LDR | | 02036nmm uu200397 4500 |
001 | | 000000334157 |
005 | | 20240805175238 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438177253 |
035 | |
▼a (MiAaPQ)AAI10828505 |
035 | |
▼a (MiAaPQ)washington:18848 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 004 |
100 | 1 |
▼a Li, Hanchuan. |
245 | 10 |
▼a Enabling Novel Sensing and Interaction with Everyday Objects using Commercial RFID Systems. |
260 | |
▼a [S.l.] :
▼b University of Washington.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 147 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
500 | |
▼a Adviser: Shwetak N. Patel. |
502 | 1 |
▼a Thesis (Ph.D.)--University of Washington, 2018. |
520 | |
▼a The Internet of Things (IoT) promises an interconnected network of smart devices that will revolutionize the way people interact with their surrounding environments. This distributed network of physical devices will open up tremendous opportunit |
520 | |
▼a The advancement of IoT has been heavily focused on creating new and smart electronic devices, while the vast majority of everyday non-smart objects are left unchecked. Techniques based on active sensors are limited by their high deployment cost |
520 | |
▼a Radio-frequency identification (RFID) has been widely adopted in the IoT industry as a standard inventory management infrastructure. In this thesis, I apply signal processing and machine learning techniques on low-level channel parameters of com |
590 | |
▼a School code: 0250. |
650 | 4 |
▼a Computer science. |
690 | |
▼a 0984 |
710 | 20 |
▼a University of Washington.
▼b Computer Science and Engineering. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0250 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999175
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |