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Yiqing Guo

Publications and source records attributed to Yiqing Guo.

30 records · Page 2Linked to original sources
arXiv2024

The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024)

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments.

astro-ph.IM↗
arXiv2023

Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model

With fewer feature dimensions, filter banks are often used in light-weight full-band speech enhancement models. In order to further enhance the coarse speech in the sub-band domain, it is necessary to apply a post-filtering for harmonic retrieval. The signal processing-based comb filters used in RNNoise and PercepNet have limited performance and may cause speech quality degradation due to inaccurate fundamental frequency estimation. To tackle this problem, we propose a learnable comb filter to enhance harmonics. Based on the sub-band model, we design a DNN-based fundamental frequency estimator to estimate the discrete fundamental frequencies and a comb filter for harmonic enhancement, which are trained via an end-to-end pattern. The experiments show the advantages of our proposed method over PecepNet and DeepFilterNet.

eess.AS↗
arXiv2023

Personalized speech enhancement combining band-split RNN and speaker attentive module

Target speaker information can be utilized in speech enhancement (SE) models to more effectively extract the desired speech. Previous works introduce the speaker embedding into speech enhancement models by means of concatenation or affine transformation. In this paper, we propose a speaker attentive module to calculate the attention scores between the speaker embedding and the intermediate features, which are used to rescale the features. By merging this module in the state-of-the-art SE model, we construct the personalized SE model for ICASSP Signal Processing Grand Challenge: DNS Challenge 5 (2023). Our system achieves a final score of 0.529 on the blind test set of track1 and 0.549 on track2.

eess.AS↗
arXiv2022

Prospective Study on Observations of γ-Ray Sources in the Galaxy Using the HADAR Experiment

The High Altitude Detection of Astronomical Radiation (HADAR) experiment is a refracting terrestrial telescope array based on the atmospheric Cherenkov imaging technique. It focuses the Cherenkov light emitted by extensive air showers through a large aperture water-lens system for observing very-high-energy-rays and cosmic rays. With the advantages of a large field-of-view (FOV) and low energy threshold, the HADAR experiment operates in a large-scale sky scanning mode to observe galactic sources. This study presents the prospects of using the HADAR experiment for the sky survey of TeV γ-ray sources from TeVCat and provids a one-year survey of statistical significance. Results from the simulation show that a total of 23 galactic point sources, including five supernova remnant sources and superbubbles, four pulsar wind nebula sources, and 14 unidentified sources, were detected in the HADAR FOV with a significance greater than 5 standard deviations (σ). The statistical significance for the Crab Nebula during one year of operation reached 346.0 σ and the one-year integral sensitivity of HADAR above 1TeV was ~1.3%-2.4% of the flux from the Crab Nebula.

astro-ph.HE↗
arXiv2022

Quantitative Assessment of DESIS Hyperspectral Data for Plant Biodiversity Estimation in Australia

Diversity of terrestrial plants plays a key role in maintaining a stable, healthy, and productive ecosystem. Though remote sensing has been seen as a promising and cost-effective proxy for estimating plant diversity, there is a lack of quantitative studies on how confidently plant diversity can be inferred from spaceborne hyperspectral data. In this study, we assessed the ability of hyperspectral data captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) for estimating plant species richness in the Southern Tablelands and Snowy Mountains regions in southeast Australia. Spectral features were firstly extracted from DESIS spectra with principal component analysis, canonical correlation analysis, and partial least squares analysis. Then regression was conducted between the extracted features and plant species richness with ordinary least squares regression, kernel ridge regression, and Gaussian process regression. Results were assessed with the coefficient of correlation ($r$) and Root-Mean-Square Error (RMSE), based on a two-fold cross validation scheme. With the best performing model, $r$ is 0.71 and RMSE is 5.99 for the Southern Tablelands region, while $r$ is 0.62 and RMSE is 6.20 for the Snowy Mountains region. The assessment results reported in this study provide supports for future studies on understanding the relationship between spaceborne hyperspectral measurements and terrestrial plant biodiversity.

cs.LG↗
arXiv2021

A preliminary study about gravitational wave radiation and cosmic heat death

We study the role of gravitational waves (GW) in the heat death of the universe. Due to the GW emission, in a very long period, dynamical systems in the universe suffer from persistent mechanical energy dissipation, evolving to a state of universal rest and death. With N-body simulations, we adopt a simple yet representative scheme to calculate the energy loss due to the GW emission. For current dark matter systems with mass $\sim10^{12}-10^{15} M_\odot$, we estimate their GW emission timescale as $\sim10^{19}-10^{25}$ years. This timescale is significantly larger than any baryon processes in the universe, but still $\sim10^{80}$ times shorter than that of the Hawking radiation. We stress that our analysis could be invalid due to many unknowns such as the dynamical chaos, the quadrupole momentum of halos, the angular momentum loss, the dynamic friction, the central black hole accretion, the dark matter decays or annihilations, the property of dark energy and the future evolution of the universe.

astro-ph.CO↗
arXiv2020

Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Possibility of estimateing the non-linear systematics using fast simulations

The tomographic AP method is so far the best method in separating the Alcock-Paczynski (AP) signal from the redshift space distortion (RSD) effects and deriving powerful constraints on cosmological parameters using the $\lesssim40h^{-1}\ \rm Mpc$ clustering region. To guarantee that the method can be easily applied to the future large scale structure (LSS) surveys, we study the possibility of estimating the systematics of the method using fast simulation method. The major contribution of the systematics comes from the non-zero redshift evolution of the RSD effects, which is quantified by $\hatξ_{Δs}(μ,z)$ in our analysis, and estimated using the BigMultidark exact N-body simulation and approximate COLA simulation samples. We find about 5\%/10\% evolution when comparing the $\hatξ_{Δs}(μ,z)$ measured as $z=0.5$/$z=1$ to the measurements at $z=0$. We checked the inaccuracy in the 2pCFs computed using COLA, and find it 5-10 times smaller than the intrinsic systematics of the tomographic AP method, indicating that using COLA to estimate the systematics is good enough. Finally, we test the effect of halo bias, and find $\lesssim$1.5\% change in $\hatξ_{Δs}$ when varying the halo mass within the range of $2\times 10^{12}$ to $10^{14}$ $M_{\odot}$. We will perform more studies to achieve an accurate and efficient estimation of the systematics in redshift range of $z=0-1.5$.

astro-ph.CO↗
arXiv2018

Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

cs.CV↗
arXiv2016

Sensitivity study of (10,100) GeV gamma-ray bursts with double shower front events from ARGO-YBJ

ARGO-YBJ, located at the YangBaJing Cosmic Ray Observatory (4300 m a.s.l., Tibet, China), is a full coverage air shower array, with an energy threshold of 300 GeV for gamma-ray astronomy. Most of the recorded events are single front showers, satisfying the trigger requirement of at least 20 particles detected in a given time window. However, in 13% of the events, two randomly arriving showers may be recorded in the same time window, and the second one, in generally smaller, does not need to satisfy the trigger condition. These events are called double front shower events. By using these small showers, well under the trigger threshold, the detector primary energy threshold can be lowered to a few tens of GeV. In this paper, the angular resolution that can be achieved with these events is evaluated by a full Monte Carlo simulation. The ARGO-YBJ sensitivity in detecting gamma-ray bursts (GRBs) by using double front shower events is also studied for various cutoff energies, time durations, and zenith angles of GRBs in the field view of ARGO.

astro-ph.HE↗
arXiv2013

On the Galactic Center Being the Main Source of Galactic Cosmic Rays as Evidenced by Recent Cosmic Ray and Gamma Ray Observations

We revisit the idea that the Galactic center (GC) is the dominant source of Galactic cosmic rays (GCRs), based on a series of new observational evidence. A unified model is proposed to explain the new phenomena of GCRs and $γ$-rays simultaneously. The GCRs are thought to be accelerated during past activities of the GC. The pair production process of GCRs in the strong radiation field due to the GC activity is responsible for the knee structure of the cosmic ray spectra. A f raction of $e^+e^-$ produced by pair production interactions, can be reaccelerated in the induced bipolar jets and be transported into the halo, leaving the Fermi $γ$-ray bubbles and WMAP microwave haze as the remnant signal. Finally, the CRs diffuse in the bulge could further interact with the interstellar medium to produce low energy $e^+e^-$. After cooling down, these positrons may annihilate to produce the 511 keV line emission as discovered by INTEGRAL.

astro-ph.HE↗
arXiv2011

Attenuation study for Tibet Water Cherenkov Muon Detector Array-A

This work aims at online calibrating the signal attenuation of the long cable used in Tibet Water Cherenkov Muon detector array-A (Tibet MD-A) under the 37 000 m2 Tibet air shower array. Based on the waterproof connection of the PMT (R3600_06) with the signal cable, and the characters of the high voltage divider, terminal reflection method is explored and used for measuring the signal attenuation with a practical way to eliminate the contribution of the pulse baseline (pedestal). Comparison measurement between it and QDC (Charge-to-Digital Conversion) data taking method are carried out by using open-ended cables, confirming that terminal reflection method is a fast and convenient method, and suitable to online calibrate the signal attenuation for Tibet MD-A. At 26\degree C, the measured attenuation coefficient with the 250-m cable, permanently connected to the PMT, is ~13.9%. Also, the cable frequency response is measured by using the sinusoidal signals, which could be used to study the time dispersion of the real signal produced with Tibet MD-A detector via Fourier analysis.

physics.ins-det↗