The rapid evolution of generative technologies necessitates reliable methods for detecting AI-generated images. A critical limitation of current detectors is their failure to generalize to images from unseen generative models, as they often overfit to source-specific semantic cues rather than learning universal generative artifacts. To overcome this, we introduce a simple yet remarkably effective pixel-level mapping pre-processing step to disrupt the pixel value distribution of images and break the fragile, non-essential semantic patterns that detectors commonly exploit as shortcuts. This forces the detector to focus on more fundamental and generalizable high-frequency traces inherent to the image generation process. Through comprehensive experiments on GAN and diffusion-based generators, we show that our approach significantly boosts the cross-generator performance of state-of-the-art detectors. Extensive analysis further verifies our hypothesis that the disruption of semantic cues is the key to generalization.

In this paper, we study the throughput optimization problem of a CSMA network with imperfect sensing by considering both miss detection errors and false alarm errors. The analysis shows that the maximum network throughput of CSMA significantly drops as the miss detection probability increases, while is less sensitive to the false alarm probability and the number of nodes. To achieve the maximum throughput, the transmission probability of each node should decrease with the miss detection probability or the number of nodes, while increase with the false alarm probability. The maximum network throughput and the optimal transmission probability are both obtained and verified by simulation results.

An RF ranging sensor based on time-interleaved Dual Frequency Continuous Wave (DFCW) radar is proposed to measure the absolute distance between two shoes to aid a dead reckoning system for pedestrian navigation. Conventional single frequency Continuous Wave (CW) radar provides accurate but ambiguous range measurement, while DFCW techniques are employed to find the approximate range of the target and to remove the ambiguity of the single frequency CW radar. The two frequencies in the proposed DFCW radar work in a Time division Duplexing (TDD) manner so that they can share the same transceiver hardware. Such a composite DFCW/CW system can be implemented with a simple direct conversion receiver which is desirable for applications such as pedestrian navigation where compact and low power consumption devices are required. The feasibility of the proposed sensor is demonstrated by a sensor prototype based on Commercial Off The Shelf (COTS) components in a practical environment. An absolute accuracy (RMS) of 0.92 mm has been achieved for a maximum test range of 600 mm. The results show that the proposed ranging sensor is promising for measuring the distance in short range applications.