Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 16...Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.展开更多
Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint r...Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.展开更多
With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-base...With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.展开更多
[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with diffe...[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with different times of steaming and exposing to the sun,and to provide a basis for the determination of the best processing technology of Polygonati Rhizoma.[Methods]SETSAIL II AQ-C 18(5μm×250 mm×4.6 mm)was used as the column,the column temperature was 30℃,pure water(A)and acetonitrile(B)were eluted gradually,0-10 min,B(5%-10%),10-30 min,B(10%-35%),30-40 min,B(35%-60%),40-45 min,B(60%-100%),flow rate 1 mL/min,absorption wavelength 200 nm.[Results]The relative retained peak area RSDs of the common peaks in the precision,reproducibility and stability tests were all less than 5%.There were 17 common peaks in the fingerprint of nine batches of samples,and the retention time of Peak 2 was basically the same as that of the reference peak of 5-HMF.Peak 4 mainly existed in the chromatogram of Sample 3 to Sample 5,peaks 5 and 11 mainly existed after Sample 3,peaks 9,14 and 16 mainly existed after Sample 6,and peaks 12 and 17 mainly existed after Sample 4.[Conclusions]A total of 17 common peaks were obtained,and the Peak 2 was the designated peak,and the chemical components of each processed product were different.展开更多
Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)w...Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)worldwide in 2020:8.8 million(48%)in females and 9.3 million(52%)in males,giving a male:female ratio of 10:9.5.It may be initiated by the action of biological,physical,or chemical agents that cause a non-lethal,permanent,DNA error on the cell with a consequence of altered cell metabolism.This altered cell metabolism include the Warburg effect,altered lipid and amino acid metabolism and production of various metabolites.It also results in unique metabolic dependencies that,in some cases,can be targeted with precision medicine and nutrition,including drugs that selectively target metabolic enzymes.Metabolic fingerprinting has been applied to the study of carcinogenesis and is particularly helpful in early diagnosis,staging and choice of treatment,thus improving health outcomes.This technology could therefore be harnessed effectively while combining with other omics technologies.展开更多
Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the ...Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the performance of RF Fingerprint(RFF)based on preamble with fixed duration, a nonlinear RF fingerprinting method based on payload symbols is proposed for the wireless OFDM communication with the bit mapping scheme of QPSK. The wireless communication system is modeled as a Hammerstein system containing the nonlinear transmitter and multipath fading channel. A parameter separation technique based on orthogonal polynomial is presented for the estimation of the parameters of the Hammerstein system. The Hammerstein system parameter separation technique is firstly used to estimate the linear parameter with the training signal, which is used to compensate the adverse effect of the linear channel for the demodulation of the successive payload symbols. The demodulated payload symbols are further used to estimate the nonlinear coefficients of the transmitter with the Hammerstein system parameter separation technique again, which is used as the novel RFF for the authentication of the QPSK-OFDM device. Numerical simulations have verified the proposed method, which can also be extended to the OFDM signals with other bit mapping schemes.展开更多
The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome t...The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching algorithms.First,a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint database.Second,a fingerprint database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint databases.Finally,a classification weighted K-nearest neighbor fingerprint matching algorithm is proposed.The estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint database.The simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.展开更多
Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF ide...Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.展开更多
High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based...High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.展开更多
Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessme...Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.展开更多
A robust radio map is essential in implementing a fingerprint-based indoor positioning system(IPS).However,the offline site survey to manually construct the radio map is time-consuming and labour-intensive.Various int...A robust radio map is essential in implementing a fingerprint-based indoor positioning system(IPS).However,the offline site survey to manually construct the radio map is time-consuming and labour-intensive.Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys.This paper presents a novel fingerprint interpolator using a multi-path loss model(MPLM)to create the virtual fingerprints from the collected sample data based on different signal paths from different access points(APs).Based on the historical signal data,the poor signal paths are identified using their standard deviations.The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths.By consideringmultipath signal propagations from different APs,the inherent noise from these signal paths can be alleviated.Firstly,locations of the signal data with standard deviations higher than the threshold are identified.The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints.The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance.Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44%compared to the conventional interpolation techniques such as the Inverse DistanceWeighting,Kriging,and single Path LossModel.As a result,we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.展开更多
The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at ...The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at defeating the PKES,we propose a novel radio frequency(RF)fingerprinting method.Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal.This feature is then analyzed using a convolutional neural network(CNN)for device identification.In evaluation,we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model.Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient(GFCC)was the most compelling feature compared to Mel-Frequency Cepstral Coefficient(MFCC),Inverse Mel-Frequency Cepstral Coefficient(IMFCC),Linear-Frequency Cepstral Coefficient(LFCC),and Bark-Frequency Cepstral Coefficient(BFCC).Additionally,we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.展开更多
Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints...Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify people.The recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this purpose.Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models.Therefore,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones.In order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and classifiers.Discriminant Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as inputs.The performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are obtained.The proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times.Three distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints.The proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and effective.The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.展开更多
The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compound...The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.展开更多
This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed alg...This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.展开更多
[Objectives]Chromatographic fingerprint analysis technology and quantitative analysis of multi-components by singlemarker(QAMS)were used to identify the authenticity and multi-component quantitative analysis ofStrepto...[Objectives]Chromatographic fingerprint analysis technology and quantitative analysis of multi-components by singlemarker(QAMS)were used to identify the authenticity and multi-component quantitative analysis ofStreptocaulon griffithiiHook,which provided experimental reference for the quality evaluation and control ofS.griffithiiHook.[Methods]Method was carried out on Agilent ZORBAX SB-C18(150 mm×4.6 mm,5μm)column with mobile phase composed of methanol-0.2%phosphoric acid solution at a flow rate of 1.0 mL/min in gradient elution mode.The column temperature was maintained at 30℃,the injection volume was 5μL,and the detection wavelengths were set at 230 nm.The HPLC fingerprint ofS.griffithiiHook was established byFingerprint Similarity Evaluation Software of Traditional Chinese Med-i cine(2012 edition),and the quality of 11 batches ofS.griffithiiHook extracts was analyzed.The content of chlorogenic acid,caffeic acid and4-methoxysalicylaldehyde inS.griffithiiHook was determined by QAMS.[Results]Thirteen common peaks were identified in the extract ofS.g riffithiiHook,and three components were identified as chlorogenic acid,caffeic acid and 4-methoxysalicylaldehyde,there was no significant difference between QAMS method and external standard method with chlorogenic acid as reference substance.[Conclusions]The established HPLC method is specific,accurate,stable and reproducible,and it can be used as an effective method for the quality control ofS.griffithii Hook.展开更多
With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each ...With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each individual and does not change over time. Genetic information has been used to identify individuals in a variety of contexts, such as criminal investigations, paternity tests, and medical research. In this study, each individual’s genetic makeup has been formatted to create a secure, unique code that incorporates various elements, such as species, gender, and the genetic identification code itself. The combinations of markers required for this code have been derived from common single nucleotide polymorphisms (SNPs), points of variation found in the human genome. The final output is in the form of a 24 numerical code with each number having three possible combinations. The custom code can then be utilized to create various modes of identification on the decentralized blockchain network as well as personalized services and products that offer users a novel way to uniquely identify themselves in ways that were not possible before.展开更多
This work first describes a simple approach for the untargeted profiling of volatile compounds for distinguishing between white duck down (WDD) and white goose down (WGD) based on resolution-optimized GC-IMS combined ...This work first describes a simple approach for the untargeted profiling of volatile compounds for distinguishing between white duck down (WDD) and white goose down (WGD) based on resolution-optimized GC-IMS combined with optimized chemometric techniques, namely PCA. The detection method for down samples was established by using GC-IMS. Meanwhile, the reason of unpleasant odors caused by WDD was explained on the basis of the characteristic volatile compounds identification. GC-IMS fingerprinting can be considered a revolutionary approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. A total of 22 compounds were successfully separated and identified through GC-IMS method, and the significant differences in volatile compounds were observed in three parts of WDD and WGD samples. The most characteristic volatile compounds of WGD belong to aldehydes, whereas carboxylic acids from WDD were detected generated by autoxidation reaction. Meanwhile, the main reason of unpleasant odor generation was possibly attributed to the high concentration of volatile carboxylic acids of WDD. Therefore, the constructed model presents a simple and efficient method of analysis and serves as a basis for down processing and quality control.展开更多
Objective:To establish HPLC fingerprint of Nauclea officinalis stems in different harvesting periods,and analyze the effect of harvest time on the quality of the medicinal materials by combining with chemical pattern ...Objective:To establish HPLC fingerprint of Nauclea officinalis stems in different harvesting periods,and analyze the effect of harvest time on the quality of the medicinal materials by combining with chemical pattern recognition.Methods:The analysis was performed on Sun Fire-C18(150 mm×4.6 mm,5μm)column.The mobile phase consisted of acetonitrile-0.1%phosphoric acid solution(gradient elution)at a flow rate of 1.0 mL/min.Results:The HPLC fingerprint of Nauclea officinalis stems was established and 12 common peaks were determined,and 3 chromatographic peaks were identified by comparison with the mixed references.There were some differences in the quality of Nauclea officinalis in different harvesting periods.The OPLS-DA analysis successfully predicted four main markers of quality difference.Conclusion:The established HPLC fingerprint could reflect the composition characteristics of Nauclea officinalis stems in different harvesting period,and the main markers that influence the composition difference of the stems could be used as key indicators for the quality control.展开更多
Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints ...Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.展开更多
基金This work was supported by the Scientific Research Foundation for High Level Talents of Qingdao Agricultural University,China(665-1120015)the National Program for Quality and Safety Risk Assessment of Agricultural Products of China(GJFP2019011)the National Natural Science Foundation of China(42207017).
文摘Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.
基金supported in part by the National Science Foundation of China under Grants U22B2027,62172297,62102262,61902276 and 62272311,Tianjin Intelligent Manufacturing Special Fund Project under Grant 20211097the China Guangxi Science and Technology Plan Project(Guangxi Science and Technology Base and Talent Special Project)under Grant AD23026096(Application Number 2022AC20001)+1 种基金Hainan Provincial Natural Science Foundation of China under Grant 622RC616CCF-Nsfocus Kunpeng Fund Project under Grant CCF-NSFOCUS202207.
文摘Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.
文摘With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.
基金Supported by General Program of Guangxi Natural Science Foundation(2022GXNSFAA035599).
文摘[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with different times of steaming and exposing to the sun,and to provide a basis for the determination of the best processing technology of Polygonati Rhizoma.[Methods]SETSAIL II AQ-C 18(5μm×250 mm×4.6 mm)was used as the column,the column temperature was 30℃,pure water(A)and acetonitrile(B)were eluted gradually,0-10 min,B(5%-10%),10-30 min,B(10%-35%),30-40 min,B(35%-60%),40-45 min,B(60%-100%),flow rate 1 mL/min,absorption wavelength 200 nm.[Results]The relative retained peak area RSDs of the common peaks in the precision,reproducibility and stability tests were all less than 5%.There were 17 common peaks in the fingerprint of nine batches of samples,and the retention time of Peak 2 was basically the same as that of the reference peak of 5-HMF.Peak 4 mainly existed in the chromatogram of Sample 3 to Sample 5,peaks 5 and 11 mainly existed after Sample 3,peaks 9,14 and 16 mainly existed after Sample 6,and peaks 12 and 17 mainly existed after Sample 4.[Conclusions]A total of 17 common peaks were obtained,and the Peak 2 was the designated peak,and the chemical components of each processed product were different.
文摘Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)worldwide in 2020:8.8 million(48%)in females and 9.3 million(52%)in males,giving a male:female ratio of 10:9.5.It may be initiated by the action of biological,physical,or chemical agents that cause a non-lethal,permanent,DNA error on the cell with a consequence of altered cell metabolism.This altered cell metabolism include the Warburg effect,altered lipid and amino acid metabolism and production of various metabolites.It also results in unique metabolic dependencies that,in some cases,can be targeted with precision medicine and nutrition,including drugs that selectively target metabolic enzymes.Metabolic fingerprinting has been applied to the study of carcinogenesis and is particularly helpful in early diagnosis,staging and choice of treatment,thus improving health outcomes.This technology could therefore be harnessed effectively while combining with other omics technologies.
文摘Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the performance of RF Fingerprint(RFF)based on preamble with fixed duration, a nonlinear RF fingerprinting method based on payload symbols is proposed for the wireless OFDM communication with the bit mapping scheme of QPSK. The wireless communication system is modeled as a Hammerstein system containing the nonlinear transmitter and multipath fading channel. A parameter separation technique based on orthogonal polynomial is presented for the estimation of the parameters of the Hammerstein system. The Hammerstein system parameter separation technique is firstly used to estimate the linear parameter with the training signal, which is used to compensate the adverse effect of the linear channel for the demodulation of the successive payload symbols. The demodulated payload symbols are further used to estimate the nonlinear coefficients of the transmitter with the Hammerstein system parameter separation technique again, which is used as the novel RFF for the authentication of the QPSK-OFDM device. Numerical simulations have verified the proposed method, which can also be extended to the OFDM signals with other bit mapping schemes.
基金supported in part by Sub Project of National Key Research and Development plan in 2020.NO.2020YFC1511704Beijing Information Science and Technology University.NO.2020KYNH212,NO.2021CGZH302+1 种基金Beijing Science and Technology Project(Grant No.Z211100004421009)in part by the National Natural Science Foundation of China(Grant No.61971048)。
文摘The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching algorithms.First,a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint database.Second,a fingerprint database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint databases.Finally,a classification weighted K-nearest neighbor fingerprint matching algorithm is proposed.The estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint database.The simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.
基金supported by Innovation Talents Promotion Program of Shaanxi Province,China(No.2021TD08)。
文摘Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.
基金supported in part by the National Natural Science Foundation of China under Grant No.61771474in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX212243+2 种基金in part by the Young Talents of Xuzhou Science and Technology Plan Project under Grant No.KC19051in part by the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University under Grant No.2021D02in part by the Open Fund of Information Photonics and Optical Communications (IPOC) (BUPT)。
文摘High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.
基金Department of Science and Technology-SERB-SRG research grant(Grant No.:SRG/2021/000750-G)and Department of Biotechnology for Ramalingaswami grant(Grant No.:BT/RLF/Re-entry/21/2020)Director,Prabodh Kumar Trivedi,of CSIR-CIMAP for providing infrastructure,facility,and funding support from CSIR,India(Grant Nos.:FC2020-23/NMITLI/TLP0001&TLP0002)We acknowledge Dr.Ritu Trivedi(CSIR-CDRI Lucknow,India)for support and Dr.Abolie Girme and Dr.Lal Hingorani(Pharmanza herbal Pvt.Ltd,India)for providing Withania somnifera samples.We acknowledge Dr.Neerja Tiwari for FT-NIR access,Ms.Manju Yadav and Ms.Namita Gupta for HPLC access,and Ms.Anju Yadav for GC-MS access.Authors would like to thank Aroma mission HCP-0007,India for funding support.Prof.Christopher T.Elliott would like to thank Bualuang ASEAN Chair Professor Fund,UK and Queen's University Belfast Fund,UK.
文摘Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.
基金funded by the Ministry of Higher EducationMalaysia under the Fundamental Research Grant Scheme(FRGS)with grant number FRGS/1/2019/ICT02/MMU/02/1.
文摘A robust radio map is essential in implementing a fingerprint-based indoor positioning system(IPS).However,the offline site survey to manually construct the radio map is time-consuming and labour-intensive.Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys.This paper presents a novel fingerprint interpolator using a multi-path loss model(MPLM)to create the virtual fingerprints from the collected sample data based on different signal paths from different access points(APs).Based on the historical signal data,the poor signal paths are identified using their standard deviations.The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths.By consideringmultipath signal propagations from different APs,the inherent noise from these signal paths can be alleviated.Firstly,locations of the signal data with standard deviations higher than the threshold are identified.The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints.The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance.Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44%compared to the conventional interpolation techniques such as the Inverse DistanceWeighting,Kriging,and single Path LossModel.As a result,we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea Government(MIST)(No.2022-0-01022,Development of Collection and Integrated Analysis Methods of Automotive Inter/Intra System Artifacts through Construction of Event-Based Experimental System).
文摘The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at defeating the PKES,we propose a novel radio frequency(RF)fingerprinting method.Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal.This feature is then analyzed using a convolutional neural network(CNN)for device identification.In evaluation,we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model.Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient(GFCC)was the most compelling feature compared to Mel-Frequency Cepstral Coefficient(MFCC),Inverse Mel-Frequency Cepstral Coefficient(IMFCC),Linear-Frequency Cepstral Coefficient(LFCC),and Bark-Frequency Cepstral Coefficient(BFCC).Additionally,we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.
文摘Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify people.The recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this purpose.Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models.Therefore,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones.In order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and classifiers.Discriminant Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as inputs.The performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are obtained.The proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times.Three distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints.The proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and effective.The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.
文摘The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.
基金Supported by Scientific Research Project of Guangxi Zhuang Autonomous Region Administration of Traditional Chinese Medicine(GZZC2019147)2020 Guangxi University Young and Middle-aged Teachers'Basic Scientific Research Ability Improvement Project(2020KY13034)+1 种基金the First Scientific Research Project of High-level Talents in the Affiliated Hospital of Youjiang Medical University for Nationalities in 2019(Y20196311)Scientific Research Project of Guangxi Zhuang Autonomous Region Administration of Traditional Chinese Medicine(20210241)。
文摘[Objectives]Chromatographic fingerprint analysis technology and quantitative analysis of multi-components by singlemarker(QAMS)were used to identify the authenticity and multi-component quantitative analysis ofStreptocaulon griffithiiHook,which provided experimental reference for the quality evaluation and control ofS.griffithiiHook.[Methods]Method was carried out on Agilent ZORBAX SB-C18(150 mm×4.6 mm,5μm)column with mobile phase composed of methanol-0.2%phosphoric acid solution at a flow rate of 1.0 mL/min in gradient elution mode.The column temperature was maintained at 30℃,the injection volume was 5μL,and the detection wavelengths were set at 230 nm.The HPLC fingerprint ofS.griffithiiHook was established byFingerprint Similarity Evaluation Software of Traditional Chinese Med-i cine(2012 edition),and the quality of 11 batches ofS.griffithiiHook extracts was analyzed.The content of chlorogenic acid,caffeic acid and4-methoxysalicylaldehyde inS.griffithiiHook was determined by QAMS.[Results]Thirteen common peaks were identified in the extract ofS.g riffithiiHook,and three components were identified as chlorogenic acid,caffeic acid and 4-methoxysalicylaldehyde,there was no significant difference between QAMS method and external standard method with chlorogenic acid as reference substance.[Conclusions]The established HPLC method is specific,accurate,stable and reproducible,and it can be used as an effective method for the quality control ofS.griffithii Hook.
文摘With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each individual and does not change over time. Genetic information has been used to identify individuals in a variety of contexts, such as criminal investigations, paternity tests, and medical research. In this study, each individual’s genetic makeup has been formatted to create a secure, unique code that incorporates various elements, such as species, gender, and the genetic identification code itself. The combinations of markers required for this code have been derived from common single nucleotide polymorphisms (SNPs), points of variation found in the human genome. The final output is in the form of a 24 numerical code with each number having three possible combinations. The custom code can then be utilized to create various modes of identification on the decentralized blockchain network as well as personalized services and products that offer users a novel way to uniquely identify themselves in ways that were not possible before.
文摘This work first describes a simple approach for the untargeted profiling of volatile compounds for distinguishing between white duck down (WDD) and white goose down (WGD) based on resolution-optimized GC-IMS combined with optimized chemometric techniques, namely PCA. The detection method for down samples was established by using GC-IMS. Meanwhile, the reason of unpleasant odors caused by WDD was explained on the basis of the characteristic volatile compounds identification. GC-IMS fingerprinting can be considered a revolutionary approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. A total of 22 compounds were successfully separated and identified through GC-IMS method, and the significant differences in volatile compounds were observed in three parts of WDD and WGD samples. The most characteristic volatile compounds of WGD belong to aldehydes, whereas carboxylic acids from WDD were detected generated by autoxidation reaction. Meanwhile, the main reason of unpleasant odor generation was possibly attributed to the high concentration of volatile carboxylic acids of WDD. Therefore, the constructed model presents a simple and efficient method of analysis and serves as a basis for down processing and quality control.
基金supported by Major Science and Technology Project of Hainan Province(ZDKJ201805)。
文摘Objective:To establish HPLC fingerprint of Nauclea officinalis stems in different harvesting periods,and analyze the effect of harvest time on the quality of the medicinal materials by combining with chemical pattern recognition.Methods:The analysis was performed on Sun Fire-C18(150 mm×4.6 mm,5μm)column.The mobile phase consisted of acetonitrile-0.1%phosphoric acid solution(gradient elution)at a flow rate of 1.0 mL/min.Results:The HPLC fingerprint of Nauclea officinalis stems was established and 12 common peaks were determined,and 3 chromatographic peaks were identified by comparison with the mixed references.There were some differences in the quality of Nauclea officinalis in different harvesting periods.The OPLS-DA analysis successfully predicted four main markers of quality difference.Conclusion:The established HPLC fingerprint could reflect the composition characteristics of Nauclea officinalis stems in different harvesting period,and the main markers that influence the composition difference of the stems could be used as key indicators for the quality control.
文摘Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.
