A new tracking error detection method using amplitude difference detection for signal waveform modulation multi-level discs

The sub-land/sub-pit affects the characteristic of the tracking error signal which is generated by the conventional differential phase detection （DPD） method in the signal waveform modulation multi-level （SWML） read-only disc. To solve this problem, this paper proposes a new tracking error detection method using amplitude difference. Based on the diffraction theory, the amplitude difference is proportional to the tracking error and is feasible to be used for obtaining the off-track information. The experimental system of the amplitude difference detection method is developed. The experimental results show that the tracking error signal derived from the new method has better performance in uniformity and signal-to-noise ratio than that derived from the conventional DPD method in the SWML read-only disc.

Read channel for signal waveform modulation multi-level disc

A novel read channel for signal waveform modulation multi-level disc is presented in this paper. This read channel employs timing recovery system and partial response maximum likelihood detector. Compared to the previous read channel composed of level detection and run-length detection, the present read channel shows superiority in capacity increase and robust performance. Especially, relying on the partial response maximum likelihood detection, lower bit error rate can be obtained.

Improved partial response maximum likelihood method combining modulation code for signal waveform modulation multi-level disc

In this paper, we describe an improved adaptive partial response maximum likelihood （PRML） method combining modulation code tbr signal waveform modulation multi-level disc. This improved adaptive PRML method employs partial response equalizer and adaptive viterbi detector combining modulation code. Compared with the traditional adaptive PRML detector, the improved PRML detector additionally employs illogical sequence detector and corrector. Illogical sequence detector and corrector can aw）id the appearance of illogical sequences effectively, which do not follow the law of modulation code for signal waveform modulation multi-level disc, and obtain the correct sequences. We implement the improved PRML detector using a DSP and an FPGA chip. The experimental results show good performance. The higher efficient and lower complexity can be obtained by using the improved PRML method than by using the previous PRML method. Meanwhile, resource utilization of the improved PRML detector is not changed, but the bit error rate （BER） is reduced by more than 20%.

Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks

Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.

Signal waveform design to detect an underwater high-speed small target

The problem of sonar signal waveform design to detect a high-speed small target in an underwater environment is discussed. From theoretical analysis, time-frequency hop signal is regarded as the most suitable signal waveform in this application. To get precise target parameter estimation ability, the signal should have high range-Doppler resolution performance. The results of signal analysis show that hop signal with frequency serial coding as Costas array has sharp ambiguity characteristic, so it can be used in an active sonar system to detect a high speed small target. A scheme of frequency coding is also presented.

Microwave dynamic large signal waveform characterization of advanced InGaP HBT for power amplifiers

In wireless mobile communications and wireless local area networks （WLAN）, advanced InGaP HBT with power amplifiers are key components. In this paper, the microwave large signal dynamic waveform characteristics of an advanced InGaP HBT are investigated experimentally for 5.8 GHz power amplifier applications. The microwave large signal waveform distortions at various input power levels, especially at large signal level, are investigated and the reasons are analyzed. The output power saturation is also explained. These analyses will be useful for power amplifier designs.

Applying image processing method to treat digital signals

In this paper, we present a novel method for digital nuclear signal processing based on image processing and recognition, which can improve signal-to-noise ratio of digital nuclear signal effectively without changing the signal shape. The digital nuclear signal with a "time-amplitude" series is converted into a grayscale image with adjustable pixel size. Template of the converted image is extracted by means of modern image processing methods, such as spatial digital low-pass filtering, image binary and the skeleton extracting of images. The needed parameters are extracted from the template image. The method of template extracting presented in this paper can be used flexibly to extract template of nuclear signals, whether the whole or even part of that, and got multi templates corresponding to the whole or partial characters of the signals. The results of image processing,along with γ-ray energy spectrum of241 Am acquired by this method, show that the new method provides a way to develop future digital nuclear instruments of high efficiency and flexibility, high density and multi parameters.

Research on the weak acoustic signal features based on magneto-acoustic effect

The electrical characteristics of tissue yield valuable information for the early diagnosis of pathological changes. Magneto-acoustic tomography( MAT) is a novel functional imaging approach for imaging electrical conductivity. It combines the advantages of high spatial resolution of ultrasonic imaging and high contrast ratio of electrical impedance tomography. It has potential application value in the early tumor diagnosis and bioelectricity monitoring. The analysis and detection of the magneto acoustic signal have a key role in improving the contrast resolution,imaging resolution and reconstruction accuracy of the electrical conductivity image. In this paper,the amplitude,the waveform characteristics and the frequency characteristics of the magnetic sound signal are review ed by the theoretical research,simulation and testing experiments of the weak magneto-acoustic signal.

SAR抗转发式干扰波形设计与优化

为提高合成孔径雷达(synthetic aperture radar,SAR)系统对抗转发式欺骗干扰的性能,提出一种基于非线性调频(non-linear frequency modulation,NLFM)信号的正交波形设计与优化技术,结合自主收发策略来优化波形组,使捷变发射的波形相互正交,从而达到在复杂环境下抑制转发式欺骗干扰的效果。首先,分析SAR系统转发式欺骗干扰的机理、波形捷变发射方法的合理性和有效性,提出利用正交波形设计进行抗干扰的方法;其次,采用S曲线法和分段函数法产生NLFM信号,基于拉格朗日算法,结合遗传算法对NLFM信号的波形组进行了优化设计;最后,通过仿真实验验证了本文方法设计的优化波形组在SAR系统中对抗转发式欺骗干扰的有效性。结果表明:由分段函数法产生NLFM波形后,在合适的干扰转发时延下,采用拉格朗日遗传算法优化NLFM波形的正交性,改善了波形的主瓣宽度和峰值旁瓣比,增强了捷变波形的正交性,提高了波形质量。

一种基于可编程逻辑器件的多功能信号源设计

为适应不同应用场景下对多种信号类型和特性的需求改变,设计一种基于可编程逻辑器件的多功能信号源,以解决通信信号输出过程存在偏差问题。基于可编程逻辑器件进行多功能信号源的电路排布,采用巴特沃斯低通滤波器对信号噪声进行处理,确定波形优化指标,利用数字频率合成技术进行信号波形优化,设计顶层逻辑模块布局。对设计的多功能信号源进行性能测试,结果表明:在改变载波频率和调制度的情况下,设计的多功能信号源在传输过程中输出的信号频谱和解调波形的偏置精度,均优于传统的信号源。

基于多模式分解和多支路并行残差网络的特高压三端混合直流输电线路故障区域诊断

特高压三端混合直流输电线路发生接地故障时,故障电气量变化存在很强的随机性和非线性,在噪声干扰下其规律性更弱,很难快速、准确地提取故障特征以诊断故障区域。鉴于此,提出一种集电流波形特征提取和故障区域诊断为一体的诊断模型。分析不同区域故障时的电流波形特点,利用数学机理不同的3种算法同时分解故障电流,避免一些容易混叠的模态子序列在单一分解方法中逃逸。利用多支路并行残差网络迅速地挖掘分解分量的多尺度空间耦合交互特征,并结合门控循环单元网络模块,进一步提取故障电流的时间耦合特征,使得特征得到显著增强。利用麻雀搜索算法优化该模型的关键参数,构建充分适应电网故障诊断的网络结构,实现故障区域的快速诊断。仿真结果表明,该方案有着较高的灵敏性、较强的抗干扰能力,满足直流保护的可靠性和速动性要求。

复杂动态负荷幅度域波形模态聚类与电能表误差敏感特征

针对复杂动态负荷游程波形模态及引起电能表误差的典型特征认识不足的问题,首先提出动态电流信号幅度域游程波形模态提取算法,提取了多种幅度域毫秒级小颗粒度游程波形模态;其次,提出LK-Shape游程波形模态聚类算法,提取了动态电流信号幅度域的6类典型游程波形模态及其快速变化特征;最后,提出导致电能表超差的两种敏感游程波形模态,并通过实验验证了该游程波形模态适于测试电能表误差,表明了所提方法的有效性和实用性。

多功能相控阵雷达行为辨识综述

多功能相控阵雷达是指具备相位控制阵列,能同时实现搜索、跟踪、制导等多种功能的雷达。随着雷达对抗的不断升级以及各国对于电子战的日益重视,作为获取多功能相控阵雷达信息的重要手段,其行为辨识技术的研究得到了进一步发展。结合目前多功能相控阵雷达行为辨识的背景与意义,对已有的不同多功能相控阵雷达信号模型进行梳理,对高密度、大噪声信号环境下的波形单元提取算法和基于深度学习的工作模式识别算法进行识别性能、适用场景等方面的系统性对比,并归纳了不同仿真数据集的实际应用情况,最后总结了该技术存在的问题并提出了未来展望。

基于STM32F407波形类型识别及参数测量装置的设计

本装置以STM32F407为核心实现对周期信号的波形识别及参数测量,装置由核心计算模块、按键、显示器、信号调理电路和频率检测电路组成,能够实现对周期信号的多通道增益采样,波形的峰峰值、频率等参数的测量,正弦波、方波及三角波等波形类型的识别。本系统通过多增益信号调理电路实现了对宽范围电压的精确测量,使用硬件过零点电路实现了对频率的精确测量,用频率测算出采样频率实现实时波形采集,通过信号变化率的方式实现了对波形类型的识别,具有电压和频率测量范围广、测量精度高、波形识别精确、响应速度快的优点。

综合零序电流信号分量的单相接地故障选线方法

文章提出了一种优化变分模态分解(VMD)参数与综合零序电流信号分量的选线方法,旨在解决目前矿井经消弧线圈接地供电系统发生单相接地故障时,存在的故障信号特征提取困难、噪声信号影响严重等问题。该方法利用鲸鱼优化VMD参数对零序电流信号进行分解,并根据非工频分量能量、工频分量极性和波形相关性的多重判据进行故障线路判别。利用RTDS/RSCAD搭建矿井供电系统模型,仿真测试了不同的故障情况,包括不同故障线路的故障接地电阻、故障初始相位角、故障点位置。结果显示,无论故障条件如何,该方法都有更加准确和可靠的选线结果。

任意波形信号发生器控制软件设计

针对传统任意波形发生器成本高,输出波形单一,平滑度不高以及波形恢复和重建能力较差等问题,文中设计了一款控制任意波形发生器输出的软件。该信号源软件通过开发波形叠加算法实现50次谐波的叠加,支持8通道用户自定义波形信号的同步输出与显示,同时通过三角插值算法增加COMTRADE文件的波形数据点,重建波形信号,支持以太网、串口多种通讯方式,并且有谐波试验、波形回放、设备参数设置等功能。测试结果表明:该软件能够模拟多种用户自定义信号,波形展示平滑度高,波形重建效果良好,对波形发生器控制软件的开发设计具有参考价值。

直流充电桩快速变化的电流和电压波形模态特性分析

针对电动汽车充电桩输出电流和电压呈现出“随机性、波动性、时变性”的快速变化特性,引发电能计量较大超差问题,文章建立充电桩快速变化直流电流和电压的随机信号数学模型及其时频域表征模型;提出快速变化波形模态提取方法,提取了6类12种快速变化直流波形模态,提出并构建了幅度域8个特征参量数学模型,表征影响电能计量的电流和电压特征;提取了典型特征参量数值和重要特性,该特征参量数值和特性可为制定直流电能表动态误差的测试评价试验条件提供理论依据。

基于非均匀间隔采样的光幕过靶信号波形获取

为确保光幕阵列的测量精度,提出一种基于非均匀采样的过靶信号波形获取方法。首先分析光幕过靶弹丸信号特性,得出该信号具有瞬态性、非周期性、大频带宽等特性;其次,对非均匀分数采样原理进行阐述;再次,设计相应信号采集系统并介绍系统组成及其工作原理;最后,通过试验验证与传统方法相比,系统测量精度满足要求,同时可降低测量成本。

低渗油藏宽带压裂中仪表压力梯度信号提纯技术

针对渗透率很低的油藏,在水平井注气过程中,水平井与裂缝布井方位、水平井层位、减氧空气氧气含量、注气速率等参数,会对油藏水驱的启动压力梯度信号形成干扰,影响后期驱油效果,也会影响低渗油藏宽带压裂作业的安全性的问题,提出一种仪表压力梯度信号提纯技术,设计存在干扰下的压力梯度拟合方法,利用拟合因子提取信号特征;油藏的压力梯度-外部干扰曲线在梯度下降阶段是一条上凹型曲线,利用其非达西渗流特征,设计cost函数实现对仪表信号数据集的代价函数求解,完成拟合;分解分量,根据流体在地层中形成侧向线性驱替规律,获取压力梯度信号的自适应谱;在外部干扰条件下获取最佳旋转角,推导获得直达波信号完成信号提纯。实验证明:该方法针对低渗油藏宽带压裂中仪表的压力梯度信号提纯效果好,而且波形没有失真,确保了提纯精度。

EMD端点效应问题解决方法的研究

经验模态分解(EMD)是近年来兴起的一种非平稳信号处理方法,但在分解过程中因产生端点效应而造成分解结果严重失真。因此,研究了一种自适应波形匹配延拓法的改进方法,该方法根据信号端部不同特点确定延拓点并进行延拓,从而解决端点效应问题。实验结果表明,改进的自适应波形匹配延拓法能避免信号EMD分解失真。将改进的自适应波形匹配延拓法的EMD结合小波软阈值去噪方法用于处理加噪超声仿真信号,得到的信噪比比小波软阈值法处理后的信噪比高33.78%,说明该方法在非线性、非平稳信号处理方面有一定优势。