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Denoising Fault-Aware Wavelet Network:A signal processing Informed Neural Network for Fault Diagnosis

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Chinese Journal of Mechanical Engineering 2023年第1期36卷 1-18页

作者： Zuogang Shang Zhibin Zhao Ruqiang Yan The State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an710049China

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.

关键词： signal processing Deep learning Explainable Denoising Fault diagnosis

A VIBRATION RECOGNITION METHOD BASED ON DEEP LEARNING AND signal processing

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工程力学 2021年第4期38卷 230-246页

作者： CHENG Zhi-gang LIAO Wen-jie CHEN Xing-yu LU Xin-zheng Civil Engineering Department Tsinghua UniversityBeijing 100084China Beijing Engineering Research Center of Steel and Concrete Composite Structures Tsinghua UniversityBeijing 100084China Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry Tsinghua UniversityBeijing 100084China

Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced ***-processing methods can extract the potent time-frequency-domain characteristics of signals;however,the performance of conventional characteristics-based classification needs to be *** used deep learning algorithms(e.g.,convolutional neural networks(CNNs))can conduct classification by extracting high-dimensional data features,with outstanding ***,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 ***,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 ***,CNNs,long short-term memory(LSTM)networks,and combined deep CNN-LSTM networks are trained for vibration recognition,according to the time-frequencydomain ***,the performance of the trained deep neural networks is evaluated and *** results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning.

关键词： vibration recognition signal processing time-frequency-domain characteristics convolutional neural network(CNN) long short-term memory(LSTM)network

Digital signal processing for High-Speed THz Communications

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Chinese Journal of Electronics 2022年第3期31卷 534-546页

作者： YU Jianjun WEI Yi School of Information Science and Technology Fudan University

The use of advanced digital signal processing(DSP) technology in the high-speed terahertz(THz) communication can effectively compensate the linear and nonlinear effects of the system and further improve the transmission performance of the system. This paper introduces the principle and application of advanced DSP algorithms such as probability shaping(PS)technology, Volterra series nonlinear compensation algorithm, Kramers-Kronig receiver, look-up table(LUT)pre-distortion compensation technology, pre-equalization and decision-directed least-mean-square equalization algorithm. Combined with DSP algorithms such as PS and LUT pre-distortion, using photon-assisted technology to successfully realize the wireless transmission of vector THz signals higher than 1 Tbit/s on the sub-THz band(D-band) 4 × 4 multiple input multiple output system.

关键词： Volterra series nonlinear compensation algorithm decision-directed least-mean-square equalization algorithm high-speed THz communications equalisers wireless channels photon-assisted technology least mean squares methods Kramers-Kronig relations look-up table pre-distortion compensation technology multiple input multiple output system advanced DSP algorithms radio receivers Volterra series high-speed terahertz communication MIMO communication probability Kramers-Kronig receiver transmission performance nonlinear effects signal processing advanced digital signal processing

Digital signal processing for coherent multi-level modulation formats

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Chinese Optics Letters 2010年第9期8卷 863-870页

作者： Xiang Zhou AT&T Labs-Research 200 Laurel Ave South Middletown NJ 07748 USA

This paper presents a review on the recent progress of digital signal processing （DSP） in the high-speed optical transmission system using single-carrier based multi-level modulation formats. The principle of several digital phase and polarization tracking algorithms recently proposed and demonstrated for future spectrally-efficient optical transmission system are discussed in detail. A novel DSP-based interference mitigation algorithm for the single-ended coherent receiver has been included. Recent technology advance on transmitter-side DSP such as digital pulse-shaping, pre-equalization and digital nonlinear compensation has also been discussed.

关键词： Coherent receivers Dense wavelength division multiplexing Optical signals Phase recovery Polarization division multiplexing signal processing

Properties of a general quaternion-valued gradient operator and its applications to signal processing

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Frontiers of Information Technology & Electronic Engineering 2016年第2期17卷 83-95页

作者： Meng-di JIANG Yi LI Wei LIU Department of Electronic and Electrical Engineering University of Sheffield School of Mathematics and Statistics University of Sheffield

The gradients of a quaternion-valued function are often required for quaternionic signal processing *** HR gradient operator provides a viable framework and has found a number of ***,the applications so far have been limited to mainly real-valued quaternion functions and linear quaternionvalued *** generalize the operator to nonlinear quaternion functions,we define a restricted version of the HR operator,which comes in two versions,the left and the right *** then present a detailed analysis of the properties of the operators,including several different product rules and chain *** the new rules,we derive explicit expressions for the derivatives of a class of regular nonlinear quaternion-valued functions,and prove that the restricted HR gradients are consistent with the gradients in the real *** an application,the derivation of the least mean square algorithm and a nonlinear adaptive algorithm is *** results based on vector sensor arrays are presented as an example to demonstrate the effectiveness of the quaternion-valued signal model and the derived signal processing algorithm.

关键词： Quaternion Gradient operator signal processing Least mean square(LMS) algorithm Nonlinear adaptive filtering Adaptive beamforming

Memristor-Based signal processing for Edge Computing

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Tsinghua Science and Technology 2022年第3期27卷 455-471页

作者： Han Zhao Zhengwu Liu Jianshi Tang Bin Gao Yufeng Zhang He Qian Huaqiang Wu the School of Integrated Circuits Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China the Department of Microelectronics Science and Technology Harbin Institute of TechnologyHarbin 150001China Beijing Innovation Center for Future Chips Tsinghua UniversityBeijing 100084China

The rapid growth of the Internet of Things(IoTs)has resulted in an explosive increase in data,and thus has raised new challenges for data processing *** computing,which settles signal processing and computing tasks at the edge of networks rather than uploading data to the cloud,can reduce the amount of data for transmission and is a promising solution to address the *** of the potential candidates for edge computing is a memristor,an emerging nonvolatile memory device that has the capability of in-memory *** this article,from the perspective of edge computing,we review recent progress on memristor-based signal processing methods,especially on the aspects of signal preprocessing and feature ***,we describe memristor-based signal classification and regression,and end-to-end signal *** all these applications,memristors serve as critical accelerators to greatly improve the overall system performance,such as power efficiency and processing ***,we discuss existing challenges and future outlooks for memristor-based signal processing systems.

关键词： memristor signal processing edge computing Internet of Things(IoTs) in-memory computing

A signal processing method for the friction-based endpoint detection system of a CMP process

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Journal of Semiconductors 2010年第12期31卷 138-142页

作者：徐驰郭东明金洙吉康仁科 Key Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education Dalian University of Technology

A signal processing method for the friction-based endpoint detection system of a chemical mechanical polishing （CMP） process is presented. The signal process method uses the wavelet threshold denoising method to reduce the noise contained in the measured original signal, extracts the Kalman filter innovation from the denoised signal as the feature signal, and judges the CMP endpoint based on the feature of the Kalman filter innovation sequence during the CMP process. Applying the signal processing method, the endpoint detection experiments of the Cu CMP process were carried out. The results show that the signal processing method can judge the endpoint of the Cu CMP process.

关键词： CMP endpoint detection signal processing

ACCELERATED SYMMETRIC ADMM AND ITS APPLICATIONS IN LARGE-SCALE signal processing

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Journal of Computational Mathematics 2024年第6期42卷 1605-1626页

作者： Jianchao Bai Ke Guo Junli Liang Yang Jing H.C.So Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518057China School of Mathematics and Statistics Northwestern Polytechnical UniversityXi’an 710129China Key Laboratory of Optimization Theory and Applications at China West Normal University of Sichuan Province School of Mathematics and InformationChina West Normal UniversityNanchong 637009China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China Department of Electrical Engineering City University of Hong KongHong KongSARChina

The alternating direction method of multipliers(ADMM)has been extensively investigated in the past decades for solving separable convex optimization problems,and surprisingly,it also performs efficiently for nonconvex *** this paper,we propose a symmetric ADMM based on acceleration techniques for a family of potentially nonsmooth and nonconvex programming problems with equality constraints,where the dual variables are updated twice with different *** proper assumptions instead of the socalled Kurdyka-Lojasiewicz inequality,convergence of the proposed algorithm as well as its pointwise iteration-complexity are analyzed in terms of the corresponding augmented Lagrangian function and the primal-dual residuals,*** of our algorithm is verified by numerical examples corresponding to signal processing applications in sparse nonconvex/convex regularized minimization.

关键词： Nonconvex optimization Symmetric ADMM Acceleration technique Complexity signal processing

Implementation of VLSI on signal processing-Based Digital Architecture Using AES Algorithm

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Computers, Materials & Continua 2023年第3期74卷 4729-4745页

作者： Mohanapriya Marimuthu Santhosh Rajendran Reshma Radhakrishnan Kalpana Rengarajan Shahzada Khurram Shafiq Ahmad Abdelaty Edrees Sayed Muhammad Shafiq Department of CSE Coimbatore Institute of TechnologyCoimbatore641014India Department of CSE Karpagam Academy of Higher EducationCoimbatore641021India Department of Electronics and Communication Engineering Veltech Multitech Dr.Rangarajan Dr.Sakunthala EngineeringChennai600062India Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Continuous improvements in very-large-scale integration(VLSI)technology and design software have significantly broadened the scope of digital signal processing(DSP)*** use of application-specific integrated circuits(ASICs)and programmable digital signal processors for many DSP applications have changed,even though new system implementations based on reconfigurable computing are becoming more *** platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation(DWT)and sophisticated computerized design techniques,which are much needed in today’s modern *** research and commercial efforts to sustain power optimization,cost savings,and improved runtime effectiveness have been initiated as initial reconfigurable technologies have ***,in this paper,it is proposed that theDWTmethod can be implemented on a fieldprogrammable gate array in a digital architecture(FPGA-DA).We examined the effects of quantization on DWTperformance in classification problems to demonstrate its reliability concerning fixed-point math *** Advanced Encryption Standard(AES)algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks(ANN)*** reducing hardware area by 57%,the proposed system has a higher throughput rate of 88.72%,reliability analysis of 95.5%compared to the other standard methods.

关键词： VLSI A ES discrete wavelet transformation signal processing