Download Intelligent Environments: Methods, Algorithms and by Dorothy Monekosso, Yoshinori Kuno, Paolo Remagnino PDF

By Dorothy Monekosso, Yoshinori Kuno, Paolo Remagnino

Researchers focused on the clever atmosphere box are practicing a destiny the place people reside in self reliant and adaptive environments; the place private and non-private areas are endowed with intelligence that boosts person event and improves caliber of life.

Each bankruptcy within the e-book describes an instance of clever surroundings, focussing at the man made intelligence method hired to create the surroundings. The ebook additionally introduces engineering options to the matter of realizing and modelling advanced scenes through multi-modal arrays of sensors. This assortment comprises contributions from key researchers within the box. subject matters coated include:

  • Ontology learning
  • Multi-agent methods
  • Multi-sensor fusion
  • Intelligent human-environment interaction
  • Advanced human-computer interaction
  • Conventional and embedded robot solutions
  • Ambient illustration and leading edge sensors

This booklet could be of curiosity to graduate scholars and researchers in academia and wishing to profit extra approximately ambient intelligence and its power influence on society.

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Extra info for Intelligent Environments: Methods, Algorithms and Applications

Example text

In partitioning methods, intermediate clusters are revisited for improvements while most hierarchical algorithms make no attempt at improving intermediate clusters. Partitioning methods generally suffer from time complexity. The distance measure used to determine the similarity between two points influences the shape of the clusters, as two points may be close according to one distance measure and far apart according to another distance measure. Some of the distance measures used are the Euclidean distance, the Manhattan distance, the Mahalanobis and the Normalized Google Distance (NGD).

2 Graphical representation of the active time (3 sensors) - outlier detected 3 Supporting Independent Living 43 given prior knowledge of the expected activities and most likely time of day for the activities, the histogram (trend) shows no outlier. 2 shows an outlier. It can be seen that a light sensor (purple, ceiling light) is active during all six periods. Investigation of the raw data indicated that the sensor status was incorrectly recorded; the light was active for an extended period of time during which it was known not to be active.

Failure of equipment to record an action, for example, a sensor failing to trigger. This was considered the most frequent and likely error. 2. Failure of equipment to take an action due to radio signal interference. 3. Random activation due to noise on mains or radio signal interference. 4. Sensor activation shorter than the sampling time during data processing. This happens particularly with motion detectors that trigger for a specific length of time. The impact of these errors is discussed in this section.

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