Titlebook: Ambient Intelligence in Everyday Life; Foreword by Emile Aa Yang Cai,Julio Abascal Book 2006 Springer-Verlag Berlin Heidelberg 2006 Ambient

背景

https://doi.org/10.1007/978-3-531-93336-8adapt how they speak to machines, so that the machines can understand them, we present a simple artificial language, based upon musical notes, that can be learned and whistled easily by most people, and so used for simple communication with robots and other kinds of machines that we use in our everyday environments.

不利

Whistling to Machinesadapt how they speak to machines, so that the machines can understand them, we present a simple artificial language, based upon musical notes, that can be learned and whistled easily by most people, and so used for simple communication with robots and other kinds of machines that we use in our everyday environments.

匍匐前进

Common Sense Reasoning – From Cyc to Intelligent Assistantwledge that every person assumes his neighbors also possess. Until that knowledge is fully represented and integrated, tools will continue to be, at best,.. Accordingly, this paper will in part present progress made in the overall Cyc Project during its twenty-year lifespan – its vision, its achieve

有说服力

cataract

A Middleware for the Deployment of Ambient Intelligent Spaceseen us and our surroundings. In consequence, this paper proposes a middleware which aims to make this vision reality following a two-fold objective: (1) to simplify the creation and deployment of physical spaces hosting smart objects and (2) to transform mobile devices into universal remote controll

artless

Ambient Interfaces for Elderly People at Home investigated. The use of musical earcons allowed the potential for an ambient interface to be compared with a traditional visual interface for older people. Results showed participants performed markedly better using the multimodal and visual interfaces than with the audio interface. In addition, b

itinerary

逃避现实

AUGUR

裂隙

https://doi.org/10.1007/978-3-322-80683-3 in realtime using multiple sensors. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated.