Jennifer Urner

Bio: Jennifer Urner is an academic researcher. The author has contributed to research in topics: Radiation pattern & Antenna measurement. The author has an hindex of 1, co-authored 1 publications receiving 565 citations.

Smart metasurface with self-adaptively reprogrammable functions

Abstract: Intelligence at either the material or metamaterial level is a goal that researchers have been pursuing. From passive to active, metasurfaces have been developed to be programmable to dynamically and arbitrarily manipulate electromagnetic (EM) wavefields. However, the programmable metasurfaces require manual control to switch among different functionalities. Here, we put forth a smart metasurface that has self-adaptively reprogrammable functionalities without human participation. The smart metasurface is capable of sensing ambient environments by integrating an additional sensor(s) and can adaptively adjust its EM operational functionality through an unmanned sensing feedback system. As an illustrative example, we experimentally develop a motion-sensitive smart metasurface integrated with a three-axis gyroscope, which can adjust self-adaptively the EM radiation beams via different rotations of the metasurface. We develop an online feedback algorithm as the control software to make the smart metasurface achieve single-beam and multibeam steering and other dynamic reactions adaptively. The proposed metasurface is extendable to other physical sensors to detect the humidity, temperature, illuminating light, and so on. Our strategy will open up a new avenue for future unmanned devices that are consistent with the ambient environment.

Minding the Billions: Ultra-wideband Localization for Deployed RFID Tags

Abstract: State-of-the-art RFID localization systems fall under two categories. The first category operates with off-the-shelf narrowband RFID tags but makes restrictive assumptions on the environment or the tag's movement patterns. The second category does not make such restrictive assumptions; however, it requires designing new ultra-wideband hardware for RFIDs and uses the large bandwidth to directly compute a tag's 3D location. Hence, while the first category is restrictive, the second one requires replacing the billions of RFIDs already produced and deployed annually. This paper presents RFind, a new technology that brings the benefits of ultra-wideband localization to the billions of RFIDs in today's world. RFind does not require changing today's passive narrowband RFID tags. Instead, it leverages their underlying physical properties to emulate a very large bandwidth and uses it for localization. Our empirical results demonstrate that RFind can emulate over 220MHz of bandwidth on tags designed with a communication bandwidth of only tens to hundreds of kHz, while remaining compliant with FCC regulations. This, combined with a new super-resolution algorithm over this bandwidth, enables RFind to perform 3D localization with sub-centimeter accuracy in each of the x/y/z dimensions, without making any restrictive assumptions on the tag's motion or the environment.

Breathfinding: A Wireless Network That Monitors and Locates Breathing in a Home

Abstract: This paper explores using RSS measurements on the links between commercial wireless devices to locate where a breathing person is located and to estimate their breathing rate, in a home, while the person is sitting, lying down, standing, or sleeping. Prior RSS-based device-free localization methods required calibration measurements to be able to locate stationary people, or did not require calibration but only located people who moved. We collect RSS measurements multiple short (3-7 minute) tests and during a longer 66 minute test, and show the location of the breathing person can be estimated, to within about 2 m average error. We describe a detector that distinguishes between sample times during which a person is moving and sample times during which a person is breathing but otherwise motionless. This detector enables removal of motion interference, i.e., RSS changes due to movements other than a person's breathing, and more accurately estimate a person's breathing rate. Being able to locate and monitor a breathing person, without calibration, is important for applications in search and rescue, health care, and security.

Personal Mobile Radars with Millimeter-Wave Massive Arrays for Indoor Mapping

Abstract: The adoption of millimeter-wave technology could open the possibility to integrate massive antenna arrays inside future 5G user mobile devices, with the possibility to enable new interesting applications. Within this context, in this paper we put forth the concept of a personal mobile radar operating at millimeter-waves and consisting of a massive array for accurate environmental mapping. Frequency selectivity and phase quantization effects are accounted for to characterize the achievable angle and range resolution necessary to collect environmental information. Successively, we propose an effective grid-based Bayesian mapping approach by introducing a new state-space model, which profits of the beneficial effects of the massive antenna array characteristics. Numerical results show that the idea herein investigated is feasible, and that a significant mapping performance is attainable even employing coarse antenna arrays provided that the number of antenna elements is sufficiently high.