光纤超外差干涉测量系统的研究

提出了一种用于绝对距离测量的光纤超外差干涉系统,简述了该方案的原理,给出了信号处理的框图,并进行了初步实验。对系统的误差分析表明,在光强较弱、信噪比差的情况下,光学元件的非理想特性以及测量信号与参考信号之间的耦合是引起测量误差的主要原因。     

对空气声速测定实验的研究

利用压电传感器测量空气中声速是普通物理实验中的综合性实验之一。在该实验中采用驻波法测量时观察到驻波振幅的最大值之间产生“次极大”现象。本文对该现象进行了分析。同时介绍了用不确定度计算过程中应注意的几点问题。     

光纤迈克尔逊干涉仪实验

本文介绍一个新型的光纤迈克尔逊干涉仪实验,通过光纤技术与传统光学技术的结合,反映了光学技术和应用的最新发展     

Optical Signal Inversion Phenomenon for 1.5 μm Derived from the Negative Nonlinear Absorption Effect in an Erbium Nitrate Solution

The modulation characteristics of the negative nonlinear absorption effect were investigated in an erbium nitrate solution using a 1510 nm laser diode. The reversed-phase waveform was obtained in the transmitted laser for a sample length of 3.0 mm. With decrease in the modulation degree, the reversed-phase waveforms were observed at modulation degrees smaller than 72%. With increase in the modulation frequency, the transmitted waveforms were asymmetrical. The optical signal inversion phenomenon for 1.5μm can be explained by considering an excited state absorption in which energy transfer occurs in a system with high concentrations of the Er3+ ion.     

Signal‐responsive gating of porous membranes by polymer brushes

Various types of signal‐responsive polymers were grafted on surfaces of porous membranes as polymer brushes. The grafted polymers shrank and extended in response to environmental signals, such as pH, ionic strength, temperature, redox reaction and photo‐irradiation. The pore size was regulated by the extent of the polymer brush. The phenomenon was observed in situ by atomic force microscopy. As a result, the substance permeation through the porous membrane was controlled in response to the signals. The permeation control was rapid in comparison with hygrogel‐type membranes, and was reversibly performed. Copyright © 2000 John Wiley & Sons, Ltd.     

Stochastic resonance in Hopfield neural networks for transmitting binary signals

We investigate the stochastic resonance phenomenon in a discrete Hopfield neural network for transmitting binary amplitude modulated signals, wherein the binary information is represented by two stored patterns. Based on the potential energy function and the input binary signal amplitude, the observed stochastic resonance phenomena involve two general noise-improvement mechanisms. A suitable amount of added noise assists or accelerates the switch of the network state vectors to follow input binary signals more correctly, yielding a lower probability of error. Moreover, at a given added noise level, the probability of error can be further reduced by the increase of the number of neurons. When the binary signals are corrupted by external heavy-tailed noise, it is found that the Hopfield neural network with a large number of neurons can outperform the matched filter in the region of low input signal-to-noise ratios per bit.     

适配体传感器检测凝血酶的研究进展

适配体是通过指数富集配体系统进化技术(SELEX)筛选得到的,能与多种目标物质高特异性、高选择性结合的寡核苷酸序列.适配体因合成简单、稳定性高、设计多样化、修饰方便、成本低等优点被用作为识别探针,并与不同的转导技术相结合,构建了多种类型的适配体传感器,并借助纳米材料独特的光、电特性,提高适配体传感器的性能.凝血酶是一种...     

畸变信号的反演修正在同步测压中的应用

本文说明了同步测压所包含的主要内容以及一套实用的同步测压系统的组成,分析了通过传压管道传递的压力信号产生畸变的原因,指出对畸变信号进行反演修正是同步测压过程中必不可少的关键环节。利用频域修正的方法可以对压力分布同步测量中的畸变信号进行反演,以消除输出信号的幅值误差和相位差,保证各个测压通道得到的信号在时间上具有良好的同步性。这样就可以通过对脉动压力分布进行面积加权积分,得到脉动气动力的时间历程。在对畸变信号进行反演修正时,得到测压系统的频率响应函数是关键,本文介绍了利用比较法来标定频响曲线的装置和方法。并通过实验演示了反演修正的效果,表明本文所采用的同步测压装置具有非常好的同步性能。     

Enhancing the Limit of Detection for Comprehensive Two‐Dimensional Gas Chromatography (GC×GC) using Bilinear Chemometric Analysis

The chemometric method referred to as the generalized rank annihilation method (GRAM) is used to improve the precision, accuracy, and resolution of comprehensive two‐dimensional gas chromatography (GC×GC) data. Because GC×GC signals follow a bilinear structure, GC×GC signals can be readily extracted from noise by chemometric techniques such as GRAM. This resulting improvement in signal‐to‐noise ratio (S/N) and detectability is referred to as bilinear signal enhancement. Here, GRAM uses bilinear signal enhancement on both resolved and unresolved GC×GC peaks that initially have a low S/N in the original GC×GC data. In this work, the chemometric method of GRAM is compared to two traditional peak integration methods for quantifying GC×GC analyte signals. One integration method uses a threshold to determine the signal of a peak of interest. With this integration method only those data points above the limit of detection and within a selected area are integrated to produce the total analyte signal for calibration and quantification. The other integration method evaluated did not employ a threshold, and simply summed all the data points in a selected region to obtain a total analyte signal. Substantial improvements in quantification precision, accuracy, and limit of detection are obtained by using GRAM, as compared to when either peak integration method is applied. In addition, the GRAM results are found to be more accurate than results obtained by peak integration, because GRAM more effectively corrects for the slight baseline offset remaining after the background subtraction of data. In the case of a 2.7‐ppm propylbenzene synthetic sample the quantification result with GRAM is 2.6 times more precise and 4.2 times more accurate than the integration method without a threshold, and 18 times more accurate than the integration method with a threshold. The limit of detection for propylbenzene was 0.6 ppm (parts per million by mass) using GRAM, without implementing any sample preconcentration prior to injection. GRAM is also demonstrated as a means to resolve overlapped signals, while enhancing the S/N. Four alkyl benzene signals of low S/N which were not resolved by GC×GC are mathematically resolved and quantified.     

Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems

Entropy-based methods have received considerable attention in the quantification of structural complexity of real-world systems. Among numerous empirical entropy algorithms, conditional entropy-based methods such as sample entropy, which are associated with amplitude distance calculation, are quite intuitive to interpret but require excessive data lengths for meaningful evaluation at large scales. To address this issue, we propose the variational embedding multiscale sample entropy (veMSE) method and conclusively demonstrate its ability to operate robustly, even with several times shorter data than the existing conditional entropy-based methods. The analysis reveals that veMSE also exhibits other desirable properties, such as the robustness to the variation in embedding dimension and noise resilience. For rigor, unlike the existing multivariate methods, the proposed veMSE assigns a different embedding dimension to every data channel, which makes its operation independent of channel permutation. The veMSE is tested on both stimulated and real world signals, and its performance is evaluated against the existing multivariate multiscale sample entropy methods. The proposed veMSE is also shown to exhibit computational advantages over the existing amplitude distance-based entropy methods.