arXiv subjects

Qihong Wu

Publications and source records attributed to Qihong Wu.

3 recordsLinked to original sources

arXiv2026

UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects

Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets. Existing algorithms rely on manually designed components, and general-purpose detectors are not optimized for UAV images, making it difficult to balance accuracy and complexity. To address these challenges, this paper proposes an end-to-end object detection framework, UFO-DETR, which integrates an LSKNet-based backbone network to optimize the receptive field and reduce the number of parameters. By combining the DAttention and AIFI modules, the model flexibly models multi-scale spatial relationships, improving multi-scale target detection performance. Additionally, the DynFreq-C3 module is proposed to enhance small target detection capability through cross-space frequency feature enhancement. Experimental results show that, compared to RT-DETR-L, the proposed method offers significant advantages in both detection performance and computational efficiency, providing an efficient solution for UAV edge computing.

cs.CV↗

arXiv2022

Cr$_3$X$_4$ (X=Se, Te) monolayers as new platform to realize robust spin filter, spin diode and spin valve

Two-dimensional ferromagnetic (FM) half-metals are promising candidates for advanced spintronic devices with small-size and high-capacity. Motivated by recent report on controlling synthesis of FM Cr$_3$Te$_4$ nanosheet, herein, to explore the potential application in spintronics, we designed spintronic devices based on Cr$_3$X$_4$ (X=Se, Te) monolayers and investigated their spin transport properties. We found that Cr$_3$Te$_4$ monolayer based device shows spin filtering and dual spin diode effect when applying bias voltage, while Cr$_3$S$_4$ monolayer is an excellent platform to realize a spin valve. The different transport properties are primarily ascribed to the semiconducting spin channel, which is close to and away from the Fermi level in Cr$_3$Te$_4$ and Cr$_3$Se$_4$ monolayers, respectively. Interestingly, the current in monolayer Cr$_3$Se$_4$ based device also displays a negative differential resistance effect (NDRE) and a high magnetoresistance ratio (up to 2*10$^3$). Moreover, we found thermally induced spin filtering effect and NDRE in Cr$_3$Se$_4$ junction when applying temperature gradient instead of bias voltage. These theoretical findings highlight the potential of Cr$_3$X$_4$ (X=Se, Te) monolayers in spintronic applications and put forward realistic materials to realize nanosale spintronic device.

cond-mat.mtrl-sci↗

arXiv2021

Data Privacy and Utility Trade-Off Based on Mutual Information Neural Estimator

In the era of big data and the Internet of Things (IoT), data owners need to share a large amount of data with the intended receivers in an insecure environment, posing a trade-off issue between user privacy and data utility. The privacy utility trade-off was facilitated through a privacy funnel based on mutual information. Nevertheless, it is challenging to characterize the mutual information accurately with small sample size or unknown distribution functions. In this article, we propose a privacy funnel based on mutual information neural estimator (MINE) to optimize the privacy utility trade-off by estimating mutual information. Instead of computing mutual information in traditional way, we estimate it using an MINE, which obtains the estimated mutual information in a trained way, ensuring that the estimation results are as precise as possible. We employ estimated mutual information as a measure of privacy and utility, and then form a problem to optimize data utility by training a neural network while the estimator's privacy discourse is less than a threshold. The simulation results also demonstrated that the estimated mutual information from MINE works very well to approximate the mutual information even with a limited number of samples to quantify privacy leakage and data utility retention, as well as optimize the privacy utility trade-off.

cs.IT↗