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.

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.

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.

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.

Development of a wide-range and fast-response digitizing pulse signal acquisition and processing system for neutron flux monitoring on EAST

The neutron count rate fluctuation reaches six orders of magnitude between the ohmic plasma scenario and high power of auxiliary heating on an experimental advanced superconducting tokamak(EAST).The measurement result of neutron flux monitoring(NFM)is a significant feedback parameter related to the acquisition of radiation protection-related information and rapid fluctuations in neutron emission induced by plasma magnetohydrodynamic activity.Therefore,a wide range and high time resolution are required for the NFM system on EAST.To satisfy these requirements,a digital pulse signal acquisition and processing system with a wide dynamic range and fast response time was developed.The present study was conducted using a field-programmable gate array(FPGA)and peripheral component interconnect extension for instrument express(PXIe)platform.The digital dual measurement modes,which are composed of the pulse-counting mode and AC coupled square integral's Campbelling mode,were designed to expand the measurement range of the signal acquisition and processing system.The time resolution of the signal acquisition and processing system was improved from 10 to 1 ms owing to utilizing highspeed analog-to-digital converters(ADCs),a high-speed PXIe communication with a direct memory access(DMA)mode,and online data preprocessing technology of FPGA.The signal acquisition and processing system was tested experimentally in the EAST radiation field.The test results showed that the time resolution of NFM was improved to 1 ms,and the dynamic range of the neutron counts rate was expanded to more than 10^(6) counts per second.The Campbelling mode was calibrated using a multipoint average linear fitting method;subsequently,the fitting coefficient reached 0.9911.Therefore,the newly developed pulse signal acquisition and processing system ensures that the NFM system meets the requirements of high-parameter experiments conducted on EAST more effectively.

Reconfigurable single-shot incoherent optical signal processing system for chirped microwave signal compression

We propose and demonstrate a reconfigurable and single-shot incoherent optical signal processing system for chirped microwave signal compression, using a programmable optical filter and a multiwavelength laser(MWL). The system is implemented by temporally modulating a specially shaped MWL followed by a suitable linear dispersive medium. A microwave dispersion value up to 1.33 ns/GHz over several GHz bandwidth is achieved based on this approach. Here we demonstrate a singleshot compression for different linearly chirped microwave signals over several GHz bandwidth. In addition, the robustness of the proposed system when input RF signals are largely distorted is also discussed.

New WA-system of kravchenko functions in digital signal processing

On the basis of modified atomic transformations the new WA-systems of Kravchenko functions are constructed.As an example the digital processing of time series of the various physical nature processing is considered.The numerical experiments and physical analysis of the results confirm the efficiency of the proposed WA-systems of Kravchenko functions.

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.

100 Gb/s coherent chaotic optical communication over 800 km fiber transmission via advanced digital signal processing

Chaotic optical communication has shown large potential as a hardware encryption method in the physical layer.As an important figure of merit,the bit rate–distance product of chaotic optical communication has been continually improved to 30 Gb/s×340 km,but it is still far from the requirement for a deployed optical fiber communication system,which is beyond 100 Gb/s×1000 km.A chaotic carrier can be considered as an analog signal and suffers from fiber channel impairments,limiting the transmission distance of high-speed chaotic optical communications.To break the limit,we propose and experimentally demonstrate a pilot-based digital signal processing scheme for coherent chaotic optical communication combined with deep-learning-based chaotic synchronization.Both transmission impairment recovery and chaotic synchronization are realized in the digital domain.The frequency offset of the lasers is accurately estimated and compensated by determining the location of the pilot tone in the frequency domain,and the equalization and phase noise compensation are jointly performed by the least mean square algorithm through the time domain pilot symbols.Using the proposed method,100 Gb∕s chaotically encrypted quadrature phase-shift keying(QPSK)signal over 800 km single-mode fiber(SMF)transmission is experimentally demonstrated.In order to enhance security,40 Gb∕s real-time chaotically encrypted QPSK signal over 800 km SMF transmission is realized by inserting pilot symbols and tone in a field-programmable gate array.This method provides a feasible approach to promote the practical application of chaotic optical communications and guarantees the high security of chaotic encryption.

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

Automatic depression recognition by intelligent speech signal processing:A systematic survey

Depression has become one of the most common mental illnesses in the world.For better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a transition from the early traditional methods based on hand‐crafted features to the application of architectures of deep learning.This paper systematically and precisely outlines the most prominent and up‐to‐date research of automatic depression recognition by intelligent speech signal processing so far.Furthermore,methods for acoustic feature extraction,algorithms for classification and regression,as well as end to end deep models are investigated and analysed.Finally,general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.

Signal Processing Circuit Design of Infrared Detection System with SO2 Concentration Based on Correlation Filter Technology

Signals from infrared detector are very weak in SO2 concentration measuring system.In order to improve the sensitivity of detection,combining with filter correlation technology and infrared absorption principle,the weak signal processing circuit is designed according to correlation detection technology.Under laboratory conditions,system performance of SO2 concentration is tested,and the experimental data are analyzed and processed.Then relationship of SO2 concentration and the measuring voltage is provided to prove that the design improves measuring sensitivity of the system.

Flexible artificial vision computing system based on FeOx optomemristor for speech recognition

With the advancement of artificial intelligence,optic in-sensing reservoir computing based on emerging semiconductor devices is high desirable for real-time analog signal processing.Here,we disclose a flexible optomemristor based on C_(27)H_(30)O_(15)/FeOx heterostructure that presents a highly sensitive to the light stimuli and artificial optic synaptic features such as short-and long-term plasticity(STP and LTP),enabling the developed optomemristor to implement complex analogy signal processing through building a real-physical dynamic-based in-sensing reservoir computing algorithm and yielding an accuracy of 94.88%for speech recognition.The charge trapping and detrapping mediated by the optic active layer of C_(27)H_(30)O_(15) that is extracted from the lotus flower is response for the positive photoconductance memory in the prepared optomemristor.This work provides a feasible organic−inorganic heterostructure as well as an optic in-sensing vision computing for an advanced optic computing system in future complex signal processing.

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.

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.

Ultrasonic Nondestructive Signals Processing Based on Matching Pursuit with Gabor Dictionary

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.

A VIBRATION RECOGNITION METHOD BASED ON DEEP LEARNING AND SIGNAL PROCESSING

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.

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.

Wavelet analysis and its application to signal processing

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.

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.