Search Results for author: Kaili Jiang

Found 7 papers, 0 papers with code

A Fast Power Spectrum Sensing Solution for Generalized Coprime Sampling

no code implementations23 Nov 2023 Kaili Jiang, Dechang Wang, Kailun Tian, HanCong Feng, Yuxin Zhao, Junyu Yuan, Bin Tang

The growing scarcity of spectrum resources, wideband spectrum sensing is required to process a prohibitive volume of data at a high sampling rate.

Wideband Spectrum Acquisition for UAV Swarm Using the Sparse Coding Fourier Transform

no code implementations14 Aug 2023 Kaili Jiang, Kailun Tian, HanCong Feng, Junyu Yuan, Bin Tang

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications.

Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver

no code implementations14 Aug 2023 Kaili Jiang, Dechang Wang, Kailun Tian, HanCong Feng, Yuxin Zhao, Sen Cao, Jian Gao, Xuying Zhang, Yanfei Li, Junyu Yuan, Ying Xiong, Bin Tang

To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper.

Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation

no code implementations13 Oct 2022 HanCong Feng, XinHai Yan, Kaili Jiang, Xinyu Zhao, Bin Tang

The automatic classification of radar waveform is a fundamental technique in electronic countermeasures (ECM). Recent supervised deep learning-based methods have achieved great success in a such classification task. However, those methods require enough labeled samples to work properly and in many circumstances, it is not available. To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels. Firstly, a pretext model is trained in a self-supervised way with the help of several data augmentation techniques to extract the class-dependent features. Next, the pseudo-supervised contrastive training is involved to further promote the separation between the extracted class-dependent features. And finally, the unsupervised problem is converted to a semi-supervised classification problem via pseudo label generation.

Classification Clustering +2

Time-dependent Orbital-free Density Functional Theory: Background and Pauli kernel approximations

no code implementations11 Feb 2021 Kaili Jiang, Michele Pavanello

The dynamic Pauli potential and associated kernel emerge as key ingredients of time-tependent orbital-free DFT.

Chemical Physics

6 nm super-resolution optical transmission and scattering spectroscopic imaging of carbon nanotubes using a nanometer-scale white light source

no code implementations8 Jun 2020 Xuezhi Ma, Qiushi Liu, Ning Yu, Da Xu, Sanggon Kim, Zebin Liu, Kaili Jiang, Bryan M. Wong, Ruoxue Yan, Ming Liu

Optical hyperspectral imaging based on absorption and scattering of photons at the visible and adjacent frequencies denotes one of the most informative and inclusive characterization methods in material research.

Super-Resolution Optics Materials Science

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