Design and Construction of a Digital Multichannel Analyzer for HPGe Detector Using Digital Signal Processing Technique

The outstanding advantage of digital signal processing (DSP) techniques and Field Programmable Gate Array (FPGA) technology is capable of improving the quality of the experimental measurements for nuclear radiation. In this article, a compact DMCA 8 K was designed and manufactured using DSP technique based on FPGA technology. In particular, the output of the preamplifier is completely processed by digital techniques which are obtained from the analog-to-digital converter (ADC) to calculate the baseline, DC offset, energy peaks, pile up, threshold discrimination and then the form of energy spectrum. The Spartan-6 board is used as a hardware for the development of the digital multichannel analyzer (DMCA), which is equipped with the 14-bit AD6645 with 62.5 Msps sample rate. The application software for instrument control, data acquisition and data processing was written under C++ builder via the RS-232 interface. The designed DMCA system has been tested with a HPGe detector using gamma sources of 60Co and 137Cs and a reference pulser.

Feature Extraction Techniques of Non-Stationary Signals for Fault Diagnosis in Machinery Systems

Previously, fault diagnosis of fixed or steady state mechanical failures (e.g., pumps in nuclear power plant turbines, engines or other key equipment) applied spectrum analysis (e.g., fast Fourier transform, FFT) to extract the frequency features as the basis for identifying the causes of failure types. However, mechanical equipment for increasingly instant speed variations (e.g., wind turbine transmissions or the mechanical arms used in 3C assemblies, etc.) mostly generate non-stationary signals, and the signal features must be averaged with analysis time which makes it difficult to identify the causes of failures. This study proposes a time frequency order spectrum method combining the short-time Fourier transform (STFT) and speed frequency order method to capture the order features of non-stationary signals. Such signal features do not change with speed, and are thus effective in identifying faults in mechanical components under non-stationary conditions. In this study, back propagation neural networks (BPNN) and time frequency order spectrum methods were used to verify faults diagnosis and obtained superior diagnosis results in non-stationary signals of gear-rotor systems.

THE DATA PROCESSING TECHNIQUE FOR INTEGRATED-OPTICS ACOUSTO-OPTIC RF SPECTRUM ANALYZER

The system of Integrated-Optics Acousto-Optic RF Spectrum Analyzer (IOAOSA)consists of a laser diode, an Acousto-Optic (A-O) modulator, geodesic lenses and CCD detectorarray. The optical signal projected on the CCD array is converted into electrical signal andprocessed by the signal processing center which consists of a TMS 32010 system and an IBM-PC.The TMS 32010 with very high speed is used in a microcomputer system. A cycle sample methodis adopted to collect the data of the CCD video signal, sampling one per 40-point. After theprocessing, the frequency bandwidth, the resolution and the dynamic range of the system aremeasured to be 100 MHz, 8 MHz and 20 dB, respectively.

Discrete Time-Frequency Signal Analysis and Processing Techniques for Non-Stationary Signals

This paper presents the methodology, properties and processing of the time-frequency techniques for non-stationary signals, which are frequently used in biomedical, communication and image processing fields. Two classes of time-frequency analysis techniques are chosen for this study. One is short-time Fourier Transform (STFT) technique from linear time-frequency analysis and the other is the Wigner-Ville Distribution (WVD) from Quadratic time-frequency analysis technique. Algorithms for both these techniques are developed and implemented on non-stationary signals for spectrum analysis. The results of this study revealed that the WVD and its classes are most suitable for time-frequency analysis.

Time-Frequency Entropy Analysis of Arc Signal in Non-Stationary Submerged Arc Welding

The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employed to calculate and analyze the stationary current signals, non-stationary current and voltage signals in the submerged arc welding process. It is obtained that time-frequency entropy of arc signal can be used as arc stability judgment criteria of submerged arc welding. Experimental results are provided to confirm the effectiveness of this approach.

Non-Stationary Signal Segmentation and Separation from Joint Time-Frequency Plane

Multi-components sinusoidal engineering signals who are non-stationary signals were considered in this study since their separation and segmentations are of great interests in many engineering fields. In most cases, the segmentation of non-stationary or multi-component signals is conducted in time domain. In this paper, we explore the advantages of applying joint time-frequency (TF) distribution of the multi-component signals to identify their segments. The Spectrogram that is known as Short-Time Fourier Transform (STFT) will be used for obtaining the time-frequency kernel. Time marginal of the computed kernel is optimally used for the signal segmentation. In order to obtain the desirable segmentation, it requires first to improve time marginal of the kernel by using two-dimensional Wiener mask filter applied to the TF kernel to mitigate and suppress non-stationary noise or interference. Additionally, a proper choice of the sliding window and its overlaying has enhanced our scheme to capture the discontinuities corresponding to the boundaries of the candidate segments.

Designing of Acousto-optic Spectrum Analyzer

The structure of the acousto-optic spectrum analyzer was investigated including the RF amplifying circuit, the optical structures and the postprocessing circuit, and the design idea of the module was applied to design the spectrum analyzer. The modularization spectrum analyzer takes on the performance stabilization and higher reliability, and according to different demands, the different modules can be used. The spectrum analyzer had such performances as the detecting frequency precision of 1 MHz, the detecting frequency error of 0.58 MHz, detecting responsivity of 90 dBm and bandwidth of 50 MHz.

Analysis of Non-stationary Signals Based on Nonlinear Chaotic Theories

In the paper, two nonlinear parameter estimation methods based on chaotic theory, surrogate data method and Lyapunov exponents, are used to distinguish the difference of non-stationary signals. After brief introducting of the corresponding algorithms, two typical different non-stationary signals with different nonlinear constraining boundaries are taken to be compared by using the above two methods respectively. The obtained results demonstrate that the apparently similar signals are distinguished effectively in a quantitative way by applying above nonlinear chaotic analyses.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

Features of energy distribution for blast vibration signals based on wavelet packet decomposition

Blast vibration analysis constitutes the foundation for studying the control of blasting vibration damage and provides the precondition of controlling blasting vibration. Based on the characteristics of short-time nonstationary random signal, the laws of energy distribution are investigated for blasting vibration signals in different blasting conditions by means of the wavelet packet analysis technique. The characteristics of wavelet transform and wavelet packet analysis are introduced. Then, blasting vibration signals of different blasting conditions are analysed by the wavelet packet analysis technique using MATLAB; energy distribution for different frequency bands is obtained. It is concluded that the energy distribution of blasting vibration signals varies with maximum decking charge,millisecond delay time and distances between explosion and the measuring point. The results show that the wavelet packet analysis method is an effective means for studying blasting seismic effect in its entirety, especially for constituting velocity-frequency criteria.

An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter

Analysis of long-term EEG signals needs that it be segmented into pseudo stationary epochs. That work is done by regarding to statistical characteristics of a signal such as amplitude and frequency. Time series measured in real world is frequently non-stationary and to extract important information from the measured time series it is significant to utilize a filter or smoother as a pre-processing step. In the proposed approach, the signal is initially filtered by Moving Average (MA) or Savitzky-Golay filter to attenuate its short-term variations. Then, changes of the amplitude or frequency of the signal is calculated by Modified Varri method which is an acceptable algorithm for segmenting a signal. By using synthetic and real EEG data, the proposed methods are compared with original approach (simple Modified Varri). The simulation results indicate the absolute advantage of the proposed methods.

矢量网络分析仪的原理及常见故障维修

简要介绍了半导体测试系统中矢量网络分析仪的基本工作原理及其部件组成;通过图片与文字描述相结合的方法,形象地叙述了5个常见故障现象,便于理解;并提供了维修方法,提升维修人员的维修经验。

高斯白噪声环境下电磁辐射测量修正

噪声对电磁辐射准确测量影响较大,特别是对一些弱信号电磁辐射测量,噪声影响不容忽视,需要对测量结果进行修正。目前传统修正办法是合理设置频谱分析仪参数,但不同频谱分析仪,参数设置会存在差别,这种方法通用性不强,而且对电磁辐射测量结果精确度提高不明显。对此,本文研究了频谱分析仪基底噪声及测量环境中噪声的特征,提出了基于高斯白噪声的修正方法,并通过实验进行分析验证。该方法能为电磁辐射准确测量与评估提供新的思路。

核脉冲信号数字自动极零设计

针对数字极零相消参数调试过程中依赖人工干预的问题,在推导出负指数衰减脉冲信号时间常数计算模型基础上,提出了自动极零算法,搭建了数字自动极零软硬件测试装置。验证实验表明,提出的负指数衰减脉冲信号时间常数计算模型,可应用于核脉冲信号时间常数的精确计算,数字自动极零算法可直接应用于数字化多道核脉冲信号自动极零相消功能的实现。

基于长循环前缀搜索的5G上行控制信道同步算法

针对现代信号分析仪中5G新空口(NR)信号物理层的调制质量测试需求,并为了解决5G NR上行控制信道的同步问题,通过对5G NR物理层协议中的帧结构以及上行控制信道的特征分析,设计了一种基于长循环前缀搜索和解调参考信号参数盲检的同步算法。首先利用长循环前缀搜索确定出半子帧的起始位置,再由此位置结合时频分布特征提取解调参考信号,然后生成本地解调参考信号对其进行参数盲检,由盲检得出的时隙索引进一步确定出半子帧的索引,从而实现对帧起始位置的确定。最后利用信号分析仪采集数据并对算法进行仿真实验,结果表明算法可以有效实现5G NR上行控制信道的同步过程,并且计算量仅为传统算法的1%。

振动传感器校准系统改造与验证

为提高设备使用率,修复故障振动传感器器校准系统,研究整套系统组成和工作原理。该文通过比较不同信号功率放大器和动态信号分析仪的性能参数,提出替换方案,改装功率放大器接线方式,计算改造后系统是否满足要求。结果表明,改造后的系统可以实现振动传感器的手动校准,调试软件可以开展加速度传感器的自动校准。使用该套系统校准结果和304所测量结果进行比对实验,证明改造后的系统校准能力符合量值传递的要求,为振动专业校准结果准确性提供保障。

基于单片机的简易逻辑分析仪设计

逻辑分析仪是一种用于分析数字系统逻辑关系的仪器。一般情况下数字电路有多路信号,而使用普通的示波器测试时,最大范围只能同时进行两路波形信号的测试,这显然不能够满足使用者的需求。针对此类问题,基于51单片机设计了简易逻辑分析仪,其特点是价格低廉,功能多样,易于上手,可满足大部分用户的需求,且经调试运行良好,有一定的应用前景。

基于微信小程序的语音频谱分析实验教学系统设计

为解决高校电子信息类专业通信类课程教学实验时长受限、地点受限等问题,帮助学生更好地理解频谱分析原理,开发基于小程序的信号发生器与频谱分析仪,利用互联网的优势,实现随时开展实验教学的效果。信号发生器可产生一定频率范围内的正弦波、方波、三角波、随机噪声、洛伦兹脉冲以及辛普森脉冲共6种常用的信号,并通过手机扬声器或耳机孔输出;频谱分析仪可对手机麦克风接收到的语音进行加窗频谱分析,并把频谱结果展现在屏幕上。使用固定型号手机进行测试,结果表明,信号发生器在产生100 Hz~10 kHz的正弦波时频率相对误差为0%,产生其它波形的频率相对误差≤1%;频谱分析仪在对正弦波音频输入做频谱分析时,峰值频率测量误差在5 Hz以内,具有良好的实验教学指导意义与现场应用适用性。

基于VxWorks系统的核信号分析处理平台设计

设计了一种基于VxWorks系统的核信号分析处理平台。该平台包含数字化多道脉冲幅度分析器、高低压电源、上位机软件等。可在单一设备实现不同类型探测器高压大小与极性的设置、前放电源调节以及成形参数调整,并对部分探测器的温度和漏电流进行检测。可提升实验或样品检测效率并降低需要使用和调试的设备数量。

矢量信号分析中高性能模拟信号采集系统的研究

现代通信涉及大量先进技术,需要有与电子通信相关的测量仪器,以捕获和分析宽带、实时矢量信号。中频数字化技术是实现通信过程的先进手段之一。针对矢量信号分析仪中对中频模拟信号采集系统高性能的要求,文中对系统各部分设计进行简要分析,应用ADS与诺顿等效变换对系统前端模拟抗混叠带通滤波器进行设计、优化与仿真;根据有大动态范围与优良信噪比的高速ADC的工作要求,对应选用的采样时钟信号各项指标进行推导计算,并提出一种应用自适应技术提高高速ADC分辨率的方法。