State/Signal Systems: Dynamic and Frequency Domain Properties

In recent years, attention has been focused on measurement of various dielectric properties in frequency domain to assess the condition in each of the dielectric media, especially in paper oil insulation systems, employed in Power Transformers. This paper details some of the findings of the laboratory investigations as well as measurements carried out on in-service Power transformers by monitoring dielectric properties in frequency domain. Interpretation has been focused on characterizing the variations in the spectral response of dielectric parameters like Dielectric loss, Capacitance and Permittivity as a function of frequency to estimate moisture content and for condition assessment of the composite insulation system of the transformer and thus to arrive at the degree of degradation of each of the insulation system.

In this paper a novel method for synchronization and cell search in 3GPP LTE downlink (DL) system has been proposed. This method performs the symbol timing and physical-layer cell identity estimation in frequency domain with less complexity, utilizing the frequency domain properties of the primary and secondary synchronization signals. The primary synchronization signals have good orthogonal and frequency domain properties, and the secondary synchronization signals have good frequency domain properties and flat spectrum.

EALQR (linear quadratic regulator with an eigenstructure assignment capability), a control design methodology previously developed by the authors, performs better than a conventional LQR or an eigenstructure assignment. However, it has a constraint on the weighting matrix, that is, the weighting matrix can be indefinite for some high-order systems. This paper analyzes the effects of an indefinite weighting matrix in EALQR on the frequency domain properties. The robustness criterion and the quantitative frequency domain properties are presented. The frequency domain properties of EALQR with an indefinite weighting matrix have been analyzed by applying them to a flight control system as a design example.

With the coming age of big data, the image signals play more and more important role in our life due to the extraordinary advance of network communication technology, and the corresponding high efficiency image processing techniques are demanded urgently. The Fourier transform is an important image processing tool which is used in a wide range of applications. Traditional Fourier transform algorithm computes on the value of each point of image, regardless of their properties in frequency domain. However, most image signals possess sparsity in frequency domain. In this paper, we present a new fast two-dimensional Fourier transform based on image sparsity. With hash function including a series of procedures such as random spectrum permutation, filtering and subsampling in frequency domain, the algorithm could identify and estimate the k largest coefficients quickly. In most sparse cases, the resulting algorithm performs faster than state-of-the-art fast Fourier transform algorithm, FFTW.

The aim of this paper is to develop a simple and efficient method for observing the fluctuating spectral density of subdiffusive Brownian motion in an overdamped periodic potential for exploring the subdiffusive property in frequency domain. Based on the general frame of linear response theory for subdiffusive fractional Fokker–Planck equation systems, an explicit relation between fluctuating spectral density and linear dynamical susceptibility is deduced, and then a method of moments based on the expansion of trigonometric functions is proposed for calculating the linear dynamic susceptibility. With the linear dynamic susceptibility available, the fluctuating spectral density is finally obtained. The numerical results demonstrate that subdiffusion weakens coherent oscillations in the periodic system, but enhances aperiodic components. Our observation embodies the fact of the Mittag–Leffler residence time distribution with an infinite mean in the subdiffusive process from the frequency domain.

The purpose of this work is to improve the orientational filter for texture image analysis. It consists of four bandpass Quadrature Polar Separable (QPS) filters with the same bandwidth but different orientation. The frequency response of the designed QPS filter consists of the independent pair set. The first is the same as the radial weighting function used in the Knutsson's filter except inserting the shift parameter to the central frequency. The second, the orientational weighting function, is characterised by a quadrature function pair which are generated by combining an exponential attenuation function. It is easy to control the filter property in frequency domain and is more effective to estimate texture image. In order to estimate the orientations and the frequency components of local texture in frequency domain, a series of experiments have been carried out. The results show that it can be used as an efficient tool in texture processing.

This paper addresses the problems of how to exploit the space and frequency properties of the wavelet coefficients, and how to design a wavelet packet coder optimally in the rate and distortion sense. From the localization properties of the wavelets, the best quantizer for a wavelet coefficient is expected to match its local characteristics, i.e., to be adaptive both in space and frequency domain. Previous image coders tended to design quantizer in a band or a class level, which limited their performances as it is difficult for the localization properties of wavelets to be exploited. Contrasting with previous coders, we introduce a new image coding framework, where the compaction properties in frequency domain are exploited through the selection of wavelet packets, and the compaction properties in space domain are exploited with the tree-structured wavelet representations. For each wavelet coefficient, its model is estimated from the quantized causal neighborhoods, therefore, the optimal quantizer is spatial-varying and rate sensitive, and the optimization problem is no longer a joint optimization problem as in the SFQ-like coders. The simulation results demonstrate that the proposed coding performance is competitive, and often is superior than those of state of art zerotree-based coding schemes.

In order to realize the intelligent evaluation of moisture content of transformer oil-paper insulation, a comprehensive evaluation model combining multi-frequency domain characteristic parameters and random forest algorithm was proposed. Firstly, on the basis of in-depth analysis of the variation of dielectric properties in frequency domain of oil-paper insulation samples under different moisture conditions, nine characteristic parameters with significant correlation with moisture content were extracted. Secondly, multiple groups of measured characteristic data in the frequency domain were normalized, and the water content of the measured sample data was evaluated by random forest algorithm under the condition of multiple characteristic data. The experimental results show that the root-mean-square error and mean absolute error of the evaluation model constructed in this paper are significantly reduced compared with the traditional model, which can provide an effective scientific basis for the subsequent quantitative evaluation of the transformer oil-paper insulation state.

Background The ultrasound technique, usually based on the transmission mode, is capable of providing the viscoelastic properties of polymers. Further techniques involving pulse-echo methods were also described in literature, but they still exhibit inaccuracies in the evaluation of the acoustic properties. Objective The manuscript focuses on an innovative approach for the characterization of the viscoelastic behavior of polymers employing the ultrasound methodology. The proposed procedure is based on the pulse-echo method in order to overcome possible inaccuracies in acoustic properties evaluation and in issues related to transmitter mode applications. Methods Starting from the pulse-echo method adopted for the acquisition, a novel formulation for data processing has been developed and described, allowing to determine the wave attenuation coefficient, in comparison to the commonly employed procedures involving ultrasound in polymers characterization, based on transmitter mode inspections. To carry out the study, a specifically designed ultrasound bench has been set up and three different polymers have been tested in the temperature range of interest. Results According to the proposed methodology, the loss factor towards the temperature is determined starting from the data acquired considering the identified attenuation coefficient and the measured sound velocities. The trustworthiness of the novel procedure has been proved comparing the obtained viscoelastic loss factor quantities to the reference master curves obtained by the standard Dynamic Mechanical Analysis characterizations carried out on the same polymer specimens. Conclusions A novel methodology involving ultrasound technology aiming to evaluate the viscoelasticity of the polymers using non-destructive approach has been developed. The results obtained are agreement with the standard viscoelastic master curves determined through the DMA.

Extracting improved mechanical properties such as high stiffness-high damping and high strength-high toughness are being investigated recently using high symmetry interlocking micro-structures. On the other hand, development of artificially engineered composite metamaterials has significantly widen the usability of such materials in multiple acoustic applications. However, investigation of elastic wave propagation through high symmetry micro-structures is still in trivial stage. In this work, a novel interlocking micro-architecture design which has been reported previously for the extraction of improved mechanical properties has been investigated to explore its acoustic responses. The finite element simulations are performed under dynamic wave propagation load at multiple scales of the geometry and for a range of material properties in frequency domain. The proposed composite structure has shown high symmetry which is uncommon in fiber-reinforced polymer composites and a desirable feature for isotropic behavior. The existence of multiple acoustic features such as band gap and near-isotropic behavior have been established. An exotic wave propagation feature, wave trapping and attenuation, has shown energy encapsulation in a series of repeating structures in a frequency range of 0.5 kHz to 2 kHz.

One of the techniques recommended for calculating the nonparametric trend (nonstationary, low-frequency time series component) is the moving trend based smoothing [10], [3], [4] typically based on linear Least Square (LS) approximates of the series in a moving window. Such algorithm splits input time series into three sections, starting, central, final ones. The procedures used in each section may be viewed as Moving Trend based Filters (MTF). In the paper MTFs properties in frequency domain are considered, from seasonal time series decomposition, smoothing and prediction efficiency perspectives. A number of MTFs enhancements is proposed, involving different approximating polynomials and final section signal corrections. In particular, a compression of the final section MTF output signal is proposed to reduce the filter delay, and then reconstruction of the missing signal by a special multiple LS approximation. It improves significantly the final section signal shape evaluation, which are essential for further prediction purposes.KeywordsMoving trend filtersfrequency domain analysistime series prediction

Linear parameter varying, finite impulse response filter design with application to reducing rise-time for rapid signal changes

Finite element analysis is used to study brain axonal injury and develop Brain White Matter (BWM) models while accounting for both the strain magnitude and the strain rate. These models are becoming more sophisticated and complicated due to the complex nature of the BMW composite structure with different material properties for each constituent phase. State-of-the-art studies, focus on employing techniques that combine information about the local axonal directionality in different areas of the brain with diagnostic tools such as Diffusion-Weighted Magnetic Resonance Imaging (Diffusion-MRI). The diffusion-MRI data offers localization and orientation information of axonal tracks which are analyzed in finite element models to simulate virtual loading scenarios. Here, a BMW biphasic material model comprised of axons and neuroglia is considered. The model’s architectural anisotropy represented by a multitude of axonal orientations, that depend on specific brain regions, adds to its complexity. During this effort, we develop a finite element method to merge micro-scale Representative Volume Elements (RVEs) with orthotropic frequency domain viscoelasticity to an integrated macro-scale BWM finite element model, which incorporates local axonal orientation. Previous studies of this group focused on building RVEs that combined different volume fractions of axons and neuroglia and simulating their anisotropic viscoelastic properties. Via the proposed model, we can assign material properties and local architecture on each element based on the information from the orientation of the axonal traces. Consecutively, a BWM finite element model is derived with fully defined both material properties and material orientation. The frequency-domain dynamic response of the BMW model is analyzed to simulate larger scale diagnostic modalities such as MRI and MRE.

A frequency domain geometric model aimed at clarifying the fundamental propagation characteristics for a wideband time-invariant channel is proposed. This modeling approach envisages a direct mapping from the frequency-domain transfer function T(f) in classic random linear systems to the geometric representation of a broadband channel. One of the main advantages of this approach is the availability of analytical expressions relating signal properties in frequency domain to channel output. A further virtue of this method is to exploit the geometric distribution of scatterers for different spectral components from a physical wave-propagation viewpoint. The workhorse is the semi-geometrically based statistical model and three heuristic rules proposed, which are an extension to the rules presented in the previous work. These rules are so proposed as to provide the missing link between any canonical model and the physical channel it represents. The power azimuthal spectrum and poster delay spectrum are calculated using this model. We further study the channel properties for a macrocell.

Performance of Machine Learning Models in Determining the GNSS Position Usage for a Loosely Coupled GNSS/IMU System