Matlab/simulink在FPGA设计中的应用

文中首先分析了MATLAB/Simulink中DSP Builder模块库在FPGA设计中优点,然后结合FSK信号的产生原理,给出了如何利用DSP Builder模块库建立FSK信号发生器模型,以及对FSK信号发生器模型进行算法级仿真和生成VHDL语言的方法,并在modelsim中对FSK信号发生器进行RTL级仿真,最后介绍了在FPGA芯片中实现FSK信号发生器的设计方法.     

独立分量分析中NLPCA-RLS算法IP核的设计

为解决实时性盲信号分离的问题,基于独立分量分析的模型,设计出了NLPCA-RLS算法的IP核.利用Simulink 和DSP Builder对算法中用到的乘法器、查找表、状态机等进行建模,通过Quartus Ⅱ综合后在Altera FPGA器件中进行硬件仿真.仿真实验分别采用人工生成的周期信号和真实的语音信号进行验证.实验结果表明,该IP核能很好的完成瞬时混合模型中盲信号的分离,具有很强的实用性.     

基于小波包的奇异信号检测方法及仿真

高压电器设备的故障大多与绝缘有关，而局部放电是造成绝缘损坏的主要原因之一。如何有效地从噪声和干扰中准确提取局部放电信号的特征，是绝缘故障在线检测的关键。针对高压电缆中的局部放电信号检测问题，提出了一种基于小波包的奇异信号检测方法。首先，分析后得到窄带信号和奇异的放电信号在小波包变换下会表现出截然不同的特性，然后利用这个特性，采用小波包的方法有效地抑制窄带干扰，并从噪声中提取出奇异的局部放电信号。仿真结果显示出在两项指标中，同步性指标大于99％，相似性指标大于90％，这证明了该算法能够从噪声和干扰中，有效地检测并提取出高压电缆中的局部放电信号。     

边缘检测算法的FPGA实现

边缘检测算法是最常见的图像处理算法. 分析了Prewitt算子和Sobel算子两种边缘检测算法的原理, 利用DSP Builder工具在MATLAB/Simulink下完成了这两种算子的图像边缘检测算法的设计, 并将该设计直接在FPGA中实现. 利用FPGA的并行处理能力, 大大缩短了图像处理所需的时间.     

基于共振稀疏分解的局部放电信号窄带干扰抑制新方法

针对电气设备局部放电信号容易被现场运行环境中的窄带干扰信号淹没、不易提取的难题,提出了基于共振稀疏分解的局部放电信号窄带干扰抑制新方法。该方法根据局部放电信号与窄带信号的振荡特征,分析并合理选择品质因子、冗余因子、分解层数和权重系数,实现了对含窄带干扰的局部放电信号的稀疏分解,并将其分解为高共振分量、低共振分量和残余分量,从而可提取出有效的局部放电信号。与小波变换阈值滤波法对比,该方法能更有效地抑制窄带干扰,且误差小、相似度高。仿真和实测数据的处理结果也验证了该方法的有效性。     

基于DSP Builder的插值滤波器的设计及FPGA实现

在数字接收机中,通常需要对采样的数据进行一定倍数的插值.采用Altera公司的DSP Builder设计工具,在MATLAB/Simulink中建立相应的算法模型并仿真.通过引入回路硬件模块,将硬件平台接入由Simulink构建的仿真测试回路中进行软硬件协同仿真,最后可以直接生成FPGA的下载文件.该方法简化了设计流程,降低了开发成本和周期,具有广阔的应用前景.     

LMS算法抑制直扩系统中窄带干扰的研究

直接序列扩频通信由于具有一定的抗干扰能力而被广泛应用,在实际通信中,当系统遭到大功率窄带干扰时,直扩系统的性能会严重恶化.针对直扩系统的抗干扰技术进行了研究,为了对窄带干扰进行抑制,提出了使用LMS自适应算法滤波器来抑制窄带干扰,来保证系统在不增加信号带宽的情况下,使直扩系统正常通信,并通过搭建仿真模型对系统进行仿真.仿真结果表明,LMS算法滤波器能有效抑制窄带干扰,证明可应用于直扩系统,能使系统的性能得到提高.     

基于光纤传感检测的局部放电信号去噪方法

针对局部放电超声信号检测过程中存在噪声干扰的问题,本文提出一种基于变分模态分解和改进小波去噪的噪声抑制方法。首先对局部放电信号进行变分模态分解,得到若干频带分布不同的本征模态分量,结合模态的峭度剔除窄带干扰主导的分量;然后利用改进的小波去噪法,将其余有效模态分量中残留的白噪声进一步滤除;最后将剩余分量进行信号重构实现噪声抑制。对仿真局部放电信号去噪的结果表明,本文方法可提升信噪比约12.1dB,且能够更加完整地保留原始信号波形。此外,在实验室环境下搭建了10kV电压等级的局部放电模拟系统,采用Sagnac光纤传感检测局部放电超声信号。实验结果表明,本文方法具有良好的去噪性能,对实测信号的噪声抑制比达到了7.504。     

一种适用于微纳卫星通信系统的数字下变频算法

提出一种适用于微纳卫星通信系统的窄带信号数字下变频的M点平均降速算法,采用数据移位和减少数据位宽的方法,解决中频信号数字下变频中镜像抑制滤波以及抽取环节带来的资源消耗多、延迟大等问题.给出了该算法的FPGA编程实现,并与一般FIR滤波器进行性能对比测试.结果表明,M点平均降速算法有良好的镜像抑制能力,同时,算法对窄带信...     

米波雷达抗窄带干扰系统的设计和实现

米波雷达的工作波段内存在窄带干扰信号,其功率与雷达目标回波相当甚至更强,严重影响目标信号的检测。提出一套抗窄带干扰系统方案,完成干扰检测、抑制和脉冲压缩等功能。系统以FPGA与高速DSP为核心,具有较强的稳定性和灵活性。外场实验数据表明,该系统能实时有效地检测并抑制干扰,达到预期的效果。     

基于特高频和CNN-LSTM-Attention算法的高压电缆故障诊断方法

随着高压电缆的加速发展和老化,由局部放电(partial discharge, PD)引起的故障问题亟须解决。为此,提出了一种基于特高频(UHF)局放技术与CNN-LSTM-Attention算法的高压电缆故障在线智能诊断方法。首先,对高压电缆的PD产生机理,以及UHF局放技术的实现过程进行描述。其次,利用巴特沃斯(Butterworth)对PD信号进行高通滤波,采用小波变换对信号进行去噪,IPLR算法对PD信号进行降维处理,进而实现特征量的准确提取。最后,建立由CNN-LSTM-Attention算法构成的智能诊断模型。模型中卷积层(CNN)提取轮廓特征,长短期记忆层(LSTM)提取信号时序特征,注意力层(Attention)学习信号重要时序部分。通过实际数据仿真表明：相比传统神经网络方法,CNN-LSTM-Attention神经网络检测方法能够准确识别高采样率的异常放电信号特征,且故障识别准确率明显提高。     

Detection of Parkinson's disease by Shifted One Dimensional Local Binary Patterns from gait

The Parkinson's disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinson's Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals.     

基于外侧腓肠肌sEMG的帕金森神经调控步态量化测评方法

帕金森(Parkinson's disease,PD)患者脑深部电刺激(deep brain stimulation,DBS)神经调控治疗迫切需要客观、量化的测评手段和分析方法.针对这一临床需求,本文提出一种基于外侧腓肠肌(lateral gastrocnemius,LG)表面肌电(surface electromyo...     

Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction

This paper proposes a method to classify idiopathic PD patients and healthy controls using both the gait characteristics of idiopathic PD patients and wavelet-based feature extraction. Using the characteristics of idiopathic PD patients who shuffle their feet while they are walking, we implemented the following three preprocessing methods: (i) we used the difference between two signals that each represented the sum of eight sensor outputs from one foot; (ii) we used the difference between the maximum and minimum records among the vertical ground reaction force outputs from the eight sensors under the left foot; and (iii) we used method (i) again, but on the signals each obtained from one foot by method (ii). After thus conducting the three preprocessing tasks, we created approximation coefficients and detail coefficients using wavelet transforms (WTs). Then, we extracted 40 features from these coefficients by using statistical approaches, including frequency distributions and their variabilities. Using the 40 features as inputs to the neural network with weighted fuzzy membership functions (NEWFM), we compared the performances of the three abovementioned methods. When idiopathic PD patients and healthy controls were classified using the NEWFM, the accuracy, specificity, and sensitivity of the results were, respectively, as follows: 74.32%, 81.63%, and 73.77% by method (i); 75.18%, 74.67%, and 75.24% by method (ii); or 77.33%, 65.48%, and 81.10% by method (iii).     

基于特高频局放技术与CG-BP算法的GIS绝缘故障类型识别方法

针对封闭式气体绝缘开关装置(Gas Insulated Switch Gear,GIS)由于生产、运输安装和运行环境等因素,引发的局部放电(Partial discharge,PD)现象而造成的绝缘故障类型问题,提出一种基于特高频(UHF)局放技术与CG-BP算法的GIS绝缘故障类型识别方法。首先,对GIS内的PD产生...     

GridFTP数据存储接口框架的研究与实现

数据存储接口(DSI)为存储系统提供一个GridFTP数据传输接口,实现对不同存储系统的支持,但是DSI开发相当复杂。该文提出一个可扩展的GridFTP数据存储接口框架,分离并实现DSI共有的核心功能,提炼一个更为简洁的开发接口。实验表明,该框架简化了存储系统DSI的开发流程,满足具体应用对数据的需求。     

Sampling the disparity space image

A central issue in stereo algorithm design is the choice of matching cost. Many algorithms simply use squared or absolute intensity differences based on integer disparity steps. In this paper, we address potential problems with such approaches. We begin with a careful analysis of the properties of the continuous disparity space image (DSI) and propose several new matching cost variants based on symmetrically matching interpolated image signals. Using stereo images with ground truth, we empirically evaluate the performance of the different cost variants and show that proper sampling can yield improved matching performance.     

A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals

Higher-order spectra (HOS) is an efficient feature extraction method used in various biomedical applications such as stages of sleep, epilepsy detection, cardiac abnormalities, and affective computing. The motive of this work was to explore the application of HOS for an automated diagnosis of Parkinson’s disease (PD) using electroencephalography (EEG) signals. Resting-state EEG signals collected from 20 PD patients with medication and 20 age-matched normal subjects were used in this study. HOS bispectrum features were extracted from the EEG signals. The obtained features were ranked using t value, and highly ranked features were used in order to develop the PD Diagnosis Index (PDDI). The PDDI is a single value, which can discriminate the two classes. Also, the ranked features were fed one by one to the various classifiers, namely decision tree (DT), fuzzy K-nearest neighbor (FKNN), K-nearest neighbor (KNN), naive bayes (NB), probabilistic neural network (PNN), and support vector machine (SVM), to choose the best classifier using minimum number of features. We have obtained an optimum mean classification accuracy of 99.62%, mean sensitivity and specificity of 100.00 and 99.25%, respectively, using the SVM classifier. The proposed PDDI can aid the clinicians in their diagnosis and help to test the efficacy of drugs.

     

PD雷达有源干扰系统的计算机仿真

为了方便有效地对PD雷达进行干扰抗干扰技术研究,需要利用某种计算机仿真技术,建立一套仿真系统,最大程度地模拟PD雷达干扰环境.基于此目的,文章首先描述了PD雷达发射信号的数学模型,重点研究了常见的两种PD雷达有源干扰方式及其作用机理,并采用MATLAB/SIMULINK仿真技术对信号,干扰等模块进行建模.仿真结果显示,建立的模型可以有效模拟对PD雷达实施的各种干扰.建模过程简单有效,因此,可以在该系统上进一步进行干扰手段改进或者抗干扰性能的仿真,有助于PD雷达干扰抗干扰技术的研究.     

Artificial recognition system for defective types of transformers by acoustic emission

An artificial recognition system of defective types for epoxy-resin transformers through acoustic emission (AE) from partial discharge (PD) experiment is proposed. PD detection is an efficient diagnosis method to prevent the failure of electric equipments arising from degrading insulation. However, most of the PD detection methods could be performed only at the shutdown period of equipments. By using AE, the online and real-time detection with defective types could be easily reached. Therefore, in this paper a series of high voltage tests were conducted on pre-faulty transformers to collect the AE signals for recognition system needed. The selected AE features instead of waveform are then extracted from these experimental AE signals for the input characteristic of recognition system. According to these features, effective identification of their defective types can be done using the proposed recognition system that combined particle swarm optimization with an artificial neural network. To demonstrate the effectiveness and feasibility of the proposed approach, the artificial recognition system is applied on both noisy and noiseless circumstances. The experiment showed encouraging results that even with 30% noise per discharge count, an 80% successful recognition rate can still be achieved.