Convolutional Neural Networks for Searching Superflares from Pixel-level Data of TESS

In this work, six convolutional neural networks (CNNs) have been trained based on %different feature images and arrays from the database including 15,638 superflare candidates on solar-type stars, which are collected from the three-years observations of Transiting Exoplanet Survey Satellite ({\em TESS}). These networks are used to replace the artificially visual inspection, which was a direct way to search for superflares, and exclude false positive events in recent years. Unlike other methods, which only used stellar light curves to search superflare signals, we try to identify superflares through {\em TESS} pixel-level data with lower risks of mixing false positive events, and give more reliable identification results for statistical analysis. The evaluated accuracy of each network is around 95.57\%. After applying ensemble learning to these networks, stacking method promotes accuracy to 97.62\% with 100\% classification rate, and voting method promotes accuracy to 99.42\% with relatively lower classification rate at 92.19\%. We find that superflare candidates with short duration and low peak amplitude have lower identification precision, as their superflare-features are hard to be identified. The database including 71,732 solar-type stars and 15,638 superflare candidates from {\em TESS} with corresponding feature images and arrays, and trained CNNs in this work are public available.Comment: 25 pages, 11 figures, 3 tables, submitte

Sparsity Signal Detection for Indoor GSSK-VLC System

In this paper, the signal detection problem in indoor visible light communication (VLC) system aided by generalized space shift keying (GSSK) is modeled as a sparse signal reconstruction problem, which has lower computational complexity by exploiting the sparse reconstruction algorithms in compressed sensing (CS). In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed based on singular value decomposition (SVD), which theoretically guarantees the feasibility of utilizing CS based sparse signal detection method in indoor GSSK-VLC system. Then, by adopting classical orthogonal matching pursuit (OMP) algorithm and compressed sampling matching pursuit (CoSaMP) algorithm, the GSSK signals are efficiently detected in the considered indoor GSSK-VLC system. Furthermore, a more efficient detection algorithm combined with OMP and maximum likelihood (ML) is also presented especially for SSK scenario. Finally, the effectiveness of the proposed sparsity aided detection algorithms in indoor GSSK-VLC system are verified by computer simulations. The results show that the proposed algorithms can achieve better bit error rate (BER) and lower computation complexity than ML based detection method. Specifically, a signal-to-noise ratio (SNR) gain as high as 12 dB is observed in the SSK scenario and about 5 dB in case of a GSSK scenario upon employing our proposed detection methods