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Comparison of Visual Attention Networks for Semantic Image Segmentation in Reminiscence Therapy
(2022)
Due to the steadily increasing age of the entire population, the number of dementia patients is steadily growing. Reminiscence therapy is an important aspect of dementia care. It is crucial to include this area in digitization as well. Modern Reminiscence sessions consist of digital media content specifically tailored to a patient’s biographical needs. To enable an automatic selection of this content, the use of Visual Attention Networks for Semantic Image Segmentation is evaluated in this work. A detailed comparison of various Neural Networks is shown, evaluated by Metric for Evaluation of Translation with Explicit Ordering (METEOR) in addition to Billingual Evaluation Study (BLEU) Score. The most promising Visual Attention Network consists of a Xception Network as Encoder and a Gated Recurrent Unit Network as Decoder.
Testing applications for SmartHome environments is quite complicated, since a real environment is not accessible or the conditions are not controllable during development time. The need to set up the whole hardware environment, increase the complexity of these systems enormously. Therefore, it is helpful to simulate the SmartHome hardware components and environment conditions (e.g. rain, heat, etc.). This paper contains an approach to improve the test and demonstration process of Internet of Things scenarios. A prototype (ScnSim: Scenario Simulator) was developed to set up scenarios. The user of the ScnSim can create her/his own scenario using items (sensors/actuators) and rules, which control the sensors and actors building the IoT enviornment. This simulator is supposed to support the user testing IoT applications or configurations of SmartHome platforms like openHAB. In addition, the ScnSim is supposed to help demonstrating showcases, for example, often demonstrated on a trade fair or as a proof of concept for a customer.
Cloud Computing an der HFU
(2010)