The movements and intentions of other ships can be determined by gathering and examining ship sound signals.The extraction and analysis of ship sound signals fundamentally support the autonomous navigation of intellig...The movements and intentions of other ships can be determined by gathering and examining ship sound signals.The extraction and analysis of ship sound signals fundamentally support the autonomous navigation of intelligent ships.Mel scale frequency cepstral coefficient(MFCC)feature parameters are improved and optimized to form NewMFCC by introducing second-order difference and wavelet packet decomposition transformation methods in this paper.Transforming sound signals into a feature vector that fully describes the dynamic characteristics of ship sound signals and the high-and low-frequency information solves the problem of the inability to transport ordinary sound signals directly as signals for training in machine learning models.Radial basis function kernels are used to conduct support vector machine classifier simulation experiments.Five types of sound signals,namely,one type of ship sound signals and four types of interference sound signals,are categorized and identified as classification targets to verify the feasibility of the classification of ship sound signals and interference signals.The proposed method improves classification accuracy by approximately 15%.展开更多
Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned sign...Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.展开更多
A novel method to extract multiple input and multiple output (MIMO) chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal g...A novel method to extract multiple input and multiple output (MIMO) chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal generators was presented. In order to separate the chaotic source signals only by using the sensor signals at receivers, a blind neural extraction algorithm based on higher-order statistic (HOS) technique was used to recover the primary chaotic signals. Simulation results show that the proposed approach has good performance in separating the primary chaotic signals even under nonideal channel.展开更多
The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise.The relationship between windowing transform andf...The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise.The relationship between windowing transform andfiltering is analyzed first in the paper.The advantage of adjustable time-frequency window of wavelet transform is introduced.Secondly the relationship between harmonic wavelet and multiple analytic band-pass filter is analyzed.The coherence of the multiple analytic band-pass filter and harmonic wavelet base function is discussed,and the characteristic that multiple analytic band-pass filter included in the harmonic wavelet transform is founded.Thirdly,by extending the harmonic wavelet transform,the concept of the adaptive harmonic window and its theoretical equation without decomposition are put forward in this paper.Then comparing with the Hanning window,the good performance of restraining side-lobe leakage possessed by adaptive harmonic window is shown,and the adaptive characteristics of window width changing and analytical center moving of the adaptive harmonic window are presented.Finally,the proposed adaptive harmonic window is applied to weak signal extraction and high frequency orbit extraction of high speed rotor under strong noise,and the satisfactory results are achieved.The application results show that the adaptive harmonic window function can be successfully applied to the actual engineering signal processing.展开更多
This paper presents a novel approach to extract the periodic signals masked by a chaotic carrier. It verifies that the driven Duffing oscillator is immune to the chaotic carrier and sensitive to certain periodic signa...This paper presents a novel approach to extract the periodic signals masked by a chaotic carrier. It verifies that the driven Duffing oscillator is immune to the chaotic carrier and sensitive to certain periodic signals. A preliminary detection scenario illustrates that the frequency and amplitude of the hidden sine wave signal can be extracted from the chaotic carrier by numerical simulation. The obtained results indicate that the hidden messages in chaotic secure communication can be eavesdropped utilizing Duffing oscillators.展开更多
The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax developme...The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraetion in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.展开更多
Rotating machine fault signal extraction becomes increasingly important in practical engineering applications.However,fault signals with low signal-to-noise ratios(SNRs)are difficult to extract,especially at the early...Rotating machine fault signal extraction becomes increasingly important in practical engineering applications.However,fault signals with low signal-to-noise ratios(SNRs)are difficult to extract,especially at the early stage of fault diagnosis.In this paper,2D line-defect phononic crystals(PCs)consisting of periodic acrylic tubes with slit are proposed for weak signal detection.The defect band,namely,the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity.The noise can be filtered by the band gap.As a result,fault signals with high SNRs can be obtained for fault feature extraction.The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies.All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals.This work will provide potential for extracting weak signals in many practical engineering applications.展开更多
Remote reflection waves, essential for acquiring high-resolution images of geological structures beyond boreholes, often suffer contamination from strong direct mode waves propagating along the borehole.Consequently, ...Remote reflection waves, essential for acquiring high-resolution images of geological structures beyond boreholes, often suffer contamination from strong direct mode waves propagating along the borehole.Consequently, the extraction of weak reflected waves becomes pivotal for optimizing migration image quality. This paper introduces a novel approach to extracting reflected waves by sequentially operating in the spatial frequency and curvelet domains. Using variation mode decomposition(VMD), single-channel spatial domain signals within the common offset gather are iteratively decomposed into high-wavenumber and low-wavenumber intrinsic mode functions(IMFs). The low-wavenumber IMF is then subtracted from the overall waveform to attenuate direct mode waves. Subsequently, the curvelet transform is employed to segregate upgoing and downgoing reflected waves within the filtered curvelet domain. As a result, direct mode waves are substantially suppressed, while the integrity of reflected waves is fully preserved. The efficacy of this approach is validated through processing synthetic and field data, underscoring its potential as a robust extraction technique.展开更多
A kind of piezoresistive response extraction method for smart cement-based composites/sensors was proposed.Two kinds of typical piezoresistive cement-based composites/sensors were fabricated by respectively adding car...A kind of piezoresistive response extraction method for smart cement-based composites/sensors was proposed.Two kinds of typical piezoresistive cement-based composites/sensors were fabricated by respectively adding carbon nanotubes and nickel powders as conductive fillers into cement paste or cement mortar.The variation in measured electrical resistance of such cement-based composites/sensors was explored without loading and under repeated compressive loading and impulsive loading.The experimental results indicate that the measured electrical resistance of piezoresistive cement-based composites/sensors exhibits a two-stage variation trend of fast increase and steady increase with measurement time without loading,and an irreversible increase after loading.This results from polarization caused by ionic conduction in these composites/sensors.After reaching a plateau,the measured electrical resistance can be divided into an electrical resistance part and an electrical capacity part.The piezoresistive responses of electrical resistance part in measured electrical resistance to loading can be extracted by eliminating the linear electrical capacity part in measured electrical resistance.展开更多
Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signa...Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequendy, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-split stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass filter to eliminate the off-band noise, and then performs time-shared blind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.展开更多
Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked pot...Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked potentials in single training sample from the EEG background noise in favor of studying the changes between the single sample response happen. The information is probably related with the different function, appearance and pathologies of the brain. At the same time this method can also be used to remove those signal’s artifacts that do not appear with EP within the same scope of time or frequency. The traditional Fourier filter can hardly attain the similar result. This method is different from other wavelet de-noising methods in which different criteria are employed in choosing wavelet coefficient. It has a biggest virtue of noting the differences among the single training sample and making use of the characteristics of high time frequency resolution to reduce the effect of interference factors to a maximum extent within the time scope that EP appear. The experiment result proves that this method is not restricted by the signal-to-noise ratio of evoked potential and electroencephalograph (EEG) and even can recognize instantaneous event under the condition of lower signal-to-noise ratio, as well as recognize the samples which evoked evident response more easily. Therefore, more evident average evoked response could be achieved by de-nosing the signals obtained through averaging out the samples that can evoke evident responses than de-nosing the average of original signals. In addition, averaging methodology can dramatically reduce the number of record samples needed, thus avoiding the effect of behavior change during the recording process. This methodology pays attention to the differences among single training sample and also accomplishes the extraction of visual evoked potentials from single trainings sample. As a result, system speed and accuracy could be improved to a great extent if this methodology is applied to brain-computer interface system based on evoked responses.展开更多
The algorithm of dense spectrum correction has been raised and proved based on the correction of discrete spectrum by fast Fourier transform.The result of simulation shows that such algorithm has advantages of high ac...The algorithm of dense spectrum correction has been raised and proved based on the correction of discrete spectrum by fast Fourier transform.The result of simulation shows that such algorithm has advantages of high accuracy and small amount of calculation.The algorithm has been successfully applied to the analysis of vibration signals from internal combustion engine.To calculate discrete spectrum,fast Fourier transform has been used to calculate the discrete spectrum by the signals acquired by the sensors on the oil pan,and the signal has been extracted from the mixed signals.展开更多
Stochastic resonance （SR） has been proved to be an effective approach to extract weak signals overwhelmed in noise. However, the detection effect of current SR models is still unsatisfactory. Here, a coupled tri-sta...Stochastic resonance （SR） has been proved to be an effective approach to extract weak signals overwhelmed in noise. However, the detection effect of current SR models is still unsatisfactory. Here, a coupled tri-stable stochastic resonance （CTSSR） model is proposed to further increase the output signal-to-noise ratio （SNR） and improve the detection effect of SR. The effects of parameters a, b, c, and r in the proposed resonance system on the SNR are studied, by which we determine a set of parameters that is relatively optimal to implement a comparison with other classical SR models. Numerical experiment results indicate that this proposed model performs better in weak signal detection applications than the classical ones with merits of higher output SNR and better anti-noise capability.展开更多
A procedure for identifying the dynamic parameter of offshore platform is presented. The present procedure consists of two key features. First uses random decrement (RD) technology to extract free vibration signal in ...A procedure for identifying the dynamic parameter of offshore platform is presented. The present procedure consists of two key features. First uses random decrement (RD) technology to extract free vibration signal in strong noise environment in which it may not white noise. Second technology which called autoregressive moving average (ARMA) was used to model the data treated by the random decrement method. In order to get rid of the color noise in the output signal response from the offshore platform an imaginary system is added in RD system and make the course of extracting performed under the state of color input by choosing the breakover condition and lead time. For eliminating multi_values of parameters identified, an updating moving average method is used. The dynamic parameters of structure under arbitrary input are identified. Example of the method as applied to a scale_model offshore platform was used to evaluate the technology of efficiency and the value of on_line.展开更多
Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time...Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to fi lter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.展开更多
In order to extract fault features of a weak signal from the strong noise and maintain signal smoothness, a new method of denoising based on the algorithm of balanced orthogonal multiwavelets is proposed. Multiwavelet...In order to extract fault features of a weak signal from the strong noise and maintain signal smoothness, a new method of denoising based on the algorithm of balanced orthogonal multiwavelets is proposed. Multiwavelets have several scaling functions and wavelet functions, and possess excellent properties that a scalar wavelet cannot satisfy simultaneously, and match the different characteristics of signals. Moreover, the balanced orthogonal multiwavelets can avoid the Gibbs phenomena and their processes have the advantages in denoising. Therefore, the denoising based on the algorithm of balanced orthogonal multiwavelets is introduced into the signal process. The algorithm of bal- anced orthogonal multiwavelet and the implementation steps of this denoising are described. The experimental compar- ison of the denoising effect between this algorithm and the traditional multiwavelet algorithm was done. The experi- ments indieate that this method is effective and feasible to extract the fault feature submerged in heavy noise.展开更多
A method of extracting normal mode attenuation coefficient from low frequency reverberation signal has been proposed.Pseudo-inverse normal mode filtering method is implemented to get single mode reverberation field fi...A method of extracting normal mode attenuation coefficient from low frequency reverberation signal has been proposed.Pseudo-inverse normal mode filtering method is implemented to get single mode reverberation field firstly.Based on the assumption of separability of modal back-scattering matrix,effective back-scattering matrix element can be calculated using single mode average reverberation intensity.Finally,mode attenuation coefficient is extracted by comparing effective back-scattering matrix elements at different ranges.The extracted mode attenuation coefficients are used to predict sound transmission loss at the same experiment area. Results show that the predicted transmission loss agrees well with the measured data.This method avoids the difficult of treating the coupling between bottom scattering attenuation and normal mode propagation attenuation.Research on extraction of mode attenuation coefficient from low frequency reverberation signal is useful for both geoacoustic inversion and rapid underwater environment assessment.展开更多
Mango firmness is one of the critical indicators for assessing internal quality and taste,as well as an indirect measure of maturity and freshness during ripening.Acoustic vibration technology has been widely applied ...Mango firmness is one of the critical indicators for assessing internal quality and taste,as well as an indirect measure of maturity and freshness during ripening.Acoustic vibration technology has been widely applied for nondestructive detection of fruit firmness.However,existing detection systems face the risk of fruit damage,prediction performance limitations,and significant influence of fruit size.This study designed a nondestructive pneumatic-electromagnetic-driven impact device based on acoustic vibration technology for firmness detection of different sizes of mango with the same impact force control.Vibration signals of 156 mangoes were acquired using an embedded accelerometer,and effective vibration signals were selected by comparing the excitation vibration response signals and the free vibration response signals.The correlation between mango reference firmness and vibration signal features was then analyzed.Based on this analysis,a prediction model for mango firmness was developed using partial least squares regression based on competitive adaptive reweighted sampling(CARS-PLSR).The results showed that the energy-type and amplitude-type statistical features in the vibration signals had a good correlation with the reference firmness(|r|≥0.45),and the mango firmness prediction model based on the vibration frequency-domain signals(CARS-PLSR)had the optimal performance.The model’s prediction determination coefficient(R_(P)^(2)),root mean square error of prediction(RMSEP),and relative percent deviation(RPD_(P))were 0.95,0.29 N/mm,and 4.20,respectively.Overall,it demonstrated that the pneumatic-electromagnetic-driven impact device integrated with an embedded accelerometer enables accurate and nondestructive detection of mango firmness.The innovative combination of pneumatic control and electromagnetic drive effectively minimizes the impact of fruit size variations and enhances prediction accuracy,demonstrating the significant potential for real-time fruit firmness sorting applications.展开更多
To improve stability and performance of the signal source and sweeping detection,as well as to extract abundant and reliable signal,the direct digital synthesis technology was employed to design the generator of the s...To improve stability and performance of the signal source and sweeping detection,as well as to extract abundant and reliable signal,the direct digital synthesis technology was employed to design the generator of the source which formed sweeping frequencies of sine wave output from 1 to 20 MHz.The planar spiral coil was con-nected as an amplitude modulation circuit.The same coil adopted differential architecture for signal detection and extraction.The MC1595 was utilized to compose a phase detector in which difference of phases varies with the change of frequencies.A low pass filter was designed to filter the carry waves of the sweeping source.Thereby the system gained abundant data and its stability was improved.Further,the spatial resolution of the system was enhanced.All of the above favors the use of software in the magnetic focused conductivity tomography system(MFCT)to reconstruct the image of conductivity within the human body.展开更多
文摘The movements and intentions of other ships can be determined by gathering and examining ship sound signals.The extraction and analysis of ship sound signals fundamentally support the autonomous navigation of intelligent ships.Mel scale frequency cepstral coefficient(MFCC)feature parameters are improved and optimized to form NewMFCC by introducing second-order difference and wavelet packet decomposition transformation methods in this paper.Transforming sound signals into a feature vector that fully describes the dynamic characteristics of ship sound signals and the high-and low-frequency information solves the problem of the inability to transport ordinary sound signals directly as signals for training in machine learning models.Radial basis function kernels are used to conduct support vector machine classifier simulation experiments.Five types of sound signals,namely,one type of ship sound signals and four types of interference sound signals,are categorized and identified as classification targets to verify the feasibility of the classification of ship sound signals and interference signals.The proposed method improves classification accuracy by approximately 15%.
基金supported by the National Natural Science Foundation of China(Grant No.42192535,42174012,42174101,41974023)the Open Fund of Hubei Luojia Laboratory(Grant No.S22H640201)。
文摘Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.
基金The Science and Technology Committee of Shanghai Municipality (No. 05DZ15004, 06DZ15013)The Project-sponsored by SRF for ROCS, SEM
文摘A novel method to extract multiple input and multiple output (MIMO) chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal generators was presented. In order to separate the chaotic source signals only by using the sensor signals at receivers, a blind neural extraction algorithm based on higher-order statistic (HOS) technique was used to recover the primary chaotic signals. Simulation results show that the proposed approach has good performance in separating the primary chaotic signals even under nonideal channel.
基金Project(51675262)supported by the National Natural Science Foundation of ChinaProject(6140210020102)supported by the Advance Research Field Fund Project of ChinaProject(2016YFD0700800)supported by the National Key Research and Development Plan of China
文摘The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise.The relationship between windowing transform andfiltering is analyzed first in the paper.The advantage of adjustable time-frequency window of wavelet transform is introduced.Secondly the relationship between harmonic wavelet and multiple analytic band-pass filter is analyzed.The coherence of the multiple analytic band-pass filter and harmonic wavelet base function is discussed,and the characteristic that multiple analytic band-pass filter included in the harmonic wavelet transform is founded.Thirdly,by extending the harmonic wavelet transform,the concept of the adaptive harmonic window and its theoretical equation without decomposition are put forward in this paper.Then comparing with the Hanning window,the good performance of restraining side-lobe leakage possessed by adaptive harmonic window is shown,and the adaptive characteristics of window width changing and analytical center moving of the adaptive harmonic window are presented.Finally,the proposed adaptive harmonic window is applied to weak signal extraction and high frequency orbit extraction of high speed rotor under strong noise,and the satisfactory results are achieved.The application results show that the adaptive harmonic window function can be successfully applied to the actual engineering signal processing.
基金supported by the National Natural Science Foundation of China (Grant Nos 60577019 and 60777041) the International Cooperation Project of Shanxi Province,China
文摘This paper presents a novel approach to extract the periodic signals masked by a chaotic carrier. It verifies that the driven Duffing oscillator is immune to the chaotic carrier and sensitive to certain periodic signals. A preliminary detection scenario illustrates that the frequency and amplitude of the hidden sine wave signal can be extracted from the chaotic carrier by numerical simulation. The obtained results indicate that the hidden messages in chaotic secure communication can be eavesdropped utilizing Duffing oscillators.
文摘The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraetion in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.
基金This paper was financially supported by the National Natural Science Foundation of China(Grant No.52175087).
文摘Rotating machine fault signal extraction becomes increasingly important in practical engineering applications.However,fault signals with low signal-to-noise ratios(SNRs)are difficult to extract,especially at the early stage of fault diagnosis.In this paper,2D line-defect phononic crystals(PCs)consisting of periodic acrylic tubes with slit are proposed for weak signal detection.The defect band,namely,the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity.The noise can be filtered by the band gap.As a result,fault signals with high SNRs can be obtained for fault feature extraction.The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies.All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals.This work will provide potential for extracting weak signals in many practical engineering applications.
基金supported by the National Natural Science Foundation of China (grant No. 42204126, 42174145, 42104132)Laoshan National Laboratory Science and Technology Innovation Project (grant No. LSKJ202203407)。
文摘Remote reflection waves, essential for acquiring high-resolution images of geological structures beyond boreholes, often suffer contamination from strong direct mode waves propagating along the borehole.Consequently, the extraction of weak reflected waves becomes pivotal for optimizing migration image quality. This paper introduces a novel approach to extracting reflected waves by sequentially operating in the spatial frequency and curvelet domains. Using variation mode decomposition(VMD), single-channel spatial domain signals within the common offset gather are iteratively decomposed into high-wavenumber and low-wavenumber intrinsic mode functions(IMFs). The low-wavenumber IMF is then subtracted from the overall waveform to attenuate direct mode waves. Subsequently, the curvelet transform is employed to segregate upgoing and downgoing reflected waves within the filtered curvelet domain. As a result, direct mode waves are substantially suppressed, while the integrity of reflected waves is fully preserved. The efficacy of this approach is validated through processing synthetic and field data, underscoring its potential as a robust extraction technique.
基金Funded by the National Natural Science Foundation of China(Nos. 51178148,50808055)the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology(No.HIT.NSRIF.2009096)the Program for New Century Excellent Talents University of China(No.NCET-0798)
文摘A kind of piezoresistive response extraction method for smart cement-based composites/sensors was proposed.Two kinds of typical piezoresistive cement-based composites/sensors were fabricated by respectively adding carbon nanotubes and nickel powders as conductive fillers into cement paste or cement mortar.The variation in measured electrical resistance of such cement-based composites/sensors was explored without loading and under repeated compressive loading and impulsive loading.The experimental results indicate that the measured electrical resistance of piezoresistive cement-based composites/sensors exhibits a two-stage variation trend of fast increase and steady increase with measurement time without loading,and an irreversible increase after loading.This results from polarization caused by ionic conduction in these composites/sensors.After reaching a plateau,the measured electrical resistance can be divided into an electrical resistance part and an electrical capacity part.The piezoresistive responses of electrical resistance part in measured electrical resistance to loading can be extracted by eliminating the linear electrical capacity part in measured electrical resistance.
基金Hong Wang's research was supported in part by the Anesthesiology Department at Wayne State University and in part by Wayne State University Research Enhancement ProgramLeyi Wang" s research was supported in part by the National Science Foundation ( No.
文摘Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequendy, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-split stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass filter to eliminate the off-band noise, and then performs time-shared blind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.
文摘Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked potentials in single training sample from the EEG background noise in favor of studying the changes between the single sample response happen. The information is probably related with the different function, appearance and pathologies of the brain. At the same time this method can also be used to remove those signal’s artifacts that do not appear with EP within the same scope of time or frequency. The traditional Fourier filter can hardly attain the similar result. This method is different from other wavelet de-noising methods in which different criteria are employed in choosing wavelet coefficient. It has a biggest virtue of noting the differences among the single training sample and making use of the characteristics of high time frequency resolution to reduce the effect of interference factors to a maximum extent within the time scope that EP appear. The experiment result proves that this method is not restricted by the signal-to-noise ratio of evoked potential and electroencephalograph (EEG) and even can recognize instantaneous event under the condition of lower signal-to-noise ratio, as well as recognize the samples which evoked evident response more easily. Therefore, more evident average evoked response could be achieved by de-nosing the signals obtained through averaging out the samples that can evoke evident responses than de-nosing the average of original signals. In addition, averaging methodology can dramatically reduce the number of record samples needed, thus avoiding the effect of behavior change during the recording process. This methodology pays attention to the differences among single training sample and also accomplishes the extraction of visual evoked potentials from single trainings sample. As a result, system speed and accuracy could be improved to a great extent if this methodology is applied to brain-computer interface system based on evoked responses.
基金Project(51176045) supported by the National Natural Science Foundation of China
文摘The algorithm of dense spectrum correction has been raised and proved based on the correction of discrete spectrum by fast Fourier transform.The result of simulation shows that such algorithm has advantages of high accuracy and small amount of calculation.The algorithm has been successfully applied to the analysis of vibration signals from internal combustion engine.To calculate discrete spectrum,fast Fourier transform has been used to calculate the discrete spectrum by the signals acquired by the sensors on the oil pan,and the signal has been extracted from the mixed signals.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61071025 and 61502538)
文摘Stochastic resonance （SR） has been proved to be an effective approach to extract weak signals overwhelmed in noise. However, the detection effect of current SR models is still unsatisfactory. Here, a coupled tri-stable stochastic resonance （CTSSR） model is proposed to further increase the output signal-to-noise ratio （SNR） and improve the detection effect of SR. The effects of parameters a, b, c, and r in the proposed resonance system on the SNR are studied, by which we determine a set of parameters that is relatively optimal to implement a comparison with other classical SR models. Numerical experiment results indicate that this proposed model performs better in weak signal detection applications than the classical ones with merits of higher output SNR and better anti-noise capability.
文摘A procedure for identifying the dynamic parameter of offshore platform is presented. The present procedure consists of two key features. First uses random decrement (RD) technology to extract free vibration signal in strong noise environment in which it may not white noise. Second technology which called autoregressive moving average (ARMA) was used to model the data treated by the random decrement method. In order to get rid of the color noise in the output signal response from the offshore platform an imaginary system is added in RD system and make the course of extracting performed under the state of color input by choosing the breakover condition and lead time. For eliminating multi_values of parameters identified, an updating moving average method is used. The dynamic parameters of structure under arbitrary input are identified. Example of the method as applied to a scale_model offshore platform was used to evaluate the technology of efficiency and the value of on_line.
基金supported by the National Natural Science Foundation of China(No.41074089)Special Financial Grant from the China Postdoctoral Science Foundation(No.201104654)
文摘Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to fi lter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.
基金supported by Scientific and Technological Foundation of Henan Province under Grant No.112102210128Science Research Project of Educational Department of Henan Province under Grant No.2011C510005
文摘In order to extract fault features of a weak signal from the strong noise and maintain signal smoothness, a new method of denoising based on the algorithm of balanced orthogonal multiwavelets is proposed. Multiwavelets have several scaling functions and wavelet functions, and possess excellent properties that a scalar wavelet cannot satisfy simultaneously, and match the different characteristics of signals. Moreover, the balanced orthogonal multiwavelets can avoid the Gibbs phenomena and their processes have the advantages in denoising. Therefore, the denoising based on the algorithm of balanced orthogonal multiwavelets is introduced into the signal process. The algorithm of bal- anced orthogonal multiwavelet and the implementation steps of this denoising are described. The experimental compar- ison of the denoising effect between this algorithm and the traditional multiwavelet algorithm was done. The experi- ments indieate that this method is effective and feasible to extract the fault feature submerged in heavy noise.
文摘A method of extracting normal mode attenuation coefficient from low frequency reverberation signal has been proposed.Pseudo-inverse normal mode filtering method is implemented to get single mode reverberation field firstly.Based on the assumption of separability of modal back-scattering matrix,effective back-scattering matrix element can be calculated using single mode average reverberation intensity.Finally,mode attenuation coefficient is extracted by comparing effective back-scattering matrix elements at different ranges.The extracted mode attenuation coefficients are used to predict sound transmission loss at the same experiment area. Results show that the predicted transmission loss agrees well with the measured data.This method avoids the difficult of treating the coupling between bottom scattering attenuation and normal mode propagation attenuation.Research on extraction of mode attenuation coefficient from low frequency reverberation signal is useful for both geoacoustic inversion and rapid underwater environment assessment.
基金financial support provided by the National Key R&D Program of China(Grant No.2022YFD2002201)the High-Level Talents Special Support Program of Zhejiang province(Grant No.2023R5250).
文摘Mango firmness is one of the critical indicators for assessing internal quality and taste,as well as an indirect measure of maturity and freshness during ripening.Acoustic vibration technology has been widely applied for nondestructive detection of fruit firmness.However,existing detection systems face the risk of fruit damage,prediction performance limitations,and significant influence of fruit size.This study designed a nondestructive pneumatic-electromagnetic-driven impact device based on acoustic vibration technology for firmness detection of different sizes of mango with the same impact force control.Vibration signals of 156 mangoes were acquired using an embedded accelerometer,and effective vibration signals were selected by comparing the excitation vibration response signals and the free vibration response signals.The correlation between mango reference firmness and vibration signal features was then analyzed.Based on this analysis,a prediction model for mango firmness was developed using partial least squares regression based on competitive adaptive reweighted sampling(CARS-PLSR).The results showed that the energy-type and amplitude-type statistical features in the vibration signals had a good correlation with the reference firmness(|r|≥0.45),and the mango firmness prediction model based on the vibration frequency-domain signals(CARS-PLSR)had the optimal performance.The model’s prediction determination coefficient(R_(P)^(2)),root mean square error of prediction(RMSEP),and relative percent deviation(RPD_(P))were 0.95,0.29 N/mm,and 4.20,respectively.Overall,it demonstrated that the pneumatic-electromagnetic-driven impact device integrated with an embedded accelerometer enables accurate and nondestructive detection of mango firmness.The innovative combination of pneumatic control and electromagnetic drive effectively minimizes the impact of fruit size variations and enhances prediction accuracy,demonstrating the significant potential for real-time fruit firmness sorting applications.
基金supported by the National Elite Youth Foundation(No.60125102).
文摘To improve stability and performance of the signal source and sweeping detection,as well as to extract abundant and reliable signal,the direct digital synthesis technology was employed to design the generator of the source which formed sweeping frequencies of sine wave output from 1 to 20 MHz.The planar spiral coil was con-nected as an amplitude modulation circuit.The same coil adopted differential architecture for signal detection and extraction.The MC1595 was utilized to compose a phase detector in which difference of phases varies with the change of frequencies.A low pass filter was designed to filter the carry waves of the sweeping source.Thereby the system gained abundant data and its stability was improved.Further,the spatial resolution of the system was enhanced.All of the above favors the use of software in the magnetic focused conductivity tomography system(MFCT)to reconstruct the image of conductivity within the human body.
