Fast and robust strain signal processing for aircraft structural health monitoring

This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis method,which can be regarded as a mixture of Gaussian-like(tiny hairy signals)and impulse-like noise(single signals with anomalous movements in peak and valley areas).Based on this,a least squares filtering method is employed to preprocess strain signals.To precisely eliminate noise or outliers in strain signals,we propose a novel variational model to generate step signals instead of strain ones.Expert judgments are employed to classify the generated signals.Based on the classification labels,whether the aircraft is structurally healthy is accurately judged.By taking the generated step count vectors and labels as an input,a discriminative neural network is proposed to realize automatic signal discrimination.The network output means whether the aircraft structure is healthy or not.Experimental results demonstrate that the proposed scheme is effective and efficient,as well as achieves more satisfactory results than other peers.

Data Analysis Methods and Signal Processing Techniques in Gravitational Wave Detection

Gravitational wave detection is one of the most cutting-edge research areas in modern physics, with its success relying on advanced data analysis and signal processing techniques. This study provides a comprehensive review of data analysis methods and signal processing techniques in gravitational wave detection. The research begins by introducing the characteristics of gravitational wave signals and the challenges faced in their detection, such as extremely low signal-to-noise ratios and complex noise backgrounds. It then systematically analyzes the application of time-frequency analysis methods in extracting transient gravitational wave signals, including wavelet transforms and Hilbert-Huang transforms. The study focuses on discussing the crucial role of matched filtering techniques in improving signal detection sensitivity and explores strategies for template bank optimization. Additionally, the research evaluates the potential of machine learning algorithms, especially deep learning networks, in rapidly identifying and classifying gravitational wave events. The study also analyzes the application of Bayesian inference methods in parameter estimation and model selection, as well as their advantages in handling uncertainties. However, the research also points out the challenges faced by current technologies, such as dealing with non-Gaussian noise and improving computational efficiency. To address these issues, the study proposes a hybrid analysis framework combining physical models and data-driven methods. Finally, the research looks ahead to the potential applications of quantum computing in future gravitational wave data analysis. This study provides a comprehensive theoretical foundation for the optimization and innovation of gravitational wave data analysis methods, contributing to the advancement of gravitational wave astronomy.

Application of fast wavelet transformation in signal processing of MEMS gyroscope

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.

Research of Crossbar Switch of High Performance Network of Signal Processing System

The new type of embedded signal processing system based on the packet switched network is achieved. According to the application field and the-characteristics of signal processing system, the RapidIO protocol is used to solve the high-speed interconnection of multi-digital signal processor （DSP）. Based on this protocol, a kind of crossbar switch module which is used to interconnect multi-DSP in the system is introduced. A route strategy, some flow control rules and error control rules, which adapt to different RapidIO network topology are also introduced. Crossbar switch performance is analyzed in detail by the probability module. By researching the technique of crossbar switch and analyzing the system performance, it has a significant meaning for building the general signal processing system.

Design of ispPAC-based Humidity Sensor Signal Processing Circuits

The widely used sensitive elements of humidity sensors can be divided into 3 types,i.e.,resistor,capacitor,and electrolyte.Humidity sensors consisting of these sensitive elements have corresponding signal processing circuit unique to each type of sensitive elements.This paper presents an ispPAC (in-system programmable Programmable Analog Circuit) -based humidity sensor signal processing circuit designed with software method and implemented with in-system programmable simulators.Practical operation shows that humidity sensor signal processing circuits of this kind,exhibit stable and reliable performance.

Low-Power Operational Amplifier for Real-Time Signal Processing System of Micro Air Vehicle

A low-power complementary metal oxide semiconductor（CMOS） operational amplifier （op-amp） for real-time signal processing of micro air vehicle （MAV） is designed in this paper.Traditional folded cascode architecture with positive channel metal oxide semiconductor（PMOS） differential input transistors and sub-threshold technology are applied under the low supply voltage.Simulation results show that this amplifier has significantly low power,while maintaining almost the same gain,bandwidth and other key performances.The power required is only 0.12 mW,which is applicable to low-power and low-voltage real-time signal acquisition and processing system.

FUNDAMENTAL COMMUNICATIONS THEORIES AND SIGNAL PROCESSING TECHNIQUES FOR AMORPHOUS CELLULAR SYSTEMS

The rapid developing of the fourth generation(4G)wireless communications has aroused tremendous demands for high speed data transmission due to the dissemination of various types of the intelligent user terminals as well as the wireless multi-media services.It is predicted that the network throughput will increase

A Low-Area, Low-Power Dynamically Reconfigurable 64-Bit Media Signal Processing Adder

Multimedia devices like cellphones, radios, televisions, and computers require low-area and energy-efficient dynamically reconfigurable data paths to process the greedy computation algorithms for real-time audio/video signal and image processing. In this paper, a novel low-area, energy-efficient 64-bit dynamically reconfigurable adder is presented. This adder can be run-time configured to different reconfigurable word lengths based on the partition signal commands provided. Moreover, the design is partitioned into sub-blocks based on functionality to save power, i.e., configuring the computation only for the necessary data path, thus avoiding the unnecessary switching power from the data path computed values that do not get used. Only functions that are needed are powered on, and the rest of the functionality is powered off. The proposed 64-bit dynamically reconfigurable media signal processing (MSP) adder is implemented in the 180 nm CMOS technology at 1.8 V, requiring an area of 39,478 μm2 and a power of 79.24 mW. The dynamic MSP adder achieves a 15.7% reduction in area and a 59.2% reduction in power than the 64-bit MSP adder.

Design of Low-Voltage, Low-Power FGMOS Based Voltage Buffer, Analog Inverter and Winner-Take-All Analog Signal Processing Circuits

This paper proposes novel floating-gate MOSFET (FGMOS) based Voltage Buffer, Analog Inverter and Winner-Take-All (WTA) circuits. The proposed circuits have low power dissipation. All proposed circuits are simulated using SPICE in 180 nm CMOS technology with supply voltages of ±1.25 V. The simulation results demonstrate increase in input range for FGMOS based voltage buffer and analog inverter and maximum power dissipation of 0.5 mW, 1.9 mW and 0.429 mW for FGMOS based voltage buffer, analog inverter and WTA circuits, respectively. The proposed circuits are intended to find applications in low voltage, low power consumer electronics.

MY FURTHERANCE OF SIGNAL PROCESSING THEORY &. ITS APPLICA-TION

The theory of digital signal treatment and its application is a discipline with wide and promising prospects, having made its debut in the 1960s. During the period from the end of the 1950s to the 1970s, I was engaged in teaching and research work in the fields of electronics,digital circuits and numerical control and participated in the development of China’s first numerical machine. At the end of the 1970s.I went to the United States

Virtual Experimental Project Design in the Digital Signal Processing Course

This paper proposes the design and development of virtual experimental projects in the Digital Signal Processing course,using MATLAB,Proteus,and CCS platforms to develop a library of typical experimental cases for biomedical engineering majors and discusses the design process.Based on these typical cases,this paper explores the secondary design for innovative engineering practice case teaching,which can promote students’understanding and mastery of digital signal processing theories,algorithms,and technologies in an intuitive,flexible,and efficient way;quickly build new innovative engineering case models and further cultivate students’engineering application ability as well as innovative thinking.

Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis

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.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalography

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.

A New Signal Processing Technique of π/4-DQPSK Modem Based on Software Radio

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.

A Novel 2-D Signal Processing Scheme for Quasi-Random Step Frequency Signal

Due to the heavy congestion in HF bands, HF radars are restricted to operating within narrow frequency bands. To improve the system bandwidth and avoid heavy interference bands, a quasi-random step frequency signal with discontinuous bands is presented. A novel two-dimensional signal processing scheme for this signal is proposed on the basis of delicate signal analysis. Simulation results demonstrate that the scheme could successfully realize the resolutions by decoupling the range-Doppler ambiguity, and effectively suppress the maximal sidelobe. Moreover, the scheme is simple and has good numerical stability.

AIRIS:Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications

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.

Signal processing for all fiber optical current transducer

The work principle of all fiber optical current transducer (AFOCT) was introduced. By analyzing the characteristic of photo-detector's output, a measurement and signal processing scheme based on sine wave modulation and demodulation was put forward for eliminating the influence of light intensity change and modulation degree change. A digital signal processing system and a calibration scheme were also advanced. The experimental data show that the mean ratio error is 0.016 74% for direct current and 0.035% for alternating current, and the correlation coefficient of linearity is up to 0.999 982 4, meeting the precision requirement of 0.2 grade. Stability experiments and temperature drift experiments show the AFOCT has a better stable capability.

Digital Signal Processing Based Real Time Vehicular Detection System

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.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

High Accuracy Time of Flight Measurement Using Digital Signal Processing Techniques for Subsea Applications

The techniques widely used in ultrasonic measurements are based on the determination of the time of flight (T.o.F). A short train of waves is transmitted and same transducer is used for reception of the reflected signal for the pulse-echo measurement applications. The amplitude of the received waveform is an envelope which starts from zero reaches to a peak and then dies out. The echoes are mostly detected by simple threshold crossing technique, which is also cause of error. In this paper digital signal processing is used to calculate the time delay in reception i.e. T.o.F, for which a maximum similarity between the reference and the delayed echo signals is obtained. To observe the effect of phase uncertainties and frequency shifts (Doppler), this processing is carried out, both directly on the actual wave shape and after extracting the envelopes of the reference and delayed echo signals. Several digital signal processing algorithms are considered and the effects of different factors such as sampling rate, resolution of digitization and S/N ratio are analyzed. Result show accuracy, computing time and cost for different techniques.