快速独立分量分析算法在局放超声阵列信号去噪中的应用

根据阵列信号去噪需要最大限度保留"相位差"信息的要求,提出采用快速独立分量分析(fast independent component analysis,FastICA)对含噪局放超声阵列信号进行去噪处理。首先通过分离矩阵U实现混合信号中源信号与噪声信号的逐次分离;然后采用波形相似性原则对分离后的信号进行盲抽取,获得去噪后局放超声信号;在此基础之上,对局放超声阵列信号模型进行了重构。同时,提出以阵列流型平均相角差、波形变化趋势、波形相似性以及去噪前后的信噪比等参数对局放超声阵列信号的去噪效果进行综合评价。最后,将FastICA用于实验室实测局放超声阵列信号的去噪。结果表明,FastICA能够很好的抑制局放超声阵列信号中的白噪声,在保证高信噪比的情况下,可最大限度地保留阵列信号的"相位差"信息,确保良好的测向效果。     

Hilbert–Huang Transform Based Approach for Measurement of Voltage Flicker Magnitude and Frequency

Voltage flicker is a non-stationary waveform for which direct spectral analysis is not appropriate. To overcome this difficulty, a Hilbert–Huang transform based technique is proposed here. Hilbert–Huang transform is a new signal processing method that can be used in the analysis of non-linear and non-stationary signals. In the suggested method, the recorded voltage signal is decomposed into Hilbert–Huang transform components, namely the empirical mode decomposition and intrinsic mode function components. These components are used in the calculation of the frequency and amplitude of voltage flicker. The clear success of empirical mode decomposition in depicting envelope variations of a sinusoidal waveform has been the main motivation for the adoption of Hilbert–Huang transform in flicker analysis. Simulations are performed over waveforms, including single- and multiple-flicker frequencies and flicker with harmonic, voltage sag, and voltage swell. The waveforms are selected as pure sinusoids, as well as harmonically rich voltage waveforms. Simulation results show that the proposed methodology constitutes a plausible way to analyze voltage flickers, making it an alternative to the available flicker analysis tools.     

接触器分断时高频过电压信号的计算机测试技术

接触器分断时会产生频率很高,幅值很大的过电压,采用一般的检测设备很难测出其动态波形,本文介绍一种基于DSP的高速数据采集系统,对接触器分断时的过电压进行采集,绘出过电压动态波形。     

基于Halbach阵列的永磁风力发电机的设计与优化

为提高电机气隙磁密的正弦度以提升电机的性能,将Halbach阵列应用于直驱式外转子永磁同步风力发电机。设计了一台1.2MW直驱式永磁同步风力发电机,通过有限元法对比分析了不同Halbach充磁阵列和传统径向充磁阵列电机的气隙磁密及感应电势波形,仿真结果显示45度Halbach永磁阵列永磁电机的气隙磁密波形和感应电势波形的正弦度最高且谐波含量最低,发电效果最佳。针对45度Halbach阵列永磁同步风力发电机进行了温度场仿真,结果表明,电机不存在局部过热情况,设计的Halbach阵列永磁同步风力发电机能够稳定运行。     

Statistical Long-Term Correlations in Dissociated Cortical Neuron Recordings

The study of nonlinear long-term correlations in neuronal signals is a central topic for advanced neural signal processing. In particular, the existence of long-term correlations in neural signals recorded via multielectrode array (MEA) could provide interesting information about changes in interneuron communications. In this study we propose a new method for long-term correlation analysis of neuronal burst activity based on the periodogram $alpha$ slope estimation of the MEA signal. We applied our method to recordings taken from cultured networks of dissociated rat cortical neurons. We show the effectiveness of the method in analyzing the activity changes as well as the temporal dynamics that take place during the development of such cultures. Results demonstrate that the $alpha$ parameter is able to divide the network development in three well-defined stages, showing pronounced variations in the long-term correlation among bursts.      

DSP在继电器试验数据高速采集中的应用

继电器分断时电路中会产生频率很高、幅值很大的过电压 ,采用一般的检测设备很难测出其动态波形 ,本文介绍一种基于DSP(数字信号处理器 )的高速数据采集系统 ,对继电器分断时的过电压进行采集 ,绘出过电压动态波形。     

Stimulation artifact in surface EMG signal: effect of the stimulation waveform, detection system, and current amplitude using hybrid stimulation technique

The purpose of this study was to investigate the amplitude properties of the artifact generated on the recorded surface electromyography (EMG) signals during transcutaneous electrical muscle stimulation. The factors which were investigated are the shape of the stimulation waveform, the distance of the stimulating electrode from the recording system, the interelectrode distance of the detection system, the spatial filter used for signal detection, and the stimulation current amplitude. Surface EMG signals were recorded during electrical stimulation of the biceps brachii motor point with a linear adhesive array of eight electrodes. Electrical stimulation was applied with seven stimulation waveforms (mono- and biphasic triangular, sinusoidal, and rectangular), generated by a specifically designed neuromuscular stimulator with hybrid output stage. The stimulation peak current was linearly increased from 0 mA to the maximum tolerated by the subject. The detection systems investigated were single and double differential with interelectrode distances multiple of 5 mm. Two trials for each contraction were performed on three different days. The average rectified artifact values (both absolute and normalized with respect to the corresponding M-wave values) were computed to investigate the artifact amplitude properties. Results indicated that, while the artifact average rectified value, normalized with respect to the M-wave amplitude, depended on the distance of the detecting electrodes from the stimulation point, it did not depend on the stimulation waveform, on the current intensity, on the interelectrode distance, and on the spatial filter. It was concluded that, using hybrid stimulation techniques, the selection of particular stimulation waveforms, interelectrode distances, or spatial filters has a minor effect on the reduction of the artifact when recording M-waves.     

基于DDS技术的波形发生器ROM压缩优化算法

针对DDS芯片因存储空间开销大导致功耗增加,可靠性降低的问题,设计了一种对直接数字频率合成（DDS）波形发生器在现场可编程门阵列（FPGA）上的ROM存储空间压缩优化算法。在不改变波形精度的前提下,通过存储幅度序列的相对增量来减少波形数据位宽的方式对ROM进行压缩,再利用幅度累加器就可以还原出真实的幅度序列。在Quartus II 13.0开发环境下搭建工程,并在FPGA开发板上测试通过。经过测试,该DDS信号发生器可产生五种不同的波形,共占据存储空间9240bit。结果表明,这种DDS优化算法比传统DDS波形发生器节省资源96%以上,能够减少系统功耗,提高系统运行速度。     

Stability of the interface between neural tissue and chronically implanted intracortical microelectrodes.

The stability of the interface between neural tissue and chronically implanted microelectrodes is very important for obtaining reliable control signals for neuroprosthetic devices. Stability is also crucial for chronic microstimulation of the cerebral cortex. However, changes of the electrode-tissue interface can be caused by a variety of mechanisms. In the present study, intracortical microelectrode arrays were implanted into the pericruciate gyrus of cats and neural activities were recorded on a regular basis for several months. An algorithm based on cluster analysis and interspike interval analysis was developed to sort the extracellular action potentials into single units. We tracked these units based on their waveform and their response to somatic stimulation or stereotypical movements by the cats. Our results indicate that, after implantation, the electrode-tissue interface may change from day-to-day over the first 1-2 weeks, week-to-week for 1-2 months, and become quite stable thereafter. A stability index is proposed to quantify the stability of the electrode-tissue interface. The reasons for the pattern of changes are discussed.     

正交波形MIMO雷达信噪比分析

正交波形MIMO雷达各阵元发射相互正交的信号波形,采用低增益的宽波束照射探测空域。从信号模型和信号处理结构入手,分析讨论了MIMO雷达和传统相控阵雷达的信噪比问题,从理论上证明了MIMO雷达要维持与相控阵雷达相当的检测性能,需要采用更长的积累时间。文中还简要分析了不同正交信号形式下的信噪比差异。构建了MIMO雷达系统仿真平台,通过仿真实验测试了MIMO雷达在不同积累时间下的信噪比,并与传统相控阵进行了比较,从而验证了理论分析的正确性。     

Detection and Extraction of Waveform Shapes for Local Signals Using Characteristic Waveform Template

Since local signals appear time‐locally and their waveforms are steep, conventional signal processing methods are generally inadequate to detect them. In this paper, by focusing on amplitude distribution forms of an observed signal, a simple signal processing method is proposed to detect the local signals and extract their waveform shapes simultaneously. Concretely, a characteristic waveform template which consists of representative data series of a simplified target local signal is newly introduced, and a certain event regulated with it is adopted. The local signals can be detected by evaluating whether the amplitude distribution forms of observed signals are associated with the event, and substitution of conditional events for associated events improves detection performance. In addition, the proposed method has been extended to extract waveform shapes by introducing an enhanced waveform template, and automatic detection and extraction of waveform shapes can be performed simultaneously. The proposed method is applied to simulation signal data, and its effectiveness is confirmed by detection performance and extracted waveform shapes for the local signals. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(3): 37–47, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21074     

A Mixed-Signal Multichip Neural Recording Interface With Bandwidth Reduction

We present a multichip structure assembled with a medical-grade stainless-steel microelectrode array intended for neural recordings from multiple channels. The design features a mixed-signal integrated circuit (IC) that handles conditioning, digitization, and time-division multiplexing of neural signals, and a digital IC that provides control, bandwidth reduction, and data communications for telemetry toward a remote host. Bandwidth reduction is achieved through action potential detection and complete capture of waveforms by means of onchip data buffering. The adopted architecture uses high parallelism and low-power building blocks for safety and long-term implantability. Both ICs are fabricated in a CMOS 0.18-$mu {rm m}$ process and are subsequently mounted on the base of the microelectrode array. The chips are stacked according to a vertical integration approach for better compactness. The presented device integrates 16 channels, and is scalable to hundreds of recording channels. Its performance was validated on a testbench with synthetic neural signals. The proposed interface presents a power consumption of 138 $mu$ W per channel, a size of 2.30 mm$^{2}$, and achieves a bandwidth reduction factor of up to 48 with typical recordings.      

基于斩波时域积分射频导纳物位仪研究与设计

电容式物位仪测量粘稠导电物位时,挂料产生的信号相位滞后真实信号相位π/4,对仪表测量精度有很大的影响。分析了电极挂料等效电路及其影响,研究了信号波形特点,提出在3π/4~7π/4处对测量信号进行斩波时域积分,可有效抵消挂料虚假信号对测量精度的影响,提高信噪比。设计了本安型二线制射频导纳物位仪,给出了硬件原理图及关键点信号波形图,建立了测试环境,使用标准云母电容对仪表进行标定,对数据进行线性拟合,以水为介质检验仪表的测量精度,实验证明仪表达到0.2级高精度设计要求。     

Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm

In this paper, a new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm. In the proposed method, suitable features of the waveform of the PQ disturbance are first extracted. These features are extracted from parameters derived from the Fourier and wavelet transforms of the signal. Then, the proposed fuzzy system classifies the type of PQ disturbances based on these features. The PSO algorithm is used to accurately determine the membership function parameters for the fuzzy systems. To test the proposed approach, the waveforms of the PQ disturbances were assumed to be in the sampled form. The impulse, interruption, swell, sag, notch, transient, harmonic, and flicker are considered as single disturbances for the voltage signal. In addition, eight possible combinations of single disturbances are considered as the PQ combined types. The capability of the proposed approach to identify these PQ disturbances is also investigated, when white Gaussian noise, with various signal to noise ratio (SNR) values, is added to the waveforms. The simulation results show that the average rate of correct identification is about 96% for different single and combined PQ disturbances under noisy conditions.     

Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area.

Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a brain-machine interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.     

Data compression of the ECG using neural network for digital Holtermonitor

A data-compression algorithm for digital Holter recording using artificial neural networks (ANNs) is described. A three-layer ANN that has a hidden layer with a few units is used to extract features of the ECG (electrocardiogram) waveform as a function of the activation levels of the hidden layer units. The number of output and input units is the same. The backpropagation algorithm is used for learning. The network is tuned with supervised signals that are the same as the input signals. One network (network 1) is used for data compression and another (network 2) is used for learning with current signals. Once the network is tuned, the common waveform features are encoded by the interconnecting weights of the network. The activation levels of the hidden units then express the respective features of the waveforms for each consecutive heartbeat.     

采用遗传神经网络的电力系统暂态信号分析方法

为了提高对存在频率波动、含有衰减直流分量的电力系统暂态信号的分析精度,提出基于遗传算法和神经网络的电力系统暂态信号分析方法.该方法先采用数字微分结合遗传算法对电力系统暂态信号参数进行粗略估计,将估计值作为神经网络训练的初始值,基波频率作为待定的权值,同时估计信号频率和各次谐波的幅值,在学习算法中对各次谐波的权值采用对应...     

基于CWD谱图和改进CNN的无线电调制分类

针对频率随时间的变化规律是不同调制信号之间最重要的区别，提出一种结合崔 威廉斯分布和改进卷积神经网络模型的无线电调制分类识别方法。在信号预处理阶段，为了更好保留信号的时频特征，引入崔-威廉斯变换将原始时间序列转换成时频图像，进而将调制信号分类问题转化成图像识别问题。在信号识别阶段，通过在卷积神经网络模型中引入残差密集块和全局平均池化层，以克服卷积神经网络模型泛化能力差和训练时间久等缺点。实验结果表明，所提方法可以有效解决梯度消失问题，具有识别率高、泛化能力强等优点。尤其是在低信噪比情况下，表现更为优异，在信噪比为-4 dB时，8种信号的分类精度便可达到100%。     

Real-time spatial-temporal signal processing with opticalnonlinearities

The instantaneous response time of parametric optical nonlinearitics enable real-time processing of, and interaction between, spatial and temporal optical waveforms. We review the various signal-processing alternatives based on three- and four-wave-mixing arrangements among spatial and temporal information carrying waveforms. The fast response time of the interaction permits information exchange between the time and space domains, providing the ability to correlate and convolve signals from the two domains. We demonstrate the usefulness of real-time signal processing with optical nonlinearities with the following experiments: converting waveforms from the time to space domain as well as from the space to time domain, spectral phase conjugation and spectral inversion of ultrafast waveforms, transmission of the spatial correlation function on an ultrafast waveform, and a suggestion for a single-shot triple autocorrelation measurement     

Repeated voltage biasing improves unit recordings by reducing resistive tissue impedances.

Reactive tissue encapsulation of chronically implanted microelectrode probes can preclude long-term recording of extracellular action potentials. We investigated an intervention strategy for functionally encapsulated microelectrode sites. This method, known as "rejuvenation," involved applying a +1.5 V dc bias to an iridium site for 4 s. Previous studies have demonstrated that rejuvenation resulted in higher signal-to-noise ratios (SNRs) by decreasing noise levels, and reduced 1-kHz site impedances by decreasing the tissue interface resistances. In this study, we have investigated: 1) the duration of a single-voltage bias session and 2) the efficacy of multiple sessions. These questions were addressed through electrophysiological recordings, cyclic voltammetry, and modeling the electrode-tissue interface via an equivalent circuit model fit to impedance spectroscopy data. In the six implants studied, we found SNRs improved for 1-7 days with a peak typically occurring within 24 h of the voltage bias. Root-mean square (RMS) noise of the extracellular recordings decreased for 1-2 days, which paralleled a similar decrease in the adsorbed tissue resistance (Ren) from the model. Implants whose SNR effects lasted more than a day showed stabilized reductions in the extracellular tissue resistance (Rex) and cellular membrane area (Am). Subsequent stimulus sessions were found to drop neural tissue parameters consistently to levels observed immediately after surgery. In most cases, these changes did parallel an improvement in SNR. These findings suggest that rejuvenation may be a useful intervention strategy to prolong the lifetime of chronically implanted microelectrodes.