org/2016/3/e88/ http://www.ncbi.nlm.nih.gov/pubmed/27439444 Online en 2291-5222 10.2196/mhealth.5284 1 Paula Glenda FerrerCheng Roann MunozRamos Jó AgilaBitsch Link Stephan MichaelJonas TimIx Portia Lynn QuetulioSee KlausWehrle inproceedings 2016-zimmermann-remp 2016 5 IEEE Proceedings of the IEEE International Conference on Communications (ICC 2016), Kuala Lumpur, Malaysia Kuala Lumpur, Malaysia ICC 2016 23.-27.5.2016 978-1-4799-6664-6 1938-1883 10.1109/ICC.2016.7510787 1 AlexanderFrömmgen TobiasErbshäuser TorstenZimmermann KlausWehrle AlejandroBuchmann inproceedings 2016-ramos-inpact-lexicon 2016 4 30 1--6 29% Acceptance rate /fileadmin/misc/2016/2016-ramos-inpact-lexicon.pdf http://inpact-psychologyconference.org/2016/conference-program/ Online Clara Pracana and Michael Wang World Institute for Advanced Research and Science

Lisbon, Portugal

Proceedings of the International Psychological Applications Conference and Trends (InPACT 2016) Lisbon, Portugal International Psychological Applications Conference and Trends (InPACT 2016) April 30 -- May 2, 2016 en 978-989-99389-6-0 1 Roann MunozRamos Paula Glenda FerrerCheng Jó AgilaBitsch Link Stephan MichaelJonas inproceedings 2016-ramos-inpact-feeling-meh 2016 4 30 1--4 29% Acceptance rate /fileadmin/misc/2016/2016-ramos-inpact-feeling-meh.pdf http://inpact-psychologyconference.org/2016/conference-program/ Online Clara Pracana and Michael Wang World Institute for Advanced Research and Science

Lisbon, Portugal

Proceedings of the International Psychological Applications Conference and Trends (InPACT 2016) Lisbon, Portugal International Psychological Applications Conference and Trends (InPACT 2016) April 30 -- May 2, 2016 en 978-989-99389-6-0 1 Roann MunozRamos Paula Glenda FerrerCheng Jó AgilaBitsch Link Stephan MichaelJonas article 2015-Jakobs-PIK PIK – Praxis der Informationsverarbeitung und Kommunikation 2016 38 4 129-133 DE 1865-8342 HelenBolke-Hermanns KaiJakobs article 2016-sdnflex_si Wiley Journal of Network Management 2016 26 1 http://onlinelibrary.wiley.com/doi/10.1002/nem.1915/pdf OliverHohlfeld ThomasZinner TheophilusBenson DavidHausheer