Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.

Continuous flow analysis of dissolved total phosphorus in seawater by UVK_(2)S_(2)O_(8) online digestion method

Several methods for analysis of dissolved total phosphorus in seawater were reviewed. Discussions were focused on UVirradiation and persulphate oxidation methods which are the most popular dissolved organic phosphorus determinationmethods presently. The compounds used for the phosphorus recovery test were categorized into three groups accordingto their chemical structure. It was found that low power UV irradiation can decompose POC or PC bonds efficientlybut may be inefficient for POP bonds. Heating-bath in acid condition is useful for decomposing POP bonds. Usingthe continuous flow analysis system (Auto-analyzer II), UV digestion and heating-bath, series experiments were carriedout based on the above analysis. Eleven model compounds were employed for the phosphorus recovery test and thefactors influencing the decomposition efficiency of dissolved compounds containing phosphorus were clarified. Finally,the optimal design for determination of dissolved total phosphorus in seawater based on the routine continuous flowanalysis system was presented. For the organic mono-phosphate, the recovery is more than 90% and a recovery of33%~51% was obtained for inorganic or organic polyphosphates. Up to now, this is the highest decompositionefficiency for dissolved phosphorus based on the continuous flow analysis system.

The continuous wavelet projections algorithm: A practical spectral-feature-mining approach for crop detection

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among features.However, feature-selection methods that satisfy both requirements are lacking. To address this issue,in this study, a novel method, the continuous wavelet projections algorithm(CWPA), was developed,which has advantages of both continuous wavelet analysis(CWA) and the successive projections algorithm(SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content(R^(2)= 0.8521)and equivalent water thickness(R^(2)= 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.

Development of a bifurcation analysis approach based on gPROMS platform

A bifurcation analysis approach is developed based on the process simulator gPROMS platform, which can automatically trace a solution path, detect and pass the bifurcation points and check the stability of solutions. The arclength continuation algorithm is incorporated as a process entity in gPROMS to overcome the limit of turning points and get multiple solutions with respect to a user-defined parameter. The bifurcation points are detected through a bifurcation test function τ which is written in C ++ routine as a foreign object connected with gPROMS through Foreign Process Interface. The stability analysis is realized by evaluating eigenvalues of the Jacobian matrix of each steady state solution. Two reference cases of an adiabatic CSTR and a homogenous azeotropic distillation from literature are studied, which successfully validate the reliability of the proposed approach. Besides the multiple steady states and Hopf bifurcation points, a more complex homoclinic bifurcation behavior is found for the distillation case compared to literature.

Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in China.Powdery mildew(Blumeria graminis)and stripe rust(Puccinia striiformis f.sp.Tritici)are two of the most prevalent winter wheat diseases in China.This study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral data.The spectral normalization process was applied prior to the analysis.Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index.In order to reduce the number of wavelet regions,correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance.Based on the selected spectral bands,vegetation indices and wavelet features,the discriminate models were established using Fisher’s linear discrimination analysis(FLDA)and support vector machine(SVM).The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses,with overall accuracies of 0.91,0.72,and 0.72 respectively for powdery mildew,stripe rust and nitrogen-water by using FLDA,and 0.79,0.67 and 0.65 respectively by using SVM.FLDA was more suitable for differentiating stresses in winter wheat,with respective accuracies of 78.1%,95.6%and 95.7%for powdery mildew,stripe rust,and nitrogen-water stress.Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification.The accuracies of high-scale and low-scale features with an overall accuracy(OA)of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88.The detection of the severity of stripe rust using this method showed an enhanced reliability(R^(2)=0.828).

A continuous/discontinuous deformation analysis(CDDA)method based on deformable blocks for fracture modeling

In the framework of finite element meshes,a novel continuous/discontinuous deformation analysis(CDDA)method is proposed in this paper for modeling of crack problems.In the present CDDA,simple polynomial interpolations are defined at the deformable block elements,and a link element is employed to connect the adjacent block elements.The CDDA is particularly suitable for modeling the fracture propagation because the switch from continuous deformation analysis to discontinuous deformation analysis is natural and convenient without additional procedures.The SIFs(stress intensity factors)for various types of cracks,such as kinked cracks or curved cracks,can be easily computed in the CDDA by using the virtual crack extension technique(VCET).Both the formulation and implementation of the VCET in CDDA are simple and straightforward.Numerical examples indicate that the present CDDA can obtain high accuracy in SIF results with simple polynomial interpolations and insensitive to mesh sizes,and can automatically simulate the crack propagation without degrading accuracy.

NUMERICAL SIMULATION OF THE TEMPERATURE FIELD IN A SOLDER BALL UNDER PULSED LASER AND ITS EFFECT ON THE VIBRATION OF THE SOLDER

The research presented here is focused on the vibration condition of a small volume solder solder ball,which is placed on the surface of a soldering pad and is exerted a pulse modulated continuous wave laser heat source. Finite element method is applied to analyzed the temperature field in the solder ball, and experi- ment is conducted to test the vibration. the results show that,that, the temperature field flucturates with the same frequency as that of the laser pulse, which in turn causes a forced vibration of the same frequency in the liquid solder ball.

Two-dimensional correlation analysis of continuous online in situ ATR-FTIR on the adsorption of butyl xanthate at the surface of α-PbO

The adsorption behavior of butyl xanthate on the surface of lead oxide was investigated using continuous online in situ attenuated total reflectance Fourier transform infrared（ATR-FTIR） spectroscopy technique and two dimensional（2D） correlation analysis.The adsorbed layer studied was prepared by coating α-PbO particles onto the surfaces of the ZnSe crystal.The appearance of spectral peaks at 1203 cm^-1,1033 cm^-1 and their red shift indicated the formation and aggregation of xanthate at the surface of α-PbO.According to 1R intensity changes after rinsing with deionized water and a NaOH solution,the adsorption was proved to be a chemisorption type.The competition between xanthate and OH^- for the surfaces leads to desorption of xanthate at higher pH.The technique of 2D correlation ATR-FTIR spectroscopy was used to evaluate the changing order of spectral intensities in the adsorption process,and the results indicated that xanthate micelles were formed at the surfaces.The adsorption kinetics of butyl xanthate was found to be a pseudo-second-order reaction model and the adsorption capacity of butyl xanthate at α-PbO was as high as 281 mg g^-1 after 150 min.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

Automatic spikes detection in seismogram

Data processing for seismic network is very complex and fussy,because a lot of data is recorded in seismicnetwork every day,which make it impossible to process these data all by manual work.Therefore,seismic datashould be processed automatically to produce a initial results about events detection and location.Afterwards,these results are reviewed and modified by analyst.In automatic processing data quality checking is important.There are three main problem data that exist in real seismic records,which include:spike,repeated data and

Mental Activity of the Brain and Emotional Stress

The article considers the biological nature and origins of emotional stress. Emotional stress is primarily formed in the mental activity of the brain in the form of pronounced long-term negative emotions and is secondarily manifested in neurophysiological mechanisms and somatovegetative processes. However, all studies of the development of emotional stress are focused on the study of central neurophysiological mechanisms, excluding the possibility of analysis for the “sources” of emotional stress, which is primarily formed in the subjective sphere of brain activity, i.e., in the mechanisms of emotions. In our studies, we propose a fundamentally new methodology for studying the mental activity of the human brain and, in particular, the mechanisms of emotions. Thereby, modern methods of psychophysiology make it possible to come closer to understanding the nature of emotional stress.

In-situ transient photovoltage study on interface electron transfer regulation of carbon dots/NiCo_(2)O_(4) photocatalyst for the enhanced overall water splitting activity

Photocatalytic hydrogen production by overall water solar-splitting is a prospective strategy to solve energy crisis.However,the rapid recombination of photogenerated electron–hole pairs deeply restricts photocatalytic activity of catalysts.Here,the in-situ transient photovoltage(TPV)technique was developed to investigate the interfacial photogenerated carrier extraction,photogenerated carrier recombination and the interfacial electron delivery kinetics of the photocatalyst.The carbon dots/NiCo_(2)O_(4)(CDs/NiCo_(2)O_(4))composite shows weakened recombination rate of photogenerated carriers due to charge storage of CDs,which enhances the photocatalytic water decomposition activity without any scavenger.CDs can accelerate the interface electron extraction about 0.09 ms,while the maximum electron storage time by CDs is up to 0.7 ms.The optimal CDs/NiCo_(2)O_(4)composite(5 wt.%CDs)displays the hydrogen production of 62µmol·h^(−1)·g^(−1) and oxygen production of 29µmol·h^(−1)·g^(−1) at normal atmosphere,which is about 4 times greater than that of pristine NiCo_(2)O_(4).This work provides sufficient evidence on the charge storage of CDs and the interfacial charge kinetics of photocatalysts on the basis of in-situ TPV tests.

Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data

Detection of yellow rust using hyperspectral data is of practical importance for disease control and prevention.As an emerging spectral analysis method,continuous wavelet analysis(CWA)has shown great potential for the detection of plant diseases and insects.Given the spectral interval of airborne or spaceborne hyperspectral sensor data differ greatly,it is important to understand the impact of spectral interval on the performance of CWA in detecting yellow rust in winter wheat.A field experiment was conducted which obtained spectral measurements of both healthy and disease-infected plants.The impacts of the mother wavelet type and spectral interval on disease detection were analyzed.The results showed that spectral features derived from all four mother wavelet types exhibited sufficient sensitivity to the occurrence of yellow rust.The Mexh wavelet slightly outperformed the others in estimating disease severity.Although the detecting accuracy generally declined with decreasing of spectral interval,relatively high accuracy levels were maintained(R^(2)>0.7)until a spectral interval of 16 nm.Therefore,it is recommended that the spectral interval of hyperspectral data should be no larger than 16 nm for the detection of yellow rust.The relatively loose spectral interval requirement permits extensive applications for disease detection with hyperspectral imagery.