arXiv subjects

Chengfeng Zhu

Publications and source records attributed to Chengfeng Zhu.

2 recordsLinked to original sources

arXiv2024

Spin texture and tunneling magnetoresistance in atomically thin CrSBr

The exploration of spin configurations and magnetoresistance in van der Waals magnetic semiconductors, particularly in the realm of thin-layer structures, is of paramount significance for the development of two-dimensional spintronic nanodevices. In this letter, we present detailed magneto-transport and photoluminescence studies on few-layer CrSBr flakes utilizing a vertical tunneling device configuration. Our investigations revealed complex magnetic states along the evolutionary path of few-layer CrSBr. We observed intermediate states exhibiting identical net magnetization demonstrate rectification properties, reminiscent of a diode-like behavior at positive and negative bias voltages. Notably, in devices with 5-layer CrSBr, we detected an intriguing positive magnetoresistive state when subjected to an in-plane magnetic field along the b-axis. The implementation of the Mott two-current model successfully calculated the tunneling resistance of different magnetic states, thereby elucidating the spin configurations responsible for the observed transport phenomena. These insights not only provide new perspectives on the intricate spin textures of two-dimensional CrSBr but also highlight the efficacy of tunneling measurements as a sensitive method for probing magnetic order in van der Waals materials.

cond-mat.mes-hall↗

arXiv2018

A framework with updateable joint images re-ranking for Person Re-identification

Person re-identification plays an important role in realistic video surveillance with increasing demand for public safety. In this paper, we propose a novel framework with rules of updating images for person re-identification in real-world surveillance system. First, Image Pool is generated by using mean-shift tracking method to automatically select video frame fragments of the target person. Second, features extracted from Image Pool by convolutional network work together to re-rank original ranking list of the main image and matching results will be generated. In addition, updating rules are designed for replacing images in Image Pool when a new image satiating with our updating critical formula in video system. These rules fall into two categories: if the new image is from the same camera as the previous updated image, it will replace one of assist images; otherwise, it will replace the main image directly. Experiments are conduced on Market-1501, iLIDS-VID and PRID-2011 and our ITSD datasets to validate that our framework outperforms on rank-1 accuracy and mAP for person re-identification. Furthermore, the update ability of our framework provides consistently remarkable accuracy rate in real-world surveillance system.

cs.CV↗