arXiv subjects

YongFeng Huang

Publications and source records attributed to YongFeng Huang.

4 recordsLinked to original sources

arXiv2023

Multi-wavelength analysis of the SN-associated low-luminosity GRB 171205A

Multi-wavelength properties of the nearby Supernova(SN)-associated low-luminosity GRB 171205A are investigated in depth to constrain its physicalan origin synthetically. The pulse width is found to be correlated with energy with a power-law index of $-0.24\pm0.07 $, which is consistent with the indices of other SN/GRBs but larger than those of long GRBs. By analyzing the overall light curve of its prompt gamma-rays and X-ray plateaus simultaneously, we infer that the early X-rays together with the gamma-rays should reflect the activities of central engine while the late X-rays may be dominated by the interaction of external shocks with circumburst material. In addition, we find that the host radio flux and offset of GRB 171205A are similar to those of other nearby low-luminosity GRBs. We adopt 9 SN/GRBs with measured offset to build a relation between peak luminosity ($L_{γ,p}$) and spectral lag ($τ$) as $L_{γ,p}\proptoτ^{-1.91\pm0.33}$. The peak luminosity and the projected physical offset of both 12 SN/GRBs and 10 KN/GRBs are found to be moderately correlated, suggesting their different progenitors. The multi-wavelength afterglow fitted with a top-hat jet model indicates that the jet half-opening angle and the viewing angle of GRB 171205A are $\thicksim$ 34.4 and 41.8 degrees, respectively, which implies that the off-axis emissions are dominated by the peripheral cocoon rather than the jet core.

astro-ph.HE↗

arXiv2020

FCEM: A Novel Fast Correlation Extract Model For Real Time Steganalysis of VoIP Stream via Multi-head Attention

Extracting correlation features between codes-words with high computational efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this paper, we utilized attention mechanisms, which have recently attracted enormous interests due to their highly parallelizable computation and flexibility in modeling correlation in sequence, to tackle steganalysis problem of Quantization Index Modulation (QIM) based steganography in compressed VoIP stream. We design a light-weight neural network named Fast Correlation Extract Model (FCEM) only based on a variant of attention called multi-head attention to extract correlation features from VoIP frames. Despite its simple form, FCEM outperforms complicated Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) models on both prediction accuracy and time efficiency. It significantly improves the best result in detecting both low embedded rates and short samples recently. Besides, the proposed model accelerates the detection speed as twice as before when the sample length is as short as 0.1s, making it a excellent method for online services.

cs.MM↗

arXiv2019

Fast Steganalysis Method for VoIP Streams

In this letter, we present a novel and extremely fast steganalysis method of Voice over IP (VoIP) streams, driven by the need for a quick and accurate detection of possible steganography in VoIP streams. We firstly analyzed the correlations in carriers. To better exploit the correlation in code-words, we mapped vector quantization code-words into a semantic space. In order to achieve high detection efficiency, only one hidden layer is utilized to extract the correlations between these code-words. Finally, based on the extracted correlation features, we used the softmax classifier to categorize the input stream carriers. To boost the performance of this proposed model, we incorporate a simple knowledge distillation framework into the training process. Experimental results show that the proposed method achieves state-of-the-art performance both in detection accuracy and efficiency. In particular, the processing time of this method on average is only about 0.05\% when sample length is as short as 0.1s, attaching strong practical value to online serving of steganography monitor.

cs.MM↗

arXiv2016

Intensity Distribution Function and Statistical Properties of Fast Radio Bursts

Fast Radio Bursts (FRBs) are intense radio flashes from the sky that are characterized by millisecond durations and Jansky-level flux densities. We carried out a statistical analysis on FRBs discovered. Their mean dispersion measure, after subtracting the contribution from the interstellar medium of our Galaxy, is found to be $\sim 660\,\rm pc\,cm^{-3}$, supporting their being from cosmological origin. Their energy released in radio band spans about two orders of magnitude, with a mean value of $\sim 10^{39}$ ergs. More interestingly, although the FRB study is still in a very early phase, the published collection of FRBs enables us to derive a useful intensity distribution function. For the 16 non-repeating FRBs detected by Parkes telescope and the Green Bank Telescope, the intensity distribution can be described as $dN/dF_{\rm obs} = (4.1 \pm 1.3) \times 10^3 \, F_{\rm obs}^{-1.1\pm0.2} \; \rm sky^{-1}\,day^{-1}$, where $F_{\rm obs}$ is the observed radio fluence in units of Jy~ms. Here the power-law index is significantly flatter than the expected value of 2.5 for standard candles distributed homogeneously in a flat Euclidean space. Based on this intensity distribution function, the Five-hundred-meter Aperture Spherical radio Telescope (FAST) will be able to detect about 5 FRBs for every 1000 hours of observation time.

astro-ph.HE↗