R
Richa Prasad
Researcher at Microsoft
Publications - 8
Citations - 717
Richa Prasad is an academic researcher from Microsoft. The author has contributed to research in topics: Wireless sensor network & Amplifier. The author has an hindex of 6, co-authored 8 publications receiving 685 citations. Previous affiliations of Richa Prasad include University of Washington.
Papers
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Proceedings ArticleDOI
Recognizing daily activities with RFID-based sensors
TL;DR: A dense sensing approach that uses RFID sensor network technology to recognize human activities in a model apartment concludes that RFid sensor networks are a promising approach for indoor activity monitoring.
Proceedings ArticleDOI
Wirelessly-Charged UHF Tags for Sensor Data Collection
TL;DR: The design and implementation of a prototype WISP-PDL is described, and results from a short demonstration study that shows it can monitor the temperature and fullness of a milk carton as it is used over the course of a day.
Journal ArticleDOI
NeuralWISP: A Wirelessly Powered Neural Interface With 1-m Range
TL;DR: The NeuralWISP is compatible with commercial RF identification readers and operates at a range up to 1 m, and includes a custom low-noise, low-power amplifier integrated circuit for processing the neural signal and an analog spike detection circuit for reducing digital computational requirements and communications bandwidth.
Proceedings ArticleDOI
RFID sensor networks with the Intel WISP
Michael Buettner,Richa Prasad,Alanson P. Sample,Daniel J. Yeager,Ben Greenstein,Joshua R. Smith,David Wetherall +6 more
TL;DR: It is believed that the small form factor and lack of battery makes the WISP an attractive alternative to motes for many of the original smart dust applications that require very small or long-lived sensors.
Proceedings ArticleDOI
NeuralWISP: An energy-harvesting wireless neural interface with 1-m range
TL;DR: The NeuralWISP is compatible with commercial RFID readers and operates at a range up to 1m, and includes a custom low-noise, low power amplifier IC for processing the neural signal and an analog spike detection circuit for reducing digital computational requirements and communications bandwidth.