-The concept of the augmented coaching ecosystem for non-obtrusive adaptive personalized elderly care is proposed on the basis of the integration of new and available ICT approaches. They include multimodal user interface (MMUI), augmented reality (AR), machine learning (ML), Internet of Things (IoT), and machine-tomachine (M2M) interactions. The ecosystem is based on the Cloud-Fog-Dew computing paradigm services, providing a full symbiosis by integrating the whole range from low level sensors up to high level services using integration efficiency inherent in synergistic use of applied technologies. Inside of this ecosystem, all of them are encapsulated in the following network layers: Dew, Fog, and Cloud computing layer. Instead of the "spaghetti connections", "mosaic of buttons", "puzzles of output data", etc., the proposed ecosystem provides the strict division in the following dataflow channels: consumer interaction channel, machine interaction channel, and caregiver interaction channel. This concept allows to decrease the physical, cognitive, and mental load on elderly care stakeholders by decreasing the secondary human-to-human (H2H), human-to-machine (H2M), and machine-to-human (M2H) interactions in favor of M2M interactions and distributed Dew Computing services environment. It allows to apply this non-obtrusive augmented reality ecosystem for effective personalized elderly care to preserve their physical, cognitive, mental and social well-being.
Test generation at the gate-level produces high-quality tests but is computationally expensive in the case of large systems. Recently, several research efforts have investigated the possibility of devising test generation methods and tools to work on high-level descriptions. The goal of these methods is to provide the designers with testability information and test sequences in the early design stages. The cost for generating test sequences in the high abstraction levels is often lower than that for generating test sequences at the gate-level, with comparable or even higher fault coverage. This paper first analyses several high-level fault models in order to select the most suitable one for estimating the testability of circuits by reasoning on their behavioral descriptions and for guiding the test generation process at the behavioral level. We assess then the effectiveness of high-level test generation with a simple ATPG algorithm, and present a novel highlevel hierarchical test generation approach to improve the results obtained by a pure high-level test generator.
This paper presents a solution to the test time minimization problem for core-based systems. We assume a hybrid BIST approach, where a test set is assembled, for each core, from pseudorandom test patterns that are generated online, and deterministic test patterns that are generated off-line and stored in the system. In this paper we propose an iterative algorithm to find the optimal combination of pseudorandom and deterministic test sets of the whole system, consisting of multiple cores, under given memory constraints, so that the total test time is minimized. Our approach employs a fast estimation methodology in order to avoid exhaustive search and to speed-up the calculation process. Experimental results have shown the efficiency of the algorithm to find a near optimal solutions.
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