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Education is the Achilles heel of successful resuscitation in cardiac arrest. Therefore, we aim to contribute to the educational efficiency by providing a novel augmented-reality (AR) guided interactive cardiopulmonary resuscitation (CPR) "trainer". For this trainer, a mixed reality smart glass, Microsoft HoloLens, and a CPR manikin covered with pressure sensors were used. To introduce the CPR procedure to a learner, an application with an intractable virtual teacher model was designed. The teaching scenario consists of the two main parts, theory and practice. In the theoretical part, the virtual teacher provides all information about the CPR procedure. Afterward, the user will be asked to perform the CPR cycles in three different stages. In the first two stages, it is aimed to gain the muscle memory with audio and optical feedback system. In the end, the performance of the participant is evaluated by the virtual teacher.
We present a study comparing the effect of real-time wearable feedback with traditional training methods for cardiopulmonary resuscitation (CPR). The aim is to ensure that the students can deliver CPR with the right compression speed and depth. On the wearable side, we test two systems: one based on a combination of visual feedback and tactile information on a smart-watch and one based on visual feedback and audio information on a Google Glass. In a trial with 50 subjects (23 trainee nurses and 27 novices,) we compare those modalities to standard human teaching that is used in nurse training. While a single traditional teaching session tends to improve only the percentage of correct depth, it has less effect on the percentage of effective CPR (depth and speed correct at the same time). By contrast, in a training session with the wearable feedback device, the average percentage of time when CPR is effective improves by up to almost 25%.
The Power and Energy Student Summit (PESS) is designed for students, young professionals and PhD-students in the field of power engineering. PESS offers the possibility to gain first experience in presentation, publication and discussion with a renowned audience of specialists. Therefore, the conference is accompanied and supervised by established scientists and experts. The venue changes every year. In 2018, the University of Kaiserslautern held the eighth PESS conference. This document presents the submissions of this conference.
Autonomous driving is disrupting the conventional automotive development. In fact, autonomous driving kicks off the consolidation of control units, i.e. the transition from distributed Electronic Control Units (ECUs) to centralized domain controllers. Platforms like Audi’s zFAS demonstrate this very clearly, where GPUs, Custom SoCs, Microcontrollers, and FPGAs are integrated on a single domain controller in order to perform sensor fusion, processing and decision making on a single Printed Circuit Board (PCB). The communication between these heterogeneous components and the algorithms for Advanced Driving Assistant Systems (ADAS) itself requires a huge amount of memory bandwidth, which will bring the Memory Wall from High Performance Computing (HPC) and data-centers directly in our cars. In this paper we highlight the roles and issues of Dynamic Random Access Memories (DRAMs) for future autonomous driving architectures.
The authors explore the intrinsic trade-off in a DRAM between the power consumption (due to refresh) and the reliability. Their unique measurement platform allows tailoring to the design constraints depending on whether power consumption, performance or reliability has the highest design priority. Furthermore, the authors show how this measurement platform can be used for reverse engineering the internal structure of DRAMs and how this knowledge can be used to improve DRAM’s reliability.