scholarly journals Analog-to-Information Conversion with Random Interval Integration

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3543
Author(s):  
Ján Šaliga ◽  
Ondrej Kováč ◽  
Imrich Andráš
Keyword(s):  
Single Channel ◽  
Random Sequence ◽  
Signal Reconstruction ◽  
Main Idea ◽  
Random Interval ◽  
Method Performance ◽  
Labview Software ◽  
Reconstructed Signal ◽  
Random Modulation ◽  
Original Measurement

A novel method of analog-to-information conversion—the random interval integration—is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors such as ECG, environmental, and other sensors, the output of which can be modeled by multi-harmonic signals. The main idea of the method is based on input signal integration by a randomly resettable integrator before the AD conversion. The integrator’s reset is controlled by a random sequence generator. The signal reconstruction employs a commonly used algorithm based on the minimalization of a distance norm between the original measurement vector and vector calculated from the reconstructed signal. The signal reconstruction is performed by solving an overdetermined problem, which is considered a state-of-the-art approach. The notable advantage of random interval integration is simple hardware implementation with commonly used components. The performance of the proposed method was evaluated using ECG signals from the MIT-BIH database, multi-sine, and own database of environmental test signals. The proposed method performance is compared to commonly used analog-to-information conversion methods: random sampling, random demodulation, and random modulation pre-integration. A comparison of the mentioned methods is performed by simulation in LabVIEW software. The achieved results suggest that the random interval integration outperforms other single-channel architectures. In certain situations, it can reach the performance of a much-more complex, but commonly used random modulation pre-integrator.

scholarly journals Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879087 ◽  
Author(s):  
Lin Zhou ◽  
Qianxiang Yu ◽  
Daozhi Liu ◽  
Ming Li ◽  
Shukai Chi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Data Storage ◽  
Wireless Sensors ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Vibration Signal ◽  
Estimation Accuracy ◽  
Offshore Structure ◽  
Vibration Signals ◽  
Reconstructed Signal

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

The Application of Pseudo-Random Sequence in the Properties Measurement of Polymer Optical Fiber

2013 ◽  
Vol 313-314 ◽  
pp. 653-657
Author(s):  
Yan Chun Wang ◽  
Chang Wei Sun
Keyword(s):  
Optical Fiber ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Random Sequence ◽  
Measuring Method ◽  
Optical Signal ◽  
Polymer Optical Fiber ◽  
Photoelectric Conversion ◽  
Noise Interference ◽  
Labview Software

A new method for the properties measurement of polymer optical fiber (POF) using pseudo-random sequence is introduced. The light source modulated by pseudo-random sequence is injected into the POF. The output optical signal after photoelectric conversion is made the correlation detection with pseudo-random sequence, and finally the correlation operation is carried out based on Labview software. Because pseudo-random sequence performs well on randomicity and it has the correlation properties similar to that of white noise, interference and noise have little influence on the peak of correlation function during the measurement, and the signal-to-noise ratio (SNR) of the output signal can be improved obviously. The measuring method is studied both theoretically and experimentally. Experimental results show that the SNR of the output signal can be improved by 25~40dB for the signal with SNR-15dB to 6dB, and the measuring precision is improved by this method.

scholarly journals Statistical View on Phase and Magnitude Information in Signal Processing

2012 ◽  
Vol 47 (3) ◽  
pp. 127-136
Author(s):  
Waldemar Popiński
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Signal Reconstruction ◽  
Time Domain Signal ◽  
Reconstructed Signal ◽  
Band Pass ◽  
Expected Values ◽  
Random Fluctuations ◽  
Phase Distortions ◽  
Complex Valued

Statistical View on Phase and Magnitude Information in Signal ProcessingIn this work the problem of reconstruction of an original complex-valued signalot,t= 0, 1, …,n- 1, from its Discrete Fourier Transform (DFT) spectrum corrupted by random fluctuations of magnitude and/or phase is investigated. It is assumed that the magnitude and/or phase of discrete spectrum values are distorted by realizations of uncorrelated random variables. The obtained results of analysis of signal reconstruction from such distorted DFT spectra concern derivation of the expected values and bounds on variances of the reconstructed signal at the observation moments. It is shown that the considered random distortions in general entail change in magnitude and/or phase of the reconstructed signal expected values, which together with imposed random deviations with finite variances can blur the similarity to the original signal. The effect of analogous random amplitude and/or phase distortions of a complex valued time domain signal on band pass filtration of distorted signal is also investigated.

scholarly journals Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis

2020 ◽  
Vol 10 (21) ◽  
pp. 7715
Author(s):  
Xiaojun Zhang ◽  
Jirui Zhu ◽  
Yaqi Wu ◽  
Dong Zhen ◽  
Minglu Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Simulation Analysis ◽  
Wavelet Packet ◽  
Signal Reconstruction ◽  
Rolling Bearing ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Integrated Method ◽  
Reconstructed Signal

An integrated method for fault detection of bearing using wavelet packet energy (WPE) and fast kurtogram (FK) is proposed. The method consists of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of WPE. Then the analyzed signal is decomposed using WPE and the energy of each decomposed signal is calculated and selected for signal reconstruction. Secondly, the reconstructed signal is analyzed by FK to select the best central frequency and bandwidth for the band-pass filter. Finally, the filtered signal is processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach are illustrated and estimated by the simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases show that the proposed method can provide more accurate fault features compared with the individual FK method.

An Effective LFM Signal Reconstruction Method for Signal Denoising

2018 ◽  
Vol 27 (09) ◽  
pp. 1850140
Author(s):  
Shan Luo ◽  
Guoan Bi ◽  
Tong Wu ◽  
Yong Xiao ◽  
Rongping Lin
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Experimental Results ◽  
Signal Denoising ◽  
Reconstruction Method ◽  
Signal To Noise ◽  
Time Frequency ◽  
Effective Time ◽  
Reconstructed Signal

One of the main challenges in signal denoising is to accurately restore useful signals in low signal-to-noise ratio (SNR) scenarios. In this paper, we investigate the signal denoising problem for multi-component linear frequency modulated (LFM) signals. An effective time-frequency (TF) analysis-based approach is proposed. Compared to the existing approaches, our proposed one can further increase the noise suppressing performance and improve the quality of the reconstructed signal. Experimental results are presented to show that the proposed denoising approach is able to effectively separate the multi-component LFM signal from the strong noise environments.

scholarly journals Acoustic emissions in the valuation of the combustion chamber pressure of an engine

2021 ◽  
Vol 2102 (1) ◽  
pp. 012014
Author(s):  
J P Rojas Suárez ◽  
J A Pabón León ◽  
M S Orjuela Abril
Keyword(s):  
Internal Combustion Engines ◽  
Signal Reconstruction ◽  
Acoustic Emissions ◽  
Chamber Pressure ◽  
Pressure Measurements ◽  
Efficient Operation ◽  
Combustion Pressure ◽  
Reconstructed Signal ◽  
Mean Pressure ◽  
The Hilbert Transform

Abstract Internal combustion engines demand advanced monitoring methodologies to promote efficient operation; particularly, the combustion pressure plays a central role in the overall performance, which promotes the utilization of transducers that hinders. Therefore, the present study introduces an acoustic emission methodology that serves for indirect combustion pressure measurements. Accordingly, the compound methodology integrates the Hilbert transform and the complex cepstrum using neural networks to accomplish pressure signal reconstruction. Results demonstrated that the proposed methodology featured robust performance while estimating pressure signals as it mitigates the combined noise effect produced by variations in engine speed, engine load, and fuel type. Moreover, the reconstructed signal facilitated the determination of key performance parameters such as peak pressure, pressure timing, and effective mean pressure. Relative error amounted to less than 10%, which ratified the robustness of the indirect pressure measurements. In conclusion, acoustic signal techniques represent an adequate approach to estimate the combustion pressure at variable engine conditions.

scholarly journals Dictionaries and algorithms for sparsity constrained image reconstruction

2021 ◽  
Author(s):  
Mathiruban Tharmalingam
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Exponential Functions ◽  
Signal To Noise ◽  
Analysis Study ◽  
Training Samples ◽  
Reconstruction Quality ◽  
Reconstructed Signal ◽  
Different Types ◽  
Signal Coding

There has been a growing interest in the different types of dictionaries that can be used in image processing applications. We propose a hybrid dictionary composed of transform based atoms and additional nonlinear atoms generated using the polynomial, rectangular and exponential functions. The additional nonlinear atoms improve signal reconstruction quality for both transient and smooth signals. To further improve signal reconstruction quality, we optimize the hybrid dictionary using training samples from the signal. We also propose a signal coding algorithm that generates additional atoms by performing a circular shift on the provided dictionary prior to coding the signal. We have evaluated the proposed methods against existing predefined dictionaries by visually examining the reconstructed images as well as evaluating the peak signal to noise ratio of the reconstructed signal. All methods proposed in this thesis improved signal reconstruction quality however; we require an in-depth cost analysis study to evaluate its limitations.

scholarly journals A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 290
Author(s):  
Jianghua Ge ◽  
Tianyu Niu ◽  
Di Xu ◽  
Guibin Yin ◽  
Yaping Wang
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Signal Reconstruction ◽  
Pearson Correlation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Multi Scale ◽  
Reconstructed Signal

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.

scholarly journals Walsh Transform and Empirical Mode Decomposition Applied to Reconstruction of Velocity and Displacement from Seismic Acceleration Measurement

2020 ◽  
Vol 10 (10) ◽  
pp. 3509 ◽  
Author(s):  
Qi Zhang ◽  
Xiang Yuan Zheng
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Vibration Signal ◽  
Walsh Transform ◽  
Ocean Engineering ◽  
Seismic Acceleration ◽  
Acceleration Signal ◽  
Mode Decomposition ◽  
Least Squares Fit ◽  
Reconstructed Signal

This paper focuses on reconstruction of dynamic velocity and displacement from seismic acceleration signal. For conventional time-domain approaches or frequency-domain approaches, due to initial values and non-negligible noise in the acceleration signal, drift and deviation in velocity and displacement are inevitable. To deal with this deficiency, this paper develops a Walsh transform and Empirical Mode Decomposition (EMD)-based integral algorithm, or WATEBI in short. In the WATEBI algorithm, the Walsh transform is employed to realize vibration signal reconstruction. Next, the EMD method is used to eliminate the residual in the reconstructed signal. Finally, the trend term in velocity and displacement is removed by linear least-squares fit. This algorithm can be straightforwardly implemented by an ordinary computer. Reconstructed displacements and velocities from vibration of a simulated single-degree-of-freedom system and two-site measured ground motions in earthquakes validated the robustness and adaptiveness of this algorithm. It can be also applied to many other areas, like mechanical engineering and ocean engineering.

The Psychological Refractory Period and Vertex Evoked Potentials

1972 ◽  
Vol 24 (2) ◽  
pp. 175-192 ◽  
Author(s):  
John Boddy
Keyword(s):  
Refractory Period ◽  
Time Course ◽  
Psychological Refractory Period ◽  
Single Channel ◽  
Random Sequence ◽  
Reaction Times ◽  
Central Processor ◽  
Sensory Evoked Potential ◽  
Sensory Evoked ◽  
Simple Rt

The averaged sensory evoked potential (EP) was recorded from the scalp (vertex to mastoid) in a psychological refractory period experiment in which 12 young adults participated. Reaction times (RTs) were measured to either both or only the second of pairs of stimuli, in different trial blocks, with inter-stimulus intervals (ISIs) of 100, 200, 300 and 400 msec occurring in random sequence. EPs were recorded at each ISI. No latency changes could be found in the prominent non-specific components (P1–N1–P2) of the EP to stimulus 2 even at ISIs where the RT was substantially delayed. Thus the notions that the RT2 delay is due to occupation of a single channel central processor by S1 and that non-specific EP components reflect the time course of information processing in underlying neural tissue, do not lend each other mutual support. Furthermore, as profound amplitude refractoriness in components P1–N1 and N1–P2 persisted at ISIs where RT was as fast or faster than simple RT, there appears to be a dissociation between “psychological refractoriness” and “physiological refractoriness”. The implications of these results are discussed.

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