This paper describes the design, fabrication, and electromechanical characteristics of inductive stents developed for intelligent stent applications. The stents, fabricated out of 316L stainless-steel tubes using laser machining, are patterned to have zigzag loops without bridge struts, and when expanded, become a helix-like structure. Highly conductive metals such as copper and gold are coated on the stents to improve their inductive/antenna function. The Q-factor of the stent is shown to increase by a factor of 7 at 150 MHz with copper coating. The expansion of the stent from 2 to 4 mm diameter results in a 3.2× increase in the inductance, obtaining ∼1 µH at a similar frequency. The stent passivated by Parylene-C film is used to characterize its resonance in different media including saline. The copper-coated inductive stent exhibits a 2.4× radial stiffness for 1 mm strain as well as a 16× bending compliance compared with a commercial stent, each of which is potentially beneficial in preventing/mitigating stent failures such as recoil as well as enabling easier navigation through intricate blood vessels. The mechanical stiffness may be tailored by adjusting stent-wire thickness while maintaining necessary coating thickness to achieve particular mechanical requirements and high inductive performance simultaneously.
Decentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametrically-specified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing the target (or the desired) event via a non-linear mapping function. In order to meet stringent energy/bandwidth requirements, sensors quantize their vector-valued observations into one or few bits and send them over error-prone (to model low-power communications) reporting channels to a fusion center (FC). Therein, a global (better) decision is taken via the proposed test. Its closed form and asymptotic (large-size WSN) performance are obtained, and the latter leveraged to optimize quantizers. The appeal of the proposed approach is confirmed via simulations.
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