A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directl...A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.展开更多
Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear d...Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation processes.BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors.When the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring.Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant analysis.In addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.展开更多
To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conven...To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.展开更多
Linear discriminant analysis and kernel vector quantization are integrated into vector quantization based speech recognition system for improving the recognition accuracy of Mandarin digits. These techniques increase ...Linear discriminant analysis and kernel vector quantization are integrated into vector quantization based speech recognition system for improving the recognition accuracy of Mandarin digits. These techniques increase the class separability and optimize the clustering procedure. Speaker-dependent (SD) and speaker-independent (SI) experiments are performed to evaluate the performance of the proposed method. The experiment results show that the proposed method is capable of reaching the word error rate of 3.76% in SD case and 6.60 % in SI case. Such a system can be suitable for being embedded in personal digital assistant(PDA), mobile phone and so on to perform voice controlling such as digit dialing, calculating, etc.展开更多
The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysph...The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this paper.We have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia detection.Several ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected dataset.The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models.According to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.展开更多
An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-neares...An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.展开更多
Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimu...Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.展开更多
We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classi...We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.展开更多
It is necessary that vision system should aid laser-cutting manipulator to position the specified part of each maize seed for getting the slice breeding genotype analysis with high throughput.Each of trivial maize see...It is necessary that vision system should aid laser-cutting manipulator to position the specified part of each maize seed for getting the slice breeding genotype analysis with high throughput.Each of trivial maize seeds should be recognized and positioned in a certain posture.Correlation area ratio(CAR)is defined as the metric of pixel attribute.A large template of round mask is adopted for seed morphological detection to measure the CAR values.We get the feature points extracted from the seed image through the isometric mapping operation.Iterative processes of linear discriminant analysis search the morphological data space to learn non-linear transformations to the space where data are linearly separable.Linear discriminant analysis utilizes the data directional distribution to position the major axis and distinguish different parts of maize seed.The labeling partition operation is applied for picking out the scattered pieces to be finely clustered.Without denoising process,the feature region could be recognized with accuracies by the synthetical methods.Extensive experiments on a large amount of seeds demonstrate the effectiveness of proposed methods.展开更多
A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate it...A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.展开更多
The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The we...The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.展开更多
Linear discrimiant analysis (LDA) has been used in face recognition. But it is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination. In order to overcome these problems,...Linear discrimiant analysis (LDA) has been used in face recognition. But it is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination. In order to overcome these problems, kernel discriminant analysis for face recognition is presented. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are given.展开更多
Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propos...Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System （IDS） to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service （DOS） and black hole attacks on vehicular ad hoe networks （VANETs）. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis （LDA） and Quadratic Diseriminant Analysis （QDA） which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.展开更多
Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single...Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.展开更多
Nitrogen(N)is the most important nutrient for plants;however,microbe-mediated N transformation under different N forms is unclear.This experiment investigated the effects of four treatments fertilized with various N f...Nitrogen(N)is the most important nutrient for plants;however,microbe-mediated N transformation under different N forms is unclear.This experiment investigated the effects of four treatments fertilized with various N forms,no N(control,CK),100%ammonium N(AN),100%nitrate N(NN),and 50%ammonium N+50%nitrate N(ANNN),on soil chemical properties,rhizosphere bacterial network,and rice growth.The ANNN treatment enhanced soil pH by 6.9%,soil organic carbon by 12%,and microbial biomass N(MBN)by 60%compared to CK.The linear discriminant effect size(LEfSe)analysis indicated four highly abundant biomarkers of bacterial communities each in the CK,NN,and AN treatments,while the ANNN treatment showed six highly abundant biomarkers with maximum effect size and linear discriminant analysis(LDA)score>4.The 16S rRNA gene-predicted functions under PICRUST indicated glutathione metabolism and proteasome and Tax4Fun recorded amino acid metabolism in the ANNN treatment.The combination of ammonium and nitrate N(i.e.,the ANNN treatment)significantly increased the expression levels of the genes encoding N metabolism,including AMT1,NRT2.1,GS1,and GOGAT1,and induced 39%,27%,35%,and 38%increase in nitrate reductase,nitrite reductase,glutamine synthetase,and glutamate synthase,respectively,in comparison to CK.In addition,the ANNN treatment promoted rice leaf photosynthetic rate by 37%,transpiration rate by 41%,CO_(2) exchange rate by 11%,and stomatal conductance by 18%compared to CK,while increased N use efficiency(NUE)by 10%and 19%,respectively,compared to the AN and NN treatments.These findings suggest that the combination of ammonium and nitrate N can promote bacterial community abundance,composition,and functional pathways by improving soil properties and can increase NUE and rice growth.This study provides a theoretical basis for the rational application of N fertilizers and the implications of this approach for future sustainable crop production.展开更多
The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral...The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral microorganism is crucial for the rapid diagnosis and early interventions of associated diseases.Traditional methods for microbial detection primarily include the plate culturing,polymerase chain reaction and enzyme-linked immunosorbent assay,which are either time-consuming or laborious.Herein,we reported a persistent luminescence-encoded multiple-channel optical sensing array and achieved the rapid and accurate identification of oral-derived microorganisms.Our results demonstrate that electrostatic attractions and hydrophobic-hydrophobic interactions dominate the binding of the persistent luminescent nanoprobes to oral microorganisms and the microbial identification process can be finished within 30 min.Specifically,a total of 7 oral-derived microorganisms demonstrate their own response patterns and were differentiated by linear discriminant analysis(LDA)with the accuracy up to 100%both in the solution and artificial saliva samples.Moreover,the persistent luminescence encoded array sensor could also discern the microorganism mixtures with the accuracy up to 100%.The proposed persistent luminescence encoding sensor arrays in this work might offer new ideas for rapid and accurate oralderived microorganism detection,and provide new ways for disease diagnosis associated with microbial metabolism.展开更多
Accurate detection of dimethyl methylphosphonate(DMMP),a simulant for chemical warfare agents,is vital for both public safety and military defense.However,conventional detection methods suffer from low selectivity,owi...Accurate detection of dimethyl methylphosphonate(DMMP),a simulant for chemical warfare agents,is vital for both public safety and military defense.However,conventional detection methods suffer from low selectivity,owing to interference from structurally similar compounds.In this study,we present a highly sensitive and selective gas sensor utilizing a solid-mounted film bulk acoustic resonator based on carbon nanotubes functionalized with hexafluoroisopropanol(HFiP)to enhance DMMP detection.This approach leverages the strong hydrogen bonding between HFiP and DMMP molecules to significantly improve the sensor’s adsorption capacity and selectivity.To further refine selectivity and at the same time solve the cross-sensitivity problem of sensitive membranes,we introduce a virtual sensor array design,generated by modulating the input power to the resonator,which enables the sensor to operate in multiple response modes across varying vibrational amplitudes.These multimodal responses are subjected to linear discriminant analysis,allowing precise differentiation of DMMP from other volatile organic compounds such as tributyl phosphate and dimethyl phthalate.Our results demonstrate superior performance in terms of both sensitivity and selectivity,offering a robust solution for detecting low-concentration DMMP in complex environments.展开更多
In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algori...In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.展开更多
A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree o...A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data.展开更多
Some dimensionality reduction （DR） approaches based on support vector machine （SVM） are proposed. But the acquirement of the projection matrix in these approaches only considers the between-class margin based on S...Some dimensionality reduction （DR） approaches based on support vector machine （SVM） are proposed. But the acquirement of the projection matrix in these approaches only considers the between-class margin based on SVM while ignoring the within-class information in data. This paper presents a new DR approach, call- ed the dimensionality reduction based on SVM and LDA （DRSL）. DRSL considers the between-class margins from SVM and LDA, and the within-class compactness from LDA to obtain the projection matrix. As a result, DRSL can realize the combination of the between-class and within-class information and fit the between-class and within-class structures in data. Hence, the obtained projection matrix increases the generalization ability of subsequent classification techniques. Experiments applied to classification techniques show the effectiveness of the proposed method.展开更多
基金The National Natural Science Foundation of China (No.61374194)
文摘A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.
基金support of National Key Research and Development Program of China(2020YFA0908303)National Natural Science Foundation of China(21878081).
文摘Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation processes.BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors.When the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring.Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant analysis.In addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.
基金The National Natural Science Foundation of China(No.61673108,61231002)
文摘To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.
文摘Linear discriminant analysis and kernel vector quantization are integrated into vector quantization based speech recognition system for improving the recognition accuracy of Mandarin digits. These techniques increase the class separability and optimize the clustering procedure. Speaker-dependent (SD) and speaker-independent (SI) experiments are performed to evaluate the performance of the proposed method. The experiment results show that the proposed method is capable of reaching the word error rate of 3.76% in SD case and 6.60 % in SI case. Such a system can be suitable for being embedded in personal digital assistant(PDA), mobile phone and so on to perform voice controlling such as digit dialing, calculating, etc.
文摘The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this paper.We have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia detection.Several ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected dataset.The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models.According to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.
文摘An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.
文摘Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.
文摘We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.
文摘It is necessary that vision system should aid laser-cutting manipulator to position the specified part of each maize seed for getting the slice breeding genotype analysis with high throughput.Each of trivial maize seeds should be recognized and positioned in a certain posture.Correlation area ratio(CAR)is defined as the metric of pixel attribute.A large template of round mask is adopted for seed morphological detection to measure the CAR values.We get the feature points extracted from the seed image through the isometric mapping operation.Iterative processes of linear discriminant analysis search the morphological data space to learn non-linear transformations to the space where data are linearly separable.Linear discriminant analysis utilizes the data directional distribution to position the major axis and distinguish different parts of maize seed.The labeling partition operation is applied for picking out the scattered pieces to be finely clustered.Without denoising process,the feature region could be recognized with accuracies by the synthetical methods.Extensive experiments on a large amount of seeds demonstrate the effectiveness of proposed methods.
文摘A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.
基金supported by the Science and Technology Project of Guangdong Province of China(Nos.2014A020213016 and 2014A020212445).
文摘The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.
文摘Linear discrimiant analysis (LDA) has been used in face recognition. But it is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination. In order to overcome these problems, kernel discriminant analysis for face recognition is presented. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are given.
文摘Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System （IDS） to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service （DOS） and black hole attacks on vehicular ad hoe networks （VANETs）. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis （LDA） and Quadratic Diseriminant Analysis （QDA） which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.
基金the financial support of the National Natural Science Foundation of China(No.22168009)。
文摘Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.
基金financially supported by the National Natural Science Foundation of China(No.32172109)the Natural Science Foundation of Guangdong Province,China(No.2021A1515010566).
文摘Nitrogen(N)is the most important nutrient for plants;however,microbe-mediated N transformation under different N forms is unclear.This experiment investigated the effects of four treatments fertilized with various N forms,no N(control,CK),100%ammonium N(AN),100%nitrate N(NN),and 50%ammonium N+50%nitrate N(ANNN),on soil chemical properties,rhizosphere bacterial network,and rice growth.The ANNN treatment enhanced soil pH by 6.9%,soil organic carbon by 12%,and microbial biomass N(MBN)by 60%compared to CK.The linear discriminant effect size(LEfSe)analysis indicated four highly abundant biomarkers of bacterial communities each in the CK,NN,and AN treatments,while the ANNN treatment showed six highly abundant biomarkers with maximum effect size and linear discriminant analysis(LDA)score>4.The 16S rRNA gene-predicted functions under PICRUST indicated glutathione metabolism and proteasome and Tax4Fun recorded amino acid metabolism in the ANNN treatment.The combination of ammonium and nitrate N(i.e.,the ANNN treatment)significantly increased the expression levels of the genes encoding N metabolism,including AMT1,NRT2.1,GS1,and GOGAT1,and induced 39%,27%,35%,and 38%increase in nitrate reductase,nitrite reductase,glutamine synthetase,and glutamate synthase,respectively,in comparison to CK.In addition,the ANNN treatment promoted rice leaf photosynthetic rate by 37%,transpiration rate by 41%,CO_(2) exchange rate by 11%,and stomatal conductance by 18%compared to CK,while increased N use efficiency(NUE)by 10%and 19%,respectively,compared to the AN and NN treatments.These findings suggest that the combination of ammonium and nitrate N can promote bacterial community abundance,composition,and functional pathways by improving soil properties and can increase NUE and rice growth.This study provides a theoretical basis for the rational application of N fertilizers and the implications of this approach for future sustainable crop production.
基金financially supported by Quanzhou high-level Talents Project Fund(No.2022C033R)the National Natural Science Foundation of China(Nos.21925401,52221001)+2 种基金the Fundamental Research Funds for the Central Universities(No.2042022rc0004)the Postdoctoral Innovative Research of Hubei Province of China(No.211000025)the interdisciplinary innovative talents foundation from Renmin Hospital of Wuhan University。
文摘The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral microorganism is crucial for the rapid diagnosis and early interventions of associated diseases.Traditional methods for microbial detection primarily include the plate culturing,polymerase chain reaction and enzyme-linked immunosorbent assay,which are either time-consuming or laborious.Herein,we reported a persistent luminescence-encoded multiple-channel optical sensing array and achieved the rapid and accurate identification of oral-derived microorganisms.Our results demonstrate that electrostatic attractions and hydrophobic-hydrophobic interactions dominate the binding of the persistent luminescent nanoprobes to oral microorganisms and the microbial identification process can be finished within 30 min.Specifically,a total of 7 oral-derived microorganisms demonstrate their own response patterns and were differentiated by linear discriminant analysis(LDA)with the accuracy up to 100%both in the solution and artificial saliva samples.Moreover,the persistent luminescence encoded array sensor could also discern the microorganism mixtures with the accuracy up to 100%.The proposed persistent luminescence encoding sensor arrays in this work might offer new ideas for rapid and accurate oralderived microorganism detection,and provide new ways for disease diagnosis associated with microbial metabolism.
基金supported by the State Key Laboratory of Pathogens and Biosecurity(Grant No.SKLPBS2240).
文摘Accurate detection of dimethyl methylphosphonate(DMMP),a simulant for chemical warfare agents,is vital for both public safety and military defense.However,conventional detection methods suffer from low selectivity,owing to interference from structurally similar compounds.In this study,we present a highly sensitive and selective gas sensor utilizing a solid-mounted film bulk acoustic resonator based on carbon nanotubes functionalized with hexafluoroisopropanol(HFiP)to enhance DMMP detection.This approach leverages the strong hydrogen bonding between HFiP and DMMP molecules to significantly improve the sensor’s adsorption capacity and selectivity.To further refine selectivity and at the same time solve the cross-sensitivity problem of sensitive membranes,we introduce a virtual sensor array design,generated by modulating the input power to the resonator,which enables the sensor to operate in multiple response modes across varying vibrational amplitudes.These multimodal responses are subjected to linear discriminant analysis,allowing precise differentiation of DMMP from other volatile organic compounds such as tributyl phosphate and dimethyl phthalate.Our results demonstrate superior performance in terms of both sensitivity and selectivity,offering a robust solution for detecting low-concentration DMMP in complex environments.
文摘In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.
基金The National Natural Science Foundation of China (No.60503023,60872160)the Natural Science Foundation for Universities ofJiangsu Province (No.08KJD520009)the Intramural Research Foundationof Nanjing University of Information Science and Technology(No.Y603)
文摘A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data.
文摘Some dimensionality reduction （DR） approaches based on support vector machine （SVM） are proposed. But the acquirement of the projection matrix in these approaches only considers the between-class margin based on SVM while ignoring the within-class information in data. This paper presents a new DR approach, call- ed the dimensionality reduction based on SVM and LDA （DRSL）. DRSL considers the between-class margins from SVM and LDA, and the within-class compactness from LDA to obtain the projection matrix. As a result, DRSL can realize the combination of the between-class and within-class information and fit the between-class and within-class structures in data. Hence, the obtained projection matrix increases the generalization ability of subsequent classification techniques. Experiments applied to classification techniques show the effectiveness of the proposed method.
