Phase Matrices Research Articles

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection.

Among precision medical techniques, medical image processing is rapidly growing as a successful tool for cancer detection. Skin cancer is one of the crucial cancer types. It is identified through computer vision (CV) techniques using dermoscopic images. The early diagnosis skin cancer from dermoscopic images can be decrease the mortality rate. We propose an automated system for skin lesion detection and classification based on statistical normal distribution and optimal feature selection. Local contrast is controlled using a brighter channel enhancement technique, and segmentation is performed through a statistical normal distribution approach. The multiplication law of probability is implemented for the fusion of segmented images. In the feature extraction phase, optimized histogram, optimized color, and gray level co-occurrences matrices features are extracted and covariance-based fusion is performed. Subsequently, optimal features are selected through a binary grasshopper optimization algorithm. The selected optimal features are finally fed to a classifier and evaluated on the ISBI 2016 and ISBI 2017 data sets. Classification accuracy is computed using different Support Vector Machine (SVM) kernel functions, and the best accuracy is obtained for the cubic function. The average accuracies of the proposed segmentation on the PH2 and ISBI 2016 data sets are 93.79 and 96.04%, respectively, for an image size 512 × 512. The accuracies of the proposed classification on the ISBI 2016 and ISBI 2017 data sets are 93.80 and 93.70%, respectively. The proposed system outperforms existing methods on selected data sets.

Principles of Quantum-like Evolution

We present a case study of quantum-like probabilities that are motivated by quantum cognition. We introduce quantum-like evolution that is l2 norm preserving but in which the matrix does not need to be unitary. We show how to map any 2 × 2 stochastic matrix to an l2 norm preserving balanced phase matrix that maps real vectors of length one into complex vectors of length one. Quantum-like evolution can simulate a probability distribution of open system in which the operator is not unitary but norm preserving. Such a kind of behaviour is studied in quantum cognition. By tensor product higher dimensional balanced phase matrices can be built. Quantum-like evolution can simulate either unitary open one by coding the phase of input vector into the phase of a balanced phase matrix, a Markov chain or an alternative evolution that can lead to fixed, periodic or chaotic behaviour resulting in strange oscillations.

Effect of Carbon Content on Bainite Transformation Kinetics and Microstructure of 4140/4150 Steels

4140 steel and 4150 steel are different only in their carbon content. A comparison between the kinetics and microstructure of the bainite transformation of both steels has been carried out under a wide range of isothermal temperatures, in which four bainite phase matrices were generated. The four phases are upper bainite, mixed upper and lower bainite, lower bainite and mixed martensite and lower bainite (lower bainite transformed under Ms temperature). The bainite transformation kinetics-morphology characteristics of the four phases were studied and compared. Kinetics behavior patterns are shown with the developed TTT diagrams and kinetics plots- (bainite volume fraction vs isothermal holding time). The upper bainite and lower bainite transition temperatures are different and the bainite reaction time span is shorter when carbon content is higher. The n values in the Johnson-Mehl- Kolmogorov-Avrami (JMKA) equation have a wider deviation when carbon content is higher, indicating more bainite reaction variation. The activation energy required for the bainite phase transformations were compared and it was found that higher activation energy is needed when carbon content is higher. The subtle microstructure morphology differences are illustrated. This work will aid future development of 4140/4150 steels with bainitic microstructures having improved mechanical properties.

A new algorithm to calculate binary phase diagrams

A new algorithm to calculate binary phase diagrams is described. The condition numbers of phase matrices are analyzed, which can explain the stability during iterations. The linear systems of equations from thermodynamic calculation are simplified. Such simplifications significantly reduce the condition numbers of phase matrices and improve the computational efficiency. The Cu-Mg and Al-Ta phase diagrams are computed by using a home-made code to verify the efficiency of this algorithm over the method utilizing direct matrix inversion without simplifications. It is highly expected that the presently developed algorithm can be generalized to calculate multi-component phase diagrams efficiently.

Learning Spatiotemporal Features of CSI for Indoor Localization With Dual-Stream 3D Convolutional Neural Networks

Recently, the research of WiFi-based indoor localization combing with deep-learning techniques has earned wide attention due to its potential applications in smart cities. In this paper, a novel fingerprinting system is proposed to achieve indoor localization via learning spatiotemporal features from channel state information (CSI) of multiple-input multiple-output wireless channels (CSI-MIMO) by a dual-stream three-dimensional (3D) convolutional neural network (DS-3DCNN). In the proposed system, the gathered raw CSI-MIMO data are firstly preprocessed through amplitude outliers elimination and phase sanitization for constructing a pair of 3D CSI-MIMO matrices including a 3D amplitude matrix and a 3D phase matrix. Next, the 3D matrices will be input to the DS-3DCNN deep neural network which consists of two parallel subnetworks with specific architecture of several convolution, batch normalization, max-pooling, and fully connected layers. Through this DS-3DCNN network, learning spatiotemporal features of CSI-MIMO is carried out simultaneously from 3D amplitude and phase matrices. And then, probabilistic classification results of two subnetworks are fused in the final output layer of the proposed DS-3DCNN based on Bayes' theorem. Moreover, in the offline training stage, a dual-stream joint optimization method is presented for efficiently optimizing network parameters. After offline training of the DS-3DCNN, in the online locating stage, current CSI-MIMO data are firstly collected from the mobile device to be located. Then probabilistic classification results are obtained from the output layer of the DS-3DCNN, and further used to approximate the posterior distribution of the mobile device's location with a Gaussian mixture model. Finally, a novel location estimation algorithm is deduced based on the minimum mean square error (MMSE) criterion. Since unique features of wireless MIMO channels are jointly learnt in spatial, temporal, and frequency domains, the proposed DS-3DCNN based fingerprinting system is reasonable to provide accurate localization results in indoor environments, which is verified in corresponding experiments.

“Naked-eye” detection of CN− from aqueous phase and other extracellular matrices: an experimental and theoretical approach mimicking the logic gate concept

Selective and real-field detection of a toxic anion (CN−) from aqueous and extracellular matrices by a cost-effective chemosensor.

Gas Chromatography with In Situ Catalytic Hydrogenolysis and Flame Ionization Detection for the Direct Measurement of Formaldehyde and Acetaldehyde in Challenging Matrices.

A gas chromatographic strategy to advance the direct detection and quantification of volatile aliphatic aldehydes such as formaldehyde and acetaldehyde in gas phase matrices without the need for sample pretreatment or concentration has been successfully developed. The catalytic hydrogenolysis of aldehydes to alkanes is conducted in situ within the 3D-printed steel jet assembly of the flame ionization detector and without any additional hardware required. Reliable conversion efficiencies of greater than 90% with respectable peak symmetries for the analytes were attained at 400 °C. Quantification of formaldehyde and acetaldehyde at parts-per-million levels over a range of 0.5-300 ppm (v/v) for formaldehyde and 0.2-430 ppm (v/v) for acetaldehyde with a respectable precision of less than 5% RSD ( n = 10) was achieved. The total analysis time was less than 10 min. Linearity with a correlation coefficient ( R2) greater than 0.9997 and measured recoveries of >99% for spike tests under the specified conditions were achieved. The 3D-printed steel jet assembly was found to be reliable and resilient to matrices such as air, water, hydrocarbons, and aromatics. An additional benefit realized with this analytical strategy is that the slight restriction induced by the presence of the catalyst in the 3D-printed jet assembly enables backflush via the inlet split vent without the need for additional pressure control or intercolumn-connection devices. The utility of this technique was demonstrated with important aldehyde applications from various segments.

Investigation of power quality disturbances by using 2D discrete orthonormal S-transform, machine learning and multi-objective evolutionary algorithms

Abstract The aim of this paper is to investigate the power quality analysis by using 2D discrete orthonormal S-transform, machine learning and multi-objective evolutionary algorithms. The fact that PQ signals are one-dimensional (1D) signals due to their nature leads to the search for feature extraction approaches based on 1D signal processing methods. Due to the electric network is getting more and more complicated day by day, it is necessary to determine effectively the disturbances events. In the proposed method, extraction of a new feature based on two-dimensional (2D) signal processing by 2D Fast Discrete Orthonormal Stockwell Transform (2D-FDOST) method and determination of the most suitable feature group by Non-dominated Sorting Genetic Algorithm II (NSGA-II) method are performed. Eleven different PQ events are synthetically produced based on mathematical modelling. 1D signals are transformed into 2D signals with equal row and column numbers. Statistical and image-based features are created on the amplitude and phase matrices obtained by 2D-FDOST method from 2D signals. The NSGA-II method, which is one of the multi-objective evolutionary optimization methods, is used to convert a large number of feature vectors into a small number of useful feature groups. NSGA-II produces the optimal solution for two different fitness functions that calculate the number of features and classifier performance. By using different machine learning classifiers for selected features, a model classifying PQ disturbances with high performance and robust structure against noisy situations is created.

Improved deformation behavior in Ti-Zr-Fe-Mn alloys comprising the C14 type Laves and β phases

Laves phase alloys are promising materials for several structural applications, but the extreme brittleness is the predominant shortcoming of a Laves matrix. One potential solution to overcome this shortcoming is to alloy Laves matrix with some soft matrix. A group of Ti-35Zr-5Fe-xMn (x = 0, 2, 4, 6, 8 wt%) alloys was cast with an aim to improve deformation in Laves alloy compositions. The phase and microstructure analyses reveal dual phase matrices, including a β phase and a C14 type Laves phase in the investigated alloys. The mechanical properties such as yield strength, hardness and plastic strain for the investigated alloys are found to be significantly sensitive to volume fraction of the Laves phase. Ti-35Zr-5Fe shows impressive ultimate compressive strength (~1.7 GPa), yield strength (1138 MPa) and large plastic strain (23.2 %). The fracture mechanisms are dependent on the microstructure of the alloys. Additionally, the work-hardening ability of the investigated alloys have also been evaluated based on the analyses of slip band patterns formed around the micro-hardness indentations. Notably, the extreme brittleness is not encountered in all the Ti-35Zr-5Fe-xMn alloys and all exhibit very good compressive elongation including the maximum (32.5 %) in Ti-35Zr-5Fe.

Multichannel Blind Sound Source Separation Using Spatial Covariance Model With Level and Time Differences and Nonnegative Matrix Factorization

This paper presents an algorithm for multichannel sound source separation using explicit modeling of level and time differences in source spatial covariance matrices (SCM). We propose a novel SCM model in which the spatial properties are modeled by the weighted sum of direction of arrival (DOA) kernels. DOA kernels are obtained as the combination of phase and level difference covariance matrices representing both time and level differences between microphones for a grid of predefined source directions. The proposed SCM model is combined with the NMF model for the magnitude spectrograms. Opposite to other SCM models in the literature, in this work, source localization is implicitly defined in the model and estimated during the signal factorization. Therefore, no localization preprocessing is required. Parameters are estimated using complex-valued nonnegative matrix factorization with both Euclidean distance and Itakura–Saito divergence. Separation performance of the proposed system is evaluated using the two-channel SiSEC development dataset and four channels signals recorded in a regular room with moderate reverberation. Finally, a comparison to other state-of-the-art methods is performed, showing better achieved separation performance in terms of SIR and perceptual measures.

Bicaudal D2 is a novel autoantibody target in systemic sclerosis that shares a key epitope with CENP-A but has a distinct clinical phenotype.

We studied the clinical correlations and epitopes of autoantibodies directed to a novel autoantigen, Bicaudal D (BICD2), in systemic sclerosis (SSc) and reviewed its relationship to centromere protein A (CENP-A). 451 SSc sera were tested for anti-BICD2 using a paramagnetic bead immunoassay and then univariate and multivariate logistic regression was used to study the association between anti-BICD2 and demographic and clinical parameters as well as other SSc-related autoantibodies. Epitope mapping was performed on solid phase matrices. 25.7% (116/451) SSc sera were anti-BICD2 positive, of which 19.0% had single specificity anti-BICD2 and 81.0% had other autoantibodies, notably anti-CENP (83/94; 88.3%). Compared to anti-BICD2 negative subjects (335/451), single specificity anti-BICD2 subjects were more likely to have an inflammatory myopathy (IM; 31.8% vs. 9.6%, p=.004) and interstitial lung disease (ILD; 52.4% vs. 29.0%, p=.024). Epitope mapping revealed a serine- and proline-rich nonapeptide SPSPGSSLP comprising amino acids 606-614 of BICD2, shared with CENP-A but not CENP-B. We observed that autoantibodies to BICD2 represent a new biomarker as they were detected in patients without other SSc-specific autoantibodies and were the second most common autoantibody identified in this SSc cohort. Our data indicate that the major cross-reactive epitope is associated with anti-CENP-A but, unlike anti-CENP, single specificity anti-BICD2 antibodies associate with ILD and IM.

Application of the weighted total field-scattering field technique to 3D-PSTD light scattering model

PSTD (Pseudo Spectral Time Domain) is an excellent model for the light scattering simulation of nonspherical aerosol particles. However, due to the particularity of its discretization form of the Maxwell's equations, the traditional Total Field/Scattering Field (TF/SF) technique for FDTD (Finite Differential Time Domain) is not applicable to PSTD, and the time-consuming pure scattering field technique is mainly applied to introduce the incident wave. To this end, the weighted TF/SF technique proposed by X. Gao is generalized and applied to the 3D-PSTD scattering model. Using this technique, the incident light can be effectively introduced by modifying the electromagnetic components in an inserted connecting region between the total field and the scattering field region with incident terms, where the incident terms are obtained by weighting the incident field by a window function. To optimally determine the thickness of connection region and the window function type for PSTD calculations, their influence on the modeling accuracy is firstly analyzed. To further verify the effectiveness and advantages of the weighted TF/SF technique, the improved PSTD model is validated against the PSTD model equipped with pure scattering field technique in both calculation accuracy and efficiency. The results show that, the performance of PSTD seems to be not sensitive to variation of window functions. The number of the connection layer required decreases with the increasing of spatial resolution, where for spatial resolution of 24 grids per wavelength, a 6-layer region is thick enough. The scattering phase matrices and integral scattering parameters obtained by the improved PSTD show an excellent consistency with those well-tested models for spherical and nonspherical particles, illustrating that the weighted TF/SF technique can introduce the incident precisely. The weighted TF/SF technique shows higher computational efficiency than pure scattering technique.

Measurement of the Microwave Refractive Index of Materials Based on Parallel Plate Waveguides

An electrical field scanning apparatus based on a parallel plate waveguide method is constructed, which collects the amplitude and phase matrices as a function of the relative position. On the basis of such data, a method for calculating the refractive index of the measured wedge samples is proposed in this paper. The measurement and calculation results of different PTFE samples reveal that the refractive index measured by the apparatus is substantially consistent with the refractive index inferred with the permittivity of the sample. The proposed refractive index calculation method proposed in this paper is a competitive method for the characterization of the refractive index of materials with positive refractive index. Since the apparatus and method can be used to measure and calculate arbitrary direction of the microwave propagation, it is believed that both of them can be applied to the negative refractive index materials, such as metamaterials or “left-handed” materials.

Synthesis, conformation, and photochemistry of difluoroacetyl isocyanate CF2HC(O)NCO and isothiocyanate CF2HC(O)NCS

Two carbonyl pseudohalogen compounds CF2HC(O)NCO and CF2HC(O)NCS have been synthesized and fully characterized by IR (gas-phase, matrix-isolation), Raman (liquid), UV–vis (gas-phase), and NMR (19F, 13C, 1H) spectroscopy. The conformational properties and photochemistry of both species have been studied by combining matrix-isolation IR spectroscopy and quantum chemical calculations with the B3LYP, MPW1PW91, CBS-QB3 methods. Two conformers (syn and anti), depending on the configuration between the CO and pseudohalogen ligands (NCO and NCS), have been unambiguously identified in both gas phase and solid noble gas matrices (Ar and Ne). Consistent with the theoretical calculations, the syn conformation is energetically more favorable. Upon irradiation with an ArF excimer laser (193 nm), CF2HC(O)NCO and CF2HC(O)NCS in cryogenic matrices eliminate CO and yield CF2HNCO and CF2HNCS, respectively. The underlying mechanism for the photochemistry of CF2HC(O)NCO is rationalized by the elimination of the carbonyl rather than the isocyanato CO moiety.

Spectral modeling of meteorites at UV-vis-NIR wavelengths

We present a novel simulation framework for assessing the spectral properties of meteorite specimens. The framework utilizes a ray-optics code, which simulates light scattering by Gaussian-random-sphere particles large compared to the wavelength of the incident light and accounts for internal diffuse scatterers. The code uses incoherent input and computes phase matrices by utilizing incoherent scattering matrices. Reflectance spectra are modeled by introducing a combination of olivine, pyroxene, and iron, the most common materials present in meteorites that dominate their spectral features. The complex refractive indices of olivine and iron are obtained from existing databases. The refractive indices of pyroxene are derived using an optimization that utilizes our ray-optics code and the measured spectrum of the material. We demonstrate our approach by applying it on the measured meteorite reflectance spectra obtained with the University of Helsinki integrating-sphere UV–vis-NIR spectrometer.

Application of Convolution Perfectly Matched Layer in MRTD scattering model for non-spherical aerosol particles and its performance analysis

The performance of absorbing boundary condition (ABC) is an important factor influencing the simulation accuracy of MRTD (Multi-Resolution Time-Domain) scattering model for non-spherical aerosol particles. To this end, the Convolution Perfectly Matched Layer (CPML), an excellent ABC in FDTD scheme, is generalized and applied to the MRTD scattering model developed by our team. In this model, the time domain is discretized by exponential differential scheme, and the discretization of space domain is implemented by Galerkin principle. To evaluate the performance of CPML, its simulation results are compared with those of BPML (Berenger's Perfectly Matched Layer) and ADE-PML (Perfectly Matched Layer with Auxiliary Differential Equation) for spherical and non-spherical particles, and their simulation errors are analyzed as well. The simulation results show that, for scattering phase matrices, the performance of CPML is better than that of BPML; the computational accuracy of CPML is comparable to that of ADE-PML on the whole, but at scattering angles where phase matrix elements fluctuate sharply, the performance of CPML is slightly better than that of ADE-PML. After orientation averaging process, the differences among the results of different ABCs are reduced to some extent. It also can be found that ABCs have a much weaker influence on integral scattering parameters (such as extinction and absorption efficiencies) than scattering phase matrices, this phenomenon can be explained by the error averaging process in the numerical volume integration.

Entropy-weighted feature-fusion method for head-pose estimation

This paper proposes a novel entropy-weighted Gabor-phase congruency (EWGP) feature descriptor for head-pose estimation on the basis of feature fusion. Gabor features are robust and invariant to differences in orientation and illuminance but are not sufficient to express the amplitude character in images. By contrast, phase congruency (PC) functions work well in amplitude expression. Both illuminance and amplitude vary over distinctive regions. Here, we employ entropy information to evaluate orientation and amplitude to execute feature fusion. More specifically, entropy is used to represent the randomness and content of information. For the first time, we seek to utilize entropy as weight information to fuse the Gabor and phase matrices in every region. The proposed EWGP feature matrix was verified on Pointing’04 and FacePix. The experimental results demonstrate that our method is superior to the state of the art in terms of MSE, MAE, and time cost.

Problem of data interpretation of laser polarization sensing of high-level clouds on the basis of theoretically calculated phase matrices of backscattering by monodisperse ice crystals

A technique for determining the microstructure parameters of high-level clouds on the basis of joint analysis of experimental and theoretical data is described. Results of the experiments on atmosphere laser sensing and theoretical calculations of light scattering by spatially oriented ice crystals are shown.

Dust-aerosol optical modeling with Gaussian spheres: Combined invariant-imbedding T-matrix and geometric-optics approach

The Gaussian sphere has been widely used as a model to study light scattering by irregular particles; and, despite extensive numerical studies, the optical properties are not thoroughly understood. Based on Gaussian spheres and using a combination of the invariant imbedding T-matrix method and an improved geometric-optics method, the single-scattering properties (namely, the 4×4 phase matrix, extinction cross section, single-scattering albedo, and asymmetry factor) are computed in the Rayleigh to geometric optics regimes. The simulations are performed with various degrees of irregularity, and the effects of particle irregularities are investigated over a wide range of particle sizes. Furthermore, the theoretical simulations based on Gaussian spheres are used to fit the measured optical properties of feldspar particles from the well-known Amsterdam–Granada light scattering database. A mixture of several shapes is shown to closely reproduce the measured phase matrices. The results may be potentially useful for remote-sensing and radiative-transfer applications involving dust aerosol.

Fabrication error analysis for diffractive optical elements used in a lithography illumination system

With the constant shrinking of printable critical dimensions in photolithography, off-axis illumination (OAI) becomes one of the effective resolution-enhancement methods facing these challenges. This, in turn, is driving much more strict requirements, such as higher diffractive efficiency of the diffractive optical elements (DOEs) used in the OAI system. Since the design algorithms to optimize DOEs’ phase profile are improved, the fabrication process becomes the main limiting factor leading to energy loss. Tolerance analysis is the general method to evaluate the fabrication accuracy requirement, which is especially useful for highly specialized deep UV applications with small structures and tight tolerances. A subpixel DOE simulation model is applied for tolerance analysis of DOEs by converting the abstractive fabrication structure errors into quantifiable subpixel phase matrices. Adopting the proposed model, four kinds of fabrication errors including misetch, misalignment, feature size error, and feature rounding error are able to be investigated. In the simulation experiments, systematic fabrication error studies of five typical DOEs used in 90-nm scanning photolithography illumination system are carried out. These results are valuable in the range of high precision DOE design algorithm and fabrication process optimization.