Time-frequency tomographic imaging of a rotating object in a narrow-band radar

2016 ◽  
Vol 8 (6) ◽  
pp. 871-879
Author(s):  
Ewa Swiercz
Keyword(s):  
Doppler Radar ◽  
Linear Time ◽  
Tomographic Imaging ◽  
Frequency Characteristics ◽  
Time Frequency ◽  
Aspect Angle ◽  
Resolution Imaging ◽  
Short Time ◽  
Rotating Target ◽  
Transmitted Frequency

The backscatter from radar object carries Doppler information of scatterers on the object determined by the radial velocity of scattering points and the radar transmitted frequency. For a rotating object this information is contained in the frequency characteristics over varying aspect angle. Frequency characteristics are used to create projections for Doppler radar tomographic imaging. This paper presents a method for high resolution imaging of a rotating target using a time-frequency transform of a returned signal as tomographic projections. The resolution of a tomographic image depends not only on radar system parameters but also depends on the resolution of input projections. The reassigned spectrogram is proposed for building of tomographic projections, due to its possibility of squeezing of frequency spread. The reassigned spectrogram is sensitive to noise so the denoising procedure in the time-frequency domain must be performed before the reassignment procedure. The denoising is performed by removing Short Time Fourier Transform (STFT) noise coefficients below the appropriate threshold. The STFT is a linear time-frequency transform and coefficients, which belong to the signal and coefficients which belong to noise can be analyzed separately. The efficiency of the proposed idea of imaging is supported by results of numerical experiments.

SECOND CARDIAC SOUND ANALYSIS TECHNIQUES AND PERFORMANCE COMPARISON

2005 ◽  
Vol 05 (03) ◽  
pp. 429-442 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wigner Distribution ◽  
Performance Comparison ◽  
Frequency Characteristics ◽  
Second Sound ◽  
Time Frequency ◽  
Cardiac Sound ◽  
And Performance ◽  
Short Time

This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the second cardiac sound S2 of the phonocardiogram signal (PCG). A comparison between these methods has shown the resolution differences between them. It is found that the spectrogram Short-Time Fourier Transform (STFT) cannot detect the two internals components of the second sound S2 (A2 and P2, atrial and pulmonary components respectively). The Wigner Distribution (WD) can provide time-frequency characteristics of the sound S2, but with insufficient diagnostic information as the two components (A2 and P2) are not accurately detected, appearing to be one component only. It is found that the wavelet transform (WT) is capable of detecting the two components, the aortic valve component A2 and pulmonary valve component P2, of the second cardiac sound S2. However, the standard Fourier transform can display these components in frequency but not the time delay between them. Furthermore, the wavelet transform provides more features and characteristics of the second sound S2 that will hemp physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.

Laplace wavelet transform theory and applications

2017 ◽  
Vol 24 (9) ◽  
pp. 1600-1620 ◽  
Author(s):  
Tariq Abuhamdia ◽  
Saied Taheri ◽  
John Burns
Keyword(s):  
Wavelet Transform ◽  
Differential Equations ◽  
Laplace Transform ◽  
Linear Time ◽  
Time Frequency ◽  
Linear Time Invariant ◽  
The Laplace Transform ◽  
Invariant Differential ◽  
Short Time ◽  
Time Invariant

This study introduces the theory of the Laplace wavelet transform (LWT). The Laplace wavelets are a generalization of the second-order under damped linear time-invariant (SOULTI) wavelets to the complex domain. This generalization produces the mother wavelet function that has been used as the Laplace pseudo wavelet or the Laplace wavelet dictionary. The study shows that the Laplace wavelet can be used to transform signals to the time-scale or time-frequency domain and can be retrieved back. The properties of the new generalization are outlined, and the characteristics of the companion wavelet transform are defined. Moreover, some similarities between the Laplace wavelet transform and the Laplace transform arise, where a relation between the Laplace wavelet transform and the Laplace transform is derived. This relation can be beneficial in evaluating the wavelet transform. The new wavelet transform has phase and magnitude, and can also be evaluated for most elementary signals. The Laplace wavelets inherit many properties from the SOULTI wavelets, and the Laplace wavelet transform inherits many properties from both the SOULTI wavelet transform and the Laplace transform. In addition, the investigation shows that both the LWT and the SOULTI wavelet transform give the particular solutions of specific related differential equations, and the particular solution of these linear time-invariant differential equations can in general be written in terms of a wavelet transform. Finally, the properties of the Laplace wavelet are verified by applications to frequency varying signals and to vibrations of mechanical systems for modes decoupling, and the results are compared with the generalized Morse and Morlet wavelets in addition to the short time Fourier transform’s results.

Inter-Harmonics Analysis Based on the STFT Applied to the Power System

2013 ◽  
Vol 662 ◽  
pp. 864-867
Author(s):  
Hui Guo ◽  
Shu Chuan Gan ◽  
Hao Wu
Keyword(s):  
Fourier Transform ◽  
Power System ◽  
Frequency Characteristics ◽  
Theoretical Base ◽  
Harmonic Detection ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Short Time

This paper introduces a method of analyzing the time-frequency characteristics for inter-harmonics by Short Time Fourier Transform (STFT). Compared with the traditional arithmetic, the STFT can acquire more precise results and provides theoretical base for inter-harmonic detection. The STFT is used to analyze the inter-harmonics by selecting proper window slides function in Matlab which can acquire more precise resolution for the time-frequency characteristics. This method is useful to eliminate the inter-harmonics in power system. So the quality of power energy can be further improved.

The STFT Applied to Inter-Harmonics Analysis in Transformer

2013 ◽  
Vol 373-375 ◽  
pp. 1595-1598
Author(s):  
Hui Luo ◽  
Hao Li ◽  
Liang Cao
Keyword(s):  
Fourier Transform ◽  
Theoretical Basis ◽  
Frequency Characteristics ◽  
Harmonic Detection ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Clear Result ◽  
Short Time

This paper introduces that the Short Time Fourier Transform (STFT) could be applied to analyze the time-frequency characteristics of inter-harmonics in transformer. Compared with the traditional Fourier Transform, the STFT could acquire more clear result, and offers theoretical basis for inter-harmonic detection. The STFT is used to analyze the inter-harmonics by selecting proper window slides function in Matlab which can obtain more precise resolution for the time-frequency characteristics. This method is useful to eliminate the inter-harmonics in transformer. It is helpful to improve the quality of power.

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

scholarly journals Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Classification Accuracy ◽  
Communication Systems ◽  
Mimo Systems ◽  
Decision Fusion ◽  
Frequency Characteristics ◽  
Modulation Classification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

scholarly journals Ultrasonic Assessment of Thickness and Bonding Quality of Coating Layer Based on Short-Time Fourier Transform and Convolutional Neural Networks

Coatings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 909
Author(s):  
Azamatjon Kakhramon ugli Malikov ◽  
Younho Cho ◽  
Young H. Kim ◽  
Jeongnam Kim ◽  
Junpil Park ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Coating Layer ◽  
Ultrasonic Pulse ◽  
Short Time Fourier Transform ◽  
Coating Materials ◽  
Time Frequency ◽  
High Attenuation ◽  
Bonding State ◽  
The Time Domain ◽  
Short Time

Ultrasonic non-destructive analysis is a promising and effective method for the inspection of protective coating materials. Offshore coating exhibits a high attenuation rate of ultrasonic energy due to the absorption and ultrasonic pulse echo testing becomes difficult due to the small amplitude of the second echo from the back wall of the coating layer. In order to address these problems, an advanced ultrasonic signal analysis has been proposed. An ultrasonic delay line was applied due to the high attenuation of the coating layer. A short-time Fourier transform (STFT) of the waveform was implemented to measure the thickness and state of bonding of coating materials. The thickness of the coating material was estimated by the projection of the STFT into the time-domain. The bonding and debonding of the coating layers were distinguished using the ratio of the STFT magnitude peaks of the two subsequent wave echoes. In addition, the advantage of the STFT-based approach is that it can accurately and quickly estimate the time of flight (TOF) of a signal even at low signal-to-noise ratios. Finally, a convolutional neural network (CNN) was applied to automatically determine the bonding state of the coatings. The time–frequency representation of the waveform was used as the input to the CNN. The experimental results demonstrated that the proposed method automatically determines the bonding state of the coatings with high accuracy. The present approach is more efficient compared to the method of estimating bonding state using attenuation.

scholarly journals Time–frequency analysis of serum with proton nuclear magnetic resonance for diagnosis of pancreatic cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Asahi Sato ◽  
Toshihiko Masui ◽  
Akitada Yogo ◽  
Takashi Ito ◽  
Keiko Hirakawa ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Carbohydrate Antigen ◽  
Serum Markers ◽  
Proton Nuclear Magnetic Resonance ◽  
Serum Samples ◽  
Time Frequency ◽  
H Nmr ◽  
Free Induction ◽  
Short Time ◽  
Long Term Survivors

AbstractAlthough serum markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen (CA19-9) have been widely used in screening for pancreatic cancer (PC), their sensitivity and specificity are unsatisfactory. Recently, a novel tool of analyzing serum using the short-time Fourier transform (STFT) of free induction decays (FIDs) obtained by 1H-NMR has been introduced. We for the first time evaluated the utility of this technology as a diagnostic tool for PC. Serum was obtained from PC patients before starting any treatments. Samples taken from individuals with benign diseases or donors for liver transplantation were obtained as controls. Serum samples from both groups underwent 1H-NMR and STFT of FIDs. STFT data were analyzed by partial least squares discriminant analysis (PLS-DA) to clarify whether differences were apparent between groups. As a result, PLS-DA score plots indicated that STFT of FIDs enabled effective classification of groups with and without PC. Additionally, in a subgroup of PC, long-term survivors (≥ 2 years) could be discriminated from short-term survivors (< 2 years), regardless of pathologic stage or CEA or CA19-9 levels. In conclusion, STFT of FIDs obtained from 1H-NMR have a potential to be a diagnostic and prognostic tool of PC.

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