A Real-Time TCP Stream Reassembly Mechanism in High-Speed Network

With the continual growth of the variety and complexity of network crime means, the traditional packet feature matching cannot detect all kinds of intrusion behaviors completely. It is urgent to reassemble network stream to perform packet processing at a semantic level above the network layer. This paper presents an efficient TCP stream reassembly mechanism for real-time processing of high-speed network traffic. By analyzing the characteristics of network stream in high-speed network and TCP connection establishment process, several polices for designing the reassembly mechanism are built. Then, the reassembly implementation is elaborated in accordance with the policies. Finally, the reassembly mechanism is compared with the traditional reassembly mechanism by the network traffic captured in a typical gigabit gateway. Experiment results illustrate that the reassembly mechanism is efficient and can satisfy the real-time property requirement of traffic analysis system in high-speed network.

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.

Development of 16-channels Compact EEG System Using Real-time High-speed Wireless Transmission

For the convenience of people with disability and for normal people, a demand for intelligent interfaces is ever increasing and therefore related studies are actively being conducted. Recently a study is being conducted to develop an interface through face expression, movement of the body and eye movements, and further more active attempts to use electrical signals(brainwave, electrocardiogram, electromyogram) measured from the human body is also actively being progressed. In addition, the development and the usage of mobile devices and smart devices are promoting these research activities even more. The brainwave is measured by electrical activities between nerve cells in the cerebral cortex using scalp electrodes. The brainwave is mainly used for diagnosis and treatment of diseases such as epilepsy, encephalitis, brain tumors and brain damage. As a result, the brainwave measurement methods and analytical methods were developed. Interface using the brainwave will not go through language or body behavior which is the result of the information processed by the brain but will pass directly to the system providing a brain-computer interface (BCI). This is possible because a variety of the brainwave appears depending on the human’s physical and mental state. Using the brainwave with the intelligent brain-computer interface or combining it with mobile devices and smart devices, regardless of space constraints, the brainwave measurement should be possible.[4,7] In this study, in order to measure the brainwave without spatial constraint, 16 channel compact brainwave measurements system using a high-speed wireless communications were designed. It was designed with a 16 channel to classify the various brainwave patterns that appear and for estimating the location of the nerve cells that triggered the brainwave. And in order to transmit the brainwave data within the channel without loss, a high-speed wireless communication must be possible that can enable a high-speed wireless transmission more sufficient than the Bluetooth, therefore, 802.11 compliant Wi-Fi communication methods were used to transfer the data to the PC. In addition, by using an analog front-end IC having a single-chip configuration with real-time digital filters, the miniaturization of the system was implemented and in order to verify the system Eye-blocking was used to observe the changes in the EEG signal.

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.

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.

A beam position measurement system of fully digital signal processing at SSRF

This fully digital beam position measurement instrument is designed for beam position monitoring and machine research in Shanghai Synchrotron Radiation Facility. The signals received from four position-sensitive detectors are narrow pulses with a repetition rate up to 499.654 MHz and a pulse width of around 100 ps, and their dynamic range could vary over more than 40 dB in machine research. By the employment of the under-sampling technique based on high-speed high-resolution A/D conversion, all the processing procedure is performed fully by the digital signal processing algorithms integrated in one single Field Programmable Gate Array. This system functions well in the laboratory and commissioning tests, demonstrating a position resolution (at the turn by turn rate of 694 kHz) better than 7 μm over the input amplitude range of -40 dBm to 10 dBm which is well beyond the requirement.

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.

Design and validation of real-time dynamic spectrum management in OFDM-based HNPLC systems

Time-varying frequency selective attenuation and colored noises are unfavorable characteristics of power line communication(PLC) channels of the low voltage networks.To overcome these disadvantages,a novel real-time dynamic spectrum management(DSM) algorithm in orthogonal frequency division multiplexing(OFDM)-based high-speed narrow-band power line communication(HNPLC) systems is proposed,and the corresponding FPGA circuit is designed and realized.Performance of the proposed DSM is validated with a large amount of network experiments under practical PLC circumstance.As the noise in each narrow subcarrier is approximately Gaussian,the proposed DSM adopts the BER/SER expression formulized via the AWGN channel to provide a handy and universal strategy for power allocation.The real-time requirement is guaranteed by choosing subcarriers in group and employing the same modulation scheme within each transmission.These measures are suitable for any modulation scheme no matter the system criterion is to maximize data rate or minimize power/BER.Algorithm design and hardware implementation of the proposed DSM are given with some flexible and efficient conversions.The DSM circuit is carried out with Xilinx KC705.Simulation and practical experiments validate that the proposed real-time DSM significantly improves system performance.

Acceleration of optical coherence tomography signal processing by multi-graphics processing units

A multi-GPU system designed for high-speed,real-time signal processing of optical coherencetomography(OCT)is described herein.For the OCT data sampled in linear wave numbers,themaximum procesing rates reached 2.95 MHz for 1024-OCT and 1.96 MHz for 2048-OCT.Data sampled using linear wavelengths were re-sampled using a time-domain interpolation method and zero-padding interpolation method to improve image quality.The maximum processing rates for1024-OCT reached 2.16 MHz for the time-domain method and 1.26 MHz for the zero-paddingmethod.The maximum processing rates for 2048-0CT reached_1.58 MHz,and 0.68 MHz,respectively.This method is capable of high-speed,real-time processing for O CT systems.

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.

基于实数化的均匀圆阵矩阵重构方法

为了减小低快拍数和低信噪比下采样协方差矩阵误差,并降低其运算复杂度,提出了一种基于实数化的均匀圆阵采样协方差矩阵重构方法。针对均匀圆阵的特点,通过组建特殊的基向量,构成特殊的重构矩阵。通过将采样协方差矩阵实数化,进一步降低了重构矩阵的复杂度。考虑到多通道不一致性对重构矩阵的影响,引入0位校正算法,提高了重构方法的稳健性。最后应用重构后的协方差矩阵进行子空间类波达方向估计(direction of arrival,DOA)。实验仿真证明,该特殊重构矩阵在实数化下与原矩阵重构能力相同;当快拍数为100、信噪比为0 dB时,双信源分辨力较重构前由74%提高到95%以上;理论重构运算复杂度降低到原来的53.99%。

高铁司机警觉度监测与干预穿戴装置研制

为准确识别、分析高铁司机疲劳状态并进行有效干预,提出一种基于脑电信号特征的疲劳识别与干预方法,并研制了集实时监测、疲劳检测和智能干预于一体的可穿戴警觉度干预装置。装置由采集终端、Web端疲劳检测设备、疲劳干预模块和警觉度预警平台组成:采集终端实时收集、分析脑电数据;Web端疲劳检测设备使用Welch法提取7个脑电特征指标计算疲劳阈值,以优化随机森林分类器识别司机疲劳状态并发送干预指令;采集终端与疲劳干预模块集成为柔性可穿戴衣物;警觉度预警平台可视化显示各模块运行状态。实验结果表明,装置的疲劳检测准确率超过90%,脑电指标显示装置疲劳干预效果良好,并具备稳定的通信性能。

集成式轴承状态实时监测系统设计

为了满足航空发动机高速轴承状态实时监测的迫切需求,研制了一种基于以太网传输的集成式多通道同步在线监测系统。系统集成了电涡流传感器前置器和IEPE加速度支持单元,满足了35~4400 MHz范围内感应探头激励和IEPE供电需求,实现转速和振动传感信号高精度采集,并通过硬件抗混叠滤波和滑动滤波算法抑制轴承的杂散干扰。试验结果表明:系统在0~9000 r/min范围内保持架转速测量误差小于2 r/min,相对误差小于0.1%,振动灵敏度为200 mV/g,实时性能好,实现轴承状态的实时在线监测。

基于毫米波雷达的心房颤动检测

提出一种基于毫米波雷达的非接触式实时心房颤动检测技术。首先使用60 GHz的PCR毫米波雷达持续采集目标对象的胸部回波信号,然后通过I-Q解调生成I-Q信号,接着基于有效点相位趋势评估法提取信号相位信息并获得微动信号,再经过数字滤波提取呼吸信号和心动信号,通过特征提取获得心脏运动特征,最后基于时域房颤波特征判断房颤发生情况。本研究的创新性主要体现在:为提取更精确的信号相位信息,提出有效点相位趋势评估法;为实现实时房颤检测,提出时域房颤判别方法。实验结果表明本研究的心房颤动检测系统对临床患者的房颤检测准确率达99.2%。

基于多核DSP的星载双基FMCW SAR成像算法实现

调频连续波(frequency modulated continuous wave,FMCW)合成孔径雷达(synthetic aperture radar,SAR)降低了传感器的峰值传输功率,使系统的重量和成本最小化,被广泛应用于机载平台。将双基地构型与FMCW技术相结合,应用于星载平台,即构成星载双基地FMCW SAR。本文对距离多普勒(range-Doppler,RD)算法进行改进,建立起一种高性能的适宜星载双基地平台的FMCW SAR成像频域算法,这种算法的处理精度明显提高,成像效果更好。基于多核数字信号处理器(digital signal processor,DSP)构建适用于星载双基SAR成像算法的并行处理架构,完成软硬件设计实现。验证了所提软件架构可以满足实时成像需求,以及算法工程化实现的可行性。

基于实时可达域的高速飞行器分组队形优化

无动力高速飞行器作为欠驱动系统,在进行编队飞行队形优化时需系统地考虑其控制特点。借助“实时可达域”这一概念,在分析高速飞行器实际可到达区域的基础上,考虑集群成员数量变化以及实际集群包含多个任务小组的情况,提出一种适用于高速飞行器集群的分组动态队形优化方法。仿真结果表明提出的队形优化方法可对队形形状和参数进行一体化优化,可灵活适应多种飞行任务,同时也适用于集群成员数量发生变化的队形重构问题,具有重要应用价值。

基于自适应VMD算法的高速铁路地震信号分析

高速铁路以确定的长度和负载在固定线路上以几乎均匀的速度长时间运行,构成了一种新的稳定且可重复的人工地震源。研究表明高速铁路地震信号具有宽频带分立谱特征。挖掘大量高速铁路地震信号中所包含的丰富信息在高速铁路运行和路基的安全监测方面具有重要的应用价值。然而,由于铁路网络系统周围环境的复杂性,实际采集数据中除高铁地震信号外,还包含有地球背景噪声以及各种人类活动所产生的噪声。如何提取实际记录中的高铁地震信号是有效利用该类信号的基础和关键。在本文中,我们提出了一种基于自适应变分模态分解(Variational mode decomposition,VMD)的高速铁路地震信号分离算法,将优化算法引入到变分模态分解中,利用能量差参数和样本熵构建适应度函数,实现对模态数及惩罚因子优化调整。此外,利用同步挤压小波变换(Synchrosqueezed wavelet transform,SSWT)对提取的高速铁路信号和现场数据进行时频分析。通过对模拟信号的处理验证后,应用到实际采集到的高铁地震数据进行分析处理。结果表明,该方法可以有效提取高铁地震信号,剔除其它背景噪声,为后续开展高铁地震的成像和反演提供基础。

高速列车大功率操纵过程分数阶建模及速度时滞预测

针对现有司机操纵策略难以完整描述列车大功率操纵过程的微观变化,设计机理特性和数据驱动模型联合驱动的分数阶建模策略,来满足高速列车无级调速下安全平稳运行的要求。针对高速列车大功率操纵模式下的大惯性特性和司机反应时间,该文首先对大功率牵引系统和制动系统的时滞特性进行分析,进而对列车操纵策略设计精简冗余工序编码策略,从闭环控制角度实现速度时滞特性精准预测;其次提出了一种新的分数阶建模方法,并设计分数阶最小二乘算法对模型中的时变参数进行精细化辨识;最后基于现场实验数据验证了所提方法的有效性。

基于国产FPGA的高速SS7信令研究与实现

高速SS7信令是一种在数字通信网中使用的公共信道信令技术,适用于无线和有线的公共交换电话网。现有高速信令解析方案可拓展性相对不足,在实际应用场景中扩展成本巨大,并且大都采用外国平台实现。随着国家信息系统软硬件平台对自主可控要求越来越高,文中提出一种基于国产高云FPGA的高速SS7信令解析方案。信令解析过程包括信令采集模块和信令过滤模块,分别对其进行了详细的介绍,并且解析信令的同时统计系统运行信息。相比现有方案,文中方案采用国产平台实现,满足自主可控要求,并对方案进行了模块化参数化设计,可以很容易地实现不同应用场景的扩展。最终设计实现了8路高速SS7信令的并发处理,经过实网测试验证,达到了预期的目的,并成功投入商用。