Parametric Spectral Estimation Methods Research Articles

Improved Hybrid Approach for Enhancing Protein Coding Regions Identification in DNA Sequences

Introduction: Identifying and predicting protein-coding regions within DNA sequences play a pivotal role in genomic research. This paper introduces an approach for identifying proteincoding regions in DNA sequences, employing a hybrid methodology that combines a digital bandpass filter with wavelet transforms and various spectral estimation techniques to enhance exon prediction. Specifically, the Haar and Daubechies wavelet transforms are applied to improve the accuracy of protein-coding region (exon) prediction, enabling the extraction of intricate details that may be obscured in the original DNA sequences. background: The identification and prediction of protein-coding regions within DNA sequences play a pivotal role in genomic research. Methods: This research showcases the utility of Haar and Daubechies wavelet transforms, both nonparametric and parametric spectral estimation methods, and the deployment of a digital band pass filter for detecting peaks in exon regions. Additionally, the application of the Electron-Ion Interaction Potential (EIIP) method for converting symbolic DNA sequences into numerical values and the utilization of sum-of-sinusoids (SoS) mathematical models with optimized parameters further enrich the toolbox for DNA sequence analysis, ensuring the success of this proposed method in modeling DNA sequences optimally and accurately identifying genes. objective: Enhanced Protein-Coding Region Identification in DNA Sequences Using Wavelet Transforms Results: The outcomes of this approach showcase a substantial enhancement in identification accuracy for protein-coding regions. In terms of peak location detection, the application of Haar and Daubechies wavelet transforms enhances the accuracy of peak localization by approximately (0.01, 3-5 dB). When employing non-parametric and parametric spectral estimation techniques, there is an improvement in peak location by approximately (0.01, 4 dB) compared to the original signal. The proposed approach also achieves higher accuracy when compared with existing methods. method: hybrid methodology that combines a digital band-pass filter with wavelet transforms and various spectral estimation techniques to enhance exon prediction. Conclusion: These findings not only bridge gaps in DNA sequence analysis but also offer a promising pathway for advancing exonic region prediction and gene identification in genomics research. The hybrid methodology presented stands as a robust contribution to the evolving landscape of genomic analysis techniques. result: The results obtained through this proposed method demonstrate significantly improved identification accuracy. These findings offer a promising avenue for DNA sequence analysis, exonic region prediction, and gene identification.

Evaluation and Compensation of the Effect of Dirt on Time-of-Flight 3D Imaging Systems

In the past few years, 3D imaging technology has received a lot of attention. Time-of-Flight (ToF) cameras based on the indirect ToF principle calculate depth information by measuring the phase shift between the emitted periodic signal and the reflected signal. When the illumination from the modulated light source reaches the same pixel through multiple paths, multipath interference will occur. Acquisitions at multiple modulation frequencies are then required to separate the interfering paths. Parametric spectral estimation methods, such as Prony’s method and its robust variants, have shown success in resolving multiple paths from multi-frequency measurements. With the widespread usage of ToF cameras in various fields, the operating environment of the camera should be taken into account in order to obtain accurate 3D images. In this work, we aim to study the effect of dirt on 3D imaging systems quantitatively. To this end, ten sheets with different transmittances, between 50% and 100% are used. To the best of our knowledge, for the first time we provide standardized methods for evaluating the effect of dirt on protective covers in the depth estimation of “flash lidar”. The accuracy of depth measurements obtained by multi-frequency multipath recovery in the absence of dust is up to 99%. However, when the light transmittance of the protective cover is less than 90%, the measurement result is already unreliable. The experiments have shown that the depth measurements obtained using the multi-frequency estimation method are more accurate in the presence of dust than the single-frequency estimation method.

Parametric Magnetotelluric Impedance Tensor Estimation

Conventional magnetotelluric (MT) impedance tensor estimation methods are generally based on robust M-estimator spectral analysis along with remote reference (RR) processing to reduce the estimation biases caused by cultural noise. Here, we propose a parametric spectral estimation and system identification-based impedance tensor estimation method and validate it with three representative data segments measured at several USArray sites. The proposed parametric method has the potential to give an accurate and robust estimate of the electromagnetic transfer impedance without an RR while maintaining a smooth and continuous response in accordance with prior physical knowledge. The approach considers the geophysical problem in addition to signal processing and system identification, providing a statistically efficient strategy for electromagnetic transfer impedance estimation of periods from 10 to 10⁴ s.

Data-Driven Haptic Texture Modeling and Rendering Based on Deep Spatio-Temporal Networks.

Data-driven approaches are commonly used to model and render haptic textures for rigid stylus-based interaction. Current state-of-the-art data-driven methodologies synthesize acceleration signals through the interpolation of samples with different input parameters based on neural networks or parametric spectral estimation methods. In this paper, we see the potential of emerging deep learning methods in this area. To this end, we designed a complete end-to-end data-driven framework to synthesize acceleration profiles based on the proposed deep spatio-temporal network. The network is trained using contact acceleration data collected through our manual scanning stylus and interaction parameters, i.e., scanning velocities, directions, and forces. The proposed network is composed of attention-aware 1D CNNs and attention-aware encoder-decoder networks to adequately capture both the local spatial features and the temporal dynamics of the acceleration signals, which are further augmented with attention mechanisms that assign weights to the features according to their contributions. For rendering, the trained network generates synthesized signals in real-time in accordance with the user's input parameters. The whole framework was numerically compared with existing state-of-the-art approaches, showing the effectiveness of the approach. Additionally, a pilot user study is conducted to demonstrate subjective similarity.

An autoregressive parametric method applied to the autowave process modelling

The authors of the paper research the topic of the autoregressive parametric spectral estimation method which is applied to oscillation (vibration). It is illustrated by the study of dynamically stable the combustion modes in a liquid-propellant engine. The scholars suppose a hypothesis that if a model parameter is selected correctly, the method offers a higher resolution. It does not require a dialogue-based weighted signal, and compensated parser/analyzer filter related to the accurate estimates of the oscillating system attributes. The survey proves that parametric spectral estimation amounts to solving an optimization problem with and seeking the autoregressive model value parameters of waveform shaping, in which the model would be as close as possible to the observed signal in reality. Based on the study, the authors design a digital model of the dynamic system that meets all the required properties.

Адаптивная оценка частоты сигнала

The paper introduces an adaptive algorithm for estimating the frequencies of harmonic components in the signal against the background of additive white noise. This method is iterative, which distinguishes it from the periodogram and parametric spectral estimation methods. The key feature of the algorithm is that it gives a reasonably accurate estimation only for the preset number of harmonic components included in the signal under study. In the original discrete signal, a frequency search was performed at each time sample using the gradient descent method. Frequency estimation is made when the frequency error value tends to a certain value. The search is based on the representation of the value of the current sample of the harmonic signal of a known frequency through the two previous values. Knowing the number of components included in the original signal sequence, it is possible to form the resulting sequence containing only residual noise samples. A mathematical model of the algorithm is given, its work is simulated for different conditions of application, the accuracy of the algorithm, i.e., frequency estimation, and the number of iterations for various signal-to-noise ratios are shown.

A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar

A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care.

Spectral analysis of railway vehicle vertical vibration under normal operating conditions

ABSTRACTThis study focuses on railway vertical random vibration–based spectral analysis under normal operating conditions. Vibration acceleration measurements are acquired using sensors mounted on the car body, bogie frame, axle bearing, and primary suspension of an Athens Metro vehicle running without passengers on a straight track. Two distinct track segments and four speeds (50, 60, 70, 80 km/h) are considered. The analysis focuses on the 0–1000 Hz frequency range using data-based non-parametric and parametric spectral estimation methods. The results indicate good agreement of the methods, the prominence of the 5–170 Hz frequency range along with higher frequencies at around 300 Hz and in the range of 600–750 Hz on the car body and the bogie frame vibration, the relatively minor dependency of the dynamics on the track segment, but their significant dependence on train speed. Yet, resonant frequencies are affected to a lesser extent, with at least 20 of them appearing as prominent.

Interferometric methods for three-dimensional target reconstruction with multipass circular SAR

The authors consider three-dimensional (3-D) target construction from SAR data collected on multiple complete circular apertures at different elevation angles. The 3-D resolution of circular SAR systems is constrained by two factors: the sparse sampling in elevation and the limited azimuthal persistence of the reflectors in the scene. Three-dimensional target reconstruction with multipass circular SAR data is further complicated by non-uniform elevation spacing in real flight paths and non-constant elevation angle throughout the circular passes. The authors first study parametric spectral estimation methods that extend standard IFSAR method of height estimation to apertures at more than two elevation angles. Next, they show that linear interpolation of the phase history data leads to unsatisfactory performance in 3-D reconstruction from non-uniformly sampled elevation passes. The authors then present a new sparsity regularised interpolation algorithm to preprocess non-uniform elevation samples to create a virtual uniformly spaced array. The authors illustrate the performance of the proposed method using data from EM simulations and data collected from a wideband X-band airborne sensor.

Applications of parametric spectral estimation methods on detection of power system harmonics

Harmonics are the major power quality problems in industrial and commercial power systems. Several methods for detection of power system harmonics have been investigated by engineers due to increasing harmonic pollution. Since the non-integer multiple harmonics (inter and sub-harmonics) become wide spread, the importance of harmonic detection has increased for sensitive filtration. This paper suggests parametric spectral estimation methods for the detection of harmonics, inter-harmonics and sub-harmonics. Yule Walker, Burg, Covariance and Modified Covariance methods are applied to generate cases. Not only integer multiple harmonics but also non-integer multiple harmonics are successfully determined in the computer simulations. Further, performances of proposed methods are compared with each other in terms of frequency resolution.

Spectral Estimation Techniques for DNA Sequence and Microarray Data Analysis

Spectral estimation techniques are widely used in modern signal processing systems. Recently, they have found important applications to the analysis of DNA data. In this paper, we review parametric and non-parametric spectral estimation methods for DNA sequence and microarray data analysis. The discrete Fourier transform (DFT) is the most commonly used technique for spectral analysis of digital signals. It can reveal the gene locations in a DNA sequence. The DFT can also be used to detect repetitive elements in a DNA sequence. The DFT produces the so-called windowing or data truncation artifacts when it is applied to a short data segment. Parametric spectral estimation methods, such as the autoregressive (AR) model, overcome this problem and can be used to obtain a high-resolution spectrum of the input signal. In this paper, we demonstrate the advantages of the AR model for the identification of protein coding regions and the detection of DNA repeats. We also review DFT and AR models and other spectral estimation techniques for the analysis of microarray time series data.

Super resolution SAR imaging via parametric spectral estimation methods

Super resolution synthetic aperture radar (SAR) image formation via sophisticated parametric spectral estimation algorithms is considered. Parametric spectral estimation methods are devised based on parametric data models and are used to estimate the model parameters. Since SAR images rather than model parameters are often used in SAR applications, we use the parameter estimates obtained with the parametric methods to simulate data matrices of large dimensions and then use the fast Fourier transform (FFT) methods on them to generate SAR images with super resolution. Experimental examples using the MSTAR and Environmental Research Institute of Michigan (ERIM) data illustrate that robust spectral estimation algorithms can generate SAR images of higher resolution than the conventional FFT methods and enhance the dominant target features.

Parametric spectral estimation to detect and diagnose faults in low speed rolling element bearings: Preliminary investigations

The monitoring of low speed bearings (⩽100 rpm) is fraught with difficulties, not the least of which is the impracticality of recording sufficiently long periods of data for appropriate data analysis to be performed. Based on current fast Fourier transform (FFT) techniques, it would be normal to analyse data for several minutes if low frequency bandwidths are being investigated. A parametric model based approach could reduce the data acquisition period to a fraction of the time taken by the FFT technique. This paper presents a brief review of the parametric spectral estimation method, as well as report on some preliminary work that compares the two methods when applied to low speed rolling element bearings.

Parametric modeling of the Wigner half-kernel and its application to spectral estimation

Parametric spectral estimation methods provide high frequency resolution and allow a compact representation. The Wigner-Ville distribution (WVD), on the other hand, presents high frequency concentration and temporal resolution, but produces spectral crossterms and abundant data. WVD autoregressive (AR) modeling combines the advantages of both techniques. Using the analytic signal to compute the WVD kernel cancels negative frequency crossterms and provides a low-order complex predictor, half the size of real AR models. Crossterms and potential kernel symmetry problems are avoided by modeling the Smoothed WVD (SWVD) half-kernel. The developed representation is analyzed and compared to real and analytic AR spectral estimators. It is shown that the SWVD half-kernel AR covariance poles produce precise frequency estimates for monocomponent signals, independent of data length and phase, just like real and analytic AR estimators, but with the advantage of excellent performance in noise. These properties are verified experimentally and extended to multicomponent signals in noise, showing that while the analytic AR estimator performs slightly better than its real counterpart, the SWVD half-kernel results in a frequency estimation error reduction of around two orders of magnitude. Parametrische Methoden der Spektralschätzung liefern eine hohe Frequenzauflösung und erlauben eine kompakte Darstellung. Auf der anderen Seite zeigt die Wigner-Ville-Verteilung (WVD) ihre hohe Frequenzkonzentration und zeitliche Auflösung, erzeugt jedoch spektrale Kreuzterme und groβe Datenmengen. Autoregressive (AR) WVD-Modellierung vereint die Vorteile beider Techniken. Der Gebrauch des analytischen Signals bei der Berechnung des WVD-Kerns löscht Kreuzterme aufgrund von negativen Frequenzen und liefert einen komplexen Prädiktor niedrigen Grades — mit dem halben Umfang des reellen AR-Modells. Kreuzterme und potentielle Kernsymmetrie-Probleme werden vermieden, indem eine geglätteter WVD (SWVD)-Halbkern modelliert wird. Die entwickelte Darstellung wird analysiert und mit reellen und analytischen AR-Spektral-schätzern verglichen. Es wird gezeigt, daβ die Pole bei der Anwendung der AR-Kovarianzmethode auf die SWVD-Halbkerne präzise Frequenzschätzungen für Eintosignale erzeugen, unabhängig von Datenlänge und Phase, genau wie reelle und analytische AR-Schätzer, aber mit dem Vorteil hervorragender Leistungsfähigkeit bei Störgeräuschen. Diese Eigenschaften werden experimentell verifiziert und auf verrauschte Mehrtonsignale erweitert. Während der analytische AR-Schätzer geringfügig besser arbeitet als sein reelles Gegenstück, zeigt sich daβ der SWVD-Halbkern-Ansatz den Frequenzschätzfehler um ca. zwei Gröβenordnungen verringert. Les méthodes paramétriques d'estimation spectrale fournissent une résolution en fréquence élevée et permettent une représentation compacte. La distribution de Wigner-Ville (WVD), par contre, présente une localisation en fréquence et une résolution temporelle élevées, mais produit des termes d'intéraction spectraux et beaucoup de données. La modélisation WVD autorégressive (AR) combine les avantages des deux techniques. L'utilisation du signal analytique pour le calcul du noyau de la WVD annule les termes d'intéraction aux fréquences négatives et fournit un prédicteur complexe d'ordre peu élevé, dont la taille est reduite de moitié par rapport aux modèles AR réels. Les problèmes dus aux termes d'intéraction et à la symétrie potentielle du noyau sont évités en modélisant le demi-noyau lisse de la WVD (SWVD). La représentation développée est analysée et comparée à des estimateurs spectraux AR réels et analytiques. Il est montré que les pôles du modèle AR deminoyau SWVD obtenus par la méthode de covariance produisent des estimées de fréquence précises dans le cas de signaux à composante unique, indépendamment de la longueur des données et de la phase, de la même façon que les estimateurs AR réels et analytiques, mais avec l'avantage de performances excellentes en présence de bruit. Ces propriétés sont vérifiées expérimentalement et étendues aux signaux multi-composantes en présence de bruit, montrant qu'alors que l'estimateur AR analytique à de légérement meilleures performances que sa contrepartie réelle, l'utilisation du demi-noyau SWVD a pour résultat une réduction de l'erreur d'estimation de la fréquence d'environ deux ordres de grandeur.

High‐resolution velocity spectra using eigenstructure methods

Stacking spectra provide maximum‐likelihood estimates for the stacking velocity, or for the ray parameter, of well separated reflections in additive white noise. However, the resolution of stacking spectra is limited by the aperture of the array and the frequency of the data. Despite these limitations, parametric spectral estimation methods achieve better resolution than does stacking. To improve resolution, the parametric methods introduce a parsimonious model for the spectrum of the data. In particular, when the data are modeled as the superposition of wavefronts, the properties of the eigenstructure of the data covariance matrix can be used to obtain high‐resolution spectra. The traditional stacking spectra can also be expressed as a function of the data covariance matrix and directly compared to the eigenstructure spectra. The superiority of the latter in separating closely interfering reflections is then apparent from a simple geometric interpretation. Eigenstructure methods were originally developed for use with narrow‐band signals, while seismic reflections are wide‐band and transient in time. Taking advantage of the full bandwidth of seismic data, we average spectra from several frequency bands. We choose each frequency band wide enough, so that we can average over time estimates of the covariance matrix. Thus, we obtain a robust estimate of the covariance matrix from short data sequences. A field‐data example shows that the high‐resolution estimators are particularly attractive for use in the estimation of local spectra in which short arrays are considered. Several realistic synthetic examples of stacking‐velocity spectra illustrate the improved performance of the new methods in comparison with conventional processing.