基于无衍射光的光互连系统的对准偏差研究

以信噪比为性能指标,以相邻光通道之间的串扰和探测器前置放大器的热噪声作为系统噪声的来源,研究了基于无衍射光的光互连系统在不同对准偏差条件下的性能.研究表明,探测器阵列的尺寸参数可作为系统对准偏差容忍度和信噪比的优化参数.扩大探测器半径,减小探测器间距可以使系统包容更大的对准偏差,但为了保证一个可以接受的信噪比,探测器间距不应过小.此外,采用无衍射光的光互连系统具有不受纵向对准偏差影响的优点.     

改进维纳滤波器及其在目标方位估计中的应用

针对复杂的水声环境以及信噪比较低的目标信号导致方位估计性能较差的问题,本文提出了一种基于改进维纳滤波器和波束形成器的方位估计方法,该方法能够抑制噪声,提高目标方位估计性能。首先利用改进维纳滤波器抑制各通道接收数据中的噪声,提高输出信噪比。在此基础上,将改进维纳滤波器的输出通过波束形成器,获得目标的方位估计。改进维纳滤波器能够通过调整滤波器参数,控制滤波器的噪声抑制能力和信号失真。因此,针对不同的波束形成器对信号失真的敏感程度不同,可以通过调节改进维纳滤波器的参数,获得噪声抑制与信号失真之间的最佳折中,从而提高输出信噪比,降低目标方位估计的信噪比门限和均方根误差。仿真和实验结果验证了本文方法。     

单矢量水听器的高分辨目标方位跟踪算法研究

根据单矢量水听器自身具有阵列流型的特点,提出了适用于对目标保持连续跟踪的空域预滤波MUSIC算法。通过调整滤波器通带中心角使其保持在目标估计方位角附近,可以消除滤波器通带中心角偏离目标真实方位角时传统预滤波MUSIC算法产生的目标方位估计误差。仿真结果表明,改进预滤波MUSIC算法可以减小甚至消除低信噪比情况下目标方位估计存在的较大误差。海试数据结果表明,阵元域MUSIC和改进预滤波MUSIC都可实现对单频脉冲信号和线性调频信号的目标方位估计,且估计结果与GPS舰位推算结果一致,但改进预滤波MUSIC算法主瓣更尖锐。对宽带航船噪声处理结果显示,改进预滤波MUSIC算法使单矢量水听器在存在目标干扰时的探测距离从2 km提升到了5 km,验证了改进预滤波MUSIC算法可实现弱目标情况下的高分辨目标方位跟踪。     

Improvement of signal-to-noise ratio by stochastic resonance in sigmoid function threshold systems, demonstrated using a CMOS inverter

Stochastic resonance (SR) has become a well-known phenomenon that can enhance weak periodic signals with the help of noise. SR is an interesting phenomenon when applied to signal processing. Although it has been proven that SR does not always improve the signal-to-noise ratio (SNR), in a strongly nonlinear system such as simple threshold system, SR does in fact improve SNR for noisy pulsed signals at appropriate noise strength. However, even in such cases, when noise is weak, the SNR is degraded. Since the noise strength cannot be known in advance, it is difficult to apply SR to real signal processing. In this paper, we focused on the shape of the threshold at which SR did not degrade the SNR when noise was weak. To achieve output change when noise was weak, we numerically analyzed a sigmoid function threshold system. When the slope around the threshold was appropriate, SNR did not degrade when noise was weak and instead was improved at suitable noise strength. We also demonstrated SNR improvement for noisy pulsed voltages using a CMOS inverter, a very common threshold device. The input-output property of a CMOS inverter resembles the sigmoid function. By inputting the noisy signal voltage to a CMOS inverter, we measured the input and output voltages and analyzed the SNRs. The results showed that SNR was effectively improved over a wide range of noise strengths.     

稀疏贝叶斯学习水声信道估计与脉冲噪声抑制方法

水声信道具有显著的稀疏特性,利用稀疏贝叶斯学习(SBL)算法能够实现稀疏水声信道的有效估计。针对SBL计算复杂度较高的问题,将广义近似消息传递-稀疏贝叶斯学习(GAMP-SBL)引入水声信道估计。该方法在SBL的框架下结合GAMP以消息传递的方式计算信道冲激响应,能够有效降低SBL的计算复杂度。针对假设背景噪声服从高斯分布的信道估计方法在脉冲噪声环境下性能下降问题,提出了基于GAMP-SBL的脉冲噪声抑制水声信道估计方法:首先利用脉冲噪声时域稀疏特性,采用GAMP-SBL估计脉冲噪声并进行抑制,然后再次利用GAMP-SBL实现水声信道估计.基于第九次北极科考冰下脉冲噪声的两次仿真结果表明,所提出的方法在归一化均方误差上相对于未进行脉冲噪声抑制的GAMP-SBL最大分别降低了18.71%,6.61%,在信道解码前误码率上最大分别降低了1.66%,4.05%,并且相对于Clipping方法更加稳健。在信噪比为20 dB时,误码率可低于10^-2。     

自适应滤波技术在激光多普勒测速仪中的应用

激光多普勒测速仪检测系统提取的光电信号中存在较大的噪声信号。为了消除这些噪声干扰, 提高激光多普勒测速仪的测量精度,提出一种新的信号处理方法,将最小均方差自适应滤波技术应用于激光多普勒测量中,利用多普勒信号和噪声信号的统计特性,以最小均方误差估计为准则,最大程度地滤除噪声信号。阐述了最小均方差自适应滤波算法的基本原理,在MATLAB平台上将其应用于理想正弦信号进行仿真,并将其应用于实测多普勒信号的处理中。仿真和实验均表明,该技术可以有效抑制激光多普勒测量中的多频率噪声的干扰,大大提高多普勒信号的信噪比和测量精度,为设计高精度的激光多普勒测速仪创造了条件。     

超微弱光子图像的相关检测及计算机模拟

在超微弱发光的研究中（例如生物发光）,由于发光强度极弱,由像增强器得到的光子图像由于样本（光子）数量太少和受系统暗噪声的影响使其信噪比极低,提出了一种基于统计学的光子图像相关积分方法和基于相关处理的光子图像处理方法,用累积光子来提高图像信噪比并得到相应灰度图像并对该方法进行了计算机模拟,同时展示了光子图像和传统灰度图像的信噪比关系,是进行微弱生物发光信号检测的有效方法。     

部分相干信息处理系统的噪声演绩(Ⅱ)——时间部分相干照明

本文研究了时间相干性对光学信息处理系统噪声演绩的影响。以输出信噪比作为信息处理系统噪声演绩的度量,系统中任意平面上的颗粒噪声及相位噪声作为主要的噪声源。结果表明,时间部分相干照明可以提高系统的输出信噪比,对系统的装置噪声有明显的抑制作用。     

增强颗粒散射中缺陷回波信号的相关加权分离谱方法

粗晶材料的超声无损检测受背散射的影响,使得其信噪比很低,且这里的噪声是与发射超声波相干的噪声,不能用简单的时间平均来消除。分离谱技术已被证明是一种抑制背散射信号、提高信噪比的良好方法,许多人为此发展了各种理论作为分离谱的后处理算法。本文介绍了一种增强超声回波信号的相关加权前处理算法。这里,取自粗晶材料标准缺陷的窄脉冲超声回波被定义为标准子波,利用子波与超声检测信号的互相关作为权系数对检测信号进行加权,此技术与分离谱处理结合起来能使提高信噪比的性能更优。实验结果显示了本文所述方法可改善诸如奥氏体不锈钢一类粗晶材料超声检测的信噪比     

Extending the articulation index to account for non-linear distortions introduced by noise-suppression algorithms

The conventional articulation index (AI) measure cannot be applied in situations where non-linear operations are involved and additive noise is present. This is because the definitions of the target and masker signals become vague following non-linear processing, as both the target and masker signals are affected. The aim of the present work is to modify the basic form of the AI measure to account for non-linear processing. This was done using a new definition of the output or effective SNR obtained following non-linear processing. The proposed output SNR definition for a specific band was designed to handle cases where the non-linear processing affects predominantly the target signal rather than the masker signal. The proposed measure also takes into consideration the fact that the input SNR in a specific band cannot be improved following any form of non-linear processing. Overall, the proposed measure quantifies the proportion of input band SNR preserved or transmitted in each band after non-linear processing. High correlation (r?=?0.9) was obtained with the proposed measure when evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech corrupted in four different real-world maskers.     

Automatic Classification of Healthy Subjects and Patients With Essential Vocal Tremor Using Probabilistic Source-Filter Model Based Noise Robust Pitch Estimation

Essential voice tremor (EVT) is a voice disorder resulting from dyscoordination within the laryngeal musculature. A low-frequency fluctuations of fundamental voice frequency or the strength of excitation amplitude is the main consequence of the disorder. The automatic classification of healthy control and EVT is useful tool for the clinicians. A typical automatic EVT classification involves three steps. The first step is to compute the pitch contour from the speech. The second step is to compute the features from the pitch contour, and the final step is to use a classifier to classify the features into healthy or EVT. It is shown that a high-resolution pitch contour estimated from the glottal closure instants (GCIs) is useful for EVT classification. The HPRC estimation can be very poor in the presence of noise. Hence, a probabilistic source filter model based noise robust GCI detection is used for HPRC estimation. The Empirical mode decomposition based feature extraction is used followed by a support vector machine classifier. The EVT classification performance is evaluated using recordings from 45 subjects. The proposed method is found to perform better than the baseline techniques in eight different additive noise conditions with six SNR levels.     

Stochastic resonance in a stochastic bistable system subject to additive white noise and dichotomous noise

The stochastic resonance (SR) in a stochastic stable system driven by a static force and a periodic square-wave signal as well as by additive white noise and dichotomous noise is considered from the point of view of the signal-to-noise ratio (SNR). It is found that the SNR exhibits SR behavior when it is plotted as a function of the noise strength of the white noise and dichotomous noise, as well as when plotted as a function of the static force. Moreover, the influence of the strength of the stochastic potential force and the correlation rate of the dichotomous noise is investigated.     

Biologically-inspired stochastic vector matching for noise-robust information processing

“End of Moore’s Law” has recently become a topic. Keeping the signal-to-noise ratio (SNR) at the same level in the future will surely increase the energy density of smaller-sized transistors. Lowering the operating voltage will prevent this, but the SNR would inevitably degrade. Meanwhile, biological systems such as cells and brains possess robustness against noise in their information processing in spite of the strong influence of stochastic thermal noise. Inspired by the information processing of organisms, we propose a stochastic computing model to acquire information from noisy signals. Our model is based on vector matching, in which the similarities between the input vector carrying external noisy signals and the reference vectors prepared in advance as memorized templates are evaluated in a stochastic manner. This model exhibited robustness against the noise strength and its performance was improved by addition of noise with an appropriate strength, which is similar to a phenomenon observed in stochastic resonance. Because the stochastic vector matching we propose here has robustness against noise, it is a candidate for noisy information processing that is driven by stochastically-operating devices with low energy consumption in future. Moreover, the stochastic vector matching may be applied to memory-based information processing like that of the brain.     

Stochastic resonance in a parallel array of linear elements

This paper studies stochastic resonance (SR) phenomenon in a parallel array of linear elements with noise. Employing the signal-to-noise ratio (SNR) theory, it obtains the output SNR, and investigates the effects on the output SNR of the system with signal-independent noise and signal-dependent noise respectively. Numerical results show: the curve of the output SNR is monotone with signal-independent noise; whereas SR appears with signal-dependent noise. Moreover, the output SNR enhances rapidly with the increase of N which is the number of elements in this parallel array linear system. This result may provide smart array of simple linear sensors which are capable of acting as noise-aided amplifiers.     

Effects of spectral smearing and temporal fine-structure distortion on the fluctuating-masker benefit for speech at a fixed signal-to-noise ratio

Normal-hearing listeners receive less benefit from momentary dips in the level of a fluctuating masker for speech processed to degrade spectral detail or temporal fine structure (TFS) than for unprocessed speech. This has been interpreted as evidence that the magnitude of the fluctuating-masker benefit (FMB) reflects the ability to resolve spectral detail and TFS. However, the FMB for degraded speech is typically measured at a higher signal-to-noise ratio (SNR) to yield performance similar to normal speech for the baseline (stationary-noise) condition. Because the FMB decreases with increasing SNR, this SNR difference might account for the reduction in FMB for degraded speech. In this study, the FMB for unprocessed and processed (TFS-removed or spectrally smeared) speech was measured in a paradigm that adjusts word-set size, rather than SNR, to equate stationary-noise performance across processing conditions. Compared at the same SNR and percent-correct level (but with different set sizes), processed and unprocessed stimuli yielded a similar FMB for four different fluctuating maskers (speech-modulated noise, one opposite-gender interfering talker, two same-gender interfering talkers, and 16-Hz interrupted noise). These results suggest that, for these maskers, spectral or TFS distortions do not directly impair the ability to benefit from momentary dips in masker level.     

SNR and functional sensitivity of BOLD and perfusion-based fMRI using arterial spin labeling with spiral SENSE at 3 T

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies using parallel imaging to reduce the readout window have reported a loss in temporal signal-to-noise ratio (SNR) that is less than would be expected given a purely thermal noise model. In this study, the impact of parallel imaging on the noise components and functional sensitivity of both BOLD and perfusion-based fMRI data was investigated. Dual-echo arterial spin labeling data were acquired on five subjects using sensitivity encoding (SENSE), at reduction factors (R) of 1, 2 and 3. Direct recording of cardiac and respiratory activity during data acquisition enabled the retrospective removal of physiological noise. The temporal SNR of the perfusion time series closely followed the thermal noise prediction of a √R loss in SNR as the readout window was shortened, with temporal SNR values (relative to the R=1 data) of 0.72 and 0.56 for the R=2 and R=3 data, respectively, after accounting for physiological noise. However, the BOLD temporal SNR decreased more slowly than predicted even after accounting for physiological noise, with relative temporal SNR values of 0.80 and 0.63 for the R=2 and R=3 data, respectively. Spectral analysis revealed that the BOLD trends were dominated by low-frequency fluctuations, which were not dominant in the perfusion data due to signal processing differences. The functional sensitivity, assessed using mean F values over activated regions of interest (ROIs), followed the temporal SNR trends for the BOLD data. However, results for the perfusion data were more dependent on the threshold used for ROI selection, most likely due to the inherently low SNR of functional perfusion data.     

Companding to improve cochlear-implant speech recognition in speech-shaped noise

Nonlinear sensory and neural processing mechanisms have been exploited to enhance spectral contrast for improvement of speech understanding in noise. The "companding" algorithm employs both two-tone suppression and adaptive gain mechanisms to achieve spectral enhancement. This study implemented a 50-channel companding strategy and evaluated its efficiency as a front-end noise suppression technique in cochlear implants. The key parameters were identified and evaluated to optimize the companding performance. Both normal-hearing (NH) listeners and cochlear-implant (CI) users performed phoneme and sentence recognition tests in quiet and in steady-state speech-shaped noise. Data from the NH listeners showed that for noise conditions, the implemented strategy improved vowel perception but not consonant and sentence perception. However, the CI users showed significant improvements in both phoneme and sentence perception in noise. Maximum average improvement for vowel recognition was 21.3 percentage points (p<0.05) at 0 dB signal-to-noise ratio (SNR), followed by 17.7 percentage points (p<0.05) at 5 dB SNR for sentence recognition and 12.1 percentage points (p<0.05) at 5 dB SNR for consonant recognition. While the observed results could be attributed to the enhanced spectral contrast, it is likely that the corresponding temporal changes caused by companding also played a significant role and should be addressed by future studies.     

The interpretation of speech reception threshold data in normal-hearing and hearing-impaired listeners: steady-state noise

Speech-in-noise-measurements are important in clinical practice and have been the subject of research for a long time. The results of these measurements are often described in terms of the speech reception threshold (SRT) and SNR loss. Using the basic concepts that underlie several models of speech recognition in steady-state noise, the present study shows that these measures are ill-defined, most importantly because the slope of the speech recognition functions for hearing-impaired listeners always decreases with hearing loss. This slope can be determined from the slope of the normal-hearing speech recognition function when the SRT for the hearing-impaired listener is known. The SII-function (i.e., the speech intelligibility index (SII) against SNR) is important and provides insights into many potential pitfalls when interpreting SRT data. Standardized SNR loss, sSNR loss, is introduced as a universal measure of hearing loss for speech in steady-state noise. Experimental data demonstrates that, unlike the SRT or SNR loss, sSNR loss is invariant to the target point chosen, the scoring method or the type of speech material.     

高斯噪声中的参数盲估计

一般的盲信号处理方法常忽略噪声的影响,而实际问题中噪声的影响是存在的。本文主要讨论了在协方差矩阵未知的加性高斯噪声中混合系数的盲估计问题。以最大似然估计为基础,本文提出一种求解参数的最优化算法,并给出了混合矩阵和协方差矩阵的计算式。采用高斯混合模型(GMM)来逼近源信号的概率密度函数,简化了算法中的积分,导出了一种实用的期望最大算法(EM)算法迭代式。计算机仿真结果表明,算法不仅能稳定收敛,而且在低信噪比下的性能也很好。