We utilize dopant-matrix strategy and emulsion polymerization to obtain aqueous afterglow dispersions from a liquid precursor, which avoids the processing of solid materials, protects organic triplets and achieves long phosphorescence...

In today's RF and microwave circuits, there is an ever-increasing demand for higher level of system integration that leads to massive computational tasks during simulation, optimization, and statistical analyses, requiring efficient modeling methods so that the whole process can be achieved reliably. Since active devices such as transistors are the core of modern RF/microwave systems, the way they are modeled in terms of accuracy and flexibility will critically influence the system design, and thus, the overall system performance. In this article, the authors present neural-and fuzzy neural-based computer-aided design techniques that can efficiently characterize and model RF/microwave transistors such as field-effect transistors and heterojunction bipolar transistors. The proposed techniques based on multilayer perceptrons neural networks and c-means clustering algorithms are demonstrated through examples.

Electro-optic data-acquisition systems encode the output from voltage-history diagnostics onto optical signals. The optical signals can propagate long distances over fiber-optic links without degrading the bandwidth of the encoded signal while protecting the recording electronics from overvoltage damage. The sinusoidal response and tolerance to high-input voltages of the Mach-Zehnder modulator used for the encoding leads to the additional advantage of a high dynamic range and a reduced need for manually swapping attenuators. We have demonstrated a single-shot, electro-optic data-acquisition system with a 600:1 dynamic range. This system provides optical isolation and a bandwidth of 6 GHz. The prototype system uses multiple optical wavelengths to allow for the multiplexing of up to eight signals onto one photodetector.