Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a ...Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.展开更多
A noise reduction method for infrared detector output signal is studied during dynamic calibration of thermocou- pie. Firstly, the deficiency of the classical filter method is analyzed and the application of the wavel...A noise reduction method for infrared detector output signal is studied during dynamic calibration of thermocou- pie. Firstly, the deficiency of the classical filter method is analyzed and the application of the wavelet analysis is introduced for signal de-noising during the dynamic testing. Secondly, the theoretical basis of wavelet analysis, the choice of wavelet base and the determination of decomposed series and threshold are analyzed. Finally, the de-noising experiment for infrared detector signal is carried out on the Matlab platform. The results indicate the proposed wavelet de-noising method is effective to remove fixed frequency and high-frequency noise; furthermore, good synchronization is achieved between the de-noised signal and the useful signal components in the original signal, which is of great significance to thermocouple modeling analys- is.展开更多
In the present study of peak particle velocity(PPV)and frequency,an improved algorithm(principal empirical mode decomposition,PEMD)based on principal component analysis(PCA)and empirical mode decomposition(EMD)is prop...In the present study of peak particle velocity(PPV)and frequency,an improved algorithm(principal empirical mode decomposition,PEMD)based on principal component analysis(PCA)and empirical mode decomposition(EMD)is proposed,with the goal of addressing poor filtering de-noising effects caused by the occurrences of modal aliasing phenomena in EMD blasting vibration signal decomposition processes.Test results showed that frequency of intrinsic mode function(IMF)components decomposed by PEMD gradually decreases and that the main frequency is unique,which eliminates the phenomenon of modal aliasing.In the simulation experiment,the signal-to-noise(SNR)and root mean square errors(RMSE)ratio of the signal de-noised by PEMD are the largest when compared to EMD and ensemble empirical mode decomposition(EEMD).The main frequency of the de-noising signal through PEMD is 75 Hz,which is closest to the frequency of the noiseless simulation signal.In geotechnical engineering blasting experiments,compared to EMD and EEMD,the signal de-noised by PEMD has the lowest level of distortion,and the frequency band is distributed in a range of 0-64 Hz,which is closest to the frequency band of the blasting vibration signal.In addition,the proportion of noise energy was the lowest,at 1.8%.展开更多
In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the ...In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.展开更多
Based on wavelet transform theory,a method for signal de-noising and singularity detection and elimination is proposed,which can reduce the noises and express local singularity.Each singularity can also be detected an...Based on wavelet transform theory,a method for signal de-noising and singularity detection and elimination is proposed,which can reduce the noises and express local singularity.Each singularity can also be detected and located through the local modulus maxima of wavelet transform.Simulation experiments are conducted with MATLAB software.The experimental results demonstrate that the method proposed in this paper is effective and feasible.展开更多
. This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet c.... This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet coefficients of the noisy signal to estimate the discontinuity of hard threshold function and soft threshold function, limiting its further application in order to overcome this shortcoming, this paper proposes a new threshold function, compared with the original threshold function, a new threshold function is simple and easy to calculate, not only with the soft threshold function is continuous. To deal with this drawback, we integrate the NN to enhance the model. Neural network belongs to the basic unsupervised learning of neural networks, the principle of competition based on the mechanism of learning and biological and the memory capacity can be increased as the number of learning patterns increases, not only offi ine learning can also be carried out on-line ＂learning while learning＂ type. The integrated algorithm can host better performance.展开更多
It＇ s a problem to be solved how to de-noise the signal of blast shock wave overpressure. In the conventional methods, the high frequency of the signal is cut directly by some mathematics algorithms, such as Fourier ...It＇ s a problem to be solved how to de-noise the signal of blast shock wave overpressure. In the conventional methods, the high frequency of the signal is cut directly by some mathematics algorithms, such as Fourier Transform, but some of the useful signal will be cut together. We adopt a new method for the signal de-noising of shock wave overpressure by wavelet analysis, There are four steps in this method. Firstly, the original signal is de-compoed. Then the time-frequency features of the signal and noise are analyzed. Thirdly, the noise is separated from the signal by only cutting its frequency while the useful signal frequency is reserved as much as possible. Lastly, the useful signal with least loss of information is recovered by reconstruction process. To verify this method, a blast shock wave signal is de-noised with FFF to make a comparison. The results show that the signal de-noised by wavelet analysis approximates the ideal signal well.展开更多
Sound waves propagate well underwater making it useful for target locating and communication.Underwater acoustic noise(UWAN)affects the reliability in applications where the noise comes from multiple sources.In this p...Sound waves propagate well underwater making it useful for target locating and communication.Underwater acoustic noise(UWAN)affects the reliability in applications where the noise comes from multiple sources.In this paper,a novel signal de-noising technique is proposed using S-transform.From the time-frequency representation,de-noising is performed using soft thresholding with universal threshold estimation which is then reconstructed.The UWAN used for the validation is sea truth data collected at Desaru beach on the eastern shore of Johor in Malaysia with the use of broadband hydrophones.The comparison is made with the more conventionally used wavelet transform de-noising method.Two types of signals are evaluated:fixed frequency signals and time-varying signals.The results demonstrate that the proposed method shows better signal to noise ratio(SNR)by 4 dB and lower root mean square error(RMSE)by 3 dB achieved at the Nyquist sampling frequency compared to the previously proposed de-noising method like wavelet transform.展开更多
Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal...Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.展开更多
With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due ...With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.展开更多
Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and ins...Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.展开更多
Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lida...Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.展开更多
An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single process...An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
The goal of a de-noising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent ...The goal of a de-noising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent work, the authors addressed a new Matlab algorithm for de-noising. A key method of the algorithm is selecting an optimal basis from a library of wavelet bases for ideal de-noising. The algorithm with an optimal basis from a library of wavelet bases for de-noising was created through making use of Matlab＇s Wavelet Toolbox. The experimental results show that the new algorithm is efficient in signal de-nosing.展开更多
In the track irregularity detection, the acceleration signals of the inertial measurement unit (IMU) output which with low frequency components and noise, this paper studied a de-noising algorithm. Based on the criter...In the track irregularity detection, the acceleration signals of the inertial measurement unit (IMU) output which with low frequency components and noise, this paper studied a de-noising algorithm. Based on the criterion of consecutive mean square error, a de-noising method for IMU acceleration signals based on empirical mode decomposition (EMD) was proposed. This method can divide the intrinsic mode functions (IMFs) derived from EMD into signal dominant modes and noise dominant modes, then the modes reflecting the important structures of a signal were combined together to form partially reconstructed de-noised signal. Simulations were conducted for simulated signals and a real IMU acceleration signals using this method. Experimental results indicate that this method can efficiently and adaptively remove noise, and this method can better meet the precision requirement.展开更多
Based on the statistical characteristics analysis of random noise power and autocorrelation function, this paper proposes a de-noising method for track state detection signal by using Empirical Mode Decomposition (EMD...Based on the statistical characteristics analysis of random noise power and autocorrelation function, this paper proposes a de-noising method for track state detection signal by using Empirical Mode Decomposition (EMD). This method is used to noise reduction refactoring for the first Intrinsic Mode Function (IMF) component in accordance with the “random sort-accumulation-average-refactoring' order. Signal autocorrelation function characteristics are used to determine the cut-off point of the dominant mode. This method was applied to test signals and the actual inertial unit signals;the experimental results show that the method can effectively remove the noise and better meet the precision requirement.展开更多
Pancreatic ductal adenocarcinoma stands out as an exceptionally fatal cancer owing to the complexities associated with its treatment and diagnosis,leading to a notably low five-year survival rate.This study offers a d...Pancreatic ductal adenocarcinoma stands out as an exceptionally fatal cancer owing to the complexities associated with its treatment and diagnosis,leading to a notably low five-year survival rate.This study offers a detailed exploration of epidemiological trends in pancreatic cancer and key molecular drivers,such as mutations in CDKN2A,KRAS,SMAD4,and TP53,along with the influence of cancer-associated fibroblasts(CAFs)on disease progression.In particular,we focused on the pivotal roles of signaling pathways such as the transforming growth factor-βand Wnt/β-catenin pathways in the development of pancreatic cancer and investigated their application in emerging therapeutic strategies.This study provides new scientific perspectives on pancreatic cancer treatment,especially in the development of precision medicine and targeted therapeutic strategies,and demonstrates the importance of signaling pathway research in the development of effective therapeutic regimens.Future studies should explore the subtypes of CAFs and their specific roles in the tumor microenvironment to devise more effective therapeutic methods.展开更多
BACKGROUND Simulated microgravity environment can lead to gastrointestinal motility disturbance.The pathogenesis of gastrointestinal motility disorders is closely related to the stem cell factor(SCF)/c-kit signaling p...BACKGROUND Simulated microgravity environment can lead to gastrointestinal motility disturbance.The pathogenesis of gastrointestinal motility disorders is closely related to the stem cell factor(SCF)/c-kit signaling pathway associated with intestinal flora and Cajal stromal cells.Moreover,intestinal flora can also affect the regulation of SCF/c-kit signaling pathway,thus affecting the expression of Cajal stromal cells.Cajal cells are the pacemakers of gastrointestinal motility.AIM To investigate the effects of Bifidobacterium lactis(B.lactis)BLa80 on the intestinal flora of rats in simulated microgravity and on the gastrointestinal motility-related SCF/c-kit pathway.METHODS The internationally recognized tail suspension animal model was used to simulate the microgravity environment,and 30 rats were randomly divided into control group,tail suspension group and drug administration tail suspension group with 10 rats in each group for a total of 28 days.The tail group was given B.lactis BLa80 by intragastric administration,and the other two groups were given water intragastric administration,the concentration of intragastric administration was 0.1 g/mL,and each rat was 1 mL/day.Hematoxylin&eosin staining was used to observe the histopathological changes in each segment of the intestine of each group,and the expression levels of SCF,c-kit,extracellular signal-regulated kinase(ERK)and p-ERK in the gastric antrum of each group were detected by Western blotting and PCR.The fecal flora and mucosal flora of rats in each group were detected by 16S rRNA.RESULTS Simulated microgravity resulted in severe exfoliation of villi of duodenum,jejunum and ileum in rats,marked damage,increased space between villi,loose arrangement,shortened columnar epithelium of colon,less folds,narrower mucosal thickness,reduced goblet cell number and crypts,and significant improvement after probiotic intervention.Simulated microgravity reduced the expressions of SCF and c-kit,and increased the expressions of ERK and P-ERK in the gastric antrum of rats.However,after probiotic intervention,the expressions of SCF and ckit were increased,while the expressions of ERK and P-ERK were decreased,with statistical significance(P<0.05).In addition,simulated microgravity can reduce the operational taxonomic unit(OTU)of the overall intestinal flora of rats,B.lactis BLa80 can increase the OTU of rats,simulated microgravity can reduce the overall richness and diversity of stool flora of rats,increase the abundance of firmicutes in stool flora of rats,and reduce the abundance of Bacteroides in stool flora of rats,most of which are mainly beneficial bacteria.Simulated microgravity can increase the overall richness and diversity of mucosal flora,increase the abundance of Bacteroides and Desulphurides in the rat mucosal flora,and decrease the abundance of firmicutes,most of which are proteobacteria.After probiotics intervention,the overall Bacteroidetes trend in simulated microgravity rats was increased.CONCLUSION B.lactis BLa80 can ameliorate intestinal mucosal injury,regulate intestinal flora,inhibit ERK expression,and activate the SCF/c-kit signaling pathway,which may have a facilitating effect on gastrointestinal motility in simulated microgravity rats.展开更多
Helicobacter pylori-associated gastritis(HPAG)is a common condition of the gastrointestinal tract.However,extensive and long-term antibiotic use has resulted in numerous adverse effects,including increased resistance,...Helicobacter pylori-associated gastritis(HPAG)is a common condition of the gastrointestinal tract.However,extensive and long-term antibiotic use has resulted in numerous adverse effects,including increased resistance,gastrointestinal dysfunction,and increased recurrence rates.When these concerns develop,traditional Chinese medicine(TCM)may have advantages.TCM is based on the concept of completeness and aims to eliminate pathogens and strengthen the body.It has the potential to prevent this condition while also boosting the rate of Helicobacter pylori eradication.This review elaborates on the mechanism of TCM treatment for HPAG based on cellular signalling pathways,which reflects the flexibility of TCM in treating diseases and the advantages of multi-level,multipathway,and multi-target treatments for HPAG.展开更多
文摘Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.
文摘A noise reduction method for infrared detector output signal is studied during dynamic calibration of thermocou- pie. Firstly, the deficiency of the classical filter method is analyzed and the application of the wavelet analysis is introduced for signal de-noising during the dynamic testing. Secondly, the theoretical basis of wavelet analysis, the choice of wavelet base and the determination of decomposed series and threshold are analyzed. Finally, the de-noising experiment for infrared detector signal is carried out on the Matlab platform. The results indicate the proposed wavelet de-noising method is effective to remove fixed frequency and high-frequency noise; furthermore, good synchronization is achieved between the de-noised signal and the useful signal components in the original signal, which is of great significance to thermocouple modeling analys- is.
基金National Natural Science Foundation of China under Grant Nos.52064015 and 51404111Jiangxi Provincial Natural Science Foundation under Grant No.20192BAB206017+1 种基金Scientific Research Project of Jiangxi Provincial Education Department under Grant No.GJJ160643the Program of Qingjiang Excellent Young Talents,Jiangxi University of Science and Technology under Grant No.JXUSTQJYX2016007。
文摘In the present study of peak particle velocity(PPV)and frequency,an improved algorithm(principal empirical mode decomposition,PEMD)based on principal component analysis(PCA)and empirical mode decomposition(EMD)is proposed,with the goal of addressing poor filtering de-noising effects caused by the occurrences of modal aliasing phenomena in EMD blasting vibration signal decomposition processes.Test results showed that frequency of intrinsic mode function(IMF)components decomposed by PEMD gradually decreases and that the main frequency is unique,which eliminates the phenomenon of modal aliasing.In the simulation experiment,the signal-to-noise(SNR)and root mean square errors(RMSE)ratio of the signal de-noised by PEMD are the largest when compared to EMD and ensemble empirical mode decomposition(EEMD).The main frequency of the de-noising signal through PEMD is 75 Hz,which is closest to the frequency of the noiseless simulation signal.In geotechnical engineering blasting experiments,compared to EMD and EEMD,the signal de-noised by PEMD has the lowest level of distortion,and the frequency band is distributed in a range of 0-64 Hz,which is closest to the frequency band of the blasting vibration signal.In addition,the proportion of noise energy was the lowest,at 1.8%.
基金The Key Program of National Natural Science of China(No.U1261205)Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.
文摘Based on wavelet transform theory,a method for signal de-noising and singularity detection and elimination is proposed,which can reduce the noises and express local singularity.Each singularity can also be detected and located through the local modulus maxima of wavelet transform.Simulation experiments are conducted with MATLAB software.The experimental results demonstrate that the method proposed in this paper is effective and feasible.
文摘. This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet coefficients of the noisy signal to estimate the discontinuity of hard threshold function and soft threshold function, limiting its further application in order to overcome this shortcoming, this paper proposes a new threshold function, compared with the original threshold function, a new threshold function is simple and easy to calculate, not only with the soft threshold function is continuous. To deal with this drawback, we integrate the NN to enhance the model. Neural network belongs to the basic unsupervised learning of neural networks, the principle of competition based on the mechanism of learning and biological and the memory capacity can be increased as the number of learning patterns increases, not only offi ine learning can also be carried out on-line ＂learning while learning＂ type. The integrated algorithm can host better performance.
文摘It＇ s a problem to be solved how to de-noise the signal of blast shock wave overpressure. In the conventional methods, the high frequency of the signal is cut directly by some mathematics algorithms, such as Fourier Transform, but some of the useful signal will be cut together. We adopt a new method for the signal de-noising of shock wave overpressure by wavelet analysis, There are four steps in this method. Firstly, the original signal is de-compoed. Then the time-frequency features of the signal and noise are analyzed. Thirdly, the noise is separated from the signal by only cutting its frequency while the useful signal frequency is reserved as much as possible. Lastly, the useful signal with least loss of information is recovered by reconstruction process. To verify this method, a blast shock wave signal is de-noised with FFF to make a comparison. The results show that the signal de-noised by wavelet analysis approximates the ideal signal well.
基金The authors would like to thank the Universiti Teknologi Malaysia(UTM)and Ministry of Higher Education(MOHE)Malaysia for supporting this work.
文摘Sound waves propagate well underwater making it useful for target locating and communication.Underwater acoustic noise(UWAN)affects the reliability in applications where the noise comes from multiple sources.In this paper,a novel signal de-noising technique is proposed using S-transform.From the time-frequency representation,de-noising is performed using soft thresholding with universal threshold estimation which is then reconstructed.The UWAN used for the validation is sea truth data collected at Desaru beach on the eastern shore of Johor in Malaysia with the use of broadband hydrophones.The comparison is made with the more conventionally used wavelet transform de-noising method.Two types of signals are evaluated:fixed frequency signals and time-varying signals.The results demonstrate that the proposed method shows better signal to noise ratio(SNR)by 4 dB and lower root mean square error(RMSE)by 3 dB achieved at the Nyquist sampling frequency compared to the previously proposed de-noising method like wavelet transform.
基金funded by National Natural Science Foundation of China(61201391)。
文摘Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.
基金This research is supported financially by Natural Science Foundation of China(Grant No.51505234,51405241,51575283).
文摘With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.
基金Supported by the China National Science and Technology Major Project(2016ZX05020005-001)
文摘Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.
文摘Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.
基金Supported by the National Natural Science Founda-tion of China (49984001)
文摘An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
文摘The goal of a de-noising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent work, the authors addressed a new Matlab algorithm for de-noising. A key method of the algorithm is selecting an optimal basis from a library of wavelet bases for ideal de-noising. The algorithm with an optimal basis from a library of wavelet bases for de-noising was created through making use of Matlab＇s Wavelet Toolbox. The experimental results show that the new algorithm is efficient in signal de-nosing.
文摘In the track irregularity detection, the acceleration signals of the inertial measurement unit (IMU) output which with low frequency components and noise, this paper studied a de-noising algorithm. Based on the criterion of consecutive mean square error, a de-noising method for IMU acceleration signals based on empirical mode decomposition (EMD) was proposed. This method can divide the intrinsic mode functions (IMFs) derived from EMD into signal dominant modes and noise dominant modes, then the modes reflecting the important structures of a signal were combined together to form partially reconstructed de-noised signal. Simulations were conducted for simulated signals and a real IMU acceleration signals using this method. Experimental results indicate that this method can efficiently and adaptively remove noise, and this method can better meet the precision requirement.
文摘Based on the statistical characteristics analysis of random noise power and autocorrelation function, this paper proposes a de-noising method for track state detection signal by using Empirical Mode Decomposition (EMD). This method is used to noise reduction refactoring for the first Intrinsic Mode Function (IMF) component in accordance with the “random sort-accumulation-average-refactoring' order. Signal autocorrelation function characteristics are used to determine the cut-off point of the dominant mode. This method was applied to test signals and the actual inertial unit signals;the experimental results show that the method can effectively remove the noise and better meet the precision requirement.
基金Supported by National Key Research and Development Program Project,No.2017YFC1700601Shaanxi Provincial Key Research and Development Program Project,No.2018SF-350Leading Talents in Scientific and Technological Innovation of the Shaanxi Province Special Support Plan,No.00518。
文摘Pancreatic ductal adenocarcinoma stands out as an exceptionally fatal cancer owing to the complexities associated with its treatment and diagnosis,leading to a notably low five-year survival rate.This study offers a detailed exploration of epidemiological trends in pancreatic cancer and key molecular drivers,such as mutations in CDKN2A,KRAS,SMAD4,and TP53,along with the influence of cancer-associated fibroblasts(CAFs)on disease progression.In particular,we focused on the pivotal roles of signaling pathways such as the transforming growth factor-βand Wnt/β-catenin pathways in the development of pancreatic cancer and investigated their application in emerging therapeutic strategies.This study provides new scientific perspectives on pancreatic cancer treatment,especially in the development of precision medicine and targeted therapeutic strategies,and demonstrates the importance of signaling pathway research in the development of effective therapeutic regimens.Future studies should explore the subtypes of CAFs and their specific roles in the tumor microenvironment to devise more effective therapeutic methods.
文摘BACKGROUND Simulated microgravity environment can lead to gastrointestinal motility disturbance.The pathogenesis of gastrointestinal motility disorders is closely related to the stem cell factor(SCF)/c-kit signaling pathway associated with intestinal flora and Cajal stromal cells.Moreover,intestinal flora can also affect the regulation of SCF/c-kit signaling pathway,thus affecting the expression of Cajal stromal cells.Cajal cells are the pacemakers of gastrointestinal motility.AIM To investigate the effects of Bifidobacterium lactis(B.lactis)BLa80 on the intestinal flora of rats in simulated microgravity and on the gastrointestinal motility-related SCF/c-kit pathway.METHODS The internationally recognized tail suspension animal model was used to simulate the microgravity environment,and 30 rats were randomly divided into control group,tail suspension group and drug administration tail suspension group with 10 rats in each group for a total of 28 days.The tail group was given B.lactis BLa80 by intragastric administration,and the other two groups were given water intragastric administration,the concentration of intragastric administration was 0.1 g/mL,and each rat was 1 mL/day.Hematoxylin&eosin staining was used to observe the histopathological changes in each segment of the intestine of each group,and the expression levels of SCF,c-kit,extracellular signal-regulated kinase(ERK)and p-ERK in the gastric antrum of each group were detected by Western blotting and PCR.The fecal flora and mucosal flora of rats in each group were detected by 16S rRNA.RESULTS Simulated microgravity resulted in severe exfoliation of villi of duodenum,jejunum and ileum in rats,marked damage,increased space between villi,loose arrangement,shortened columnar epithelium of colon,less folds,narrower mucosal thickness,reduced goblet cell number and crypts,and significant improvement after probiotic intervention.Simulated microgravity reduced the expressions of SCF and c-kit,and increased the expressions of ERK and P-ERK in the gastric antrum of rats.However,after probiotic intervention,the expressions of SCF and ckit were increased,while the expressions of ERK and P-ERK were decreased,with statistical significance(P<0.05).In addition,simulated microgravity can reduce the operational taxonomic unit(OTU)of the overall intestinal flora of rats,B.lactis BLa80 can increase the OTU of rats,simulated microgravity can reduce the overall richness and diversity of stool flora of rats,increase the abundance of firmicutes in stool flora of rats,and reduce the abundance of Bacteroides in stool flora of rats,most of which are mainly beneficial bacteria.Simulated microgravity can increase the overall richness and diversity of mucosal flora,increase the abundance of Bacteroides and Desulphurides in the rat mucosal flora,and decrease the abundance of firmicutes,most of which are proteobacteria.After probiotics intervention,the overall Bacteroidetes trend in simulated microgravity rats was increased.CONCLUSION B.lactis BLa80 can ameliorate intestinal mucosal injury,regulate intestinal flora,inhibit ERK expression,and activate the SCF/c-kit signaling pathway,which may have a facilitating effect on gastrointestinal motility in simulated microgravity rats.
基金Supported by National Natural Science Foundation of China,No.82374323and Hunan Graduate Research Innovation Project,No.2023CX15.
文摘Helicobacter pylori-associated gastritis(HPAG)is a common condition of the gastrointestinal tract.However,extensive and long-term antibiotic use has resulted in numerous adverse effects,including increased resistance,gastrointestinal dysfunction,and increased recurrence rates.When these concerns develop,traditional Chinese medicine(TCM)may have advantages.TCM is based on the concept of completeness and aims to eliminate pathogens and strengthen the body.It has the potential to prevent this condition while also boosting the rate of Helicobacter pylori eradication.This review elaborates on the mechanism of TCM treatment for HPAG based on cellular signalling pathways,which reflects the flexibility of TCM in treating diseases and the advantages of multi-level,multipathway,and multi-target treatments for HPAG.
