Student Ensemble: Wind Symphony

Center for the Performing ArtsSunday AfternoonSeptember 13, 20153:00 p.m

Student Ensemble: Symphonic Winds

Center for the Performing ArtsMarch 23, 2016Wednesday Evening8:00 p.m

Systemarchitektur und Fahrmanöver zum sicheren Anhalten modularer automatisierter Fahrzeuge

Maschinelle Systeme übernehmen einen immer größer werdenden Anteil der dynamischen Fahraufgabe automatisierter Fahrzeuge. Funktionale Degradationen können die Fähigkeiten dieser Systeme negativ beeinflussen, sodass sie die Fahraufgabe nicht weiter erfüllen können. In diesen Fällen wird bei höher automatisierten Systemen die Fahraufgabe von einer maschinellen Rückfallebene übernommen. Im Rahmen des Forschungsprojekts UNICARagil wird eine modulare und dienstbasierte funktionale Fahrzeugarchitektur entwickelt, für die in diesem Beitrag die Anforderungen und die Systemarchitektur einer geeigneten funktionalen Rückfallebene vorgestellt werden und der weitere Forschungsbedarf hinsichtlich der erforderlichen Fähigkeiten der Teilfunktionen, ihrer gegenseitigen Abhängigkeiten und der Absicherung der Teil- und Gesamtfunktionen erläutert wird

Approach to Maintain a Safe State of an Automated Vehicle in Case of Unsafe Desired Behavior

For automated driving, higher levels of automation pose new challenges in terms of safety. In this paper, we develop a generic behavior safety framework that maintains a safe vehicle state even in case of system failures. It is applicable to different configurations of automated driving system architectures. We verify the designed generic behavior safety framework by applying it to two different architectures from both projects PRORETA 5 and UNICARagil. The previously defined safety requirements are met with both applications, which indicates that the developed generic safety framework is also valid for other configurations of automated driving systems

Real-Time Pose Graph SLAM based on Radar

This work presents a real-time pose graph based Simultaneous Localization and Mapping (SLAM) system for automotive Radar. The algorithm constructs a map from Radar detections using the Iterative Closest Point (ICP) method to match consecutive scans obtained from a single, front-facing Radar sensor. The algorithm is evaluated on a range of real-world datasets and shows mean translational errors as low as 0.62 m and demonstrates robustness on long tracks. Using a single Radar, our proposed system achieves state-of-the-art performance when compared to other Radar-based SLAM algorithms that use multiple, higher-resolution Radars

Impact of Swiprosin-1/Efhd2 on Adult Hippocampal Neurogenesis

Swiprosin-1/Efhd2 (Efhd2) is highly expressed in the CNS during development and in the adult. EFHD2 is regulated by Ca2+ binding, stabilizes F-actin, and promotes neurite extension. Previous studies indicated a dysregulation of EFHD2 in human Alzheimer's disease brains. We hypothesized a detrimental effect of genetic ablation of Efhd2 on hippocampal integrity and specifically investigated adult hippocampal neurogenesis. Efhd2 was expressed throughout adult neuronal development and in mature neurons. We observed a severe reduction of the survival of adult newborn neurons in Efhd2 knockouts, starting at the early neuroblast stage. Spine formation and dendrite growth of newborn neurons were compromised in full Efhd2 knockouts, but not upon cell- autonomous Efhd2 deletion. Together with our finding of severe hippocampal tauopathy in Efhd2 knockout mice, these data connect Efhd2 to impaired synaptic plasticity as present in Alzheimer's disease and identify a role of Efhd2 in neuronal survival and synaptic integration in the adult hippocampus

Data-driven Derivation of Requirements for a Lidar Sensor Model

Safety assurance in virtual driving simulation environments requires accurate sensor models. However, generally accepted quality criteria for sensor models do not yet exist. In this work, we investigate the model quality needed for a Lidar sensor model for virtual validation. We seek to answer the question, whether neglecting sensor effects in a simplified sensor model might lead to a measurable difference in performance of the sensor model compared to a real sensor. A data-driven approach has been chosen to identify relevant features for object classification in Lidar pointclouds which need to be accurately represented in simulations. The contribution of our work is two-fold: Firstly, we identify important features for object detection in point clouds from Lidar data. For this, we apply object classification algorithms to pointcloud segments, for which a variety of geometric, stochastic, and sensor-specific features have been calculated. Using filter models, principal component analysis (PCA), and embedded models, each feature is assessed and ranked on an individual basis. Secondly, we derive implications for Lidar sensor models based on our findings. We investigate variations in classification quality by succesively removing groups of features from our feature set. Our results show, that to make sensor models suitable for the validation of object detection algorithms, the accurate representation of simple geometric features in synthetic pointclouds is sufficient in many cases. Our method can also be used to support the derivation of requirements and validation criteria for sensor models

Tideglusib Rescues Neurite Pathology of SPG11 iPSC Derived Cortical Neurons

Mutations in SPG11 cause a complicated autosomal recessive form of hereditary spastic paraplegia (HSP). Mechanistically, there are indications for the dysregulation of the GSK3β/βCat signaling pathway in SPG11. In this study, we tested the therapeutic potential of the GSK3β inhibitor, tideglusib, to rescue neurodegeneration associated characteristics in an induced pluripotent stem cells (iPSCs) derived neuronal model from SPG11 patients and matched healthy controls as well as a CRISPR-Cas9 mediated SPG11 knock-out line and respective control. SPG11-iPSC derived cortical neurons, as well as the genome edited neurons exhibited shorter and less complex neurites than controls. Administration of tideglusib to these lines led to the rescue of neuritic impairments. Moreover, the treatment restored increased cell death and ameliorated the membranous inclusions in iPSC derived SPG11 neurons. Our results provide a first evidence for the rescue of neurite pathology in SPG11-HSP by tideglusib. The current lack of disease-modifying treatments for SPG11 and related types of complicated HSP renders tideglusib a candidate compound for future clinical application