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.

High Resolution Radar Real-Time Signal and Information Processing

Radar is an electronic device that uses radio waves to determine the range, angle, or velocity of objects. Real-time signal and information processor is an important module for real-time positioning, imaging, detection and recognition of targets. With the development of ultra-wideband technology, synthetic aperture technology, signal and information processing technology, the radar coverage, detection accuracy and resolution have been greatly improved, especially in terms of one-dimensional(1D) high-resolution radar detection, tracking, recognition, and two-dimensional(2D) synthetic aperture radar imaging technology. Meanwhile, for the application of radar detection and remote sensing with high resolution and wide swath, the amount of data has been greatly increased. Therefore, the radar is required to have low-latency and real-time processing capability under the constraints of size, weight and power consumption. This paper systematically introduces the new technology of high resolution radar and real-time signal and information processing. The key problems and solutions are discussed, including the detection and tracking of 1D high-resolution radar, the accurate signal modeling and wide-swath imaging for geosynchronous orbit synthetic aperture radar, and real-time signal and information processing architecture and efficient algorithms. Finally, the latest research progress and representative results are presented, and the development trends are prospected.

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.

Real-Time Discrete Adaptive Control of Robot Arm Based on Digital Signal Processing

A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.

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.

Training of Multi-layered Neural Network for Data Enlargement Processing Using an Activity Function

In this paper, we present a study on activity functions for an MLNN (multi-layered neural network) and propose a suitable activity function for data enlargement processing. We have carefully studied the training performance of Sigmoid, ReLu, Leaky-ReLu and L & exp. activity functions for few inputs to multiple output training patterns. Our MLNNs model has L hidden layers with two or three inputs to four or six outputs data variations by BP (backpropagation) NN (neural network) training. We focused on the multi teacher training signals to investigate and evaluate the training performance in MLNNs to select the best and good activity function for data enlargement and hence could be applicable for image and signal processing (synaptic divergence) along with the proposed methods with convolution networks. We specifically used four activity functions from which we found out that L & exp. activity function can suite DENN (data enlargement neural network) training since it could give the highest percentage training abilities compared to the other activity functions of Sigmoid, ReLu and Leaky-ReLu during simulation and training of data in the network. And finally, we recommend L & exp. function to be good for MLNNs and may be applicable for signal processing of data and information enlargement because of its performance training characteristics with multiple teacher training patterns using original generated data and hence can be tried with CNN (convolution neural networks) of image processing.

A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi M_(S) 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.

TIME-DOMAIN INTERPOLATION ON GRAPHICS PROCESSING UNIT

The signal processing speed of spectral domain optical coherence tomography(SD-OCT)has become a bottleneck in a lot of medical applications.Recently,a time-domain interpolation method was proposed.This method can get better signal-to-noise ratio(SNR)but much-reduced signal processing time in SD-OCT data processing as compared with the commonly used zeropadding interpolation method.Additionally,the resampled data can be obtained by a few data and coefficients in the cutoff window.Thus,a lot of interpolations can be performed simultaneously.So,this interpolation method is suitable for parallel computing.By using graphics processing unit(GPU)and the compute unified device architecture(CUDA)program model,time-domain interpolation can be accelerated significantly.The computing capability can be achieved more than 250,000 A-lines,200,000 A-lines,and 160,000 A-lines in a second for 2,048 pixel OCT when the cutoff length is L=11,L=21,and L=31,respectively.A frame SD-OCT data(400A-lines×2,048 pixel per line)is acquired and processed on GPU in real time.The results show that signal processing time of SD-OCT can befinished in 6.223 ms when the cutoff length L=21,which is much faster than that on central processing unit(CPU).Real-time signal processing of acquired data can be realized.

Real-time measurement of high rotational projectile axial acceleration based on 2-axis acceleration sensor

The real-time measurement principle of high rotational projectile＇s angular velocity based on 2-axis acceleration sensor and the axial acceleration measurement error caused by the installation error are discussed.The 2-axis acceleration sensor is applied to measure the high rotational projectile＇s angular velocity and the measurement value of axial acceleration,the axial acceleration of the high rotational projectile equals the measurement value of axial acceleration subtracting the centrifugal acceleration component,so that the high-accuracy real-time measurement of axial acceleration is realized.The memory test has confirmed the strike tally of the theoretical analysis and the test result.The measurement technique can satisfy the high-accuracy measurement of the high rotational projectile axial acceleration in the self-determination course correction fuze projectile.

Real-Time Maqam Estimation Model in Max/MSP Configured for the Nāy

Automatic maqam estimation is considered significant toward improving multimedia live music performances and automatic accompaniment. This contribution proposed a real-time maqam estimation model developed in the visual programming language MAX/MSP and configured for the nāydukah. The model’s design stood on basic formulas of Arab music maqamat as explained in theory and applied in practice. The model consisted of different layers of competition;the first was for the identification of the instant tonic of the melodic figure, and the second was for the recognition of its identifying E (E, E half-flat and E flat). Those two competitions were used to estimate the maqam in real-time. Then, accumulated estimation results were used to estimate the maqam in longer durations;five-second and full duration. The model was evaluated using professionally performed nāy improvisations. Results reflected a success in estimating all the studied maqamat when the full improvisation was considered. In addition, results were very good for real-time and five-second estimation where average estimation confidence was 75.98% and 80.04%, respectively.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

电力电缆局放在线监测神经网络自动识别精度的提升方法

为了尽可能降低电缆线路局放在线监测系统装置运行中对局放信号识别的误判误报的可能性,对局放的判读机理和识别精度进行了改进和提升。基于现场运行系统回收的数万个疑似电缆内部局放的告警记录数据进行了分析分类,对判别程序中沿用的局放自动逻辑判别程序追加了相间信号相关性过滤程序,对既有的神经网络在构造上作了优选优化,对神经网络学习数据的前置处理进行了改进。对改进提升后的局放判别程序采用了包括现场记录,模拟信号发生器和人工局放模型加电压实验的3种局放数据进行检验。结果表明,改进提升后的局放判别程序不但能够使现场的非局放告警数据的告警率降低到5%的期待水平,同时也提高了识别人工模拟产生的各种类型局放信号的准确性,大大降低了局放在线监测系统误判误报的可能性。

基于判决反馈的VDES解调系统设计与FPGA实现

甚高频数据交换系统(Very high frequency Data Exchange System,VDES)作为新一代船舶通信系统,具有广阔的应用前景。由于卫星相对船舶的高速运动,VDES中上行应用特定消息(Application-specific Message,ASM)链路会产生较大的多普勒频移,在接收端仅依靠已知训练序列估计的频偏等信道参数无法满足正确解调的性能要求。为此提出一种基于判决反馈的解调方法,通过分段解调,缩短每次解调的数据长度,提高解调时对频偏的容忍度,并利用每段解调的结果作为下一段未解调数据的导频,估计出当前数据中的信道参数。仿真结果表明,所提算法相较于无反馈相干解调算法性能大大提升。在上述研究的基础上,在可编程逻辑器件上实现了对ASM无导频上行链路的正确解调。

基于UA数据同步的民机CBIC系统通信方法研究

随着民用飞机电源系统逐渐智能化,现代民用飞机的座舱显示系统中也加入了断路器显示与控制相关界面,实现在驾驶舱中达到对电源系统断路器的可视及可控。基于保证驾驶舱显示界面数据完整性的目的,结合ARINC661标准与应用软件的功能,提出了一种具备UA间数据同步的CBIC系统通信方法。针对ARINC661规范和CBIC系统进行简单概述,在对CBIC系统的具体通信流程进行分析后,设计出一种UA之间数据同步的方法,并通过单片机进行仿真实验,实现了UA切换时数据与指令的一致,该方法可以为后续相关的研究工作提供参考。

基于HHT-LSTM的冬奥会临时设施运行趋势预测方法研究

针对冬奥会延庆赛区临时设施的安全性和可使用性,本文充分结合信号处理算法与深度神经网络,提出了一种由希尔伯特黄变换(Hilbert-Huang transform,HHT)对时序数据进行信号分解和信号特征提取,长短期记忆网络(long short-term memory,LSTM)进行临时设施运行趋势预测2部分构成模型。该模型基于受严寒天气和大客流诱发的看台振动等一系列外因影响所测得的真实振动和倾角数据,实现对设施进行有效的预测,以避免发生安全问题,解决了由于受数据中一些无关特征因素的干扰导致预测准确度低的问题。论文提出的方法与循环神经网络(recurrent neural network,RNN)、门控循环网络(gated recurrent neural network,GRU)、双向RNN和双向GRU等运行趋势预测方法进行比较,验证了本文方法的可行性和有效性,实验结果也说明所提出的模型在此类任务中表现非常出色。

数据手套用柔性传感系统及信号特征分析技术

对数据手套采集信息的各组成部件进行归纳,总结数据手套中的传感系统,重点探讨柔性传感器的制备和传感阵列在手套中的集成。综述数据手套在不同应用场景下对采集信号的处理模式,特别介绍深度学习及神经网络计算方法在手势识别中的研究进展。最后,总结数据手套需突破的问题及未来发展趋势。

混沌理论在减振弹簧故障检测中的应用研究

针对振动筛减振弹簧在长时间工作后会出现失效的问题,在分析减振弹簧动力学特性和有限元仿真的基础上,提出一种运用混沌理论分析检测减振弹簧故障工况的方法。首先设计振动实验监测减振弹簧在不同故障工况下的振动信号,然后通过时频分析研究不同工况下的信号差异,最后运用混沌理论计算振动信号的混沌特性参数。由此发现减振弹簧故障时系统处于混沌状态,且不同工况下最大李普诺夫指数、关联维数等具有明显差异,可以用于减振弹簧的故障检测研究。

面向八通道肌电信号处理的微控制器设计

该文提出了一种基于嵌入式ARM系统的面向肌电信号处理的微控制器设计方案。利用肌电信号采集和处理模块对人体表面肌电进行采集、滤波和放大处理,通过A/D转换器将处理后的数据传输到嵌入式ARM系统中,实现肌肉运动状态的实时监测。为了实现高速采集模拟信号并将其转换为数字信号,该文在硬件设计中采用了内置8通道的12位A/D转换器和转存A/D数据的DMA控制器于一体的nRF52840芯片解决方案。实验结果表明,设计的肌电微控制器具有高精度、高稳定性和快速响应的特点,能够满足基于表面肌电信号的手势识别或假手控制等研究的要求。

基于MATLAB建模的电磁兼容测试中接收机数字化设计

随着虚拟仪器的快速发展,对如何将虚拟仪器的可定制性、易扩展、价格低廉等优势应用于电磁兼容测试的接收机进行了研究。通过让数字化虚拟仪器部分取代乃至完全取代传统仪器,已成为日后电磁兼容技术革新的关注焦点。基于MATLAB软件搭建了接收机的模型,实现了接收机的数字化设计。其中中频滤波器的滤波窗函数采用高斯函数,带宽通过修正系数拟合;检波器经过傅里叶变换,实现信号的频谱计算以及包络线绘制。模型通过频选信号的变化,能够实现峰值和平均值两种检波效果。实际测试结果与模型仿真结果误差小于10%,符合企业测试要求。该模型具有较强的扩展性与兼容性,能对整车与高低压零部件的电磁兼容问题进行一定的监测,设计经验对后续电磁兼容测试过程整体数字化发展有着重要意义。

智能农机嵌入式数据信息库的设计

嵌入式系统是一种特定功能的计算机系统,具有小巧、高效、低功耗的特点,可实现实时控制、数据采集、信号处理、通信等功能,为现代农业生产提供高效、可靠的数据管理和分析平台。该文阐述了嵌入式系统的结构组成及工作原理,选择STM32嵌入式微控制器架构,Keil MDK作为嵌入式开发环境,结合ALIENTEK战舰STM32F103开发板进行开发,使用多路模拟量输入通道,作为智能农机嵌入式数据信息库的输入,经过处理和分析后用于农机的智能控制、优化调节等,提高农机工作效率和智能化水平。该信息库的设计将为数字化农业发展提供重要技术支撑,对智能农机在提升农业生产效率和可持续发展方面起到一定的推动作用。