Frequency-domain Fitting Research Articles

Research on Pedestrian and Cyclist Classification Method Based on Micro-Doppler Effect.

In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver's field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification.

A Frequency Domain Fitting Algorithm Method for Automotive Suspension Structure under Colored Noise

The suspension of a car has different structural forms but usually consists of springs, shock absorbers, guiding mechanisms, etc. As a vehicle moves, the terrain often induces a multifaceted non-white noise vibration within the vehicle. Research on this type of vibration often uses the operational modal analysis (OMA) method, due to its advantages of not requiring knowledge of excitation signals. The disadvantage is that it can only analyze systems under white noise excitation, otherwise it will bring errors. So, this paper proposes a frequency domain fitting algorithm (FDFA) based on colored noise excitation. Initially, an exposition on the foundational principles of the FDFA technique was provided, followed by a demonstration of the modal identification approach. Subsequently, a simulation scenario involving a cantilever beam, akin to a suspension system, was chosen for examination in three instances, revealing that the frequency discrepancies are under 2.94%, and for damping coefficients, they are less than 2.76%. In conclusion, the paper’s introduced FDFA technique, along with the frequency–spatial domain decomposition (FSDD) approach, were employed to determine the modal characteristics of aluminum cantilever beams subjected to four distinct colored noise stimulations. The findings indicate that when utilizing the FDFA technique, the error in modal frequency is kept below 2.5%, while the error for the damping ratio does not exceed 15%. Compared with FSDD, the accuracy was improved.

Null Phase Assumption-Based Technique for Constructing the Target Model of Seismic Irregularity on High-Speed Railways

High-speed railways are “lifeline projects” that shoulder the heavy responsibility of transporting relief supplies and medical forces for the first time after earthquakes. To ensure the train’s safety after earthquakes, it is of great urgency to ascertain a post-earthquake speed threshold. To that end, a target model for seismic irregularity emerges as a key parameter. In this paper, a null phase assumption-based technique for the mutual conversion between evolutionary power spectral density and non-stationary signal was proposed. Taking a high-speed railway track-bridge system as the research object, the target model of seismic irregularities was constructed based on the proposed technique. The rationality of the target model of seismic irregularities was verified, and the construction parameter settings were discussed. Moreover, a simplified frequency-domain fitting method for the target model of seismic irregularities was proposed based on the spectral decomposition theory. According to the research findings, the null phase assumption-based technique is capable of performing interconversion between seismic irregularity and its evolutionary power spectral density with satisfactory accuracy. It is recommended to set the minimum number of seismic irregularities, spatial sampling interval, hop size, and length of window function as 50, 0.25[Formula: see text]m, 1, and 40 to 100, respectively.

Model parameter estimations of the multi-channel turbulence response from flutter flight tests based on autoregressive spectra

Signal processing of flutter flight test data enables verification of aircraft flutter design, and the signal from a flutter flight test excited by atmospheric turbulence is a particularly important form of the flutter test. Owing to the randomness of atmospheric turbulence excitation, multi-channel analysis of turbulence responses at various positions in the same component can improve the analytical accuracy of flutter signal processing. The relationship between the maximum singular value of the multi-channel turbulence response power spectral density matrix and the system self-power spectral density function is elucidated herein using a frequency domain decomposition method. However, there is a contradiction in the power spectral density function between the spectral line density and the spectral line smoothing calculated based on the periodogram of the frequency domain decomposition. By applying an autoregressive spectral model, the power spectral density function of the turbulence response is calculated to achieve spectral line smoothing and sufficient spectral line density. Additionally, the power spectral density function is then used to construct the power spectral density function matrix of the multi-channel turbulence response, and the maximum singular value curve is calculated based on the singular value decomposition of each spectral pin. Finally, the modal parameters of the turbulence response signal are estimated via multi-modal frequency domain fitting. The developed approach is validated based on simulations and flutter flight test turbulence response signals.

Measurement of Film Structure Using Time-Frequency-Domain Fitting and White-Light Scanning Interferometry

A new technique is proposed for measuring film structure based on the combination of time- and frequency-domain fitting and white-light scanning interferometry. The approach requires only single scanning and employs a fitting method to obtain the film thickness and the upper surface height in the frequency and time domains, respectively. The cross-correlation function is applied to obtain the initial value of the upper surface height, thereby making the fitting process more accurate. Standard films (SiO2) with different thicknesses were measured to verify the accuracy and reliability of the proposed method, and the three-dimensional topographies of the upper and lower surfaces of the films were reconstructed.

Wafer Stage Modal Model Identification by Modified Vector Fitting Method

Vector fitting is modified in this study for the parametric identification of a model with an undamped rigid body mode in the frequency domain. The modal model of a six-degree-of-freedom (6DOF) wafer stage is identified using the modified vector fitting method. The modal model is typically fitted from the matrix fraction description (MFD) form of the transfer function, which increases the cost function. The vector fitting in frequency domain provides an approach to fitting the modal model directly from frequency response functions(FRFs) via a partial rational basis function, the poles of which can be obtained by pole relocation technology. The traditional vector fitting method is not applicable in identifying systems containing rigid body modes. The method is reformulated here and effectively applied for wafer stage identification. The accuracy of the fitted model is validated by control loop simulation.

Pitch contours curve frequency domain fitting with vocabulary matching based music generation

Pitch contours curve frequency domain fitting with vocabulary matching based music generation

Vector‐fitting‐based quantitative SSCI analysis for series‐compensated wind power systems

The occurrence of unstable subsynchronous control interaction (SSCI) between wind farms and series-compensated networks poses a considerable threat to the safety of power system. Different from the conventional eigenvalue analysis and Nyquist method, the black-box-model based RLC fitting technique becomes more attractive for the assessment of the SSCI risk. However, the method has merely been verified by radial networks whose SSCI mode can be well explained by an RLC circuit. A practical wind power system, however, can be meshed networks comprising multiple geographically distributed wind farms and series capacitors. Thus, it is questionable whether the application of the RLC fitting technique to such systems can still yield a sufficient accuracy. To clarify this aspect, in this work, the performance of RLC fitting in various test systems was examined. The results indicated that the aggregation technique considerably influences the accuracy of the assessment, and the RLC fitting cannot be applied when multiple series capacitors are involved in the SSCI mode. To address this problem, a more advanced frequency-domain fitting technique known as vector fitting was adopted, and its robustness and effectiveness were verified considering a modified The Electric Reliability Council of Texas (ERCOT) system.

T165. ANTI-GLUTAMATERGIC PROPERTY OF N-ACETYLCYSTEINE DOCUMENTED IN VIVO WITH 1H MRS

BackgroundDeficits of brain glutathione (GSH), the primary antioxidant in living tissue, and associated redox imbalance are postulated to be implicated in schizophrenia. However, due to poor blood-brain barrier permeability of GSH, direct supplementation is ineffective in restoring its brain levels. Therefore, there has been great interest in investigating N-acetylcysteine (NAC), as a prodrug that can be deacetylated to supply cysteine (Cys), which crosses the BBB and is the rate-limiting substrate in brain GSH synthesis and restoration. Once inside the cell, Cys combines with glutamate (Glu) to initiate GSH synthesis in a reaction that is catalyzed by gamma-glutamyl cysteine ligase (GCL), the rate-limiting enzyme. Likely due to the in situ reaction of NAC-supplied Cys with Glu to spur GSH synthesis, NAC is believed to have anti-glutamatergic properties. However, direct in vivo evidence of NAC as an anti-glutamatergic agent is currently lacking. In this study, we used 1H MRS to monitor changes in brain levels of both GSH and Glu in response to 4 weeks of daily supplementation with NAC in healthy volunteer subjects. We postulated that 4 weeks of NAC treatment would elevate GSH and decrease Glu levels.MethodsSubjects: Participants recruited for this study consisted of 12 mentally and physically healthy human volunteer (HV) subjects.NAC Supplementation: To investigate the effects of dietary NAC supplementation on cortical GSH and Glu levels, each HV subject underwent 1H MRS scans at baseline. Then a 4-week supplement of 900mg NAC tablets was provided, to be taken 2 per day for a daily NAC dose of 1800mg. Finally, each subject was brought back after 4 weeks for the post-NAC 1H MRS scans to assess the effect of the treatment on cortical GSH and Glu levels.Brain 1H MRS: In vivo cortical GSH spectra were recorded in 15 min on a 3.0 T GE MRI system from a 3cmx3cmx2cm occipital cortex voxel, using the standard J-editing technique, with TE/TR 68/1500ms and 290 interleaved excitations (580 total). Levels of Glu, uncontaminated with glutamine, were obtained from the same occipital cortex voxel in 6 min using the CT-PRESS method, with TE/TR 139ms/1500ms, and 129 chemical shift encoding steps in increments of 0.8ms in t1 dimension. Peak areas for both GSH and Glu were derived by frequency-domain fitting of the recorded spectra. The resulting peak areas were then expressed as ratios relative to the area of the unsuppressed tissue water signal in the voxel.ResultsGSH Levels: The effect of 4 weeks of NAC supplementation on GSH levels was a numerical increase that did not reach statistical significance relative to baseline (p=0.33).Glu LevelsFollowing 4 weeks of NAC supplementation cortical Glu levels decreased significantly compared to baseline (p=0.04).DiscussionThis study has shown that following 4 weeks of taking NAC, brain Glu levels in HV decreased significantly, even though a numerical increase in GSH levels did not reach statistical significance. The observed Glu decrease at 4 weeks suggests a net increase in the consumption of intracellular Glu reserves in the GCL-catalyzed reaction that combines NAC-supplied Cys with Glu in GSH synthesis. The failure of GSH levels to increase significantly after NAC likely reflected the established “feedback inhibition” mechanism whereby GSH synthesis is shut off in a tissue with normal levels, with de novo synthesis due to NAC occurring only in a subset of subjects with “low” GSH levels (i.e., below the sample mean) at baseline. In summary, the results of this study support the role of NAC as an anti-glutamatergic agent, which seems to modulate Glu levels by increasing the availability of intracellular Cys that then combines with Glu to initiate GSH synthesis, leading to a lowering of total Glu levels as measured with MRS

A Simplified Time-Domain Fitting Method Based on Fractional Operational Matrix for Cole Parameter Estimation

The Cole model is widely used in bioimpedance spectroscopy (BIS) applications for evaluating the contents and status of biological tissues. Traditional frequency-domain fitting methods for Cole parameter estimation (CPE) often need wideband direct or indirect BIS measurements. The newly emerged time-domain fitting methods do not require BIS measurement, but they usually have unsatisfactory estimation accuracy and heavily rely on some specific excitations. This paper proposes a novel time-domain fitting method based on the fractional operational matrix (FOM) for CPE. First, the Cole model is represented as a FOM-based algebraic equation. Then, the model-based voltage response can be calculated as the product of the sampled current excitation and the FOM-based Cole equation. Finally, the Cole parameter can be estimated by minimizing the sum of squares error between the sampled voltage response and the model-based voltage response using an iterative nonlinear least-squares fitting algorithm. Experimental measurements are verified on three 2R1C circuits and in vivo leg muscle of a healthy volunteer. The results show that the Cole parameter can be accurately estimated by the proposed FOM-based fitting method directly using the sampled current and voltage signals. This paper establishes an effective time-domain fitting method for CPE, which could greatly simplify the hardware and software designs without the need for the BIS measurement.

Pole-Selective Residue Perturbation Technique for Passivity Enforcement of FDNEs

This paper presents a new passivity enforcement technique for frequency-dependent network equivalents expressed as rational models. The main feature of the proposed technique is that only a few residues of the rational model are perturbed, so that the passivity enforcement procedure becomes computationally more efficient compared to existing methods. The proposed technique is verified on practical network cases. It is demonstrated that the accuracy of the perturbed model is maintained in frequency-domain fitting and time-domain simulations of transients.

Rational approximation of fractional order systems by vector fitting method

A novel method for approximating fractional order systems is presented. Vector fitting is involved in this method. As the basis of approximation of fractional order systems, approximation of fractional order operators is mostly achieved by curve fitting in frequency domain, such as the well-known Oustaloup’s method. However, these methods have several serious defects in principle. A new perspective based on system identification is adopted to deal with approximation of fractional order operators in this paper. Moreover, nonzero initial condition for approximating fractional order systems is considered. And the proposed assignment of initial values for the Caputo case offers an effective solution for the simulation with nonzero initial condition. Finally, numerical examples are given to verify the efficiency of the proposed method.

Design of Improved Fractional Order Integrators using Indirect Discretization Method

paper has adopted rational approximation based on Regular Newton method, for frequency domain fitting of transfer functions of fractional order integrators (FOIs). Further, different discretized mathematical models of one-half, one-third and one-fourth order integrators based on Al-Alaoui operator and New optimized four segment operator have been also developed using these rational approximations by indirect discretization technique. All the proposed models of FOIs are found to be stable when investigated for stability. Simulation results of magnitude responses, phase responses and absolute magnitude errors show that the proposed FOIs obtained by approximations based on Regular Newton method, clearly outperform the other existing approximation techniques which have been used for designing fractional order operators. Results of absolute magnitude errors for all proposed fractional order integrators have been reported to be as low as 0.01, in range 0.35 ≤ ω ≤ 1 π radians of full band of normalized frequency. Among the proposed FOIs, the one-half, one-third and one- fourth order models based on Al-Alaoui operator (for 2 nd iterations) are noticeable with tremendously improved results with absolute magnitude errors of ≤ 0.004 in complete normalized frequency range.

Effects of pyruvate dose on in vivo metabolism and quantification of hyperpolarized 13C spectra

Real-time in vivo measurements of metabolites are performed by signal enhancement of [1-(13)C]pyruvate using dynamic nuclear polarization, rapid dissolution and intravenous injection, acquisition of free induction decay signals and subsequent quantification of spectra. The commonly injected dose of hyperpolarized pyruvate is larger than typical tracer doses, with measurement before complete dilution of the injected bolus. Pyruvate is in exchange with its downstream metabolites lactate, alanine and bicarbonate. A transient exposure to high pyruvate blood concentrations may cause the saturation of cellular uptake and metabolic conversion. The goal of this study was to examine the effects of a [1-(13)C]pyruvate bolus on metabolic conversion in vivo. Spectra were quantified by three different methods: frequency-domain fitting with LCModel, time-domain fitting with AMARES and simple linear least-squares fitting in the time domain. Since the simple linear least-squares approach showed bleeding artifacts and LCModel produced noisier time signals. AMARES performed best in the quantification of in vivo hyperpolarized pyruvate spectra. We examined pyruvate doses of 0.1-0.4 mmol/kg (body mass) in male Wistar rats by acquiring slice-selective free induction decay signals in slices dominated by heart, liver and kidney tissue. Dose effects were noted in all cases, except for alanine in the cardiac slice below the dose of 0.2 mmol/kg. Our results indicate unlimited cellular uptake of pyruvate up to this dose and limited enzymatic activity of lactate dehydrogenase. In the cardiac slice above 0.2 mmol/kg and in liver and kidney slices, reflect limited cellular uptake or enzymatic activity, or a combination of both effects. The results indicate that the dose of pyruvate must be recognized as an important determinant for metabolic tissue kinetics, and saturation effects must be taken into account for the quantitative interpretation of the observed results.

Quantitative magnetic resonance spectroscopy of schizophrenia: Relationship between decreased N‐acetylaspartate and frontal lobe dysfunction

Numerous studies using proton magnetic resonance spectroscopy (1H-MRS) have detected signal changes in schizophrenia. However, most studies investigated relative concentrations such as N-acetylaspartate/creatine plus phosphocreatine (NAA/Cre) and choline-containing compounds/creatine plus phosphocreatine (Cho/Cre), and individual metabolite concentrations have not been clarified. Using absolute quantification measurement of 1H-MRS, the aim of the present paper was to demonstrate the changes in metabolite concentrations in the frontal lobe of patients with chronic schizophrenia. The 1H-MRS was performed in the left frontal lobe in 14 patients with schizophrenia and in 13 healthy comparison subjects. Individual MRS peak concentration was quantified based on a frequency-domain fitting program: LCModel. The scores on the Positive and Negative Symptoms Scale and Wisconsin Card Sorting Test were used for clinical assessment. The NAA concentration was reduced in schizophrenic patients (average, 7.94 mmol/L, t=2.28, P<0.05) compared with healthy subjects (average=8.45 mmol/L) while choline, creatine or NAA/Cre ratio did not show any differences. The reduction in NAA concentration had a significant correlation with the severity of negative symptoms (r=-0.536, P<0.05) and poor performance in Wisconsin Card Sorting Test (r=-0.544, P<0.05). Using quantitative MRS, decreased NAA concentration was confirmed in the left frontal lobe of schizophrenic patients and was demonstrated to be correlated with negative symptoms and cognitive dysfunction in schizophrenia.

An automated algorithm for combining multivoxel MRS data acquired with phased‐array coils

To develop a fully automated algorithm for combining multivoxel magnetic resonance spectroscopy (MRS) data acquired with a phased-array coil. The frequency-domain fitting method of LCModel (Provencher SW, Magn Reson Med 1993;30:672-679) was utilized to analyze the individual data sets. The phase corrections and the metabolite areas were then extracted from the LCModel output files for each individual spectrum. These areas were used to determine the dominant metabolite for each spatial location and to combine the individual spectra in a weighted manner. The combination of MRS data acquired from a phantom and the brains of normal volunteers with a four array coil yielded improved signal-to-noise ratio (SNR) in all voxels. The average improvement in SNR of the combined spectrum, as compared with the best of the individual spectra at each spatial location, was 1.4. In the phantom, the predicted SNR improvement of two-fold was achieved at the center of the sample. In the brain, the maximum improvement was 1.8, due to sampling of the ventricles in the center of the sample. The method described in this report provides a means for employing phased-array coils in MRS with the same advantages as those found in MRI.

Frequency-Selective Quantification of Biomedical Magnetic Resonance Spectroscopy Data

In this paper the possibility of obtaining accurate estimates of parameters of selected peaks in the presence of unknown or uninteresting spectral features in biomedical magnetic resonance spectroscopy (MRS) signals is investigated. This problem is denoted by frequency-selective parameter estimation. A new time-domain technique based on maximum-phase finite impulse response (FIR) filters is presented. The proposed method is compared to a number of existing approaches: the application of a weighting function in the time domain, frequency domain fitting using a polynomial baseline, and the time-domain HSVD filter method. The ease of use and low computational complexity of the FIR filter method make it an attractive approach for frequency-selective parameter estimation. The methods are validated using simulations of relevant 13C and 31P MRS examples.

Versatile frequency domain fitting using time domain models and prior knowledge.

An iterative nonlinear least-squares fitting algorithm in the frequency domain using time domain models for quantification of complex frequency domain MR spectra is presented. The algorithm allows incorporation of prior knowledge and has both the advantage of time-domain fitting with respect to handling the problem of missing data points and truncated data sets and of frequency-domain fitting with respect to multiple frequency-selective fitting. The described algorithm can handle, in addition to Lorentzian and Gaussian lineshapes, Voigt and nonanalytic lineshapes. The program allows the user the design of his own fitting strategy to optimize the probability of reaching the global least-squares minimum. The application of the fitting program is illustrated with examples from in vivo 1H-, 31P-, and 13C-MR spectroscopy.

Limiting electrical response of conductive and dielectric systems, stretched-exponential behavior, and discrimination between fitting models

Given a fitting model, such as the Kohlrausch–Williams–Watts (KWW)/stretched-exponential response, three plausible approaches to fitting small-signal frequency or time-response data are described and compared. Fitting can be carried out with either of two conductive-system formalisms or with a dielectric-system one. Methods are discussed and illustrated for deciding which of the three approaches is most pertinent for a given data set. Limiting low- and high-frequency log–log slopes for each of the four immittance levels are presented for several common models; cutoff effects are considered; and an anomaly in the approach to a single-relaxation-time Debye response for one of the conductive-system approaches is identified and explained. It is found that the temporal response function for the most appropriate conductive-system dispersion (CSD) approach, designated the CSD1, one long used in approximate form for frequency-response data analysis, does not lead to stretched-exponential transient behavior when a KWW response model is considered. Frequency-domain fitting methods and approaches are illustrated and discriminated using 321 and 380 K Na2O–3SiO2 data sets. The CSD1 approach using a KWW model is found to be most appropriate for fitting these data exceedingly closely with a complex nonlinear least-squares procedure available in the free computer program LEVM. Detailed examination and simulation of the approximate, long-used CSD1 modulus fitting formalism shows the unfortunate results of its failure to include separately the effects of the always present high-frequency-limiting dielectric constant, εD∞. The stretched-exponential exponent, β, associated with this fitting approach has always been misidentified in the past, and even after its reinterpretation, the result is likely to be sufficiently approximate that most physical conclusions derived from such fitting will need reevaluation.

Identification of Solar Collector Dynamics Using Physical Model-Based Approach

A system dynamics model of flat-plate solar collectors was derived and identified here. A nonlinear physical model was first derived from a two-node concept and energy conservation principle. The model was then approximated by the linear perturbation equations which were Laplace transformed and solved to lead to a distributed model in terms of the transfer functions. A model reduction was further employed to yield a linear time-invariant model with parameters as functions of steady-state operating conditions. The model parameters were identified by a dynamic test with step inputs at various operating conditions using frequency response analysis and model fitting in frequency domain. The identified parameters were then fitted to a function of steady-state mass flowrate mw. Thus, the model can describe the system dynamics behavior under various operating conditions through the identified parameters. The simulations using the model were shown to agree very well with the test results.