TY - JOUR A1 - Morgenthal, Guido A1 - Eick, Jan Frederick A1 - Rau, Sebastian A1 - Taraben, Jakob T1 - Wireless Sensor Networks Composed of Standard Microcomputers and Smartphones for Applications in Structural Health Monitoring JF - Sensors - Special Issue Selected Papers from 7th Asia-Pacific Workshop on Structural Health Monitoring N2 - Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. This paper presents a software framework for WiFi-based wireless sensor networks composed of low-cost mass market single-board computers. A number of specific system-level software components were developed to enable robust data acquisition, data processing, sensor network communication, and timing with a focus on structural health monitoring (SHM) applications. The framework was validated on Raspberry Pi computers, and its performance was studied in detail. The paper presents several characteristics of the measurement quality such as sampling accuracy and time synchronization and discusses the specific limitations of the system. The implementation includes a complementary smartphone application that is utilized for data acquisition, visualization, and analysis. A prototypical implementation further demonstrates the feasibility of integrating smartphones as data acquisition nodes into the network, utilizing their internal sensors. The measurement system was employed in several monitoring campaigns, three of which are documented in detail. The suitability of the system is evaluated based on comparisons of target quantities with reference measurements. The results indicate that the presented system can robustly achieve a measurement performance commensurate with that required in many typical SHM tasks such as modal identification. As such, it represents a cost-effective alternative to more traditional monitoring solutions. KW - Structural Health Monitoring KW - Mikrocomputer KW - Smartphone KW - Schwingungsmessung KW - Wireless sensor network KW - Raspberry Pi KW - Smartphones KW - Vibration measurements KW - OA-Publikationsfonds2019 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20190514-39123 UR - https://www.mdpi.com/1424-8220/19/9/2070 VL - 2019 IS - Volume 19, Issue 9, 2070 PB - MDPI ER - TY - JOUR A1 - Brombach, Benjamin A1 - Bruns, Erich A1 - Bimber, Oliver T1 - Subobject Detection through Spatial Relationships on Mobile Phones N2 - We present a novel image classification technique for detecting multiple objects (called subobjects) in a single image. In addition to image classifiers, we apply spatial relationships among the subobjects to verify and to predict locations of detected and undetected subobjects, respectively. By continuously refining the spatial relationships throughout the detection process, even locations of completely occluded exhibits can be determined. Finally, all detected subobjects are labeled and the user can select the object of interest for retrieving corresponding multimedia information. This approach is applied in the context of PhoneGuide, an adaptive museum guidance system for camera-equipped mobile phones. We show that the recognition of subobjects using spatial relationships is up to 68% faster than related approaches without spatial relationships. Results of a field experiment in a local museum illustrate that unexperienced users reach an average recognition rate for subobjects of 85.6% under realistic conditions. KW - Objekterkennung KW - Smartphone KW - Subobjekterkennung KW - Räumliche Beziehungen KW - Neuronales Netz KW - Museumsführer KW - Subobject Detection KW - Spatial Relationships KW - Neural Networks KW - Museum Guidance Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20081007-14296 ER -