A flexible organic artificial synapse(OAS)for tunable time-frequency signal processing was fabricated using a tri-blend film that had been fabricated using a one-step solution method.When combined with a chitosan film...A flexible organic artificial synapse(OAS)for tunable time-frequency signal processing was fabricated using a tri-blend film that had been fabricated using a one-step solution method.When combined with a chitosan film,this OAS can achieve an ultrashort-term retention time of only 49 ms for instant electricalcomputing applications;this is the shortest retention time yet achieved by a two-terminal artificial synapse.An array of these flexible OASs can withstand a high bending strain of 5%for 10^(4) cycles;this deformation endurance is a new record.The OAS was also sensitive to the number and frequency of electrical inputs;a tunable cut-off frequency enables dynamic filtering for use in image detail enhancement.This work provides a new resource for development of future neuromorphic computing devices。展开更多
In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-t...In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-time graph signal processing(SGSP)approach and a modified convolution recurrent network.Similar to the traditional shorttime time-frequency transformation,SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations,where the connection and weight flexibility of each vertex are fully taken into account.In the presence of HSI,SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement.Two SGSP methods,adjacency singular value decomposition and implicit graph transformation,are designed to capture relationships among the sampling points in the segmented signals.Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment.When the interference-to-signal ratio exceeds 27dB,only our proposed GSPCRN can achieve the interference mitigation.展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as...While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as complex 3D survey planning,low signal-tonoise ratio raw data,inadequate near-surface velocity modeling,and imaging inaccuracy have long hindered the advancement of seismic exploration across this region.Through a problem-solving approach rooted in geological target analysis,this research systematically investigates the behavioral patterns of nodal seismometer-based high-density seismic acquisition in loess plateau.Tailored advancements in waveform enhancement and depth velocity modelling methodologies have been engineered.Field validations confirm that the optimized workflow demonstrates marked improvements in amplitude preservation and imaging resolution,offering novel insights for future reservoir characterization endeavors.展开更多
Focused on the non-statlonarity and real-time analysis of signal in flutter test with progression variable speed （FTPVS）, a new method of recursive time-frequency analysis is presented. The time-varying system is tr...Focused on the non-statlonarity and real-time analysis of signal in flutter test with progression variable speed （FTPVS）, a new method of recursive time-frequency analysis is presented. The time-varying system is tracked on-line by building a time-varying parameter model, and then the relevant parameter spectrum can be obtained. The feasibility and advantages of the method are examined by digital simulation. The results of FTPVS at low-speed wind-tunnel promise the engineering application perspective of the method.展开更多
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat...With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.展开更多
This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-preci...This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-precision laser interferometric displacement measurement.A stable power supply module is designed to provide low-noise voltage to the entire circuit.An analog circuit system is constructed,including key circuits such as photoelectric sensors,I-V amplification,zero adjustment,fully differential amplification,and amplitude modulation filtering.To acquire and process signals,the PMAC Acc24E3 data acquisition card is selected,which realizes phase demodulation through reversible square wave counting,inverts displacement information,and a visual interface for the host computer is designed.Experimental verification shows that the designed system achieves micrometer-level measurement accuracy within a range of 0-10mm,with a maximum measurement error of less than 1.2μm,a maximum measurement speed of 6m/s,and a resolution better than 0.158μm.展开更多
Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are explorin...Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.展开更多
Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial loss...Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety.The capacitors forming the output filter,typically aluminumelectrolytic capacitors(AECs),are among the most critical and susceptible components in power converters.The electrolyte in AECs often evaporates over time,causing the internal resistance to rise and the capacitance to drop,ultimately leading to component failure.Detecting this fault requires measuring the current in the capacitor,rendering the method invasive and frequently impractical due to spatial constraints or operational limitations imposed by the integration of a current sensor in the capacitor branch.This article proposes the implementation of an online noninvasive fault diagnosis technique for estimating the Equivalent Series Resistance(ESR)and Capacitance(C)values of the capacitor,employing a combination of signal processing techniques(SPT)and machine learning(ML)algorithms.This solution relies solely on the converter’s input and output signals,therefore making it a non-invasive approach.The ML algorithm used was linear regression,applied to 27 attributes,21 of which were generated through feature engineering to enhance the model’s performance.The proposed solution demonstrates an R^(2) score greater than 0.99 in the estimation of both ESR and C.展开更多
Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert p...Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert performance.This survey reviews the principal model families as convolutional,recurrent,generative,reinforcement,autoencoder,and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation,classification,reconstruction,and anomaly detection.A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust,context-aware predictions.To support clinical adoption,we outline post-hoc explainability techniques(Grad-CAM,SHAP,LIME)and describe emerging intrinsically interpretable designs that expose decision logic to end users.Regulatory guidance from the U.S.FDA,the European Medicines Agency,and the EU AI Act is summarised,linking transparency and lifecycle-monitoring requirements to concrete development practices.Remaining challenges as data imbalance,computational cost,privacy constraints,and cross-domain generalization are discussed alongside promising solutions such as federated learning,uncertainty quantification,and lightweight 3-D architectures.The article therefore offers researchers,clinicians,and policymakers a concise,practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.展开更多
Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional...Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.展开更多
Decomposition and reconstruction of Mallat fast wavelet transformation （WT） is described. A fast algorithm, which can greatly decrease the processing burden and can be very easy for hardware implementation in real-t...Decomposition and reconstruction of Mallat fast wavelet transformation （WT） is described. A fast algorithm, which can greatly decrease the processing burden and can be very easy for hardware implementation in real-time, is analyzed. The algorithm will no longer have the processing of decimation and interpolation of usual WT. The formulae of the decomposition and the reconstruction are given. Simulation results of the MEMS （micro-electro mechanical systems） gyroscope drift signal show that the algorithm spends much less processing time to finish the de-noising process than the usual WT. And the de-noising effect is the same. The fast algorithm has been implemented in a TMS320C6713 digital signal processor. The standard variance of the gyroscope static drift signal decreases from 78. 435 5 （°）/h to 36. 763 5 （°）/h. It takes 0. 014 ms to process all input data and can meet the real-time analysis of signal.展开更多
A television based multistatic radar system is described. The commercial television transmitter is used as the illuminator in the multistatic radar system. The reflected commercial television signals are measured by ...A television based multistatic radar system is described. The commercial television transmitter is used as the illuminator in the multistatic radar system. The reflected commercial television signals are measured by an array of sensors. A data processing scheme is developed that adapts to the poor signal processing ability. The innovation is focused on the construction of the observation space, which could reduce the non linearity error. The new method leads to better system stability than the traditional one. Monte Carlo simulation is utilized and compared with the traditional method.展开更多
In order to detect and process underground vibration signal, this paper presents a system with the combination of software and hardware. The hardware part consists of sensor, memory chips, USB, etc. , which is respons...In order to detect and process underground vibration signal, this paper presents a system with the combination of software and hardware. The hardware part consists of sensor, memory chips, USB, etc. , which is responsible for capturing original signals from sensors. The software part is a virtual oscilloscope based on LabWindows/CVI （C vitual instrument）, which not only has the functions of traditional oscilloscope but also can analyze and process vibration signals in special ways. The experimental results show that the designed system is stable, reliable and easy to be operated, which can meet practical requirements.展开更多
The success of ultrasonic nondestructive testing technology depends not only on the generation and measurement of the desired waveform, but also on the signal processing of the measured waves. The traditional time-dom...The success of ultrasonic nondestructive testing technology depends not only on the generation and measurement of the desired waveform, but also on the signal processing of the measured waves. The traditional time-domain methods have been partly successful in identifying small cracks, but not so successful in estimating crack size, especially in strong backscattering noise. Sparse signal representation can provide sparse information that represents the signal time-frequency signature, which can also be used in processing ultrasonic nondestructive signals. A novel ultrasonic nondestructive signal processing algorithm based on signal sparse representation is proposed. In order to suppress noise, matching pursuit algorithm with Gabor dictionary is selected as the signal decomposition method. Precise echoes information, such as crack location and size, can be estimated by quantitative analysis with Gabor atom. To verify the performance, the proposed algorithm is applied to computer simulation signal and experimental ultrasonic signals which represent multiple backscattered echoes from a thin metal plate with artificial holes. The results show that this algorithm not only has an excellent performance even when dealing with signals in the presence of strong noise, but also is successful in estimating crack location and size. Moreover, the algorithm can be applied to data compression of ultrasonic nondestructive signal.展开更多
Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods dif...Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.展开更多
The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was...The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was used to decompose two blasting seismic signals with the continuous wavelet transforms (CWT). The resultshows that wavelet analysis is the better method to help us determine the essential factors which create damage effectsthan Fourier analysis.展开更多
Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research comm...Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.展开更多
DQPSK modem has been chosen as the modem scheme in many mobile communication systems. A new signal processing technique of π/4-DQPSK modem based on software radio is discussed in this paper. Unlike many other softwar...DQPSK modem has been chosen as the modem scheme in many mobile communication systems. A new signal processing technique of π/4-DQPSK modem based on software radio is discussed in this paper. Unlike many other software radio solutions to the subject, we choose a universal digital radio baseband processor operating as the co-processor of DSP. Only the core algorithms for signal processing are implemented with DSP. Thus the computation burden on DSP is reduced significantly. Compared with the traditional ones, the technique mentioned in this paper is more promising and attractive. It is extremely compact and power-efficient, which is often required by a mobile communication system. The implementation of baseband signal processing for π/4-DQPSK modem on this platform is illustrated in detail. Special emphases are laid on the architecture of the system and the algorithms used in the baseband signal processing. Finally, some experimental results are presented and the performances of the signal processing and compensation algorithms are evaluated through computer simulations.展开更多
Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery.Therefore,it is difficult to extract,analyze,and...Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery.Therefore,it is difficult to extract,analyze,and diagnose mechanical fault features.To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery,a study on the time-frequency feature extraction method of multi-source shock signals is conducted.Combining the characteristics of reciprocating mechanical vibration signals,a targeted optimization method considering the variational modal decomposition(VMD)mode number and second penalty factor is proposed,which completed the adaptive decomposition of coupled signals.Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals,a new bilateral adaptive Laplace wavelet(BALW)is established.A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search(HS)method.A multi-source shock simulation signal is established,and actual data on the valve fault are obtained through diesel engine fault experiments.The fault recognition rate of the intake and exhaust valve clearance is above 90%and the extraction accuracy of the shock start position is improved by 10°.展开更多
基金supported by the National Key R&D Program of China(Nos.2022YFE0198200,2022YFA1200044)the National Science Fund for Distinguished Young Scholars of China(No.T2125005)+1 种基金the Tianjin Science Foundation for Distinguished Young Scholars(No.19JCJQJC61000)the Shenzhen Science and Technology Project(No.JCYj20210324121002008).
文摘A flexible organic artificial synapse(OAS)for tunable time-frequency signal processing was fabricated using a tri-blend film that had been fabricated using a one-step solution method.When combined with a chitosan film,this OAS can achieve an ultrashort-term retention time of only 49 ms for instant electricalcomputing applications;this is the shortest retention time yet achieved by a two-terminal artificial synapse.An array of these flexible OASs can withstand a high bending strain of 5%for 10^(4) cycles;this deformation endurance is a new record.The OAS was also sensitive to the number and frequency of electrical inputs;a tunable cut-off frequency enables dynamic filtering for use in image detail enhancement.This work provides a new resource for development of future neuromorphic computing devices。
基金supported by he National Social Science Found of China(2022-SKJJ-B-112).
文摘In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-time graph signal processing(SGSP)approach and a modified convolution recurrent network.Similar to the traditional shorttime time-frequency transformation,SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations,where the connection and weight flexibility of each vertex are fully taken into account.In the presence of HSI,SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement.Two SGSP methods,adjacency singular value decomposition and implicit graph transformation,are designed to capture relationships among the sampling points in the segmented signals.Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment.When the interference-to-signal ratio exceeds 27dB,only our proposed GSPCRN can achieve the interference mitigation.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
文摘While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as complex 3D survey planning,low signal-tonoise ratio raw data,inadequate near-surface velocity modeling,and imaging inaccuracy have long hindered the advancement of seismic exploration across this region.Through a problem-solving approach rooted in geological target analysis,this research systematically investigates the behavioral patterns of nodal seismometer-based high-density seismic acquisition in loess plateau.Tailored advancements in waveform enhancement and depth velocity modelling methodologies have been engineered.Field validations confirm that the optimized workflow demonstrates marked improvements in amplitude preservation and imaging resolution,offering novel insights for future reservoir characterization endeavors.
文摘Focused on the non-statlonarity and real-time analysis of signal in flutter test with progression variable speed （FTPVS）, a new method of recursive time-frequency analysis is presented. The time-varying system is tracked on-line by building a time-varying parameter model, and then the relevant parameter spectrum can be obtained. The feasibility and advantages of the method are examined by digital simulation. The results of FTPVS at low-speed wind-tunnel promise the engineering application perspective of the method.
基金supported by Natural Science Foundation of China(No.62371231)Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001Jiangsu Provincial Key Research and Development Program(No.BE2023027).
文摘With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.
文摘This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-precision laser interferometric displacement measurement.A stable power supply module is designed to provide low-noise voltage to the entire circuit.An analog circuit system is constructed,including key circuits such as photoelectric sensors,I-V amplification,zero adjustment,fully differential amplification,and amplitude modulation filtering.To acquire and process signals,the PMAC Acc24E3 data acquisition card is selected,which realizes phase demodulation through reversible square wave counting,inverts displacement information,and a visual interface for the host computer is designed.Experimental verification shows that the designed system achieves micrometer-level measurement accuracy within a range of 0-10mm,with a maximum measurement error of less than 1.2μm,a maximum measurement speed of 6m/s,and a resolution better than 0.158μm.
文摘Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.
文摘Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety.The capacitors forming the output filter,typically aluminumelectrolytic capacitors(AECs),are among the most critical and susceptible components in power converters.The electrolyte in AECs often evaporates over time,causing the internal resistance to rise and the capacitance to drop,ultimately leading to component failure.Detecting this fault requires measuring the current in the capacitor,rendering the method invasive and frequently impractical due to spatial constraints or operational limitations imposed by the integration of a current sensor in the capacitor branch.This article proposes the implementation of an online noninvasive fault diagnosis technique for estimating the Equivalent Series Resistance(ESR)and Capacitance(C)values of the capacitor,employing a combination of signal processing techniques(SPT)and machine learning(ML)algorithms.This solution relies solely on the converter’s input and output signals,therefore making it a non-invasive approach.The ML algorithm used was linear regression,applied to 27 attributes,21 of which were generated through feature engineering to enhance the model’s performance.The proposed solution demonstrates an R^(2) score greater than 0.99 in the estimation of both ESR and C.
基金supported by the Science Committee of the Ministry of Higher Education and Science of the Republic of Kazakhstan within the framework of grant AP23489899“Applying Deep Learning and Neuroimaging Methods for Brain Stroke Diagnosis”.
文摘Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert performance.This survey reviews the principal model families as convolutional,recurrent,generative,reinforcement,autoencoder,and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation,classification,reconstruction,and anomaly detection.A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust,context-aware predictions.To support clinical adoption,we outline post-hoc explainability techniques(Grad-CAM,SHAP,LIME)and describe emerging intrinsically interpretable designs that expose decision logic to end users.Regulatory guidance from the U.S.FDA,the European Medicines Agency,and the EU AI Act is summarised,linking transparency and lifecycle-monitoring requirements to concrete development practices.Remaining challenges as data imbalance,computational cost,privacy constraints,and cross-domain generalization are discussed alongside promising solutions such as federated learning,uncertainty quantification,and lightweight 3-D architectures.The article therefore offers researchers,clinicians,and policymakers a concise,practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.
基金Aeronautical Science Foundation of China (20071551016)
文摘Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.
基金The National High Technology Research and Devel-opment Program of China (863Program) (No2002AA812038)
文摘Decomposition and reconstruction of Mallat fast wavelet transformation （WT） is described. A fast algorithm, which can greatly decrease the processing burden and can be very easy for hardware implementation in real-time, is analyzed. The algorithm will no longer have the processing of decimation and interpolation of usual WT. The formulae of the decomposition and the reconstruction are given. Simulation results of the MEMS （micro-electro mechanical systems） gyroscope drift signal show that the algorithm spends much less processing time to finish the de-noising process than the usual WT. And the de-noising effect is the same. The fast algorithm has been implemented in a TMS320C6713 digital signal processor. The standard variance of the gyroscope static drift signal decreases from 78. 435 5 （°）/h to 36. 763 5 （°）/h. It takes 0. 014 ms to process all input data and can meet the real-time analysis of signal.
文摘A television based multistatic radar system is described. The commercial television transmitter is used as the illuminator in the multistatic radar system. The reflected commercial television signals are measured by an array of sensors. A data processing scheme is developed that adapts to the poor signal processing ability. The innovation is focused on the construction of the observation space, which could reduce the non linearity error. The new method leads to better system stability than the traditional one. Monte Carlo simulation is utilized and compared with the traditional method.
基金National Natural Science Foundation of China(No.61302159,61227003,61301259)Natural Science Foundation of Shanxi Province(No.2012021011-2)The Project Sponsored by Scientific Research for the Returned Overseas Chinese Scholars,Shanxi Province(No.2013-083)
文摘In order to detect and process underground vibration signal, this paper presents a system with the combination of software and hardware. The hardware part consists of sensor, memory chips, USB, etc. , which is responsible for capturing original signals from sensors. The software part is a virtual oscilloscope based on LabWindows/CVI （C vitual instrument）, which not only has the functions of traditional oscilloscope but also can analyze and process vibration signals in special ways. The experimental results show that the designed system is stable, reliable and easy to be operated, which can meet practical requirements.
基金supported by National Natural Science Foundation of China (Grant No. 60672108, Grant No. 60372020)
文摘The success of ultrasonic nondestructive testing technology depends not only on the generation and measurement of the desired waveform, but also on the signal processing of the measured waves. The traditional time-domain methods have been partly successful in identifying small cracks, but not so successful in estimating crack size, especially in strong backscattering noise. Sparse signal representation can provide sparse information that represents the signal time-frequency signature, which can also be used in processing ultrasonic nondestructive signals. A novel ultrasonic nondestructive signal processing algorithm based on signal sparse representation is proposed. In order to suppress noise, matching pursuit algorithm with Gabor dictionary is selected as the signal decomposition method. Precise echoes information, such as crack location and size, can be estimated by quantitative analysis with Gabor atom. To verify the performance, the proposed algorithm is applied to computer simulation signal and experimental ultrasonic signals which represent multiple backscattered echoes from a thin metal plate with artificial holes. The results show that this algorithm not only has an excellent performance even when dealing with signals in the presence of strong noise, but also is successful in estimating crack location and size. Moreover, the algorithm can be applied to data compression of ultrasonic nondestructive signal.
基金National Natural Science Foundation of China (Grant Nos. 51835009, 52105116)China Postdoctoral Science Foundation (Grant Nos. 2021M692557, 2021TQ0263)。
文摘Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.
文摘The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was used to decompose two blasting seismic signals with the continuous wavelet transforms (CWT). The resultshows that wavelet analysis is the better method to help us determine the essential factors which create damage effectsthan Fourier analysis.
基金supported by the Auckland Medical Research Foundation,No.1117017(to CPU)
文摘Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.
文摘DQPSK modem has been chosen as the modem scheme in many mobile communication systems. A new signal processing technique of π/4-DQPSK modem based on software radio is discussed in this paper. Unlike many other software radio solutions to the subject, we choose a universal digital radio baseband processor operating as the co-processor of DSP. Only the core algorithms for signal processing are implemented with DSP. Thus the computation burden on DSP is reduced significantly. Compared with the traditional ones, the technique mentioned in this paper is more promising and attractive. It is extremely compact and power-efficient, which is often required by a mobile communication system. The implementation of baseband signal processing for π/4-DQPSK modem on this platform is illustrated in detail. Special emphases are laid on the architecture of the system and the algorithms used in the baseband signal processing. Finally, some experimental results are presented and the performances of the signal processing and compensation algorithms are evaluated through computer simulations.
基金Supported by National Natural Science Foundation of China (Grant Nos.52101343,52201351)。
文摘Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery.Therefore,it is difficult to extract,analyze,and diagnose mechanical fault features.To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery,a study on the time-frequency feature extraction method of multi-source shock signals is conducted.Combining the characteristics of reciprocating mechanical vibration signals,a targeted optimization method considering the variational modal decomposition(VMD)mode number and second penalty factor is proposed,which completed the adaptive decomposition of coupled signals.Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals,a new bilateral adaptive Laplace wavelet(BALW)is established.A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search(HS)method.A multi-source shock simulation signal is established,and actual data on the valve fault are obtained through diesel engine fault experiments.The fault recognition rate of the intake and exhaust valve clearance is above 90%and the extraction accuracy of the shock start position is improved by 10°.
