0000000000476343
AUTHOR
Enrico Daidone
Short-Term Sensory Data Prediction in Ambient Intelligence Scenarios
Predicting data is a crucial ability for resource-constrained devices like the nodes of a Wireless Sensor Network. In the context of Ambient Intelligence scenarios, in particular, short-term sensory data prediction becomes a key enabler for more difficult tasks such as prolonging network lifetime, reducing the amount of communication required and improving user-environment interaction. In this chapter we propose a software module designed for clustered wireless sensor networks, able to predict various environmental quantities, namely temperature, humidity and light. The software module is supported by an ontology that describes the topology of the AmI scenario and the effects of the actuato…
A Heterogeneous Sensor and Actuator Network Architecture for Ambient Intelligence
One of the most important characteristics of a typical ambient intelligence scenario is the presence of a number of sensors and actuators that capture information about user preferences and activities. Such nodes, i.e., sensors and actuators, are often based on different technologies so that types of networks which are typically different coexist in a real system, for example, in a home or a building. In this chapter we present a heterogeneous sensor and actuator network architecture designed to separate network management issues from higher, intelligent layers. The effectiveness of the solution proposed here was evaluated using an experimental scenario involving the monitoring of an office…
Automatic skull stripping in MRI based on morphological filters and fuzzy c-means segmentation
In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is presented. Skull stripping is a process that allows to separate the brain from the rest of tissues. The proposed method is based on a 2D brain extraction making use of fuzzy c-means segmentation and morphological operators applied on transversal slices. The approach is extended to the 3D case, taking into account the result obtained from the preceding slice to solve the organ splitting problem. The proposed approach is compared with BET (Brain Extraction Tool) implemented in MRIcro software.