Radio frequency fingerprinting (RFF) is a technique that identifies wireless devices by exploiting unique hardware-induced imperfections measured in their transmitted RF signals. We propose a lightweight RFF method by leveraging temporal variations in RF signals. By combining multi-scale feature extraction through coarse and fine segmentation with DTW-based similarity features, our approach achieves an accuracy of 0.9882 and a Macro-F1 score of 0.988 in our experiment using the Wi-Fi RF data collected from 120 devices.

In this study, we propose a framework to support upgrading a gaming simulation by designers. Here, designers are those who are directly involved in the design and construction of a gaming specification. A gaming simulation provides its participants with experiences in a virtual social system. Upgrade refers to the modification of the basic specification of a gaming simulation in response to feedback from the external environment and its reconstruction accordingly. To demonstrate the usefulness of the proposed framework, we present an example that describes the operational processes of upgrade an existing gaming simulation, BARNGA, following the demand to make it one that a single player is able to play.