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A Distributed Newton Method for Processing Signals Defined on the Large-Scale Networks 认领 引用 被引量：1
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作者 Yanhai Zhang Junzheng Jiang +1 位作者 Haitao Wang Mou Ma 《China Communications》 SCIE CSCD 2023年第5期315-329,共15页
In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously pe... In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm. 展开更多
关键词 graph signal processing distributed Newton method active network decomposition secondorder algorithm
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Microseismic signal processing and rockburst disaster identification:A multi-task deep learning and machine learning approach 认领 引用 被引量：1
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作者 Chunchi Ma Weihao Xu +3 位作者 Xuefeng Ran Tianbin Li Hang Zhang Dongwei Xing 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期441-456,共16页
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
关键词 Underground engineering Microseismic signal processing Deep learning Multi-task Rockburst identification
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Introduction to the Special Issue on Recent Advances in Signal Processing and Computer Vision 认领 引用
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作者 Bo Yang Chao Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期33-36,共4页
Over the past decade, artificial intelligence, particularly deep learning, has fundamentally reshaped the fields of signal processing and computer vision. As mentioned in the introduction to this special issue, we are... Over the past decade, artificial intelligence, particularly deep learning, has fundamentally reshaped the fields of signal processing and computer vision. As mentioned in the introduction to this special issue, we are witnessing a significant paradigm shift. AI has evolved from recognizing the world through classification and detection to simulating it through generative models and synthesis. Most recently, AI has begun to impact the real world through embodied intelligence and robotic interaction. This special issue reflects this trajectory, presenting original research articles, reviews and methodological advances in areas such as multimodal learning, 3D vision, generative modelling, medical image analysis and autonomous systems.The 12 contributions included here reflect the current frontiers of AI, shedding light on the challenges and opportunities involved in bridging the gap between virtual intelligence and physical reality.Reliable and interpretable AI is of paramount importance in medical applications. This issue contains several papers that address various aspects of this challenge, ranging from signal-enhanced diagnosis to surgical perception and 3D reconstruction. 展开更多
关键词 deep learning robotic interaction signal processing computer vision generative models embodied intelligence recognizing world simulating
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Enhancing Convolution Recurrent Network with Graph Signal Processing:High Suppressive Interference Mitigation 认领 引用
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作者 Guo Pengcheng Yu Miao +1 位作者 Gu Miaomiao Ren Bingyin 《China Communications》 SCIE EI CSCD 2026年第1期255-272,共18页
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
关键词 adjacency matrix short-time graph signal processing signal enhancement wireless communications
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Intelligent online monitoring and processing of electrical discharge and arc compound milling signals 认领 引用
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作者 Haoxiang LU Yonghong LIU +7 位作者 Xinlei WU Qiang LI Pengxin ZHANG Ziang WANG Lixin WANG Maoyuan ZHANG Fanbo MENG Renjie JI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期723-740,共18页
Electrical Discharge and Arc Compound Milling(EDACM)presents significant advantages in terms of high efficiency and low cost for machining difficult-to-machine materials such as superalloys,titanium alloys,and ultra-h... Electrical Discharge and Arc Compound Milling(EDACM)presents significant advantages in terms of high efficiency and low cost for machining difficult-to-machine materials such as superalloys,titanium alloys,and ultra-high-strength steels,thereby exhibiting substantial potential for the manufacturing of critical aerospace components.However,its application is constrained by low machining accuracy and poor surface quality,highlighting the necessity for online monitoring and processing of EDACM signals to enable intelligent machining through dynamic adjustment of discharge parameters and enhance both machining accuracy and surface quality.However,the occurrence of atypical discharge waveforms and signal drift during the EDACM process further complicates online monitoring.To address these challenges,this paper proposes the Intelligent Online Monitoring and Processing Method(IOMPM)for EDACM signals,utilizing a hybrid neural network structure that integrates One-Dimensional Convolutional Neural Networks(1DCNNs)with Long Short-Term Memory(LSTM)networks.An online processing theoretical model is developed to enable automatic identification and real-time calibration of the pulse period start point during the EDACM process,effectively overcoming the limitations of existing methods in terms of real-time performance and classification accuracy.The method constructs a dataset composed exclusively of current signals,incorporating 10%atypical discharge waveforms to improve the model's adaptability to actual machining conditions.Using this dataset,the hybrid neural network model achieves an accuracy of 98.68%on the test set.Experimental validation confirms both the accuracy and real-time capability of the IOMPM,laying a solid foundation for enhancing both machining accuracy and surface quality,as well as for fulfilling the stringent quality and reliability requirements of aerospace manufacturing. 展开更多
关键词 Deep learning Electrical Discharge and Arc Compound Milling(EDACM) Electrical Discharge Machining(EDM) Intelligent online monitoring Online signal processing
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Research on Signal Processing Architecture Design and Compatibility of Multimodal Perception System for Emergency Rescue Unmanned Aerial Vehicles 认领 引用
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作者 Jiahang Zhu 《Journal of Electronic Research and Application》 2026年第4期22-35,共14页
In response to the core pain points in emergency rescue scenarios,such as the strong heterogeneity of multi-modal sensing signals,low efficiency of collaborative processing,and insufficient hardware adaptability,this ... In response to the core pain points in emergency rescue scenarios,such as the strong heterogeneity of multi-modal sensing signals,low efficiency of collaborative processing,and insufficient hardware adaptability,this paper reviews the application status of multi-modal sensing technology in emergency rescue fields,analyzes the key issues of the existing signal processing system,and constructs a three-level modular signal processing architecture of“sensing-transmission-pre-processing”.The sensing layer inputs the raw signals and outputs signals in a unified data format;the transmission layer inputs multiple signals,outputs these signals with priority,different paths,and strategies;the pre-processing layer processes the input signals and transmits the information to the unmanned aerial vehicle(UAV)terminal.This research clearly defines the core principles and modular logic of the architecture design,and explores the transmission optimization,resource allocation,and hardware compatibility schemes of different signal types from a technical perspective,demonstrating the balance between the general and specific paths of the architecture.The research results can provide architecture-level design references for the multi-modal sensing module of the airborne-ground integrated emergency rescue UAV,solve the problem of heterogeneous signal collaborative processing,and improve the overall response efficiency and reliability of the emergency rescue system. 展开更多
关键词 Emergency rescue unmanned aircraft Multi-modal perception Signal processing architecture Modular design Technical adaptability
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Intelligent Estimation of ESR and C in AECs for Buck Converters Using Signal Processing and ML Regression 认领 引用
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作者 Acácio M.R.Amaral 《Computers, Materials & Continua》 SCIE EI 2025年第11期3825-3859,共35页
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 R2 score greater than 0.99 in the estimation of both ESR and C. 展开更多
关键词 Buck converter boost converter AECs fault detection predictive maintenance signal processing techniques feature engineering linear regression and K-nearest neighbors
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Adaptive optimal consensus over networked multi-agent systems:a digital signal-driven hybrid iteration approach 认领 引用
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作者 Jun Li Jiansong Lu +1 位作者 Lianghao Ji Huaqing Li 《Digital Communications and Networks》 SCIE EI CSCD 2026年第5期825-835,共11页
The integration of Digital Signal Processing(DSP)and Reinforcement Learning(RL)for optimal consensus control in Networked Multi-Agent Systems(NMASs)has garnered significant research attention.However,prior research en... The integration of Digital Signal Processing(DSP)and Reinforcement Learning(RL)for optimal consensus control in Networked Multi-Agent Systems(NMASs)has garnered significant research attention.However,prior research encounters some limitations:1)dependency on initial admissible control policies,and 2)systemic data redundancy arising from ineffective data governance in distributed architectures and slow convergence rates of conventional RL algorithms.To overcome these challenges,this paper proposes a Distributed Collaborative Iteration Adaptive Dynamic Programming(DCIADP)framework.The methodology reformulates the solution of Hamilton-Jacobi-Bellman(HJB)equations by integrating Value Iteration(VI)and Policy Iteration(PI)within a unified architecture,eliminating reliance on prior knowledge of system dynamics.Specifically,a dynamic factor is introduced to synergistically integrate the complementary strengths of VI and PI,achieving accelerated convergence while bypassing the initialization requirement for admissible policies.This innovation significantly mitigates computational overhead in distributed nodes during localized DSP operations.Furthermore,a self-tuning mechanism dynamically optimizes the factor,enhancing adaptability to heterogeneous network conditions.Through rigorous theoretical analysis,the proposed framework is proven to ensure asymptotic convergence and Lyapunov stability.Practical implementation is realized through actor-critic Neural Networks(NNs),incorporating an experience replay mechanism to exploit temporal correlation characteristics in networked data streams.This enables derivation of optimal control policies solely from transmitted network signals,independent of explicit system parameter knowledge.The framework thus establishes a resource-eicient adaptive control paradigm for bandwidth-constrained networked MASs.Finally,several numerical simulations validate the effectiveness and superiority of the proposed approach. 展开更多
关键词 Off-policy Optimal control Multi-agent systems Reinforcement learning Digital signal processing
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Signal processing and machine learning techniques in DC microgrids:a review 认领 引用
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作者 Kanche Anjaiah Jonnalagadda Divya +1 位作者 Eluri N.V.D.V.Prasad Renu Sharma 《Global Energy Interconnection》 EI CSCD 2025年第4期598-624,共27页
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
关键词 DC microgrids Mathematical approach Signal processing technique Machine learning technique Hybrid model Detection
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Deep Learning in Biomedical Image and Signal Processing:A Survey 认领 引用
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作者 Batyrkhan Omarov 《Computers, Materials & Continua》 SCIE EI 2025年第11期2195-2253,共59页
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
关键词 Deep learning biomedical imaging signal processing neural networks image segmentation disease classification drug discovery patient monitoring robotic surgery artificial intelligence in healthcare
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A CNN-Transformer Hybrid Model for Real-Time Recognition of Affective Tactile Biosignals 认领 引用
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作者 Chang Xu Xianbo Yin +1 位作者 Zhiyong Zhou Bomin Liu 《Computers, Materials & Continua》 SCIE EI 2026年第4期2343-2356,共14页
This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals.The proposed framework combines convolutional neural networks(CNNs)to extract spatial and local temporal fea... This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals.The proposed framework combines convolutional neural networks(CNNs)to extract spatial and local temporal features with the Transformer encoder that captures long-range dependencies in time-series data through multi-head attention.Model performance was evaluated on two widely used tactile biosignal datasets,HAART and CoST,which contain diverse affective touch gestures recorded from pressure sensor arrays.TheCNN-Transformer model achieved recognition rates of 93.33%on HAART and 80.89%on CoST,outperforming existing methods on both benchmarks.By incorporating temporal windowing,the model enables instantaneous prediction,improving generalization across gestures of varying duration.These results highlight the effectiveness of deep learning for tactile biosignal processing and demonstrate the potential of theCNN-Transformer approach for future applications in wearable sensors,affective computing,and biomedical monitoring. 展开更多
关键词 Tactile biosignals affective touch recognition wearable sensors signal processing human-machine interaction
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Research and Implementation of Fusion Filtering Algorithms for Meteor Radar Echo Signals 认领 引用
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作者 Jin Yujian Yang Xiaoming +1 位作者 Huang Songtao Chen Xiaolong 《China Communications》 SCIE EI CSCD 2026年第5期15-30,共16页
Meteor radars are widely used to study the dynamics of the mesosphere and lower thermosphere,where the accuracy of atmospheric observations depends critically on the quality of meteor echo signals.This study focuses o... Meteor radars are widely used to study the dynamics of the mesosphere and lower thermosphere,where the accuracy of atmospheric observations depends critically on the quality of meteor echo signals.This study focuses on improving echo signal quality,as meteor trail echoes are transient,low-SNR,and highly susceptible to interference.Four filtering algorithms-wavelet denoising–bilateral filtering(WDBF),wavelet denoising–guided filtering(WD-GF),extended Kalman–guided filtering(EK-GF),and an improved convolutional neural network(ICNN)-based method are examined through theoretical analysis and numerical simulations.The optimal algorithm is further integrated into the digital acquisition and processing unit of a meteor radar system.The WD-GF method shows superior denoising performance and robustness,yielding an average SNR improvement of 9.3 dB relative to the raw signal.Long-term field observations verify its effectiveness,demonstrating a 15.26%increase in detected meteors.The proposed WD-GF filtering algorithm significantly improves meteor radar detection capability and measurement accuracy,providing a practical and efficient solution for high-precision,real-time atmospheric observations. 展开更多
关键词 atmospheric observation fusion filtering meteor burst communication meteor radar signal processing
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Design of Differential Signal Processing Circuitry for Single-Frequency Laser Interferometry Displacement Measurement 认领 引用
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作者 Songxiang Liu Jingping Yan Can Tang 《Journal of Electronic Research and Application》 2025年第2期258-267,共10页
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
关键词 Displacement Measurement Weak Signal Processing Differential Signal Data Acquisition
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High-Frequency Analog Signal Processing in Integrated Circuits 认领 引用
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作者 Zhuoqing Wu 《Journal of Electronic Research and Application》 2025年第7期136-141,共6页
This paper focuses on high-frequency analog signal processing in integrated circuits,encompassing key aspects such as electromagnetic wave propagation in semiconductor media,device modeling,circuit architecture,noise ... This paper focuses on high-frequency analog signal processing in integrated circuits,encompassing key aspects such as electromagnetic wave propagation in semiconductor media,device modeling,circuit architecture,noise modeling,and power integrity.It analyzes the influence of these factors on signal processing performance and discusses corresponding technical approaches.In addition,the paper addresses representative applications in 5G communications,automotive radar,and medical imaging systems.Future research directions in high-frequency analog integrated circuit design are also discussed. 展开更多
关键词 High-frequency analog signal processing Integrated circuits Circuit architecture
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Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis 认领 引用 被引量：20
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作者 Zuogang Shang Zhibin Zhao Ruqiang Yan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第1期1-18,共18页
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
关键词 Signal processing Deep learning Explainable Denoising Fault diagnosis
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Ultrasonic Nondestructive Signals Processing Based on Matching Pursuit with Gabor Dictionary 认领 引用 被引量：9
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作者 GUO Jinku WU Jinying +1 位作者 YANG Xiaojun LIU Guangbin 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第4期591-595,共5页
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
关键词 ultrasonic signal processing sparse representation matching pursuit Gabor dictionary
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AIRIS:Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications 认领 引用 被引量：6
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作者 Shun Zhang Muye Li +2 位作者 Mengnan Jian Yajun Zhao Feifei Gao 《China Communications》 SCIE EI CSCD 2021年第7期158-171,共14页
Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communicati... Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communications systems.Meanwhile,it has been recently admitted that implementing artificial intelligence(AI)into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments.Besides the conventional model-driven approaches,AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data.Hence,AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching,insufficient resource,hardware impairment,as well as dynamical transmissions.As one of the earliest survey papers,we will introduce the merging of AI and RIS,called AIRIS,over various signal processing topics,including environmental sensing,channel acquisition,beamforming design,and resource scheduling,etc.We will also discuss the challenges of AIRIS and present some interesting future directions. 展开更多
关键词 reconfigurable intelligent surface artifi-cial intelligence deep learning deep reinforcement learning signal processing
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Digital Signal Processing Based Real Time Vehicular Detection System 认领 引用 被引量：5
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作者 杨兆选 林涛 +2 位作者 李香萍 刘春义 高健 《Transactions of Tianjin University》 EI CAS 2005年第2期119-124,共6页
Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is ... Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation. 展开更多
关键词 intelligent transportation system vehicular detection digital signal processing loop emulation background differencing
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Application of fast wavelet transformation in signal processing of MEMS gyroscope 认领 引用 被引量：7
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作者 吉训生 王寿荣 许宜申 《Journal of Southeast University(English Edition)》 EI CAS 2006年第4期510-513,共4页
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
关键词 wavelet transformation signal processing gyroscope threshold
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A VIBRATION RECOGNITION METHOD BASED ON DEEP LEARNING AND SIGNAL PROCESSING 认领 引用 被引量：7
20
作者 CHENG Zhi-gang LIAO Wen-jie +1 位作者 CHEN Xing-yu LU Xin-zheng 《工程力学》 EI CSCD 北大核心 2021年第4期230-246,共17页
Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification.Signal-processing methods can ex... Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification.Signal-processing methods can extract the potent time-frequency-domain characteristics of signals;however,the performance of conventional characteristics-based classification needs to be improved.Widely used deep learning algorithms(e.g.,convolutional neural networks(CNNs))can conduct classification by extracting high-dimensional data features,with outstanding performance.Hence,combining the advantages of signal processing and deep-learning algorithms can significantly enhance vibration recognition performance.A novel vibration recognition method based on signal processing and deep neural networks is proposed herein.First,environmental vibration signals are collected;then,signal processing is conducted to obtain the coefficient matrices of the time-frequency-domain characteristics using three typical algorithms:the wavelet transform,Hilbert-Huang transform,and Mel frequency cepstral coefficient extraction method.Subsequently,CNNs,long short-term memory(LSTM)networks,and combined deep CNN-LSTM networks are trained for vibration recognition,according to the time-frequencydomain characteristics.Finally,the performance of the trained deep neural networks is evaluated and validated.The results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning. 展开更多
关键词 vibration recognition signal processing time-frequency-domain characteristics convolutional neural network(CNN) long short-term memory(LSTM)network
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