Intensity Matrix Research Articles

A modified approach for semi-quantitative estimation of physical vulnerability of buildings exposed to different landslide intensity scenarios

ABSTRACTLandslides are the most common natural disasters in mountainous regions, being responsible for significant loss of life as well as damage to critical infrastructure and properties. As the world population grows, people tend to move to higher locations to construct buildings, thereby making structures vulnerable due to landslides. This paper discusses previous research on the vulnerability assessment of structures exposed to landslides and presents a modified semi-quantitative approach to assess the scenario-based physical vulnerability of buildings based on their resistance ability and landslide intensity. Resistance ability is determined by integrating expert knowledge-based resistance factors assigned to five primary building parameters. Landslide intensity matrix defining different intensity levels is proposed based on combinations of landslide velocity and volume. Physical vulnerability of buildings is estimated and classified as class I, II or III for scenario-based low to very high landslide intensity. Finally, the application of the model is illustrated with a case study of 71 buildings from Garhwal Himalayas, India.

An optical Hash function construction based on equal modulus decomposition for authentication verification

This paper proposes a Hash based authentication scheme that employs equal modulus decomposition (EMD) and fractional joint transform correlator. An 8-bit grey scale image is separated into its constituent bit plane matrices. Each bit plane matrix is subjected to EMD encryption procedure employing numerically generated random phase mask (RPM) and amplitude mask (AM). The EMD encryption results into two complex functions, which are used for the generation of phase mask and amplitude mask for the next bit plane matrix. A two round EMD procedure is carried out to obtain an 8-bit intensity matrix which is Fourier transformed and its central frequency components are extracted to obtain a 256-bit Hash value. Numerical simulations have been carried out to validate the effectiveness and performance of the proposed scheme.

Shock wave plasma generation in low pressure ambient gas from powder sample using subtarget supported micro mesh as a sample holder and its potential applications for sensitive analysis of powder samples

We present in this report the results of experimental study on the spectrochemical analysis of powder samples using subtarget supported micro mesh (SSMM) sample holder in low pressure ambient gases. The study is substantiated by establishing the analyte excitation mechanism with the evidence of shock wave plasma generation and the high temperature induced subsequently required for the thermal excitation and emission of the ablated atoms. The application of SSMM sample holder using Cu subtarget and stainless steel micro mesh to a number of powder samples in low pressure ambient gases are shown to produce generally sharp emission lines with low background, without suffering from intensity reduction and matrix effect commonly found in the use of pelletized powder samples. The same excellent spectral quality is demonstrated by its application to the analysis of rice samples which is the major staple diets in a large number of countries. In particular the analysis of Zn in rice is shown to exhibit a linear calibration line with extrapolated zero intercept and a detection limit of <0.87 μg/g which is promising for quantitative analysis.

A Parallel Algorithm for Wavelet Transform-Based Color Image Compression

Abstract Wavelet transforms emerge as one of the popular techniques in image compression. This technique is accepted by the JPEG Committee for the next-generation image compression standard JPEG-2000. Convolution-based strategy is widely used in calculating the wavelet transform of the image. A convolution-based wavelet transform consists of a large number of multiplications and additions. A color image consists of a two-dimensional matrix each for red, green, and blue colors. An ordinary way to calculate the wavelet transform of a color image includes calculating the transform of the intensity matrix of the red, green, and blue components. In this article, we present a parallel algorithm for calculating the convolution-based wavelet transform of the red, green, and blue intensity components simultaneously in color images, which can run on commonly used processors. This means that it needs no extra hardware. The results are also compared to the nonparallel algorithm based on compression time, mean square error, compression ratio, and peak signal-to-noise ratio. Complexity analysis and comparative complexity analysis with some other papers are also shown here.

The Highest Mutation in mtDNA Hypervariable Region and Application of Biostatistics with Nucleotide Base X t-n in Determining the Identity of the Mutation through a Transition Intensity Matrix

Human mitochondrial DNA (mtDNA) have been used intensively in the field of forensic identification of victims or suspects of crime through biological evidence. The number of mtDNA molecules in a single cell are in the tens of thousands which enable analysis of samples very little or damaged. Till now there is no standard method for identification using mtDNA in a mass disaster victims such as natural disasters, wars and accidents so that the identification process can not run fast. This study found C16.223t variants in mtDNA sequences that can be used to divide the database into two groups so as to accelerate the process of identification through a mathematical algorithm. This variant has the highest frequency (29.7%) of the 91 polymorphic human mtDNA HVS1 along the 300 nucleotide (16,024-16,324) derived from the NCBI database as much as 142 sequences. MtDNA sequences obtained from data collection Papuan human mtDNA groups that have been published in the NCBI. The next variant that can be used as a classifier in a row in the sequence is 16,311; 16,304; 16,189; and 16,270 with the identity (T→c). For a matrix Q is reversible so the matrix and could have the opposite diagonal. Thus the above equation can be solved by using the diagonal method that can be written: U³ = Uµ רUµ aµ†1 . This equation could count the number of transitions and transversion substitution mutations that occur in a nucleotide sequence of mtDNA. With this grouping, the database can be reduced so as to accelerate the process of identification of samples. Expected method of grouping by the variant with the highest frequency can be developed in the codification database for forensic interest such as the police or the mtDNA database purposes of study anthropology and evolutionary biology.

Positioning and Imaging Detection of Corona Discharge in Air with Double Helix Acoustic Sensors Array

Corona discharge could be a serious hazard to the outdoor insulation of a high-voltage (HV) system and also is an important detection issue for insulation status diagnosis. In this study, a double-helix-ultrasonic-array (DHUA) sensor was developed for corona positioning, which showed a good sensitivity to the ultrasonic signal originating from corona discharge and a satisfied directional response in static beam patterns. Based on the matting-based image fusion method, the acoustic intensity matrix was fused to a visual scene in real-time. This ultrasonic visualization technology was then evaluated with a simulation test and performed in a real air insulated substation (AIS), and showed a satisfied performance in terms of sensitivity and spatial location resolution.

Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography.

We propose a novel suite of algorithms for automatically segmenting the airway lumen and mucus in endobronchial optical coherence tomography (OCT) data sets, as well as a novel approach for quantifying the contents of the mucus. Mucus and lumen were segmented using a robust, multi-stage algorithm that requires only minimal input regarding sheath geometry. The algorithm performance was highly accurate in a wide range of airway and noise conditions. Mucus was classified using mean backscattering intensity and grey level co-occurrence matrix (GLCM) statistics. We evaluated our techniques in vivo in asthmatic and non-asthmatic volunteers.

Radiomics predicts clinical outcome in primary gastroesophageal junction adenocarcinoma treated by chemo/radiotherapy and surgery

Abstract Purpose Radiomics has shown great promise to use quantifiable imaging characteristics to predict the behavior and prognosis of neoplasms. This is the first study to evaluate whether radiomic texture analysis can predict outcomes in gastroesophageal junction adenocarcinoma (GEJAC) treated with neoadjuvant chemoradiotherapy (CRT). Materials and Methods Pretreatment contrast-enhanced CT images of 146 patients with stage II-III GEJAC were reviewed (2009–2011), and randomly split into training and validation groups at a 1:1 ratio stratified with baseline clinical characteristics. Whole-tumor texture was assessed using quantitative image features based on intensity, shape, and gray-level co-occurrence matrix. The relevant pretreatment texture features, in addition to the significant baseline clinical features to predict survival, were identified using multivariate Cox proportional hazard regression model with stepwise variable selection in the training sample and verified in the validation sample, to facilitate the proposal of a multi-point index for standard patient pre-treatment risk classification. Results Of the factors identified in the training cohort independently associated with OS, only shape compactness (p = 0.04) and pathologic grade differentiation (PDG) (p = 0.02) were confirmed in the validation sample. Using both parameters, we created a 3-point risk classification index: low-risk (well-moderate PDG and high compactness), medium-risk (poor PDG or low compactness), and high-risk (poor PDG and low compactness). The risk index showed a strong negative association with postoperative pathologic complete response (pCR) (p = 0.04). Median OS for the high-, medium-, and low-risk groups were 23, 51, and ≥72 months, respectively (p Conclusion Radiomic texture analysis can be used to stratify patients with GEJAC receiving trimodality therapy based on prognosis. The risk scoring system based on shape compactness and PDG shows a great potential for pre-treatment risk classification to guide surgical resection in locally advanced disease. Though in need of greater validation, these hypothesis-generating data could provide a unique platform of personalized oncologic care.

A stochastic Expectation–Maximisation (EM) algorithm for construction of mortality tables

AbstractIn this paper, we present an extension of the model proposed by Lin &amp; Liu that uses the concept of physiological age to model the ageing process by using phase-type distributions to calculate the probability of death. We propose a finite-state Markov jump process to model the hypothetical ageing process in which it is possible the transition rates between non-consecutive physiological ages. Since the Markov process has only a single absorbing state, the death time follows a phase-type distribution. Thus, to build a mortality table the challenge is to estimate this matrix based on the records of the ageing process. Considering the nature of the data, we consider two cases: having continuous time information of the ageing process, and the more interesting and realistic case, having reports of the process just in determined times. If the ageing process is only observed at discrete time points we have a missing data problem, thus, we use a stochastic Expectation–Maximisation (SEM) algorithm to find the maximum likelihood estimator of the intensity matrix. And in order to do that, we build Markov bridges which are sampled using the Bisection method. The theory is illustrated by a simulation study and used to fit real data.

Finding the probability distribution of states in the fuzzy markov systems

A problem on finding the stationary distributions of probabilities of states for the Markov systems under conditions of uncertainty is solved. It is assumed that parameters of the analyzed Markov and semi-Markov systems (matrix of transition intensities, analytical description of distribution functions of the durations of being in states of the system before exiting, as well as a matrix of transition probabilities) are not clearly assigned. In order to describe the fuzziness, we employ the Gaussian membership functions, as well as functions of the type. The appropriate procedure of systems analysis is based on the developed technology for solving the systems of linear algebraic equations with fuzzy coefficients. In the problem on analysis of a semi-Markov system, the estimation of components of the stationary distribution of probabilities of states of the system is obtained by the minimization of a complex criterion. The criterion considers the measure of deviation of the desired distribution from the modal one, as well as the level of compactness of membership functions of the fuzzy result of solution. In this case, we apply the rule introduced for the calculation of expected value of fuzzy numbers. The criterion proposed is modified through the introduction of weight coefficients, which consider possible differences in the levels of requirements to different components of the criterion.

Atherosclerosis and Metabolic Syndrome-significance of Inflammation, Urgency of Weight Loss and Extracorporeal Removal of Proinflammatory and Proatherogenic Substances

The link between atherosclerosis and metabolic syndrome is through system inflammation. Molecular mechanisms of this link can be explained by hypothesis that arterial hypertension, central obesity and insulin resistance are connected through dysfunction of endothelium and inflammation, where major role play tumor necrosis factor alpha (TNF-a) and reactive oxygen species (ROS), that promotes modulation of insulin receptor substrare2 (IRS2) signaling, insulin resistance, nuclear factor NF-kB (NF-kB) and IκB kinase β activation, endothelial dysfunction, oxidized low density lipoproteins accumulation, vascular inflammatory responses with vascular smooth muscular cells apoptosis and atherosclerotic plaque instability. TNF-a and ROS overproduction closely linked with central obesity and adipocytokines deregulation. Morphological substrate of atherosclerosis-plaque, it’s stability and evolution depends on intensity proinflammatory mediators and matrix metalloproteinases production, that closely bind with metabolic syndrome components, such as central obesity with adipocytokines production; arterial hypertension with endothelial dysfunction; glucose intolerance, insulin resistance and its intracellular signaling disturbances; oxidized low density lipoproteins accumulation and oxidative stress.

Theory of propagation of spectrum and correlations of radiation in optically dense gas in the case of the closed excitation contour

This work is devoted to generalization of the semi-classical theory of interaction of broadband laser radiation with the atomic gas at the room temperature in the cell in the case of the closed excitation contour. The atomic density matrix equations and spectrum and correlations transport equations have been derived for excitation by fluctuating field with Gaussian statistics. It is shown that the spatial oscillations of radiation intensity and atomic density matrix can be excited. It was found that such medium can serve as a filter of incoherent part of the radiation.

Diatom growth responses to photoperiod and light are predictable from diel reductant generation

Light drives phytoplankton productivity, so phytoplankton must exploit variable intensities and durations of light exposure, depending upon season, latitude, and depth. We analyzed the growth, photophysiology and composition of small, Thalassiosira pseudonana, and large, Thalassiosira punctigera, centric diatoms from temperate, coastal marine habitats, responding to a matrix of photoperiods and growth light intensities. T. pseudonana showed fastest growth rates under long photoperiods and low to moderate light intensities, while the larger T. punctigera showed fastest growth rates under short photoperiods and higher light intensities. Photosystem II function and content responded primarily to instantaneous growth light intensities during the photoperiod, while diel carbon fixation and RUBISCO content responded more to photoperiod duration than to instantaneous light intensity. Changing photoperiods caused species‐specific changes in the responses of photochemical yield (e−/photon) to growth light intensity. These photophysiological variables showed complex responses to photoperiod and to growth light intensity. Growth rate also showed complex responses to photoperiod and growth light intensity. But these complex responses resolved into a close relation between growth rate and the cumulative daily generation of reductant, across the matrix of photoperiods and light intensities.

On the Use of the $l_{2}$-Norm for Texture Analysis of Polarimetric SAR Data

In this paper, the use of the $l_{2}$ -norm, or Span, of the scattering vectors is suggested for texture analysis of polarimetric synthetic aperture radar (SAR) data, with the benefits that we need neither an analysis of the polarimetric channels separately nor a filtering of the data to analyze the statistics. Based on the product model, the distribution of the $l_{2}$ -norm is studied. Closed expressions of the probability density functions under the assumptions of several texture distributions are provided. To utilize the statistical properties of the $l_{2}$ -norm, quantities including normalized moments and log-cumulants are derived, along with corresponding estimators and estimation variances. Results on both simulated and real SAR data show that the use of statistics based on the $l_{2}$ -norm brings advantages in several aspects with respect to the normalized intensity moments and matrix variate log-cumulants.

Simulation and Calibration of Options Prices under a Levy-Type Stochastic Dynamic and Semi Markov Market Switching Regimes Processes

This work mainly highlights the benefits of derivative pricing in a semi Markov switching market. We explore the main differences between Markov and Semi Markov regime switching models. The three main problems we deal with are, (1) historical parameter calibration through the recently developed LLGMM method, (2) effects of semi Markov parameters on option prices and (3) comparison of Heston model, semi Markov regime model and Markov regime model calibration performances over both sequential option price calibration and the overall implied volatility surface of the market. Employing the LLGMM method, parameters of the spot price process described by a linear Levy-type stochastic differential equation under semi-Markov structural perturbations are calibrated to observed prices. From Fourier space time stepping and Carr and Madan methods, risk neutral parameters of the spot price are calibrated and interpreted. American and European style vanilla and exotic option prices are simulated. The presented results are shown in the context of a piecewise constant semi-Markov intensity matrix approximating a Weibull intensity matrix. We obtain that under the conditional minimum entropy martingale measure, option prices predictably increase as the regime risk increases through the intensity matrix of the semi Markov process. Calibration and simulation results demonstrate noticeable effects of semi-Markov parameters on option prices and a demonstrably better calibration fit of the Black Scholes model over the entire volatility surface, in a market with semi Markov regimes.

The Limit Behaviour of Imprecise Continuous-Time Markov Chains

We study the limit behaviour of a nonlinear differential equation whose solution is a superadditive generalisation of a stochastic matrix, prove convergence, and provide necessary and sufficient conditions for ergodicity. In the linear case, the solution of our differential equation is equal to the matrix exponential of an intensity matrix and can then be interpreted as the transition operator of a homogeneous continuous-time Markov chain. Similarly, in the generalised nonlinear case that we consider, the solution can be interpreted as the lower transition operator of a specific set of non-homogeneous continuous-time Markov chains, called an imprecise continuous-time Markov chain. In this context, our convergence result shows that for a fixed initial state, an imprecise continuous-time Markov chain always converges to a limiting distribution, and our ergodicity result provides a necessary and sufficient condition for this limiting distribution to be independent of the initial state.

Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects.

The effect of noise on image features has yet to be studied in depth. Our objective was to explore how significantly image features are affected by the addition of uncorrelated noise to an image. The signal-to-noise ratio and noise power spectrum were calculated for a positron emission tomography/computed tomography scanner using a Ge-68 phantom. The conventional and respiratory-gated positron emission tomography/computed tomography images of 31 patients with lung cancer were retrospectively examined. Multiple sets of noise images were created for each original image by adding Gaussian noise of varying standard deviation equal to 2.5%, 4.0%, and 6.0% of the maximum intensity for positron emission tomography images and 10, 20, 50, 80, and 120 Hounsfield units for computed tomography images. Image features were extracted from all images, and percentage differences between the original image and the noise image feature values were calculated. These features were then categorized according to the noise sensitivity. The contour-dependent shape descriptors averaged below 4% difference in positron emission tomography and below 13% difference in computed tomography between noise and original images. Gray level size zone matrix features were the most sensitive to uncorrelated noise exhibiting average differences >200% for conventional and respiratory-gated images in computed tomography and 90% in positron emission tomography. Image feature differences increased as the noise level increased for shape, intensity, and gray-level co-occurrence matrix features in positron emission tomography and for gray-level co-occurrence matrix and gray-level size zone matrix features in conventional computed tomography. Investigators should be aware of the noise effects on image features.

Theoretical analysis of controllability of interference phenomena between partially coherent fields in the intensity matrix theory

We analyze the controllability of interference phenomena between partially coherent fields by introducing the Wigner distribution function (WDF) and entropy, which is defined using the intensity matrix [H. Gamo, J. Opt. Soc. Am. 47, 976 (1957)]. The analytical derivation of the WDF and entropy is presented for a partially coherent imaging system consisting of two pinholes illuminated by a circular source. It is shown that the WDF, defined in the 4D space–spatial frequency region, and entropy can be useful tools to understand how one can freely and quantitatively control the interference when any optical components in the partially coherent imaging system are changed.

Realization of consensus of multi-agent systems with stochastically mixed interactions.

In this paper, we propose a new consensus model in which the interactions among agents stochastically switch between attraction and repulsion. Such a positive-and-negative mechanism is described by the white-noise-based coupling. Analytic criteria for the consensus and non-consensus in terms of the eigenvalues of the noise intensity matrix are derived, which provide a better understanding of the constructive roles of random interactions. Specifically, we discover a positive role of noise coupling that noise can accelerate the emergence of consensus. We find that the converging speed of the multi-agent network depends on the square of the second smallest eigenvalue of its graph Laplacian. The influence of network topologies on the consensus time is also investigated.

Performance evaluation of GLCM and pixel intensity matrix for skin texture analysis

Summary Texture refers to visual patterns or spatial arrangement of pixels. Texture analysis is one of the features in image processing that is used to analyze the images captured by the imaging devices on human skin. Skin texture analysis plays a vital role in assessing the skin health and in the diagnosis of skin disorders such as allergic skin disorders, viral skin disease, bacterial skin diseases and fungal skin diseases. Texture analysis is carried out by one of the methods namely, structural, statistical, model based, transform based techniques. Statistical texture analysis depends mainly on feature extraction which may be done using GLCM (grey level co-occurrence matrix) and WDM (wavelength division multiplexing) techniques. The extracted features are used to classify texture. In this paper a study of skin texture analysis is carried out by comparing GLCM features and pixel intensity matrix parameters by experimenting with cheek and dorsal skin samples. The results obtained show that pixel intensity matrix parameters are more helpful than GLCM for analyzing the texture of skin.