TY - JOUR A1 - Scharfenberg, Georg A1 - Mottok, Jürgen A1 - Artmann, Christina A1 - Hobelsberger, Martin A1 - Paric, Ivan A1 - Großmann, Benjamin A1 - Pohlt, Clemens A1 - Wackerbarth, Alena A1 - Pausch, Uli A1 - Heidrich, Christiane A1 - Fadanelli, Martin A1 - Elsner, Michael A1 - Pöcher, Daniel A1 - Pittroff, Lenz A1 - Beer, Stefan A1 - Brückl, Oliver A1 - Haslbeck, Matthias A1 - Sterner, Michael A1 - Thema, Martin A1 - Muggenthaler, Nicole A1 - Lenck, Thorsten A1 - Götz, Philipp A1 - Eckert, Fabian A1 - Deubzer, Michael A1 - Stingl, Armin A1 - Simsek, Erol A1 - Krämer, Stefan A1 - Großmann, Benjamin A1 - Schlegl, Thomas A1 - Niedersteiner, Sascha A1 - Berlehner, Thomas A1 - Joblin, Mitchell A1 - Mauerer, Wolfgang A1 - Apel, Sven A1 - Siegmund, Janet A1 - Riehle, Dirk A1 - Weber, Joachim A1 - Palm, Christoph A1 - Zobel, Martin A1 - Al-Falouji, Ghassan A1 - Prestel, Dietmar A1 - Scharfenberg, Georg A1 - Mandl, Roland A1 - Deinzer, Arnulf A1 - Halang, W. A1 - Margraf-Stiksrud, Jutta A1 - Sick, Bernhard A1 - Deinzer, Renate A1 - Scherzinger, Stefanie A1 - Klettke, Meike A1 - Störl, Uta A1 - Wiech, Katharina A1 - Kubata, Christoph A1 - Sindersberger, Dirk A1 - Monkman, Gareth J. A1 - Dollinger, Markus A1 - Dembianny, Sven A1 - Kölbl, Andreas A1 - Welker, Franz A1 - Meier, Matthias A1 - Thumann, Philipp A1 - Swidergal, Krzysztof A1 - Wagner, Marcus A1 - Haug, Sonja A1 - Vernim, Matthias A1 - Seidenstücker, Barbara A1 - Weber, Karsten A1 - Arsan, Christian A1 - Schone, Reinhold A1 - Münder, Johannes A1 - Schroll-Decker, Irmgard A1 - Dillinger, Andrea Elisabeth A1 - Fuchshofer, Rudolf A1 - Monkman, Gareth J. A1 - Shamonin (Chamonine), Mikhail A1 - Geith, Markus A. A1 - Koch, Fabian A1 - Ühlin, Christian A1 - Schratzenstaller, Thomas A1 - Saßmannshausen, Sean Patrick A1 - Auchter, Eberhard A1 - Kriz, Willy A1 - Springer, Othmar A1 - Thumann, Maria A1 - Kusterle, Wolfgang A1 - Obermeier, Andreas A1 - Udalzow, Anton A1 - Schmailzl, Anton A1 - Hierl, Stefan A1 - Langer, Christoph A1 - Schreiner, Rupert ED - Baier, Wolfgang T1 - Forschungsbericht 2015 T3 - Forschungsberichte der OTH Regensburg - 2015 Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13867 SN - 978-3-00-048589-3 CY - Regensburg ER - TY - JOUR A1 - Weber, Karsten A1 - Rinke, Bernhard A1 - Alwardt, Christian T1 - Dr. Seltsam, oder wie ich lernte, die Drohne zu lieben BT - Ein Überblick aktueller Debatten zum Drohneneinsatz JF - TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis N2 - Drohnen werden inzwischen in vielen und sehr unterschiedlichen Kon-texten verwendet. Aus dem Blickwinkel der Technikfolgenabschätzung (TA) scheint es daher sinnvoll, den Umfang der momentanen und zu-künftigen Nutzung von Drohnen und daraus resultierende Implikatio-nen näher zu beleuchten und eine Bestandsaufnahme durchzuführen. Darüber hinaus sollen die voraussichtlichen Pfade der weiteren Tech-nikentwicklung, relevante Akteure und deren Interessenslage sowie zu-künftige Anwendungspotenziale und Einsatzfelder analysiert werden KW - drones KW - civil use KW - military use KW - technology assessment Y1 - 2018 U6 - https://doi.org/10.14512/tatup.27.3.11 VL - 27 IS - 3 SP - 11 EP - 13 PB - Institute for Technology Assessment and Systems Analysis / KIT ER - TY - JOUR A1 - Hinterberger, Lukas A1 - Fischer, Sebastian A1 - Weber, Bernhard A1 - Neubauer, Katrin A1 - Hackenberg, Rudolf T1 - Extended Definition of the Proposed Open Standard for IoT Device IdentificAtion and RecoGnition (IoTAG) JF - International Journal on Advances in Internet Technology N2 - Internet of Things (IoT) devices are critical to operate and maintain, because of their number and high connectivity. A lot of security issues concern IoT devices and the networks they are integrated. To help getting an overview of an IoT network, the devices and the security, we propose a scoring system to get a good impression of IT security. This system generates single scores for each device, using features like encryption, update behavior, etc. Furthermore, a summarized score for the whole network is calculated, to show the status of the network security in an easy way for the administrator. To enable the scoring system, a precise list of the existing devices and their operating status is necessary. To achieve this, we present an open standard for the IoT Device IdentificAtion and RecoGnition (short IoTAG), which requires that devices report, e.g., their name, an unique ID, the firmware version and the supported encryption. The proposed standard is described in detail and an implementation guideline is given in this paper. Additionally, information about how to realize the serialization, the integrity and the communication with IoTAG. KW - Internet of Things KW - device identification KW - open standard KW - security rating KW - IoTAG Y1 - 2020 UR - https://www.thinkmind.org/articles/inttech_v13_n34_2020_2.pdf VL - 13 IS - 3 & 4 SP - 110 EP - 121 PB - IARIA ER - TY - CHAP A1 - Hinterberger, Lukas A1 - Weber, Bernhard A1 - Fischer, Sebastian A1 - Neubauer, Katrin A1 - Hackenberg, Rudolf T1 - IoT Device IdentificAtion and RecoGnition (IoTAG) T2 - CLOUD COMPUTING 2020, The Eleventh International Conference on Cloud Computing, GRIDs and Virtualization N2 - To ensure the secure operation of IoT devices in the future, they must be continuously monitored. This starts with an inventory of the devices, checking for a current software version and extends to the encryption algorithms and active services used. Based on this information, a security analysis and rating of the whole network is possible. To solve this challenge in the growing network environments, we present a proposal for a standard. With the IoT Device IdentificAtion and RecoGnition (IoTAG), each IoT device reports its current status to a central location as required and provides information on security. This information includes a unique ID, the exact device name, the current software version, active services, cryptographic methods used, etc. The information is signed to make misuse more difficult and to ensure that the device can always be uniquely identified. In this paper, we introduce IoTAG in detail and describe the necessary requirements. KW - Internet of Things KW - device identification KW - IoTAG KW - security rating Y1 - 2020 UR - https://thinkmind.org/articles/cloud_computing_2020_2_10_28002.pdf SN - 978-1-61208-778-8 SP - 17 EP - 23 PB - IARIA ER - TY - CHAP A1 - Weber, Bernhard A1 - Hinterberger, Lukas A1 - Fischer, Sebastian A1 - Hackenberg, Rudolf ED - Duncan, Bob ED - Lee, Yong Woo ED - Popescu, Manuela T1 - How to Prevent Misuse of IoTAG? T2 - Cloud Computing 2021: the Twelfth International Conference on Cloud Computing, GRIDs, and Virtualization: April 18-22, 2021, Porto, Portugal N2 - Since IoT devices are potentially insecure and offer great attack potential, in our past research we presented IoTAG, a solution where devices communicate security-related information about themselves. However, since this information can also be exploited by attackers, we present in this paper a solution against the misuse of IoTAG. In doing so, we address the two biggest problems: authentication and pairing with a trusted device. This is solved by introducing a pairing process, which uses the simultaneous authentication of equals algorithm to securely exchange and verify each others signature, and by using the server and client authentication provided by HTTP over TLS. We provide the minimum requirements and evaluate the methods used. The emphasis is on known and already proven methods. Additionally, we analyze the potential consequences of an attacker tapping the IoTAG information. Finally, we conclude that the solution successfully prevents access to IoTAG by unauthorized clients on the same network. KW - Internet of Things KW - IoTAG KW - device pairing KW - device authentication KW - trusted connection Y1 - 2021 UR - https://www.thinkmind.org/articles/cloud_computing_2021_1_40_28006.pdf SN - 978-1-61208-845-7 SN - 2308-4294 PB - IARIA CY - Wilmington, DE, USA ER - TY - BOOK A1 - Reichenbach, Hartmut A1 - Bruss, Werner A1 - Bürger, Michael A1 - Fiedler, Manfred A1 - Göbel, Manuel A1 - Middendorf, Bernhard A1 - Neidhart, Thomas A1 - Pollnow, Elke A1 - Ramler-Kowollik, Marcel A1 - Scholz, Enrico A1 - Simon, Jana A1 - Sosinka, Katharina A1 - Spirkl, Florian A1 - Trunk, Ulrich A1 - Wagner, Achim A1 - Wagner, Bernd A1 - Weber, Steffen ED - Forschungsgesellschaft für Straßen- und Verkehrswesen FGSV e.V., T1 - Merkblatt für die Herstellung und Verwendung von zeitweise fließfähigen, selbstverdichtenden Verfüllbaustoffen (ZFSV) im Erdbau N2 - Das „Merkblatt für die Herstellung und Verwendung von zeitweise fließfähigen, selbstverdichtenden Verfüllbaustoffen“ (M ZFSV), Ausgabe 2025, befasst sich mit der Herstellung und der Anwendung zeitweise fließfähiger, selbstverdichtender Verfüllbaustoffe (ZFSV) gemäß den „Zusätzlichen Technischen Vertragsbedingungen und Richtlinien für Erdarbeiten im Straßenbau“ (ZTV E-StB) (FGSV 599) beim Bau von Straßen und Verkehrsflächen. Für andere Bereiche des Bauwesens kann dieses Merkblatt ebenfalls angewendet werden. Das M ZFSV enthält Qualitätsanforderungen für diese Baustoffe und Baustoffgemische. Die Anforderungen schließen die verschiedenen Anwendungsgebiete der Baustoffe und Baustoffgemische, die Herstellung, Lieferung und den Einbau sowie die Prüfungen und die Qualitätssicherung ein. Vorab wird ebenso auf Anwendungsgebiete und Planungsgrundsätze eingegangen. Ergänzt werden die umfangreichen Ausführungen durch mehrere Anhänge. Y1 - 2025 SN - 978-3-86446-419-5 PB - FGSV Verlag CY - Köln ET - Ausgabe 2025 ER - TY - JOUR A1 - Graßmann, Felix A1 - Mengelkamp, Judith A1 - Brandl, Caroline A1 - Harsch, Sebastian A1 - Zimmermann, Martina E. A1 - Linkohr, Birgit A1 - Peters, Annette A1 - Heid, Iris M. A1 - Palm, Christoph A1 - Weber, Bernhard H. F. T1 - A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography JF - Ophtalmology N2 - Purpose Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. Participants. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. Methods. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. Main Outcome Measures. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. Results. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%–92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly. Conclusions Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age. KW - Senile Makuladegeneration KW - Krankheitsverlauf KW - Mustererkennung KW - Maschinelles Lernen Y1 - 2018 U6 - https://doi.org/10.1016/j.ophtha.2018.02.037 N1 - Corresponding authors: Bernhard H. F. Weber, University of Regensburg, and Christoph Palm VL - 125 IS - 9 SP - 1410 EP - 1420 PB - Elsevier ER -