Efficient stochastic parallel gradient descent training for on-chip optical processor

In recent years,space-division multiplexing(SDM)technology,which involves transmitting data information on multiple parallel channels for efficient capacity scaling,has been widely used in fiber and free-space optical communication sys-tems.To enable flexible data management and cope with the mixing between different channels,the integrated reconfig-urable optical processor is used for optical switching and mitigating the channel crosstalk.However,efficient online train-ing becomes intricate and challenging,particularly when dealing with a significant number of channels.Here we use the stochastic parallel gradient descent(SPGD)algorithm to configure the integrated optical processor,which has less com-putation than the traditional gradient descent(GD)algorithm.We design and fabricate a 6×6 on-chip optical processor on silicon platform to implement optical switching and descrambling assisted by the online training with the SPDG algorithm.Moreover,we apply the on-chip processor configured by the SPGD algorithm to optical communications for optical switching and efficiently mitigating the channel crosstalk in SDM systems.In comparison with the traditional GD al-gorithm,it is found that the SPGD algorithm features better performance especially when the scale of matrix is large,which means it has the potential to optimize large-scale optical matrix computation acceleration chips.

A Distributed Newton Method for Processing Signals Defined on the Large-Scale Networks

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.

Multifunctional mixed analog/digital signal processor based on integrated photonics

Photonic signal processing offers a versatile and promising toolkit for contemporary scenarios ranging from digital optical communication to analog microwave operation.Compared to its electronic counterpart,it eliminates inherent bandwidth limitations and meanwhile exhibits the potential to provide unparalleled scalability and flexibility,particularly through integrated photonics.However,by far the on-chip solutions for optical signal processing are often tailored to specific tasks,which lacks versatility across diverse applications.Here,we propose a streamlined chip-level signal processing architecture that integrates different active and passive building blocks in silicon-on-insulator(SOI)platform with a compact and efficient manner.Comprehensive and in-depth analyses for the architecture are conducted at levels of device,system,and application.Accompanied by appropriate configuring schemes,the photonic circuitry supports loading and processing both analog and digital signals simultaneously.Three distinct tasks are facilitated with one single chip across several mainstream fields,spanning optical computing,microwave photonics,and optical communications.Notably,it has demonstrated competitive performance in functions like image processing,spectrum filtering,and electro-optical bandwidth equalization.Boasting high universality and a compact form factor,the proposed architecture is poised to be instrumental for next-generation functional fusion systems.

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.

2-D DIRECTION FINDING OF COHERENT SIGNALS VIA TEMPORO-SPATIAL PROCESSING

In most wireless communication systems, two-dimensional Directions-Of-Arrival (DOA) of multipath signals need to be found for spatial selective transmission. However, it is quite difficult to find their DOAs due to the coherent nature of multipath signals and considerable computations when performing 2-D searches. In this paper, a new algorithm to estimate 2-D DOA of multiple narrow-band signals is proposed. A DOA cyclic matrix is constructed whose eigenvalues and eigenvectors can be simultaneously used to extract 2-D DOA without 2-D searches. By exploiting the temporal property of cyclostationarity, the signal detection capability is significantly improved. Besides, based on the decorrelation model for mobile terminal signals, the algorithm can be effectively extended to the coherent case without spatial smoothing and the loss of array aperture. Simulation results are given to illustrate the performance of the new algorithm.

Extraction of Buried Signals in Noise: Correlated Processes

In this paper, we propose extraction of signals correlated with noise in which they are buried. Proposed extraction method uses no a-priori information on the buried signal and works independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Extraction of buried correlated signals is achieved without averaging in the time or frequency domain.

Extraction of Signals Buried in Noise: Non-Ergodic Processes

In this paper, we propose extraction of signals buried in non-ergodic processes. It is shown that the proposed method extracts signals defined in a non-ergodic framework without averaging or smoothing in the direct time or frequency domain. Extraction is achieved independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Performances of the pro-posed extraction method and comparative results with other methods are demonstrated via experimental Doppler velocimetry measurements.

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.

DIGITAL PROCESSING OF THE POLARIZATION STATE OF GEOPHYSICAL ULF SIGNALS

In this paper, the evaluation by running window smoothing is used for the digital processing of the polarization of geophysical ULF signals. The observed signals are resolved into two orthogonal complex components so that it is no longer necessary to consider the phase and amplitude of the signals simultaneously.

A New Processing Method for the Nonlinear Signals Produced by Electromagnetic Flowmeters in Conditions of Pipe Partial Filling

When a pipe is partially filled with a given working liquid,the relationship between the electromotive force(EMF)measured by the sensor(flowmeter)and the average velocity is nonlinear and non-monotonic.This relationship varies with the inclination of the pipe,the fluid density,the pipe wall friction coefficient,and other factors.Therefore,existing measurement methods cannot meet the accuracy requirements of many industrial applications.In this study,a new processing method is proposed by which the flow rate can be measured with an ordinary electromagnetic flowmeter even if the pipe is only partially filled.First,a B-spline curve fitting method is applied to a limited set of measurements.Second,matrix inversion required in the B-spline curve method is optimized in order to reduce the number of needed computations.Dedicated experimental tests prove that the proposed method can effectively measure the average flow velocity of the fluid.When the fluid level of the pipeline is between 50%and 100%,the relative error is less than 3.5%.

Description and analysis of non-stationary signals by nested semi-Markov processes

The possibility of describing the time-dependent processes of scattering by underlying surfaces and the clear sky, as well as the seasonal behaviour of the refractive index of troposphere by using nested semi-Markov processes has been consid- ered. Local Gaussian models can be used to describe the process inside each phase state. The possibility of describing the sta- tistics of reflections from the sea and the refractive index by using Kravchenko finite functions has been shown for the first time.

Processing Human Colonic Pressure Signals by Using Overdetermined ICA

Independent component analysis （ICA） is a widely used method for blind source separation （BSS）. The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis （PCA）, the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches.

Big Data Analytics Using Graph Signal Processing

The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration(VLSI)technology and design software have significantly broadened the scope of digital signal processing(DSP)applications.The 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 complex.Adaptable 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 world.New research and commercial efforts to sustain power optimization,cost savings,and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged.Hence,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 implementations.The 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)method.By 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.

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.

充电桩充电模块功率器件故障诊断研究综述

大功率直流充电桩是未来电动汽车充电设施的发展方向,充电模块是直流充电桩最重要以及故障率最高的部件,其中功率器件开路故障较为常见。为保证充电模块安全可靠运行,需要对充电模块功率器件开路故障进行准确识别和定位。文中首先对充电模块拓扑结构和故障类型进行分析,然后分别对基于解析模型、基于信号处理和基于知识的功率器件开路故障诊断方法进行总结,分别介绍了各类方法的基本思想、研究进展和优缺点,最后总结并展望充电模块功率器件开路故障诊断方法未来的研究和发展方向。

基于模糊神经网络的电网消防预警算法

针对传统基于阈值判别方法的电网火灾预警系统预测精度低、抗干扰能力弱的问题,提出了一种基于模糊神经网络的电网消防预警算法。该算法利用神经网络学习大规模电网数据,使用模糊逻辑推理算法来提升预测结果的推理能力,并通过结合神经网络对大规模数据的学习能力和模糊逻辑算法的推理能力来分析电网线路参数,从而提升电网消防预警系统的精度和抗干扰能力。实验与仿真结果表明,所提出方法能显著提升电网火灾的预警精度,且使用模糊逻辑推理可以得到更符合实际情况的电网火灾预警结果。

合成孔径雷达高分辨率成像虚拟仿真实验平台设计

合成孔径雷达(synthetic aperture radar,SAR)系统组成复杂,搭载于飞机、卫星等运动平台上才可实现数据获取,难以开展基于SAR雷达实物的实验教学。为解决此问题,设计并搭建了SAR高分辨率成像虚拟仿真实验平台。该虚拟仿真实验平台将系统设计技术、实验设计技术、数据采集过程、信号处理技术、图像评估技术等SAR实验的完整流程进行了高展示度、高保真度、高交互性的计算机模拟。一方面,该虚拟仿真实验可以帮助学生理解SAR设计、工作和应用的流程,巩固相关课程基础知识,激发学习雷达信号处理知识的兴趣;另一方面,可以考查学生对相关知识的综合应用能力,增强学生解决实际问题的技能。该仿真平台已成功应用于虚拟仿真教学课程实践,教学效果良好,对培养新工科技术型人才具有重要意义。

基于STM32的辨音识别系统的设计与应用

声音识别技术能够用于多种环境参数检测,本文采用STM32的DSP技术,对全向拾音器采集到环境声音进行快速傅里叶变换(FFT),获取待测声音的主频率和次频率,通过液晶屏显示采集信号的强度和主要频率分量。该设备能够有效对环境声音的主要参赛进行检测,在噪声检测和一般设备运行故障检测具有较高的适用性。

基于信号处理的液压泵故障检测方法研究综述

液压泵的故障诊断是其正常工作和健康管理的关键,基于信号处理的液压泵诊断方法已经成为主流。近年来,学者们对液压泵故障诊断的研究非常活跃,但对液压泵故障分析和诊断方法缺少系统的总结和分析。通过对液压泵相关文献进行统计分析,系统地总结了液压泵故障产生的原因、故障诊断的基本方法及研究进展,指出了液压泵故障分析和诊断领域的发展前景,为研究人员和相关维修人员提供了参考价值并指明了研究方向。