scholarly journals Spectral Analysis of Stationary Signals Based on Two Simplified Arrangements of Chirp Transform Spectrometer

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 65
Author(s):  
Quan Zhao ◽  
Ling Tong ◽  
Bo Gao
Keyword(s):  
Spectral Analysis ◽  
Frequency Conversion ◽  
Frequency Resolution ◽  
Matching Problem ◽  
System Sensitivity ◽  
Deep Space Exploration ◽  
Spectrum Measurement ◽  
Stationary Signals ◽  
Simulation And Experiment ◽  
Remote Sensing Systems

The classical two-channel push-pull chirp transform spectrometer (CTS) has been widely applied in satellite-borne remote sensing systems for earth observation and deep space exploration. In this paper, we present two simplified structures with single M(l)-C(s) CTS arrangements for the spectral analysis of stationary signals. A simplified CTS system with a single M(l)-C(s) arrangement and a time delay line was firstly developed. Another simplified structure of CTS with a M(l)-C(s) arrangement and a frequency conversion channel was also developed for spectral analysis of stationary signals. Simulation and experiment results demonstrate that the two simplified arrangements can both realize spectrum measurement for the stationary signals and obtain the same frequency resolution, amplitude accuracy and system sensitivity as that of the classical two-channel push–pull CTS system. Compared to the classical CTS structure, the two simplified arrangements require fewer devices, save power consumption and have reduced mass. The matching problem between the two channels can be avoided in the two simplified arrangements. The simplified CTS arrangements may have potential application in the spectrum measurement of stationary signals in the field of aviation and spaceflight.

scholarly journals A Study of Parameters Setting of the STADZT

10.14311/1654 ◽  
2012 ◽  
Vol 52 (5) ◽  
Author(s):  
Václav Turoň
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Frequency Resolution ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Stationary Signals ◽  
Zolotarev Polynomials ◽  
Non Stationary Signal ◽  
Short Time ◽  
Main Influence

This paper deals with the new time-frequency Short-Time Approximated Discrete Zolotarev Transform (STADZT), which is based on symmetrical Zolotarev polynomials. Due to the special properties of these polynomials, STADZT can be used for spectral analysis of stationary and non-stationary signals with the better time and frequency resolution than the widely used Short-Time Fourier Transform (STFT). This paper describes the parameters of STADZT that have the main influence on its properties and behaviour. The selected parameters include the shape and length of the segmentation window, and the segmentation overlap. Because STADZT is very similar to STFT, the paper includes a comparison of the spectral analysis of a non-stationary signal created by STADZT and by STFT with various settings of the parameters.

scholarly journals Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

2010 ◽  
Vol 20 (1) ◽  
pp. 135-147 ◽  
Author(s):  
Ewa Świercz
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Pattern Matching ◽  
Classification Algorithm ◽  
Statistical Distribution ◽  
Classification Rule ◽  
Matching Problem ◽  
Time Frequency ◽  
Stationary Signals ◽  
Non Stationary Signal

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distributionA new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requiresa prioriknowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to themaxdiscriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

scholarly journals Optimum Resolution Bandwidth for Spectral Analysis of Stationary Random Vibration Data

1993 ◽  
Vol 1 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Allan G. Piersol
Keyword(s):  
Spectral Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Random Vibration ◽  
Damping Ratio ◽  
Frequency Resolution ◽  
Power Spectral ◽  
Averaging Time ◽  
Resolution Bandwidth

This article presents a methodology for selecting the frequency resolution bandwidth for the spectral analysis of stationary random vibration signals in an optimum manner. Specifically, the resolution bandwidth that will produce power spectral density estimates with a minimum mean square error is determined for any given measurement duration (averaging time), and methods of approximating the optimum bandwidth using practical spectral analysis procedures are detailed. The determination of the optimum resolution bandwidth requires an estimate for the damping ratio of the vibrating structure that produced the measured vibration signal and the analysis averaging time. It is shown that the optimum resolution bandwidth varies approximately with the 0.8 power of the damping ratio and the bandwidth center frequency, and the −0.2 power of the averaging time. Also, any resolution bandwidth within ±50% of the optimum bandwidth will produce power spectral density (PSD) estimates with an error that is no more than 25% above the minimum achievable error. If a damping ratio of about 5% for structural resonances is assumed, a constant percentage resolution bandwidth of 1/12 octave, but no less than 2.5 Hz, will provide a near optimum PSD analysis for an averaging time of 2 seconds over the frequency range from 20 to 2000 Hz. A simple scaling formula allows the determination of appropriate bandwidths for other damping ratios and averaging times.

Research on Frequency Conversion ＆ Selective Measurement and Error Correction Algorithms in Grounding Characteristics Measuring System

2012 ◽  
Vol 605-607 ◽  
pp. 1068-1073
Author(s):  
Si Qi Wang ◽  
Xi Wen Chen ◽  
Zi Juan Guo ◽  
Xu Wang
Keyword(s):  
Frequency Conversion ◽  
Phase Error ◽  
Sampling Rate ◽  
Estimation Method ◽  
Signal Amplitude ◽  
Measuring System ◽  
Frequency Resolution ◽  
Frequency Error ◽  
High Precision Measurement ◽  
Data Length

The paper concerns algorithms error and parameters correction technology of frequency conversion＆selective measurement in grounding characteristics measuring system. It analyzes estimation method of signal amplitude phase based on Fourier transform, calculates the sensitive degree of the method to frequency error, and also presents several major parameters for the algorithm, including sampling rate, data length and selection condition of frequency resolution in order to meet the high precision measurement requirements. Furthermore, the paper analyzes correction algorithm of amplitude and phase error caused by unknown nonlinearity of circuit during the practical measurement.

scholarly journals Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

2020 ◽  
Author(s):  
Eduardo Arrufat-Pié ◽  
Mario Estévez-Báez ◽  
José Mario Estévez-Carreras ◽  
Calixto Machado Curbelo ◽  
Gerry Leisman ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
A Priori ◽  
Quantitative Eeg ◽  
Frequency Resolution ◽  
Intrinsic Mode Functions ◽  
Multivariate Empirical Mode Decomposition ◽  
Healthy Humans ◽  
Mode Decomposition ◽  
Mixing Problem

AbstractConsidering the properties of the empirical mode decomposition to extract from a signal its natural oscillatory components known as intrinsic mode functions (IMFs), the spectral analysis of these IMFs could provide a novel alternative for the quantitative EEG analysis without a priori establish more or less arbitrary band limits. This approach has begun to be used in the last years for studies of EEG records of patients included in database repositories or including a low number of individuals or of limited EEG leads, but a detailed study in healthy humans has not yet been reported. Therefore, in this study the aims were to explore and describe the main spectral indices of the IMFs of the EEG in healthy humans using a method based on the FFT and another on the Hilbert-Huang transform (HHT). The EEG of 34 healthy volunteers was recorded and decomposed using a recently developed multivariate empirical mode decomposition algorithm. Extracted IMFs were submitted to spectral analysis with, and the results were compared with an ANOVA test. The first six decomposed IMFs from the EEG showed frequency values in the range of the classical bands of the EEG (1.5 to 56 Hz). Both methods showed in general similar results for mean weighted frequencies and estimations of power spectral density, although the HHT is recommended because of its better frequency resolution. It was shown the presence of the mode-mixing problem producing a slight overlapping of spectral frequencies mainly between the IMF3 and IMF4 modes.

The Hilbert-Huang Spectral Analysis Method Applied to VIV

2006 ◽  
Author(s):  
Celso P. Pesce ◽  
Andre´ L. C. Fujarra ◽  
Leonardo K. Kubota
Keyword(s):  
Spectral Analysis ◽  
Stationary Time Series ◽  
Analysis Method ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Modulation ◽  
Mode Decomposition ◽  
Highly Nonlinear ◽  
Stationary Signals ◽  
Spectral Analysis Method

Vortex-Induced Vibration (VIV) is a highly nonlinear dynamic phenomenon. Usual spectral analysis methods rely on the hypotheses of linear and stationary dynamics. A new method envisaged to treat nonlinear and non-stationary signals was presented by Huang et al. [1] : The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. This technique, called thereafter the Hilbert-Huang transform (or spectral analysis) method, is here applied to VIV phenomena, aiming at disclosing some hidden dynamic characteristics, such as the time-modulation and jumps of multi-branched response frequencies and their related energy spectra.

The spectral analysis of cyclo-non-stationary signals

2016 ◽  
Vol 75 ◽  
pp. 280-300 ◽  
Author(s):  
D. Abboud ◽  
S. Baudin ◽  
J. Antoni ◽  
D. Rémond ◽  
M. Eltabach ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Stationary Signals

Synchronization between Driving and Illumination Signals in a High Frequency Stroboscopic Imaging System

2011 ◽  
Vol 70 ◽  
pp. 327-332
Author(s):  
Cheng Fei Wang ◽  
Ying Jun Xu ◽  
Fei Peng Zhu ◽  
Xiao Yuan He
Keyword(s):  
Light Source ◽  
High Frequency ◽  
Relative Phase ◽  
Frequency Conversion ◽  
Imaging System ◽  
Phase Error ◽  
Frequency Resolution ◽  
Signal Frequency ◽  
Drive Signals ◽  
The Impact

In a stroboscopic imaging system, in order to make images to be correctly captured the synchronization must be strictly satisfied. Direct digital synthesizer (DDS) is a new frequency synthesizing technique used for creating arbitrary waveforms from a single, fixed-frequency reference clock. It has many advantages such as fast frequency conversion, high resolution of frequency and keeping phase continuous while frequency switches. By applying the DDS technique, this paper presents a method to generate the illumination signal of light source and the drive signal of device, and more attention is paid to how to perform the precise synchronization between the two signals. By adjusting the relative delay of the illumination and the drive signals, the phase can be changed. Allowing a sequence of images depicting the motion over the interval studied to be captured, which yields a complete picture of the vibration of a specimen or the surface structure of a MEMS device. Experimental results show that the high frequency of a signal could be up to 120 MHz with frequency resolution 4μHz. The phase resolution of 2-16 can be achieved by the proposed method. The relative phase error between illumination and driven signals could be down to 2-16. It is relatively easy to a synchronization illumination signal of light source and drive signal of MEMS accurately. However, as the two signals' transmission paths are different, their transmission delays are different also. Accordingly, a phase error would be caused and it would change as the signal frequency changes. Moreover, if the drive signal of external objects is used, the phase error, which is introduced via inaccuracy synchronizing the frequency of signal, would be superposed, indeed. These phase errors would affect the quality of imaging and the accuracy of measurement. In this paper, an effective method to adjust the errors is proposed. By means of specially designed hardware circuit, the error of phase and frequency synchronization between illumination signal of light source and drive signal of MEMS can be adjusted automatically. The proposed method improves the accuracy in synchronism and reduces the impact of the error of signal synchronization in the system. It is proved that the minimum relative phase error between illumination signal of light source and drive signal of MEMS can be reduced by more than 10 times using this method.

Wavelet-based Segmentation and Feature Extraction of Heart Sounds for Intelligent PDA-based Phonocardiography

2007 ◽  
Vol 46 (02) ◽  
pp. 135-141 ◽  
Author(s):  
H. Nazeran
Keyword(s):  
Feature Extraction ◽  
Spectral Analysis ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Digital Signal ◽  
Heart Sounds ◽  
Segmentation Algorithm ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Frequency Information

Summary Objectives : Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography combined with modern digital signal processing techniques has strongly renewed researchers' interest in studying heart sounds and murmurs.The aim of this work is to investigate the applicability of different spectral analysis methods to heart sound signals and explore their suitability for PDA-based implementation. Methods : Fourier transform (FT), short-time Fourier transform (STFT) and wavelet transform (WT) are used to perform spectral analysis on heart sounds. A segmentation algorithm based on Shannon energy is used to differentiate between first and second heartsounds. Then wavelet transform is deployed again to extract 64 features of heart sounds. Results : The FT provides valuable frequency information but the timing information is lost during the transformation process. The STFT or spectrogram provides valuable time-frequency information but there is a trade-off between time and frequency resolution. Waveletanalysis, however, does not suffer from limitations of the STFT and provides adequate time and frequency resolution to accurately characterize the normal and pathological heartsounds. Conclusions : The results show that the wavelet-based segmentation algorithm is quite effective in localizing the important components of both normal and abnormal heart sounds. They also demonstrate that wavelet-based feature extraction provides suitable feature vectors which are clearly differentiable and useful for automatic classification of heart sounds.

scholarly journals Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2912
Author(s):  
Joaquin Luque ◽  
Davide Anguita ◽  
Francisco Pérez ◽  
Robert Denda
Keyword(s):  
Spectral Analysis ◽  
Electricity Demand ◽  
Frequency Resolution ◽  
Electrical Networks ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Sequence Representation ◽  
Electrical Demand ◽  
Computer Resources

The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.

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