ON DIRECT METHODS FOR TIME-LIMITED SIGNAL AND IMAGE RECONSTRUCTION AND ENHANCEMENT

The discrete Fourier transform (DFT) can be considered as an observing system, which has an input f, an output F, and a response with additive noise E. In many applications, part of the frequency spectrum/frequency information is missing or unavailable due to the passage of the time-limited signal through a band-limited system, for example, the discrete Fourier system. We suggest improving the resolution of the reconstruction of signals and images using a novel approach for the solution of the discrete Fourier system and by image enhancement. We note that the reconstruction of a time-limited signal can be simply realized by only using either the real part or the imaginary part of the DFT matrix. Therefore, based on the study of the special structure of the real and imaginary parts of the discrete Fourier matrix, a fast direct computational method is developed that utilizes explicit formulas for the truncated singular value decomposition (TSVD) obtained recently by the authors. For improving the resolution of the reconstructions, enhancement by logarithm transform is applied. This fast direct computational method is superior to other direct methods such as LU decomposition, QR decomposition, classical SVD and classical TSVD. The explicit TSVD along with the enhancement can be considered as a useful tool for signal and image reconstructions. Numerical tests for signal and image reconstructions and enhancements are given as well.

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A novel approach to the computation of one-loop three- and four-point functions: I. The real mass case

Progress of Theoretical and Experimental Physics ◽

10.1093/ptep/ptz114 ◽

2019 ◽

Vol 2019 (11) ◽

Cited By ~ 4

Author(s):

J Ph Guillet ◽

E Pilon ◽

Y Shimizu ◽

M S Zidi

Keyword(s):

Building Blocks ◽

The Real ◽

Alternative Method ◽

Novel Approach ◽

Reduction Methods ◽

Real Mass ◽

Novel Method ◽

Algebraic Reduction ◽

The One ◽

Mass Case

Abstract This article is the first of a series of three presenting an alternative method of computing the one-loop scalar integrals. This novel method enjoys a couple of interesting features as compared with the method closely following ’t Hooft and Veltman adopted previously. It directly proceeds in terms of the quantities driving algebraic reduction methods. It applies to the three-point functions and, in a similar way, to the four-point functions. It also extends to complex masses without much complication. Lastly, it extends to kinematics more general than that of the physical, e.g., collider processes relevant at one loop. This last feature may be useful when considering the application of this method beyond one loop using generalized one-loop integrals as building blocks.

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Minimum entropy deconvolution and simplicity: A noniterative algorithm

Geophysics ◽

10.1190/1.1441919 ◽

1985 ◽

Vol 50 (3) ◽

pp. 394-413 ◽

Cited By ~ 43

Author(s):

Carlos A. Cabrelli

Keyword(s):

Synthetic Data ◽

Computational Method ◽

Minimum Entropy ◽

Autocorrelation Matrix ◽

Numerical Tests ◽

Convolution Model ◽

Single Input ◽

Definition Of ◽

Single Matrix ◽

New Criterion

Minimum entropy deconvolution (MED) is a technique developed by Wiggins (1978) with the purpose of separating the components of a signal, as the convolution model of a smooth wavelet with a series of impulses. The advantage of this method, as compared with traditional methods, is that it obviates strong hypotheses over the components, which require only the simplicity of the output. The degree of simplicity is measured with the Varimax norm for factor analysis. An iterative algorithm for computation of the filter is derived from this norm, having as an outstanding characteristic its stability in presence of noise. Geometrical analysis of the Varimax norm suggests the definition of a new criterion for simplicity: the D norm. In case of multiple inputs, the D norm is obtained through modification of the kurtosis norm. One of the most outstanding characteristics of the new criterion, by comparison with the Varimax norm, is that a noniterative algorithm for computation of the deconvolution filter can be derived from the D norm. This is significant because the standard MED algorithm frequently requires in each iteration the inversion of an autocorrelation matrix whose order is the length of the filter, while the new algorithm derived from the D norm requires the inversion of a single matrix. On the other hand, results of numerical tests, performed jointly with Graciela A. Canziani, show that the new algorithm produces outputs of greater simplicity than those produced by the traditional MED algorithm. These considerations imply that the D criterion yields a new computational method for minimum entropy deconvolution. A section of numerical examples is included, where the results of an extensive simulation study with synthetic data are analyzed. The numerical computations show in all cases a remarkable improvement resulting from use of the D norm. The properties of stability in the presence of noise are preserved as shown in the examples. In the case of a single input, the relation between the D norm and the spiking filter is analyzed (Appendix B).

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Framework Design of Computational Method Using Singular Value Decomposition for Gender Classification in Words Usage

Proceedings of the International Conference on Science, Technology and Social Sciences (ICSTSS) 2012 ◽

10.1007/978-981-287-077-3_60 ◽

2014 ◽

pp. 507-517

Author(s):

Roslan Sadjirin ◽

Haslinda Noradzan ◽

Kamisah Ariffin ◽

Noli Maishara Nordin

Keyword(s):

Singular Value Decomposition ◽

Singular Value ◽

Computational Method ◽

Gender Classification ◽

Framework Design ◽

Value Decomposition

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On the Effects of Structural Coupling on Piezoelectric Energy Harvesting Systems Subject to Random Base Excitation

Aerospace ◽

10.3390/aerospace7070093 ◽

2020 ◽

Vol 7 (7) ◽

pp. 93

Author(s):

Hamidreza Masoumi ◽

Hamid Moeenfard ◽

Hamed Haddad Khodaparast ◽

Michael I. Friswell

Keyword(s):

Energy Harvesting ◽

Mechanical Response ◽

Harmonic Excitation ◽

Point Of View ◽

Analytical Models ◽

Energy Harvesters ◽

Piezoelectric Energy ◽

Novel Approach ◽

Band Limited ◽

Multi Body

The current research investigates the novel approach of coupling separate energy harvesters in order to scavenge more power from a stochastic point of view. To this end, a multi-body system composed of two cantilever harvesters with two identical piezoelectric patches is considered. The beams are interconnected through a linear spring. Assuming a stochastic band limited white noise excitation of the base, the statistical properties of the mechanical response and those of the generated voltages are derived in closed form. Moreover, analytical models are derived for the expected value of the total harvested energy. In order to maximize the expected generated power, an optimization is performed to determine the optimum physical and geometrical characteristics of the system. It is observed that by properly tuning the harvester parameters, the energy harvesting performance of the structure is remarkably improved. Furthermore, using an optimized energy harvester model, this study shows that the coupling of the beams negatively affects the scavenged power, contrary to the effect previously demonstrated for harvesters under harmonic excitation. The qualitative and quantitative knowledge resulting from this analysis can be effectively employed for the realistic design and modelling of coupled multi-body structures under stochastic excitations.

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Numerical solutions of symmetric saddle point problem by direct methods

Demonstratio Mathematica ◽

10.1515/dema-2013-0463 ◽

2013 ◽

Vol 46 (3) ◽

Author(s):

Alicja Smoktunowicz ◽

Felicja Okulicka-Dłużewska

Keyword(s):

Saddle Point ◽

Numerical Solutions ◽

Direct Methods ◽

Qr Decomposition ◽

Saddle Point Problem ◽

Singular Value ◽

Point Problem ◽

Numerical Comparison ◽

Augmented System ◽

Value Decomposition

AbstractNumerical stability of two main direct methods for solving the symmetric saddle point problem are analyzed. The first one is a generalization of Golub’s method for the augmented system formulation (ASF) and uses the Householder QR decomposition. The second method is supported by the singular value decomposition (SVD). Numerical comparison of some direct methods are given.

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Geodesic Distance on Gaussian Manifolds to Reduce the Statistical Errors in the Investigation of Complex Systems

Complexity ◽

10.1155/2019/5986562 ◽

2019 ◽

Vol 2019 ◽

pp. 1-24 ◽

Cited By ~ 1

Author(s):

Michele Lungaroni ◽

Andrea Murari ◽

Emmanuele Peluso ◽

Pasqualino Gaudio ◽

Michela Gelfusa

Keyword(s):

Euclidean Distance ◽

Additive Noise ◽

Geodesic Distance ◽

Type Ii ◽

Additional Source ◽

Numerical Tests ◽

Important Improvement ◽

Scientific Expertise ◽

Type Ii Errors ◽

Robust Statistical Methods

In the last years the reputation of medical, economic, and scientific expertise has been strongly damaged by a series of false predictions and contradictory studies. The lax application of statistical principles has certainly contributed to the uncertainty and loss of confidence in the sciences. Various assumptions, generally held as valid in statistical treatments, have proved their limits. In particular, since some time it has emerged quite clearly that even slightly departures from normality and homoscedasticity can affect significantly classic significance tests. Robust statistical methods have been developed, which can provide much more reliable estimates. On the other hand, they do not address an additional problem typical of the natural sciences, whose data are often the output of delicate measurements. The data can therefore not only be sampled from a nonnormal pdf but also be affected by significant levels of Gaussian additive noise of various amplitude. To tackle this additional source of uncertainty, in this paper it is shown how already developed robust statistical tools can be usefully complemented with the Geodesic Distance on Gaussian Manifolds. This metric is conceptually more appropriate and practically more effective, in handling noise of Gaussian distribution, than the traditional Euclidean distance. The results of a series of systematic numerical tests show the advantages of the proposed approach in all the main aspects of statistical inference, from measures of location and scale to size effects and hypothesis testing. Particularly relevant is the reduction even of 35% in Type II errors, proving the important improvement in power obtained by applying the methods proposed in the paper. It is worth emphasizing that the proposed approach provides a general framework, in which also noise of different statistical distributions can be dealt with.

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Learning the Value of Teamwork to Form Efficient Teams

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6192 ◽

2020 ◽

Vol 34 (05) ◽

pp. 7063-7070

Author(s):

Ryan Beal ◽

Narayan Changder ◽

Timothy Norman ◽

Sarvapali Ramchurn

Keyword(s):

Team Performance ◽

Real World ◽

Performance Data ◽

Team Formation ◽

World Cup ◽

Fifa World Cup ◽

The Real ◽

Agent Interactions ◽

Novel Approach ◽

Network Metrics

In this paper we describe a novel approach to team formation based on the value of inter-agent interactions. Specifically, we propose a model of teamwork that considers outcomes from chains of interactions between agents. Based on our model, we devise a number of network metrics to capture the contribution of interactions between agents. This is then used to learn the value of teamwork from historical team performance data. We apply our model to predict team performance and validate our approach using real-world team performance data from the 2018 FIFA World Cup. Our model is shown to better predict the real-world performance of teams by up to 46% compared to models that ignore inter-agent interactions.

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A Novel Approach to Predict the Real Time Sentimental Analysis by Naive Bayes & RNN Algorithm during the COVID Pandemic in UAE

2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) ◽

10.1109/ccci49893.2020.9256587 ◽

2020 ◽

Author(s):

Abdulrahman Radaideh ◽

Fikri Dweiri ◽

Mohammad Obaidat

Keyword(s):

Real Time ◽

Naive Bayes ◽

Naïve Bayes ◽

The Real ◽

Novel Approach

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Solving the real eigenvalues of hermitian quadratic eigenvalue problems via bisection

Electronic Journal of Linear Algebra ◽

10.13001/1081-3810.1979 ◽

2015 ◽

Vol 30 ◽

pp. 721-743 ◽

Cited By ~ 3

Author(s):

Hao Li ◽

Yunfeng Cai

Keyword(s):

Eigenvalue Problems ◽

Quadratic Eigenvalue Problem ◽

The Real ◽

Numerical Tests ◽

Spring System ◽

Quadratic Eigenvalue Problems ◽

The Difference ◽

Mass Spring ◽

Quadratic Eigenvalue ◽

Inertia Index

This paper considers solving the real eigenvalues of the Quadratic Eigenvalue Problem (QEP) Q(\lambda)x =(\lambda^2M+\lambdaC+K)x = 0 in a given interval (a, b), where the coefficient matrices M, C, K are Hermitian and M is nonsingular. First, an inertia theorem for the QEP is proven, which characterizes the difference of inertia index between Hermitian matrices Q(a) and Q(b). Several useful corollaries are then obtained, where it is shown that the number of real eigenvalues of QEP Q(\lambda)x = 0 in the interval (a, b) is no less than the absolute value of the difference of the negative inertia index between Q(a) and Q(b); furthermore, when all real eigenvalues in (a, b) are semi-simple with the same sign characteristic, the inequality becomes an equality. Based on the established theory, the bisection method (with preprocessing) can be used to compute the real eigenvalues of the QEP by computing the inertia indices. Applications to the calculation of the equienergy lines with k.p model, and also a non-overdamped mass-spring system are presented in the numerical tests.

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