arXiv subjects

Jinghua Zhang

Publications and source records attributed to Jinghua Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2023

The Pre-explosion Environments and The Progenitor of SN 2023ixf from the Hobby Eberly Telescope Dark Energy Experiment (HETDEX)

Supernova (SN) 2023ixf was discovered on May 19th, 2023. The host galaxy, M101, was observed by the Hobby Eberly Telescope Dark Energy Experiment (HETDEX) collaboration over the period April 30, 2020 -- July 10, 2020, using the Visible Integral-field Replicable Unit Spectrograph (VIRUS; $3470\lesssimλ\lesssim5540$ Å) on the 10-m Hobby-Eberly Telescope (HET). The fiber filling factor within $\pm$ 30 arcsec of SN 2023ixf is 80% with a spatial resolution of 1 arcsec. The r<5.5 arcsec surroundings are 100% covered. This allows us to analyze the spatially resolved pre-explosion local environments of SN 2023ixf with nebular emission lines. The 2-dimensional (2D) maps of the extinction and the star-formation rate (SFR) surface density ($Σ_{\rm SFR}$) show weak increasing trends in the radial distributions within the r<5.5 arcsec regions, suggesting lower values of extinction and SFR in the vicinity of the progenitor of SN 2023ixf. The median extinction and that of the surface density of SFR within r<3 arcsec are $E(B-V)=0.06\pm0.14$, and $Σ_{\rm SFR}=10^{-5.44\pm0.66}~\rm M_{\odot}\cdot yr^{-1}\cdot arcsec^{-2}$. There is no significant change in extinction before and after the explosion. The gas metallicity does not change significantly with the separation from SN 2023ixf. The metal-rich branch of the $R_{23}$ calculations indicates that the gas metallicity around SN 2023ixf is similar to the solar metallicity ($\sim Z_{\odot}$). The archival deep images from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) show a clear detection of the progenitor of SN 2023ixf in the $z$-band at $22.778\pm0.063$ mag, but non-detections in the remaining four bands of CFHTLS ($u,g,r,i$). The results suggest a massive progenitor of $\approx$ 22 $M_\odot$.

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arXiv2023

Near-Infrared Ca II Triplet As An Stellar Activity Indicator: Library and Comparative Study

We have established and released a new stellar index library of the Ca II Triplet, which serves as an indicator for characterizing the chromospheric activity of stars. The library is based on data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS) Data Release 9 (DR9). To better reflect the chromospheric activity of stars, we have defined new indices $R$ and $R^{+}$. The library includes measurements of $R$ and $R^{+}$ for each Ca II infrared triplet (IRT) from 699,348 spectra of 562,863 F, G and K-type solar-like stars with Signal-to-Noise Ratio (SNR) higher than 100, as well as the stellar atmospheric parameters and basic information inherited from the LAMOST LRS Catalog. We compared the differences between the 3 individual index of the Ca II Triplet and also conducted a comparative analysis of $R^{+}_{\lambda8542}$ to the Ca II H&K $S$ and $R^+_{HK}$ index database. We find the fraction of low active stars decreases with $T_{eff}$ and the fraction of high active first decrease with decreasing temperature and turn to increase with decreasing temperature at 5800K. We also find a significant fraction of stars that show high activity index in both Ca II H&K and IRT are binaries with low activity, some of them could be discriminated in Ca II H&K $S$ index and $R^{+}_{\lambda8542}$ space. This newly stellar library serves as a valuable resource for studying chromospheric activity in stars and can be used to improve our comprehension of stellar magnetic activity and other astrophysical phenomena.

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arXiv2023

Age of FGK Dwarfs Observed with LAMOST and GALAH: Considering the Oxygen Enhancement

Varying oxygen abundance could impact the modeling-inferred ages. This work aims to estimate the ages of dwarfs considering observed oxygen abundance. To characterize 67,503 LAMOST and 4,006 GALAH FGK-type dwarf stars, we construct a grid of stellar models which take into account oxygen abundance as an independent model input. Compared with ages determined with commonly-used $α$-enhanced models, we find a difference of $\sim$9% on average when the observed oxygen abundance is considered. The age differences between the two types of models are correlated to [Fe/H] and [O/$α$], and they are relatively significant on stars with [Fe/H] $\lesssim$ -0.6 dex. Generally, varying 0.2 dex in [O/$α$] will alter the age estimates of metal-rich (-0.2 $<$ [Fe/H] $<$ 0.2) stars by $\sim$10%, and relatively metal-poor (-1 $<$ [Fe/H] $<$ -0.2) stars by $\sim$15%. Of the low-O stars with [Fe/H] $<$ 0.1 dex and [O/$α$] $\sim$ -0.2 dex, many have fractional age differences of $\geq$ 10%, and even reach up to 27%. The fractional age difference of high-O stars with [O/$α$] $\sim$ 0.4 dex reaches up to -33% to -42% at [Fe/H] $\lesssim$ -0.6 dex. We also analyze the chemical properties of these stars. We find a decreasing trend of [Fe/H] with age from 7.5-9 Gyr to 5-6.5 Gyr for the stars from the LAMOST and GALAH. The [O/Fe] of these stars increases with decreasing age from 7.5-9 Gyr to 3-4 Gyr, indicating that the younger population is more O-rich.

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arXiv2023

Deep Learning for Iris Recognition: A Review

Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

cs.CV↗

arXiv2023

ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos

The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities. So far, the detection of sperms and impurities still largely relies on the traditional image processing and detection techniques which only yield limited performance and often require manual intervention in the detection process, therefore unfavorably escalating the time cost and injecting the subjective bias into the analysis. Encouraged by the successes of deep learning methods in numerous object detection tasks, here we report a deep learning model based on Double Branch Feature Extraction Network (DBFEN) and Cross-conjugate Feature Pyramid Networks (CCFPN).DBFEN is designed to extract visual features from tiny objects with a double branch structure, and CCFPN is further introduced to fuse the features extracted by DBFEN to enhance the description of position and high-level semantic information. Our work is the pioneer of introducing deep learning approaches to the detection of sperms and impurities. Experiments show that the highest AP50 of the sperm and impurity detection is 91.13% and 59.64%, which lead its competitors by a substantial margin and establish new state-of-the-art results in this problem.

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arXiv2022

Artificial Neural Networks for Finger Vein Recognition: A Survey

Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. They are almost impossible to be stolen and difficult to interfere with by external conditions. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, it without relying on feature engineering and have superior performance. To summarize the development of finger vein recognition based on artificial neural networks, this paper collects 149 related papers. First, we introduce the background of finger vein recognition and the motivation of this survey. Then, the development history of artificial neural networks and the representative networks on finger vein recognition tasks are introduced. The public datasets that are widely used in finger vein recognition are then described. After that, we summarize the related finger vein recognition tasks based on classical neural networks and deep neural networks, respectively. Finally, the challenges and potential development directions in finger vein recognition are discussed. To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.

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arXiv2022

An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder--decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97\% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting.

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arXiv2022

Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trend

Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields. Because veins are buried beneath the skin tissue, finger vein image recognition has an unparalleled advantage, which is not easily disturbed by external factors. This review summarizes 46 papers about deep learning for finger vein image recognition from 2017 to 2021. These papers are summarized according to the tasks of deep neural networks. Besides, we present the challenges and potential development directions of finger vein image recognition.

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arXiv2022

ET White Paper: To Find the First Earth 2.0

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries.

astro-ph.IM↗

arXiv2022

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

With the acceleration of urbanization and living standards, microorganisms play increasingly important roles in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The changing trend can be adjusted in time and provided a basis for the improvement. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image segmentation methods with periods. This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.

cs.CV↗

arXiv2022

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The analysis of microorganisms is essential for making full use of different microorganisms. The conventional analysis methods are laborious and time-consuming. Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it. However, the automatic microorganism image analysis faces many challenges, such as the requirement of a robust algorithm caused by various application occasions, insignificant features and easy under-segmentation caused by the image characteristic, and various analysis tasks. Therefore, we conduct this review to comprehensively discuss the characteristics of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are presented. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

cs.CV↗

arXiv2021

A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

eess.IV↗

arXiv2021

Lithium Evolution of Giant Stars Observed by LAMOST and Kepler

Mapping lithium evolution for evolved stars will provide restrictions and constraints on the fundamental stellar interior physical processes, which further shed light on our understanding of the theory of stellar structure and evolution. Based on a sample of 1,848 giants with known evolutionary phases and lithium abundances from the LAMOST-\kepler{} and LAMOST-\emph{K}2 fields, we construct mass-radius diagrams to characterize the evolutionary features of lithium. The stars at red giant branch (RGB) phase show natural depletion along with their stellar evolution, particularly, there is no obvious crowd stars with anomalously high Li abundances near the bump. Most of the low-mass stars reaching their zero-age sequence of core-helium-burning (ZAHeB) have Li abundances around $\sim1.0$\,dex, which show an increase of Li abundance by $\sim0.6$\,dex compared to the stars above the bump of RGB. This suggests the helium flash can be responsible for moderate Li production. While for super Li-rich stars, some special mechanisms should be considered during helium flash. Other scenarios, such as merger, could also be interpretations given the Li-rich stars can be found at anytime during the steady state phase of core He-burning. During the core He-burning (HeB) phase, there is no indication of obvious lithium depletion.

astro-ph.SR↗

arXiv2021

A Comparison for Patch-level Classification of Deep Learning Methods on Transparent Environmental Microorganism Images: from Convolutional Neural Networks to Visual Transformers

Nowadays, analysis of Transparent Environmental Microorganism Images (T-EM images) in the field of computer vision has gradually become a new and interesting spot. This paper compares different deep learning classification performance for the problem that T-EM images are challenging to analyze. We crop the T-EM images into 8 * 8 and 224 * 224 pixel patches in the same proportion and then divide the two different pixel patches into foreground and background according to ground truth. We also use four convolutional neural networks and a novel ViT network model to compare the foreground and background classification experiments. We conclude that ViT performs the worst in classifying 8 * 8 pixel patches, but it outperforms most convolutional neural networks in classifying 224 * 224 pixel patches.

cs.CV↗

arXiv2021

Secrecy Outage Probability of Cognitive Small-Cell Network with Unreliable Backhaul Connections

In this paper, we investigate the secrecy performance of underlay cognitive small-cell radio network with unreliable backhaul connections. The secondary cognitive small-cell transmitters are connected to macro base station by wireless backhaul links. The small-cell network is sharing the same spectrum with the primary network ensuring that a desired outage probability constraint in the primary network is always satisfied. We propose an optimal transmitter selection (OTS) scheme for small-cell network to transfer information to the destination. The closed-form expression of secrecy outage probability are derived. Our result shows that increasing the primary transmitter's transmit power and the number of small-cell transmitter can improve the system performance. The backhaul reliability of secondary and the desired outage probability of the primary also have significant impact on the system.

eess.SP↗

arXiv2021

Secrecy Performance of Small-Cell Networks with Transmitter Selection and Unreliable Backhaul under Spectrum Sharing Environment

We investigate the secrecy performance of an underlay small-cell cognitive radio network under unreliable backhaul connections. The small-cell network shares the same spectrum with the primary network, ensuring that a desired outage probability constraint is always met in the primary network. {To improve the security of the small-cell cognitive network, we propose three sub-optimal small-cell transmitter selection schemes,} namely sub-optimal transmitter selection, minimal interference selection, and minimal eavesdropping selection. Closed-form expressions of the non-zero secrecy rate, secrecy outage probability, and ergodic secrecy capacity are provided for the schemes along with asymptotic expressions. {We also propose an optimal selection scheme and compare performances with the sub-optimal selection schemes.} {Computable expressions for the non-zero secrecy rate and secrecy outage probability are presented for the optimal selection scheme.} Our results show that by increasing the primary transmitter's power and the number of small-cell transmitters, the system performance improves. The selection scheme, the backhaul reliability, and the primary user quality-of-service constraint also have a significant impact on secrecy performance.

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arXiv2021

A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

Environmental microorganism (EM) offers a high-efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms. The pairwise deep learning features technique combines the capability of handcrafted and deep learning features. In this technique we, leverage the Shi and Tomasi interest points by extracting deep learning features from patches which are centered at interest points locations. Then, to increase the number of potential features that have intermediate spatial characteristics between nearby interest points, we use Delaunay triangulation theorem and straight-line geometric theorem to pair the nearby deep learning features. The potential of pairwise features is justified on the classification of EMs using SVMs, k-NN, and Random Forest classifier. The pairwise features obtain outstanding results of 99.17%, 91.34%, 91.32%, 91.48%, and 99.56%, which are the increase of about 5.95%, 62.40%, 62.37%, 61.84%, and 3.23% in accuracy, F1-score, recall, precision, and specificity respectively, compared to non-paired deep learning features.

cs.CV↗

arXiv2021

EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks

Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. The EMDS-5 dataset has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. Image denoising uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the aspect of edge detection, six edge detection operators are used to detect the edges of the images, and two evaluation indicators, peak-signal to noise ratio and mean structural similarity, are used for evaluation. Image segmentation includes single-object image segmentation and multi-object image segmentation. Six methods are used for single-object image segmentation, while k-means and U-net are used for multi-object segmentation.We extract nine features from the images in EMDS-5 and use the Support Vector Machine classifier for testing. In terms of image classification, we select the VGG16 feature to test different classifiers. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval. We select the last layer of features of these two deep learning networks as feature vectors. We use mean average precision as the evaluation index for retrieval.

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