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Ambient Diagnostics
Yang Cai
Ambient Diagnostics addresses innovative methods for discovering patterns from affordable devices, such as mobile phones, watches, cameras, and game interfaces, to interpret multimedia data for personal health monitoring and diagnosis. This is the first comprehensive textbook on multidisciplinary innovations in affordable healthcare--from sensory fusion, pattern detection, to classification.
Connecting the Dots
The material in this book combines sensing, pattern recognition, and visual design, and is divided into four parts, which cover fundamentals, multimedia intelligence, pervasive sensors, and crowdsourcing. The author describes basic pattern discovery models, sound, color, motion and video analytics, and pattern discovery from games and social networks. Each chapter contains the material's main concepts, as well as case studies, and extensive study questions.
Reflects the rapidly growing platforms for remote sensing, gaming, and social networking
Incorporates cognitive tests such as fatigue detection
Includes pseudo code and sample code
Provides vision algorithms and multimedia analytics
Covers Multimedia Intelligence Extensively
Ambient Diagnostics includes concepts for ambient technologies such as point-and-search, the pill camera, active sensing with Kinect, digital human labs, negative and relative feature spaces, and semantic representations. The book also introduces methods for collective intelligence from online video games and social media.
404 pages, 353 black & white illustrations, 11 black & white tables
| Media | Böcker Inbunden Bok (Inbunden bok med hårda pärmar och skyddsomslag) |
| Releasedatum | 1 december 2014 |
| ISBN13 | 9781466510418 |
| Utgivare | Taylor & Francis Inc |
| Antal sidor | 408 |
| Mått | 150 × 220 × 20 mm · 726 g |
| Språk | Engelska |
![Cover for Yang Cai · Instinctive Computing (Inbunden Bok) [1st ed. 2016 edition] (2017)](https://imusic.b-cdn.net/images/item/original/765/9781447172765.jpg?yang-cai-2017-instinctive-computing-inbunden-bok&class=scaled&v=1653436639)
