PubMed · 15038258
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Bryan Bergeron. 2004. Wireless interference with telemetry equipment?. https://doi.org/10.3810/pgm.2004.03.1468
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Explore the source record for details and available documents.
This paper presents a low-phase-noise, hybrid LC-tank, analog frequency modulator for wireless biotelemetry employing on-chip NMOS varactors in the inversion region as the frequency tuning element. We demonstrate that a correct estimate for the destination signal-to-noise ratio, which quantifies the quality of the wirelessly received signal in a frequency-modulated biotelemetry system, is only achieved after taking into account the large-signal oscillation effect on the tank varactor. A prototype chip is fabricated using AMI 1.5-microm double-poly double-metal n-well CMOS process, and exhibits a measured gain factor of 1.21 MHz/V in the mid-range of the tuning voltage and a phase noise of -88.6 dBc/Hz at 10-kHz offset from the 95.1-MHz carrier while dissipating 1.48 mW from a 3 V power supply leading to a figure of merit (FOM) of -166.5 dBc/Hz. The VCO is successfully interfaced with a penetrating silicon microelectrode with 700 microm2 iridium recording sites for wireless in vitro recording of a 50 Hz simulated normal sinus rhythm signal from saline over a distance of approximately 0.25 m. Given a typical gain of approximately 40 dB for fully integrated front-end bioamplifiers, a wireless recording microsystem employing this VCO would be capable of detecting input biopotentials down to the submilivolt range.
A low-cost reconfigurable embedded apparatus for two-dimensional (2-D) motion detection has been developed. This paper briefly outlines the embedded reconfigurable system architecture, and presents in-depth the 2-D motion detection model, which is directly mapped to reconfigurable hardware. Emphasis is placed on the hardware ability to adapt to individual needs of kinetically challenged persons by altering detection thresholds and delays, thus resulting into an efficient low-cost reconfigurable hardware implementation of the model. This paper also presents how the model detects complex motions through a vocabulary of simple motions, and how the system is trained to individual users' needs. Experimental results and integrated applications of the model for text processing are also presented.