Exact(10)
It turns out that each specific type of intelligent sensor environment has its own adoption issues.
The data association and management scheme is proposed to complement two different types of signals in heterogeneous sensor environment.
We present 3SoSM, an approach to optimize interactions between application requirements and wireless sensor environment in real-time.
The success of an intelligent sensor environment is mainly determined by the extent to which it is adopted by users.
Biosensors, which are used for collecting information from interaction between biomolecules and related sensor environment, have been researched extensively in healthcare, biomedical, and life science area [1 8].
In this research, a study on health analysis and prediction for the wellness and health sensor environment, and the technologies supporting long-term care is discussed.
Similar(50)
However, in dense sensor environments, some abnormal sensors or conflicted information will increase the complexity and the time of track fusion greatly, even lead to opposite results.
The approach is mainly targeted for surveillance systems (e.g., Ambient Assisted Living [33]) to model trajectories of persons or to model routines within sensor environments.
Based on four case studies, we developed and tested questionnaires that contribute as an instrument for evaluating existing sensor environments, or during the design phase.
The concept of STFD was developed in the context of the evaluation of quadratic TFDs to account for nonstationary signals in a multi-sensor environment.
Aiming to provide a transparent and efficient mobility infrastructure for the mHealth field we designed and developed a context-aware IPv6 flow mobility management scheme for Android focusing on mobile patient monitoring and tele-consultation use-cases in a multi-sensor environment.
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