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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

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

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

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

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

AIRIS:Artificial Intelligence Enhanced signal processing in Reconfigurable Intelligent Surface Communications

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China Communications 2021年第7期18卷 158-171页

作者： Shun Zhang Muye Li Mengnan Jian Yajun Zhao Feifei Gao State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China Algorithm Department of Wireless Product R&D Institute ZTE CorporationBeijing 100029China Institute for Artificial Intelligence Tsinghua University(THUAI)State Key Lab of Intelligent Technologies and SystemsTsinghua UniversityBeijing National Research Center for Information Science and Technology(BNRist)Department of AutomationTsinghua University Beijing100084China

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 ***,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 *** 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 ***,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 *** 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,*** will also discuss the challenges of AIRIS and present some interesting future directions.

关键词： reconfigurable intelligent surface artifi-cial intelligence deep learning deep reinforcement learning 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

Bicoid signal Extraction with a Selection of Parametric and Nonparametric signal processing Techniques

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Genomics, Proteomics & Bioinformatics 2015年第3期13卷 183-191页

作者： Zara Ghodsi Emmanuel Sirimal Silva Hossein Hassani The Statistical Research Centre Bournemouth University Institute for International Energy Studies (ⅡES)

The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraetion in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.

关键词： Bicoid Drosophila melanogaster signal extraction signal processing

Wavelet analysis and its application to signal processing

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International Journal of Minerals,Metallurgy and Materials 1997年第3期11卷 49-53页

作者： HE　Jun WU Yalun (Resource Engineering School, University of Science and Technology Beijing, Beijing 100083, China) Univ of Science and Technology Beijing Beijing China

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.

关键词： wavelet analysis signal processing wavelet transform blasting seismic signal