Fluorescence spectral imaging for characterization of tissue based on multivariate statistical analysis

A novel spectral imaging method for the classification of light-induced autofluorescence spectra based on principal component analysis (PCA), a multivariate statistical analysis technique commonly used for studying the statistical characteristics of spectral data, is proposed and investigated. A set of optical spectral filters related to the diagnostically relevant principal components is proposed to process autofluorescence signals optically and generate principal component score images of the examined tissue simultaneously. A diagnostic image is then formed on the basis of an algorithm that relates the principal component scores to tissue pathology. With autofluorescence spectral data collected from nasopharyngeal tissue in vivo, a set of principal component filters was designed to process the autofluorescence signal, and the PCA-based diagnostic algorithms were developed to classify the spectral signal. Simulation results demonstrate that the proposed spectral imaging system can differentiate carcinoma lesions from normal tissue with a sensitivity of 95% and specificity of 93%. The optimal design of principal filters and the optimal selection of PCA-based algorithms were investigated to improve the diagnostic accuracy. The robustness of the spectral imaging method against noise in the autofluorescence signal was studied as well.     

Applications of moving window two-dimensional correlation spectroscopy to analysis of phase transitions and spectra classification

Our recently proposed idea of moving window two-dimensional (2D) correlation spectroscopy, which partitions a data set into series of relatively small submatrices (windows) and calculates their covariance maps in succession, is tested for three convoluted data set. Phase-transition temperatures of oleic acid and poly-(N-isopropylacrylamide) in an aqueous solution are sought by analyzing covariances of their temperature-dependent near-infrared and infrared spectra, respectively, while Raman spectra of three kinds of polyethylene (PE) pellets are investigated to find the spectral differences among them and to classify randomly ordered spectra by a sample-sample (SS) covariance map. The criterion of mean of standard deviation of covariance matrices is used as an indicator of the crucial information present in these matrices so that only a few of them are discussed in details. The results are obtained quickly after very simple calculations and are studied at length. The baseline variation is not removed prior to the calculations but is found to be of use for the determination of the phase-transition temperatures. Randomly ordered Raman spectra of the PE pellets are classified by innovatively used and interpreted SS slice spectra, with the relation to principal component analysis discussed.     

Interpretation of static time-of-flight secondary ion mass spectra of adsorbed protein films by multivariate pattern recognition

Multivariate analysis has become increasingly common in the analysis of multidimensional spectral data. We previously showed that the multivariate analysis technique principal component analysis (PCA) is an excellent method for interpreting the static time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of adsorbed protein films. PCA is an unsupervised pattern recognition technique that loses resolution between spectra of different proteins as more proteins are added to the data set due to large within-group variation. The supervised pattern recognition techniques discriminant principal component analysis (DPCA) and linear discriminant analysis (LDA), which aim to control within-group variation while maximizing between-group separation to enhance discrimination between groups, were compared with PCA using data sets of TOF-SIMS spectra of proteins adsorbed onto mica and PTFE substrates. DPCA and LDA quantitatively improved discrimination between groups and provided different information about the data than PCA. LDA was able to classify unknown samples with a misclassification rate lower than PCA or DPCA. Both unsupervised and supervised pattern recognition techniques are useful for the interpretation and classification of static TOF-SIMS spectra of adsorbed protein films.     

Spectrum of Doppler ultrasound signals from nonstationary blood flow

A new formulation for the Doppler signal generation process in pulsatile flow has been developed enabling easier identification and quantification of the mechanisms involved in spectral broadening and the development of a simple estimation formula for the measured rms spectral width. The accuracy of the estimation formula was tested by comparing it with the spectral widths found by using conventional spectral estimation on simulated Doppler signals from pulsatile flow. The influence of acceleration, sample volume size, and time window duration on the Doppler spectral width was investigated for flow with blunt and parabolic velocity profiles passing through Gaussian-shaped sample volumes. Our results show that, for short duration windows, the spectral width is dominated by window broadening and that acceleration has a small effect on the spectral width. For long duration windows, the effect of acceleration must be taken into account. The size of the sample volume affects the spectral width of the Doppler signal in two ways: by intrinsic broadening and by the range of velocities passing through it. These effects act in opposite directions. The simple spectral width estimation formula was shown to have excellent agreement with widths calculated using the model and indicates the potential for correcting not only for window and nonstationarity broadening but also for intrinsic broadening.     

Portable Fourier transform infrared spectroradiometer for field measurements of radiance and emissivity

A hand-held, battery-powered Fourier transform infrared spectroradiometer weighing 12.5 kg has been developed for the field measurement of spectral radiance from the Earth's surface and atmosphere in the 3-5-μm and 8-14-μm atmospheric windows, with a 6-cm(-1) spectral resolution. Other versions of this instrument measure spectral radiance between 0.4 and 20 μm, using different optical materials and detectors, with maximum spectral resolutions of 1 cm(-1). The instrument tested here has a measured noise-equivalent delta T of 0.01 °C, and it measures surface emissivities, in the field, with an accuracy of 0.02 or better in the 8-14-μm window (depending on atmospheric conditions), and within 0.04 in accessible regions of the 3-5-μm window. The unique, patented design of the interferometer has permitted operation in weather ranging from 0 to 45 °C and 0 to 100% relative humidity, and in vibration-intensive environments such as moving helicopters. The instrument has made field measurements of radiance and emissivity for 3 yr without loss of optical alignment. We describe the design of the instrument and discuss methods used to calibrate spectral radiance and calculate spectral emissivity from radiance measurements. Examples of emissivity spectra are shown for both the 3-5-μm and 8-14-μm atmospheric windows.     

The chemometrics approach applied to FTIR spectral data for the analysis of rice bran oil in extra virgin olive oil

Among eleven studied vegetable oils, rice bran oil (RBO) has the close similarity to extra virgin olive oil (EVOO) in terms of FTIR spectra, as shown in the score plot of first and second principal components. The peak intensities at 18 frequency regions were used as matrix variables in principal component analysis (PCA). Consequently, the presence of RBO in EVOO is difficult to detect. This study aimed to use the chemometrics approach, namely discriminant analysis (DA) and multivariate calibrations of partial least square and principle component regression to analyze RBO in EVOO. DA was used for the classification of EVOO and EVOO mixed with RBO. Multivariate calibrations were exploited for the quantification of RBO in EVOO. The combined frequency regions of 1200-900 and 3020-3000 cm^− 1 were used for such analysis. The results showed that no misclassification was reported for the classification of EVOO and EVOO mixed with RBO. Partial least square regression either using normal or first derivative FTIR spectra can be successfully used for the quantification of RBO in EVOO. In addition, analysis of fatty acid composition can complement the results obtained from FTIR spectral data.     

Two-dimensional correlation analysis and waterfall plots for detecting positional fluctuations of spectral changes

In this study, we demonstrate the potentials and pitfalls of using various waterfall plots, such as conventional waterfall plots, two-dimensional (2D) gradient maps, moving window two-dimensional analysis (MW2D), perturbation-correlation moving window two-dimensional analysis (PCMW2D), and moving window principal component analysis two-dimensional correlation analysis (MWPCA2D), in the detection of the existence of band position shifts. Waterfall plots of the simulated spectral datasets are compared with conventional 2D correlation spectra. Different waterfall plots give different features in differentiating the behaviors of frequency shift versus two overlapped bands. Two-dimensional correlation spectra clearly show the very characteristic cluster pattern for both band position shifts and two overlapped bands. The vivid pattern differences are readily detectable in various waterfalls plots. Various types of waterfall plots of temperature-dependent infrared (IR) spectra of ethylene glycol, which does not have the actual band shift but only two overlapped bands, and of Fourier transform infrared (FT-IR) spectra of 2 wt% acetone in a mixed solvent of CHCl(3)/CCl(4) demonstrate that waterfall plots are not able to unambiguously detect the difference between real band shift and two overlapped bands. Thus, the presence or lack of the asynchronous 2D butterfly pattern seems like the most effective diagnostic tool for band shift detection.     

Simultaneous Statistical Inference

The effect of misclassification, for initial samples of finite size from multivariate normal populations, on the linear discriminant function (Anderson's classification statistic [l]) has been considered by analysing the results of sampling experiments. (Lachenbruch [2]). This paper presents an alternative approach by which the effect of misclassification is expressed in the form of asymptotic expansions of degrees higher than previously available. Although the results of these expansions are not always in agreement with the conclusions drawn by Lachenbruch from his sampling experiments in which the sample size was moderately large, the conclusion from the asymptotic approach is that Lachenbruch's large sample results (obtained when the sample size is infinite) hold in most cases. In those instances in which they apparently do not, a general condition for them to hold is obtained.     

Elastic net grouping variable selection combined with partial least squares regression (EN-PLSR) for the analysis of strongly multi-collinear spectroscopic data

In this paper a novel wavelength region selection algorithm, called elastic net grouping variable selection combined with partial least squares regression (EN-PLSR), is proposed for multi-component spectral data analysis. The EN-PLSR algorithm can automatically select successive strongly correlated prediction variable groups related to the response variable using two steps. First, a portion of the correlated predictors are selected and divided into subgroups by means of the grouping effect of elastic net estimation. Then, a recursive leave-one-group-out strategy is employed to further shrink the variable groups in terms of the root mean square error of cross-validation (RMSECV) criterion. The performance of the algorithm with real near-infrared (NIR) spectroscopic data sets shows that the EN-PLSR algorithm is competitive with full-spectrum PLS and moving window partial least squares (MWPLS) regression methods and it is suitable for use with strongly correlated spectroscopic data.     

Source identification of underground fuel spills by solid-phase microextraction/high-resolution gas chromatography/genetic algorithms

Solid-phase microextraction (SPME), capillary column gas chromatography, and pattern recognition methods were used to develop a potential method for typing jet fuels so a spill sample in the environment can be traced to its source. The test data consisted of gas chromatograms from 180 neat jet fuel samples representing common aviation turbine fuels found in the United States (JP-4, Jet-A, JP-7, JPTS, JP-5, JP-8). SPME sampling of the fuel's headspace afforded well-resolved reproducible profiles, which were standardized using special peak-matching software. The peak-matching procedure yielded 84 standardized retention time windows, though not all peaks were present in all gas chromatograms. A genetic algorithm (GA) was employed to identify features (in the standardized chromatograms of the neat jet fuels) suitable for pattern recognition analysis. The GA selected peaks, whose two largest principal components showed clustering of the chromatograms on the basis of fuel type. The principal component analysis routine in the fitness function of the GA acted as an information filter, significantly reducing the size of the search space, since it restricted the search to feature subsets whose variance is primarily about differences between the various fuel types in the training set. In addition, the GA focused on those classes and/or samples that were difficult to classify as it trained using a form of boosting. Samples that consistently classify correctly were not as heavily weighted as samples that were difficult to classify. Over time, the GA learned its optimal parameters in a manner similar to a perceptron. The pattern recognition GA integrated aspects of strong and weak learning to yield a "smart" one-pass procedure for feature selection.     

Spectral Matched Filters in Coherent Laser Radar

Abstract

Insight into the properties of maximum likelihood Doppler frequency shift estimators, which function as spectral domain matched filters, is often obtained more readily from examination of their spectral correlating functions, and lag windows than from simulated performance data. These functions are compared and contrasted with reference to lidar applications for the three principal estimators.     

Investigation of polypeptide conformational transitions with two-dimensional Raman optical activity correlation analysis, applying autocorrelation and moving window approaches

The study of conformational transitions in polypeptides is not only important for the understanding of folding mechanisms responsible for the self-assembly of proteins but also for the investigation of the misfolding of proteins that can result in diseases including cystic fibrosis, Alzheimer's, and Parkinson's diseases. Our recent studies developing two-dimensional Raman optical activity (ROA) correlation analysis have proven to be successful in the investigation of polypeptide conformational transitions. However, the complexity of the ROA spectra, and the 2D correlation synchronous and asynchronous plots, makes data analysis detailed and complex, requiring great care in interpretation of 2D correlation rules. By utilizing the 2D correlation approaches of autocorrelation and moving windows it has been possible to gain further information from the ROA spectral data sets in a simpler and more consistent way. The most significant spectral intensity changes have been easily identified, facilitating appropriate interpretation of synchronous plots, and transition phases have been identified in the moving window plots, directly relating spectral intensity changes to the perturbation.     

Spectral histogram using the minimization algorithm-theory and applications to flaw detection

In ultrasonic flaw detection in large grained materials, backscattered grain noise often masks the flaw signal. To enhance the flaw visibility, a frequency diverse statistical filtering technique known as split-spectrum processing has been developed. This technique splits the received wideband signal into an ensemble of narrowband signals exhibiting different signal-to-noise ratios (SNR). Using a minimization algorithm, SNR enhancement can be obtained at the output. The nonlinear properties of the frequency diverse statistic filter are characterized based on the spectral histogram, which is the statistical distribution of the spectral windows selected by the minimization algorithm. The theoretical analysis indicates that the spectral histogram is similar in nature to the Wiener filter transfer function. Therefore, the optimal filter frequency region can be determined adaptively based on the spectral histogram without prior knowledge of the signal and noise spectra.     

Cloud manufacturing resources fuzzy classification based on genetic simulated annealing algorithm

To solve the problem of fuzzy classification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on genetic algorithm (GA), simulated annealing (SA) and fuzzy C-means clustering algorithm (FCM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource; the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer; the fitness function is constructed by fuzzy clustering algorithm (FCM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective.     

Tunable double optomechanical induced transparency in an optomechanical system with Bose–Einstein condensate

We investigate the double optomechanically induced transparency (OMIT) of a weak problem field in a hybrid optomechanical system, composed of a Bose–Einstein condensate (BEC), a movable mirror and an optical cavity. Contrast to the single OMIT window in a traditional optomechanical system, the frequency difference between the BEC and the moving mirror in our system can lead to the splitting of the single OMIT window into two transparency windows. Interestingly, the splitting of the two windows varies near linearly with the frequency difference and is robust against the cavity decay. This property can be applied to detect the frequency of the movable mirror. Besides, the driving power and the BEC-cavity coupling strength play a key role in controlling the width of the two transparency windows.     

基于人工鱼群算法-极限学习机的多手势精准识别

手势作为人机交互的重要方式,因灵活性与便捷性强,已成为控制领域的研究重点。针对上肢康复机器人手势识别技术存在的不足,结合特征组合与滑动窗口法,提出一种基于人工鱼群算法（artificial fish swarm algorithm,AFSA）优化的极限学习机（extreme learning machine,ELM）的多手势精准识别方法,以提高手势识别的准确率。首先,运用表面肌电测量系统采集人体常用的8种手势对应的表面肌电信号（surface electromyography,SEMG）,作为后续分类模型的信号源,并运用去噪技术与起止点检测技术对SEMG进行预处理;然后,选取通过主成分分析（principal components analysis,PCA）降维处理后的最优特征组合与最优滑动窗口;接着,采用AFSA搜寻ELM的最优输入权值和隐含阈值,以提高其分类准确率;最后,对AFSA优化的ELM（AFSA-ELM）分类模型、反向传播（back propagation,BP）神经网络分类模型和未优化的ELM分类模型进行比较,以验证所提出方法的精准性。实验结果表明,结合最优特征组合与最优滑动窗口设计的AFSA-ELM分类模型对多种手势的平均识别准确率高达97.4%,比BP神经网络分类模型和未优化的ELM分类模型分别高3.5%和1.6%,验证了所提出方法的识别精准性。研究结果可为手势识别提供新思路,进而为人体上肢动作的深度分析和上肢康复机器人手势识别算法的优化提供理论基础和参考。     

Adaptive optimal design of active thermoelectric windows using surrogate modeling

This paper develops an optimal design and an optimal operating strategy for Active Thermoelectric (ATE) windows. The proposed ATE window design uses thermostats to actively control thermoelectric (TE) units and fans to regulate the heat transfer through the windows. To achieve high energy efficiency, optimization of the ATE window design seeks to simultaneously minimize the heat transferred through the window and the net power consumption. The ATE windows should adapt to varying climatic conditions. The heat transfer and the power supplies are optimized under a prescribed set of climatic conditions. Based on the optimal results obtained for these conditions, surrogate models are developed to represent the optimal modes of operation as functions of the climatic conditions, namely (i) ambient temperature, (ii) wind speed, and (iii) solar radiation. To this end, Radial Basis Functions (RBF) are used. The results show that the ATE windows provide significantly improved insulation compared to traditional windows under varying climatic conditions. Moreover, it was found that the ATE window operates at a superior efficiency than a standard HVAC system, particularly in colder climates.     

A Novel Prediction Method for Hardness Using Auto-regressive Spectrum of Barkhausen Noise

In this study, a novel method for predicting hardness of ferromagnetic alloy based on the magnetic Barkhausen noise (MBN) is proposed. A set of new frequency features of MBN and a new hardness prediction method are proposed. The new features are derived from the first and second derivative of the auto-regressive spectrum of MBN signal. The new automatic hardness prediction method include Bag-of-Words, principal component analysis and back propagate neural network optimized by ensemble learning. The experimental results of the hardness classification show that the new features are superior to the previous features—the misclassification rate using the new features is less than 0.67%, while the misclassification rate using the previous features is about 2%. The efficiency of the new method is also proved by hardness classification experiment. Compared with the traditional time-domain method and the previous frequency domain method, the misclassification rate of the new method decreased significantly from 25% to less than 1%. In addition, the new method is highly automatic, so it is more versatile than manual algorithms. The above characteristics make the proposed new method suitable for predicting the hardness of ferromagnetic alloys in practice.     

Multiple target detection using split spectrum processing and groupdelay moving entropy

The split spectrum processing technique obtains a frequency-diverse ensemble of narrow-band signals through a filterbank then recombines them nonlinearly to improve target visibility. Although split spectrum processing is an effective method for suppressing grain noise in ultrasonic nondestructive testing, its application was mainly limited to the detection of single targets or multiple targets having similar spectral characteristics. In this paper, the group delay moving entropy technique is introduced primarily to enhance the performance of split spectrum processing in detecting multiple targets which exhibit different spectral characteristics (i.e., variations in target signal center frequency and bandwidth). This is likely to occur in complex, dispersive, and nonhomogeneous media such as composites, layered, and clad materials, etc. The analysis shows that the group delay moving entropy method can be used effectively to select the optimal frequency region for split spectrum processing when detecting such targets. Based on an iterative procedure that combines group delay moving entropy and split spectrum processing, multiple targets can be identified one at a time, and subsequently eliminated by using time domain windows. The removal of the dominant target improves the detection of the remaining weaker targets. Simulation results are presented which demonstrate the feasibility of the multistep split spectrum processing technique for detecting multiple targets in such materials     

Single-machine due-window assignment scheduling based on common flow allowance,learning effect and resource allocation

This article considers a single-machine due-window assignment scheduling problem based on a common flow allowance (i.e. all jobs have slack due window (SLKW)). We assume that the actual processing time of a job is a function of its position in a sequence (learning effect) and its continuously divisible and non-renewable resource allocation. The problem is to determine the optimal due windows, the optimal resource allocation and the processing sequence simultaneously to minimise costs for earliness, tardiness, the window location, window size, makespan and resource consumption. For a linear or a convex function of the amount of a resource allocated to the job, we provide a polynomial time algorithm, respectively. Some extensions of the problem are also shown.