By Hideyuki Nakashima, Hamid Aghajan, Juan Carlos Augusto
Ambient Intelligence (AmI) has lately been followed as a time period concerning a multidisciplinary topic which embraces numerous pre-existing fields of laptop technology and engineering. Given the variety of strength functions this dating obviously extends to different components of technological know-how, comparable to schooling, well-being and social care, leisure, activities, and transportation, to call a number of. AmI brings those assets and plenty of different components jointly to supply versatile and clever prone to clients performing of their environments.
Handbook of Ambient Intelligence and shrewdpermanent Environments is a entire presentation of the most recent advancements within the burgeoning examine quarter of ambient intelligence and shrewdpermanent environments. Written through major overseas specialists, this seminal reference organizes all significant options, theories, methodologies, traits, and demanding situations right into a coherent, unified repository.
About this handbook:
- Offers a present and thorough assessment of Ambient Intelligence and examines the relative actual infrastructure of shrewdpermanent environments
- Features application-oriented assurance and provides present initiatives at the subject
- Provides assurance from prime researchers and practitioners in laptop technology and engineering communities
- Describes infastructure and the way sensors are networked and used in program settings
- Explores know-how that may be equipped over a networked sensing infrastructure to make assets largely on hand in an unobtrusive way
- Studies the interplay among people and artifiical systems
- Probes advancements that goal to make synthetic platforms extra rational
- Addresses Multi-Agent Systems—systems that give a contribution to the assets of a man-made procedure that are used to appreciate diversified sitautions and choose intelligently
- Investigates a variety of software ans good as attention of the effect this know-how may have in day-by-day lives.
- Provides perception into the various contemporary significant initiatives built round the world.
This entire quantity is an outstanding instrument for examine scientists, practitioners, senior undergraduate and graduate scholars in machine technological know-how and engineering. This e-book additionally offers an invaluable textual content for execs operating in provider technological know-how, schooling, schooling, healthiness and social care, leisure, activities, transportation and concrete development.
Read or Download Handbook of Ambient Intelligence and Smart Environments PDF
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Extra resources for Handbook of Ambient Intelligence and Smart Environments
The information is then transferred to high-level reasoning modules for knowledge accumulation in applications involving behavior monitoring, or for reacting to the situation in applications based on ambient intelligence and smart environments. Another type of interaction between vision and higher-level application context may involve visualization of events or human actions which can take the form of video distribution or avatarbased visual communication . In both kinds of interfaces, two-way communication between the vision and data processing modules on the one hand, and high-level reasoning and visualization modules on the other hand can enable effective information acquisition by guiding vision to the features of interest, and validating the vision output based on prior knowledge, context, and accumulated behavior models.
However, most of these techniques have been applied in the context of a single resident. A current looming issue for intelligent environment systems is performing these same techniques when multiple residents are present in the environment. This chapter investigates the problem of attributing sensor events to individuals in a multi-resident intelligent environment. Specifically, a naive Bayesian classifier and a hidden Markov model are used to identify the resident responsible for a sensor event.
This chapter investigates the problem of attributing sensor events to individuals in a multi-resident intelligent environment. Specifically, a naive Bayesian classifier and a hidden Markov model are used to identify the resident responsible for a sensor event. Results of experimental validation in a real intelligent workplace testbed are presented and the unique issues that arise in addressing this challenging problem are discussed. 4. Mobile Agents Mobile agent technology has been promoted as an emerging technology that makes it much easier to design, implement, and maintain distributed systems, including cloud computing and sensor networks.