Interactive design is an emerging trend in dementia care environments. This article describes a research project aiming at the design and development of novel spatial objects with narrative attributes that incorporate embedded technology and textiles to support the wellbeing of people living with dementia. In collaboration with people with dementia, this interdisciplinary research project focuses on the question of how innovative spatial objects can be incorporated into dementia long-term care settings, transforming the space into a comforting and playful narrative environment that can enhance self-esteem while also facilitating communication between people living with dementia, family, and staff members. The research methodologies applied are qualitative, including Action Research. Participatory design methods with the experts by experience—the people with dementia—and health professionals have been used to inform the study. Early findings from this research are presented as design solutions comprising a series of spatial object prototypes with embedded technology and textiles. The prototypes were evaluated primarily by researchers, health professionals, academics, and design practitioners in terms of functionality, aesthetics, and their potential to stimulate engagement. The research is ongoing, and the aim is to evaluate the prototypes by using ethnographic and sensory ethnography methods and, consequently, further develop them through co-design workshops with people living with dementia.
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In this paper, artificial intelligence tools are implemented in order to predict trajectory positions, as well as channel performance of an optical wireless communications link. Case studies for industrial scenarios are considered to this aim. In a first stage, system parameters are optimized using a hybrid multi-objective optimization (HMO) procedure based on the grey wolf optimizer and the non-sorting genetic algorithm III with the goal of simultaneously maximizing power and spectral efficiency. In a second stage, we demonstrate that a long short-term memory neural network (LSTM) is able to predict positions, as well as channel gain. In this way, the VLC links can be configured with the optimal parameters provided by the HMO. The success of the proposed LSTM architectures was validated by training and test root-mean square error evaluations below 1%.
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Traditional IMU based PDR systems suffer from rapidly growing drift effects due to the inherent bias of the inertial sensor. Many existing solutions to mitigate this problem use aiding sensors or information as heuristics or map data. We propose a new optimization framework to solve the PDR estimation problem where the sensors biases are explicitly included as state variables and therefore be used to correct for bias effects in the PDR. By using a smoothing approach and exploiting the rigid structure of a MIMU array one can solve for the slowly varying sensor biases. This paper presents the method and gives an exemplary result of a walking trial. Good agreements in the position and orientation with an optical reference system were found. Moreover, accelerometer and gyroscope biases could be estimated accordingly. Further research includes the performance of more experiments under various conditions such that a more quantitative evaluation can be obtained. In addition, an exploration of a (pseudo) realtime filter version would be valuable such that the system can be applied online.
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The increasing complexity of digital networks on which society depends on renders these networks increasingly vulnerable to attacks. Focusing on physical layer security, we propose to develop single-spatial-mode optical Physical Unclonable Functions (PUFs) as an authentication solution for quantum and classical communication links. These novel PUFs are read out through standard optical fibers or free-space links. Several implementations of single-mode PUFs are proposed exploiting the time / frequency domain for the encoding challenge / response space. Together with the PUFs, we will develop tools to generate challenge-forming few-photon light pulses and to validate PUF responses at the few-photon level and take specific steps toward wide implementation.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
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