COOPERATIVE MIMO SPECTRUM SENSING BASED ON RANDOM MATRIX THEORY

Random Matrix Theory (RMT) is a valuable tool for describing the asymptotic behavior of multiple systems,especially for large matrices. In this paper,using asymptotic random matrix theory,a new cooperative Multiple-Input Multiple-Output (MIMO) scheme for spectrum sensing is proposed,which shows how asymptotic free property of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for Cognitive Radios (CRs). Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance compared with the energy detection techniques even in the case of a small sample of observations.

Compressive Wideband Spectrum Sensing Based on Random Matrix Theory

Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based compressive wideband spectrum sensing(ECWSS) scheme using random matrix theory(RMT) was proposed in this paper.The ECWSS directly utilized the compressive measurements based on compressive sampling(CS) theory to perform wideband spectrum sensing without requiring signal recovery,which could greatly reduce computational complexity and data acquisition burden.In the ECWSS,to alleviate the communication overhead of secondary user(SU),the sensors around SU carried out compressive sampling at the sub-Nyquist rate instead of SU.Furthermore,the exact probability density function of extreme eigenvalues was used to set the threshold.Theoretical analyses and simulation results show that compared with the existing eigenvalue-based sensing schemes,the ECWSS has much lower computational complexity and cost with no significant detection performance degradation.

Verification of the Validity of the NPT Treatment in Hereditary Spastic Paraplegia: An Investigation Performed by Application of Random Matrix Theory

We have applied the Random Matrix Theory in order to examine the validity of the NPT treatment in HSP. We have investigated the pathology examining the sEMG recorded signal for about eight minutes. We have performed standard electromyographic investigations as well as we have applied the RMT method of analysis. We have investigated the sEMG signals before and after the NPT treatment. The application of a so robust method as the RMT evidences that the NPT treatment was able to induce a net improvement of the disease respect to the pathological status before NPT.

Fast Protection for Collector Lines in Large-Scale Wind Farms Based on Random Matrix Theory

The impact of large-scale wind farms on power system stability should be carefully investigated,in which mal-functions usually exist in the collector line's relay protection.In order to solve this challenging problem,a novel time-domain protection scheme for collector lines,based on random matrix theory(RMT),is proposed in this paper.First,the collected currents are preprocessed to form time series data.Then,a real-time sliding time window is used to form a consecutive time series data matrix.Based on RMT,mean spectral radius(MSR)is used to analyze time series data characteristics after real-time calculations are performed.Case studies demonstrate that RMT is independent from fault locations and fault types.In particular,faulty and non-faulty collector lines can be accurately and efficiently identified compared with traditional protection schemes.

Evaluating the Vulnerability of Integrated Electricity-heat-gas Systems Based on the High-dimensional Random Matrix Theory

Faced with the tight coupling of multi energy sources,the interaction between different energy supply systems makes it difficult for integrated energy systems(IES)to identify weak nodes.Based on the analysis of the data generated by the actual operation of IES,this paper proposes a weak node identification method based on random matrix theory(RMT).First,establish a unified power flow model for IES.Secondly.introduce RMT and the characteristics of weak nodes,without considering the detailed physical model of the system,using historical data and real-time data to construct the random matrix.Thirdly,the two limit spectrum distribution functions(Marchenko-Pastur law and ring law)are used to qualitatively analyze the system’s operating status,calculate linear eigenvalue statistics such as mean spectral radius(MSR),and establish the weak node identification model based on entropy theory.Finally,the simulation of IES verifies the effectiveness of the proposed method and provides a new approach for the identification of weak nodes in IES.

An improved subspace weighting method using random matrix theory

The weighting subspace fitting(WSF)algorithm performs better than the multi-signal classification(MUSIC)algorithm in the case of low signal-to-noise ratio(SNR)and when signals are correlated.In this study,we use the random matrix theory(RMT)to improve WSF.RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate.The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance.Using the asymptotic results of the norm of the projection from the sample covariance matrix signal subspace onto the real signal in the random matrix theory,the method of calculating WSF is obtained.Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.

Spatio-Temporal Correlation Analysis of Global Temperature Based on the Correlation Matrix Theory

Based on the NCEP/NCAR reanalysis daily mean temperature data from 1948 to 2005 and random time series of the same size,temperature correlation matrixes（TCMs） and random correlation matrixes（RCMs） are constructed and compared.The results show that there are meaningful true correlations as well as correlation＂noises＂in the TCMs.The true correlations contain short range correlations（SRCs） among temperature series of neighboring grid points as well as long range correlations（LRCs） among temperature series of different regions,such as the El Nino area and the warm pool areas of the Pacific,the Indian Ocean,the Atlantic,etc.At different time scales,these two kinds of correlations show different features：at 1-10-day scale,SRCs are more important than LRCs;while at 15-day-or-more scale,the importance of SRCs and LRCs decreases and increases respectively,compared with the case of 1-10-day scale.It is found from the analyses of eigenvalues and eigenvectors of TCMs and corresponding RCMs that most correlation information is contained in several eigenvectors of TCMs with relatively larger eigenvalues,and the projections of global temperature series onto these eigenvectors are able to reflect the overall characteristics of global temperature changes to some extent.Besides,the correlation coefficients（CCs） of grid point temperature series show significant temporal and spatial variations.The average CCs over 1950-1956,1972-1977,and 1996-2000 are significantly higher than average while that over the periods 1978-1982 and 1991-1996 are opposite,suggesting a distinctive oscillation of quasi-10-20 yr.Spatially,the CCs at 1-and 15-day scales both show band-like zonal distributions;the zonally averaged CCs at 1-day scale display a better latitudinal symmetry,while they are relatively worse at 15-day scale because of sea-land contrast of the Northern and Southern Hemisphere.However,the meridionally averaged CCs at 15-day scale display a longitudinal quasi-symmetry.

Centrality of the collision and random matrix theory

I discuss the results from a study of the central ^12CC collisions at 4.2 A GeV/c. The data have been analyzed using a new method based on the Random Matrix Theory. The simulation data coming from the Ultra Relativistic Quantum Molecular Dynamics code were used in the analyses. I found that the behavior of the nearest neighbor spacing distribution for the protons, neutrons and neutral pions depends critically on the multiplicity of secondary particles for simulated data. I conclude that the obtained results offer the possibility of fixing the centrality using the critical values of the multiplicity.

Fault Location Detection of Transmission Lines in Noise Environments Based on Random Matrix Theory

Fault detection and location are critically significant applications of a supervisory control system in a smart grid.The methods,based on random matrix theory(RMT),have been practiced using measurements to detect short circuit faults occurring on transmission lines.However,the diagnostic accuracy is infuenced by the noise signal in the measurements.The relationship between mean eigenvalue of a random matrix and noise is detected in this paper,and the defects of the Mean Spectral Radius(MSR),as an indicator to detect faults,are theoretically determined,along with a novel indicator of the shifting degree of maximum eigenvalue and its threshold.By comparing the indicator and the threshold,the occurrence of a fault can be assessed.Finally,an augmented matrix is constructed to locate the fault area.The proposed method can effectively achieve fault detection via the RMT without any influence of noise,and also does not depend on system models.The experiment results are based on the IEEE 39-bus system.Also,actual provincial grid data is applied to validate the effectiveness of the proposed method.

Central nucleus-nucleus collisions at relativistic energies with a new method based on Random Matrix Theory

Using the method based on Random Matrix Theory （RMT）, the results for the nearest-neighbor distributions obtained from the experimental data on ^12C-C collisions at 4.2 AGeV/c have been discussed and compared with the simulated data on ^12C-C collisions at 4.2 AGeV/c produced with the aid of the Dubna Cascade Model. The results show that the correlation of secondary particles decreases with an increasing number of charged particles Nch. These observed changes in the nearest-neighbor distributions of charged particles could be associated with the centrality variation of the collisions.

An optimized cluster density matrix embedding theory

We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study the distant correlations of the Heisenberg J_(1)-J_(2)model on the square lattice.We find that the intermediate phase(0.43≤sssim J_(2)≤sssim 0.62)is divided into two parts.One part is a near-critical region(0.43≤J_(2)≤0.50).The other part is the plaquette valence bond solid(PVB)state(0.51≤J_(2)≤0.62).The spin correlations decay exponentially as a function of distance in the PVB.

Graph Theory and Matrix Approach(GTMA)Model for the Selec­tion of the Femoral-Component of Total Knee Joint Replacement

Total Knee Replacement(TKR)is the increasing trend now a day,in revision surgery which is associated with aseptic loosening,which is a challenging research for the TKR component.The selection of optimal material loosening can be controlled at some limits.This paper is going to consider the best material selected among a number of alternative materials for the femoral component(FC)by using Graph Theory.Here GTMA process used for optimization of material and a systematic technique introduced through sensitivity analysis to find out the more reliable result.Obtained ranking suggests the use of optimized material over the other existing material.By following GTMA Co_Cr-alloys(wrought-Co-Ni-Cr-Mo)and Co_Cr-alloys(cast-able-Co-Cr-Mo)are on the 1st and 2nd position respectively.

Research on big data applications in Global Energy Interconnection

Construction of Global Energy Interconnection(GEI) is regarded as an effective way to utilize clean energy and it has been a hot research topic in recent years. As one of the enabling technologies for GEI, big data is accompanied with the sharing, fusion and comprehensive application of energy related data all over the world. The paper analyzes the technology innovation direction of GEI and the advantages of big data technologies in supporting GEI development, and then gives some typical application scenarios to illustrate the application value of big data. Finally, the architecture for applying random matrix theory in GEI is presented.

LESSER KNOWN MIRACLES OF BURGERS EQUATION

This article is a short introduction to the surprising appearance of Burgers equation in some basic probabilistic models.

Biexponential distribution of open times of a toy channel model

The biexponential distributions of open times are observed in various types of ion channels. In this paper, by discussing a simple channel model, we show that there are two different schemes to understand the biexponential distribution of open times. One scheme is mathematically strict based on generator matrix theory, while the other one has a clear physical explanation according to an approximation process with numerical simulation of Markovian channel dynamics. Our comparison results suggest that even for biologically complex channels, in addition to carrying out a stochastic simulation, the strict theoretical analysis should be considered to understand the multiple exponential distributions of open times.

Intraband dynamics and terahertz emission in biased semiconductor superlattices coupled to double far-infrared pulses

This paper studies both the intraband polarization and terahertz emission of a semiconductor superlattice in combined dc and ac electric fields by using the superposition of two identical time delayed and phase shifted optical pulses. By adjusting the delay between these two optical pulses, our results show that the intraband polarization is sensitive to the time delay. The peak values appear again for the terahertz emission intensity due to the superposition of two optical pulses. The emission lines of terahertz blueshift and redshift in different ac electric fields and dynamic localization appears. The emission lines of THz only appear to blueshift when the biased superlattice is driven by a single optical pulse. Due to excitonic dynamic localization, the terahertz emission intensity decays with time in different dc and ac electric fields. These are features of this superlattice which distinguish it from a supertattice generated by a single optical pulse to drive it.

Dynamics of molecular alignment steered by a few-cycle terahertz laser pulse

The field-free alignment of molecule Cl CN is investigated by using a terahertz few-cycle pulse(THz FCP)based on the time-dependent density matrix theory.It is shown that a high degree of molecular alignment can be obtained by changing the matching number of the THz FCPs in the adiabatic regime and the non-adiabatic regime.The matching number can affect both the maximum value of the alignment and the time at which it is achieved.It is also found that a higher degree of alignment can be achieved by using the THz FCP at lower intensity and there exists an optimal threshold of molecular alignment with the increase of the field amplitude.Also found is the frequency sensitive region in which the degree of maximum alignment can be enhanced greatly by modulating the center frequencies of different THz FCPs.The investigation demonstrates that comparing with a THz single-cycle pulse,a better result of the field-free alignment can be created by a THz FCP at a constant rotational temperature of molecule.

On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence

In this paper,we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation matrix.The underlying population is allowed to have a general dependence structure.The result no longer follows the generalized Marcenko-Pastur law,which is brand new.It is the first result on rank correlation matrices with dependence.As applications,we study Kendall’s rank correlation matrix for multivariate normal distributions with a general covariance matrix.From these results,we further gain insights into Kendall’s rank correlation matrix and its connections with the sample covariance/correlation matrix.

Structure and Curvatures of Trajectories of a 2D Log-Gas

A model is constructed to study the statistical properties of irregular trajectories of a log-gas whose positions are those of the complex eigenvalues of the unitary Ginibre ensemble. It is shown that statistically the trajectories form a structure that reveals the eigenvalue departure positions. It is also shown that the curvatures of the ensemble of trajectories are Cauchy distributed.

THE GENERAL STRESS STRAIN RELATION OF SOILS INVOLVING THE ROTATION OF PRINCIPAL STRESS AXES

In the light of matrix theory, the character of stress increment which causes the rotation of principal stress axes is analysed and the general stress increment is decomposed into two parts: coaxial part and rotational part. Based on these, the complex three dimensional (3-D) problem involving the rotation of principal stress axes is simplified to the combination of the 3-D coaxial model and the theory about pure rotation of principal stress axes that is only around one principal stress axes. The difficulty of analysis is reduced significantly. The concrete calculating method of general 3-D problem is provided and other applications are also presented.